Intercomparison of Downscaling Methods on Hydrological Impact for Earth System Model of NE United States

NASA Astrophysics Data System (ADS)

Yang, P.; Fekete, B. M.; Rosenzweig, B.; Lengyel, F.; Vorosmarty, C. J.

2012-12-01

Atmospheric dynamics are essential inputs to Regional-scale Earth System Models (RESMs). Variables including surface air temperature, total precipitation, solar radiation, wind speed and humidity must be downscaled from coarse-resolution, global General Circulation Models (GCMs) to the high temporal and spatial resolution required for regional modeling. However, this downscaling procedure can be challenging due to the need to correct for bias from the GCM and to capture the spatiotemporal heterogeneity of the regional dynamics. In this study, the results obtained using several downscaling techniques and observational datasets were compared for a RESM of the Northeast Corridor of the United States. Previous efforts have enhanced GCM model outputs through bias correction using novel techniques. For example, the Climate Impact Research at Potsdam Institute developed a series of bias-corrected GCMs towards the next generation climate change scenarios (Schiermeier, 2012; Moss et al., 2010). Techniques to better represent the heterogeneity of climate variables have also been improved using statistical approaches (Maurer, 2008; Abatzoglou, 2011). For this study, four downscaling approaches to transform bias-corrected HADGEM2-ES Model output (daily at .5 x .5 degree) to the 3'*3'(longitude*latitude) daily and monthly resolution required for the Northeast RESM were compared: 1) Bilinear Interpolation, 2) Daily bias-corrected spatial downscaling (D-BCSD) with Gridded Meteorological Datasets (developed by Abazoglou 2011), 3) Monthly bias-corrected spatial disaggregation (M-BCSD) with CRU(Climate Research Unit) and 4) Dynamic Downscaling based on Weather Research and Forecast (WRF) model. Spatio-temporal analysis of the variability in precipitation was conducted over the study domain. Validation of the variables of different downscaling methods against observational datasets was carried out for assessment of the downscaled climate model outputs. The effects of using the different approaches to downscale atmospheric variables (specifically air temperature and precipitation) for use as inputs to the Water Balance Model (WBMPlus, Vorosmarty et al., 1998;Wisser et al., 2008) for simulation of daily discharge and monthly stream flow in the Northeast US for a 100-year period in the 21st century were also assessed. Statistical techniques especially monthly bias-corrected spatial disaggregation (M-BCSD) showed potential advantage among other methods for the daily discharge and monthly stream flow simulation. However, Dynamic Downscaling will provide important complements to the statistical approaches tested.

Can bias correction and statistical downscaling methods improve the skill of seasonal precipitation forecasts?

NASA Astrophysics Data System (ADS)

Manzanas, R.; Lucero, A.; Weisheimer, A.; Gutiérrez, J. M.

2018-02-01

Statistical downscaling methods are popular post-processing tools which are widely used in many sectors to adapt the coarse-resolution biased outputs from global climate simulations to the regional-to-local scale typically required by users. They range from simple and pragmatic Bias Correction (BC) methods, which directly adjust the model outputs of interest (e.g. precipitation) according to the available local observations, to more complex Perfect Prognosis (PP) ones, which indirectly derive local predictions (e.g. precipitation) from appropriate upper-air large-scale model variables (predictors). Statistical downscaling methods have been extensively used and critically assessed in climate change applications; however, their advantages and limitations in seasonal forecasting are not well understood yet. In particular, a key problem in this context is whether they serve to improve the forecast quality/skill of raw model outputs beyond the adjustment of their systematic biases. In this paper we analyze this issue by applying two state-of-the-art BC and two PP methods to downscale precipitation from a multimodel seasonal hindcast in a challenging tropical region, the Philippines. To properly assess the potential added value beyond the reduction of model biases, we consider two validation scores which are not sensitive to changes in the mean (correlation and reliability categories). Our results show that, whereas BC methods maintain or worsen the skill of the raw model forecasts, PP methods can yield significant skill improvement (worsening) in cases for which the large-scale predictor variables considered are better (worse) predicted by the model than precipitation. For instance, PP methods are found to increase (decrease) model reliability in nearly 40% of the stations considered in boreal summer (autumn). Therefore, the choice of a convenient downscaling approach (either BC or PP) depends on the region and the season.

A user-targeted synthesis of the VALUE perfect predictor experiment

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Widmann, Martin; Gutierrez, Jose; Kotlarski, Sven; Hertig, Elke; Wibig, Joanna; Rössler, Ole; Huth, Radan

2016-04-01

VALUE is an open European network to validate and compare downscaling methods for climate change research. A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods. VALUE's main approach to validation is user-focused: starting from a specific user problem, a validation tree guides the selection of relevant validation indices and performance measures. We consider different aspects: (1) marginal aspects such as mean, variance and extremes; (2) temporal aspects such as spell length characteristics; (3) spatial aspects such as the de-correlation length of precipitation extremes; and multi-variate aspects such as the interplay of temperature and precipitation or scale-interactions. Several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur. Experiment 1 (perfect predictors): what is the isolated downscaling skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Experiment 2 (Global climate model predictors): how is the overall representation of regional climate, including errors inherited from global climate models? Experiment 3 (pseudo reality): do methods fail in representing regional climate change? Here, we present a user-targeted synthesis of the results of the first VALUE experiment. In this experiment, downscaling methods are driven with ERA-Interim reanalysis data to eliminate global climate model errors, over the period 1979-2008. As reference data we use, depending on the question addressed, (1) observations from 86 meteorological stations distributed across Europe; (2) gridded observations at the corresponding 86 locations or (3) gridded spatially extended observations for selected European regions. With more than 40 contributing methods, this study is the most comprehensive downscaling inter-comparison project so far. The results clearly indicate that for several aspects, the downscaling skill varies considerably between different methods. For specific purposes, some methods can therefore clearly be excluded.

Statistical downscaling of mean temperature, maximum temperature, and minimum temperature on the Loess Plateau, China

NASA Astrophysics Data System (ADS)

Jiang, L.

2017-12-01

Climate change is considered to be one of the greatest environmental threats. Global climate models (GCMs) are the primary tool used for studying climate change. However, GCMs are limited because of their coarse spatial resolution and inability to resolve important sub-grid scale features such as terrain and clouds. Statistical downscaling methods can be used to downscale large-scale variables to local-scale. In this study, we assess the applicability of the Statistical Downscaling Model (SDSM) in downscaling the outputs from Beijing Normal University Earth System Model (BNU-ESM). The study focus on the the Loess Plateau, China, and the variables for downscaling include daily mean temperature (TMEAN), maximum temperature (TMAX) and minimum temperature (TMIN). The results show that SDSM performs well for these three climatic variables on the Loess Plateau. After downscaling, the root mean square errors for TMEAN, TMAX, TMIN for BNU-ESM were reduced by 70.9%, 75.1%, and 67.2%, respectively. All the rates of change in TMEAN, TMAX and TMIN during the 21st century decreased after SDSM downscaling. We also show that SDSM can effectively reduce uncertainty, compared with the raw model outputs. TMEAN uncertainty was reduced by 27.1%, 26.8%, and 16.3% for the future scenarios of RCP 2.6, RCP 4.5 and RCP 8.5, respectively. The corresponding reductions in uncertainty were 23.6%, 30.7%, and 18.7% for TMAX; 37.6%, 31.8%, and 23.2% for TMIN.

New statistical downscaling for Canada

NASA Astrophysics Data System (ADS)

Murdock, T. Q.; Cannon, A. J.; Sobie, S.

2013-12-01

This poster will document the production of a set of statistically downscaled future climate projections for Canada based on the latest available RCM and GCM simulations - the North American Regional Climate Change Assessment Program (NARCCAP; Mearns et al. 2007) and the Coupled Model Intercomparison Project Phase 5 (CMIP5). The main stages of the project included (1) downscaling method evaluation, (2) scenarios selection, (3) production of statistically downscaled results, and (4) applications of results. We build upon a previous downscaling evaluation project (Bürger et al. 2012, Bürger et al. 2013) in which a quantile-based method (Bias Correction/Spatial Disaggregation - BCSD; Werner 2011) provided high skill compared with four other methods representing the majority of types of downscaling used in Canada. Additional quantile-based methods (Bias-Correction/Constructed Analogues; Maurer et al. 2010 and Bias-Correction/Climate Imprint ; Hunter and Meentemeyer 2005) were evaluated. A subset of 12 CMIP5 simulations was chosen based on an objective set of selection criteria. This included hemispheric skill assessment based on the CLIMDEX indices (Sillmann et al. 2013), historical criteria used previously at the Pacific Climate Impacts Consortium (Werner 2011), and refinement based on a modified clustering algorithm (Houle et al. 2012; Katsavounidis et al. 1994). Statistical downscaling was carried out on the NARCCAP ensemble and a subset of the CMIP5 ensemble. We produced downscaled scenarios over Canada at a daily time resolution and 300 arc second (~10 km) spatial resolution from historical runs for 1951-2005 and from RCP 2.6, 4.5, and 8.5 projections for 2006-2100. The ANUSPLIN gridded daily dataset (McKenney et al. 2011) was used as a target. It has national coverage, spans the historical period of interest 1951-2005, and has daily time resolution. It uses interpolation of station data based on thin-plate splines. This type of method has been shown to have superior skill in interpolating RCM data over North America (McGinnis et al. 2012). An early application of the new dataset was to provide projections of climate extremes for adaptation planning by the British Columbia Ministry of Transportation and Infrastructure. Recently, certain stretches of highway have experienced extreme precipitation events resulting in substantial damage to infrastructure. As part of the planning process to refurbish or replace components of these highways, information about the magnitude and frequency of future extreme events are needed to inform the infrastructure design. The increased resolution provided by downscaling improves the representation of topographic features, particularly valley temperature and precipitation effects. A range of extreme values, from simple daily maxima and minima to complex multi-day and threshold-based climate indices were computed and analyzed from the downscaled output. Selected results from this process and how the projections of precipitation extremes are being used in the context of highway infrastructure planning in British Columbia will be presented.

A method for deterministic statistical downscaling of daily precipitation at a monsoonal site in Eastern China

NASA Astrophysics Data System (ADS)

Liu, Yonghe; Feng, Jinming; Liu, Xiu; Zhao, Yadi

2017-12-01

Statistical downscaling (SD) is a method that acquires the local information required for hydrological impact assessment from large-scale atmospheric variables. Very few statistical and deterministic downscaling models for daily precipitation have been conducted for local sites influenced by the East Asian monsoon. In this study, SD models were constructed by selecting the best predictors and using generalized linear models (GLMs) for Feixian, a site in the Yishu River Basin and Shandong Province. By calculating and mapping Spearman rank correlation coefficients between the gridded standardized values of five large-scale variables and daily observed precipitation, different cyclonic circulation patterns were found for monsoonal precipitation in summer (June-September) and winter (November-December and January-March); the values of the gridded boxes with the highest absolute correlations for observed precipitation were selected as predictors. Data for predictors and predictands covered the period 1979-2015, and different calibration and validation periods were divided when fitting and validating the models. Meanwhile, the bootstrap method was also used to fit the GLM. All the above thorough validations indicated that the models were robust and not sensitive to different samples or different periods. Pearson's correlations between downscaled and observed precipitation (logarithmically transformed) on a daily scale reached 0.54-0.57 in summer and 0.56-0.61 in winter, and the Nash-Sutcliffe efficiency between downscaled and observed precipitation reached 0.1 in summer and 0.41 in winter. The downscaled precipitation partially reflected exact variations in winter and main trends in summer for total interannual precipitation. For the number of wet days, both winter and summer models were able to reflect interannual variations. Other comparisons were also made in this study. These results demonstrated that when downscaling, it is appropriate to combine a correlation-based predictor selection across a spatial domain with GLM modeling.

Downscaling Thermal Infrared Radiance for Subpixel Land Surface Temperature Retrieval

PubMed Central

Liu, Desheng; Pu, Ruiliang

2008-01-01

Land surface temperature (LST) retrieved from satellite thermal sensors often consists of mixed temperature components. Retrieving subpixel LST is therefore needed in various environmental and ecological studies. In this paper, we developed two methods for downscaling coarse resolution thermal infrared (TIR) radiance for the purpose of subpixel temperature retrieval. The first method was developed on the basis of a scale-invariant physical model on TIR radiance. The second method was based on a statistical relationship between TIR radiance and land cover fraction at high spatial resolution. The two methods were applied to downscale simulated 990-m ASTER TIR data to 90-m resolution. When validated against the original 90-m ASTER TIR data, the results revealed that both downscaling methods were successful in capturing the general patterns of the original data and resolving considerable spatial details. Further quantitative assessments indicated a strong agreement between the true values and the estimated values by both methods. PMID:27879844

Downscaling Thermal Infrared Radiance for Subpixel Land Surface Temperature Retrieval.

PubMed

Liu, Desheng; Pu, Ruiliang

2008-04-06

Land surface temperature (LST) retrieved from satellite thermal sensors often consists of mixed temperature components. Retrieving subpixel LST is therefore needed in various environmental and ecological studies. In this paper, we developed two methods for downscaling coarse resolution thermal infrared (TIR) radiance for the purpose of subpixel temperature retrieval. The first method was developed on the basis of a scale-invariant physical model on TIR radiance. The second method was based on a statistical relationship between TIR radiance and land cover fraction at high spatial resolution. The two methods were applied to downscale simulated 990-m ASTER TIR data to 90-m resolution. When validated against the original 90-m ASTER TIR data, the results revealed that both downscaling methods were successful in capturing the general patterns of the original data and resolving considerable spatial details. Further quantitative assessments indicated a strong agreement between the true values and the estimated values by both methods.

Uncertainty in projected point precipitation extremes for hydrological impact analysis of climate change

NASA Astrophysics Data System (ADS)

Van Uytven, Els; Willems, Patrick

2017-04-01

Current trends in the hydro-meteorological variables indicate the potential impact of climate change on hydrological extremes. Therefore, they trigger an increased importance climate adaptation strategies in water management. The impact of climate change on hydro-meteorological and hydrological extremes is, however, highly uncertain. This is due to uncertainties introduced by the climate models, the internal variability inherent to the climate system, the greenhouse gas scenarios and the statistical downscaling methods. In view of the need to define sustainable climate adaptation strategies, there is a need to assess these uncertainties. This is commonly done by means of ensemble approaches. Because more and more climate models and statistical downscaling methods become available, there is a need to facilitate the climate impact and uncertainty analysis. A Climate Perturbation Tool has been developed for that purpose, which combines a set of statistical downscaling methods including weather typing, weather generator, transfer function and advanced perturbation based approaches. By use of an interactive interface, climate impact modelers can apply these statistical downscaling methods in a semi-automatic way to an ensemble of climate model runs. The tool is applicable to any region, but has been demonstrated so far to cases in Belgium, Suriname, Vietnam and Bangladesh. Time series representing future local-scale precipitation, temperature and potential evapotranspiration (PET) conditions were obtained, starting from time series of historical observations. Uncertainties on the future meteorological conditions are represented in two different ways: through an ensemble of time series, and a reduced set of synthetic scenarios. The both aim to span the full uncertainty range as assessed from the ensemble of climate model runs and downscaling methods. For Belgium, for instance, use was made of 100-year time series of 10-minutes precipitation observations and daily temperature and PET observations at Uccle and a large ensemble of 160 global climate model runs (CMIP5). They cover all four representative concentration pathway based greenhouse gas scenarios. While evaluating the downscaled meteorological series, particular attention was given to the performance of extreme value metrics (e.g. for precipitation, by means of intensity-duration-frequency statistics). Moreover, the total uncertainty was decomposed in the fractional uncertainties for each of the uncertainty sources considered. Research assessing the additional uncertainty due to parameter and structural uncertainties of the hydrological impact model is ongoing.

Statistical downscaling of precipitation using long short-term memory recurrent neural networks

NASA Astrophysics Data System (ADS)

Misra, Saptarshi; Sarkar, Sudeshna; Mitra, Pabitra

2017-11-01

Hydrological impacts of global climate change on regional scale are generally assessed by downscaling large-scale climatic variables, simulated by General Circulation Models (GCMs), to regional, small-scale hydrometeorological variables like precipitation, temperature, etc. In this study, we propose a new statistical downscaling model based on Recurrent Neural Network with Long Short-Term Memory which captures the spatio-temporal dependencies in local rainfall. The previous studies have used several other methods such as linear regression, quantile regression, kernel regression, beta regression, and artificial neural networks. Deep neural networks and recurrent neural networks have been shown to be highly promising in modeling complex and highly non-linear relationships between input and output variables in different domains and hence we investigated their performance in the task of statistical downscaling. We have tested this model on two datasets—one on precipitation in Mahanadi basin in India and the second on precipitation in Campbell River basin in Canada. Our autoencoder coupled long short-term memory recurrent neural network model performs the best compared to other existing methods on both the datasets with respect to temporal cross-correlation, mean squared error, and capturing the extremes.

A hybrid downscaling procedure for estimating the vertical distribution of ambient temperature in local scale

NASA Astrophysics Data System (ADS)

Yiannikopoulou, I.; Philippopoulos, K.; Deligiorgi, D.

2012-04-01

The vertical thermal structure of the atmosphere is defined by a combination of dynamic and radiation transfer processes and plays an important role in describing the meteorological conditions at local scales. The scope of this work is to develop and quantify the predictive ability of a hybrid dynamic-statistical downscaling procedure to estimate the vertical profile of ambient temperature at finer spatial scales. The study focuses on the warm period of the year (June - August) and the method is applied to an urban coastal site (Hellinikon), located in eastern Mediterranean. The two-step methodology initially involves the dynamic downscaling of coarse resolution climate data via the RegCM4.0 regional climate model and subsequently the statistical downscaling of the modeled outputs by developing and training site-specific artificial neural networks (ANN). The 2.5ox2.5o gridded NCEP-DOE Reanalysis 2 dataset is used as initial and boundary conditions for the dynamic downscaling element of the methodology, which enhances the regional representivity of the dataset to 20km and provides modeled fields in 18 vertical levels. The regional climate modeling results are compared versus the upper-air Hellinikon radiosonde observations and the mean absolute error (MAE) is calculated between the four grid point values nearest to the station and the ambient temperature at the standard and significant pressure levels. The statistical downscaling element of the methodology consists of an ensemble of ANN models, one for each pressure level, which are trained separately and employ the regional scale RegCM4.0 output. The ANN models are theoretically capable of estimating any measurable input-output function to any desired degree of accuracy. In this study they are used as non-linear function approximators for identifying the relationship between a number of predictor variables and the ambient temperature at the various vertical levels. An insight of the statistically derived input-output transfer functions is obtained by utilizing the ANN weights method, which quantifies the relative importance of the predictor variables in the estimation procedure. The overall downscaling performance evaluation incorporates a set of correlation and statistical measures along with appropriate statistical tests. The hybrid downscaling method presented in this work can be extended to various locations by training different site-specific ANN models and the results, depending on the application, can be used for assisting the understanding of the past, present and future climatology. ____________________________ This research has been co-financed by the European Union and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF) - Research Funding Program: Heracleitus II: Investing in knowledge society through the European Social Fund.

Identification of reliable gridded reference data for statistical downscaling methods in Alberta

NASA Astrophysics Data System (ADS)

Eum, H. I.; Gupta, A.

2017-12-01

Climate models provide essential information to assess impacts of climate change at regional and global scales. However, statistical downscaling methods have been applied to prepare climate model data for various applications such as hydrologic and ecologic modelling at a watershed scale. As the reliability and (spatial and temporal) resolution of statistically downscaled climate data mainly depend on a reference data, identifying the most reliable reference data is crucial for statistical downscaling. A growing number of gridded climate products are available for key climate variables which are main input data to regional modelling systems. However, inconsistencies in these climate products, for example, different combinations of climate variables, varying data domains and data lengths and data accuracy varying with physiographic characteristics of the landscape, have caused significant challenges in selecting the most suitable reference climate data for various environmental studies and modelling. Employing various observation-based daily gridded climate products available in public domain, i.e. thin plate spline regression products (ANUSPLIN and TPS), inverse distance method (Alberta Townships), and numerical climate model (North American Regional Reanalysis) and an optimum interpolation technique (Canadian Precipitation Analysis), this study evaluates the accuracy of the climate products at each grid point by comparing with the Adjusted and Homogenized Canadian Climate Data (AHCCD) observations for precipitation, minimum and maximum temperature over the province of Alberta. Based on the performance of climate products at AHCCD stations, we ranked the reliability of these publically available climate products corresponding to the elevations of stations discretized into several classes. According to the rank of climate products for each elevation class, we identified the most reliable climate products based on the elevation of target points. A web-based system was developed to allow users to easily select the most reliable reference climate data at each target point based on the elevation of grid cell. By constructing the best combination of reference data for the study domain, the accurate and reliable statistically downscaled climate projections could be significantly improved.

Downscaling SMAP Soil Moisture Using Geoinformation Data and Geostatistics

NASA Astrophysics Data System (ADS)

Xu, Y.; Wang, L.

2017-12-01

Soil moisture is important for agricultural and hydrological studies. However, ground truth soil moisture data for wide area is difficult to achieve. Microwave remote sensing such as Soil Moisture Active Passive (SMAP) can offer a solution for wide coverage. However, existing global soil moisture products only provide observations at coarse spatial resolutions, which often limit their applications in regional agricultural and hydrological studies. This paper therefore aims to generate fine scale soil moisture information and extend soil moisture spatial availability. A statistical downscaling scheme is presented that incorporates multiple fine scale geoinformation data into the downscaling of coarse scale SMAP data in the absence of ground measurement data. Geoinformation data related to soil moisture patterns including digital elevation model (DEM), land surface temperature (LST), land use and normalized difference vegetation index (NDVI) at a fine scale are used as auxiliary environmental variables for downscaling SMAP data. Generalized additive model (GAM) and regression tree are first conducted to derive statistical relationships between SMAP data and auxiliary geoinformation data at an original coarse scale, and residuals are then downscaled to a finer scale via area-to-point kriging (ATPK) by accounting for the spatial correlation information of the input residuals. The results from standard validation scores as well as the triple collocation (TC) method against soil moisture in-situ measurements show that the downscaling method can significantly improve the spatial details of SMAP soil moisture while maintain the accuracy.

Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella.

PubMed

Guentchev, Galina S; Rood, Richard B; Ammann, Caspar M; Barsugli, Joseph J; Ebi, Kristie; Berrocal, Veronica; O'Neill, Marie S; Gronlund, Carina J; Vigh, Jonathan L; Koziol, Ben; Cinquini, Luca

2016-02-29

Foodborne diseases have large economic and societal impacts worldwide. To evaluate how the risks of foodborne diseases might change in response to climate change, credible and usable climate information tailored to the specific application question is needed. Global Climate Model (GCM) data generally need to, both, be downscaled to the scales of the application to be usable, and represent, well, the key characteristics that inflict health impacts. This study presents an evaluation of temperature-based heat indices for the Washington D.C. area derived from statistically downscaled GCM simulations for 1971-2000--a necessary step in establishing the credibility of these data. The indices approximate high weekly mean temperatures linked previously to occurrences of Salmonella infections. Due to bias-correction, included in the Asynchronous Regional Regression Model (ARRM) and the Bias Correction Constructed Analogs (BCCA) downscaling methods, the observed 30-year means of the heat indices were reproduced reasonably well. In April and May, however, some of the statistically downscaled data misrepresent the increase in the number of hot days towards the summer months. This study demonstrates the dependence of the outcomes to the selection of downscaled climate data and the potential for misinterpretation of future estimates of Salmonella infections.

AI-based (ANN and SVM) statistical downscaling methods for precipitation estimation under climate change scenarios

NASA Astrophysics Data System (ADS)

Mehrvand, Masoud; Baghanam, Aida Hosseini; Razzaghzadeh, Zahra; Nourani, Vahid

2017-04-01

Since statistical downscaling methods are the most largely used models to study hydrologic impact studies under climate change scenarios, nonlinear regression models known as Artificial Intelligence (AI)-based models such as Artificial Neural Network (ANN) and Support Vector Machine (SVM) have been used to spatially downscale the precipitation outputs of Global Climate Models (GCMs). The study has been carried out using GCM and station data over GCM grid points located around the Peace-Tampa Bay watershed weather stations. Before downscaling with AI-based model, correlation coefficient values have been computed between a few selected large-scale predictor variables and local scale predictands to select the most effective predictors. The selected predictors are then assessed considering grid location for the site in question. In order to increase AI-based downscaling model accuracy pre-processing has been developed on precipitation time series. In this way, the precipitation data derived from various GCM data analyzed thoroughly to find the highest value of correlation coefficient between GCM-based historical data and station precipitation data. Both GCM and station precipitation time series have been assessed by comparing mean and variances over specific intervals. Results indicated that there is similar trend between GCM and station precipitation data; however station data has non-stationary time series while GCM data does not. Finally AI-based downscaling model have been applied to several GCMs with selected predictors by targeting local precipitation time series as predictand. The consequences of recent step have been used to produce multiple ensembles of downscaled AI-based models.

A framework for evaluating statistical downscaling performance under changing climatic conditions (Invited)

NASA Astrophysics Data System (ADS)

Dixon, K. W.; Balaji, V.; Lanzante, J.; Radhakrishnan, A.; Hayhoe, K.; Stoner, A. K.; Gaitan, C. F.

2013-12-01

Statistical downscaling (SD) methods may be viewed as generating a value-added product - a refinement of global climate model (GCM) output designed to add finer scale detail and to address GCM shortcomings via a process that gleans information from a combination of observations and GCM-simulated climate change responses. Making use of observational data sets and GCM simulations representing the same historical period, cross-validation techniques allow one to assess how well an SD method meets this goal. However, lacking observations of future, the extent to which a particular SD method's skill might degrade when applied to future climate projections cannot be assessed in the same manner. Here we illustrate and describe extensions to a 'perfect model' experimental design that seeks to quantify aspects of SD method performance both for a historical period (1979-2008) and for late 21st century climate projections. Examples highlighting cases in which downscaling performance deteriorates in future climate projections will be discussed. Also, results will be presented showing how synthetic datasets having known statistical properties may be used to further isolate factors responsible for degradations in SD method skill under changing climatic conditions. We will describe a set of input files used to conduct these analyses that are being made available to researchers who wish to utilize this experimental framework to evaluate SD methods they have developed. The gridded data sets cover a region centered on the contiguous 48 United States with a grid spacing of approximately 25km, have daily time resolution (e.g., maximum and minimum near-surface temperature and precipitation), and represent a total of 120 years of model simulations. This effort is consistent with the 2013 National Climate Predictions and Projections Platform Quantitative Evaluation of Downscaling Workshop goal of supporting a community approach to promote the informed use of downscaled climate projections.

Statistical Downscaling and Bias Correction of Climate Model Outputs for Climate Change Impact Assessment in the U.S. Northeast

NASA Technical Reports Server (NTRS)

Ahmed, Kazi Farzan; Wang, Guiling; Silander, John; Wilson, Adam M.; Allen, Jenica M.; Horton, Radley; Anyah, Richard

2013-01-01

Statistical downscaling can be used to efficiently downscale a large number of General Circulation Model (GCM) outputs to a fine temporal and spatial scale. To facilitate regional impact assessments, this study statistically downscales (to 1/8deg spatial resolution) and corrects the bias of daily maximum and minimum temperature and daily precipitation data from six GCMs and four Regional Climate Models (RCMs) for the northeast United States (US) using the Statistical Downscaling and Bias Correction (SDBC) approach. Based on these downscaled data from multiple models, five extreme indices were analyzed for the future climate to quantify future changes of climate extremes. For a subset of models and indices, results based on raw and bias corrected model outputs for the present-day climate were compared with observations, which demonstrated that bias correction is important not only for GCM outputs, but also for RCM outputs. For future climate, bias correction led to a higher level of agreements among the models in predicting the magnitude and capturing the spatial pattern of the extreme climate indices. We found that the incorporation of dynamical downscaling as an intermediate step does not lead to considerable differences in the results of statistical downscaling for the study domain.

Climatic change on the Gulf of Fonseca (Central America) using two-step statistical downscaling of CMIP5 model outputs

NASA Astrophysics Data System (ADS)

Ribalaygua, Jaime; Gaitán, Emma; Pórtoles, Javier; Monjo, Robert

2018-05-01

A two-step statistical downscaling method has been reviewed and adapted to simulate twenty-first-century climate projections for the Gulf of Fonseca (Central America, Pacific Coast) using Coupled Model Intercomparison Project (CMIP5) climate models. The downscaling methodology is adjusted after looking for good predictor fields for this area (where the geostrophic approximation fails and the real wind fields are the most applicable). The method's performance for daily precipitation and maximum and minimum temperature is analysed and revealed suitable results for all variables. For instance, the method is able to simulate the characteristic cycle of the wet season for this area, which includes a mid-summer drought between two peaks. Future projections show a gradual temperature increase throughout the twenty-first century and a change in the features of the wet season (the first peak and mid-summer rainfall being reduced relative to the second peak, earlier onset of the wet season and a broader second peak).

Assessment of hi-resolution multi-ensemble statistical downscaling regional climate scenarios over Japan

NASA Astrophysics Data System (ADS)

Dairaku, K.

2017-12-01

The Asia-Pacific regions are increasingly threatened by large scale natural disasters. Growing concerns that loss and damages of natural disasters are projected to further exacerbate by climate change and socio-economic change. Climate information and services for risk assessments are of great concern. Fundamental regional climate information is indispensable for understanding changing climate and making decisions on when and how to act. To meet with the needs of stakeholders such as National/local governments, spatio-temporal comprehensive and consistent information is necessary and useful for decision making. Multi-model ensemble regional climate scenarios with 1km horizontal grid-spacing over Japan are developed by using CMIP5 37 GCMs (RCP8.5) and a statistical downscaling (Bias Corrected Spatial Disaggregation (BCSD)) to investigate uncertainty of projected change associated with structural differences of the GCMs for the periods of historical climate (1950-2005) and near future climate (2026-2050). Statistical downscaling regional climate scenarios show good performance for annual and seasonal averages for precipitation and temperature. The regional climate scenarios show systematic underestimate of extreme events such as hot days of over 35 Celsius and annual maximum daily precipitation because of the interpolation processes in the BCSD method. Each model projected different responses in near future climate because of structural differences. The most of CMIP5 37 models show qualitatively consistent increase of average and extreme temperature and precipitation. The added values of statistical/dynamical downscaling methods are also investigated for locally forced nonlinear phenomena, extreme events.

Characterizing sources of uncertainty from global climate models and downscaling techniques

USGS Publications Warehouse

Wootten, Adrienne; Terando, Adam; Reich, Brian J.; Boyles, Ryan; Semazzi, Fred

2017-01-01

In recent years climate model experiments have been increasingly oriented towards providing information that can support local and regional adaptation to the expected impacts of anthropogenic climate change. This shift has magnified the importance of downscaling as a means to translate coarse-scale global climate model (GCM) output to a finer scale that more closely matches the scale of interest. Applying this technique, however, introduces a new source of uncertainty into any resulting climate model ensemble. Here we present a method, based on a previously established variance decomposition method, to partition and quantify the uncertainty in climate model ensembles that is attributable to downscaling. We apply the method to the Southeast U.S. using five downscaled datasets that represent both statistical and dynamical downscaling techniques. The combined ensemble is highly fragmented, in that only a small portion of the complete set of downscaled GCMs and emission scenarios are typically available. The results indicate that the uncertainty attributable to downscaling approaches ~20% for large areas of the Southeast U.S. for precipitation and ~30% for extreme heat days (> 35°C) in the Appalachian Mountains. However, attributable quantities are significantly lower for time periods when the full ensemble is considered but only a sub-sample of all models are available, suggesting that overconfidence could be a serious problem in studies that employ a single set of downscaled GCMs. We conclude with recommendations to advance the design of climate model experiments so that the uncertainty that accrues when downscaling is employed is more fully and systematically considered.

Comparison Of Downscaled CMIP5 Precipitation Datasets For Projecting Changes In Extreme Precipitation In The San Francisco Bay Area.

NASA Technical Reports Server (NTRS)

Milesi, Cristina; Costa-Cabral, Mariza; Rath, John; Mills, William; Roy, Sujoy; Thrasher, Bridget; Wang, Weile; Chiang, Felicia; Loewenstein, Max; Podolske, James

2014-01-01

Water resource managers planning for the adaptation to future events of extreme precipitation now have access to high resolution downscaled daily projections derived from statistical bias correction and constructed analogs. We also show that along the Pacific Coast the Northern Oscillation Index (NOI) is a reliable predictor of storm likelihood, and therefore a predictor of seasonal precipitation totals and likelihood of extremely intense precipitation. Such time series can be used to project intensity duration curves into the future or input into stormwater models. However, few climate projection studies have explored the impact of the type of downscaling method used on the range and uncertainty of predictions for local flood protection studies. Here we present a study of the future climate flood risk at NASA Ames Research Center, located in South Bay Area, by comparing the range of predictions in extreme precipitation events calculated from three sets of time series downscaled from CMIP5 data: 1) the Bias Correction Constructed Analogs method dataset downscaled to a 1/8 degree grid (12km); 2) the Bias Correction Spatial Disaggregation method downscaled to a 1km grid; 3) a statistical model of extreme daily precipitation events and projected NOI from CMIP5 models. In addition, predicted years of extreme precipitation are used to estimate the risk of overtopping of the retention pond located on the site through simulations of the EPA SWMM hydrologic model. Preliminary results indicate that the intensity of extreme precipitation events is expected to increase and flood the NASA Ames retention pond. The results from these estimations will assist flood protection managers in planning for infrastructure adaptations.

Evaluating the Appropriateness of Downscaled Climate Information for Projecting Risks of Salmonella

PubMed Central

Guentchev, Galina S.; Rood, Richard B.; Ammann, Caspar M.; Barsugli, Joseph J.; Ebi, Kristie; Berrocal, Veronica; O’Neill, Marie S.; Gronlund, Carina J.; Vigh, Jonathan L.; Koziol, Ben; Cinquini, Luca

2016-01-01

Foodborne diseases have large economic and societal impacts worldwide. To evaluate how the risks of foodborne diseases might change in response to climate change, credible and usable climate information tailored to the specific application question is needed. Global Climate Model (GCM) data generally need to, both, be downscaled to the scales of the application to be usable, and represent, well, the key characteristics that inflict health impacts. This study presents an evaluation of temperature-based heat indices for the Washington D.C. area derived from statistically downscaled GCM simulations for 1971–2000—a necessary step in establishing the credibility of these data. The indices approximate high weekly mean temperatures linked previously to occurrences of Salmonella infections. Due to bias-correction, included in the Asynchronous Regional Regression Model (ARRM) and the Bias Correction Constructed Analogs (BCCA) downscaling methods, the observed 30-year means of the heat indices were reproduced reasonably well. In April and May, however, some of the statistically downscaled data misrepresent the increase in the number of hot days towards the summer months. This study demonstrates the dependence of the outcomes to the selection of downscaled climate data and the potential for misinterpretation of future estimates of Salmonella infections. PMID:26938544

On the application of the Principal Component Analysis for an efficient climate downscaling of surface wind fields

NASA Astrophysics Data System (ADS)

Chavez, Roberto; Lozano, Sergio; Correia, Pedro; Sanz-Rodrigo, Javier; Probst, Oliver

2013-04-01

With the purpose of efficiently and reliably generating long-term wind resource maps for the wind energy industry, the application and verification of a statistical methodology for the climate downscaling of wind fields at surface level is presented in this work. This procedure is based on the combination of the Monte Carlo and the Principal Component Analysis (PCA) statistical methods. Firstly the Monte Carlo method is used to create a huge number of daily-based annual time series, so called climate representative years, by the stratified sampling of a 33-year-long time series corresponding to the available period of the NCAR/NCEP global reanalysis data set (R-2). Secondly the representative years are evaluated such that the best set is chosen according to its capability to recreate the Sea Level Pressure (SLP) temporal and spatial fields from the R-2 data set. The measure of this correspondence is based on the Euclidean distance between the Empirical Orthogonal Functions (EOF) spaces generated by the PCA (Principal Component Analysis) decomposition of the SLP fields from both the long-term and the representative year data sets. The methodology was verified by comparing the selected 365-days period against a 9-year period of wind fields generated by dynamical downscaling the Global Forecast System data with the mesoscale model SKIRON for the Iberian Peninsula. These results showed that, compared to the traditional method of dynamical downscaling any random 365-days period, the error in the average wind velocity by the PCA's representative year was reduced by almost 30%. Moreover the Mean Absolute Errors (MAE) in the monthly and daily wind profiles were also reduced by almost 25% along all SKIRON grid points. These results showed also that the methodology presented maximum error values in the wind speed mean of 0.8 m/s and maximum MAE in the monthly curves of 0.7 m/s. Besides the bulk numbers, this work shows the spatial distribution of the errors across the Iberian domain and additional wind statistics such as the velocity and directional frequency. Additional repetitions were performed to prove the reliability and robustness of this kind-of statistical-dynamical downscaling method.

Hydrological responses to dynamically and statistically downscaled climate model output

USGS Publications Warehouse

Wilby, R.L.; Hay, L.E.; Gutowski, W.J.; Arritt, R.W.; Takle, E.S.; Pan, Z.; Leavesley, G.H.; Clark, M.P.

2000-01-01

Daily rainfall and surface temperature series were simulated for the Animas River basin, Colorado using dynamically and statistically downscaled output from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) re-analysis. A distributed hydrological model was then applied to the downscaled data. Relative to raw NCEP output, downscaled climate variables provided more realistic stimulations of basin scale hydrology. However, the results highlight the sensitivity of modeled processes to the choice of downscaling technique, and point to the need for caution when interpreting future hydrological scenarios.

Multi-Site and Multi-Variables Statistical Downscaling Technique in the Monsoon Dominated Region of Pakistan

NASA Astrophysics Data System (ADS)

Khan, Firdos; Pilz, Jürgen

2016-04-01

South Asia is under the severe impacts of changing climate and global warming. The last two decades showed that climate change or global warming is happening and the first decade of 21st century is considered as the warmest decade over Pakistan ever in history where temperature reached 53 0C in 2010. Consequently, the spatio-temporal distribution and intensity of precipitation is badly effected and causes floods, cyclones and hurricanes in the region which further have impacts on agriculture, water, health etc. To cope with the situation, it is important to conduct impact assessment studies and take adaptation and mitigation remedies. For impact assessment studies, we need climate variables at higher resolution. Downscaling techniques are used to produce climate variables at higher resolution; these techniques are broadly divided into two types, statistical downscaling and dynamical downscaling. The target location of this study is the monsoon dominated region of Pakistan. One reason for choosing this area is because the contribution of monsoon rains in this area is more than 80 % of the total rainfall. This study evaluates a statistical downscaling technique which can be then used for downscaling climatic variables. Two statistical techniques i.e. quantile regression and copula modeling are combined in order to produce realistic results for climate variables in the area under-study. To reduce the dimension of input data and deal with multicollinearity problems, empirical orthogonal functions will be used. Advantages of this new method are: (1) it is more robust to outliers as compared to ordinary least squares estimates and other estimation methods based on central tendency and dispersion measures; (2) it preserves the dependence among variables and among sites and (3) it can be used to combine different types of distributions. This is important in our case because we are dealing with climatic variables having different distributions over different meteorological stations. The proposed model will be validated by using the (National Centers for Environmental Prediction / National Center for Atmospheric Research) NCEP/NCAR predictors for the period of 1960-1990 and validated for 1990-2000. To investigate the efficiency of the proposed model, it will be compared with the multivariate multiple regression model and with dynamical downscaling climate models by using different climate indices that describe the frequency, intensity and duration of the variables of interest. KEY WORDS: Climate change, Copula, Monsoon, Quantile regression, Spatio-temporal distribution.

Climate Change Projection for the Department of Energy's Savannah River Site

NASA Astrophysics Data System (ADS)

Werth, D. W.

2014-12-01

As per recent Department of Energy (DOE) sustainability requirements, the Savannah River National Laboratory (SRNL) is developing a climate projection for the DOE's Savannah River Site (SRS) near Aiken, SC. This will comprise data from both a statistical and a dynamic downscaling process, each interpolated to the SRS. We require variables most relevant to operational activities at the site (such as the US Forest Service's forest management program), and select temperature, precipitation, wind, and humidity as being most relevant to energy and water resource requirements, fire and forest ecology, and facility and worker safety. We then develop projections of the means and extremes of these variables, estimate the effect on site operations, and develop long-term mitigation strategies. For example, given that outdoor work while wearing protective gear is a daily facet of site operations, heat stress is of primary importance to work planning, and we use the downscaled data to estimate changes in the occurrence of high temperatures. For the statistical downscaling, we use global climate model (GCM) data from the Climate Model Intercomparison Project, version 5 (CMIP-5), which was used in the IPCC Fifth Assessment Report (AR5). GCM data from five research groups was selected, and two climate change scenarios - RCP 4.5 and RCP 8.5 - are used with observed data from site instruments and other databases to produce the downscaled projections. We apply a quantile regression downscaling method, which involves the use of the observed cumulative distribution function to correct that of the GCM. This produces a downscaled projection with an interannual variability closer to that of the observed data and allows for more extreme values in the projections, which are often absent in GCM data. The statistically downscaled data is complemented with dynamically downscaled data from the NARCCAP database, which comprises output from regional climate models forced with GCM output from the CMIP-3 database of GCM simulations. Applications of the downscaled climate projections to some of the unique operational needs of a large DOE weapons complex site are described.

Assessment of the scale effect on statistical downscaling quality at a station scale using a weather generator-based model

USDA-ARS?s Scientific Manuscript database

The resolution of General Circulation Models (GCMs) is too coarse to assess the fine scale or site-specific impacts of climate change. Downscaling approaches including dynamical and statistical downscaling have been developed to meet this requirement. As the resolution of climate model increases, it...

Improvement of downscaled rainfall and temperature across generations over the Western Himalayan region of India

NASA Astrophysics Data System (ADS)

Das, L.; Dutta, M.; Akhter, J.; Meher, J. K.

2016-12-01

It is a challenging task to create station level (local scale) climate change information over the mountainous locations of Western Himalayan Region (WHR) in India because of limited data availability and poor data quality. In the present study, missing values of station data were handled through Multiple Imputation Chained Equation (MICE) technique. Finally 22 numbers of rain gauge and 16 number of temperature station data having continuous record during 1901­2005 and 1969­2009 period respectively were considered as reference stations for developing downscaled rainfall and temperature time series from five commonly available GCMs in the IPCC's different generation assessment reports namely 2nd, 3rd, 4th and 5th hereafter known as SAR, TAR, AR4 and AR5 respectively. Downscaled models were developed using the combined data from the ERA-interim reanalysis and GCMs historical runs (in spite of forcing were not identical in different generation) as predictor and station level rainfall and temperature as predictands. Station level downscaled rainfall and temperature time series were constructed for five GCMs available in each generation. Regional averaged downscaled time series comprising of all stations was prepared for each model and generation and the downscaled results were compared with observed time series. Finally an Overall Model Improvement Index (OMII) was developed using the downscaling results, which was used to investigate the model improvement across generations as well as the improvement of downscaling results obtained from the Empirical Statistical Downscaling (ESD) methods. In case of temperature, models have improved from SAR to AR5 over the study area. In all most all the GCMs TAR is showing worst performance over the WHR by considering the different statistical indices used in this study. In case of precipitation, no model has shown gradual improvement from SAR to AR5 both for interpolated and downscaled values.

Validation of spatial variability in downscaling results from the VALUE perfect predictor experiment

NASA Astrophysics Data System (ADS)

Widmann, Martin; Bedia, Joaquin; Gutiérrez, Jose Manuel; Maraun, Douglas; Huth, Radan; Fischer, Andreas; Keller, Denise; Hertig, Elke; Vrac, Mathieu; Wibig, Joanna; Pagé, Christian; Cardoso, Rita M.; Soares, Pedro MM; Bosshard, Thomas; Casado, Maria Jesus; Ramos, Petra

2016-04-01

VALUE is an open European network to validate and compare downscaling methods for climate change research. Within VALUE a systematic validation framework to enable the assessment and comparison of both dynamical and statistical downscaling methods has been developed. In the first validation experiment the downscaling methods are validated in a setup with perfect predictors taken from the ERA-interim reanalysis for the period 1997 - 2008. This allows to investigate the isolated skill of downscaling methods without further error contributions from the large-scale predictors. One aspect of the validation is the representation of spatial variability. As part of the VALUE validation we have compared various properties of the spatial variability of downscaled daily temperature and precipitation with the corresponding properties in observations. We have used two test validation datasets, one European-wide set of 86 stations, and one higher-density network of 50 stations in Germany. Here we present results based on three approaches, namely the analysis of i.) correlation matrices, ii.) pairwise joint threshold exceedances, and iii.) regions of similar variability. We summarise the information contained in correlation matrices by calculating the dependence of the correlations on distance and deriving decorrelation lengths, as well as by determining the independent degrees of freedom. Probabilities for joint threshold exceedances and (where appropriate) non-exceedances are calculated for various user-relevant thresholds related for instance to extreme precipitation or frost and heat days. The dependence of these probabilities on distance is again characterised by calculating typical length scales that separate dependent from independent exceedances. Regionalisation is based on rotated Principal Component Analysis. The results indicate which downscaling methods are preferable if the dependency of variability at different locations is relevant for the user.

Statistical downscaling and future scenario generation of temperatures for Pakistan Region

NASA Astrophysics Data System (ADS)

Kazmi, Dildar Hussain; Li, Jianping; Rasul, Ghulam; Tong, Jiang; Ali, Gohar; Cheema, Sohail Babar; Liu, Luliu; Gemmer, Marco; Fischer, Thomas

2015-04-01

Finer climate change information on spatial scale is required for impact studies than that presently provided by global or regional climate models. It is especially true for regions like South Asia with complex topography, coastal or island locations, and the areas of highly heterogeneous land-cover. To deal with the situation, an inexpensive method (statistical downscaling) has been adopted. Statistical DownScaling Model (SDSM) employed for downscaling of daily minimum and maximum temperature data of 44 national stations for base time (1961-1990) and then the future scenarios generated up to 2099. Observed as well as Predictors (product of National Oceanic and Atmospheric Administration) data were calibrated and tested on individual/multiple basis through linear regression. Future scenario was generated based on HadCM3 daily data for A2 and B2 story lines. The downscaled data has been tested, and it has shown a relatively strong relationship with the observed in comparison to ECHAM5 data. Generally, the southern half of the country is considered vulnerable in terms of increasing temperatures, but the results of this study projects that in future, the northern belt in particular would have a possible threat of increasing tendency in air temperature. Especially, the northern areas (hosting the third largest ice reserves after the Polar Regions), an important feeding source for Indus River, are projected to be vulnerable in terms of increasing temperatures. Consequently, not only the hydro-agricultural sector but also the environmental conditions in the area may be at risk, in future.

An application of hybrid downscaling model to forecast summer precipitation at stations in China

NASA Astrophysics Data System (ADS)

Liu, Ying; Fan, Ke

2014-06-01

A pattern prediction hybrid downscaling method was applied to predict summer (June-July-August) precipitation at China 160 stations. The predicted precipitation from the downscaling scheme is available one month before. Four predictors were chosen to establish the hybrid downscaling scheme. The 500-hPa geopotential height (GH5) and 850-hPa specific humidity (q85) were from the skillful predicted output of three DEMETER (Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction) general circulation models (GCMs). The 700-hPa geopotential height (GH7) and sea level pressure (SLP) were from reanalysis datasets. The hybrid downscaling scheme (HD-4P) has better prediction skill than a conventional statistical downscaling model (SD-2P) which contains two predictors derived from the output of GCMs, although two downscaling schemes were performed to improve the seasonal prediction of summer rainfall in comparison with the original output of the DEMETER GCMs. In particular, HD-4P downscaling predictions showed lower root mean square errors than those based on the SD-2P model. Furthermore, the HD-4P downscaling model reproduced the China summer precipitation anomaly centers more accurately than the scenario of the SD-2P model in 1998. A hybrid downscaling prediction should be effective to improve the prediction skill of summer rainfall at stations in China.

Statistical downscaling of mean temperature, maximum temperature, and minimum temperature on the Loess Plateau, China

NASA Astrophysics Data System (ADS)

Lin, Jiang; Miao, Chiyuan

2017-04-01

Climate change is considered to be one of the greatest environmental threats. This has urged scientific communities to focus on the hot topic. Global climate models (GCMs) are the primary tool used for studying climate change. However, GCMs are limited because of their coarse spatial resolution and inability to resolve important sub-grid scale features such as terrain and clouds. Statistical downscaling methods can be used to downscale large-scale variables to local-scale. In this study, we assess the applicability of the widely used Statistical Downscaling Model (SDSM) for the Loess Plateau, China. The observed variables included daily mean temperature (TMEAN), maximum temperature (TMAX) and minimum temperature (TMIN) from 1961 to 2005. The and the daily atmospheric data were taken from reanalysis data from 1961 to 2005, and global climate model outputs from Beijing Normal University Earth System Model (BNU-ESM) from 1961 to 2099 and from observations . The results show that SDSM performs well for these three climatic variables on the Loess Plateau. After downscaling, the root mean square errors for TMEAN, TMAX, TMIN for BNU-ESM were reduced by 70.9%, 75.1%, and 67.2%, respectively. All the rates of change in TMEAN, TMAX and TMIN during the 21st century decreased after SDSM downscaling. We also show that SDSM can effectively reduce uncertainty, compared with the raw model outputs. TMEAN uncertainty was reduced by 27.1%, 26.8%, and 16.3% for the future scenarios of RCP 2.6, RCP 4.5 and RCP 8.5, respectively. The corresponding reductions in uncertainty were 23.6%, 30.7%, and 18.7% for TMAX, ; and 37.6%, 31.8%, and 23.2% for TMIN.

Sensitivity of Statistical Downscaling Techniques to Reanalysis Choice and Implications for Regional Climate Change Scenarios

NASA Astrophysics Data System (ADS)

Manzanas, R., Sr.; Brands, S.; San Martin, D., Sr.; Gutiérrez, J. M., Sr.

2014-12-01

This work shows that local-scale climate projections obtained by means of statistical downscaling are sensitive to the choice of reanalysis used for calibration. To this aim, a Generalized Linear Model (GLM) approach is applied to downscale daily precipitation in the Philippines. First, the GLMs are trained and tested -under a cross-validation scheme- separately for two distinct reanalyses (ERA-Interim and JRA-25) for the period 1981-2000. When the observed and downscaled time-series are compared, the attained performance is found to be sensitive to the reanalysis considered if climate change signal bearing variables (temperature and/or specific humidity) are included in the predictor field. Moreover, performance differences are shown to be in correspondence with the disagreement found between the raw predictors from the two reanalyses. Second, the regression coefficients calibrated either with ERA-Interim or JRA-25 are subsequently applied to the output of a Global Climate Model (MPI-ECHAM5) in order to assess the sensitivity of local-scale climate change projections (up to 2100) to reanalysis choice. In this case, the differences detected in present climate conditions are considerably amplified, leading to "delta-change" estimates differing by up to a 35% (on average for the entire country) depending on the reanalysis used for calibration. Therefore, reanalysis choice is shown to importantly contribute to the uncertainty of local-scale climate change projections, and, consequently, should be treated with equal care as other, well-known, sources of uncertainty -e.g., the choice of the GCM and/or downscaling method.- Implications of the results for the entire tropics, as well as for the Model Output Statistics downscaling approach are also briefly discussed.

Improve projections of changes in southern African summer rainfall through comprehensive multi-timescale empirical statistical downscaling

NASA Astrophysics Data System (ADS)

Dieppois, B.; Pohl, B.; Eden, J.; Crétat, J.; Rouault, M.; Keenlyside, N.; New, M. G.

2017-12-01

The water management community has hitherto neglected or underestimated many of the uncertainties in climate impact scenarios, in particular, uncertainties associated with decadal climate variability. Uncertainty in the state-of-the-art global climate models (GCMs) is time-scale-dependant, e.g. stronger at decadal than at interannual timescales, in response to the different parameterizations and to internal climate variability. In addition, non-stationarity in statistical downscaling is widely recognized as a key problem, in which time-scale dependency of predictors plays an important role. As with global climate modelling, therefore, the selection of downscaling methods must proceed with caution to avoid unintended consequences of over-correcting the noise in GCMs (e.g. interpreting internal climate variability as a model bias). GCM outputs from the Coupled Model Intercomparison Project 5 (CMIP5) have therefore first been selected based on their ability to reproduce southern African summer rainfall variability and their teleconnections with Pacific sea-surface temperature across the dominant timescales. In observations, southern African summer rainfall has recently been shown to exhibit significant periodicities at the interannual timescale (2-8 years), quasi-decadal (8-13 years) and inter-decadal (15-28 years) timescales, which can be interpret as the signature of ENSO, the IPO, and the PDO over the region. Most of CMIP5 GCMs underestimate southern African summer rainfall variability and their teleconnections with Pacific SSTs at these three timescales. In addition, according to a more in-depth analysis of historical and pi-control runs, this bias is might result from internal climate variability in some of the CMIP5 GCMs, suggesting potential for bias-corrected prediction based empirical statistical downscaling. A multi-timescale regression based downscaling procedure, which determines the predictors across the different timescales, has thus been used to simulate southern African summer rainfall. This multi-timescale procedure shows much better skills in simulating decadal timescales of variability compared to commonly used statistical downscaling approaches.

Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wood, Andrew W; Leung, Lai R; Sridhar, V

Six approaches for downscaling climate model outputs for use in hydrologic simulation were evaluated, with particular emphasis on each method's ability to produce precipitation and other variables used to drive a macroscale hydrology model applied at much higher spatial resolution than the climate model. Comparisons were made on the basis of a twenty-year retrospective (1975–1995) climate simulation produced by the NCAR-DOE Parallel Climate Model (PCM), and the implications of the comparison for a future (2040–2060) PCM climate scenario were also explored. The six approaches were made up of three relatively simple statistical downscaling methods – linear interpolation (LI), spatial disaggregationmore » (SD), and bias-correction and spatial disaggregation (BCSD) – each applied to both PCM output directly (at T42 spatial resolution), and after dynamical downscaling via a Regional Climate Model (RCM – at ½-degree spatial resolution), for downscaling the climate model outputs to the 1/8-degree spatial resolution of the hydrological model. For the retrospective climate simulation, results were compared to an observed gridded climatology of temperature and precipitation, and gridded hydrologic variables resulting from forcing the hydrologic model with observations. The most significant findings are that the BCSD method was successful in reproducing the main features of the observed hydrometeorology from the retrospective climate simulation, when applied to both PCM and RCM outputs. Linear interpolation produced better results using RCM output than PCM output, but both methods (PCM-LI and RCM-LI) lead to unacceptably biased hydrologic simulations. Spatial disaggregation of the PCM output produced results similar to those achieved with the RCM interpolated output; nonetheless, neither PCM nor RCM output was useful for hydrologic simulation purposes without a bias-correction step. For the future climate scenario, only the BCSD-method (using PCM or RCM) was able to produce hydrologically plausible results. With the BCSD method, the RCM-derived hydrology was more sensitive to climate change than the PCM-derived hydrology.« less

Climate change projections for winter precipitation over Tropical America using statistical downscaling

NASA Astrophysics Data System (ADS)

Palomino-Lemus, Reiner; Córdoba-Machado, Samir; Quishpe-Vásquez, César; García-Valdecasas-Ojeda, Matilde; Raquel Gámiz-Fortis, Sonia; Castro-Díez, Yolanda; Jesús Esteban-Parra, María

2017-04-01

In this study the Principal Component Regression (PCR) method has been used as statistical downscaling technique for simulating boreal winter precipitation in Tropical America during the period 1950-2010, and then for generating climate change projections for 2071-2100 period. The study uses the Global Precipitation Climatology Centre (GPCC, version 6) data set over the Tropical America region [30°N-30°S, 120°W-30°W] as predictand variable in the downscaling model. The mean monthly sea level pressure (SLP) from the National Center for Environmental Prediction - National Center for Atmospheric Research (NCEP-NCAR reanalysis project), has been used as predictor variable, covering a more extended area [30°N-30°S, 180°W-30°W]. Also, the SLP outputs from 20 GCMs, taken from the Coupled Model Intercomparison Project (CMIP5) have been used. The model data include simulations with historical atmospheric concentrations and future projections for the representative concentration pathways RCP2.6, RCP4.5, and RCP8.5. The ability of the different GCMs to simulate the winter precipitation in the study area for present climate (1971-2000) was analyzed by calculating the differences between the simulated and observed precipitation values. Additionally, the statistical significance at 95% confidence level of these differences has been estimated by means of the bilateral rank sum test of Wilcoxon-Mann-Whitney. Finally, to project winter precipitation in the area for the period 2071-2100, the downscaling model, recalibrated for the total period 1950-2010, was applied to the SLP outputs of the GCMs under the RCP2.6, RCP4.5, and RCP8.5 scenarios. The results show that, generally, for present climate the statistical downscaling shows a high ability to faithfully reproduce the precipitation field, while the simulations performed directly by using not downscaled outputs of GCMs strongly distort the precipitation field. For future climate, the projected predictions under the RCP4.5 and RCP8.5 scenarios show large areas with significant changes. For the RCP2.6 scenario, projected results present a predominance of very moderate decreases in rainfall, although significant in some models. Keywords: climate change projections, precipitation, Tropical America, statistical downscaling. Acknowledgements: This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).

Structural uncertainty of downscaled climate model output in a difficult-to-resolve environment: data sparseness and parameterization error contribution to statistical and dynamical downscaling output in the U.S. Caribbean region

NASA Astrophysics Data System (ADS)

Terando, A. J.; Grade, S.; Bowden, J.; Henareh Khalyani, A.; Wootten, A.; Misra, V.; Collazo, J.; Gould, W. A.; Boyles, R.

2016-12-01

Sub-tropical island nations may be particularly vulnerable to anthropogenic climate change because of predicted changes in the hydrologic cycle that would lead to significant drying in the future. However, decision makers in these regions have seen their adaptation planning efforts frustrated by the lack of island-resolving climate model information. Recently, two investigations have used statistical and dynamical downscaling techniques to develop climate change projections for the U.S. Caribbean region (Puerto Rico and U.S. Virgin Islands). We compare the results from these two studies with respect to three commonly downscaled CMIP5 global climate models (GCMs). The GCMs were dynamically downscaled at a convective-permitting scale using two different regional climate models. The statistical downscaling approach was conducted at locations with long-term climate observations and then further post-processed using climatologically aided interpolation (yielding two sets of projections). Overall, both approaches face unique challenges. The statistical approach suffers from a lack of observations necessary to constrain the model, particularly at the land-ocean boundary and in complex terrain. The dynamically downscaled model output has a systematic dry bias over the island despite ample availability of moisture in the atmospheric column. Notwithstanding these differences, both approaches are consistent in projecting a drier climate that is driven by the strong global-scale anthropogenic forcing.

Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting.

PubMed

Owens, M J; Horbury, T S; Wicks, R T; McGregor, S L; Savani, N P; Xiong, M

2014-06-01

Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind "noise," which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical "downscaling" of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme. Solar wind models must be downscaled in order to drive magnetospheric models Ensemble downscaling is more effective than deterministic downscaling The magnetosphere responds nonlinearly to small-scale solar wind fluctuations.

Predicting and downscaling ENSO impacts on intraseasonal precipitation statistics in California: The 1997/98 event

USGS Publications Warehouse

Gershunov, A.; Barnett, T.P.; Cayan, D.R.; Tubbs, T.; Goddard, L.

2000-01-01

Three long-range forecasting methods have been evaluated for prediction and downscaling of seasonal and intraseasonal precipitation statistics in California. Full-statistical, hybrid-dynamical - statistical and full-dynamical approaches have been used to forecast El Nin??o - Southern Oscillation (ENSO) - related total precipitation, daily precipitation frequency, and average intensity anomalies during the January - March season. For El Nin??o winters, the hybrid approach emerges as the best performer, while La Nin??a forecasting skill is poor. The full-statistical forecasting method features reasonable forecasting skill for both La Nin??a and El Nin??o winters. The performance of the full-dynamical approach could not be evaluated as rigorously as that of the other two forecasting schemes. Although the full-dynamical forecasting approach is expected to outperform simpler forecasting schemes in the long run, evidence is presented to conclude that, at present, the full-dynamical forecasting approach is the least viable of the three, at least in California. The authors suggest that operational forecasting of any intraseasonal temperature, precipitation, or streamflow statistic derivable from the available records is possible now for ENSO-extreme years.

Statistical downscaling of IPCC sea surface wind and wind energy predictions for U.S. east coastal ocean, Gulf of Mexico and Caribbean Sea

NASA Astrophysics Data System (ADS)

Yao, Zhigang; Xue, Zuo; He, Ruoying; Bao, Xianwen; Song, Jun

2016-08-01

A multivariate statistical downscaling method is developed to produce regional, high-resolution, coastal surface wind fields based on the IPCC global model predictions for the U.S. east coastal ocean, the Gulf of Mexico (GOM), and the Caribbean Sea. The statistical relationship is built upon linear regressions between the empirical orthogonal function (EOF) spaces of a cross- calibrated, multi-platform, multi-instrument ocean surface wind velocity dataset (predictand) and the global NCEP wind reanalysis (predictor) over a 10 year period from 2000 to 2009. The statistical relationship is validated before applications and its effectiveness is confirmed by the good agreement between downscaled wind fields based on the NCEP reanalysis and in-situ surface wind measured at 16 National Data Buoy Center (NDBC) buoys in the U.S. east coastal ocean and the GOM during 1992-1999. The predictand-predictor relationship is applied to IPCC GFDL model output (2.0°×2.5°) of downscaled coastal wind at 0.25°×0.25° resolution. The temporal and spatial variability of future predicted wind speeds and wind energy potential over the study region are further quantified. It is shown that wind speed and power would significantly be reduced in the high CO2 climate scenario offshore of the mid-Atlantic and northeast U.S., with the speed falling to one quarter of its original value.

Downscaling Satellite Precipitation with Emphasis on Extremes: A Variational ℓ1-Norm Regularization in the Derivative Domain

NASA Astrophysics Data System (ADS)

Foufoula-Georgiou, E.; Ebtehaj, A. M.; Zhang, S. Q.; Hou, A. Y.

2014-05-01

The increasing availability of precipitation observations from space, e.g., from the Tropical Rainfall Measuring Mission (TRMM) and the forthcoming Global Precipitation Measuring (GPM) Mission, has fueled renewed interest in developing frameworks for downscaling and multi-sensor data fusion that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying rainfall fields. Of special interest is the reproduction of extreme precipitation intensities and gradients, as these are directly relevant to hazard prediction. In this paper, we present a new formalism for downscaling satellite precipitation observations, which explicitly allows for the preservation of some key geometrical and statistical properties of spatial precipitation. These include sharp intensity gradients (due to high-intensity regions embedded within lower-intensity areas), coherent spatial structures (due to regions of slowly varying rainfall), and thicker-than-Gaussian tails of precipitation gradients and intensities. Specifically, we pose the downscaling problem as a discrete inverse problem and solve it via a regularized variational approach (variational downscaling) where the regularization term is selected to impose the desired smoothness in the solution while allowing for some steep gradients (called ℓ1-norm or total variation regularization). We demonstrate the duality between this geometrically inspired solution and its Bayesian statistical interpretation, which is equivalent to assuming a Laplace prior distribution for the precipitation intensities in the derivative (wavelet) space. When the observation operator is not known, we discuss the effect of its misspecification and explore a previously proposed dictionary-based sparse inverse downscaling methodology to indirectly learn the observation operator from a data base of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case studies featuring the downscaling of a hurricane precipitation field.

Evaluation of Statistical Downscaling Skill at Reproducing Extreme Events

NASA Astrophysics Data System (ADS)

McGinnis, S. A.; Tye, M. R.; Nychka, D. W.; Mearns, L. O.

2015-12-01

Climate model outputs usually have much coarser spatial resolution than is needed by impacts models. Although higher resolution can be achieved using regional climate models for dynamical downscaling, further downscaling is often required. The final resolution gap is often closed with a combination of spatial interpolation and bias correction, which constitutes a form of statistical downscaling. We use this technique to downscale regional climate model data and evaluate its skill in reproducing extreme events. We downscale output from the North American Regional Climate Change Assessment Program (NARCCAP) dataset from its native 50-km spatial resolution to the 4-km resolution of University of Idaho's METDATA gridded surface meterological dataset, which derives from the PRISM and NLDAS-2 observational datasets. We operate on the major variables used in impacts analysis at a daily timescale: daily minimum and maximum temperature, precipitation, humidity, pressure, solar radiation, and winds. To interpolate the data, we use the patch recovery method from the Earth System Modeling Framework (ESMF) regridding package. We then bias correct the data using Kernel Density Distribution Mapping (KDDM), which has been shown to exhibit superior overall performance across multiple metrics. Finally, we evaluate the skill of this technique in reproducing extreme events by comparing raw and downscaled output with meterological station data in different bioclimatic regions according to the the skill scores defined by Perkins et al in 2013 for evaluation of AR4 climate models. We also investigate techniques for improving bias correction of values in the tails of the distributions. These techniques include binned kernel density estimation, logspline kernel density estimation, and transfer functions constructed by fitting the tails with a generalized pareto distribution.

Downscaling Satellite Precipitation with Emphasis on Extremes: A Variational 1-Norm Regularization in the Derivative Domain

NASA Technical Reports Server (NTRS)

Foufoula-Georgiou, E.; Ebtehaj, A. M.; Zhang, S. Q.; Hou, A. Y.

2013-01-01

The increasing availability of precipitation observations from space, e.g., from the Tropical Rainfall Measuring Mission (TRMM) and the forthcoming Global Precipitation Measuring (GPM) Mission, has fueled renewed interest in developing frameworks for downscaling and multi-sensor data fusion that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying rainfall fields. Of special interest is the reproduction of extreme precipitation intensities and gradients, as these are directly relevant to hazard prediction. In this paper, we present a new formalism for downscaling satellite precipitation observations, which explicitly allows for the preservation of some key geometrical and statistical properties of spatial precipitation. These include sharp intensity gradients (due to high-intensity regions embedded within lower-intensity areas), coherent spatial structures (due to regions of slowly varying rainfall),and thicker-than-Gaussian tails of precipitation gradients and intensities. Specifically, we pose the downscaling problem as a discrete inverse problem and solve it via a regularized variational approach (variational downscaling) where the regularization term is selected to impose the desired smoothness in the solution while allowing for some steep gradients(called 1-norm or total variation regularization). We demonstrate the duality between this geometrically inspired solution and its Bayesian statistical interpretation, which is equivalent to assuming a Laplace prior distribution for the precipitation intensities in the derivative (wavelet) space. When the observation operator is not known, we discuss the effect of its misspecification and explore a previously proposed dictionary-based sparse inverse downscaling methodology to indirectly learn the observation operator from a database of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case studies featuring the downscaling of a hurricane precipitation field.

Accounting for Global Climate Model Projection Uncertainty in Modern Statistical Downscaling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johannesson, G

2010-03-17

Future climate change has emerged as a national and a global security threat. To carry out the needed adaptation and mitigation steps, a quantification of the expected level of climate change is needed, both at the global and the regional scale; in the end, the impact of climate change is felt at the local/regional level. An important part of such climate change assessment is uncertainty quantification. Decision and policy makers are not only interested in 'best guesses' of expected climate change, but rather probabilistic quantification (e.g., Rougier, 2007). For example, consider the following question: What is the probability that themore » average summer temperature will increase by at least 4 C in region R if global CO{sub 2} emission increases by P% from current levels by time T? It is a simple question, but one that remains very difficult to answer. It is answering these kind of questions that is the focus of this effort. The uncertainty associated with future climate change can be attributed to three major factors: (1) Uncertainty about future emission of green house gasses (GHG). (2) Given a future GHG emission scenario, what is its impact on the global climate? (3) Given a particular evolution of the global climate, what does it mean for a particular location/region? In what follows, we assume a particular GHG emission scenario has been selected. Given the GHG emission scenario, the current batch of the state-of-the-art global climate models (GCMs) is used to simulate future climate under this scenario, yielding an ensemble of future climate projections (which reflect, to some degree our uncertainty of being able to simulate future climate give a particular GHG scenario). Due to the coarse-resolution nature of the GCM projections, they need to be spatially downscaled for regional impact assessments. To downscale a given GCM projection, two methods have emerged: dynamical downscaling and statistical (empirical) downscaling (SDS). Dynamic downscaling involves configuring and running a regional climate model (RCM) nested within a given GCM projection (i.e., the GCM provides bounder conditions for the RCM). On the other hand, statistical downscaling aims at establishing a statistical relationship between observed local/regional climate variables of interest and synoptic (GCM-scale) climate predictors. The resulting empirical relationship is then applied to future GCM projections. A comparison of the pros and cons of dynamical versus statistical downscaling is outside the scope of this effort, but has been extensively studied and the reader is referred to Wilby et al. (1998); Murphy (1999); Wood et al. (2004); Benestad et al. (2007); Fowler et al. (2007), and references within those. The scope of this effort is to study methodology, a statistical framework, to propagate and account for GCM uncertainty in regional statistical downscaling assessment. In particular, we will explore how to leverage an ensemble of GCM projections to quantify the impact of the GCM uncertainty in such an assessment. There are three main component to this effort: (1) gather the necessary climate-related data for a regional SDS study, including multiple GCM projections, (2) carry out SDS, and (3) assess the uncertainty. The first step is carried out using tools written in the Python programming language, while analysis tools were developed in the statistical programming language R; see Figure 1.« less

Assessing the Assessment Methods: Climate Change and Hydrologic Impacts

NASA Astrophysics Data System (ADS)

Brekke, L. D.; Clark, M. P.; Gutmann, E. D.; Mizukami, N.; Mendoza, P. A.; Rasmussen, R.; Ikeda, K.; Pruitt, T.; Arnold, J. R.; Rajagopalan, B.

2014-12-01

The Bureau of Reclamation, the U.S. Army Corps of Engineers, and other water management agencies have an interest in developing reliable, science-based methods for incorporating climate change information into longer-term water resources planning. Such assessments must quantify projections of future climate and hydrology, typically relying on some form of spatial downscaling and bias correction to produce watershed-scale weather information that subsequently drives hydrology and other water resource management analyses (e.g., water demands, water quality, and environmental habitat). Water agencies continue to face challenging method decisions in these endeavors: (1) which downscaling method should be applied and at what resolution; (2) what observational dataset should be used to drive downscaling and hydrologic analysis; (3) what hydrologic model(s) should be used and how should these models be configured and calibrated? There is a critical need to understand the ramification of these method decisions, as they affect the signal and uncertainties produced by climate change assessments and, thus, adaptation planning. This presentation summarizes results from a three-year effort to identify strengths and weaknesses of widely applied methods for downscaling climate projections and assessing hydrologic conditions. Methods were evaluated from two perspectives: historical fidelity, and tendency to modulate a global climate model's climate change signal. On downscaling, four methods were applied at multiple resolutions: statistically using Bias Correction Spatial Disaggregation, Bias Correction Constructed Analogs, and Asynchronous Regression; dynamically using the Weather Research and Forecasting model. Downscaling results were then used to drive hydrologic analyses over the contiguous U.S. using multiple models (VIC, CLM, PRMS), with added focus placed on case study basins within the Colorado Headwaters. The presentation will identify which types of climate changes are expressed robustly across methods versus those that are sensitive to method choice; which method choices seem relatively more important; and where strategic investments in research and development can substantially improve guidance on climate change provided to water managers.

A climate-based multivariate extreme emulator of met-ocean-hydrological events for coastal flooding

NASA Astrophysics Data System (ADS)

Camus, Paula; Rueda, Ana; Mendez, Fernando J.; Tomas, Antonio; Del Jesus, Manuel; Losada, Iñigo J.

2015-04-01

Atmosphere-ocean general circulation models (AOGCMs) are useful to analyze large-scale climate variability (long-term historical periods, future climate projections). However, applications such as coastal flood modeling require climate information at finer scale. Besides, flooding events depend on multiple climate conditions: waves, surge levels from the open-ocean and river discharge caused by precipitation. Therefore, a multivariate statistical downscaling approach is adopted to reproduce relationships between variables and due to its low computational cost. The proposed method can be considered as a hybrid approach which combines a probabilistic weather type downscaling model with a stochastic weather generator component. Predictand distributions are reproduced modeling the relationship with AOGCM predictors based on a physical division in weather types (Camus et al., 2012). The multivariate dependence structure of the predictand (extreme events) is introduced linking the independent marginal distributions of the variables by a probabilistic copula regression (Ben Ayala et al., 2014). This hybrid approach is applied for the downscaling of AOGCM data to daily precipitation and maximum significant wave height and storm-surge in different locations along the Spanish coast. Reanalysis data is used to assess the proposed method. A commonly predictor for the three variables involved is classified using a regression-guided clustering algorithm. The most appropriate statistical model (general extreme value distribution, pareto distribution) for daily conditions is fitted. Stochastic simulation of the present climate is performed obtaining the set of hydraulic boundary conditions needed for high resolution coastal flood modeling. References: Camus, P., Menéndez, M., Méndez, F.J., Izaguirre, C., Espejo, A., Cánovas, V., Pérez, J., Rueda, A., Losada, I.J., Medina, R. (2014b). A weather-type statistical downscaling framework for ocean wave climate. Journal of Geophysical Research, doi: 10.1002/2014JC010141. Ben Ayala, M.A., Chebana, F., Ouarda, T.B.M.J. (2014). Probabilistic Gaussian Copula Regression Model for Multisite and Multivariable Downscaling, Journal of Climate, 27, 3331-3347.

Downscaled projections of Caribbean coral bleaching that can inform conservation planning.

PubMed

van Hooidonk, Ruben; Maynard, Jeffrey Allen; Liu, Yanyun; Lee, Sang-Ki

2015-09-01

Projections of climate change impacts on coral reefs produced at the coarse resolution (~1°) of Global Climate Models (GCMs) have informed debate but have not helped target local management actions. Here, projections of the onset of annual coral bleaching conditions in the Caribbean under Representative Concentration Pathway (RCP) 8.5 are produced using an ensemble of 33 Coupled Model Intercomparison Project phase-5 models and via dynamical and statistical downscaling. A high-resolution (~11 km) regional ocean model (MOM4.1) is used for the dynamical downscaling. For statistical downscaling, sea surface temperature (SST) means and annual cycles in all the GCMs are replaced with observed data from the ~4-km NOAA Pathfinder SST dataset. Spatial patterns in all three projections are broadly similar; the average year for the onset of annual severe bleaching is 2040-2043 for all projections. However, downscaled projections show many locations where the onset of annual severe bleaching (ASB) varies 10 or more years within a single GCM grid cell. Managers in locations where this applies (e.g., Florida, Turks and Caicos, Puerto Rico, and the Dominican Republic, among others) can identify locations that represent relative albeit temporary refugia. Both downscaled projections are different for the Bahamas compared to the GCM projections. The dynamically downscaled projections suggest an earlier onset of ASB linked to projected changes in regional currents, a feature not resolved in GCMs. This result demonstrates the value of dynamical downscaling for this application and means statistically downscaled projections have to be interpreted with caution. However, aside from west of Andros Island, the projections for the two types of downscaling are mostly aligned; projected onset of ASB is within ±10 years for 72% of the reef locations. © 2015 The Authors. Global Change Biology Published by John Wiley & Sons Ltd.

Examining the Performance of Statistical Downscaling Methods: Toward Matching Applications to Data Products

NASA Astrophysics Data System (ADS)

Dixon, K. W.; Lanzante, J. R.; Adams-Smith, D.

2017-12-01

Several challenges exist when seeking to use future climate model projections in a climate impacts study. A not uncommon approach is to utilize climate projection data sets derived from more than one future emissions scenario and from multiple global climate models (GCMs). The range of future climate responses represented in the set is sometimes taken to be indicative of levels of uncertainty in the projections. Yet, GCM outputs are deemed to be unsuitable for direct use in many climate impacts applications. GCM grids typically are viewed as being too coarse. Additionally, regional or local-scale biases in a GCM's simulation of the contemporary climate that may not be problematic from a global climate modeling perspective may be unacceptably large for a climate impacts application. Statistical downscaling (SD) of climate projections - a type of post-processing that uses observations to inform the refinement of GCM projections - is often used in an attempt to account for GCM biases and to provide additional spatial detail. "What downscaled climate projection is the best one to use" is a frequently asked question, but one that is not always easy to answer, as it can be dependent on stakeholder needs and expectations. Here we present results from a perfect model experimental design illustrating how SD method performance can vary not only by SD method, but how performance can also vary by location, season, climate variable of interest, amount of projected climate change, SD configuration choices, and whether one is interested in central tendencies or the tails of the distribution. Awareness of these factors can be helpful when seeking to determine the suitability of downscaled climate projections for specific climate impacts applications. It also points to the potential value of considering more than one SD data product in a study, so as to acknowledge uncertainties associated with the strengths and weaknesses of different downscaling methods.

Statistical downscaling rainfall using artificial neural network: significantly wetter Bangkok?

NASA Astrophysics Data System (ADS)

Vu, Minh Tue; Aribarg, Thannob; Supratid, Siriporn; Raghavan, Srivatsan V.; Liong, Shie-Yui

2016-11-01

Artificial neural network (ANN) is an established technique with a flexible mathematical structure that is capable of identifying complex nonlinear relationships between input and output data. The present study utilizes ANN as a method of statistically downscaling global climate models (GCMs) during the rainy season at meteorological site locations in Bangkok, Thailand. The study illustrates the applications of the feed forward back propagation using large-scale predictor variables derived from both the ERA-Interim reanalyses data and present day/future GCM data. The predictors are first selected over different grid boxes surrounding Bangkok region and then screened by using principal component analysis (PCA) to filter the best correlated predictors for ANN training. The reanalyses downscaled results of the present day climate show good agreement against station precipitation with a correlation coefficient of 0.8 and a Nash-Sutcliffe efficiency of 0.65. The final downscaled results for four GCMs show an increasing trend of precipitation for rainy season over Bangkok by the end of the twenty-first century. The extreme values of precipitation determined using statistical indices show strong increases of wetness. These findings will be useful for policy makers in pondering adaptation measures due to flooding such as whether the current drainage network system is sufficient to meet the changing climate and to plan for a range of related adaptation/mitigation measures.

The role of observational reference data for climate downscaling: Insights from the VALUE COST Action

NASA Astrophysics Data System (ADS)

Kotlarski, Sven; Gutiérrez, José M.; Boberg, Fredrik; Bosshard, Thomas; Cardoso, Rita M.; Herrera, Sixto; Maraun, Douglas; Mezghani, Abdelkader; Pagé, Christian; Räty, Olle; Stepanek, Petr; Soares, Pedro M. M.; Szabo, Peter

2016-04-01

VALUE is an open European network to validate and compare downscaling methods for climate change research (http://www.value-cost.eu). A key deliverable of VALUE is the development of a systematic validation framework to enable the assessment and comparison of downscaling methods. Such assessments can be expected to crucially depend on the existence of accurate and reliable observational reference data. In dynamical downscaling, observational data can influence model development itself and, later on, model evaluation, parameter calibration and added value assessment. In empirical-statistical downscaling, observations serve as predictand data and directly influence model calibration with corresponding effects on downscaled climate change projections. We here present a comprehensive assessment of the influence of uncertainties in observational reference data and of scale-related issues on several of the above-mentioned aspects. First, temperature and precipitation characteristics as simulated by a set of reanalysis-driven EURO-CORDEX RCM experiments are validated against three different gridded reference data products, namely (1) the EOBS dataset (2) the recently developed EURO4M-MESAN regional re-analysis, and (3) several national high-resolution and quality-controlled gridded datasets that recently became available. The analysis reveals a considerable influence of the choice of the reference data on the evaluation results, especially for precipitation. It is also illustrated how differences between the reference data sets influence the ranking of RCMs according to a comprehensive set of performance measures.

Validation of non-stationary precipitation series for site-specific impact assessment: comparison of two statistical downscaling techniques

NASA Astrophysics Data System (ADS)

Mullan, Donal; Chen, Jie; Zhang, Xunchang John

2016-02-01

Statistical downscaling (SD) methods have become a popular, low-cost and accessible means of bridging the gap between the coarse spatial resolution at which climate models output climate scenarios and the finer spatial scale at which impact modellers require these scenarios, with various different SD techniques used for a wide range of applications across the world. This paper compares the Generator for Point Climate Change (GPCC) model and the Statistical DownScaling Model (SDSM)—two contrasting SD methods—in terms of their ability to generate precipitation series under non-stationary conditions across ten contrasting global climates. The mean, maximum and a selection of distribution statistics as well as the cumulative frequencies of dry and wet spells for four different temporal resolutions were compared between the models and the observed series for a validation period. Results indicate that both methods can generate daily precipitation series that generally closely mirror observed series for a wide range of non-stationary climates. However, GPCC tends to overestimate higher precipitation amounts, whilst SDSM tends to underestimate these. This infers that GPCC is more likely to overestimate the effects of precipitation on a given impact sector, whilst SDSM is likely to underestimate the effects. GPCC performs better than SDSM in reproducing wet and dry day frequency, which is a key advantage for many impact sectors. Overall, the mixed performance of the two methods illustrates the importance of users performing a thorough validation in order to determine the influence of simulated precipitation on their chosen impact sector.

User's Manual for Downscaler Fusion Software

EPA Science Inventory

Recently, a series of 3 papers has been published in the statistical literature that details the use of downscaling to obtain more accurate and precise predictions of air pollution across the conterminous U.S. This downscaling approach combines CMAQ gridded numerical model output...

TopoSCALE v.1.0: downscaling gridded climate data in complex terrain

NASA Astrophysics Data System (ADS)

Fiddes, J.; Gruber, S.

2014-02-01

Simulation of land surface processes is problematic in heterogeneous terrain due to the the high resolution required of model grids to capture strong lateral variability caused by, for example, topography, and the lack of accurate meteorological forcing data at the site or scale it is required. Gridded data products produced by atmospheric models can fill this gap, however, often not at an appropriate spatial resolution to drive land-surface simulations. In this study we describe a method that uses the well-resolved description of the atmospheric column provided by climate models, together with high-resolution digital elevation models (DEMs), to downscale coarse-grid climate variables to a fine-scale subgrid. The main aim of this approach is to provide high-resolution driving data for a land-surface model (LSM). The method makes use of an interpolation of pressure-level data according to topographic height of the subgrid. An elevation and topography correction is used to downscale short-wave radiation. Long-wave radiation is downscaled by deriving a cloud-component of all-sky emissivity at grid level and using downscaled temperature and relative humidity fields to describe variability with elevation. Precipitation is downscaled with a simple non-linear lapse and optionally disaggregated using a climatology approach. We test the method in comparison with unscaled grid-level data and a set of reference methods, against a large evaluation dataset (up to 210 stations per variable) in the Swiss Alps. We demonstrate that the method can be used to derive meteorological inputs in complex terrain, with most significant improvements (with respect to reference methods) seen in variables derived from pressure levels: air temperature, relative humidity, wind speed and incoming long-wave radiation. This method may be of use in improving inputs to numerical simulations in heterogeneous and/or remote terrain, especially when statistical methods are not possible, due to lack of observations (i.e. remote areas or future periods).

Using multiple climate projections for assessing hydrological response to climate change in the Thukela River Basin, South Africa

NASA Astrophysics Data System (ADS)

Graham, L. Phil; Andersson, Lotta; Horan, Mark; Kunz, Richard; Lumsden, Trevor; Schulze, Roland; Warburton, Michele; Wilk, Julie; Yang, Wei

This study used climate change projections from different regional approaches to assess hydrological effects on the Thukela River Basin in KwaZulu-Natal, South Africa. Projecting impacts of future climate change onto hydrological systems can be undertaken in different ways and a variety of effects can be expected. Although simulation results from global climate models (GCMs) are typically used to project future climate, different outcomes from these projections may be obtained depending on the GCMs themselves and how they are applied, including different ways of downscaling from global to regional scales. Projections of climate change from different downscaling methods, different global climate models and different future emissions scenarios were used as input to simulations in a hydrological model to assess climate change impacts on hydrology. A total of 10 hydrological change simulations were made, resulting in a matrix of hydrological response results. This matrix included results from dynamically downscaled climate change projections from the same regional climate model (RCM) using an ensemble of three GCMs and three global emissions scenarios, and from statistically downscaled projections using results from five GCMs with the same emissions scenario. Although the matrix of results does not provide complete and consistent coverage of potential uncertainties from the different methods, some robust results were identified. In some regards, the results were in agreement and consistent for the different simulations. For others, particularly rainfall, the simulations showed divergence. For example, all of the statistically downscaled simulations showed an annual increase in precipitation and corresponding increase in river runoff, while the RCM downscaled simulations showed both increases and decreases in runoff. According to the two projections that best represent runoff for the observed climate, increased runoff would generally be expected for this basin in the future. Dealing with such variability in results is not atypical for assessing climate change impacts in Africa and practitioners are faced with how to interpret them. This work highlights the need for additional, well-coordinated regional climate downscaling for the region to further define the range of uncertainties involved.

Optimising predictor domains for spatially coherent precipitation downscaling

NASA Astrophysics Data System (ADS)

Radanovics, S.; Vidal, J.-P.; Sauquet, E.; Ben Daoud, A.; Bontron, G.

2012-04-01

Relationships between local precipitation (predictands) and large-scale circulation (predictors) are used for statistical downscaling purposes in various contexts, from medium-term forecasting to climate change impact studies. For hydrological purposes like flood forecasting, the downscaled precipitation spatial fields have furthermore to be coherent over possibly large basins. This thus first requires to know what predictor domain can be associated to the precipitation over each part of the studied basin. This study addresses this issue by identifying the optimum predictor domains over the whole of France, for a specific downscaling method based on a analogue approach and developed by Ben Daoud et al. (2011). The downscaling method used here is based on analogies on different variables: temperature, relative humidity, vertical velocity and geopotentials. The optimum predictor domain has been found to consist of the nearest grid cell for all variables except geopotentials (Ben Daoud et al., 2011). Moreover, geopotential domains have been found to be sensitive to the target location by Obled et al. (2002), and the present study thus focuses on optimizing the domains of this specific predictor over France. The predictor domains for geopotential at 500 hPa and 1000 hPa are optimised for 608 climatologically homogeneous zones in France using the ERA-40 reanalysis data for the large-scale predictors and local precipitation from the Safran near-surface atmospheric reanalysis (Vidal et al., 2010). The similarity of geopotential fields is measured by the Teweles and Wobus shape criterion. The predictive skill of different predictor domains for the different regions is tested with the Continuous Ranked Probability Score (CRPS) for the 25 best analogue days found with the statistical downscaling method. Rectangular predictor domains of different sizes, shapes and locations are tested, and the one that leads to the smallest CRPS for the zone in question is retained. The resulting optimised domains are analysed for defining regions where neighbouring zones have equal or similar predictor domains and identifying which French river basins contain zones associated with different predictor domains, i.e. are exposed to different meteorological influences. The above analysis will be used (1) to extend the statistical downscaling method of Ben Daoud et al. (2011) to the whole of France and (2) to develop it further in order to achieve spatially coherent forecasts while preserving the predictive skill on the local scale. Ben Daoud, A., Sauquet, E., Lang, M., Bontron, G., and Obled, C. (2011). Precipitation forecasting through an analog sorting technique: a comparative study. Advances in Geosciences, 29:103-107. doi: 10.5194/adgeo-29-103-2011 Obled, C., Bontron, G., and Garçon, R. (2002). Quantitative precipitation forecasts: a statistical adaptation of model outputs through an analogues sorting approach. Atmospheric Research, 63(3-4):303-324. doi: 10.1016/S0169-8095(02)00038-8 Vidal, J.-P., Martin, E., Franchistéguy, L., Baillon, M., and Soubeyroux, J.-M. (2010) A 50-year high-resolution atmospheric reanalysis over France with the Safran system. International Journal of Climatology, 30:1627-1644. doi: 10.1002/joc.2003

Downscaling climate model output for water resources impacts assessment (Invited)

NASA Astrophysics Data System (ADS)

Maurer, E. P.; Pierce, D. W.; Cayan, D. R.

2013-12-01

Water agencies in the U.S. and around the globe are beginning to wrap climate change projections into their planning procedures, recognizing that ongoing human-induced changes to hydrology can affect water management in significant ways. Future hydrology changes are derived using global climate model (GCM) projections, though their output is at a spatial scale that is too coarse to meet the needs of those concerned with local and regional impacts. Those investigating local impacts have employed a range of techniques for downscaling, the process of translating GCM output to a more locally-relevant spatial scale. Recent projects have produced libraries of publicly-available downscaled climate projections, enabling managers, researchers and others to focus on impacts studies, drawing from a shared pool of fine-scale climate data. Besides the obvious advantage to data users, who no longer need to develop expertise in downscaling prior to examining impacts, the use of the downscaled data by hundreds of people has allowed a crowdsourcing approach to examining the data. The wide variety of applications employed by different users has revealed characteristics not discovered during the initial data set production. This has led to a deeper look at the downscaling methods, including the assumptions and effect of bias correction of GCM output. Here new findings are presented related to the assumption of stationarity in the relationships between large- and fine-scale climate, as well as the impact of quantile mapping bias correction on precipitation trends. The validity of these assumptions can influence the interpretations of impacts studies using data derived using these standard statistical methods and help point the way to improved methods.

Optimising predictor domains for spatially coherent precipitation downscaling

NASA Astrophysics Data System (ADS)

Radanovics, S.; Vidal, J.-P.; Sauquet, E.; Ben Daoud, A.; Bontron, G.

2013-10-01

Statistical downscaling is widely used to overcome the scale gap between predictors from numerical weather prediction models or global circulation models and predictands like local precipitation, required for example for medium-term operational forecasts or climate change impact studies. The predictors are considered over a given spatial domain which is rarely optimised with respect to the target predictand location. In this study, an extended version of the growing rectangular domain algorithm is proposed to provide an ensemble of near-optimum predictor domains for a statistical downscaling method. This algorithm is applied to find five-member ensembles of near-optimum geopotential predictor domains for an analogue downscaling method for 608 individual target zones covering France. Results first show that very similar downscaling performances based on the continuous ranked probability score (CRPS) can be achieved by different predictor domains for any specific target zone, demonstrating the need for considering alternative domains in this context of high equifinality. A second result is the large diversity of optimised predictor domains over the country that questions the commonly made hypothesis of a common predictor domain for large areas. The domain centres are mainly distributed following the geographical location of the target location, but there are apparent differences between the windward and the lee side of mountain ridges. Moreover, domains for target zones located in southeastern France are centred more east and south than the ones for target locations on the same longitude. The size of the optimised domains tends to be larger in the southeastern part of the country, while domains with a very small meridional extent can be found in an east-west band around 47° N. Sensitivity experiments finally show that results are rather insensitive to the starting point of the optimisation algorithm except for zones located in the transition area north of this east-west band. Results also appear generally robust with respect to the archive length considered for the analogue method, except for zones with high interannual variability like in the Cévennes area. This study paves the way for defining regions with homogeneous geopotential predictor domains for precipitation downscaling over France, and therefore de facto ensuring the spatial coherence required for hydrological applications.

Climate change, estuaries and anadromous fish habitat in the northeastern United States: models, downscaling and uncertainty

NASA Astrophysics Data System (ADS)

Muhling, B.; Gaitan, C. F.; Tommasi, D.; Saba, V. S.; Stock, C. A.; Dixon, K. W.

2016-02-01

Estuaries of the northeastern United States provide essential habitat for many anadromous fishes, across a range of life stages. Climate change is likely to impact estuarine environments and habitats through multiple pathways. Increasing air temperatures will result in a warming water column, and potentially increased stratification. In addition, changes to precipitation patterns may alter freshwater inflow dynamics, leading to altered seasonal salinity regimes. However, the spatial resolution of global climate models is generally insufficient to resolve these processes at the scale of individual estuaries. Global models can be downscaled to a regional resolution using a variety of dynamical and statistical methods. In this study, we examined projections of estuarine conditions, and future habitat extent, for several anadromous fishes in the Chesapeake Bay using different statistical downscaling methods. Sources of error from physical and biological models were quantified, and key areas of uncertainty were highlighted. Results suggested that future projections of the distribution and recruitment of species most strongly linked to freshwater flow dynamics had the highest levels of uncertainty. The sensitivity of different life stages to environmental conditions, and the population-level responses of anadromous species to climate change, were identified as important areas for further research.

Spatial Scale Gap Filling Using an Unmanned Aerial System: A Statistical Downscaling Method for Applications in Precision Agriculture.

PubMed

Hassan-Esfahani, Leila; Ebtehaj, Ardeshir M; Torres-Rua, Alfonso; McKee, Mac

2017-09-14

Applications of satellite-borne observations in precision agriculture (PA) are often limited due to the coarse spatial resolution of satellite imagery. This paper uses high-resolution airborne observations to increase the spatial resolution of satellite data for related applications in PA. A new variational downscaling scheme is presented that uses coincident aerial imagery products from "AggieAir", an unmanned aerial system, to increase the spatial resolution of Landsat satellite data. This approach is primarily tested for downscaling individual band Landsat images that can be used to derive normalized difference vegetation index (NDVI) and surface soil moisture (SSM). Quantitative and qualitative results demonstrate promising capabilities of the downscaling approach enabling effective increase of the spatial resolution of Landsat imageries by orders of 2 to 4. Specifically, the downscaling scheme retrieved the missing high-resolution feature of the imageries and reduced the root mean squared error by 15, 11, and 10 percent in visual, near infrared, and thermal infrared bands, respectively. This metric is reduced by 9% in the derived NDVI and remains negligibly for the soil moisture products.

Spatial Scale Gap Filling Using an Unmanned Aerial System: A Statistical Downscaling Method for Applications in Precision Agriculture

PubMed Central

Hassan-Esfahani, Leila; Ebtehaj, Ardeshir M.; McKee, Mac

2017-01-01

Applications of satellite-borne observations in precision agriculture (PA) are often limited due to the coarse spatial resolution of satellite imagery. This paper uses high-resolution airborne observations to increase the spatial resolution of satellite data for related applications in PA. A new variational downscaling scheme is presented that uses coincident aerial imagery products from “AggieAir”, an unmanned aerial system, to increase the spatial resolution of Landsat satellite data. This approach is primarily tested for downscaling individual band Landsat images that can be used to derive normalized difference vegetation index (NDVI) and surface soil moisture (SSM). Quantitative and qualitative results demonstrate promising capabilities of the downscaling approach enabling effective increase of the spatial resolution of Landsat imageries by orders of 2 to 4. Specifically, the downscaling scheme retrieved the missing high-resolution feature of the imageries and reduced the root mean squared error by 15, 11, and 10 percent in visual, near infrared, and thermal infrared bands, respectively. This metric is reduced by 9% in the derived NDVI and remains negligibly for the soil moisture products. PMID:28906428

High resolution probabilistic precipitation forecast over Spain combining the statistical downscaling tool PROMETEO and the AEMET short range EPS system (AEMET/SREPS)

NASA Astrophysics Data System (ADS)

Cofino, A. S.; Santos, C.; Garcia-Moya, J. A.; Gutierrez, J. M.; Orfila, B.

2009-04-01

The Short-Range Ensemble Prediction System (SREPS) is a multi-LAM (UM, HIRLAM, MM5, LM and HRM) multi analysis/boundary conditions (ECMWF, UKMetOffice, DWD and GFS) run twice a day by AEMET (72 hours lead time) over a European domain, with a total of 5 (LAMs) x 4 (GCMs) = 20 members. One of the main goals of this project is analyzing the impact of models and boundary conditions in the short-range high-resolution forecasted precipitation. A previous validation of this method has been done considering a set of climate networks in Spain, France and Germany, by interpolating the prediction to the gauge locations (SREPS, 2008). In this work we compare these results with those obtained by using a statistical downscaling method to post-process the global predictions, obtaining an "advanced interpolation" for the local precipitation using climate network precipitation observations. In particular, we apply the PROMETEO downscaling system based on analogs and compare the SREPS ensemble of 20 members with the PROMETEO statistical ensemble of 5 (analog ensemble) x 4 (GCMs) = 20 members. Moreover, we will also compare the performance of a combined approach post-processing the SREPS outputs using the PROMETEO system. References: SREPS 2008. 2008 EWGLAM-SRNWP Meeting (http://www.aemet.es/documentos/va/divulgacion/conferencias/prediccion/Ewglam/PRED_CSantos.pdf)

Gridded Calibration of Ensemble Wind Vector Forecasts Using Ensemble Model Output Statistics

NASA Astrophysics Data System (ADS)

Lazarus, S. M.; Holman, B. P.; Splitt, M. E.

2017-12-01

A computationally efficient method is developed that performs gridded post processing of ensemble wind vector forecasts. An expansive set of idealized WRF model simulations are generated to provide physically consistent high resolution winds over a coastal domain characterized by an intricate land / water mask. Ensemble model output statistics (EMOS) is used to calibrate the ensemble wind vector forecasts at observation locations. The local EMOS predictive parameters (mean and variance) are then spread throughout the grid utilizing flow-dependent statistical relationships extracted from the downscaled WRF winds. Using data withdrawal and 28 east central Florida stations, the method is applied to one year of 24 h wind forecasts from the Global Ensemble Forecast System (GEFS). Compared to the raw GEFS, the approach improves both the deterministic and probabilistic forecast skill. Analysis of multivariate rank histograms indicate the post processed forecasts are calibrated. Two downscaling case studies are presented, a quiescent easterly flow event and a frontal passage. Strengths and weaknesses of the approach are presented and discussed.

SDSM-DC: A smarter approach to downscaling for decision-making? (Invited)

NASA Astrophysics Data System (ADS)

Wilby, R. L.; Dawson, C. W.

2011-12-01

General Circulation Model (GCM) output has been used for downscaling and impact assessments for at least 25 years. Downscaling methods raise awareness about risks posed by climate variability and change to human and natural systems. However, there are relatively few instances where these analyses have translated into actionable information for adaptation. One reason is that conventional ';top down' downscaling typically yields very large uncertainty bounds in projected impacts at regional and local scales. Consequently, there are growing calls to use downscaling tools in smarter ways that refocus attention on the decision problem rather than on the climate modelling per se. The talk begins with an overview of various application of the Statistical DownScaling Model (SDSM) over the last decade. This sample offers insights to downscaling practice in terms of regions and sectors of interest, modes of application and adaptation outcomes. The decision-centred rationale and functionality of the latest version of SDSM is then explained. This new downscaling tool does not require GCM input but enables the user to generate plausible daily weather scenarios that may be informed by climate model and/or palaeoenvironmental information. Importantly, the tool is intended for stress-testing adaptation options rather than for exhaustive analysis of uncertainty components. The approach is demonstrated by downscaling multi-basin, multi-elevation temperature and precipitation scenarios for the Upper Colorado River Basin. These scenarios are used alongside other narratives of future conditions that might potential affect the security of water supplies, and for evaluating steps that can be taken to manage these risks.

SDSM-DC: A smarter approach to downscaling for decision-making? (Invited)

NASA Astrophysics Data System (ADS)

Wilby, R. L.; Dawson, C. W.

2013-12-01

General Circulation Model (GCM) output has been used for downscaling and impact assessments for at least 25 years. Downscaling methods raise awareness about risks posed by climate variability and change to human and natural systems. However, there are relatively few instances where these analyses have translated into actionable information for adaptation. One reason is that conventional ';top down' downscaling typically yields very large uncertainty bounds in projected impacts at regional and local scales. Consequently, there are growing calls to use downscaling tools in smarter ways that refocus attention on the decision problem rather than on the climate modelling per se. The talk begins with an overview of various application of the Statistical DownScaling Model (SDSM) over the last decade. This sample offers insights to downscaling practice in terms of regions and sectors of interest, modes of application and adaptation outcomes. The decision-centred rationale and functionality of the latest version of SDSM is then explained. This new downscaling tool does not require GCM input but enables the user to generate plausible daily weather scenarios that may be informed by climate model and/or palaeoenvironmental information. Importantly, the tool is intended for stress-testing adaptation options rather than for exhaustive analysis of uncertainty components. The approach is demonstrated by downscaling multi-basin, multi-elevation temperature and precipitation scenarios for the Upper Colorado River Basin. These scenarios are used alongside other narratives of future conditions that might potential affect the security of water supplies, and for evaluating steps that can be taken to manage these risks.

Model Independence in Downscaled Climate Projections: a Case Study in the Southeast United States

NASA Astrophysics Data System (ADS)

Gray, G. M. E.; Boyles, R.

2016-12-01

Downscaled climate projections are used to deduce how the climate will change in future decades at local and regional scales. It is important to use multiple models to characterize part of the future uncertainty given the impact on adaptation decision making. This is traditionally employed through an equally-weighted ensemble of multiple GCMs downscaled using one technique. Newer practices include several downscaling techniques in an effort to increase the ensemble's representation of future uncertainty. However, this practice may be adding statistically dependent models to the ensemble. Previous research has shown a dependence problem in the GCM ensemble in multiple generations, but has not been shown in the downscaled ensemble. In this case study, seven downscaled climate projections on the daily time scale are considered: CLAREnCE10, SERAP, BCCA (CMIP5 and CMIP3 versions), Hostetler, CCR, and MACA-LIVNEH. These data represent 83 ensemble members, 44 GCMs, and two generations of GCMs. Baseline periods are compared against the University of Idaho's METDATA gridded observation dataset. Hierarchical agglomerative clustering is applied to the correlated errors to determine dependent clusters. Redundant GCMs across different downscaling techniques show the most dependence, while smaller dependence signals are detected within downscaling datasets and across generations of GCMs. These results indicate that using additional downscaled projections to increase the ensemble size must be done with care to avoid redundant GCMs and the process of downscaling may increase the dependence of those downscaled GCMs. Climate model generation does not appear dissimilar enough to be treated as two separate statistical populations for ensemble building at the local and regional scales.

Ensemble downscaling in coupled solar wind-magnetosphere modeling for space weather forecasting

PubMed Central

Owens, M J; Horbury, T S; Wicks, R T; McGregor, S L; Savani, N P; Xiong, M

2014-01-01

Advanced forecasting of space weather requires simulation of the whole Sun-to-Earth system, which necessitates driving magnetospheric models with the outputs from solar wind models. This presents a fundamental difficulty, as the magnetosphere is sensitive to both large-scale solar wind structures, which can be captured by solar wind models, and small-scale solar wind “noise,” which is far below typical solar wind model resolution and results primarily from stochastic processes. Following similar approaches in terrestrial climate modeling, we propose statistical “downscaling” of solar wind model results prior to their use as input to a magnetospheric model. As magnetospheric response can be highly nonlinear, this is preferable to downscaling the results of magnetospheric modeling. To demonstrate the benefit of this approach, we first approximate solar wind model output by smoothing solar wind observations with an 8 h filter, then add small-scale structure back in through the addition of random noise with the observed spectral characteristics. Here we use a very simple parameterization of noise based upon the observed probability distribution functions of solar wind parameters, but more sophisticated methods will be developed in the future. An ensemble of results from the simple downscaling scheme are tested using a model-independent method and shown to add value to the magnetospheric forecast, both improving the best estimate and quantifying the uncertainty. We suggest a number of features desirable in an operational solar wind downscaling scheme. Key Points Solar wind models must be downscaled in order to drive magnetospheric models Ensemble downscaling is more effective than deterministic downscaling The magnetosphere responds nonlinearly to small-scale solar wind fluctuations PMID:26213518

Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

NASA Astrophysics Data System (ADS)

Soares dos Santos, T.; Mendes, D.; Rodrigues Torres, R.

2016-01-01

Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANNs) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon; northeastern Brazil; and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model output and observed monthly precipitation. We used general circulation model (GCM) experiments for the 20th century (RCP historical; 1970-1999) and two scenarios (RCP 2.6 and 8.5; 2070-2100). The model test results indicate that the ANNs significantly outperform the MLR downscaling of monthly precipitation variability.

Artificial neural networks and multiple linear regression model using principal components to estimate rainfall over South America

NASA Astrophysics Data System (ADS)

dos Santos, T. S.; Mendes, D.; Torres, R. R.

2015-08-01

Several studies have been devoted to dynamic and statistical downscaling for analysis of both climate variability and climate change. This paper introduces an application of artificial neural networks (ANN) and multiple linear regression (MLR) by principal components to estimate rainfall in South America. This method is proposed for downscaling monthly precipitation time series over South America for three regions: the Amazon, Northeastern Brazil and the La Plata Basin, which is one of the regions of the planet that will be most affected by the climate change projected for the end of the 21st century. The downscaling models were developed and validated using CMIP5 model out- put and observed monthly precipitation. We used GCMs experiments for the 20th century (RCP Historical; 1970-1999) and two scenarios (RCP 2.6 and 8.5; 2070-2100). The model test results indicate that the ANN significantly outperforms the MLR downscaling of monthly precipitation variability.

Rainfall Downscaling Conditional on Upper-air Atmospheric Predictors: Improved Assessment of Rainfall Statistics in a Changing Climate

NASA Astrophysics Data System (ADS)

Langousis, Andreas; Mamalakis, Antonis; Deidda, Roberto; Marrocu, Marino

2015-04-01

To improve the level skill of Global Climate Models (GCMs) and Regional Climate Models (RCMs) in reproducing the statistics of rainfall at a basin level and at hydrologically relevant temporal scales (e.g. daily), two types of statistical approaches have been suggested. One is the statistical correction of climate model rainfall outputs using historical series of precipitation. The other is the use of stochastic models of rainfall to conditionally simulate precipitation series, based on large-scale atmospheric predictors produced by climate models (e.g. geopotential height, relative vorticity, divergence, mean sea level pressure). The latter approach, usually referred to as statistical rainfall downscaling, aims at reproducing the statistical character of rainfall, while accounting for the effects of large-scale atmospheric circulation (and, therefore, climate forcing) on rainfall statistics. While promising, statistical rainfall downscaling has not attracted much attention in recent years, since the suggested approaches involved complex (i.e. subjective or computationally intense) identification procedures of the local weather, in addition to demonstrating limited success in reproducing several statistical features of rainfall, such as seasonal variations, the distributions of dry and wet spell lengths, the distribution of the mean rainfall intensity inside wet periods, and the distribution of rainfall extremes. In an effort to remedy those shortcomings, Langousis and Kaleris (2014) developed a statistical framework for simulation of daily rainfall intensities conditional on upper air variables, which accurately reproduces the statistical character of rainfall at multiple time-scales. Here, we study the relative performance of: a) quantile-quantile (Q-Q) correction of climate model rainfall products, and b) the statistical downscaling scheme of Langousis and Kaleris (2014), in reproducing the statistical structure of rainfall, as well as rainfall extremes, at a regional level. This is done for an intermediate-sized catchment in Italy, i.e. the Flumendosa catchment, using climate model rainfall and atmospheric data from the ENSEMBLES project (http://ensembleseu.metoffice.com). In doing so, we split the historical rainfall record of mean areal precipitation (MAP) in 15-year calibration and 45-year validation periods, and compare the historical rainfall statistics to those obtained from: a) Q-Q corrected climate model rainfall products, and b) synthetic rainfall series generated by the suggested downscaling scheme. To our knowledge, this is the first time that climate model rainfall and statistically downscaled precipitation are compared to catchment-averaged MAP at a daily resolution. The obtained results are promising, since the proposed downscaling scheme is more accurate and robust in reproducing a number of historical rainfall statistics, independent of the climate model used and the length of the calibration period. This is particularly the case for the yearly rainfall maxima, where direct statistical correction of climate model rainfall outputs shows increased sensitivity to the length of the calibration period and the climate model used. The robustness of the suggested downscaling scheme in modeling rainfall extremes at a daily resolution, is a notable feature that can effectively be used to assess hydrologic risk at a regional level under changing climatic conditions. Acknowledgments The research project is implemented within the framework of the Action «Supporting Postdoctoral Researchers» of the Operational Program "Education and Lifelong Learning" (Action's Beneficiary: General Secretariat for Research and Technology), and is co-financed by the European Social Fund (ESF) and the Greek State. CRS4 highly acknowledges the contribution of the Sardinian regional authorities.

GPCC - A weather generator-based statistical downscaling tool for site-specific assessment of climate change impacts

USDA-ARS?s Scientific Manuscript database

Resolution of climate model outputs are too coarse to be used as direct inputs to impact models for assessing climate change impacts on agricultural production, water resources, and eco-system services at local or site-specific scales. Statistical downscaling approaches are usually used to bridge th...

Downscaling and hydrological uncertainties in 20th century hydrometeorological reconstructions over France

NASA Astrophysics Data System (ADS)

Vidal, Jean-Philippe; Caillouet, Laurie; Dayon, Gildas; Boé, Julien; Sauquet, Eric; Thirel, Guillaume; Graff, Benjamin

2017-04-01

The record length of streamflow observations is generally limited to the last 50 years, which is not enough to properly explore the natural hydrometeorological variability, a key to better understand the effects of anthropogenic climate change. This work proposes a comparison of different hydrometeorological reconstruction datasets over France built on the downscaling of the NOAA 20th century global extended reanalysis (20CR, Compo et al., 2011). It aims at assessing the uncertainties related to these reconstructions and improving our knowledge of the multi-decadal hydrometeorological variability over the 20th century. High-resolution daily meteorological reconstructions over the period 1871-2012 are obtained with two statistical downscaling methods based on the analogue approach: the deterministic ANALOG method (Dayon et al., 2015) and the probabilistic SCOPE method (Caillouet et al., 2016). These reconstructions are then used as forcings for the GR6J lumped conceptual rainfall-runoff model and the SIM physically-based distributed hydrological model, in order to derive daily streamflow reconstructions over a set of around 70 reference near-natural catchments. Results show a large multi-decadal streamflow variability over the last 140 years, which is however relatively consistent over France. Empirical estimates of three types of uncertainty - structure of the downscaling method, small-scale internal variability, and hydrological model structure - show roughly equal contributions to the streamflow uncertainty at the annual time scale, with values as high as 20% of the interannual mean. Caillouet, L., Vidal, J.-P., Sauquet, E., and Graff, B.: Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France, Clim. Past, 12, 635-662, doi:10.5194/cp-12-635-2016, 2016. Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Matsui, N., Allan, R. J., Yin, X., Gleason, B. E., Vose, R. S., Rutledge, G., Bessemoulin, P., Brönnimann, S., Brunet, M., Crouthamel, R. I., Grant, A. N., Groisman, P. Y., Jones, P. D., Kruk, M. C., Kruger, A. C., Marshall, G. J., Maugeri, M., Mok, H. Y., Nordli, Ø., Ross, T. F., Trigo, R. M., Wang, X. L., Woodruff, S. D., and Worley, S. J.: The Twentieth Century Reanalysis Project. Q. J. R. Meteorol. Soc., 137, 1-28, doi:10.1002/qj.776, 2011. Dayon, G., Boé, J., and Martin, E.: Transferability in the future climate of a statistical downscaling method for precipitation in France, J. Geophys. Res.-Atmos., 120, 1023-1043, doi: 10.1002/2014JD022236, 2015.

Application of SDSM and LARS-WG for simulating and downscaling of rainfall and temperature

NASA Astrophysics Data System (ADS)

Hassan, Zulkarnain; Shamsudin, Supiah; Harun, Sobri

2014-04-01

Climate change is believed to have significant impacts on the water basin and region, such as in a runoff and hydrological system. However, impact studies on the water basin and region are difficult, since general circulation models (GCMs), which are widely used to simulate future climate scenarios, do not provide reliable hours of daily series rainfall and temperature for hydrological modeling. There is a technique named as "downscaling techniques", which can derive reliable hour of daily series rainfall and temperature due to climate scenarios from the GCMs output. In this study, statistical downscaling models are used to generate the possible future values of local meteorological variables such as rainfall and temperature in the selected stations in Peninsular of Malaysia. The models are: (1) statistical downscaling model (SDSM) that utilized the regression models and stochastic weather generators and (2) Long Ashton research station weather generator (LARS-WG) that only utilized the stochastic weather generators. The LARS-WG and SDSM models obviously are feasible methods to be used as tools in quantifying effects of climate change condition in a local scale. SDSM yields a better performance compared to LARS-WG, except SDSM is slightly underestimated for the wet and dry spell lengths. Although both models do not provide identical results, the time series generated by both methods indicate a general increasing trend in the mean daily temperature values. Meanwhile, the trend of the daily rainfall is not similar to each other, with SDSM giving a relatively higher change of annual rainfall compared to LARS-WG.

A comparison of dynamical and statistical downscaling methods for regional wave climate projections along French coastlines.

NASA Astrophysics Data System (ADS)

Laugel, Amélie; Menendez, Melisa; Benoit, Michel; Mattarolo, Giovanni; Mendez, Fernando

2013-04-01

Wave climate forecasting is a major issue for numerous marine and coastal related activities, such as offshore industries, flooding risks assessment and wave energy resource evaluation, among others. Generally, there are two main ways to predict the impacts of the climate change on the wave climate at regional scale: the dynamical and the statistical downscaling of GCM (Global Climate Model). In this study, both methods have been applied on the French coast (Atlantic , English Channel and North Sea shoreline) under three climate change scenarios (A1B, A2, B1) simulated with the GCM ARPEGE-CLIMAT, from Météo-France (AR4, IPCC). The aim of the work is to characterise the wave climatology of the 21st century and compare the statistical and dynamical methods pointing out advantages and disadvantages of each approach. The statistical downscaling method proposed by the Environmental Hydraulics Institute of Cantabria (Spain) has been applied (Menendez et al., 2011). At a particular location, the sea-state climate (Predictand Y) is defined as a function, Y=f(X), of several atmospheric circulation patterns (Predictor X). Assuming these climate associations between predictor and predictand are stationary, the statistical approach has been used to project the future wave conditions with reference to the GCM. The statistical relations between predictor and predictand have been established over 31 years, from 1979 to 2009. The predictor is built as the 3-days-averaged squared sea level pressure gradient from the hourly CFSR database (Climate Forecast System Reanalysis, http://cfs.ncep.noaa.gov/cfsr/). The predictand has been extracted from the 31-years hindcast sea-state database ANEMOC-2 performed with the 3G spectral wave model TOMAWAC (Benoit et al., 1996), developed at EDF R&D LNHE and Saint-Venant Laboratory for Hydraulics and forced by the CFSR 10m wind field. Significant wave height, peak period and mean wave direction have been extracted with an hourly-resolution at 110 coastal locations along the French coast. The model, based on the BAJ parameterization of the source terms (Bidlot et al, 2007) was calibrated against ten years of GlobWave altimeter observations (2000-2009) and validated through deep and shallow water buoy observations. The dynamical downscaling method has been performed with the same numerical wave model TOMAWAC used for building ANEMOC-2. Forecast simulations are forced by the 10m wind fields of ARPEGE-CLIMAT (A1B, A2, B1) from 2010 to 2100. The model covers the Atlantic Ocean and uses a spatial resolution along the French and European coast of 10 and 20 km respectively. The results of the model are stored with a time resolution of one hour. References: Benoit M., Marcos F., and F. Becq, (1996). Development of a third generation shallow-water wave model with unstructured spatial meshing. Proc. 25th Int. Conf. on Coastal Eng., (ICCE'1996), Orlando (Florida, USA), pp 465-478. Bidlot J-R, Janssen P. and Adballa S., (2007). A revised formulation of ocean wave dissipation and its model impact, technical memorandum ECMWF n°509. Menendez, M., Mendez, F.J., Izaguirre,C., Camus, P., Espejo, A., Canovas, V., Minguez, R., Losada, I.J., Medina, R. (2011). Statistical Downscaling of Multivariate Wave Climate Using a Weather Type Approach, 12th International Workshop on Wave Hindcasting and Forecasting and 3rd Coastal Hazard Symposium, Kona (Hawaii).

Application of statistical downscaling technique for the production of wine grapes (Vitis vinifera L.) in Spain

NASA Astrophysics Data System (ADS)

Gaitán Fernández, E.; García Moreno, R.; Pino Otín, M. R.; Ribalaygua Batalla, J.

2012-04-01

Climate and soil are two of the most important limiting factors for agricultural production. Nowadays climate change has been documented in many geographical locations affecting different cropping systems. The General Circulation Models (GCM) has become important tools to simulate the more relevant aspects of the climate expected for the XXI century in the frame of climatic change. These models are able to reproduce the general features of the atmospheric dynamic but their low resolution (about 200 Km) avoids a proper simulation of lower scale meteorological effects. Downscaling techniques allow overcoming this problem by adapting the model outcomes to local scale. In this context, FIC (Fundación para la Investigación del Clima) has developed a statistical downscaling technique based on a two step analogue methods. This methodology has been broadly tested on national and international environments leading to excellent results on future climate models. In a collaboration project, this statistical downscaling technique was applied to predict future scenarios for the grape growing systems in Spain. The application of such model is very important to predict expected climate for the different growing crops, mainly for grape, where the success of different varieties are highly related to climate and soil. The model allowed the implementation of agricultural conservation practices in the crop production, detecting highly sensible areas to negative impacts produced by any modification of climate in the different regions, mainly those protected with protected designation of origin, and the definition of new production areas with optimal edaphoclimatic conditions for the different varieties.

Downscaling wind and wavefields for 21st century coastal flood hazard projections in a region of complex terrain

USGS Publications Warehouse

O'Neill, Andrea; Erikson, Li; Barnard, Patrick

2017-01-01

While global climate models (GCMs) provide useful projections of near-surface wind vectors into the 21st century, resolution is not sufficient enough for use in regional wave modeling. Statistically downscaled GCM projections from Multivariate Adaptive Constructed Analogues provide daily averaged near-surface winds at an appropriate spatial resolution for wave modeling within the orographically complex region of San Francisco Bay, but greater resolution in time is needed to capture the peak of storm events. Short-duration high wind speeds, on the order of hours, are usually excluded in statistically downscaled climate models and are of key importance in wave and subsequent coastal flood modeling. Here we present a temporal downscaling approach, similar to constructed analogues, for near-surface winds suitable for use in local wave models and evaluate changes in wind and wave conditions for the 21st century. Reconstructed hindcast winds (1975–2004) recreate important extreme wind values within San Francisco Bay. A computationally efficient method for simulating wave heights over long time periods was used to screen for extreme events. Wave hindcasts show resultant maximum wave heights of 2.2 m possible within the Bay. Changes in extreme over-water wind speeds suggest contrasting trends within the different regions of San Francisco Bay, but 21th century projections show little change in the overall magnitude of extreme winds and locally generated waves.

Tethys – A Python Package for Spatial and Temporal Downscaling of Global Water Withdrawals

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Xinya; Vernon, Chris R.; Hejazi, Mohamad I.

Downscaling of water withdrawals from regional/national to local scale is a fundamental step and also a common problem when integrating large scale economic and integrated assessment models with high-resolution detailed sectoral models. Tethys, an open-access software written in Python, is developed with statistical downscaling algorithms, to spatially and temporally downscale water withdrawal data to a finer scale. The spatial resolution will be downscaled from region/basin scale to grid (0.5 geographic degree) scale and the temporal resolution will be downscaled from year to month. Tethys is used to produce monthly global gridded water withdrawal products based on estimates from the Globalmore » Change Assessment Model (GCAM).« less

Tethys – A Python Package for Spatial and Temporal Downscaling of Global Water Withdrawals

DOE PAGES

Li, Xinya; Vernon, Chris R.; Hejazi, Mohamad I.; ...

2018-02-09

Downscaling of water withdrawals from regional/national to local scale is a fundamental step and also a common problem when integrating large scale economic and integrated assessment models with high-resolution detailed sectoral models. Tethys, an open-access software written in Python, is developed with statistical downscaling algorithms, to spatially and temporally downscale water withdrawal data to a finer scale. The spatial resolution will be downscaled from region/basin scale to grid (0.5 geographic degree) scale and the temporal resolution will be downscaled from year to month. Tethys is used to produce monthly global gridded water withdrawal products based on estimates from the Globalmore » Change Assessment Model (GCAM).« less

The Impact of Climate Projection Method on the Analysis of Climate Change in Semi-arid Basins

NASA Astrophysics Data System (ADS)

Halper, E.; Shamir, E.

2016-12-01

In small basins with arid climates, rainfall characteristics are highly variable and stream flow is tightly coupled with the nuances of rainfall events (e.g. hourly precipitation patterns Climate change assessments in these basins typically employ CMIP5 projections downscaled with Bias Corrected Statistical Downscaling and Bias Correction/Constructed Analogs (BCSD-BCCA) methods, but these products have drawbacks. Specifically, BCSD-BCCA these projections do not explicitly account for localized physical precipitation mechanisms (e.g. monsoon and snowfall) that are essential to many hydrological systems in the U. S. Southwest. An investigation of the impact of different types of precipitation projections for two kinds of hydrologic studies is being conducted under the U.S. Bureau of Reclamation's Science and Technology Grant Program. An innovative modeling framework consisting of a weather generator of likely hourly precipitation scenarios, coupled with rainfall-runoff, river routing and groundwater models, has been developed in the Nogales, Arizona area. This framework can simulate the impact of future climate on municipal water operations. This framework allows the rigorous comparison of the BCSD-BCCA methods with alternative approaches including rainfall output from dynamical downscaled Regional Climate Models (RCM), a stochastic rainfall generator forced by either Global Climate Models (GCM) or RCM, and projections using historical records conditioned on either GCM or RCM. The results will provide guide for the use of climate change projections into hydrologic studies of semi-arid areas. The project extends this comparison to analyses of flood control. Large flows on the Bill Williams River are a concern for the operation of dams along the Lower Colorado River. After adapting the weather generator for this region, we will evaluate the model performance for rainfall and stream flow, with emphasis on statistical features important to the specific needs of flood management. The end product of the research is to develop a test to guide selection of a precipitation projection method (including downscaling procedure) for a given region and objective.

Statistical downscaling of daily precipitation over Llobregat river basin in Catalonia (Spain) using three downscaling methods.

NASA Astrophysics Data System (ADS)

Ballinas, R.; Versini, P.-A.; Sempere, D.; Escaler, I.

2009-09-01

Any long-term change in the patterns of average weather in a global or regional scale is called climate change. It may cause a progressive increase of atmospheric temperature and consequently may change the amount, frequency and intensity of precipitation. All these changes of meteorological parameters may modify the water cycle: run-off, infiltration, aquifer recharge, etc. Recent studies in Catalonia foresee changes in hydrological systems caused by climate change. This will lead to alterations in the hydrological cycle that could impact in land use, in the regimen of water extractions, in the hydrological characteristics of the territory and reduced groundwater recharge. Besides, can expect a loss of flow in rivers. In addition to possible increases in the frequency of extreme rainfall, being necessary to modify the design of infrastructure. Because this, it work focuses on studying the impacts of climate change in one of the most important basins in Catalonia, the Llobregat River Basin. The basin is the hub of the province of Barcelona. It is a highly populated and urbanized catchment, where water resources are used for different purposes, as drinking water production, agricultural irrigation, industry and hydro-electrical energy production. In consequence, many companies and communities depend on these resources. To study the impact of climate change in the Llobregat basin, storms (frequency, intensity) mainly, we will need regional climate change information. A regional climate is determined by interactions at large, regional and local scales. The general circulation models (GCMs) are run at too coarse resolution to permit accurate description of these regional and local interactions. So far, they have been unable to provide consistent estimates of climate change on a local scale. Several regionalization techniques have been developed to bridge the gap between the large-scale information provided by GCMs and fine spatial scales required for regional and environmental impact studies. Downscaling methods to assess the effect of large-scale circulations on local parameters have. Statistical downscaling methods are based on the view that regional climate can be conditioned by two factors: large-scale climatic state and regional/local features. Local climate information is derived by first developing a statistical model which relates large-scale variables or "predictors" for which GCMs are trustable to regional or local surface "predictands" for which models are less skilful. The main advantage of these methods is that they are computationally inexpensive, and can be applied to outputs from different GCM experiments. Three statistical downscaling methods are applied: Analogue method, Delta Change and Direct Forcing. These methods have been used to determine daily precipitation projections at rain gauge location to study the intensity, frequency and variability of storms in a context of climate change in the Llobregat River Basin in Catalonia, Spain. This work is part of the European project "Water Change" (included in the LIFE + Environment Policy and Governance program). It deals with Medium and long term water resources modelling as a tool for planning and global change adaptation. Two stakeholders involved in the project provided the historical time series: Catalan Water Agency (ACA) and the State Meteorological Agency (AEMET).

Statistical Downscaling of WRF-Chem Model: An Air Quality Analysis over Bogota, Colombia

NASA Astrophysics Data System (ADS)

Kumar, Anikender; Rojas, Nestor

2015-04-01

Statistical downscaling is a technique that is used to extract high-resolution information from regional scale variables produced by coarse resolution models such as Chemical Transport Models (CTMs). The fully coupled WRF-Chem (Weather Research and Forecasting with Chemistry) model is used to simulate air quality over Bogota. Bogota is a tropical Andean megacity located over a high-altitude plateau in the middle of very complex terrain. The WRF-Chem model was adopted for simulating the hourly ozone concentrations. The computational domains were chosen of 120x120x32, 121x121x32 and 121x121x32 grid points with horizontal resolutions of 27, 9 and 3 km respectively. The model was initialized with real boundary conditions using NCAR-NCEP's Final Analysis (FNL) and a 1ox1o (~111 km x 111 km) resolution. Boundary conditions were updated every 6 hours using reanalysis data. The emission rates were obtained from global inventories, namely the REanalysis of the TROpospheric (RETRO) chemical composition and the Emission Database for Global Atmospheric Research (EDGAR). Multiple linear regression and artificial neural network techniques are used to downscale the model output at each monitoring stations. The results confirm that the statistically downscaled outputs reduce simulated errors by up to 25%. This study provides a general overview of statistical downscaling of chemical transport models and can constitute a reference for future air quality modeling exercises over Bogota and other Colombian cities.

Demeter-W

DOE Office of Scientific and Technical Information (OSTI.GOV)

2017-09-27

Demeter-W, an open-access software written in Python, consists of extensible module packages. It is developed with statistical downscaling algorithms, to spatially and temporally downscale water demand data into finer scale. The spatial resolution will be downscaled from region/basin scale to grid (0.5 geographic degree) scale and the temporal resolution will be downscaled from year to month. For better understanding of the driving forces and patterns for global water withdrawal, the researchers is able to utilize Demeter-W to reconstruct the data sets to examine the issues related to water withdrawals at fine spatial and temporal scales.

Statistical Downscaling of Gusts During Extreme European Winter Storms Using Radial-Basis-Function Networks

NASA Astrophysics Data System (ADS)

Voigt, M.; Lorenz, P.; Kruschke, T.; Osinski, R.; Ulbrich, U.; Leckebusch, G. C.

2012-04-01

Winterstorms and related gusts can cause extensive socio-economic damages. Knowledge about the occurrence and the small scale structure of such events may help to make regional estimations of storm losses. For a high spatial and temporal representation, the use of dynamical downscaling methods (RCM) is a cost-intensive and time-consuming option and therefore only applicable for a limited number of events. The current study explores a methodology to provide a statistical downscaling, which offers small scale structured gust fields from an extended large scale structured eventset. Radial-basis-function (RBF) networks in combination with bidirectional Kohonen (BDK) maps are used to generate the gustfields on a spatial resolution of 7 km from the 6-hourly mean sea level pressure field from ECMWF reanalysis data. BDK maps are a kind of neural network which handles supervised classification problems. In this study they are used to provide prototypes for the RBF network and give a first order approximation for the output data. A further interpolation is done by the RBF network. For the training process the 50 most extreme storm events over the North Atlantic area from 1957 to 2011 are used, which have been selected from ECMWF reanalysis datasets ERA40 and ERA-Interim by an objective wind based tracking algorithm. These events were downscaled dynamically by application of the DWD model chain GME → COSMO-EU. Different model parameters and their influence on the quality of the generated high-resolution gustfields are studied. It is shown that the statistical RBF network approach delivers reasonable results in modeling the regional gust fields for untrained events.

Extension of the SIM Hydrometeorological Reanalysis Over the Entire 20th Century by Combination of Observations and Statistical Downscaling

NASA Astrophysics Data System (ADS)

Minvielle, M.; Céron, J.; Page, C.

2013-12-01

The SAFRAN-ISBA-MODCOU (SIM) system is a combination of three different components: an atmospheric analysis system (SAFRAN) providing the atmospheric forcing for a land surface model (ISBA) that computes surface water and energy budgets and a hydrological model (MODCOU) that provides river flows and level of several aquifers. The variables generated by the SIM chain constitute the SIM reanalysis and the current version only covers the 1958-2012 period. However, long climate datasets are required for evaluation and verification of climate hindcasts/forecasts and to isolate the contribution of natural decadal variability from that of anthropogenic forcing to climate variations. The aim of this work is to extend of the fine-mesh SIM reanalysis to the entire 20th century, especially focusing on temperature and rainfall over France, but also soil wetness and river flows. This extension will first allow a detailed investigation of the influence of decadal variability on France at very fine spatial scales and will provide crucial information for climate model evaluation. Before 1958, the density of available observations from Météo-France necessary to force SAFRAN (rainfall, snow, wind, temperature, humidity, cloudiness) is much lower than today, and not sufficient to produce a correct SIM reanalysis. That's why is has been decided to use the available atmospheric observations over the past decades combined to a statistical downscaling algorithm to overcome the lack of observations. The DSCLIM software package implemented by the CERFACS and using a weather typing based statistical methodology will be used as statistical downscaling method to reconstruct the atmospheric variables necessary to force the ISBA-MODCOU hydrological component. The first stage of this work was to estimate and compare the bias and strengths of the two approaches in their ability to reconstruct the past decades. In this sense, SIM hydro-meteorological experiments were performed for some recent years, with a number of observations artificially reduced to a number similar to years 1910, 1930 and 1950. Concurrently, the same recent years have been downscaled by DSCLIM and used to force ISBA-MODCOU. Afterwards, some additional experiments with some modified parameters in the DSCLIM algorithm have been performed in order to adapt the methodology to the study case, and thus trying to improve its performances. Several configurations of the DSCLIM algorithm were applied to the entire century, using the NOAA20CR reanalysis as large-scale predictor. The reconstructed atmospheric variables are compared to the available observations over the entire century to estimate the ability of the statistical downscaling method to reproduce a correct interannual to multidecadal variability. Finally, a novel method is tested: available observations over past decades are introduced in the DSCLIM algorithm, in order to obtain a reconstructed dataset as realistic as possible.

Climate Change Impacts at Department of Defense

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kotamarthi, Rao; Wang, Jiali; Zoebel, Zach

This project is aimed at providing the U.S. Department of Defense (DoD) with a comprehensive analysis of the uncertainty associated with generating climate projections at the regional scale that can be used by stakeholders and decision makers to quantify and plan for the impacts of future climate change at specific locations. The merits and limitations of commonly used downscaling models, ranging from simple to complex, are compared, and their appropriateness for application at installation scales is evaluated. Downscaled climate projections are generated at selected DoD installations using dynamic and statistical methods with an emphasis on generating probability distributions of climatemore » variables and their associated uncertainties. The sites selection and selection of variables and parameters for downscaling was based on a comprehensive understanding of the current and projected roles that weather and climate play in operating, maintaining, and planning DoD facilities and installations.« less

Development of incremental dynamical downscaling and analysis system for regional scale climate change projections

NASA Astrophysics Data System (ADS)

Wakazuki, Yasutaka; Hara, Masayuki; Fujita, Mikiko; Ma, Xieyao; Kimura, Fujio

2013-04-01

Regional scale climate change projections play an important role in assessments of influences of global warming and include statistical (SD) and dynamical downscaling (DD) approaches. One of DD methods is developed basing on the pseudo-global-warming (PGW) method developed by Kimura and Kitoh (2007) in this study. In general, DD uses regional climate model (RCM) with lateral boundary data. In PGW method, the climatological mean difference estimated by GCMs are added to the objective analysis data (ANAL), and the data are used as the lateral boundary data in the future climate simulations. The ANAL is also used as the lateral boundary conditions of the present climate simulation. One of merits of the PGW method is that influences of biases of GCMs in RCM simulations are reduced. However, the PGW method does not treat climate changes in relative humidity, year-to-year variation, and short-term disturbances. The developing new downscaling method is named as the incremental dynamical downscaling and analysis system (InDDAS). The InDDAS treat climate changes in relative humidity and year-to-year variations. On the other hand, uncertainties of climate change projections estimated by many GCMs are large and are not negligible. Thus, stochastic regional scale climate change projections are expected for assessments of influences of global warming. Many RCM runs must be performed to make stochastic information. However, the computational costs are huge because grid size of RCM runs should be small to resolve heavy rainfall phenomena. Therefore, the number of runs to make stochastic information must be reduced. In InDDAS, climatological differences added to ANAL become statistically pre-analyzed information. The climatological differences of many GCMs are divided into mean climatological difference (MD) and departures from MD. The departures are analyzed by principal component analysis, and positive and negative perturbations (positive and negative standard deviations multiplied by departure patterns (eigenvectors)) with multi modes are added to MD. Consequently, the most likely future states are calculated with climatological difference of MD. For example, future states in cases that temperature increase is large and small are calculated with MD plus positive and negative perturbations of the first mode.

Post-processing of multi-hydrologic model simulations for improved streamflow projections

NASA Astrophysics Data System (ADS)

khajehei, sepideh; Ahmadalipour, Ali; Moradkhani, Hamid

2016-04-01

Hydrologic model outputs are prone to bias and uncertainty due to knowledge deficiency in model and data. Uncertainty in hydroclimatic projections arises due to uncertainty in hydrologic model as well as the epistemic or aleatory uncertainties in GCM parameterization and development. This study is conducted to: 1) evaluate the recently developed multi-variate post-processing method for historical simulations and 2) assess the effect of post-processing on uncertainty and reliability of future streamflow projections in both high-flow and low-flow conditions. The first objective is performed for historical period of 1970-1999. Future streamflow projections are generated for 10 statistically downscaled GCMs from two widely used downscaling methods: Bias Corrected Statistically Downscaled (BCSD) and Multivariate Adaptive Constructed Analogs (MACA), over the period of 2010-2099 for two representative concentration pathways of RCP4.5 and RCP8.5. Three semi-distributed hydrologic models were employed and calibrated at 1/16 degree latitude-longitude resolution for over 100 points across the Columbia River Basin (CRB) in the pacific northwest USA. Streamflow outputs are post-processed through a Bayesian framework based on copula functions. The post-processing approach is relying on a transfer function developed based on bivariate joint distribution between the observation and simulation in historical period. Results show that application of post-processing technique leads to considerably higher accuracy in historical simulations and also reducing model uncertainty in future streamflow projections.

Climate Change Implications for Tropical Islands: Interpolating and Interpreting Statistically Downscaled GCM Projections for Management and Planning

Treesearch

Azad Henareh Khalyani; William A. Gould; Eric Harmsen; Adam Terando; Maya Quinones; Jaime A. Collazo

2016-01-01

Climate change and water resources in a tropical island system: propagation of uncertainty from statistically downscaled climate models to hydrologic models

Treesearch

Ashley E. Van Beusekom; William A. Gould; Adam J. Terando; Jaime A. Collazo

2015-01-01

Many tropical islands have limited water resources with historically increasing demand, all potentially affected by a changing climate. The effects of climate change on island hydrology are difficult to model due to steep local precipitation gradients and sparse data. Thiswork uses 10 statistically downscaled general circulationmodels (GCMs) under two greenhouse gas...

Multi objective climate change impact assessment using multi downscaled climate scenarios

NASA Astrophysics Data System (ADS)

Rana, Arun; Moradkhani, Hamid

2016-04-01

Global Climate Models (GCMs) are often used to downscale the climatic parameters on a regional and global scale. In the present study, we have analyzed the changes in precipitation and temperature for future scenario period of 2070-2099 with respect to historical period of 1970-2000 from a set of statistically downscaled GCM projections for Columbia River Basin (CRB). Analysis is performed using 2 different statistically downscaled climate projections namely the Bias Correction and Spatial Downscaling (BCSD) technique generated at Portland State University and the Multivariate Adaptive Constructed Analogs (MACA) technique, generated at University of Idaho, totaling to 40 different scenarios. Analysis is performed on spatial, temporal and frequency based parameters in the future period at a scale of 1/16th of degree for entire CRB region. Results have indicated in varied degree of spatial change pattern for the entire Columbia River Basin, especially western part of the basin. At temporal scales, winter precipitation has higher variability than summer and vice-versa for temperature. Frequency analysis provided insights into possible explanation to changes in precipitation.

Verification of GCM-generated regional seasonal precipitation for current climate and of statistical downscaling estimates under changing climate conditions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Busuioc, A.; Storch, H. von; Schnur, R.

Empirical downscaling procedures relate large-scale atmospheric features with local features such as station rainfall in order to facilitate local scenarios of climate change. The purpose of the present paper is twofold: first, a downscaling technique is used as a diagnostic tool to verify the performance of climate models on the regional scale; second, a technique is proposed for verifying the validity of empirical downscaling procedures in climate change applications. The case considered is regional seasonal precipitation in Romania. The downscaling model is a regression based on canonical correlation analysis between observed station precipitation and European-scale sea level pressure (SLP). Themore » climate models considered here are the T21 and T42 versions of the Hamburg ECHAM3 atmospheric GCM run in time-slice mode. The climate change scenario refers to the expected time of doubled carbon dioxide concentrations around the year 2050. Generally, applications of statistical downscaling to climate change scenarios have been based on the assumption that the empirical link between the large-scale and regional parameters remains valid under a changed climate. In this study, a rationale is proposed for this assumption by showing the consistency of the 2 x CO{sub 2} GCM scenarios in winter, derived directly from the gridpoint data, with the regional scenarios obtained through empirical downscaling. Since the skill of the GCMs in regional terms is already established, it is concluded that the downscaling technique is adequate for describing climatically changing regional and local conditions, at least for precipitation in Romania during winter.« less

Effects of future climate conditions on terrestrial export from coastal southern California

NASA Astrophysics Data System (ADS)

Feng, D.; Zhao, Y.; Raoufi, R.; Beighley, E.; Melack, J. M.

2015-12-01

The Santa Barbara Coastal - Long Term Ecological Research Project (SBC-LTER) is focused on investigating the relative importance of land and ocean processes in structuring giant kelp forest ecosystems. Understanding how current and future climate conditions influence terrestrial export is a central theme for the project. Here we combine the Hillslope River Routing (HRR) model and daily precipitation and temperature downscaled using statistical downscaling based on localized constructed Analogs (LOCA) to estimate recent streamflow dynamics (2000 to 2014) and future conditions (2015 to 2100). The HRR model covers the SBC-LTER watersheds from just west of the Ventura River to Point Conception; a land area of roughly 800 km2 with 179 watersheds ranging from 0.1 to 123 km2. The downscaled climate conditions have a spatial resolution of 6 km by 6 km. Here, we use the Penman-Monteith method with the Food and Agriculture Organization of the United Nations (FAO) limited climate data approximations and land surface conditions (albedo, leaf area index, land cover) measured from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua satellites to estimate potential evapotranspiration (PET). The HRR model is calibrated for the period 2000 to 2014 using USGS and LTER streamflow. An automated calibration technique is used. For future climate scenarios, we use mean 8-day land cover conditions. Future streamflow, ET and soil moisture statistics are presented and based on downscaled P and T from ten climate model projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5).

Statistical Downscaling of General Circulation Model Outputs to Precipitation Accounting for Non-Stationarities in Predictor-Predictand Relationships

PubMed Central

Sachindra, D. A.; Perera, B. J. C.

2016-01-01

This paper presents a novel approach to incorporate the non-stationarities characterised in the GCM outputs, into the Predictor-Predictand Relationships (PPRs) in statistical downscaling models. In this approach, a series of 42 PPRs based on multi-linear regression (MLR) technique were determined for each calendar month using a 20-year moving window moved at a 1-year time step on the predictor data obtained from the NCEP/NCAR reanalysis data archive and observations of precipitation at 3 stations located in Victoria, Australia, for the period 1950–2010. Then the relationships between the constants and coefficients in the PPRs and the statistics of reanalysis data of predictors were determined for the period 1950–2010, for each calendar month. Thereafter, using these relationships with the statistics of the past data of HadCM3 GCM pertaining to the predictors, new PPRs were derived for the periods 1950–69, 1970–89 and 1990–99 for each station. This process yielded a non-stationary downscaling model consisting of a PPR per calendar month for each of the above three periods for each station. The non-stationarities in the climate are characterised by the long-term changes in the statistics of the climate variables and above process enabled relating the non-stationarities in the climate to the PPRs. These new PPRs were then used with the past data of HadCM3, to reproduce the observed precipitation. It was found that the non-stationary MLR based downscaling model was able to produce more accurate simulations of observed precipitation more often than conventional stationary downscaling models developed with MLR and Genetic Programming (GP). PMID:27997609

Statistical Downscaling of General Circulation Model Outputs to Precipitation Accounting for Non-Stationarities in Predictor-Predictand Relationships.

PubMed

Sachindra, D A; Perera, B J C

2016-01-01

This paper presents a novel approach to incorporate the non-stationarities characterised in the GCM outputs, into the Predictor-Predictand Relationships (PPRs) in statistical downscaling models. In this approach, a series of 42 PPRs based on multi-linear regression (MLR) technique were determined for each calendar month using a 20-year moving window moved at a 1-year time step on the predictor data obtained from the NCEP/NCAR reanalysis data archive and observations of precipitation at 3 stations located in Victoria, Australia, for the period 1950-2010. Then the relationships between the constants and coefficients in the PPRs and the statistics of reanalysis data of predictors were determined for the period 1950-2010, for each calendar month. Thereafter, using these relationships with the statistics of the past data of HadCM3 GCM pertaining to the predictors, new PPRs were derived for the periods 1950-69, 1970-89 and 1990-99 for each station. This process yielded a non-stationary downscaling model consisting of a PPR per calendar month for each of the above three periods for each station. The non-stationarities in the climate are characterised by the long-term changes in the statistics of the climate variables and above process enabled relating the non-stationarities in the climate to the PPRs. These new PPRs were then used with the past data of HadCM3, to reproduce the observed precipitation. It was found that the non-stationary MLR based downscaling model was able to produce more accurate simulations of observed precipitation more often than conventional stationary downscaling models developed with MLR and Genetic Programming (GP).

Multisite rainfall downscaling and disaggregation in a tropical urban area

NASA Astrophysics Data System (ADS)

Lu, Y.; Qin, X. S.

2014-02-01

A systematic downscaling-disaggregation study was conducted over Singapore Island, with an aim to generate high spatial and temporal resolution rainfall data under future climate-change conditions. The study consisted of two major components. The first part was to perform an inter-comparison of various alternatives of downscaling and disaggregation methods based on observed data. This included (i) single-site generalized linear model (GLM) plus K-nearest neighbor (KNN) (S-G-K) vs. multisite GLM (M-G) for spatial downscaling, (ii) HYETOS vs. KNN for single-site disaggregation, and (iii) KNN vs. MuDRain (Multivariate Rainfall Disaggregation tool) for multisite disaggregation. The results revealed that, for multisite downscaling, M-G performs better than S-G-K in covering the observed data with a lower RMSE value; for single-site disaggregation, KNN could better keep the basic statistics (i.e. standard deviation, lag-1 autocorrelation and probability of wet hour) than HYETOS; for multisite disaggregation, MuDRain outperformed KNN in fitting interstation correlations. In the second part of the study, an integrated downscaling-disaggregation framework based on M-G, KNN, and MuDRain was used to generate hourly rainfall at multiple sites. The results indicated that the downscaled and disaggregated rainfall data based on multiple ensembles from HadCM3 for the period from 1980 to 2010 could well cover the observed mean rainfall amount and extreme data, and also reasonably keep the spatial correlations both at daily and hourly timescales. The framework was also used to project future rainfall conditions under HadCM3 SRES A2 and B2 scenarios. It was indicated that the annual rainfall amount could reduce up to 5% at the end of this century, but the rainfall of wet season and extreme hourly rainfall could notably increase.

Comparison of Grid Nudging and Spectral Nudging Techniques for Dynamical Climate Downscaling within the WRF Model

NASA Astrophysics Data System (ADS)

Fan, X.; Chen, L.; Ma, Z.

2010-12-01

Climate downscaling has been an active research and application area in the past several decades focusing on regional climate studies. Dynamical downscaling, in addition to statistical methods, has been widely used in downscaling as the advanced modern numerical weather and regional climate models emerge. The utilization of numerical models enables that a full set of climate variables are generated in the process of downscaling, which are dynamically consistent due to the constraints of physical laws. While we are generating high resolution regional climate, the large scale climate patterns should be retained. To serve this purpose, nudging techniques, including grid analysis nudging and spectral nudging, have been used in different models. There are studies demonstrating the benefit and advantages of each nudging technique; however, the results are sensitive to many factors such as nudging coefficients and the amount of information to nudge to, and thus the conclusions are controversy. While in a companion work of developing approaches for quantitative assessment of the downscaled climate, in this study, the two nudging techniques are under extensive experiments in the Weather Research and Forecasting (WRF) model. Using the same model provides fair comparability. Applying the quantitative assessments provides objectiveness of comparison. Three types of downscaling experiments were performed for one month of choice. The first type is serving as a base whereas the large scale information is communicated through lateral boundary conditions only; the second is using the grid analysis nudging; and the third is using spectral nudging. Emphases are given to the experiments of different nudging coefficients and nudging to different variables in the grid analysis nudging; while in spectral nudging, we focus on testing the nudging coefficients, different wave numbers on different model levels to nudge.

Importance of Preserving Cross-correlation in developing Statistically Downscaled Climate Forcings and in estimating Land-surface Fluxes and States

NASA Astrophysics Data System (ADS)

Das Bhowmik, R.; Arumugam, S.

2015-12-01

Multivariate downscaling techniques exhibited superiority over univariate regression schemes in terms of preserving cross-correlations between multiple variables- precipitation and temperature - from GCMs. This study focuses on two aspects: (a) develop an analytical solutions on estimating biases in cross-correlations from univariate downscaling approaches and (b) quantify the uncertainty in land-surface states and fluxes due to biases in cross-correlations in downscaled climate forcings. Both these aspects are evaluated using climate forcings available from both historical climate simulations and CMIP5 hindcasts over the entire US. The analytical solution basically relates the univariate regression parameters, co-efficient of determination of regression and the co-variance ratio between GCM and downscaled values. The analytical solutions are compared with the downscaled univariate forcings by choosing the desired p-value (Type-1 error) in preserving the observed cross-correlation. . For quantifying the impacts of biases on cross-correlation on estimating streamflow and groundwater, we corrupt the downscaled climate forcings with different cross-correlation structure.

Prognostic Aspects of Sub-seasonal Rainfall Characteristics using the Outputs of General Circulation Model: An Application of Statistical Downscaling and Temporal Disaggregation

NASA Astrophysics Data System (ADS)

Singh, A.; Mohanty, U. C.; Ghosh, K.

2015-12-01

Most regions of India experience varied rainfall duration during the southwest monsoon, changes in which exhibit major impact not only agriculture, but also other sectors like hydrology, agriculture, food and fodder storage etc. In addition, changes in sub-seasonal rainfall characteristics highly impact the rice production. As part of the endeavor seasonal climate outlook, as well as information for weather within climate may be helpful for advance planning and risk management in agriculture. The General Circulation Model (GCM) provide an alternative to gather information for weather within climate but variability is very low in comparison to observation. On the other hand, the spatial resolution of GCM predicted rainfall is not found at the observed station/grid point. To tackle the problem, initially a statistical downscaling over 19 station of Odisha state is undertaken using the atmospheric parameters predicted by a GCM (NCEP-CFSv2). For the purpose, an extended domain is taken for analyzing the significant zone for the atmospheric parameters like zonal wind at 850hPa, Sea Surface Temperature (SST), geopotential height. A statistical model using the pattern projection method is further developed based on empirical orthogonal function. The downscaled rainfall is found better in association with station observation in comparison to raw GCM prediction in view of deterministic and probabilistic skill measure. Further, the sub-seasonal and seasonal forecast from the GCMs can be used at different time steps for risk management. Therefore, downscaled seasonal/monthly rainfall is further converted to sub-seasonal/daily time scale using a non-homogeneous markov model. The simulated weather sequences are further compared with the observed sequence in view of categorical rainfall events. The outcomes suggest that the rainfall amount are overestimated for excess rainfall and henceforth larger excess rainfall events can be realized. The skill for prediction of rainfall events corresponding to lower thresholds is found higher. A detail discussion regarding skill of spatial downscale rainfall at observed stations and its further representation of sub-seasonal characteristics (spells, less rainfall, heavy rainfall, and moderate rainfall events) of rainfall for disaggregated outputs will be presented.

Uncertainty Assessment of the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP) Dataset

NASA Technical Reports Server (NTRS)

Wang, Weile; Nemani, Ramakrishna R.; Michaelis, Andrew; Hashimoto, Hirofumi; Dungan, Jennifer L.; Thrasher, Bridget L.; Dixon, Keith W.

2016-01-01

The NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) dataset is comprised of downscaled climate projections that are derived from 21 General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) and across two of the four greenhouse gas emissions scenarios (RCP4.5 and RCP8.5). Each of the climate projections includes daily maximum temperature, minimum temperature, and precipitation for the periods from 1950 through 2100 and the spatial resolution is 0.25 degrees (approximately 25 km x 25 km). The GDDP dataset has received warm welcome from the science community in conducting studies of climate change impacts at local to regional scales, but a comprehensive evaluation of its uncertainties is still missing. In this study, we apply the Perfect Model Experiment framework (Dixon et al. 2016) to quantify the key sources of uncertainties from the observational baseline dataset, the downscaling algorithm, and some intrinsic assumptions (e.g., the stationary assumption) inherent to the statistical downscaling techniques. We developed a set of metrics to evaluate downscaling errors resulted from bias-correction ("quantile-mapping"), spatial disaggregation, as well as the temporal-spatial non-stationarity of climate variability. Our results highlight the spatial disaggregation (or interpolation) errors, which dominate the overall uncertainties of the GDDP dataset, especially over heterogeneous and complex terrains (e.g., mountains and coastal area). In comparison, the temporal errors in the GDDP dataset tend to be more constrained. Our results also indicate that the downscaled daily precipitation also has relatively larger uncertainties than the temperature fields, reflecting the rather stochastic nature of precipitation in space. Therefore, our results provide insights in improving statistical downscaling algorithms and products in the future.

Stochastic Downscaling of Digital Elevation Models

NASA Astrophysics Data System (ADS)

Rasera, Luiz Gustavo; Mariethoz, Gregoire; Lane, Stuart N.

2016-04-01

High-resolution digital elevation models (HR-DEMs) are extremely important for the understanding of small-scale geomorphic processes in Alpine environments. In the last decade, remote sensing techniques have experienced a major technological evolution, enabling fast and precise acquisition of HR-DEMs. However, sensors designed to measure elevation data still feature different spatial resolution and coverage capabilities. Terrestrial altimetry allows the acquisition of HR-DEMs with centimeter to millimeter-level precision, but only within small spatial extents and often with dead ground problems. Conversely, satellite radiometric sensors are able to gather elevation measurements over large areas but with limited spatial resolution. In the present study, we propose an algorithm to downscale low-resolution satellite-based DEMs using topographic patterns extracted from HR-DEMs derived for example from ground-based and airborne altimetry. The method consists of a multiple-point geostatistical simulation technique able to generate high-resolution elevation data from low-resolution digital elevation models (LR-DEMs). Initially, two collocated DEMs with different spatial resolutions serve as an input to construct a database of topographic patterns, which is also used to infer the statistical relationships between the two scales. High-resolution elevation patterns are then retrieved from the database to downscale a LR-DEM through a stochastic simulation process. The output of the simulations are multiple equally probable DEMs with higher spatial resolution that also depict the large-scale geomorphic structures present in the original LR-DEM. As these multiple models reflect the uncertainty related to the downscaling, they can be employed to quantify the uncertainty of phenomena that are dependent on fine topography, such as catchment hydrological processes. The proposed methodology is illustrated for a case study in the Swiss Alps. A swissALTI3D HR-DEM (with 5 m resolution) and a SRTM-derived LR-DEM from the Western Alps are used to downscale a SRTM-based LR-DEM from the eastern part of the Alps. The results show that the method is capable of generating multiple high-resolution synthetic DEMs that reproduce the spatial structure and statistics of the original DEM.

Statistical downscaling modeling with quantile regression using lasso to estimate extreme rainfall

NASA Astrophysics Data System (ADS)

Santri, Dewi; Wigena, Aji Hamim; Djuraidah, Anik

2016-02-01

Rainfall is one of the climatic elements with high diversity and has many negative impacts especially extreme rainfall. Therefore, there are several methods that required to minimize the damage that may occur. So far, Global circulation models (GCM) are the best method to forecast global climate changes include extreme rainfall. Statistical downscaling (SD) is a technique to develop the relationship between GCM output as a global-scale independent variables and rainfall as a local- scale response variable. Using GCM method will have many difficulties when assessed against observations because GCM has high dimension and multicollinearity between the variables. The common method that used to handle this problem is principal components analysis (PCA) and partial least squares regression. The new method that can be used is lasso. Lasso has advantages in simultaneuosly controlling the variance of the fitted coefficients and performing automatic variable selection. Quantile regression is a method that can be used to detect extreme rainfall in dry and wet extreme. Objective of this study is modeling SD using quantile regression with lasso to predict extreme rainfall in Indramayu. The results showed that the estimation of extreme rainfall (extreme wet in January, February and December) in Indramayu could be predicted properly by the model at quantile 90th.

On the downscaling of actual evapotranspiration maps based on combination of MODIS and landsat-based actual evapotranspiration estimates

USGS Publications Warehouse

Singh, Ramesh K.; Senay, Gabriel B.; Velpuri, Naga Manohar; Bohms, Stefanie; Verdin, James P.

2014-01-01

 Downscaling is one of the important ways of utilizing the combined benefits of the high temporal resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) images and fine spatial resolution of Landsat images. We have evaluated the output regression with intercept method and developed the Linear with Zero Intercept (LinZI) method for downscaling MODIS-based monthly actual evapotranspiration (AET) maps to the Landsat-scale monthly AET maps for the Colorado River Basin for 2010. We used the 8-day MODIS land surface temperature product (MOD11A2) and 328 cloud-free Landsat images for computing AET maps and downscaling. The regression with intercept method does have limitations in downscaling if the slope and intercept are computed over a large area. A good agreement was obtained between downscaled monthly AET using the LinZI method and the eddy covariance measurements from seven flux sites within the Colorado River Basin. The mean bias ranged from −16 mm (underestimation) to 22 mm (overestimation) per month, and the coefficient of determination varied from 0.52 to 0.88. Some discrepancies between measured and downscaled monthly AET at two flux sites were found to be due to the prevailing flux footprint. A reasonable comparison was also obtained between downscaled monthly AET using LinZI method and the gridded FLUXNET dataset. The downscaled monthly AET nicely captured the temporal variation in sampled land cover classes. The proposed LinZI method can be used at finer temporal resolution (such as 8 days) with further evaluation. The proposed downscaling method will be very useful in advancing the application of remotely sensed images in water resources planning and management.

Assessment of the uncertainty and predictive power of large-scale predictors for nonlinear precipitation downscaling in the European Arctic (Invited)

NASA Astrophysics Data System (ADS)

Sauter, T.

2013-12-01

Despite the extensive research on downscaling methods there is still little consensus about the choice of useful atmospheric predictor variables. Besides the general decision of a proper statistical downscaling model, the selection of an informative predictor set is crucial for the accuracy and stability of the resulting downscaled time series. These requirements must be fullfilled by both the atmospheric variables and the predictor domains in terms of geographical location and spatial extend, to which in general not much attention is paid. However, only a limited number of studies is interested in the predictive capability of the predictor domain size or shape, and the question to what extent variability of neighboring grid points influence local-scale events. In this study we emphasized the spatial relationships between observed daily precipitation and selected number of atmospheric variables for the European Arctic. Several nonlinear regression models are used to link the large-scale predictors obtained from reanalysed Weather Research and Forecast model runs to the local-scale observed precipitation. Inferences on the sources of uncertainty are then drawn from variance based sensitivity measures, which also permit to capture interaction effects between individual predictors. The information is further used to develop more parsimonious downscaling models with only small decreases in accuracy. Individual predictors (without interactions) account for almost 2/3 of the total output variance, while the remaining fraction is solely due to interactions. Neglecting predictor interactions in the screening process will lead to some loss of information. Hence, linear screening methods are insufficient as they neither account for interactions nor for non-additivity as given by many nonlinear prediction algorithms.

Testing a Weather Generator for Downscaling Climate Change Projections over Switzerland

NASA Astrophysics Data System (ADS)

Keller, Denise E.; Fischer, Andreas M.; Liniger, Mark A.; Appenzeller, Christof; Knutti, Reto

2016-04-01

Climate information provided by global or regional climate models (RCMs) are often too coarse and prone to substantial biases, making it impossible to directly use daily time-series of the RCMs for local assessments and in climate impact models. Hence, statistical downscaling becomes necessary. For the Swiss National Climate Change Initiative (CH2011), a delta-change approach was used to provide daily climate projections at the local scale. This data have the main limitations that changes in variability, extremes and in the temporal structure, such as changes in the wet day frequency, are not reproduced. The latter is a considerable downside of the delta-change approach for many impact applications. In this regard, stochastic weather generators (WGs) are an appealing technique that allow the simulation of multiple realizations of synthetic weather sequences consistent with the locally observed weather statistics and its future changes. Here, we analyse a Richardson-type weather generator (WG) as an alternative method to downscale daily precipitation, minimum and maximum temperature. The WG is calibrated for 26 Swiss stations and the reference period 1980-2009. It is perturbed with change factors derived from 12 RCMs (ENSEMBLES) to represent the climate of 2070-2099 assuming the SRES A1B emission scenario. The WG can be run in multi-site mode, making it especially attractive for impact-modelers that rely on a realistic spatial structure in downscaled time-series. The results from the WG are benchmarked against the original delta-change approach that applies mean additive or multiplicative adjustments to the observations. According to both downscaling methods, the results reveal area-wide mean temperature increases and a precipitation decrease in summer, consistent with earlier studies. For the summer drying, the WG indicates primarily a decrease in wet-day frequency and correspondingly an increase in mean dry spell length by around 18% - 40% at low-elevation stations. By construction, these potential changes cannot be represented by a delta-change approach. In winter, both methods project a shortening of the frost period (-30 to -60 days) and a decrease of snow days (-20% to -100%). The WG demonstrates though, that almost present-day conditions in snow-days could still occur in the future. As expected, both methods have difficulties in representing extremes. If users focus on changes in temporal sequences and need a large number of future realizations that are spatially consistent, it is recommended to use data from a WG instead of a delta-change approach.

Development and Evaluation of High-Resolution Climate Simulations Over the Mountainous Northeastern United States

NASA Technical Reports Server (NTRS)

Winter, Jonathan M.; Beckage, Brian; Bucini, Gabriela; Horton, Radley M.; Clemins, Patrick J.

2016-01-01

The mountain regions of the northeastern United States are a critical socioeconomic resource for Vermont, New York State, New Hampshire, Maine, and southern Quebec. While global climate models (GCMs) are important tools for climate change risk assessment at regional scales, even the increased spatial resolution of statistically downscaled GCMs (commonly approximately 1/ 8 deg) is not sufficient for hydrologic, ecologic, and land-use modeling of small watersheds within the mountainous Northeast. To address this limitation, an ensemble of topographically downscaled, high-resolution (30"), daily 2-m maximum air temperature; 2-m minimum air temperature; and precipitation simulations are developed for the mountainous Northeast by applying an additional level of downscaling to intermediately downscaled (1/ 8 deg) data using high-resolution topography and station observations. First, observed relationships between 2-m air temperature and elevation and between precipitation and elevation are derived. Then, these relationships are combined with spatial interpolation to enhance the resolution of intermediately downscaled GCM simulations. The resulting topographically downscaled dataset is analyzed for its ability to reproduce station observations. Topographic downscaling adds value to intermediately downscaled maximum and minimum 2-m air temperature at high-elevation stations, as well as moderately improves domain-averaged maximum and minimum 2-m air temperature. Topographic downscaling also improves mean precipitation but not daily probability distributions of precipitation. Overall, the utility of topographic downscaling is dependent on the initial bias of the intermediately downscaled product and the magnitude of the elevation adjustment. As the initial bias or elevation adjustment increases, more value is added to the topographically downscaled product.

The effects of climate downscaling technique and observational data set on modeled ecological responses.

PubMed

Pourmokhtarian, Afshin; Driscoll, Charles T; Campbell, John L; Hayhoe, Katharine; Stoner, Anne M K

2016-07-01

Assessments of future climate change impacts on ecosystems typically rely on multiple climate model projections, but often utilize only one downscaling approach trained on one set of observations. Here, we explore the extent to which modeled biogeochemical responses to changing climate are affected by the selection of the climate downscaling method and training observations used at the montane landscape of the Hubbard Brook Experimental Forest, New Hampshire, USA. We evaluated three downscaling methods: the delta method (or the change factor method), monthly quantile mapping (Bias Correction-Spatial Disaggregation, or BCSD), and daily quantile regression (Asynchronous Regional Regression Model, or ARRM). Additionally, we trained outputs from four atmosphere-ocean general circulation models (AOGCMs) (CCSM3, HadCM3, PCM, and GFDL-CM2.1) driven by higher (A1fi) and lower (B1) future emissions scenarios on two sets of observations (1/8º resolution grid vs. individual weather station) to generate the high-resolution climate input for the forest biogeochemical model PnET-BGC (eight ensembles of six runs).The choice of downscaling approach and spatial resolution of the observations used to train the downscaling model impacted modeled soil moisture and streamflow, which in turn affected forest growth, net N mineralization, net soil nitrification, and stream chemistry. All three downscaling methods were highly sensitive to the observations used, resulting in projections that were significantly different between station-based and grid-based observations. The choice of downscaling method also slightly affected the results, however not as much as the choice of observations. Using spatially smoothed gridded observations and/or methods that do not resolve sub-monthly shifts in the distribution of temperature and/or precipitation can produce biased results in model applications run at greater temporal and/or spatial resolutions. These results underscore the importance of carefully considering field observations used for training, as well as the downscaling method used to generate climate change projections, for smaller-scale modeling studies. Different sources of variability including selection of AOGCM, emissions scenario, downscaling technique, and data used for training downscaling models, result in a wide range of projected forest ecosystem responses to future climate change. © 2016 by the Ecological Society of America.

Current and future pluvial flood hazard analysis for the city of Antwerp

NASA Astrophysics Data System (ADS)

Willems, Patrick; Tabari, Hossein; De Niel, Jan; Van Uytven, Els; Lambrechts, Griet; Wellens, Geert

2016-04-01

For the city of Antwerp in Belgium, higher rainfall extremes were observed in comparison with surrounding areas. The differences were found statistically significant for some areas and may be the result of the heat island effect in combination with the higher concentrations of aerosols. A network of 19 rain gauges but with varying records length (the longest since the 1960s) and continuous radar data for 10 years were combined to map the spatial variability of rainfall extremes over the city at various durations from 15 minutes to 1 day together with the uncertainty. The improved spatial rainfall information was used as input in the sewer system model of the city to analyze the frequency of urban pluvial floods. Comparison with historical flood observations from various sources (fire brigade and media) confirmed that the improved spatial rainfall information also improved sewer impact results on both the magnitude and frequency of the sewer floods. Next to these improved urban flood impact results for recent and current climatological conditions, the new insights on the local rainfall microclimate were also helpful to enhance future projections on rainfall extremes and pluvial floods in the city. This was done by improved statistical downscaling of all available CMIP5 global climate model runs (160 runs) for the 4 RCP scenarios, as well as the available EURO-CORDEX regional climate model runs. Two types of statistical downscaling methods were applied for that purpose (a weather typing based method, and a quantile perturbation approach), making use of the microclimate results and its dependency on specific weather types. Changes in extreme rainfall intensities were analyzed and mapped as a function of the RCP scenario, together with the uncertainty, decomposed in the uncertainties related to the climate models, the climate model initialization or limited length of the 30-year time series (natural climate variability) and the statistical downscaling (albeit limited to two types of methods). These were finally transferred into future pluvial flash flood hazard maps for the city together with the uncertainties, and are considered as basis for spatial planning and adaptation.

Spatial, temporal and frequency based climate change assessment in Columbia River Basin using multi downscaled-scenarios

NASA Astrophysics Data System (ADS)

Rana, Arun; Moradkhani, Hamid

2016-07-01

Uncertainties in climate modelling are well documented in literature. Global Climate Models (GCMs) are often used to downscale the climatic parameters on a regional scale. In the present work, we have analyzed the changes in precipitation and temperature for future scenario period of 2070-2099 with respect to historical period of 1970-2000 from statistically downscaled GCM projections in Columbia River Basin (CRB). Analysis is performed using two different statistically downscaled climate projections (with ten GCMs downscaled products each, for RCP 4.5 and RCP 8.5, from CMIP5 dataset) namely, those from the Bias Correction and Spatial Downscaling (BCSD) technique generated at Portland State University and from the Multivariate Adaptive Constructed Analogs (MACA) technique, generated at University of Idaho, totaling to 40 different scenarios. The two datasets for BCSD and MACA are downscaled from observed data for both scenarios projections i.e. RCP4.5 and RCP8.5. Analysis is performed using spatial change (yearly scale), temporal change (monthly scale), percentile change (seasonal scale), quantile change (yearly scale), and wavelet analysis (yearly scale) in the future period from the historical period, respectively, at a scale of 1/16th of degree for entire CRB region. Results have indicated in varied degree of spatial change pattern for the entire Columbia River Basin, especially western part of the basin. At temporal scales, winter precipitation has higher variability than summer and vice versa for temperature. Most of the models have indicated considerate positive change in quantiles and percentiles for both precipitation and temperature. Wavelet analysis provided insights into possible explanation to changes in precipitation.

A multimodal wave spectrum-based approach for statistical downscaling of local wave climate

USGS Publications Warehouse

Hegermiller, Christie; Antolinez, Jose A A; Rueda, Ana C.; Camus, Paula; Perez, Jorge; Erikson, Li; Barnard, Patrick; Mendez, Fernando J.

2017-01-01

Characterization of wave climate by bulk wave parameters is insufficient for many coastal studies, including those focused on assessing coastal hazards and long-term wave climate influences on coastal evolution. This issue is particularly relevant for studies using statistical downscaling of atmospheric fields to local wave conditions, which are often multimodal in large ocean basins (e.g. the Pacific). Swell may be generated in vastly different wave generation regions, yielding complex wave spectra that are inadequately represented by a single set of bulk wave parameters. Furthermore, the relationship between atmospheric systems and local wave conditions is complicated by variations in arrival time of wave groups from different parts of the basin. Here, we address these two challenges by improving upon the spatiotemporal definition of the atmospheric predictor used in statistical downscaling of local wave climate. The improved methodology separates the local wave spectrum into “wave families,” defined by spectral peaks and discrete generation regions, and relates atmospheric conditions in distant regions of the ocean basin to local wave conditions by incorporating travel times computed from effective energy flux across the ocean basin. When applied to locations with multimodal wave spectra, including Southern California and Trujillo, Peru, the new methodology improves the ability of the statistical model to project significant wave height, peak period, and direction for each wave family, retaining more information from the full wave spectrum. This work is the base of statistical downscaling by weather types, which has recently been applied to coastal flooding and morphodynamic applications.

Spatial downscaling algorithm of TRMM precipitation based on multiple high-resolution satellite data for Inner Mongolia, China

NASA Astrophysics Data System (ADS)

Duan, Limin; Fan, Keke; Li, Wei; Liu, Tingxi

2017-12-01

Daily precipitation data from 42 stations in Inner Mongolia, China for the 10 years period from 1 January 2001 to 31 December 2010 was utilized along with downscaled data from the Tropical Rainfall Measuring Mission (TRMM) with a spatial resolution of 0.25° × 0.25° for the same period based on the statistical relationships between the normalized difference vegetation index (NDVI), meteorological variables, and digital elevation models (https://en.wikipedia.org/wiki/Digital_elevation_model) (DEM) using the leave-one-out (LOO) cross validation method and multivariate step regression. The results indicate that (1) TRMM data can indeed be used to estimate annual precipitation in Inner Mongolia and there is a linear relationship between annual TRMM and observed precipitation; (2) there is a significant relationship between TRMM-based precipitation and predicted precipitation, with a spatial resolution of 0.50° × 0.50°; (3) NDVI and temperature are important factors influencing the downscaling of TRMM precipitation data for DEM and the slope is not the most significant factor affecting the downscaled TRMM data; and (4) the downscaled TRMM data reflects spatial patterns in annual precipitation reasonably well, showing less precipitation falling in west Inner Mongolia and more in the south and southeast. The new approach proposed here provides a useful alternative for evaluating spatial patterns in precipitation and can thus be applied to generate a more accurate precipitation dataset to support both irrigation management and the conservation of this fragile grassland ecosystem.

Stepping inside the niche: microclimate data are critical for accurate assessment of species' vulnerability to climate change

PubMed Central

Storlie, Collin; Merino-Viteri, Andres; Phillips, Ben; VanDerWal, Jeremy; Welbergen, Justin; Williams, Stephen

2014-01-01

To assess a species' vulnerability to climate change, we commonly use mapped environmental data that are coarsely resolved in time and space. Coarsely resolved temperature data are typically inaccurate at predicting temperatures in microhabitats used by an organism and may also exhibit spatial bias in topographically complex areas. One consequence of these inaccuracies is that coarsely resolved layers may predict thermal regimes at a site that exceed species' known thermal limits. In this study, we use statistical downscaling to account for environmental factors and develop high-resolution estimates of daily maximum temperatures for a 36 000 km2 study area over a 38-year period. We then demonstrate that this statistical downscaling provides temperature estimates that consistently place focal species within their fundamental thermal niche, whereas coarsely resolved layers do not. Our results highlight the need for incorporation of fine-scale weather data into species' vulnerability analyses and demonstrate that a statistical downscaling approach can yield biologically relevant estimates of thermal regimes. PMID:25252835

Projection of spatial and temporal changes of rainfall in Sarawak of Borneo Island using statistical downscaling of CMIP5 models

NASA Astrophysics Data System (ADS)

Sa'adi, Zulfaqar; Shahid, Shamsuddin; Chung, Eun-Sung; Ismail, Tarmizi bin

2017-11-01

This study assesses the possible changes in rainfall patterns of Sarawak in Borneo Island due to climate change through statistical downscaling of General Circulation Models (GCM) projections. Available in-situ observed rainfall data were used to downscale the future rainfall from ensembles of 20 GCMs of Coupled Model Intercomparison Project phase 5 (CMIP5) for four Representative Concentration Pathways (RCP) scenarios, namely, RCP2.6, RCP4.5, RCP6.0 and RCP8.5. Model Output Statistics (MOS) based downscaling models were developed using two data mining approaches known as Random Forest (RF) and Support Vector Machine (SVM). The SVM was found to downscale all GCMs with normalized mean square error (NMSE) of 48.2-75.2 and skill score (SS) of 0.94-0.98 during validation. The results show that the future projection of the annual rainfalls is increasing and decreasing on the region-based and catchment-based basis due to the influence of the monsoon season affecting the coast of Sarawak. The ensemble mean of GCMs projections reveals the increased and decreased mean of annual precipitations at 33 stations with the rate of 0.1% to 19.6% and one station with the rate of - 7.9% to - 3.1%, respectively under all RCP scenarios. The remaining 15 stations showed inconsistency neither increasing nor decreasing at the rate of - 5.6% to 5.2%, but mainly showing a trend of decreasing rainfall during the first period (2010-2039) followed by increasing rainfall for the period of 2070-2099.

Precipitation projections under GCMs perspective and Turkish Water Foundation (TWF) statistical downscaling model procedures

NASA Astrophysics Data System (ADS)

Dabanlı, İsmail; Şen, Zekai

2018-04-01

The statistical climate downscaling model by the Turkish Water Foundation (TWF) is further developed and applied to a set of monthly precipitation records. The model is structured by two phases as spatial (regional) and temporal downscaling of global circulation model (GCM) scenarios. The TWF model takes into consideration the regional dependence function (RDF) for spatial structure and Markov whitening process (MWP) for temporal characteristics of the records to set projections. The impact of climate change on monthly precipitations is studied by downscaling Intergovernmental Panel on Climate Change-Special Report on Emission Scenarios (IPCC-SRES) A2 and B2 emission scenarios from Max Plank Institute (EH40PYC) and Hadley Center (HadCM3). The main purposes are to explain the TWF statistical climate downscaling model procedures and to expose the validation tests, which are rewarded in same specifications as "very good" for all stations except one (Suhut) station in the Akarcay basin that is in the west central part of Turkey. Eventhough, the validation score is just a bit lower at the Suhut station, the results are "satisfactory." It is, therefore, possible to say that the TWF model has reasonably acceptable skill for highly accurate estimation regarding standard deviation ratio (SDR), Nash-Sutcliffe efficiency (NSE), and percent bias (PBIAS) criteria. Based on the validated model, precipitation predictions are generated from 2011 to 2100 by using 30-year reference observation period (1981-2010). Precipitation arithmetic average and standard deviation have less than 5% error for EH40PYC and HadCM3 SRES (A2 and B2) scenarios.

On the impact of using downscaled reanalysis data instead of direct measurements for modeling the mass balance of a tropical glacier (Cordillera Blanca, Peru)

NASA Astrophysics Data System (ADS)

Galos, Stephan; Hofer, Marlis; Marzeion, Ben; Mölg, Thomas; Großhauser, Martin

2013-04-01

Due to their setting, tropical glaciers are sensitive indicators of mid-tropospheric meteorological variability and climate change. Furthermore these glaciers are of particular interest because they respond faster to climatic changes than glaciers located in mid- or high-latitudes. As long-term direct meteorological measurements in such remote environments are scarce, reanalysis data (e.g. ERA-Interim) provide a highly valuable source of information. Reanalysis datasets (i) enable a temporal extension of data records gained by direct measurements and (ii) provide information from regions where direct measurements are not available. In order to properly derive the physical exchange processes between glaciers and atmosphere from reanalysis data, downscaling procedures are required. In the present study we investigate if downscaled atmospheric variables (air temperature and relative humidity) from a reanalysis dataset can be used as input for a physically based, high resolution energy and mass balance model. We apply a well validated empirical-statistical downscaling model, fed with ERA-Interim data, to an automated weather station (AWS) on the surface of Glaciar Artesonraju (8.96° S | 77.63° W). The downscaled data is then used to replace measured air temperature and relative humidity in the input for the energy and mass balance model, which was calibrated using ablation data from stakes and a sonic ranger. In order to test the sensitivity of the modeled mass balance to the downscaled data, the results are compared to a reference model run driven solely with AWS data as model input. We finally discuss the results and present future perspectives for further developing this method.

Downscaling Indicators of Forest Habitat Structure from National Assessments

Treesearch

Kurt H. Riitters

2005-01-01

Downscaling is an important problem because consistent large-area assessments of forest habitat structure, while feasible, are only feasible when using relatively coarse data and indicators. Techniques are needed to enable more detailed and local interpretations of the national statistics. Using the results of national assessments from land-cover maps, this paper...

An Archive of Downscaled WCRP CMIP3 Climate Projections for Planning Applications in the Contiguous United States

NASA Astrophysics Data System (ADS)

Brekke, L. D.; Pruitt, T.; Maurer, E. P.; Duffy, P. B.

2007-12-01

Incorporating climate change information into long-term evaluations of water and energy resources requires analysts to have access to climate projection data that have been spatially downscaled to "basin-relevant" resolution. This is necessary in order to develop system-specific hydrology and demand scenarios consistent with projected climate scenarios. Analysts currently have access to "climate model" resolution data (e.g., at LLNL PCMDI), but not spatially downscaled translations of these datasets. Motivated by a common interest in supporting regional and local assessments, the U.S. Bureau of Reclamation and LLNL (through support from the DOE National Energy Technology Laboratory) have teamed to develop an archive of downscaled climate projections (temperature and precipitation) with geographic coverage consistent with the North American Land Data Assimilation System domain, encompassing the contiguous United States. A web-based information service, hosted at LLNL Green Data Oasis, has been developed to provide Reclamation, LLNL, and other interested analysts free access to archive content. A contemporary statistical method was used to bias-correct and spatially disaggregate projection datasets, and was applied to 112 projections included in the WCRP CMIP3 multi-model dataset hosted by LLNL PCMDI (i.e. 16 GCMs and their multiple simulations of SRES A2, A1b, and B1 emissions pathways).

Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France

DOE Office of Scientific and Technical Information (OSTI.GOV)

Caillouet, Laurie; Vidal, Jean -Philippe; Sauquet, Eric

This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871–2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late nineteenth century onwards. The SANDHY (Stepwise ANalogue Downscaling method for HYdrology) statistical downscaling method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the Safran high-resolution near-surface reanalysis,more » available from 1958 onwards only. SANDHY provides a daily ensemble of 125 analogue dates over the 1871–2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping the structure of the SANDHY method unchanged while reducing those seasonal biases. The calendar selection keeps the analogues closest to the target calendar day. The stepwise selection applies two new analogy steps based on similarity of the sea surface temperature (SST) and the large-scale 2 m temperature ( T). Comparisons to the Safran reanalysis over 1959–2007 and to homogenized series over the whole twentieth century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable method suitable in a quantitative precipitation forecast context, the stepwise subselection method allows for potential season shifts and SST trends and is therefore better suited for climate reconstructions and climate change studies. Furthermore, the probabilistic downscaling of 20CR over the period 1871–2012 with the SANDHY probabilistic downscaling method combined with the stepwise subselection thus constitutes a perfect framework for assessing the recent observed meteorological events but also future events projected by climate change impact studies and putting them in a historical perspective.« less

Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France

DOE PAGES

Caillouet, Laurie; Vidal, Jean -Philippe; Sauquet, Eric; ...

2016-03-16

This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871–2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late nineteenth century onwards. The SANDHY (Stepwise ANalogue Downscaling method for HYdrology) statistical downscaling method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the Safran high-resolution near-surface reanalysis,more » available from 1958 onwards only. SANDHY provides a daily ensemble of 125 analogue dates over the 1871–2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping the structure of the SANDHY method unchanged while reducing those seasonal biases. The calendar selection keeps the analogues closest to the target calendar day. The stepwise selection applies two new analogy steps based on similarity of the sea surface temperature (SST) and the large-scale 2 m temperature ( T). Comparisons to the Safran reanalysis over 1959–2007 and to homogenized series over the whole twentieth century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable method suitable in a quantitative precipitation forecast context, the stepwise subselection method allows for potential season shifts and SST trends and is therefore better suited for climate reconstructions and climate change studies. Furthermore, the probabilistic downscaling of 20CR over the period 1871–2012 with the SANDHY probabilistic downscaling method combined with the stepwise subselection thus constitutes a perfect framework for assessing the recent observed meteorological events but also future events projected by climate change impact studies and putting them in a historical perspective.« less

Analyzing the future climate change of Upper Blue Nile River basin using statistical downscaling techniques

NASA Astrophysics Data System (ADS)

Fenta Mekonnen, Dagnenet; Disse, Markus

2018-04-01

Climate change is becoming one of the most threatening issues for the world today in terms of its global context and its response to environmental and socioeconomic drivers. However, large uncertainties between different general circulation models (GCMs) and coarse spatial resolutions make it difficult to use the outputs of GCMs directly, especially for sustainable water management at regional scale, which introduces the need for downscaling techniques using a multimodel approach. This study aims (i) to evaluate the comparative performance of two widely used statistical downscaling techniques, namely the Long Ashton Research Station Weather Generator (LARS-WG) and the Statistical Downscaling Model (SDSM), and (ii) to downscale future climate scenarios of precipitation, maximum temperature (Tmax) and minimum temperature (Tmin) of the Upper Blue Nile River basin at finer spatial and temporal scales to suit further hydrological impact studies. The calibration and validation result illustrates that both downscaling techniques (LARS-WG and SDSM) have shown comparable and good ability to simulate the current local climate variables. Further quantitative and qualitative comparative performance evaluation was done by equally weighted and varying weights of statistical indexes for precipitation only. The evaluation result showed that SDSM using the canESM2 CMIP5 GCM was able to reproduce more accurate long-term mean monthly precipitation but LARS-WG performed best in capturing the extreme events and distribution of daily precipitation in the whole data range. Six selected multimodel CMIP3 GCMs, namely HadCM3, GFDL-CM2.1, ECHAM5-OM, CCSM3, MRI-CGCM2.3.2 and CSIRO-MK3 GCMs, were used for downscaling climate scenarios by the LARS-WG model. The result from the ensemble mean of the six GCM showed an increasing trend for precipitation, Tmax and Tmin. The relative change in precipitation ranged from 1.0 to 14.4 % while the change for mean annual Tmax may increase from 0.4 to 4.3 °C and the change for mean annual Tmin may increase from 0.3 to 4.1 °C. The individual result of the HadCM3 GCM has a good agreement with the ensemble mean result. HadCM3 from CMIP3 using A2a and B2a scenarios and canESM2 from CMIP5 GCMs under RCP2.6, RCP4.5 and RCP8.5 scenarios were downscaled by SDSM. The result from the two GCMs under five different scenarios agrees with the increasing direction of three climate variables (precipitation, Tmax and Tmin). The relative change of the downscaled mean annual precipitation ranges from 2.1 to 43.8 % while the change for mean annual Tmax and Tmin may increase in the range from 0.4 to 2.9 °C and from 0.3 to 1.6 °C respectively.

Influence of Scale Effect and Model Performance in Downscaling ASTER Land Surface Temperatures to a Very High Spatial Resolution in an Agricultural Area

NASA Astrophysics Data System (ADS)

Zhou, J.; Li, G.; Liu, S.; Zhan, W.; Zhang, X.

2015-12-01

At present land surface temperatures (LSTs) can be generated from thermal infrared remote sensing with spatial resolutions from ~100 m to tens of kilometers. However, LSTs with high spatial resolution, e.g. tens of meters, are still lack. The purpose of LST downscaling is to generate LSTs with finer spatial resolutions than their native spatial resolutions. The statistical linear or nonlinear regression models are most frequently used for LST downscaling. The basic assumption of these models is the scale-invariant relationships between LST and its descriptors, which is questioned but rare researches have been reported. In addition, few researches can be found for downscaling satellite LST or TIR data to a high spatial resolution, i.e. better than 100 m or even finer. The lack of LST with high spatial resolution cannot satisfy the requirements of applications such as evapotranspiration mapping at the field scale. By selecting a dynamically developing agricultural oasis as the study area, the aim of this study is to downscale the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LSTs to 15 m, to satisfy the requirement of evapotranspiration mapping at the field scale. Twelve ASTER images from May to September in 2012, covering the entire growth stage of maize, were selected. Four statistical models were evaluated, including one global model, one piecewise model, and two local models. The influence from scale effect in downscaling LST was quantified. The downscaled LSTs are evaluated from accuracy and image quality. Results demonstrate that the influence from scale effect varies according to models and the maize growth stage. Significant influence about -4 K to 6 K existed at the early stage and weaker influence existed in the middle stage. When compared with the ground measured LSTs, the downscaled LSTs resulted from the global and local models yielded higher accuracies and better image qualities than the local models. In addition to the vegetation indices, the surface albedo is an important descriptor for downscaling LST through explaining its spatial variation induced by soil moisture.

CMIP5 downscaling and its uncertainty in China

NASA Astrophysics Data System (ADS)

Yue, TianXiang; Zhao, Na; Fan, ZeMeng; Li, Jing; Chen, ChuanFa; Lu, YiMin; Wang, ChenLiang; Xu, Bing; Wilson, John

2016-11-01

A comparison between the Coupled Model Intercomparison Project Phase 5 (CMIP5) data and observations at 735 meteorological stations indicated that mean annual temperature (MAT) was underestimated about 1.8 °C while mean annual precipitation (MAP) was overestimated about 263 mm in general across the whole of China. A statistical analysis of China-CMIP5 data demonstrated that MAT exhibits spatial stationarity, while MAP exhibits spatial non-stationarity. MAT and MAP data from the China-CMIP5 dataset were downscaled by combining statistical approaches with a method for high accuracy surface modeling (HASM). A statistical transfer function (STF) of MAT was formulated using minimized residuals output by HASM with an ordinary least squares (OLS) linear equation that used latitude and elevation as independent variables, abbreviated as HASM-OLS. The STF of MAP under a BOX-COX transformation was derived as a combination of minimized residuals output by HASM with a geographically weight regression (GWR) using latitude, longitude, elevation and impact coefficient of aspect as independent variables, abbreviated as HASM-GB. Cross validation, using observational data from the 735 meteorological stations across China for the period 1976 to 2005, indicates that the largest uncertainty occurred on the Tibet plateau with mean absolute errors (MAEs) of MAT and MAP as high as 4.64 °C and 770.51 mm, respectively. The downscaling processes of HASM-OLS and HASM-GB generated MAEs of MAT and MAP that were 67.16% and 77.43% lower, respectively across the whole of China on average, and 88.48% and 97.09% lower for the Tibet plateau.

Multi-model Ensemble Regional Climate Projection of the Maritime Continent using the MIT Regional Climate Model

NASA Astrophysics Data System (ADS)

Kang, S.; IM, E. S.; Eltahir, E. A. B.

2016-12-01

In this study, the future change in precipitation due to global warming is investigated over the Maritime Continent using the MIT Regional Climate Model (MRCM). A total of nine 30-year projections under multi-GCMs (CCSM, MPI, ACCESS) and multi-scenarios of emissions (Control, RCP4.5, RCP8.5) are dynamically downscaled using the MRCM with 12km horizontal resolution. Since downscaled results tend to systematically overestimate the precipitation regardless of GCM used as lateral boundary conditions, the Parametric Quantile Mapping (PQM) is applied to reduce this wet bias. The cross validation for the control simulation shows that the PQM method seems to retain the spatial pattern and temporal variability of raw simulation, however it effectively reduce the wet bias. Based on ensemble projections produced by dynamical downscaling and statistical bias correction, a reduction of future precipitation is discernible, in particular during dry season (June-July-August). For example, intense precipitation in Singapore is expected to be reduced in RCP8.5 projection compared to control simulation. However, the geographical patterns and magnitude of changes still remain uncertain, suffering from statistical insignificance and a lack of model agreement. Acknowledgements This research is supported by the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise programme. The Center for Environmental Sensing and Modeling is an interdisciplinary research group of the Singapore-MIT Alliance for Research and Technology

The Schaake shuffle: A method for reconstructing space-time variability in forecasted precipitation and temperature fields

USGS Publications Warehouse

Clark, M.R.; Gangopadhyay, S.; Hay, L.; Rajagopalan, B.; Wilby, R.

2004-01-01

A number of statistical methods that are used to provide local-scale ensemble forecasts of precipitation and temperature do not contain realistic spatial covariability between neighboring stations or realistic temporal persistence for subsequent forecast lead times. To demonstrate this point, output from a global-scale numerical weather prediction model is used in a stepwise multiple linear regression approach to downscale precipitation and temperature to individual stations located in and around four study basins in the United States. Output from the forecast model is downscaled for lead times up to 14 days. Residuals in the regression equation are modeled stochastically to provide 100 ensemble forecasts. The precipitation and temperature ensembles from this approach have a poor representation of the spatial variability and temporal persistence. The spatial correlations for downscaled output are considerably lower than observed spatial correlations at short forecast lead times (e.g., less than 5 days) when there is high accuracy in the forecasts. At longer forecast lead times, the downscaled spatial correlations are close to zero. Similarly, the observed temporal persistence is only partly present at short forecast lead times. A method is presented for reordering the ensemble output in order to recover the space-time variability in precipitation and temperature fields. In this approach, the ensemble members for a given forecast day are ranked and matched with the rank of precipitation and temperature data from days randomly selected from similar dates in the historical record. The ensembles are then reordered to correspond to the original order of the selection of historical data. Using this approach, the observed intersite correlations, intervariable correlations, and the observed temporal persistence are almost entirely recovered. This reordering methodology also has applications for recovering the space-time variability in modeled streamflow. ?? 2004 American Meteorological Society.

Development and Application of a Soil Moisture Downscaling Method for Mobility Assessment

DTIC Science & Technology

2011-05-01

instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send...REPORT Development and Application of a Soil Moisture Downscaling Method for Mobility Assessment 14. ABSTRACT 16. SECURITY CLASSIFICATION OF: Soil...cells). Thus, a method is required to downscale intermediate-resolution patterns to finer resolutions. Fortunately, fine-resolution variations in

Meteorological Contribution to Variability in Particulate Matter Concentrations

NASA Astrophysics Data System (ADS)

Woods, H. L.; Spak, S. N.; Holloway, T.

2006-12-01

Local concentrations of fine particulate matter (PM) are driven by a number of processes, including emissions of aerosols and gaseous precursors, atmospheric chemistry, and meteorology at local, regional, and global scales. We apply statistical downscaling methods, typically used for regional climate analysis, to estimate the contribution of regional scale meteorology to PM mass concentration variability at a range of sites in the Upper Midwestern U.S. Multiple years of daily PM10 and PM2.5 data, reported by the U.S. Environmental Protection Agency (EPA), are correlated with large-scale meteorology over the region from the National Centers for Environmental Prediction (NCEP) reanalysis data. We use two statistical downscaling methods (multiple linear regression, MLR, and analog) to identify which processes have the greatest impact on aerosol concentration variability. Empirical Orthogonal Functions of the NCEP meteorological data are correlated with PM timeseries at measurement sites. We examine which meteorological variables exert the greatest influence on PM variability, and which sites exhibit the greatest response to regional meteorology. To evaluate model performance, measurement data are withheld for limited periods, and compared with model results. Preliminary results suggest that regional meteorological processes account over 50% of aerosol concentration variability at study sites.

Assessment of a stochastic downscaling methodology in generating an ensemble of hourly future climate time series

NASA Astrophysics Data System (ADS)

Fatichi, S.; Ivanov, V. Y.; Caporali, E.

2013-04-01

This study extends a stochastic downscaling methodology to generation of an ensemble of hourly time series of meteorological variables that express possible future climate conditions at a point-scale. The stochastic downscaling uses general circulation model (GCM) realizations and an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). Marginal distributions of factors of change are computed for several climate statistics using a Bayesian methodology that can weight GCM realizations based on the model relative performance with respect to a historical climate and a degree of disagreement in projecting future conditions. A Monte Carlo technique is used to sample the factors of change from their respective marginal distributions. As a comparison with traditional approaches, factors of change are also estimated by averaging GCM realizations. With either approach, the derived factors of change are applied to the climate statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series of meteorological variables that can be considered to be representative of future climate conditions. In this study, the time series are generated in an ensemble mode to fully reflect the uncertainty of GCM projections, climate stochasticity, as well as uncertainties of the downscaling procedure. Applications of the methodology in reproducing future climate conditions for the periods of 2000-2009, 2046-2065 and 2081-2100, using the period of 1962-1992 as the historical baseline are discussed for the location of Firenze (Italy). The inferences of the methodology for the period of 2000-2009 are tested against observations to assess reliability of the stochastic downscaling procedure in reproducing statistics of meteorological variables at different time scales.

Backcasting long-term climate data: evaluation of hypothesis

NASA Astrophysics Data System (ADS)

Saghafian, Bahram; Aghbalaghi, Sara Ghasemi; Nasseri, Mohsen

2018-05-01

Most often than not, incomplete datasets or short-term recorded data in vast regions impedes reliable climate and water studies. Various methods, such as simple correlation with stations having long-term time series, are practiced to infill or extend the period of observation at stations with missing or short-term data. In the current paper and for the first time, the hypothesis on the feasibility of extending the downscaling concept to backcast local observation records using large-scale atmospheric predictors is examined. Backcasting is coined here to contrast forecasting/projection; the former is implied to reconstruct in the past, while the latter represents projection in the future. To assess our hypotheses, daily and monthly statistical downscaling models were employed to reconstruct past precipitation data and lengthen the data period. Urmia and Tabriz synoptic stations, located in northwestern Iran, constituted two case study stations. SDSM and data-mining downscaling model (DMDM) daily as well as the group method of data handling (GMDH) and model tree (Mp5) monthly downscaling models were trained with National Center for Environmental Prediction (NCEP) data. After training, reconstructed precipitation data of the past was validated against observed data. Then, the data was fully extended to the 1948 to 2009 period corresponding to available NCEP data period. The results showed that DMDM performed superior in generation of monthly average precipitation compared with the SDSM, Mp5, and GMDH models, although none of the models could preserve the monthly variance. This overall confirms practical value of the proposed approach in extension of the past historic data, particularly for long-term climatological and water budget studies.

Downscaling Satellite Land Surface Temperatures in Urban Regions for Surface Energy Balance Study and Heat Index Development

NASA Astrophysics Data System (ADS)

Norouzi, H.; Bah, A.; Prakash, S.; Nouri, N.; Blake, R.

2017-12-01

A great percentage of the world's population reside in urban areas that are exposed to the threats of global and regional climate changes and associated extreme weather events. Among them, urban heat islands have significant health and economic impacts due to higher thermal gradients of impermeable surfaces in urban regions compared to their surrounding rural areas. Therefore, accurate characterization of the surface energy balance in urban regions are required to predict these extreme events. High spatial resolution Land surface temperature (LST) in the scale of street level in the cities can provide wealth of information to study surface energy balance and eventually providing a reliable heat index. In this study, we estimate high-resolution LST maps using combination of LandSat 8 and infrared based satellite products such as Moderate Resolution Imaging Spectroradiometer (MODIS) and newly launched Geostationary Operational Environmental Satellite-R Series (GOES-R). Landsat 8 provides higher spatial resolution (30 m) estimates of skin temperature every 16 days. However, MODIS and GOES-R have lower spatial resolution (1km and 4km respectively) with much higher temporal resolution. Several statistical downscaling methods were investigated to provide high spatiotemporal LST maps in urban regions. The results reveal that statistical methods such as Principal Component Analysis (PCA) can provide reliable estimations of LST downscaling with 2K accuracy. Other methods also were tried including aggregating (up-scaling) the high-resolution data to a coarse one to examine the limitations and to build the model. Additionally, we deployed flux towers over distinct materials such as concrete, asphalt, and rooftops in New York City to monitor the sensible and latent heat fluxes through eddy covariance method. To account for the incoming and outgoing radiation, a 4-component radiometer is used that can observe both incoming and outgoing longwave and shortwave radiation. This enables us to accurately build the relationship between LST, air temperature, and the heat index in the future.

Stepping inside the niche: microclimate data are critical for accurate assessment of species' vulnerability to climate change.

PubMed

Storlie, Collin; Merino-Viteri, Andres; Phillips, Ben; VanDerWal, Jeremy; Welbergen, Justin; Williams, Stephen

2014-09-01

To assess a species' vulnerability to climate change, we commonly use mapped environmental data that are coarsely resolved in time and space. Coarsely resolved temperature data are typically inaccurate at predicting temperatures in microhabitats used by an organism and may also exhibit spatial bias in topographically complex areas. One consequence of these inaccuracies is that coarsely resolved layers may predict thermal regimes at a site that exceed species' known thermal limits. In this study, we use statistical downscaling to account for environmental factors and develop high-resolution estimates of daily maximum temperatures for a 36 000 km(2) study area over a 38-year period. We then demonstrate that this statistical downscaling provides temperature estimates that consistently place focal species within their fundamental thermal niche, whereas coarsely resolved layers do not. Our results highlight the need for incorporation of fine-scale weather data into species' vulnerability analyses and demonstrate that a statistical downscaling approach can yield biologically relevant estimates of thermal regimes. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Revealing Risks in Adaptation Planning: expanding Uncertainty Treatment and dealing with Large Projection Ensembles during Planning Scenario development

NASA Astrophysics Data System (ADS)

Brekke, L. D.; Clark, M. P.; Gutmann, E. D.; Wood, A.; Mizukami, N.; Mendoza, P. A.; Rasmussen, R.; Ikeda, K.; Pruitt, T.; Arnold, J. R.; Rajagopalan, B.

2015-12-01

Adaptation planning assessments often rely on single methods for climate projection downscaling and hydrologic analysis, do not reveal uncertainties from associated method choices, and thus likely produce overly confident decision-support information. Recent work by the authors has highlighted this issue by identifying strengths and weaknesses of widely applied methods for downscaling climate projections and assessing hydrologic impacts. This work has shown that many of the methodological choices made can alter the magnitude, and even the sign of the climate change signal. Such results motivate consideration of both sources of method uncertainty within an impacts assessment. Consequently, the authors have pursued development of improved downscaling techniques spanning a range of method classes (quasi-dynamical and circulation-based statistical methods) and developed approaches to better account for hydrologic analysis uncertainty (multi-model; regional parameter estimation under forcing uncertainty). This presentation summarizes progress in the development of these methods, as well as implications of pursuing these developments. First, having access to these methods creates an opportunity to better reveal impacts uncertainty through multi-method ensembles, expanding on present-practice ensembles which are often based only on emissions scenarios and GCM choices. Second, such expansion of uncertainty treatment combined with an ever-expanding wealth of global climate projection information creates a challenge of how to use such a large ensemble for local adaptation planning. To address this challenge, the authors are evaluating methods for ensemble selection (considering the principles of fidelity, diversity and sensitivity) that is compatible with present-practice approaches for abstracting change scenarios from any "ensemble of opportunity". Early examples from this development will also be presented.

Probabilistic regional climate projection in Japan using a regression model with CMIP5 multi-model ensemble experiments

NASA Astrophysics Data System (ADS)

Ishizaki, N. N.; Dairaku, K.; Ueno, G.

2016-12-01

We have developed a statistical downscaling method for estimating probabilistic climate projection using CMIP5 multi general circulation models (GCMs). A regression model was established so that the combination of weights of GCMs reflects the characteristics of the variation of observations at each grid point. Cross validations were conducted to select GCMs and to evaluate the regression model to avoid multicollinearity. By using spatially high resolution observation system, we conducted statistically downscaled probabilistic climate projections with 20-km horizontal grid spacing. Root mean squared errors for monthly mean air surface temperature and precipitation estimated by the regression method were the smallest compared with the results derived from a simple ensemble mean of GCMs and a cumulative distribution function based bias correction method. Projected changes in the mean temperature and precipitation were basically similar to those of the simple ensemble mean of GCMs. Mean precipitation was generally projected to increase associated with increased temperature and consequent increased moisture content in the air. Weakening of the winter monsoon may affect precipitation decrease in some areas. Temperature increase in excess of 4 K was expected in most areas of Japan in the end of 21st century under RCP8.5 scenario. The estimated probability of monthly precipitation exceeding 300 mm would increase around the Pacific side during the summer and the Japan Sea side during the winter season. This probabilistic climate projection based on the statistical method can be expected to bring useful information to the impact studies and risk assessments.

Evaluation of NLDAS 12-km and downscaled 1-km temperature products in New York State for potential use in health exposure response studies

NASA Astrophysics Data System (ADS)

Estes, M. G., Jr.; Insaf, T.; Crosson, W. L.; Al-Hamdan, M. Z.

2017-12-01

Heat exposure metrics (maximum and minimum daily temperatures,) have a close relationship with human health. While meteorological station data provide a good source of point measurements, temporal and spatially consistent temperature data are needed for health studies. Reanalysis data such as the North American Land Data Assimilation System's (NLDAS) 12-km gridded product are an effort to resolve spatio-temporal environmental data issues; the resolution may be too coarse to accurately capture the effects of elevation, mixed land/water areas, and urbanization. As part of this NASA Applied Sciences Program funded project, the NLDAS 12-km air temperature product has been downscaled to 1-km using MODIS Land Surface Temperature patterns. Limited validation of the native 12-km NLDAS reanalysis data has been undertaken. Our objective is to evaluate the accuracy of both the 12-km and 1-km downscaled products using the US Historical Climatology Network station data geographically dispersed across New York State. Statistical methods including correlation, scatterplots, time series and summary statistics were used to determine the accuracy of the remotely-sensed maximum and minimum temperature products. The specific effects of elevation and slope on remotely-sensed temperature product accuracy were determined with 10-m digital elevation data that were used to calculate percent slope and link with the temperature products at multiple scales. Preliminary results indicate the downscaled temperature product improves accuracy over the native 12-km temperature product with average correlation improvements from 0.81 to 0.85 for minimum and 0.71 to 0.79 for maximum temperatures in 2009. However, the benefits vary temporally and geographically. Our results will inform health studies using remotely-sensed temperature products to determine health risk from excessive heat by providing a more robust assessment of the accuracy of the 12-km NLDAS product and additional accuracy gained from the 1-km downscaled product. Also, the results will be shared with the National Weather Service to determine potential benefits to heat warning systems and evaluated for inclusion in the Centers of Disease Control and Prevention (CDC) Environmental Public Health Tracking Network as a resource for the health community.

Impacts of climate change and internal climate variability on french rivers streamflows

NASA Astrophysics Data System (ADS)

Dayon, Gildas; Boé, Julien; Martin, Eric

2016-04-01

The assessment of the impacts of climate change often requires to set up long chains of modeling, from the model to estimate the future concentration of greenhouse gases to the impact model. Throughout the modeling chain, sources of uncertainty accumulate making the exploitation of results for the development of adaptation strategies difficult. It is proposed here to assess the impacts of climate change on the hydrological cycle over France and the associated uncertainties. The contribution of the uncertainties from greenhouse gases emission scenario, climate models and internal variability are addressed in this work. To have a large ensemble of climate simulations, the study is based on Global Climate Models (GCM) simulations from the Coupled Model Intercomparison Phase 5 (CMIP5), including several simulations from the same GCM to properly assess uncertainties from internal climate variability. Simulations from the four Radiative Concentration Pathway (RCP) are downscaled with a statistical method developed in a previous study (Dayon et al. 2015). The hydrological system Isba-Modcou is then driven by the downscaling results on a 8 km grid over France. Isba is a land surface model that calculates the energy and water balance and Modcou a hydrogeological model that routes the surface runoff given by Isba. Based on that framework, uncertainties uncertainties from greenhouse gases emission scenario, climate models and climate internal variability are evaluated. Their relative importance is described for the next decades and the end of this century. In a last part, uncertainties due to internal climate variability on streamflows simulated with downscaled GCM and Isba-Modcou are evaluated against observations and hydrological reconstructions on the whole 20th century. Hydrological reconstructions are based on the downscaling of recent atmospheric reanalyses of the 20th century and observations of temperature and precipitation. We show that the multi-decadal variability of streamflows observed in the 20th century is generally weaker in the hydrological simulations done with the historical simulations from climate models. References: Dayon et al. (2015), Transferability in the future climate of a statistical downscaling mehtod for precipitation in France, J. Geophys. Res. Atmos., 120, 1023-1043, doi:10.1002/2014JD022236

A New High Resolution Climate Dataset for Climate Change Impacts Assessments in New England

NASA Astrophysics Data System (ADS)

Komurcu, M.; Huber, M.

2016-12-01

Assessing regional impacts of climate change (such as changes in extreme events, land surface hydrology, water resources, energy, ecosystems and economy) requires much higher resolution climate variables than those available from global model projections. While it is possible to run global models in higher resolution, the high computational cost associated with these simulations prevent their use in such manner. To alleviate this problem, dynamical downscaling offers a method to deliver higher resolution climate variables. As part of an NSF EPSCoR funded interdisciplinary effort to assess climate change impacts on New Hampshire ecosystems, hydrology and economy (the New Hampshire Ecosystems and Society project), we create a unique high-resolution climate dataset for New England. We dynamically downscale global model projections under a high impact emissions scenario using the Weather Research and Forecasting model (WRF) with three nested grids of 27, 9 and 3 km horizontal resolution with the highest resolution innermost grid focusing over New England. We prefer dynamical downscaling over other methods such as statistical downscaling because it employs physical equations to progressively simulate climate variables as atmospheric processes interact with surface processes, emissions, radiation, clouds, precipitation and other model components, hence eliminates fix relationships between variables. In addition to simulating mean changes in regional climate, dynamical downscaling also allows for the simulation of climate extremes that significantly alter climate change impacts. We simulate three time slices: 2006-2015, 2040-2060 and 2080-2100. This new high-resolution climate dataset (with more than 200 variables saved in hourly (six hourly) intervals for the highest resolution domain (outer two domains)) along with model input and restart files used in our WRF simulations will be publicly available for use to the broader scientific community to support in-depth climate change impacts assessments for New England. We present results focusing on future changes in New England extreme events.

Downscaled climate projections for the Southeast United States: evaluation and use for ecological applications

USGS Publications Warehouse

Wootten, Adrienne; Smith, Kara; Boyles, Ryan; Terando, Adam; Stefanova, Lydia; Misra, Vasru; Smith, Tom; Blodgett, David L.; Semazzi, Fredrick

2014-01-01

Climate change is likely to have many effects on natural ecosystems in the Southeast U.S. The National Climate Assessment Southeast Technical Report (SETR) indicates that natural ecosystems in the Southeast are likely to be affected by warming temperatures, ocean acidification, sea-level rise, and changes in rainfall and evapotranspiration. To better assess these how climate changes could affect multiple sectors, including ecosystems, climatologists have created several downscaled climate projections (or downscaled datasets) that contain information from the global climate models (GCMs) translated to regional or local scales. The process of creating these downscaled datasets, known as downscaling, can be carried out using a broad range of statistical or numerical modeling techniques. The rapid proliferation of techniques that can be used for downscaling and the number of downscaled datasets produced in recent years present many challenges for scientists and decisionmakers in assessing the impact or vulnerability of a given species or ecosystem to climate change. Given the number of available downscaled datasets, how do these model outputs compare to each other? Which variables are available, and are certain downscaled datasets more appropriate for assessing vulnerability of a particular species? Given the desire to use these datasets for impact and vulnerability assessments and the lack of comparison between these datasets, the goal of this report is to synthesize the information available in these downscaled datasets and provide guidance to scientists and natural resource managers with specific interests in ecological modeling and conservation planning related to climate change in the Southeast U.S. This report enables the Southeast Climate Science Center (SECSC) to address an important strategic goal of providing scientific information and guidance that will enable resource managers and other participants in Landscape Conservation Cooperatives to make science-based climate change adaptation decisions.

Downscaling Global Emissions and Its Implications Derived from Climate Model Experiments

PubMed Central

Abe, Manabu; Kinoshita, Tsuguki; Hasegawa, Tomoko; Kawase, Hiroaki; Kushida, Kazuhide; Masui, Toshihiko; Oka, Kazutaka; Shiogama, Hideo; Takahashi, Kiyoshi; Tatebe, Hiroaki; Yoshikawa, Minoru

2017-01-01

In climate change research, future scenarios of greenhouse gas and air pollutant emissions generated by integrated assessment models (IAMs) are used in climate models (CMs) and earth system models to analyze future interactions and feedback between human activities and climate. However, the spatial resolutions of IAMs and CMs differ. IAMs usually disaggregate the world into 10–30 aggregated regions, whereas CMs require a grid-based spatial resolution. Therefore, downscaling emissions data from IAMs into a finer scale is necessary to input the emissions into CMs. In this study, we examined whether differences in downscaling methods significantly affect climate variables such as temperature and precipitation. We tested two downscaling methods using the same regionally aggregated sulfur emissions scenario obtained from the Asian-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) model. The downscaled emissions were fed into the Model for Interdisciplinary Research on Climate (MIROC). One of the methods assumed a strong convergence of national emissions intensity (e.g., emissions per gross domestic product), while the other was based on inertia (i.e., the base-year remained unchanged). The emissions intensities in the downscaled spatial emissions generated from the two methods markedly differed, whereas the emissions densities (emissions per area) were similar. We investigated whether the climate change projections of temperature and precipitation would significantly differ between the two methods by applying a field significance test, and found little evidence of a significant difference between the two methods. Moreover, there was no clear evidence of a difference between the climate simulations based on these two downscaling methods. PMID:28076446

Calculating distributed glacier mass balance for the Swiss Alps from RCM output: Development and testing of downscaling and validation methods

NASA Astrophysics Data System (ADS)

Machguth, H.; Paul, F.; Kotlarski, S.; Hoelzle, M.

2009-04-01

Climate model output has been applied in several studies on glacier mass balance calculation. Hereby, computation of mass balance has mostly been performed at the native resolution of the climate model output or data from individual cells were selected and statistically downscaled. Little attention has been given to the issue of downscaling entire fields of climate model output to a resolution fine enough to compute glacier mass balance in rugged high-mountain terrain. In this study we explore the use of gridded output from a regional climate model (RCM) to drive a distributed mass balance model for the perimeter of the Swiss Alps and the time frame 1979-2003. Our focus lies on the development and testing of downscaling and validation methods. The mass balance model runs at daily steps and 100 m spatial resolution while the RCM REMO provides daily grids (approx. 18 km resolution) of dynamically downscaled re-analysis data. Interpolation techniques and sub-grid parametrizations are combined to bridge the gap in spatial resolution and to obtain daily input fields of air temperature, global radiation and precipitation. The meteorological input fields are compared to measurements at 14 high-elevation weather stations. Computed mass balances are compared to various sets of direct measurements, including stake readings and mass balances for entire glaciers. The validation procedure is performed separately for annual, winter and summer balances. Time series of mass balances for entire glaciers obtained from the model run agree well with observed time series. On the one hand, summer melt measured at stakes on several glaciers is well reproduced by the model, on the other hand, observed accumulation is either over- or underestimated. It is shown that these shifts are systematic and correlated to regional biases in the meteorological input fields. We conclude that the gap in spatial resolution is not a large drawback, while biases in RCM output are a major limitation to model performance. The development and testing of methods to reduce regionally variable biases in entire fields of RCM output should be a focus of pursuing studies.

Results from the VALUE perfect predictor experiment: process-based evaluation

NASA Astrophysics Data System (ADS)

Maraun, Douglas; Soares, Pedro; Hertig, Elke; Brands, Swen; Huth, Radan; Cardoso, Rita; Kotlarski, Sven; Casado, Maria; Pongracz, Rita; Bartholy, Judit

2016-04-01

Until recently, the evaluation of downscaled climate model simulations has typically been limited to surface climatologies, including long term means, spatial variability and extremes. But these aspects are often, at least partly, tuned in regional climate models to match observed climate. The tuning issue is of course particularly relevant for bias corrected regional climate models. In general, a good performance of a model for these aspects in present climate does therefore not imply a good performance in simulating climate change. It is now widely accepted that, to increase our condidence in climate change simulations, it is necessary to evaluate how climate models simulate relevant underlying processes. In other words, it is important to assess whether downscaling does the right for the right reason. Therefore, VALUE has carried out a broad process-based evaluation study based on its perfect predictor experiment simulations: the downscaling methods are driven by ERA-Interim data over the period 1979-2008, reference observations are given by a network of 85 meteorological stations covering all European climates. More than 30 methods participated in the evaluation. In order to compare statistical and dynamical methods, only variables provided by both types of approaches could be considered. This limited the analysis to conditioning local surface variables on variables from driving processes that are simulated by ERA-Interim. We considered the following types of processes: at the continental scale, we evaluated the performance of downscaling methods for positive and negative North Atlantic Oscillation, Atlantic ridge and blocking situations. At synoptic scales, we considered Lamb weather types for selected European regions such as Scandinavia, the United Kingdom, the Iberian Pensinsula or the Alps. At regional scales we considered phenomena such as the Mistral, the Bora or the Iberian coastal jet. Such process-based evaluation helps to attribute biases in surface variables to underlying processes and ultimately to improve climate models.

Regional downscaling of temporal resolution in near-surface wind from statistically downscaled Global Climate Models (GCMs) for use in San Francisco Bay coastal flood modeling

NASA Astrophysics Data System (ADS)

O'Neill, A.; Erikson, L. H.; Barnard, P.

2013-12-01

While Global Climate Models (GCMs) provide useful projections of near-surface wind vectors into the 21st century, resolution is not sufficient enough for use in regional wave modeling. Statistically downscaled GCM projections from Multivariate Adaptive Constructed Analogues (MACA) provide daily near-surface winds at an appropriate spatial resolution for wave modeling within San Francisco Bay. Using 30 years (1975-2004) of climatological data from four representative stations around San Francisco Bay, a library of example daily wind conditions for four corresponding over-water sub-regions is constructed. Empirical cumulative distribution functions (ECDFs) of station conditions are compared to MACA GFDL hindcasts to create correction factors, which are then applied to 21st century MACA wind projections. For each projection day, a best match example is identified via least squares error among all stations from the library. The best match's daily variation in velocity components (u/v) is used as an analogue of representative wind variation and is applied at 3-hour increments about the corresponding sub-region's projected u/v values. High temporal resolution reconstructions using this methodology on hindcast MACA fields from 1975-2004 accurately recreate extreme wind values within the San Francisco Bay, and because these extremes in wind forcing are of key importance in wave and subsequent coastal flood modeling, this represents a valuable method of generating near-surface wind vectors for use in coastal flood modeling.

Comparison of present global reanalysis datasets in the context of a statistical downscaling method for precipitation prediction

NASA Astrophysics Data System (ADS)

Horton, Pascal; Weingartner, Rolf; Brönnimann, Stefan

2017-04-01

The analogue method is a statistical downscaling method for precipitation prediction. It uses similarity in terms of synoptic-scale predictors with situations in the past in order to provide a probabilistic prediction for the day of interest. It has been used for decades in a context of weather or flood forecasting, and is more recently also applied to climate studies, whether for reconstruction of past weather conditions or future climate impact studies. In order to evaluate the relationship between synoptic scale predictors and the local weather variable of interest, e.g. precipitation, reanalysis datasets are necessary. Nowadays, the number of available reanalysis datasets increases. These are generated by different atmospheric models with different assimilation techniques and offer various spatial and temporal resolutions. A major difference between these datasets is also the length of the archive they provide. While some datasets start at the beginning of the satellite era (1980) and assimilate these data, others aim at homogeneity on a longer period (e.g. 20th century) and only assimilate conventional observations. The context of the application of analogue methods might drive the choice of an appropriate dataset, for example when the archive length is a leading criterion. However, in many studies, a reanalysis dataset is subjectively chosen, according to the user's preferences or the ease of access. The impact of this choice on the results of the downscaling procedure is rarely considered and no comprehensive comparison has been undertaken so far. In order to fill this gap and to advise on the choice of appropriate datasets, nine different global reanalysis datasets were compared in seven distinct versions of analogue methods, over 300 precipitation stations in Switzerland. Significant differences in terms of prediction performance were identified. Although the impact of the reanalysis dataset on the skill score varies according to the chosen predictor, be it atmospheric circulation or thermodynamic variables, some hierarchy between the datasets is often preserved. This work can thus help choosing an appropriate dataset for the analogue method, or raise awareness of the consequences of using a certain dataset.

Downscale climate change scenarios over the Western Himalayan region of India using multi-generation CMIP experiments

NASA Astrophysics Data System (ADS)

Das, Lalu; Meher, Jitendra K.; Akhter, Javed

2017-04-01

Assessing climate change information over the Western Himalayan Region (WHR) of India is crucial but challenging task due to its limited numbers of station data containing huge missing values. The issues of missing values of station data were replaced the Multiple Imputation Chained Equation (MICE) technique. Finally 22 numbers of rain gauge stations having continuous data during 1901-2005 and 16 numbers stations having continuous temperature data during 1969-2009 were considered as " reference stations for assessing rainfall and temperature trends in addition to evaluation of the GCMs available in the Coupled Model Intercomparison Project, Phase 3 (CMIP3) and phase 5 (CMIP5) over WRH. Station data indicates that the winter warming is higher and rapid (1.05oC) than other seasons and less warming in the post monsoon season in the last 41 years. Area averaged using 22 station data indicates that monsoon and winter rainfall has decreased by -5 mm and -320 mm during 1901-2000 while pre-monsoon and post monsoon showed an increasing trends of 21 mm and 13 mm respectively. Present study is constructed the downscaled climate change information at station locations (22 and 16 stations for rainfall and temperature respectively) over the WHR from the GCMs commonly available in the IPCC's different generations assessment reports namely 2nd, 3rd, 4th and 5th thereafter known as SAR, TAR, AR4 and AR5 respectively. Once the downscaled results are obtained for each generation model outputs, then a comparison of studies is carried out from the results of each generation. Finally an overall model improvement index (OMII) is developed using the downscaling results which is used to investigate the model improvement across generations as well as the improvement of downscaling results obtained from the empirical statistical downscaling (ESD) methods. In general, the results indicate that there is a gradual improvement of GCMs simulations as well as downscaling results across generation. Key words: MICE Techniques, CMIP3, CMIP5, ESD and OMII

Inevitable end-of-21st-century trends toward earlier surface runoff timing in California's Sierra Nevada Mountains

NASA Astrophysics Data System (ADS)

Schwartz, M. A.; Hall, A. D.; Sun, F.; Walton, D.; Berg, N.

2015-12-01

Hybrid dynamical-statistical downscaling is used to produce surface runoff timing projections for California's Sierra Nevada, a high-elevation mountain range with significant seasonal snow cover. First, future climate change projections (RCP8.5 forcing scenario, 2081-2100 period) from five CMIP5 global climate models (GCMs) are dynamically downscaled. These projections reveal that future warming leads to a shift toward earlier snowmelt and surface runoff timing throughout the Sierra Nevada region. Relationships between warming and surface runoff timing from the dynamical simulations are used to build a simple statistical model that mimics the dynamical model's projected surface runoff timing changes given GCM input or other statistically-downscaled input. This statistical model can be used to produce surface runoff timing projections for other GCMs, periods, and forcing scenarios to quantify ensemble-mean changes, uncertainty due to intermodel variability and consequences stemming from choice of forcing scenario. For all CMIP5 GCMs and forcing scenarios, significant trends toward earlier surface runoff timing occur at elevations below 2500m. Thus, we conclude that trends toward earlier surface runoff timing by the end-of-the-21st century are inevitable. The changes to surface runoff timing diagnosed in this study have implications for many dimensions of climate change, including impacts on surface hydrology, water resources, and ecosystems.

Uncertainties of statistical downscaling from predictor selection: Equifinality and transferability

NASA Astrophysics Data System (ADS)

Fu, Guobin; Charles, Stephen P.; Chiew, Francis H. S.; Ekström, Marie; Potter, Nick J.

2018-05-01

The nonhomogeneous hidden Markov model (NHMM) statistical downscaling model, 38 catchments in southeast Australia and 19 general circulation models (GCMs) were used in this study to demonstrate statistical downscaling uncertainties caused by equifinality to and transferability. That is to say, there could be multiple sets of predictors that give similar daily rainfall simulation results for both calibration and validation periods, but project different amounts (or even directions of change) of rainfall changing in the future. Results indicated that two sets of predictors (Set 1 with predictors of sea level pressure north-south gradient, u-wind at 700 hPa, v-wind at 700 hPa, and specific humidity at 700 hPa and Set 2 with predictors of sea level pressure north-south gradient, u-wind at 700 hPa, v-wind at 700 hPa, and dewpoint temperature depression at 850 hPa) as inputs to the NHMM produced satisfactory results of seasonal rainfall in comparison with observations. For example, during the model calibration period, the relative errors across the 38 catchments ranged from 0.48 to 1.76% with a mean value of 1.09% for the predictor Set 1, and from 0.22 to 2.24% with a mean value of 1.16% for the predictor Set 2. However, the changes of future rainfall from NHMM projections based on 19 GCMs produced projections with a different sign for these two different sets of predictors: Set 1 predictors project an increase of future rainfall with magnitudes depending on future time periods and emission scenarios, but Set 2 predictors project a decline of future rainfall. Such divergent projections may present a significant challenge for applications of statistical downscaling as well as climate change impact studies, and could potentially imply caveats in many existing studies in the literature.

Downscaling global land-use/land-cover projections for use in region-level state-and-transition simulation modeling

USGS Publications Warehouse

Sherba, Jason T.; Sleeter, Benjamin M.; Davis, Adam W.; Parker, Owen P.

2015-01-01

Global land-use/land-cover (LULC) change projections and historical datasets are typically available at coarse grid resolutions and are often incompatible with modeling applications at local to regional scales. The difficulty of downscaling and reapportioning global gridded LULC change projections to regional boundaries is a barrier to the use of these datasets in a state-and-transition simulation model (STSM) framework. Here we compare three downscaling techniques to transform gridded LULC transitions into spatial scales and thematic LULC classes appropriate for use in a regional STSM. For each downscaling approach, Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) LULC projections, at the 0.5 × 0.5 cell resolution, were downscaled to seven Level III ecoregions in the Pacific Northwest, United States. RCP transition values at each cell were downscaled based on the proportional distribution between ecoregions of (1) cell area, (2) land-cover composition derived from remotely-sensed imagery, and (3) historic LULC transition values from a LULC history database. Resulting downscaled LULC transition values were aggregated according to their bounding ecoregion and “cross-walked” to relevant LULC classes. Ecoregion-level LULC transition values were applied in a STSM projecting LULC change between 2005 and 2100. While each downscaling methods had advantages and disadvantages, downscaling using the historical land-use history dataset consistently apportioned RCP LULC transitions in agreement with historical observations. Regardless of the downscaling method, some LULC projections remain improbable and require further investigation.

Towards the Goal of Modular Climate Data Services: An Overview of NCPP Applications and Software

NASA Astrophysics Data System (ADS)

Koziol, B. W.; Cinquini, L.; Treshansky, A.; Murphy, S.; DeLuca, C.

2013-12-01

In August 2013, the National Climate Predictions and Projections Platform (NCPP) organized a workshop focusing on the quantitative evaluation of downscaled climate data products (QED-2013). The QED-2013 workshop focused on real-world application problems drawn from several sectors (e.g. hydrology, ecology, environmental health, agriculture), and required that downscaled downscaled data products be dynamically accessed, generated, manipulated, annotated, and evaluated. The cyberinfrastructure elements that were integrated to support the workshop included (1) a wiki-based project hosting environment (Earth System CoG) with an interface to data services provided by an Earth System Grid Federation (ESGF) data node; (2) metadata tools provided by the Earth System Documentation (ES-DOC) collaboration; and (3) a Python-based library OpenClimateGIS (OCGIS) for subsetting and converting NetCDF-based climate data to GIS and tabular formats. Collectively, this toolset represents a first deployment of a 'ClimateTranslator' that enables users to access, interpret, and apply climate information at local and regional scales. This presentation will provide an overview of these components above, how they were used in the workshop, and discussion of current and potential integration. The long-term strategy for this software stack is to offer the suite of services described on a customizable, per-project basis. Additional detail on the three components is below. (1) Earth System CoG is a web-based collaboration environment that integrates data discovery and access services with tools for supporting governance and the organization of information. QED-2013 utilized these capabilities to share with workshop participants a suite of downscaled datasets, associated images derived from those datasets, and metadata files describing the downscaling techniques involved. The collaboration side of CoG was used for workshop organization, discussion, and results. (2) The ES-DOC Questionnaire, Viewer, and Comparator are web-based tools for the creation and use of model and experiment documentation. Workshop participants used the Questionnaire to generate metadata on regional downscaling models and statistical downscaling methods, and the Viewer to display the results. A prototype Comparator was available to compare properties across dynamically downscaled models. (3) OCGIS is a Python (v2.7) package designed for geospatial manipulation, subsetting, computation, and translation of Climate and Forecasting (CF)-compliant climate datasets - either stored in local NetCDF files, or files served through THREDDS data servers.

Downscaling RCP8.5 daily temperatures and precipitation in Ontario using localized ensemble optimal interpolation (EnOI) and bias correction

NASA Astrophysics Data System (ADS)

Deng, Ziwang; Liu, Jinliang; Qiu, Xin; Zhou, Xiaolan; Zhu, Huaiping

2017-10-01

A novel method for daily temperature and precipitation downscaling is proposed in this study which combines the Ensemble Optimal Interpolation (EnOI) and bias correction techniques. For downscaling temperature, the day to day seasonal cycle of high resolution temperature of the NCEP climate forecast system reanalysis (CFSR) is used as background state. An enlarged ensemble of daily temperature anomaly relative to this seasonal cycle and information from global climate models (GCMs) are used to construct a gain matrix for each calendar day. Consequently, the relationship between large and local-scale processes represented by the gain matrix will change accordingly. The gain matrix contains information of realistic spatial correlation of temperature between different CFSR grid points, between CFSR grid points and GCM grid points, and between different GCM grid points. Therefore, this downscaling method keeps spatial consistency and reflects the interaction between local geographic and atmospheric conditions. Maximum and minimum temperatures are downscaled using the same method. For precipitation, because of the non-Gaussianity issue, a logarithmic transformation is used to daily total precipitation prior to conducting downscaling. Cross validation and independent data validation are used to evaluate this algorithm. Finally, data from a 29-member ensemble of phase 5 of the Coupled Model Intercomparison Project (CMIP5) GCMs are downscaled to CFSR grid points in Ontario for the period from 1981 to 2100. The results show that this method is capable of generating high resolution details without changing large scale characteristics. It results in much lower absolute errors in local scale details at most grid points than simple spatial downscaling methods. Biases in the downscaled data inherited from GCMs are corrected with a linear method for temperatures and distribution mapping for precipitation. The downscaled ensemble projects significant warming with amplitudes of 3.9 and 6.5 °C for 2050s and 2080s relative to 1990s in Ontario, respectively; Cooling degree days and hot days will significantly increase over southern Ontario and heating degree days and cold days will significantly decrease in northern Ontario. Annual total precipitation will increase over Ontario and heavy precipitation events will increase as well. These results are consistent with conclusions in many other studies in the literature.

Quantifying the intra-annual uncertainties in climate change assessment over 10 sub-basins across the Pacific Northwest US

NASA Astrophysics Data System (ADS)

Ahmadalipour, Ali; Moradkhani, Hamid; Rana, Arun

2017-04-01

Uncertainty is an inevitable feature of climate change impact assessments. Understanding and quantifying different sources of uncertainty is of high importance, which can help modeling agencies improve the current models and scenarios. In this study, we have assessed the future changes in three climate variables (i.e. precipitation, maximum temperature, and minimum temperature) over 10 sub-basins across the Pacific Northwest US. To conduct the study, 10 statistically downscaled CMIP5 GCMs from two downscaling methods (i.e. BCSD and MACA) were utilized at 1/16 degree spatial resolution for the historical period of 1970-2000 and future period of 2010-2099. For the future projections, two future scenarios of RCP4.5 and RCP8.5 were used. Furthermore, Bayesian Model Averaging (BMA) was employed to develop a probabilistic future projection for each climate variable. Results indicate superiority of BMA simulations compared to individual models. Increasing temperature and precipitation are projected at annual timescale. However, the changes are not uniform among different seasons. Model uncertainty shows to be the major source of uncertainty, while downscaling uncertainty significantly contributes to the total uncertainty, especially in summer.

Probabilistic projections of 21st century climate change over Northern Eurasia

NASA Astrophysics Data System (ADS)

Monier, E.; Sokolov, A. P.; Schlosser, C. A.; Scott, J. R.; Gao, X.

2013-12-01

We present probabilistic projections of 21st century climate change over Northern Eurasia using the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model that couples an earth system model of intermediate complexity, with a two-dimensional zonal-mean atmosphere, to a human activity model. Regional climate change is obtained by two downscaling methods: a dynamical downscaling, where the IGSM is linked to a three dimensional atmospheric model; and a statistical downscaling, where a pattern scaling algorithm uses climate-change patterns from 17 climate models. This framework allows for key sources of uncertainty in future projections of regional climate change to be accounted for: emissions projections; climate system parameters (climate sensitivity, strength of aerosol forcing and ocean heat uptake rate); natural variability; and structural uncertainty. Results show that the choice of climate policy and the climate parameters are the largest drivers of uncertainty. We also nd that dierent initial conditions lead to dierences in patterns of change as large as when using different climate models. Finally, this analysis reveals the wide range of possible climate change over Northern Eurasia, emphasizing the need to consider all sources of uncertainty when modeling climate impacts over Northern Eurasia.

Probabilistic projections of 21st century climate change over Northern Eurasia

NASA Astrophysics Data System (ADS)

Monier, Erwan; Sokolov, Andrei; Schlosser, Adam; Scott, Jeffery; Gao, Xiang

2013-12-01

We present probabilistic projections of 21st century climate change over Northern Eurasia using the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model that couples an Earth system model of intermediate complexity with a two-dimensional zonal-mean atmosphere to a human activity model. Regional climate change is obtained by two downscaling methods: a dynamical downscaling, where the IGSM is linked to a three-dimensional atmospheric model, and a statistical downscaling, where a pattern scaling algorithm uses climate change patterns from 17 climate models. This framework allows for four major sources of uncertainty in future projections of regional climate change to be accounted for: emissions projections, climate system parameters (climate sensitivity, strength of aerosol forcing and ocean heat uptake rate), natural variability, and structural uncertainty. The results show that the choice of climate policy and the climate parameters are the largest drivers of uncertainty. We also find that different initial conditions lead to differences in patterns of change as large as when using different climate models. Finally, this analysis reveals the wide range of possible climate change over Northern Eurasia, emphasizing the need to consider these sources of uncertainty when modeling climate impacts over Northern Eurasia.

Projections and downscaling of 21st century temperatures, precipitation, radiative fluxes and winds for the southwestern US, with focus on the Lake Tahoe basin

USGS Publications Warehouse

Dettinger, Michael D.

2013-01-01

Recent projections of global climate changes in response to increasing greenhouse-gas concentrations in the atmosphere include warming in the Southwestern US and, especially, in the vicinity of Lake Tahoe of from about +3°C to +6°C by end of century and changes in precipitation on the order of 5-10 % increases or (more commonly) decreases, depending on the climate model considered. Along with these basic changes, other climate variables like solar insolation, downwelling (longwave) radiant heat, and winds may change. Together these climate changes may result in changes in the hydrology of the Tahoe basin and potential changes in lake overturning and ecological regimes. Current climate projections, however, are generally spatially too coarse (with grid cells separated by 1 to 2° latitude and longitude) for direct use in assessments of the vulnerabilities of the much smaller Tahoe basin. Thus, daily temperatures, precipitation, winds, and downward radiation fluxes from selected global projections have been downscaled by a statistical method called the constructed-analogues method onto 10 to 12 km grids over the Southwest and especially over Lake Tahoe. Precipitation, solar insolation and winds over the Tahoe basin change only moderately (and with indeterminate signs) in the downscaled projections, whereas temperatures and downward longwave fluxes increase along with imposed increases in global greenhouse-gas concentrations.

Simulation of an ensemble of future climate time series with an hourly weather generator

NASA Astrophysics Data System (ADS)

Caporali, E.; Fatichi, S.; Ivanov, V. Y.; Kim, J.

2010-12-01

There is evidence that climate change is occurring in many regions of the world. The necessity of climate change predictions at the local scale and fine temporal resolution is thus warranted for hydrological, ecological, geomorphological, and agricultural applications that can provide thematic insights into the corresponding impacts. Numerous downscaling techniques have been proposed to bridge the gap between the spatial scales adopted in General Circulation Models (GCM) and regional analyses. Nevertheless, the time and spatial resolutions obtained as well as the type of meteorological variables may not be sufficient for detailed studies of climate change effects at the local scales. In this context, this study presents a stochastic downscaling technique that makes use of an hourly weather generator to simulate time series of predicted future climate. Using a Bayesian approach, the downscaling procedure derives distributions of factors of change for several climate statistics from a multi-model ensemble of GCMs. Factors of change are sampled from their distributions using a Monte Carlo technique to entirely account for the probabilistic information obtained with the Bayesian multi-model ensemble. Factors of change are subsequently applied to the statistics derived from observations to re-evaluate the parameters of the weather generator. The weather generator can reproduce a wide set of climate variables and statistics over a range of temporal scales, from extremes, to the low-frequency inter-annual variability. The final result of such a procedure is the generation of an ensemble of hourly time series of meteorological variables that can be considered as representative of future climate, as inferred from GCMs. The generated ensemble of scenarios also accounts for the uncertainty derived from multiple GCMs used in downscaling. Applications of the procedure in reproducing present and future climates are presented for different locations world-wide: Tucson (AZ), Detroit (MI), and Firenze (Italy). The stochastic downscaling is carried out with eight GCMs from the CMIP3 multi-model dataset (IPCC 4AR, A1B scenario).

Performance of Statistical Temporal Downscaling Techniques of Wind Speed Data Over Aegean Sea

NASA Astrophysics Data System (ADS)

Gokhan Guler, Hasan; Baykal, Cuneyt; Ozyurt, Gulizar; Kisacik, Dogan

2016-04-01

Wind speed data is a key input for many meteorological and engineering applications. Many institutions provide wind speed data with temporal resolutions ranging from one hour to twenty four hours. Higher temporal resolution is generally required for some applications such as reliable wave hindcasting studies. One solution to generate wind data at high sampling frequencies is to use statistical downscaling techniques to interpolate values of the finer sampling intervals from the available data. In this study, the major aim is to assess temporal downscaling performance of nine statistical interpolation techniques by quantifying the inherent uncertainty due to selection of different techniques. For this purpose, hourly 10-m wind speed data taken from 227 data points over Aegean Sea between 1979 and 2010 having a spatial resolution of approximately 0.3 degrees are analyzed from the National Centers for Environmental Prediction (NCEP) The Climate Forecast System Reanalysis database. Additionally, hourly 10-m wind speed data of two in-situ measurement stations between June, 2014 and June, 2015 are considered to understand effect of dataset properties on the uncertainty generated by interpolation technique. In this study, nine statistical interpolation techniques are selected as w0 (left constant) interpolation, w6 (right constant) interpolation, averaging step function interpolation, linear interpolation, 1D Fast Fourier Transform interpolation, 2nd and 3rd degree Lagrange polynomial interpolation, cubic spline interpolation, piecewise cubic Hermite interpolating polynomials. Original data is down sampled to 6 hours (i.e. wind speeds at 0th, 6th, 12th and 18th hours of each day are selected), then 6 hourly data is temporally downscaled to hourly data (i.e. the wind speeds at each hour between the intervals are computed) using nine interpolation technique, and finally original data is compared with the temporally downscaled data. A penalty point system based on coefficient of variation root mean square error, normalized mean absolute error, and prediction skill is selected to rank nine interpolation techniques according to their performance. Thus, error originated from the temporal downscaling technique is quantified which is an important output to determine wind and wave modelling uncertainties, and the performance of these techniques are demonstrated over Aegean Sea indicating spatial trends and discussing relevance to data type (i.e. reanalysis data or in-situ measurements). Furthermore, bias introduced by the best temporal downscaling technique is discussed. Preliminary results show that overall piecewise cubic Hermite interpolating polynomials have the highest performance to temporally downscale wind speed data for both reanalysis data and in-situ measurements over Aegean Sea. However, it is observed that cubic spline interpolation performs much better along Aegean coastline where the data points are close to the land. Acknowledgement: This research was partly supported by TUBITAK Grant number 213M534 according to Turkish Russian Joint research grant with RFBR and the CoCoNET (Towards Coast to Coast Network of Marine Protected Areas Coupled by Wİnd Energy Potential) project funded by European Union FP7/2007-2013 program.

Seasonal Atmospheric and Oceanic Predictions

NASA Technical Reports Server (NTRS)

Roads, John; Rienecker, Michele (Technical Monitor)

2003-01-01

Several projects associated with dynamical, statistical, single column, and ocean models are presented. The projects include: 1) Regional Climate Modeling; 2) Statistical Downscaling; 3) Evaluation of SCM and NSIPP AGCM Results at the ARM Program Sites; and 4) Ocean Forecasts.

An initial assessment of a SMAP soil moisture disaggregation scheme using TIR surface evaporation data over the continental United States

NASA Astrophysics Data System (ADS)

Mishra, Vikalp; Ellenburg, W. Lee; Griffin, Robert E.; Mecikalski, John R.; Cruise, James F.; Hain, Christopher R.; Anderson, Martha C.

2018-06-01

The Soil Moisture Active Passive (SMAP) mission is dedicated toward global soil moisture mapping. Typically, an L-band microwave radiometer has spatial resolution on the order of 36-40 km, which is too coarse for many specific hydro-meteorological and agricultural applications. With the failure of the SMAP active radar within three months of becoming operational, an intermediate (9-km) and finer (3-km) scale soil moisture product solely from the SMAP mission is no longer possible. Therefore, the focus of this study is a disaggregation of the 36-km resolution SMAP passive-only surface soil moisture (SSM) using the Soil Evaporative Efficiency (SEE) approach to spatial scales of 3-km and 9-km. The SEE was computed using thermal-infrared (TIR) estimation of surface evaporation over Continental U.S. (CONUS). The disaggregation results were compared with the 3 months of SMAP-Active (SMAP-A) and Active/Passive (AP) products, while comparisons with SMAP-Enhanced (SMAP-E), SMAP-Passive (SMAP-P), as well as with more than 180 Soil Climate Analysis Network (SCAN) stations across CONUS were performed for a 19 month period. At the 9-km spatial scale, the TIR-Downscaled data correlated strongly with the SMAP-E SSM both spatially (r = 0.90) and temporally (r = 0.87). In comparison with SCAN observations, overall correlations of 0.49 and 0.47; bias of -0.022 and -0.019 and unbiased RMSD of 0.105 and 0.100 were found for SMAP-E and TIR-Downscaled SSM across the Continental U.S., respectively. At 3-km scale, TIR-Downscaled and SMAP-A had a mean temporal correlation of only 0.27. In terms of gain statistics, the highest percentage of SCAN sites with positive gains (>55%) was observed with the TIR-Downscaled SSM at 9-km. Overall, the TIR-based downscaled SSM showed strong correspondence with SMAP-E; compared to SCAN, and overall both SMAP-E and TIR-Downscaled performed similarly, however, gain statistics show that TIR-Downscaled SSM slightly outperformed SMAP-E.

Sub-daily Statistical Downscaling of Meteorological Variables Using Neural Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kumar, Jitendra; Brooks, Bjørn-Gustaf J.; Thornton, Peter E

2012-01-01

A new open source neural network temporal downscaling model is described and tested using CRU-NCEP reanal ysis and CCSM3 climate model output. We downscaled multiple meteorological variables in tandem from monthly to sub-daily time steps while also retaining consistent correlations between variables. We found that our feed forward, error backpropagation approach produced synthetic 6 hourly meteorology with biases no greater than 0.6% across all variables and variance that was accurate within 1% for all variables except atmospheric pressure, wind speed, and precipitation. Correlations between downscaled output and the expected (original) monthly means exceeded 0.99 for all variables, which indicates thatmore » this approach would work well for generating atmospheric forcing data consistent with mass and energy conserved GCM output. Our neural network approach performed well for variables that had correlations to other variables of about 0.3 and better and its skill was increased by downscaling multiple correlated variables together. Poor replication of precipitation intensity however required further post-processing in order to obtain the expected probability distribution. The concurrence of precipitation events with expected changes in sub ordinate variables (e.g., less incident shortwave radiation during precipitation events) were nearly as consistent in the downscaled data as in the training data with probabilities that differed by no more than 6%. Our downscaling approach requires training data at the target time step and relies on a weak assumption that climate variability in the extrapolated data is similar to variability in the training data.« less

Downscaling climate information for local disease mapping.

PubMed

Bernardi, M; Gommes, R; Grieser, J

2006-06-01

The study of the impacts of climate on human health requires the interdisciplinary efforts of health professionals, climatologists, biologists, and social scientists to analyze the relationships among physical, biological, ecological, and social systems. As the disease dynamics respond to variations in regional and local climate, climate variability affects every region of the world and the diseases are not necessarily limited to specific regions, so that vectors may become endemic in other regions. Climate data at local level are thus essential to evaluate the dynamics of vector-borne disease through health-climate models and most of the times the climatological databases are not adequate. Climate data at high spatial resolution can be derived by statistical downscaling using historical observations but the method is limited by the availability of historical data at local level. Since the 90s', the statistical interpolation of climate data has been an important priority of the Agrometeorology Group of the Food and Agriculture Organization of the United Nations (FAO), as they are required for agricultural planning and operational activities at the local level. Since 1995, date of the first FAO spatial interpolation software for climate data, more advanced applications have been developed such as SEDI (Satellite Enhanced Data Interpolation) for the downscaling of climate data, LOCCLIM (Local Climate Estimator) and the NEW_LOCCLIM in collaboration with the Deutscher Wetterdienst (German Weather Service) to estimate climatic conditions at locations for which no observations are available. In parallel, an important effort has been made to improve the FAO climate database including at present more than 30,000 stations worldwide and expanding the database from developing countries coverage to global coverage.

The Use of Statistical Downscaling to Project Regional Climate Changes as they Relate to Future Energy Production

NASA Astrophysics Data System (ADS)

Werth, D. W.; O'Steen, L.; Chen, K.; Altinakar, M. S.; Garrett, A.; Aleman, S.; Ramalingam, V.

2010-12-01

Global climate change has the potential for profound impacts on society, and poses significant challenges to government and industry in the areas of energy security and sustainability. Given that the ability to exploit energy resources often depends on the climate, the possibility of climate change means we cannot simply assume that the untapped potential of today will still exist in the future. Predictions of future climate are generally based on global climate models (GCMs) which, due to computational limitations, are run at spatial resolutions of hundreds of kilometers. While the results from these models can predict climatic trends averaged over large spatial and temporal scales, their ability to describe the effects of atmospheric phenomena that affect weather on regional to local scales is inadequate. We propose the use of several optimized statistical downscaling techniques that can infer climate change at the local scale from coarse resolution GCM predictions, and apply the results to assess future sustainability for two sources of energy production dependent on adequate water resources: nuclear power (through the dissipation of waste heat from cooling towers, ponds, etc.) and hydroelectric power. All methods will be trained with 20th century data, and applied to data from the years 2040-2049 to get the local-scale changes. Models of cooling tower operation and hydropower potential will then use the downscaled data to predict the possible changes in energy production, and the implications of climate change on plant siting, design, and contribution to the future energy grid can then be examined.

Impacts of precipitation and potential evapotranspiration patterns on downscaling soil moisture in regions with large topographic relief

NASA Astrophysics Data System (ADS)

Cowley, Garret S.; Niemann, Jeffrey D.; Green, Timothy R.; Seyfried, Mark S.; Jones, Andrew S.; Grazaitis, Peter J.

2017-02-01

Soil moisture can be estimated at coarse resolutions (>1 km) using satellite remote sensing, but that resolution is poorly suited for many applications. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales coarse-resolution soil moisture using fine-resolution topographic, vegetation, and soil data to produce fine-resolution (10-30 m) estimates of soil moisture. The EMT+VS model performs well at catchments with low topographic relief (≤124 m), but it has not been applied to regions with larger ranges of elevation. Large relief can produce substantial variations in precipitation and potential evapotranspiration (PET), which might affect the fine-resolution patterns of soil moisture. In this research, simple methods to downscale temporal average precipitation and PET are developed and included in the EMT+VS model, and the effects of spatial variations in these variables on the surface soil moisture estimates are investigated. The methods are tested against ground truth data at the 239 km2 Reynolds Creek watershed in southern Idaho, which has 1145 m of relief. The precipitation and PET downscaling methods are able to capture the main features in the spatial patterns of both variables. The space-time Nash-Sutcliffe coefficients of efficiency of the fine-resolution soil moisture estimates improve from 0.33 to 0.36 and 0.41 when the precipitation and PET downscaling methods are included, respectively. PET downscaling provides a larger improvement in the soil moisture estimates than precipitation downscaling likely because the PET pattern is more persistent through time, and thus more predictable, than the precipitation pattern.

Downscaling of daily precipitation using a hybrid model of Artificial Neural Network, Wavelet, and Quantile Mapping in Gharehsoo River Basin, Iran

NASA Astrophysics Data System (ADS)

Taie Semiromi, M.; Koch, M.

2017-12-01

Although linear/regression statistical downscaling methods are very straightforward and widely used, and they can be applied to a single predictor-predictand pair or spatial fields of predictors-predictands, the greatest constraint is the requirement of a normal distribution of the predictor and the predictand values, which means that it cannot be used to predict the distribution of daily rainfall because it is typically non-normal. To tacked with such a limitation, the current study aims to introduce a new developed hybrid technique taking advantages from Artificial Neural Networks (ANNs), Wavelet and Quantile Mapping (QM) for downscaling of daily precipitation for 10 rain-gauge stations located in Gharehsoo River Basin, Iran. With the purpose of daily precipitation downscaling, the study makes use of Second Generation Canadian Earth System Model (CanESM2) developed by Canadian Centre for Climate Modeling and Analysis (CCCma). Climate projections are available for three representative concentration pathways (RCPs) namely RCP 2.6, RCP 4.5 and RCP 8.5 for up to 2100. In this regard, 26 National Centers for Environmental Prediction (NCEP) reanalysis large-scale variables which have potential physical relationships with precipitation, were selected as candidate predictors. Afterwards, predictor screening was conducted using correlation, partial correlation and explained variance between predictors and predictand (precipitation). Depending on each rain-gauge station between two and three predictors were selected which their decomposed details (D) and approximation (A) obtained from discrete wavelet analysis were fed as inputs to the neural networks. After downscaling of daily precipitation, bias correction was conducted using quantile mapping. Out of the complete time series available, i.e. 1978-2005, two third of which namely 1978-1996 was used for calibration of QM and the reminder, i.e. 1997-2005 was considered for the validation. Result showed that the proposed hybrid method supported by QM for bias-correction could quite satisfactorily simulate daily precipitation. Also, results indicated that under all RCPs, precipitation will be more or less than 12% decreased by 2100. However, precipitation will be less decreased under RCP 8.5 compared with RCP 4.5.

Application of Unmanned Aerial Systems in Spatial Downscaling of Landsat VIR imageries of Agricultural Fields

NASA Astrophysics Data System (ADS)

Torres, A.; Hassan Esfahani, L.; Ebtehaj, A.; McKee, M.

2016-12-01

While coarse space-time resolution of satellite observations in visible to near infrared (VIR) is a serious limiting factor for applications in precision agriculture, high resolution remotes sensing observation by the Unmanned Aerial Systems (UAS) systems are also site-specific and still practically restrictive for widespread applications in precision agriculture. We present a modern spatial downscaling approach that relies on new sparse approximation techniques. The downscaling approach learns from a large set of coincident low- and high-resolution satellite and UAS observations to effectively downscale the satellite imageries in VIR bands. We focus on field experiments using the AggieAirTM platform and Landsat 7 ETM+ and Landsat 8 OLI observations obtained in an intensive field campaign in 2013 over an agriculture field in Scipio, Utah. The results show that the downscaling methods can effectively increase the resolution of Landsat VIR imageries by the order of 2 to 4 from 30 m to 15 and 7.5 m, respectively. Specifically, on average, the downscaling method reduces the root mean squared errors up to 26%, considering bias corrected AggieAir imageries as the reference.

Quantifying the Value of Downscaled Climate Model Information for Adaptation Decisions: When is Downscaling a Smart Decision?

NASA Astrophysics Data System (ADS)

Terando, A. J.; Wootten, A.; Eaton, M. J.; Runge, M. C.; Littell, J. S.; Bryan, A. M.; Carter, S. L.

2015-12-01

Two types of decisions face society with respect to anthropogenic climate change: (1) whether to enact a global greenhouse gas abatement policy, and (2) how to adapt to the local consequences of current and future climatic changes. The practice of downscaling global climate models (GCMs) is often used to address (2) because GCMs do not resolve key features that will mediate global climate change at the local scale. In response, the development of downscaling techniques and models has accelerated to aid decision makers seeking adaptation guidance. However, quantifiable estimates of the value of information are difficult to obtain, particularly in decision contexts characterized by deep uncertainty and low system-controllability. Here we demonstrate a method to quantify the additional value that decision makers could expect if research investments are directed towards developing new downscaled climate projections. As a proof of concept we focus on a real-world management problem: whether to undertake assisted migration for an endangered tropical avian species. We also take advantage of recently published multivariate methods that account for three vexing issues in climate impacts modeling: maximizing climate model quality information, accounting for model dependence in ensembles of opportunity, and deriving probabilistic projections. We expand on these global methods by including regional (Caribbean Basin) and local (Puerto Rico) domains. In the local domain, we test whether a high resolution (2km) dynamically downscaled GCM reduces the multivariate error estimate compared to the original coarse-scale GCM. Initial tests show little difference between the downscaled and original GCM multivariate error. When propagated through to a species population model, the Value of Information analysis indicates that the expected utility that would accrue to the manager (and species) if this downscaling were completed may not justify the cost compared to alternative actions.

Spatial Downscaling of TRMM Precipitation using MODIS product in the Korean Peninsula

NASA Astrophysics Data System (ADS)

Cho, H.; Choi, M.

2013-12-01

Precipitation is a major driving force in the water cycle. But, it is difficult to provide spatially distributed precipitation data from isolated individual in situ. The Tropical Rainfall Monitoring Mission (TRMM) satellite can provide precipitation data with relatively coarse spatial resolution (0.25° scale) at daily basis. In order to overcome the coarse spatial resolution of TRMM precipitation products, we conducted a downscaling technique using a scaling parameter from the Moderate Resolution Imaging Spectroradiometers (MODIS) sensor. In this study, statistical relations between precipitation estimates derived from the TRMM satellite and the normalized difference vegetation index (NDVI) which is obtained from the MODIS sensor in TERRA satellite are found for different spatial scales on the Korean peninsula in northeast Asia. We obtain the downscaled precipitation mapping by regression equation between yearly TRMM precipitations values and annual average NDVI aggregating 1km to 25 degree. The downscaled precipitation is validated using time series of the ground measurements precipitation dataset provided by Korea Meteorological Organization (KMO) from 2002 to 2005. To improve the spatial downscaling of precipitation, we will conduct a study about correlation between precipitation and land surface temperature, perceptible water and other hydrological parameters.

An enhanced archive facilitating climate impacts analysis

USGS Publications Warehouse

Maurer, E.P.; Brekke, L.; Pruitt, T.; Thrasher, B.; Long, J.; Duffy, P.; Dettinger, M.; Cayan, D.; Arnold, J.

2014-01-01

We describe the expansion of a publicly available archive of downscaled climate and hydrology projections for the United States. Those studying or planning to adapt to future climate impacts demand downscaled climate model output for local or regional use. The archive we describe attempts to fulfill this need by providing data in several formats, selectable to meet user needs. Our archive has served as a resource for climate impacts modelers, water managers, educators, and others. Over 1,400 individuals have transferred more than 50 TB of data from the archive. In response to user demands, the archive has expanded from monthly downscaled data to include daily data to facilitate investigations of phenomena sensitive to daily to monthly temperature and precipitation, including extremes in these quantities. New developments include downscaled output from the new Coupled Model Intercomparison Project phase 5 (CMIP5) climate model simulations at both the monthly and daily time scales, as well as simulations of surface hydrologi- cal variables. The web interface allows the extraction of individual projections or ensemble statistics for user-defined regions, promoting the rapid assessment of model consensus and uncertainty for future projections of precipitation, temperature, and hydrology. The archive is accessible online (http://gdo-dcp.ucllnl.org/downscaled_ cmip_projections).

Projecting the Influence of Climate Change on Extreme Ground-level Ozone Events in Selected Ontario Cities =

NASA Astrophysics Data System (ADS)

Leung, Kinson He Yin

Ground-level ozone (O3) is perhaps one of the most familiar pollutants in Ontario, Canada because it is associated with most smog alerts in the province. O3 varies on a number of spatial and temporal scales, primarily due to meteorological variability and the impact of long-range transport of its precursors on the photochemical processes. The goal of this thesis is to project the change in the probability of occurrence of future Extreme Ground-level Ozone Events (EGLOEs) due to changes in atmospheric conditions as a result of climate change for cities located in the southern, eastern and northern parts of Ontario, Canada by using a combination of General Circulation / Global Climate Models (GCMs) and statistical downscaling. These Ontario cities are Toronto, Windsor, London, Kingston, Ottawa, Thunder Bay, Sudbury and North Bay. The successful downscaling method used in this research to generate city-specific climate change scenarios was the Statistical DownScaling Model (SDSM) version 4.2.2, which is a hybrid of regression-based and stochastic weather-generator downscaling methods. The results indicate that the mean values of the daily maximum ground-level ozone concentrations could increase by up to 12-17% in Southern Ontario, 8-16% in Eastern Ontario and 1.5-9% in Northern Ontario by the end of the century due largely to changes in long-range transport. Three important themes emerge from the results: 1) the research successfully model O3 concentration in a region where long-range transport plays a substantial role. 2) The clear confirmation regarding the role of long-range transport in determining O 3 concentration in most areas of Ontario. 3) The projected increase of ozone in Ontario, due largely to an increase of long-range transport, caused by shifting atmospheric dynamics rather than a direct temperature effect on ozone production. Moreover, the results indicate that the future Southern, Eastern and Northern Ontario's EGLOEs with the O3 concentration ≥ 80 ppb (the current Ontario 1-hour Ambient Air Quality criterion for extreme ozone concentration) will have an increase of over 60%, 50% and 62% respectively by the year of 2100 under the different future scenarios in the third version of the Coupled Global Climate Model (CGCM3) and the Hadley Centre's Climate Model (HadCM3).

Integrating K-means Clustering with Kernel Density Estimation for the Development of a Conditional Weather Generation Downscaling Model

NASA Astrophysics Data System (ADS)

Chen, Y.; Ho, C.; Chang, L.

2011-12-01

In previous decades, the climate change caused by global warming increases the occurrence frequency of extreme hydrological events. Water supply shortages caused by extreme events create great challenges for water resource management. To evaluate future climate variations, general circulation models (GCMs) are the most wildly known tools which shows possible weather conditions under pre-defined CO2 emission scenarios announced by IPCC. Because the study area of GCMs is the entire earth, the grid sizes of GCMs are much larger than the basin scale. To overcome the gap, a statistic downscaling technique can transform the regional scale weather factors into basin scale precipitations. The statistic downscaling technique can be divided into three categories include transfer function, weather generator and weather type. The first two categories describe the relationships between the weather factors and precipitations respectively based on deterministic algorithms, such as linear or nonlinear regression and ANN, and stochastic approaches, such as Markov chain theory and statistical distributions. In the weather type, the method has ability to cluster weather factors, which are high dimensional and continuous variables, into weather types, which are limited number of discrete states. In this study, the proposed downscaling model integrates the weather type, using the K-means clustering algorithm, and the weather generator, using the kernel density estimation. The study area is Shihmen basin in northern of Taiwan. In this study, the research process contains two steps, a calibration step and a synthesis step. Three sub-steps were used in the calibration step. First, weather factors, such as pressures, humidities and wind speeds, obtained from NCEP and the precipitations observed from rainfall stations were collected for downscaling. Second, the K-means clustering grouped the weather factors into four weather types. Third, the Markov chain transition matrixes and the conditional probability density function (PDF) of precipitations approximated by the kernel density estimation are calculated respectively for each weather types. In the synthesis step, 100 patterns of synthesis data are generated. First, the weather type of the n-th day are determined by the results of K-means clustering. The associated transition matrix and PDF of the weather type were also determined for the usage of the next sub-step in the synthesis process. Second, the precipitation condition, dry or wet, can be synthesized basing on the transition matrix. If the synthesized condition is dry, the quantity of precipitation is zero; otherwise, the quantity should be further determined in the third sub-step. Third, the quantity of the synthesized precipitation is assigned as the random variable of the PDF defined above. The synthesis efficiency compares the gap of the monthly mean curves and monthly standard deviation curves between the historical precipitation data and the 100 patterns of synthesis data.

Projecting the potential evapotranspiration by coupling different formulations and input data reliabilities: The possible uncertainty source for climate change impacts on hydrological regime

NASA Astrophysics Data System (ADS)

Wang, Weiguang; Li, Changni; Xing, Wanqiu; Fu, Jianyu

2017-12-01

Representing atmospheric evaporating capability for a hypothetical reference surface, potential evapotranspiration (PET) determines the upper limit of actual evapotranspiration and is an important input to hydrological models. Due that present climate models do not give direct estimates of PET when simulating the hydrological response to future climate change, the PET must be estimated first and is subject to the uncertainty on account of many existing formulae and different input data reliabilities. Using four different PET estimation approaches, i.e., the more physically Penman (PN) equation with less reliable input variables, more empirical radiation-based Priestley-Taylor (PT) equation with relatively dependable downscaled data, the most simply temperature-based Hamon (HM) equation with the most reliable downscaled variable, and downscaling PET directly by the statistical downscaling model, this paper investigated the differences of runoff projection caused by the alternative PET methods by a well calibrated abcd monthly hydrological model. Three catchments, i.e., the Luanhe River Basin, the Source Region of the Yellow River and the Ganjiang River Basin, representing a large climatic diversity were chosen as examples to illustrate this issue. The results indicated that although similar monthly patterns of PET over the period 2021-2050 for each catchment were provided by the four methods, the magnitudes of PET were still slightly different, especially for spring and summer months in the Luanhe River Basin and the Source Region of the Yellow River with relatively dry climate feature. The apparent discrepancy in magnitude of change in future runoff and even the diverse change direction for summer months in the Luanhe River Basin and spring months in the Source Region of the Yellow River indicated that the PET method related uncertainty occurred, especially in the Luanhe River Basin and the Source Region of the Yellow River with smaller aridity index. Moreover, the possible reason of discrepancies in uncertainty between three catchments was quantitatively discussed by the contribution analysis based on climatic elasticity method. This study can provide beneficial reference to comprehensively understand the impacts of climate change on hydrological regime and thus improve the regional strategy for future water resource management.

Evaluation of uncertainty in capturing the spatial variability and magnitudes of extreme hydrological events for the uMngeni catchment, South Africa

NASA Astrophysics Data System (ADS)

Kusangaya, Samuel; Warburton Toucher, Michele L.; van Garderen, Emma Archer

2018-02-01

Downscaled General Circulation Models (GCMs) output are used to forecast climate change and provide information used as input for hydrological modelling. Given that our understanding of climate change points towards an increasing frequency, timing and intensity of extreme hydrological events, there is therefore the need to assess the ability of downscaled GCMs to capture these extreme hydrological events. Extreme hydrological events play a significant role in regulating the structure and function of rivers and associated ecosystems. In this study, the Indicators of Hydrologic Alteration (IHA) method was adapted to assess the ability of simulated streamflow (using downscaled GCMs (dGCMs)) in capturing extreme river dynamics (high and low flows), as compared to streamflow simulated using historical climate data from 1960 to 2000. The ACRU hydrological model was used for simulating streamflow for the 13 water management units of the uMngeni Catchment, South Africa. Statistically downscaled climate models obtained from the Climate System Analysis Group at the University of Cape Town were used as input for the ACRU Model. Results indicated that, high flows and extreme high flows (one in ten year high flows/large flood events) were poorly represented both in terms of timing, frequency and magnitude. Simulated streamflow using dGCMs data also captures more low flows and extreme low flows (one in ten year lowest flows) than that captured in streamflow simulated using historical climate data. The overall conclusion was that although dGCMs output can reasonably be used to simulate overall streamflow, it performs poorly when simulating extreme high and low flows. Streamflow simulation from dGCMs must thus be used with caution in hydrological applications, particularly for design hydrology, as extreme high and low flows are still poorly represented. This, arguably calls for the further improvement of downscaling techniques in order to generate climate data more relevant and useful for hydrological applications such as in design hydrology. Nevertheless, the availability of downscaled climatic output provide the potential of exploring climate model uncertainties in different hydro climatic regions at local scales where forcing data is often less accessible but more accurate at finer spatial scales and with adequate spatial detail.

The effects of climate downscaling technique and observational data set on modeled ecological responses

Treesearch

Afshin Pourmokhtarian; Charles T. Driscoll; John L. Campbell; Katharine Hayhoe; Anne M. K. Stoner

2016-01-01

Assessments of future climate change impacts on ecosystems typically rely on multiple climate model projections, but often utilize only one downscaling approach trained on one set of observations. Here, we explore the extent to which modeled biogeochemical responses to changing climate are affected by the selection of the climate downscaling method and training...

Statistical Downscaling Of Local Climate In The Alpine Region

NASA Astrophysics Data System (ADS)

Kaspar, Severin; Philipp, Andreas; Jacobeit, Jucundus

2016-04-01

The impact of climate change on the alpine region was disproportional strong in the past decades compared to the surrounding areas, which becomes manifest in a higher increase in surface air temperature. Beside the thermal changes also implications for the hydrological cycle may be expected, acting as a very important factor not only for the ecosystem but also for mankind, in the form of water security or considering economical aspects like winter tourism etc. Therefore, in climate impact studies, it is necessary to focus on variables with high influence on the hydrological cycle, for example temperature, precipitation, wind, humidity and radiation. The aim of this study is to build statistical downscaling models which are able to reproduce temperature and precipitation at the mountainous alpine weather stations Zugspitze and Sonnblick and to further project these models into the future to identify possible changes in the behavior of these climate variables and with that in the hydrological cycle. Beside facing a in general very complex terrain in this high elevated regions, we have the advantage of a more direct atmospheric influence on the meteorology of the exposed weather stations from the large scale circulation. Two nonlinear statistical methods are developed to model the station-data series on a daily basis: On the one hand a conditional classification approach was used and on the other hand a model based on artificial neural networks (ANNs) was built. The latter is in focus of this presentation. One of the important steps of developing a new model approach is to find a reliable predictor setup with e.g. informative predictor variables or adequate location and size of the spatial domain. The question is: Can we include synoptic background knowledge to identify an optimal domain for an ANN approach? The yet developed ANN setups and configurations show promising results in downscaling both, temperature (up to 80 % of explained variance) and precipitation (up to 60 % of explained variance).

Application of Statistical Downscaling Techniques to Predict Rainfall and Its Spatial Analysis Over Subansiri River Basin of Assam, India

NASA Astrophysics Data System (ADS)

Barman, S.; Bhattacharjya, R. K.

2017-12-01

The River Subansiri is the major north bank tributary of river Brahmaputra. It originates from the range of Himalayas beyond the Great Himalayan range at an altitude of approximately 5340m. Subansiri basin extends from tropical to temperate zones and hence exhibits a great diversity in rainfall characteristics. In the Northern and Central Himalayan tracts, precipitation is scarce on account of high altitudes. On the other hand, Southeast part of the Subansiri basin comprising the sub-Himalayan and the plain tract in Arunachal Pradesh and Assam, lies in the tropics. Due to Northeast as well as Southwest monsoon, precipitation occurs in this region in abundant quantities. Particularly, Southwest monsoon causes very heavy precipitation in the entire Subansiri basin during May to October. In this study, the rainfall over Subansiri basin has been studied at 24 different locations by multiple linear and non-linear regression based statistical downscaling techniques and by Artificial Neural Network based model. APHRODITE's gridded rainfall data of 0.25˚ x 0.25˚ resolutions and climatic parameters of HadCM3 GCM of resolution 2.5˚ x 3.75˚ (latitude by longitude) have been used in this study. It has been found that multiple non-linear regression based statistical downscaling technique outperformed the other techniques. Using this method, the future rainfall pattern over the Subansiri basin has been analyzed up to the year 2099 for four different time periods, viz., 2020-39, 2040-59, 2060-79, and 2080-99 at all the 24 locations. On the basis of historical rainfall, the months have been categorized as wet months, months with moderate rainfall and dry months. The spatial changes in rainfall patterns for all these three types of months have also been analyzed over the basin. Potential decrease of rainfall in the wet months and months with moderate rainfall and increase of rainfall in the dry months are observed for the future rainfall pattern of the Subansiri basin.

Projections of the Ganges-Brahmaputra precipitation: downscaled from GCM predictors

USGS Publications Warehouse

Pervez, Md Shahriar; Henebry, Geoffrey M.

2014-01-01

Downscaling Global Climate Model (GCM) projections of future climate is critical for impact studies. Downscaling enables use of GCM experiments for regional scale impact studies by generating regionally specific forecasts connecting global scale predictions and regional scale dynamics. We employed the Statistical Downscaling Model (SDSM) to downscale 21st century precipitation for two data-sparse hydrologically challenging river basins in South Asia—the Ganges and the Brahmaputra. We used CGCM3.1 by Canadian Center for Climate Modeling and Analysis version 3.1 predictors in downscaling the precipitation. Downscaling was performed on the basis of established relationships between historical Global Summary of Day observed precipitation records from 43 stations and National Center for Environmental Prediction re-analysis large scale atmospheric predictors. Although the selection of predictors was challenging during the set-up of SDSM, they were found to be indicative of important physical forcings in the basins. The precipitation of both basins was largely influenced by geopotential height: the Ganges precipitation was modulated by the U component of the wind and specific humidity at 500 and 1000 h Pa pressure levels; whereas, the Brahmaputra precipitation was modulated by the V component of the wind at 850 and 1000 h Pa pressure levels. The evaluation of the SDSM performance indicated that model accuracy for reproducing precipitation at the monthly scale was acceptable, but at the daily scale the model inadequately simulated some daily extreme precipitation events. Therefore, while the downscaled precipitation may not be the suitable input to analyze future extreme flooding or drought events, it could be adequate for analysis of future freshwater availability. Analysis of the CGCM3.1 downscaled precipitation projection with respect to observed precipitation reveals that the precipitation regime in each basin may be significantly impacted by climate change. Precipitation during and after the monsoon is likely to increase in both basins under the A1B and A2 emission scenarios; whereas, the pre-monsoon precipitation is likely to decrease. Peak monsoon precipitation is likely to shift from July to August, and may impact the livelihoods of large rural populations linked to subsistence agriculture in the basins. Uncertainty analysis of the downscaled precipitation indicated that the uncertainty in the downscaled precipitation was less than the uncertainty in the original CGCM3.1 precipitation; hence, the CGCM3.1 downscaled precipitation was a better input for the regional hydrological impact studies. However, downscaled precipitation from multiple GCMs is suggested for comprehensive impact studies.

Development of probabilistic regional climate scenario in East Asia

NASA Astrophysics Data System (ADS)

Dairaku, K.; Ueno, G.; Ishizaki, N. N.

2015-12-01

Climate information and services for Impacts, Adaptation and Vulnerability (IAV) Assessments are of great concern. In order to develop probabilistic regional climate information that represents the uncertainty in climate scenario experiments in East Asia (CORDEX-EA and Japan), the probability distribution of 2m air temperature was estimated by using developed regression model. The method can be easily applicable to other regions and other physical quantities, and also to downscale to finer-scale dependent on availability of observation dataset. Probabilistic climate information in present (1969-1998) and future (2069-2098) climate was developed using CMIP3 SRES A1b scenarios 21 models and the observation data (CRU_TS3.22 & University of Delaware in CORDEX-EA, NIAES AMeDAS mesh data in Japan). The prototype of probabilistic information in CORDEX-EA and Japan represent the quantified structural uncertainties of multi-model ensemble experiments of climate change scenarios. Appropriate combination of statistical methods and optimization of climate ensemble experiments using multi-General Circulation Models (GCMs) and multi-regional climate models (RCMs) ensemble downscaling experiments are investigated.

Seasonal drought predictability in Portugal using statistical-dynamical techniques

NASA Astrophysics Data System (ADS)

Ribeiro, A. F. S.; Pires, C. A. L.

2016-08-01

Atmospheric forecasting and predictability are important to promote adaption and mitigation measures in order to minimize drought impacts. This study estimates hybrid (statistical-dynamical) long-range forecasts of the regional drought index SPI (3-months) over homogeneous regions from mainland Portugal, based on forecasts from the UKMO operational forecasting system, with lead-times up to 6 months. ERA-Interim reanalysis data is used for the purpose of building a set of SPI predictors integrating recent past information prior to the forecast launching. Then, the advantage of combining predictors with both dynamical and statistical background in the prediction of drought conditions at different lags is evaluated. A two-step hybridization procedure is performed, in which both forecasted and observed 500 hPa geopotential height fields are subjected to a PCA in order to use forecasted PCs and persistent PCs as predictors. A second hybridization step consists on a statistical/hybrid downscaling to the regional SPI, based on regression techniques, after the pre-selection of the statistically significant predictors. The SPI forecasts and the added value of combining dynamical and statistical methods are evaluated in cross-validation mode, using the R2 and binary event scores. Results are obtained for the four seasons and it was found that winter is the most predictable season, and that most of the predictive power is on the large-scale fields from past observations. The hybridization improves the downscaling based on the forecasted PCs, since they provide complementary information (though modest) beyond that of persistent PCs. These findings provide clues about the predictability of the SPI, particularly in Portugal, and may contribute to the predictability of crops yields and to some guidance on users (such as farmers) decision making process.

Analysis of the interannual variability of tropical cyclones striking the California coast based on statistical downscaling

NASA Astrophysics Data System (ADS)

Mendez, F. J.; Rueda, A.; Barnard, P.; Mori, N.; Nakajo, S.; Espejo, A.; del Jesus, M.; Diez Sierra, J.; Cofino, A. S.; Camus, P.

2016-02-01

Hurricanes hitting California have a very low ocurrence probability due to typically cool ocean temperature and westward tracks. However, damages associated to these improbable events would be dramatic in Southern California and understanding the oceanographic and atmospheric drivers is of paramount importance for coastal risk management for present and future climates. A statistical analysis of the historical events is very difficult due to the limited resolution of atmospheric and oceanographic forcing data available. In this work, we propose a combination of: (a) statistical downscaling methods (Espejo et al, 2015); and (b) a synthetic stochastic tropical cyclone (TC) model (Nakajo et al, 2014). To build the statistical downscaling model, Y=f(X), we apply a combination of principal component analysis and the k-means classification algorithm to find representative patterns from a potential TC index derived from large-scale SST fields in Eastern Central Pacific (predictor X) and the associated tropical cyclone ocurrence (predictand Y). SST data comes from NOAA Extended Reconstructed SST V3b providing information from 1854 to 2013 on a 2.0 degree x 2.0 degree global grid. As data for the historical occurrence and paths of tropical cycloneas are scarce, we apply a stochastic TC model which is based on a Monte Carlo simulation of the joint distribution of track, minimum sea level pressure and translation speed of the historical events in the Eastern Central Pacific Ocean. Results will show the ability of the approach to explain seasonal-to-interannual variability of the predictor X, which is clearly related to El Niño Southern Oscillation. References Espejo, A., Méndez, F.J., Diez, J., Medina, R., Al-Yahyai, S. (2015) Seasonal probabilistic forecasting of tropical cyclone activity in the North Indian Ocean, Journal of Flood Risk Management, DOI: 10.1111/jfr3.12197 Nakajo, S., N. Mori, T. Yasuda, and H. Mase (2014) Global Stochastic Tropical Cyclone Model Based on Principal Component Analysis and Cluster Analysis, Journal of Applied Meteorology and Climatology, DOI: 10.1175/JAMC-D-13-08.1

Use of statistically and dynamically downscaled atmospheric model output for hydrologic simulations in three mountainous basins in the western United States

USGS Publications Warehouse

Hay, L.E.; Clark, M.P.

2003-01-01

This paper examines the hydrologic model performance in three snowmelt-dominated basins in the western United States to dynamically- and statistically downscaled output from the National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis (NCEP). Runoff produced using a distributed hydrologic model is compared using daily precipitation and maximum and minimum temperature timeseries derived from the following sources: (1) NCEP output (horizontal grid spacing of approximately 210 km); (2) dynamically downscaled (DDS) NCEP output using a Regional Climate Model (RegCM2, horizontal grid spacing of approximately 52 km); (3) statistically downscaled (SDS) NCEP output; (4) spatially averaged measured data used to calibrate the hydrologic model (Best-Sta) and (5) spatially averaged measured data derived from stations located within the area of the RegCM2 model output used for each basin, but excluding Best-Sta set (All-Sta). In all three basins the SDS-based simulations of daily runoff were as good as runoff produced using the Best-Sta timeseries. The NCEP, DDS, and All-Sta timeseries were able to capture the gross aspects of the seasonal cycles of precipitation and temperature. However, in all three basins, the NCEP-, DDS-, and All-Sta-based simulations of runoff showed little skill on a daily basis. When the precipitation and temperature biases were corrected in the NCEP, DDS, and All-Sta timeseries, the accuracy of the daily runoff simulations improved dramatically, but, with the exception of the bias-corrected All-Sta data set, these simulations were never as accurate as the SDS-based simulations. This need for a bias correction may be somewhat troubling, but in the case of the large station-timeseries (All-Sta), the bias correction did indeed 'correct' for the change in scale. It is unknown if bias corrections to model output will be valid in a future climate. Future work is warranted to identify the causes for (and removal of) systematic biases in DDS simulations, and improve DDS simulations of daily variability in local climate. Until then, SDS based simulations of runoff appear to be the safer downscaling choice.

Coupling climate and hydrological models to evaluate the impact of climate change on run of the river hydropower schemes from UK study sites

NASA Astrophysics Data System (ADS)

Pasten-Zapata, Ernesto; Jones, Julie; Moggridge, Helen

2015-04-01

As climate change is expected to generate variations on the Earth's precipitation and temperature, the water cycle will also experience changes. Consequently, water users will have to be prepared for possible changes in future water availability. The main objective of this research is to evaluate the impacts of climate change on river regimes and the implications to the operation and feasibility of run of the river hydropower schemes by analyzing four UK study sites. Run of the river schemes are selected for analysis due to their higher dependence to the available river flow volumes when compared to storage hydropower schemes that can rely on previously accumulated water volumes (linked to poster in session HS5.3). Global Climate Models (GCMs) represent the main tool to assess future climate change. In this research, Regional Climate Models (RCMs), which dynamically downscale GCM outputs providing higher resolutions, are used as starting point to evaluate climate change within the study catchments. RCM daily temperature and precipitation will be downscaled to an appropriate scale for impact studies and bias corrected using different statistical methods: linear scaling, local intensity scaling, power transformation, variance scaling and delta change correction. The downscaled variables will then be coupled to hydrological models that have been previously calibrated and validated against observed daily river flow data. The coupled hydrological and climate models will then be used to simulate historic river flows that are compared to daily observed values in order to evaluate the model accuracy. As this research will employ several different RCMs (from the EURO-CORDEX simulations), downscaling and bias correction methodologies, greenhouse emission scenarios and hydrological models, the uncertainty of each element will be estimated. According to their uncertainty magnitude, a prediction of the best downscaling approach (or approaches) is expected to be obtained. The current progress of the project will be presented along with the steps to be followed in the future.

Using Random Forest to Improve the Downscaling of Global Livestock Census Data

PubMed Central

Nicolas, Gaëlle; Robinson, Timothy P.; Wint, G. R. William; Conchedda, Giulia; Cinardi, Giuseppina; Gilbert, Marius

2016-01-01

Large scale, high-resolution global data on farm animal distributions are essential for spatially explicit assessments of the epidemiological, environmental and socio-economic impacts of the livestock sector. This has been the major motivation behind the development of the Gridded Livestock of the World (GLW) database, which has been extensively used since its first publication in 2007. The database relies on a downscaling methodology whereby census counts of animals in sub-national administrative units are redistributed at the level of grid cells as a function of a series of spatial covariates. The recent upgrade of GLW1 to GLW2 involved automating the processing, improvement of input data, and downscaling at a spatial resolution of 1 km per cell (5 km per cell in the earlier version). The underlying statistical methodology, however, remained unchanged. In this paper, we evaluate new methods to downscale census data with a higher accuracy and increased processing efficiency. Two main factors were evaluated, based on sample census datasets of cattle in Africa and chickens in Asia. First, we implemented and evaluated Random Forest models (RF) instead of stratified regressions. Second, we investigated whether models that predicted the number of animals per rural person (per capita) could provide better downscaled estimates than the previous approach that predicted absolute densities (animals per km2). RF models consistently provided better predictions than the stratified regressions for both continents and species. The benefit of per capita over absolute density models varied according to the species and continent. In addition, different technical options were evaluated to reduce the processing time while maintaining their predictive power. Future GLW runs (GLW 3.0) will apply the new RF methodology with optimized modelling options. The potential benefit of per capita models will need to be further investigated with a better distinction between rural and agricultural populations. PMID:26977807

A statistical downscaling approach for roadside NO2 concentrations: Application to a WRF-Chem study for Berlin

NASA Astrophysics Data System (ADS)

Kuik, Friderike; Lauer, Axel; von Schneidemesser, Erika; Butler, Tim

2017-04-01

Many European cities continue to struggle with meeting the European air quality limits for NO2. In Berlin, Germany, most of the exceedances in NO2 recorded at monitoring sites near busy roads can be largely attributed to emissions from traffic. In order to assess the impact of changes in traffic emissions on air quality at policy relevant scales, we combine the regional atmosphere-chemistry transport model WRF-Chem at a resolution of 1kmx1km with a statistical downscaling approach. Here, we build on the recently published study evaluating the performance of a WRF-Chem setup in representing observed urban background NO2 concentrations from Kuik et al. (2016) and extend this setup by developing and testing an approach to statistically downscale simulated urban background NO2 concentrations to street level. The approach uses a multilinear regression model to relate roadside NO2 concentrations observed with the municipal monitoring network with observed NO2 concentrations at urban background sites and observed traffic counts. For this, the urban background NO2 concentrations are decomposed into a long term, a synoptic and a diurnal component using the Kolmogorov-Zurbenko filtering method. We estimate the coefficients of the regression model for five different roadside stations in Berlin representing different street types. In a next step we combine the coefficients with simulated urban background concentrations and observed traffic counts, in order to estimate roadside NO2 concentrations based on the results obtained with WRF-Chem at the five selected stations. In a third step, we extrapolate the NO2 concentrations to all major roads in Berlin. The latter is based on available data for Berlin of daily mean traffic counts, diurnal and weekly cycles of traffic as well as simulated urban background NO2 concentrations. We evaluate the NO2 concentrations estimated with this method at street level for Berlin with additional observational data from stationary measurements and mobile measurements conducted during a campaign in summer 2014. The results show that this approach allows us to estimate NO2 concentrations at roadside reasonably well. The approach can be applied when observations show a strong correlation between roadside NO2 concentrations and traffic emissions from a single type of road. The method, however, shows weaknesses for intersections where observed NO2 concentrations are influenced by traffic on several different roads. We then apply this downscaling approach to estimate the impact of different traffic emission scenarios both on urban background and street level NO2 concentrations. References Kuik, F., Lauer, A., Churkina, G., Denier van der Gon, H. A. C., Fenner, D., Mar, K. A., and Butler, T. M.: Air quality modelling in the Berlin-Brandenburg region using WRF-Chem v3.7.1: sensitivity to resolution of model grid and input data, Geosci. Model Dev., 9, 4339-4363, doi:10.5194/gmd-9-4339-2016, 2016.

Dynamical Downscaling of NASA/GISS ModelE: Continuous, Multi-Year WRF Simulations

NASA Astrophysics Data System (ADS)

Otte, T.; Bowden, J. H.; Nolte, C. G.; Otte, M. J.; Herwehe, J. A.; Faluvegi, G.; Shindell, D. T.

2010-12-01

The WRF Model is being used at the U.S. EPA for dynamical downscaling of the NASA/GISS ModelE fields to assess regional impacts of climate change in the United States. The WRF model has been successfully linked to the ModelE fields in their raw hybrid vertical coordinate, and continuous, multi-year WRF downscaling simulations have been performed. WRF will be used to downscale decadal time slices of ModelE for recent past, current, and future climate as the simulations being conducted for the IPCC Fifth Assessment Report become available. This presentation will focus on the sensitivity to interior nudging within the RCM. The use of interior nudging for downscaled regional climate simulations has been somewhat controversial over the past several years but has been recently attracting attention. Several recent studies that have used reanalysis (i.e., verifiable) fields as a proxy for GCM input have shown that interior nudging can be beneficial toward achieving the desired downscaled fields. In this study, the value of nudging will be shown using fields from ModelE that are downscaled using WRF. Several different methods of nudging are explored, and it will be shown that the method of nudging and the choices made with respect to how nudging is used in WRF are critical to balance the constraint of ModelE against the freedom of WRF to develop its own fields.

Downscaling Coarse Actual ET Data Using Land Surface Resistance

NASA Astrophysics Data System (ADS)

Shen, T.

2017-12-01

This study proposed a new approach of downscaling ETWATCH 1km actual evapotranspiration (ET) product to a spatial resolution of 30m using land surface resistance that simulated mainly from monthly Landsat8 data and Jarvis method, which combined the benefits of both high temporal resolution of ETWATCH product and fine spatial resolution of Landsat8. The driving factor, surface resistance (Rs), was chosen for the reason that could reflect the transfer ability of vapor flow over canopy. Combined resistance Rs both upon canopy conditions, atmospheric factors and available water content of soil, which remains stable inside one ETWATCH pixel (1km). In this research, we used ETWATCH 1km ten-day actual ET product from April to October in a total of twenty-one images and monthly 30 meters cloud-free NDVI of 2013 (two images from HJ as a substitute due to cloud contamination) combined meteorological indicators for downscaling. A good agreement and correlation were obtained between the downscaled data and three flux sites observation in the middle reach of Heihe basin. The downscaling results show good consistency with the original ETWATCH 1km data both temporal and spatial scale over different land cover types with R2 ranged from 0.8 to 0.98. Besides, downscaled result captured the progression of vegetation transpiration well. This study proved the practicability of new downscaling method in the water resource management.

Seasonal Prediction of Regional Surface Air Temperature and First-flowering Date in South Korea using Dynamical Downscaling

NASA Astrophysics Data System (ADS)

Ahn, J. B.; Hur, J.

2015-12-01

The seasonal prediction of both the surface air temperature and the first-flowering date (FFD) over South Korea are produced using dynamical downscaling (Hur and Ahn, 2015). Dynamical downscaling is performed using Weather Research and Forecast (WRF) v3.0 with the lateral forcing from hourly outputs of Pusan National University (PNU) coupled general circulation model (CGCM) v1.1. Gridded surface air temperature data with high spatial (3km) and temporal (daily) resolution are obtained using the physically-based dynamical models. To reduce systematic bias, simple statistical correction method is then applied to the model output. The FFDs of cherry, peach and pear in South Korea are predicted for the decade of 1999-2008 by applying the corrected daily temperature predictions to the phenological thermal-time model. The WRF v3.0 results reflect the detailed topographical effect, despite having cold and warm biases for warm and cold seasons, respectively. After applying the correction, the mean temperature for early spring (February to April) well represents the general pattern of observation, while preserving the advantages of dynamical downscaling. The FFD predictabilities for the three species of trees are evaluated in terms of qualitative, quantitative and categorical estimations. Although FFDs derived from the corrected WRF results well predict the spatial distribution and the variation of observation, the prediction performance has no statistical significance or appropriate predictability. The approach used in the study may be helpful in obtaining detailed and useful information about FFD and regional temperature by accounting for physically-based atmospheric dynamics, although the seasonal predictability of flowering phenology is not high enough. Acknowledgements This work was carried out with the support of the Rural Development Administration Cooperative Research Program for Agriculture Science and Technology Development under Grant Project No. PJ009953 and Project No. PJ009353, Republic of Korea. Reference Hur, J., J.-B. Ahn, 2015. Seasonal Prediction of Regional Surface Air Temperature and First-flowering Date over South Korea, Int. J. Climatol., DOI: 10.1002/joc.4323.

Fundamental statistical relationships between monthly and daily meteorological variables: Temporal downscaling of weather based on a global observational dataset

NASA Astrophysics Data System (ADS)

Sommer, Philipp; Kaplan, Jed

2016-04-01

Accurate modelling of large-scale vegetation dynamics, hydrology, and other environmental processes requires meteorological forcing on daily timescales. While meteorological data with high temporal resolution is becoming increasingly available, simulations for the future or distant past are limited by lack of data and poor performance of climate models, e.g., in simulating daily precipitation. To overcome these limitations, we may temporally downscale monthly summary data to a daily time step using a weather generator. Parameterization of such statistical models has traditionally been based on a limited number of observations. Recent developments in the archiving, distribution, and analysis of "big data" datasets provide new opportunities for the parameterization of a temporal downscaling model that is applicable over a wide range of climates. Here we parameterize a WGEN-type weather generator using more than 50 million individual daily meteorological observations, from over 10'000 stations covering all continents, based on the Global Historical Climatology Network (GHCN) and Synoptic Cloud Reports (EECRA) databases. Using the resulting "universal" parameterization and driven by monthly summaries, we downscale mean temperature (minimum and maximum), cloud cover, and total precipitation, to daily estimates. We apply a hybrid gamma-generalized Pareto distribution to calculate daily precipitation amounts, which overcomes much of the inability of earlier weather generators to simulate high amounts of daily precipitation. Our globally parameterized weather generator has numerous applications, including vegetation and crop modelling for paleoenvironmental studies.

US EPA 2012 Air Quality Fused Surface for the Conterminous U.S. Map Service

EPA Pesticide Factsheets

This web service contains a polygon layer that depicts fused air quality predictions for 2012 for census tracts in the conterminous United States. Fused air quality predictions (for ozone and PM2.5) are modeled using a Bayesian space-time downscaling fusion model approach described in a series of three published journal papers: 1) (Berrocal, V., Gelfand, A. E. and Holland, D. M. (2012). Space-time fusion under error in computer model output: an application to modeling air quality. Biometrics 68, 837-848; 2) Berrocal, V., Gelfand, A. E. and Holland, D. M. (2010). A bivariate space-time downscaler under space and time misalignment. The Annals of Applied Statistics 4, 1942-1975; and 3) Berrocal, V., Gelfand, A. E., and Holland, D. M. (2010). A spatio-temporal downscaler for output from numerical models. J. of Agricultural, Biological,and Environmental Statistics 15, 176-197) is used to provide daily, predictive PM2.5 (daily average) and O3 (daily 8-hr maximum) surfaces for 2012. Summer (O3) and annual (PM2.5) means calculated and published. The downscaling fusion model uses both air quality monitoring data from the National Air Monitoring Stations/State and Local Air Monitoring Stations (NAMS/SLAMS) and numerical output from the Models-3/Community Multiscale Air Quality (CMAQ). Currently, predictions at the US census tract centroid locations within the 12 km CMAQ domain are archived. Predictions at the CMAQ grid cell centroids, or any desired set of locations co

Dynamical Downscaling of Global Circulation Models With the Weather Research and Forecast Model in the Northern Great Plains

NASA Astrophysics Data System (ADS)

Burtch, D.; Mullendore, G. L.; Kennedy, A. D.; Simms, M.; Kirilenko, A.; Coburn, J.

2015-12-01

Understanding the impacts of global climate change on regional scales is crucial for accurate decision-making by state and local governments. This is especially true in North Dakota, where climate change can have significant consequences on agriculture, its traditionally strongest economic sector. This region of the country shows a high variability in precipitation, especially in the summer months and so the focus of this study is on warm season processes over decadal time scales. The Weather Research and Forecast (WRF) model is used to dynamically downscale two Global Circulation Models (GCMs) from the CMIP5 ensemble in order to determine the microphysical parameterization and nudging techniques (spectral or analysis) best suited for this region. The downscaled domain includes the entirety of North Dakota at a horizontal resolution of 5 km. In addition, smaller domains of 1 km horizontal resolution are centered over regions of focused hydrological importance. The dynamically downscaled simulations are compared with both gridded observational data and statistically downscaled data to evaluate the performance of the simulations. Preliminary results have shown a marked difference between the two downscaled GCMs in terms of temperature and precipitation bias. Choice of microphysical parameterization has not shown to create any significant differences in the temperature fields. However, the precipitation fields do appear to be most affected by the microphysical parameterization, regardless of the choice of GCM. Implications on the unique water resource challenges faced in this region will also be discussed.

Toward Robust and Efficient Climate Downscaling for Wind Energy

NASA Astrophysics Data System (ADS)

Vanvyve, E.; Rife, D.; Pinto, J. O.; Monaghan, A. J.; Davis, C. A.

2011-12-01

This presentation describes a more accurate and economical (less time, money and effort) wind resource assessment technique for the renewable energy industry, that incorporates innovative statistical techniques and new global mesoscale reanalyzes. The technique judiciously selects a collection of "case days" that accurately represent the full range of wind conditions observed at a given site over a 10-year period, in order to estimate the long-term energy yield. We will demonstrate that this new technique provides a very accurate and statistically reliable estimate of the 10-year record of the wind resource by intelligently choosing a sample of ±120 case days. This means that the expense of downscaling to quantify the wind resource at a prospective wind farm can be cut by two thirds from the current industry practice of downscaling a randomly chosen 365-day sample to represent winds over a "typical" year. This new estimate of the long-term energy yield at a prospective wind farm also has far less statistical uncertainty than the current industry standard approach. This key finding has the potential to reduce significantly market barriers to both onshore and offshore wind farm development, since insurers and financiers charge prohibitive premiums on investments that are deemed to be high risk. Lower uncertainty directly translates to lower perceived risk, and therefore far more attractive financing terms could be offered to wind farm developers who employ this new technique.

A Statistical Bias Correction Tool for Generating Climate Change Scenarios in Indonesia based on CMIP5 Datasets

NASA Astrophysics Data System (ADS)

Faqih, A.

2017-03-01

Providing information regarding future climate scenarios is very important in climate change study. The climate scenario can be used as basic information to support adaptation and mitigation studies. In order to deliver future climate scenarios over specific region, baseline and projection data from the outputs of global climate models (GCM) is needed. However, due to its coarse resolution, the data have to be downscaled and bias corrected in order to get scenario data with better spatial resolution that match the characteristics of the observed data. Generating this downscaled data is mostly difficult for scientist who do not have specific background, experience and skill in dealing with the complex data from the GCM outputs. In this regards, it is necessary to develop a tool that can be used to simplify the downscaling processes in order to help scientist, especially in Indonesia, for generating future climate scenario data that can be used for their climate change-related studies. In this paper, we introduce a tool called as “Statistical Bias Correction for Climate Scenarios (SiBiaS)”. The tool is specially designed to facilitate the use of CMIP5 GCM data outputs and process their statistical bias corrections relative to the reference data from observations. It is prepared for supporting capacity building in climate modeling in Indonesia as part of the Indonesia 3rd National Communication (TNC) project activities.

Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset

NASA Astrophysics Data System (ADS)

Lange, Stefan

2018-05-01

Many meteorological forcing datasets include bias-corrected surface downwelling longwave and shortwave radiation (rlds and rsds). Methods used for such bias corrections range from multi-year monthly mean value scaling to quantile mapping at the daily timescale. An additional downscaling is necessary if the data to be corrected have a higher spatial resolution than the observational data used to determine the biases. This was the case when EartH2Observe (E2OBS; Calton et al., 2016) rlds and rsds were bias-corrected using more coarsely resolved Surface Radiation Budget (SRB; Stackhouse Jr. et al., 2011) data for the production of the meteorological forcing dataset EWEMBI (Lange, 2016). This article systematically compares various parametric quantile mapping methods designed specifically for this purpose, including those used for the production of EWEMBI rlds and rsds. The methods vary in the timescale at which they operate, in their way of accounting for physical upper radiation limits, and in their approach to bridging the spatial resolution gap between E2OBS and SRB. It is shown how temporal and spatial variability deflation related to bilinear interpolation and other deterministic downscaling approaches can be overcome by downscaling the target statistics of quantile mapping from the SRB to the E2OBS grid such that the sub-SRB-grid-scale spatial variability present in the original E2OBS data is retained. Cross validations at the daily and monthly timescales reveal that it is worthwhile to take empirical estimates of physical upper limits into account when adjusting either radiation component and that, overall, bias correction at the daily timescale is more effective than bias correction at the monthly timescale if sampling errors are taken into account.

Future Temperatures and Precipitations in the Arid Northern-Central Chile: A Multi-Model Downscaling Approach

NASA Astrophysics Data System (ADS)

Souvignet, M.; Heinrich, J.

2010-03-01

Downscaling of global climate outputs is necessary to transfer projections of potential climate change scenarios to local levels. This is of special interest to dry mountainous areas, which are particularly vulnerable to climate change due to risks of reduced freshwater availability. These areas play a key role for hydrology since they usually receive the highest local precipitation rates stored in form of snow and glaciers. In the central-northern Chile (Norte Chico, 26-33ºS), where agriculture still serves as a backbone of the economy as well as ensures the well being of people, the knowledge of water resources availability is essential. The region is characterised by a semiarid climate with a mean annual precipitation inferior to 100mm. Moreover, the local climate is also highly influenced by the ENSO phenomenon, which accounts for the strong inter-annual variability in precipitation patterns. Although historical and spatially extensive precipitation data in the headwaters of the basins in this region are not readily available, records at coastal stations show worrisome trends. For instance, the average precipitation in La Serena, the most important city located in the Coquimbo Region, has decreased dramatically in the past 100 years. The 30-year monthly average has decreased from 170 mm in the early 20th century to values less than 80 mm nowadays. Climate Change is expected to strengthen this pattern in the region, and therefore strongly influence local hydrological patterns. The objectives of this study are i) to develop climate change scenarios (2046-2099) for the Norte Chico using multi-model predictions in terms of temperatures and precipitations, and ii) to compare the efficiency of two downscaling techniques in arid mountainous regions. In addition, this study aims at iii) providing decision makers with sound analysis of potential impact of Climate Change on streamflow in the region. For the present study, future local climate scenarios were developed for maximum, minimum temperature and precipitation in the research area based on four different General Circulation Models (GCMs). On the first hand, the Statistical Downscaling Model (SDSM) was used. This model is based on a multiple linear regression method and is best described as a hybrid of the stochastic weather generator and transfer function methods. One common advantage of statistical downscaling is that it ensures the maintenance of local spatial and temporal variability in generating realistic data time series. On the other hand and for comparison purposes, the Change Factor method was used. This methodology is relatively straightforward and ideal for rapid climate change assessment. The outputs of the HadCM3, CGCM3.1, GDFL-CM2 and MRI-CGCM2.3.2 A1 and B2 scenarios were downscaled with both methodologies and thereafter compared by means of several hydro-meteorological indices for a 55-years period (2045-2099). Preliminary results indicate that local temperatures are expected to rise in the region, whereas precipitations may decrease. However, minimum and maximum temperatures might increase at a faster rate at higher altitude areas. In addition, the Cordillera mountain range may encounter and longer winters with a dramatic decrease of icing days (Tmax<0°C). As for precipitation, both SRES scenarios for all models return a diminishing tendency, though the A2 scenario results show a faster decrease rate. Results indicate potential strong inter-seasonal and inter-annual perturbations in Rainfall in the region. Consequently, the Norte Chico will possibly see its streamflow strongly impacted with a resulting high variability at the seasonal and inter-annual level. A probabilistic analysis of the projections of the four GCMs provided a better representation of uncertainties linked with downscaled scenarios. Whereas maximum and minimum temperatures were accurately simulated by both downscaling methods, precipitation simulations returned weaker results. SDSM proved to have a poor ability to simulate extreme rainfall events and few conclusions could be drawn with respect to future occurrences of ENSO phenomena. On the other hand, the change factor method reproduced comparatively better historical precipitations. Despite all sources of error and uncertainties, which must be taken into account when handling the projections, this study addresses an issue that goes beyond local concerns and aims at developing a better understanding of impacts of climate change in fragile environments such as the arid and semiarid transition zone of north-central Chile. Its additional applied component goes therefore beyond the classical comparative study and aims at supporting stakeholders in their processes of decision making.

Climate change effects on extreme flows of water supply area in Istanbul: utility of regional climate models and downscaling method.

PubMed

Kara, Fatih; Yucel, Ismail

2015-09-01

This study investigates the climate change impact on the changes of mean and extreme flows under current and future climate conditions in the Omerli Basin of Istanbul, Turkey. The 15 regional climate model output from the EU-ENSEMBLES project and a downscaling method based on local implications from geophysical variables were used for the comparative analyses. Automated calibration algorithm is used to optimize the parameters of Hydrologiska Byråns Vattenbalansavdel-ning (HBV) model for the study catchment using observed daily temperature and precipitation. The calibrated HBV model was implemented to simulate daily flows using precipitation and temperature data from climate models with and without downscaling method for reference (1960-1990) and scenario (2071-2100) periods. Flood indices were derived from daily flows, and their changes throughout the four seasons and year were evaluated by comparing their values derived from simulations corresponding to the current and future climate. All climate models strongly underestimate precipitation while downscaling improves their underestimation feature particularly for extreme events. Depending on precipitation input from climate models with and without downscaling the HBV also significantly underestimates daily mean and extreme flows through all seasons. However, this underestimation feature is importantly improved for all seasons especially for spring and winter through the use of downscaled inputs. Changes in extreme flows from reference to future increased for the winter and spring and decreased for the fall and summer seasons. These changes were more significant with downscaling inputs. With respect to current time, higher flow magnitudes for given return periods will be experienced in the future and hence, in the planning of the Omerli reservoir, the effective storage and water use should be sustained.

A downscaling method for the assessment of local climate change

NASA Astrophysics Data System (ADS)

Bruno, E.; Portoghese, I.; Vurro, M.

2009-04-01

The use of complimentary models is necessary to study the impact of climate change scenarios on the hydrological response at different space-time scales. However, the structure of GCMs is such that their space resolution (hundreds of kilometres) is too coarse and not adequate to describe the variability of extreme events at basin scale (Burlando and Rosso, 2002). To bridge the space-time gap between the climate scenarios and the usual scale of the inputs for hydrological prediction models is a fundamental requisite for the evaluation of climate change impacts on water resources. Since models operate a simplification of a complex reality, their results cannot be expected to fit with climate observations. Identifying local climate scenarios for impact analysis implies the definition of more detailed local scenario by downscaling GCMs or RCMs results. Among the output correction methods we consider the statistical approach by Déqué (2007) reported as a ‘Variable correction method' in which the correction of model outputs is obtained by a function build with the observation dataset and operating a quantile-quantile transformation (Q-Q transform). However, in the case of daily precipitation fields the Q-Q transform is not able to correct the temporal property of the model output concerning the dry-wet lacunarity process. An alternative correction method is proposed based on a stochastic description of the arrival-duration-intensity processes in coherence with the Poissonian Rectangular Pulse scheme (PRP) (Eagleson, 1972). In this proposed approach, the Q-Q transform is applied to the PRP variables derived from the daily rainfall datasets. Consequently the corrected PRP parameters are used for the synthetic generation of statistically homogeneous rainfall time series that mimic the persistency of daily observations for the reference period. Then the PRP parameters are forced through the GCM scenarios to generate local scale rainfall records for the 21st century. The statistical parameters characterizing daily storm occurrence, storm intensity and duration needed to apply the PRP scheme are considered among STARDEX collection of extreme indices.

Downscaling U.S. public opinion about climate change and the 'Six Americas' to states, cities, and counties

NASA Astrophysics Data System (ADS)

Marlon, J. R.; Howe, P. D.; Leiserowitz, A.

2013-12-01

For climate change communication to be most effective, messages should be targeted to the characteristics of local audiences. In the U.S., 'Six Americas' have been identified among the public based on their response to the climate change issue. The distribution of these different 'publics' varies between states and communities, yet data about public opinion at the sub-national scale remains scarce. In this presentation, we describe a methodology to statistically downscale results from national-level surveys about the Six Americas, climate literacy, and other aspects of public opinion to smaller areas, including states, metropolitan areas, and counties. The method utilizes multilevel regression with poststratification (MRP) to model public opinion at various scales using a large national-level survey dataset. We present state and county-level estimates of two key beliefs about climate change: belief that climate change is happening, and belief in the scientific consensus about climate change. We further present estimates of how the Six Americas vary across the U.S.

Assessment of regional downscaling simulations for long term mean, excess and deficit Indian Summer Monsoons

NASA Astrophysics Data System (ADS)

Varikoden, Hamza; Mujumdar, M.; Revadekar, J. V.; Sooraj, K. P.; Ramarao, M. V. S.; Sanjay, J.; Krishnan, R.

2018-03-01

This study undertakes a comprehensive assessment of dynamical downscaling of summer monsoon (June-September; JJAS) rainfall over heterogeneous regions namely the Western Ghats (WG), Central India (CI) and North-Eastern Region (NER) for long term mean, excess and deficit episodes for the historical period from 1951 to 2005. This downscaling assessment is based on six Coordinated Regional Climate Downscaling Experiments (CORDEX) for South Asia (SAS) region, their five driving Global Climate Models (GCM) simulations along with observations from India Meteorological Department (IMD) and Asian Precipitation Highly Resolved Observational Integrated Towards Evaluation for Water Resources (APHRODITE). The analysis reveals an overall reduction of dry bias in rainfall across the regions of Indian sub-continent in most of the downscaled CORDEX-SAS models and in their ensemble mean as compared to that of driving GCMs. The interannual variabilities during historical period are reasonably captured by the ensemble means of CORDEX-SAS simulations with an underestimation of 0.43%, 38% and 52% for the WG, CI and NER, respectively. Upon careful examination of the CORDEX-SAS models and their driving GCMs revealed considerable improvement in the regionally downscaled rainfall. The value addition of dynamical downscaling is apparent over the WG in Regional Climate Model (RCM) simulations with an improvement of more than 30% for the long term mean, excess and deficit episodes from their driving GCMs. In the case of NER, the improvement in the downscaled rainfall product is more than 10% for all the episodes. However, the value addition in the CORDEX-SAS simulations for CI region, dominantly influenced by synoptic scale processes, is not clear. Nevertheless, the reduction of dry bias in the complex topographical regions is remarkable. The relative performance of dynamical downscaling of rainfall over complex topography in response to local forcing and orographic lifting depict the value addition (30% over WG and 10% over NER, with a statistical significance of more than 5% level), when compared with the synoptic scale system induced rainfall over the plains of central-India.

Analyzing the Multiscale Processes in Tropical Cyclone Genesis Associated with African Easterly Waves using the PEEMD. Part I: Downscaling Processes

NASA Astrophysics Data System (ADS)

Wu, Y.; Shen, B. W.; Cheung, S.

2016-12-01

Recent advance in high-resolution global hurricane simulations and visualizations have collectively suggested the importance of both downscaling and upscaling processes in the formation and intensification of TCs. To reveal multiscale processes from massive volume of global data for multiple years, a scalable Parallel Ensemble Empirical Mode Decomposition (PEEMD) method has been developed for the analysis. In this study, the PEEMD is applied to analyzing 10-year (2004-2013) ERA-Interim global 0.750 resolution reanalysis data to explore the role of the downscaling processes in tropical cyclogenesis associated with African Easterly Waves (AEWs). Using the PEEMD, raw data are decomposed into oscillatory Intrinsic Function Modes (IMFs) that represent atmospheric systems of the various length scales and the trend mode that represents a non-oscillatory large scale environmental flow. Among oscillatory modes, results suggest that the third oscillatory mode (IMF3) is statistically correlated with the TC/AEW scale systems. Therefore, IMF3 and trend mode are analyzed in details. Our 10-year analysis shows that more than 50% of the AEW associated hurricanes reveal the association of storms' formation with the significant downscaling shear transfer from the larger-scale trend mode to the smaller scale IMF3. Future work will apply the PEEMD to the analysis of higher-resolution datasets to explore the role of the upscaling processes provided by the convection (or TC) in the development of the TC (or AEW). Figure caption: The tendency for horizontal wind shear for the total winds (black line), IMF3 (blue line), and trend mode (red line) and SLP (black dotted line) along the storm track of Helene (2006).

Actor groups, related needs, and challenges at the climate downscaling interface

NASA Astrophysics Data System (ADS)

Rössler, Ole; Benestad, Rasmus; Diamando, Vlachogannis; Heike, Hübener; Kanamaru, Hideki; Pagé, Christian; Margarida Cardoso, Rita; Soares, Pedro; Maraun, Douglas; Kreienkamp, Frank; Christodoulides, Paul; Fischer, Andreas; Szabo, Peter

2016-04-01

At the climate downscaling interface, numerous downscaling techniques and different philosophies compete on being the best method in their specific terms. Thereby, it remains unclear to what extent and for which purpose these downscaling techniques are valid or even the most appropriate choice. A common validation framework that compares all the different available methods was missing so far. The initiative VALUE closes this gap with such a common validation framework. An essential part of a validation framework for downscaling techniques is the definition of appropriate validation measures. The selection of validation measures should consider the needs of the stakeholder: some might need a temporal or spatial average of a certain variable, others might need temporal or spatial distributions of some variables, still others might need extremes for the variables of interest or even inter-variable dependencies. Hence, a close interaction of climate data providers and climate data users is necessary. Thus, the challenge in formulating a common validation framework mirrors also the challenges between the climate data providers and the impact assessment community. This poster elaborates the issues and challenges at the downscaling interface as it is seen within the VALUE community. It suggests three different actor groups: one group consisting of the climate data providers, the other two groups being climate data users (impact modellers and societal users). Hence, the downscaling interface faces classical transdisciplinary challenges. We depict a graphical illustration of actors involved and their interactions. In addition, we identified four different types of issues that need to be considered: i.e. data based, knowledge based, communication based, and structural issues. They all may, individually or jointly, hinder an optimal exchange of data and information between the actor groups at the downscaling interface. Finally, some possible ways to tackle these issues are discussed.

Quantifying the mass loss of peripheral Greenland glaciers and ice caps (1958-2014).

NASA Astrophysics Data System (ADS)

Noël, Brice; van de Berg, Willem Jan; Machguth, Horst; van den Broeke, Michiel

2016-04-01

Since the 2000s, mass loss from Greenland peripheral glaciers and ice caps (GICs) has accelerated, becoming an important contributor to sea level rise. Under continued warming throughout the 21st century, GICs might yield up to 7.5 to 11 mm sea level rise, with increasing dominance of surface runoff at the expense of ice discharge. However, despite multiple observation campaigns, little remains known about the contribution of GICs to total Greenland mass loss. Furthermore, the relatively coarse resolutions in regional climate models, i.e. 5 km to 20 km, fail to represent the small scale patterns of surface mass balance (SMB) components over these topographically complex regions including also narrow valley glaciers. Here, we present a novel approach to quantify the contribution of GICs to surface melt and runoff, based on an elevation dependent downscaling method. GICs daily SMB components at 1 km resolution are obtained by statistically downscaling the outputs of RACMO2.3 at 11 km resolution to a down-sampled version of the GIMP DEM for the period 1958-2014. This method has recently been successfully validated over the Greenland ice sheet and is now applied to GICs. In this study, we first evaluate the 1 km daily downscaled GICs SMB against a newly available and comprehensive dataset of ablation stake measurements. Then, we investigate present-day trends of meltwater production and SMB for different regions and estimate GICs contribution to total Greenland mass loss. These data are considered valuable for model evaluation and prediction of future sea level rise.

Climate change impacts in Zhuoshui watershed, Taiwan

NASA Astrophysics Data System (ADS)

Chao, Yi-Chiung; Liu, Pei-Ling; Cheng, Chao-Tzuen; Li, Hsin-Chi; Wu, Tingyeh; Chen, Wei-Bo; Shih, Hung-Ju

2017-04-01

There are 5.3 typhoons hit Taiwan per year on average in last decade. Typhoon Morakot in 2009, the most severe typhoon, causes huge damage in Taiwan, including 677 casualty and roughly NT 110 billion (3.3 billion USD) in economic loss. Some researches documented that typhoon frequency will decrease but increase in intensity in western North Pacific region. It is usually preferred to use high resolution dynamical model to get better projection of extreme events; because coarse resolution models cannot simulate intense extreme events. Under that consideration, dynamical downscaling climate data was chosen to describe typhoon satisfactorily. One of the aims for Taiwan Climate Change Projection and Information Platform (TCCIP) is to demonstrate the linkage between climate change data and watershed impact models. The purpose is to understand relative disasters induced by extreme rainfall (typhoons) under climate change in watersheds including landslides, debris flows, channel erosion and deposition, floods, and economic loss. The study applied dynamic downscaling approach to release climate change projected typhoon events under RCP 8.5, the worst-case scenario. The Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) and FLO-2D models, then, were used to simulate hillslope disaster impacts in the upstream of Zhuoshui River. CCHE1D model was used to elevate the sediment erosion or deposition in channel. FVCOM model was used to asses a flood impact in urban area in the downstream. Finally, whole potential loss associate with these typhoon events was evaluated by the Taiwan Typhoon Loss Assessment System (TLAS) under climate change scenario. Results showed that the total loss will increase roughly by NT 49.7 billion (1.6 billion USD) in future in Zhuoshui watershed in Taiwan. The results of this research could help to understand future impact; however model bias still exists. Because typhoon track is a critical factor to consider regional disaster risk and the projection of typhoon is still highly uncertain and typhoon number is very limited in a single model simulation. Since Taiwan is a small island, different typhoon tracks induce different level of disaster impacts in watersheds. Therefore, more samples dynamic downscaled typhoon events are needed for analysis to improve and increase reliability in future. Considering dynamical downscaling methods consume massive computing power, developing a new statistical downscaling approach and new method to release daily climate change data to hourly data could be a short-term solution.

Evaluating the uncertainty of predicting future climate time series at the hourly time scale

NASA Astrophysics Data System (ADS)

Caporali, E.; Fatichi, S.; Ivanov, V. Y.

2011-12-01

A stochastic downscaling methodology is developed to generate hourly, point-scale time series for several meteorological variables, such as precipitation, cloud cover, shortwave radiation, air temperature, relative humidity, wind speed, and atmospheric pressure. The methodology uses multi-model General Circulation Model (GCM) realizations and an hourly weather generator, AWE-GEN. Probabilistic descriptions of factors of change (a measure of climate change with respect to historic conditions) are computed for several climate statistics and different aggregation times using a Bayesian approach that weights the individual GCM contributions. The Monte Carlo method is applied to sample the factors of change from their respective distributions thereby permitting the generation of time series in an ensemble fashion, which reflects the uncertainty of climate projections of future as well as the uncertainty of the downscaling procedure. Applications of the methodology and probabilistic expressions of certainty in reproducing future climates for the periods, 2000 - 2009, 2046 - 2065 and 2081 - 2100, using the 1962 - 1992 period as the baseline, are discussed for the location of Firenze (Italy). The climate predictions for the period of 2000 - 2009 are tested against observations permitting to assess the reliability and uncertainties of the methodology in reproducing statistics of meteorological variables at different time scales.

Soil moisture downscaling using a simple thermal based proxy

NASA Astrophysics Data System (ADS)

Peng, Jian; Loew, Alexander; Niesel, Jonathan

2016-04-01

Microwave remote sensing has been largely applied to retrieve soil moisture (SM) from active and passive sensors. The obvious advantage of microwave sensor is that SM can be obtained regardless of atmospheric conditions. However, existing global SM products only provide observations at coarse spatial resolutions, which often hamper their applications in regional hydrological studies. Therefore, various downscaling methods have been proposed to enhance the spatial resolution of satellite soil moisture products. The aim of this study is to investigate the validity and robustness of a simple Vegetation Temperature Condition Index (VTCI) downscaling scheme over different climates and regions. Both polar orbiting (MODIS) and geostationary (MSG SEVIRI) satellite data are used to improve the spatial resolution of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative (ESA CCI) soil moisture, which is a merged product based on both active and passive microwave observations. The results from direct validation against soil moisture in-situ measurements, spatial pattern comparison, as well as seasonal and land use analyses show that the downscaling method can significantly improve the spatial details of CCI soil moisture while maintain the accuracy of CCI soil moisture. The application of the scheme with different satellite platforms and over different regions further demonstrate the robustness and effectiveness of the proposed method. Therefore, the VTCI downscaling method has the potential to facilitate relevant hydrological applications that require high spatial and temporal resolution soil moisture.

Evaluation of dynamically downscaled extreme temperature using a spatially-aggregated generalized extreme value (GEV) model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Jiali; Han, Yuefeng; Stein, Michael L.

2016-02-10

The Weather Research and Forecast (WRF) model downscaling skill in extreme maximum daily temperature is evaluated by using the generalized extreme value (GEV) distribution. While the GEV distribution has been used extensively in climatology and meteorology for estimating probabilities of extreme events, accurately estimating GEV parameters based on data from a single pixel can be difficult, even with fairly long data records. This work proposes a simple method assuming that the shape parameter, the most difficult of the three parameters to estimate, does not vary over a relatively large region. This approach is applied to evaluate 31-year WRF-downscaled extreme maximummore » temperature through comparison with North American Regional Reanalysis (NARR) data. Uncertainty in GEV parameter estimates and the statistical significance in the differences of estimates between WRF and NARR are accounted for by conducting bootstrap resampling. Despite certain biases over parts of the United States, overall, WRF shows good agreement with NARR in the spatial pattern and magnitudes of GEV parameter estimates. Both WRF and NARR show a significant increase in extreme maximum temperature over the southern Great Plains and southeastern United States in January and over the western United States in July. The GEV model shows clear benefits from the regionally constant shape parameter assumption, for example, leading to estimates of the location and scale parameters of the model that show coherent spatial patterns.« less

Evaluation of LIS-based Soil Moisture and Evapotranspiration in the Korean Peninsula

NASA Astrophysics Data System (ADS)

Jung, H. C.; Kang, D. H.; Kim, E. J.; Yoon, Y.; Kumar, S.; Peters-Lidard, C. D.; Baeck, S. H.; Hwang, E.; Chae, H.

2017-12-01

K-water is the South Korean national water agency. It is the government-funded private agency for water resource development that provides both civil and industrial water in S. Korea. K-water is interested in exploring how earth remote sensing and modeling can help their tasks. In this context, the NASA Land Information System (LIS) is implemented to simulate land surface processes in the Korean Peninsula. The Noah land surface model with Multi-Parameterization, version 3.6 (Noah-MP) is used to reproduce the water budget variables on a 1 km spatial resolution grid with a daily temporal resolution. The Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) datasets is used to force the system. The rainfall data are spatially downscaled from high resolution WorldClim precipitation climatology. The other meteorological inputs (i.e. air temperature, humidity, pressure, winds, radiation) are also downscaled by statistical methods (i.e. lapse-rate, slope-aspect). Additional model experiments are conducted with local rainfall datasets and soil maps to replace the downscaled MERRA-2 precipitation field and the hybrid STATSGO/FAO soil texture, respectively. For the evaluation of model performance, daily soil moisture and evapotranspiration measurements at several stations are compared to the LIS-based outputs. This study demonstrates that application of NASA's LIS can enhance drought and flood prediction capabilities in South Asia and Korea.

Climate Change Impact Assessment in Pacific North West Using Copula based Coupling of Temperature and Precipitation variables

NASA Astrophysics Data System (ADS)

Qin, Y.; Rana, A.; Moradkhani, H.

2014-12-01

The multi downscaled-scenario products allow us to better assess the uncertainty of the changes/variations of precipitation and temperature in the current and future periods. Joint Probability distribution functions (PDFs), of both the climatic variables, might help better understand the interdependence of the two, and thus in-turn help in accessing the future with confidence. Using the joint distribution of temperature and precipitation is also of significant importance in hydrological applications and climate change studies. In the present study, we have used multi-modelled statistically downscaled-scenario ensemble of precipitation and temperature variables using 2 different statistically downscaled climate dataset. The datasets used are, 10 Global Climate Models (GCMs) downscaled products from CMIP5 daily dataset, namely, those from the Bias Correction and Spatial Downscaling (BCSD) technique generated at Portland State University and from the Multivariate Adaptive Constructed Analogs (MACA) technique, generated at University of Idaho, leading to 2 ensemble time series from 20 GCM products. Thereafter the ensemble PDFs of both precipitation and temperature is evaluated for summer, winter, and yearly periods for all the 10 sub-basins across Columbia River Basin (CRB). Eventually, Copula is applied to establish the joint distribution of two variables enabling users to model the joint behavior of the variables with any level of correlation and dependency. Moreover, the probabilistic distribution helps remove the limitations on marginal distributions of variables in question. The joint distribution is then used to estimate the change trends of the joint precipitation and temperature in the current and future, along with estimation of the probabilities of the given change. Results have indicated towards varied change trends of the joint distribution of, summer, winter, and yearly time scale, respectively in all 10 sub-basins. Probabilities of changes, as estimated by the joint precipitation and temperature, will provide useful information/insights for hydrological and climate change predictions.

Forest-stressing climate factors on the US West Coast as simulated by CMIP5

NASA Astrophysics Data System (ADS)

Rupp, D. E.; Buotte, P.; Hicke, J. A.; Law, B. E.; Mote, P.; Sharp, D.; Zhenlin, Y.

2013-12-01

The rate of forest mortality has increased significantly in western North America since the 1970s. Causes include insect attacks, fire, and soil water deficit, all of which are interdependent. We first identify climate factors that stress forests by reducing photosynthesis and hydraulic conductance, and by promoting bark beetle infestation and wildfire. Examples of such factors may be two consecutive years of extreme summer precipitation deficit, or prolonged vapor pressure deficit exceeding some threshold. Second, we quantify the frequency and magnitude of these climate factors in 20th and 21st century climates, as simulated by global climate models (GCMs) in Coupled Model Intercomparison Project phase 5 (CMIP5), of Washington, Oregon, and California in the western US. Both ';raw' (i.e., original spatial resolution) and statistically downscaled simulations are considered, the latter generated using the Multivariate Adaptive Constructed Analogs (MACA) method. CMIP5 models that most faithfully reproduce the observed historical statistics of these climate factors are identified. Furthermore, significant changes in the statistics between the 20th and 21st centuries are reported. A subsequent task will be to use a selected subset of MACA-downscaled CMIP5 simulations to force the Community Land Model, version 4.5 (CLM 4.5). CLM 4.5 will be modified to better simulate forest mortality and to couple CLM with an economic model. The ultimate goal of this study is to understand the interactions and the feedbacks by which the market and the forest ecosystem influence each other.

Statistical downscaling of summer precipitation over northwestern South America

NASA Astrophysics Data System (ADS)

Palomino Lemus, Reiner; Córdoba Machado, Samir; Raquel Gámiz Fortis, Sonia; Castro Díez, Yolanda; Jesús Esteban Parra, María

2015-04-01

In this study a statistical downscaling (SD) model using Principal Component Regression (PCR) for simulating summer precipitation in Colombia during the period 1950-2005, has been developed, and climate projections during the 2071-2100 period by applying the obtained SD model have been obtained. For these ends the Principal Components (PCs) of the SLP reanalysis data from NCEP were used as predictor variables, while the observed gridded summer precipitation was the predictand variable. Period 1950-1993 was utilized for calibration and 1994-2010 for validation. The Bootstrap with replacement was applied to provide estimations of the statistical errors. All models perform reasonably well at regional scales, and the spatial distribution of the correlation coefficients between predicted and observed gridded precipitation values show high values (between 0.5 and 0.93) along Andes range, north and north Pacific of Colombia. Additionally, the ability of the MIROC5 GCM to simulate the summer precipitation in Colombia, for present climate (1971-2005), has been analyzed by calculating the differences between the simulated and observed precipitation values. The simulation obtained by this GCM strongly overestimates the precipitation along a horizontal sector through the center of Colombia, especially important at the east and west of this country. However, the SD model applied to the SLP of the GCM shows its ability to faithfully reproduce the rainfall field. Finally, in order to get summer precipitation projections in Colombia for the period 1971-2100, the downscaled model, recalibrated for the total period 1950-2010, has been applied to the SLP output from MIROC5 model under the RCP2.6, RCP4.5 and RCP8.5 scenarios. The changes estimated by the SD models are not significant under the RCP2.6 scenario, while for the RCP4.5 and RCP8.5 scenarios a significant increase of precipitation appears regard to the present values in all the regions, reaching around the 27% in northern Colombia region under the RCP8.5 scenario. Keywords: Statistical downscaling, precipitation, Principal Component Regression, climate change, Colombia. ACKNOWLEDGEMENTS This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).

Downscaling essential climate variable soil moisture using multisource data from 2003 to 2010 in China

NASA Astrophysics Data System (ADS)

Wang, Hui-Lin; An, Ru; You, Jia-jun; Wang, Ying; Chen, Yuehong; Shen, Xiao-ji; Gao, Wei; Wang, Yi-nan; Zhang, Yu; Wang, Zhe; Quaye-Ballard, Jonathan Arthur

2017-10-01

Soil moisture plays an important role in the water cycle within the surface ecosystem, and it is the basic condition for the growth of plants. Currently, the spatial resolutions of most soil moisture data from remote sensing range from ten to several tens of km, while those observed in-situ and simulated for watershed hydrology, ecology, agriculture, weather, and drought research are generally <1 km. Therefore, the existing coarse-resolution remotely sensed soil moisture data need to be downscaled. This paper proposes a universal and multitemporal soil moisture downscaling method suitable for large areas. The datasets comprise land surface, brightness temperature, precipitation, and soil and topographic parameters from high-resolution data and active/passive microwave remotely sensed essential climate variable soil moisture (ECV_SM) data with a spatial resolution of 25 km. Using this method, a total of 288 soil moisture maps of 1-km resolution from the first 10-day period of January 2003 to the last 10-day period of December 2010 were derived. The in-situ observations were used to validate the downscaled ECV_SM. In general, the downscaled soil moisture values for different land cover and land use types are consistent with the in-situ observations. Mean square root error is reduced from 0.070 to 0.061 using 1970 in-situ time series observation data from 28 sites distributed over different land uses and land cover types. The performance was also assessed using the GDOWN metric, a measure of the overall performance of the downscaling methods based on the same dataset. It was positive in 71.429% of cases, indicating that the suggested method in the paper generally improves the representation of soil moisture at 1-km resolution.

Spoilt for choice - A comparison of downscaling approaches for hydrological impact studies

NASA Astrophysics Data System (ADS)

Rössler, Ole; Fischer, Andreas; Kotlarski, Sven; Keller, Denise; Liniger, Mark; Weingartner, Rolf

2017-04-01

With the increasing number of available climate downscaling approaches, users are often faced with the luxury problem of which downscaling method to apply in a climate change impact assessment study. In Switzerland, for instance, the new generation of local scale climate scenarios CH2018 will be based on quantile mapping (QM), replacing the previous delta change (DC) method. Parallel to those two methods, a multi-site weather generator (WG) was developed to meet specific user needs. The question poses which downscaling method is the most suitable for a given application. Here, we analyze the differences of the three approaches in terms of hydro-meteorological responses in the Swiss pre-Alps in terms of mean values as well as indices of extremes. The comparison of the three different approaches was carried out in the frame of a hydrological impact assessment study that focused on different runoff characteristics and their related meteorological indices in the meso-scale catchment of the river Thur ( 1700 km2), Switzerland. For this purpose, we set up the hydrological model WaSiM-ETH under present (1980-2009) and under future conditions (2070-2099), assuming the SRES A1B emission scenario. Input to the three downscaling approaches were 10 GCM-RCM simulations of the ENSEMBLES project, while eight meteorological station observations served as the reference. All station data, observed and downscaled, were interpolated to obtain meteorological fields of temperature and precipitation required by the hydrological model. For the present-day reference period we evaluated the ability of each downscaling method to reproduce today's hydro-meteorological patterns. In the scenario runs, we focused on the comparison of change signals for each hydro-meteorological parameter generated by the three downscaling techniques. The evaluation exercise reveals that QM and WG perform equally well in representing present day average conditions, but that QM outperforms WG in reproducing indices related to extreme conditions like the number of drought events or multi-day rain sums. In terms of mean monthly discharge changes, the three downscaling methods reveal notable differences: DC shows the strongest (in summer) and less pronounced (in winter) change signal. Regarding some extreme features of runoff like frequency of droughts and the low flow level, DC shows similar change signals compared to QM and WG. This was unexpected as DC is commonly reported to fail in terms of projecting extreme changes. In contrast, QM mostly shows the strongest change signals for the 10 different extreme related indices, due to its ability to pick up more features of the climate change signals from the RCM. This indicates that DC and also WG miss some aspects, especially for flood related indices. Hence, depending on the target variable of interest, DC and QM typically provide the full range of change signals, while WG mostly lies in between both method. However, it offers the great advantage of multiple realizations combined with inter-variable consistency.

Dynamical Downscaling of Meteorology from a Global Model by WRF towards Resolving US PM2.5 Distributions for the Mid 21st Century

NASA Astrophysics Data System (ADS)

Kunwar, S.; Bowden, J.; Milly, G.; Previdi, M. J.; Fiore, A. M.; West, J. J.

2017-12-01

In the coming decades, anthropogenically induced climate change will likely impact PM2.5 through both changing meteorology and feedback in natural emissions. A major goal of our project is to assess changes in PM2.5 levels over the continental US due to climate variability and change for the period 2005-2065. We will achieve this by using regional models to dynamically downscale coarse resolution (20 × 20) meteorology and air chemistry from a global model to finer spatial resolution (12 km), improving air quality projections for regions and subregions of the US (NE, SE, SW, NW, Midwest, Intermountain West). We downscale from GFDL CM3 simulations of the RCP8.5 scenario for the years 2006-2100 with aerosol and ozone precursor emissions fixed at 2005 levels. We carefully select model years from the global simulations that sample the range of PM2.5 distributions for different US regions at mid 21st century (2050-2065). Here we will show results for the meteorological downscaling (using WRF version 3.8.1) for this project, including a performance evaluation for meteorological variables with respect to the global model. In the future, the downscaled meteorology presented here will be used to drive air quality downscaling in CMAQ (version 5.2). Analysis of the resulting PM2.5 statistics for US regions, as well as the drivers for PM2.5 changes, will be important in supporting informed policies for air quality (also health and visibility) planning for different US regions for the next five decades.

A review of spatial downscaling of satellite remotely sensed soil moisture

NASA Astrophysics Data System (ADS)

Peng, Jian; Loew, Alexander; Merlin, Olivier; Verhoest, Niko E. C.

2017-06-01

Satellite remote sensing technology has been widely used to estimate surface soil moisture. Numerous efforts have been devoted to develop global soil moisture products. However, these global soil moisture products, normally retrieved from microwave remote sensing data, are typically not suitable for regional hydrological and agricultural applications such as irrigation management and flood predictions, due to their coarse spatial resolution. Therefore, various downscaling methods have been proposed to improve the coarse resolution soil moisture products. The purpose of this paper is to review existing methods for downscaling satellite remotely sensed soil moisture. These methods are assessed and compared in terms of their advantages and limitations. This review also provides the accuracy level of these methods based on published validation studies. In the final part, problems and future trends associated with these methods are analyzed.

Improving GEFS Weather Forecasts for Indian Monsoon with Statistical Downscaling

NASA Astrophysics Data System (ADS)

Agrawal, Ankita; Salvi, Kaustubh; Ghosh, Subimal

2014-05-01

Weather forecast has always been a challenging research problem, yet of a paramount importance as it serves the role of 'key input' in formulating modus operandi for immediate future. Short range rainfall forecasts influence a wide range of entities, right from agricultural industry to a common man. Accurate forecasts actually help in minimizing the possible damage by implementing pre-decided plan of action and hence it is necessary to gauge the quality of forecasts which might vary with the complexity of weather state and regional parameters. Indian Summer Monsoon Rainfall (ISMR) is one such perfect arena to check the quality of weather forecast not only because of the level of intricacy in spatial and temporal patterns associated with it, but also the amount of damage it can cause (because of poor forecasts) to the Indian economy by affecting agriculture Industry. The present study is undertaken with the rationales of assessing, the ability of Global Ensemble Forecast System (GEFS) in predicting ISMR over central India and the skill of statistical downscaling technique in adding value to the predictions by taking them closer to evidentiary target dataset. GEFS is a global numerical weather prediction system providing the forecast results of different climate variables at a fine resolution (0.5 degree and 1 degree). GEFS shows good skills in predicting different climatic variables but fails miserably over rainfall predictions for Indian summer monsoon rainfall, which is evident from a very low to negative correlation values between predicted and observed rainfall. Towards the fulfilment of second rationale, the statistical relationship is established between the reasonably well predicted climate variables (GEFS) and observed rainfall. The GEFS predictors are treated with multicollinearity and dimensionality reduction techniques, such as principal component analysis (PCA) and least absolute shrinkage and selection operator (LASSO). Statistical relationship is established between the principal components and observed rainfall over training period and predictions are obtained for testing period. The validations show high improvements in correlation coefficient between observed and predicted data (0.25 to 0.55). The results speak in favour of statistical downscaling methodology which shows the capability to reduce the gap between observed data and predictions. A detailed study is required to be carried out by applying different downscaling techniques to quantify the improvements in predictions.

a Spiral-Based Downscaling Method for Generating 30 M Time Series Image Data

NASA Astrophysics Data System (ADS)

Liu, B.; Chen, J.; Xing, H.; Wu, H.; Zhang, J.

2017-09-01

The spatial detail and updating frequency of land cover data are important factors influencing land surface dynamic monitoring applications in high spatial resolution scale. However, the fragmentized patches and seasonal variable of some land cover types (e. g. small crop field, wetland) make it labor-intensive and difficult in the generation of land cover data. Utilizing the high spatial resolution multi-temporal image data is a possible solution. Unfortunately, the spatial and temporal resolution of available remote sensing data like Landsat or MODIS datasets can hardly satisfy the minimum mapping unit and frequency of current land cover mapping / updating at the same time. The generation of high resolution time series may be a compromise to cover the shortage in land cover updating process. One of popular way is to downscale multi-temporal MODIS data with other high spatial resolution auxiliary data like Landsat. But the usual manner of downscaling pixel based on a window may lead to the underdetermined problem in heterogeneous area, result in the uncertainty of some high spatial resolution pixels. Therefore, the downscaled multi-temporal data can hardly reach high spatial resolution as Landsat data. A spiral based method was introduced to downscale low spatial and high temporal resolution image data to high spatial and high temporal resolution image data. By the way of searching the similar pixels around the adjacent region based on the spiral, the pixel set was made up in the adjacent region pixel by pixel. The underdetermined problem is prevented to a large extent from solving the linear system when adopting the pixel set constructed. With the help of ordinary least squares, the method inverted the endmember values of linear system. The high spatial resolution image was reconstructed on the basis of high spatial resolution class map and the endmember values band by band. Then, the high spatial resolution time series was formed with these high spatial resolution images image by image. Simulated experiment and remote sensing image downscaling experiment were conducted. In simulated experiment, the 30 meters class map dataset Globeland30 was adopted to investigate the effect on avoid the underdetermined problem in downscaling procedure and a comparison between spiral and window was conducted. Further, the MODIS NDVI and Landsat image data was adopted to generate the 30m time series NDVI in remote sensing image downscaling experiment. Simulated experiment results showed that the proposed method had a robust performance in downscaling pixel in heterogeneous region and indicated that it was superior to the traditional window-based methods. The high resolution time series generated may be a benefit to the mapping and updating of land cover data.

Statistical Downscaling in Multi-dimensional Wave Climate Forecast

NASA Astrophysics Data System (ADS)

Camus, P.; Méndez, F. J.; Medina, R.; Losada, I. J.; Cofiño, A. S.; Gutiérrez, J. M.

2009-04-01

Wave climate at a particular site is defined by the statistical distribution of sea state parameters, such as significant wave height, mean wave period, mean wave direction, wind velocity, wind direction and storm surge. Nowadays, long-term time series of these parameters are available from reanalysis databases obtained by numerical models. The Self-Organizing Map (SOM) technique is applied to characterize multi-dimensional wave climate, obtaining the relevant "wave types" spanning the historical variability. This technique summarizes multi-dimension of wave climate in terms of a set of clusters projected in low-dimensional lattice with a spatial organization, providing Probability Density Functions (PDFs) on the lattice. On the other hand, wind and storm surge depend on instantaneous local large-scale sea level pressure (SLP) fields while waves depend on the recent history of these fields (say, 1 to 5 days). Thus, these variables are associated with large-scale atmospheric circulation patterns. In this work, a nearest-neighbors analog method is used to predict monthly multi-dimensional wave climate. This method establishes relationships between the large-scale atmospheric circulation patterns from numerical models (SLP fields as predictors) with local wave databases of observations (monthly wave climate SOM PDFs as predictand) to set up statistical models. A wave reanalysis database, developed by Puertos del Estado (Ministerio de Fomento), is considered as historical time series of local variables. The simultaneous SLP fields calculated by NCEP atmospheric reanalysis are used as predictors. Several applications with different size of sea level pressure grid and with different temporal domain resolution are compared to obtain the optimal statistical model that better represents the monthly wave climate at a particular site. In this work we examine the potential skill of this downscaling approach considering perfect-model conditions, but we will also analyze the suitability of this methodology to be used for seasonal forecast and for long-term climate change scenario projection of wave climate.

Statistical downscaling of sub-daily (6-hour) temperature in Romania, by means of artificial neural networks

NASA Astrophysics Data System (ADS)

Birsan, Marius-Victor; Dumitrescu, Alexandru; Cǎrbunaru, Felicia

2016-04-01

The role of statistical downscaling is to model the relationship between large-scale atmospheric circulation and climatic variables on a regional and sub-regional scale, making use of the predictions of future circulation generated by General Circulation Models (GCMs) in order to capture the effects of climate change on smaller areas. The study presents a statistical downscaling model based on a neural network-based approach, by means of multi-layer perceptron networks. Sub-daily temperature data series from 81 meteorological stations over Romania, with full data records are used as predictands. As large-scale predictor, the NCEP/NCAD air temperature data at 850 hPa over the domain 20-30E / 40-50N was used, at a spatial resolution of 2.5×2.5 degrees. The period 1961-1990 was used for calibration, while the validation was realized over the 1991-2010 interval. Further, in order to estimate future changes in air temperature for 2021-2050 and 2071-2100, air temperature data at 850 hPa corresponding to the IPCC A1B scenario was extracted from the CNCM33 model (Meteo-France) and used as predictor. This work has been realized within the research project "Changes in climate extremes and associated impact in hydrological events in Romania" (CLIMHYDEX), code PN II-ID-2011-2-0073, financed by the Romanian Executive Agency for Higher Education Research, Development and Innovation Funding (UEFISCDI).

Optimized AVHRR land surface temperature downscaling method for local scale observations: case study for the coastal area of the Gulf of Gdańsk

NASA Astrophysics Data System (ADS)

Chybicki, Andrzej; Łubniewski, Zbigniew

2017-09-01

Satellite imaging systems have known limitations regarding their spatial and temporal resolution. The approaches based on subpixel mapping of the Earth's environment, which rely on combining the data retrieved from sensors of higher temporal and lower spatial resolution with the data characterized by lower temporal but higher spatial resolution, are of considerable interest. The paper presents the downscaling process of the land surface temperature (LST) derived from low resolution imagery acquired by the Advanced Very High Resolution Radiometer (AVHRR), using the inverse technique. The effective emissivity derived from another data source is used as a quantity describing thermal properties of the terrain in higher resolution, and allows the downsampling of low spatial resolution LST images. The authors propose an optimized downscaling method formulated as the inverse problem and show that the proposed approach yields better results than the use of other downsampling methods. The proposed method aims to find estimation of high spatial resolution LST data by minimizing the global error of the downscaling. In particular, for the investigated region of the Gulf of Gdansk, the RMSE between the AVHRR image downscaled by the proposed method and the Landsat 8 LST reference image was 2.255°C with correlation coefficient R equal to 0.828 and Bias = 0.557°C. For comparison, using the PBIM method, it was obtained RMSE = 2.832°C, R = 0.775 and Bias = 0.997°C for the same satellite scene. It also has been shown that the obtained results are also good in local scale and can be used for areas much smaller than the entire satellite imagery scene, depicting diverse biophysical conditions. Specifically, for the analyzed set of small sub-datasets of the whole scene, the obtained RSME between the downscaled and reference image was smaller, by approx. 0.53°C on average, in the case of applying the proposed method than in the case of using the PBIM method.

Relating Solar Resource Variability to Cloud Type

NASA Astrophysics Data System (ADS)

Hinkelman, L. M.; Sengupta, M.

2012-12-01

Power production from renewable energy (RE) resources is rapidly increasing. Generation of renewable energy is quite variable since the solar and wind resources that form the inputs are, themselves, inherently variable. There is thus a need to understand the impact of renewable generation on the transmission grid. Such studies require estimates of high temporal and spatial resolution power output under various scenarios, which can be created from corresponding solar resource data. Satellite-based solar resource estimates are the best source of long-term solar irradiance data for the typically large areas covered by transmission studies. As satellite-based resource datasets are generally available at lower temporal and spatial resolution than required, there is, in turn, a need to downscale these resource data. Downscaling in both space and time requires information about solar irradiance variability, which is primarily a function of cloud types and properties. In this study, we analyze the relationship between solar resource variability and satellite-based cloud properties. One-minute resolution surface irradiance data were obtained from a number of stations operated by the National Oceanic and Atmospheric Administration (NOAA) under the Surface Radiation (SURFRAD) and Integrated Surface Irradiance Study (ISIS) networks as well as from NREL's Solar Radiation Research Laboratory (SRRL) in Golden, Colorado. Individual sites were selected so that a range of meteorological conditions would be represented. Cloud information at a nominal 4 km resolution and half hour intervals was derived from NOAA's Geostationary Operation Environmental Satellite (GOES) series of satellites. Cloud class information from the GOES data set was then used to select and composite irradiance data from the measurement sites. The irradiance variability for each cloud classification was characterized using general statistics of the fluxes themselves and their variability in time, as represented by ramps computed for time scales from 10 s to 0.5 hr. The statistical relationships derived using this method will be presented, comparing and contrasting the statistics computed for the different cloud types. The implications for downscaling irradiances from satellites or forecast models will also be discussed.

Climate downscaling effects on predictive ecological models: a case study for threatened and endangered vertebrates in the southeastern United States

USGS Publications Warehouse

Bucklin, David N.; Watling, James I.; Speroterra, Carolina; Brandt, Laura A.; Mazzotti, Frank J.; Romañach, Stephanie S.

2013-01-01

High-resolution (downscaled) projections of future climate conditions are critical inputs to a wide variety of ecological and socioeconomic models and are created using numerous different approaches. Here, we conduct a sensitivity analysis of spatial predictions from climate envelope models for threatened and endangered vertebrates in the southeastern United States to determine whether two different downscaling approaches (with and without the use of a regional climate model) affect climate envelope model predictions when all other sources of variation are held constant. We found that prediction maps differed spatially between downscaling approaches and that the variation attributable to downscaling technique was comparable to variation between maps generated using different general circulation models (GCMs). Precipitation variables tended to show greater discrepancies between downscaling techniques than temperature variables, and for one GCM, there was evidence that more poorly resolved precipitation variables contributed relatively more to model uncertainty than more well-resolved variables. Our work suggests that ecological modelers requiring high-resolution climate projections should carefully consider the type of downscaling applied to the climate projections prior to their use in predictive ecological modeling. The uncertainty associated with alternative downscaling methods may rival that of other, more widely appreciated sources of variation, such as the general circulation model or emissions scenario with which future climate projections are created.

Streamflow changes in the Sierra Nevada, California, simulated using a statistically downscaled general circulation model scenario of climate change

USGS Publications Warehouse

Wilby, Robert L.; Dettinger, Michael D.

2000-01-01

Simulations of future climate using general circulation models (GCMs) suggest that rising concentrations of greenhouse gases may have significant consequences for the global climate. Of less certainty is the extent to which regional scale (i.e., sub-GCM grid) environmental processes will be affected. In this chapter, a range of downscaling techniques are critiqued. Then a relatively simple (yet robust) statistical downscaling technique and its use in the modelling of future runoff scenarios for three river basins in the Sierra Nevada, California, is described. This region was selected because GCM experiments driven by combined greenhouse-gas and sulphate-aerosol forcings consistently show major changes in the hydro-climate of the southwest United States by the end of the 21st century. The regression-based downscaling method was used to simulate daily rainfall and temperature series for streamflow modelling in three Californian river basins under current-and future-climate conditions. The downscaling involved just three predictor variables (specific humidity, zonal velocity component of airflow, and 500 hPa geopotential heights) supplied by the U.K. Meteorological Office couple ocean-atmosphere model (HadCM2) for the grid point nearest the target basins. When evaluated using independent data, the model showed reasonable skill at reproducing observed area-average precipitation, temperature, and concomitant streamflow variations. Overall, the downscaled data resulted in slight underestimates of mean annual streamflow due to underestimates of precipitation in spring and positive temperature biases in winter. Differences in the skill of simulated streamflows amongst the three basins were attributed to the smoothing effects of snowpack on streamflow responses to climate forcing. The Merced and American River basins drain the western, windward slope of the Sierra Nevada and are snowmelt dominated, whereas the Carson River drains the eastern, leeward slope and is a mix of rainfall runoff and snowmelt runoff. Simulated streamflow in the American River responds rapidly and sensitively to daily-scale temperature and precipitation fluctuations and errors; in the Merced and Carson Rivers, the response to the same short-term influences is much less. Consequently, the skill of simulated flows was significantly lower in the American River model than in the Carson and Merced. The physiography of the three basins also accounts for differences in their sensitivities to future climate change. Increases in winter precipitation exceeding +100% coupled with mean temperature rises greater than +2°C result in increased winter streamflows in all three basins. In the Merced and Carson basins, these streamflow increases reflect large changes in winter snowpack, whereas the streamflow changes in the lower elevation American basin are driven primarily by rainfall runoff. Furthermore, reductions in winter snowpack in the American River basin, owing to less precipitation falling as snow and earlier melting of snow at middle elevations, lead to less spring and summer streamflow. Taken collectively, the downscaling results suggest significant changes to both the timing and magnitude of streamflows in the Sierra Nevada by the end of the 21st Century. In the higher elevation basins, the HadCM2 scenario implies more annual streamflow and more streamflow during the spring and summer months that are critical for water-resources management in California. Depending on the relative significance of rainfall runoff and snowmelt, each basin responds in its own way to regional climate forcing. Generally, then, climate scenarios need to be specified — by whatever means — with sufficient temporal and spatial resolution to capture subtle orographic influences if projections of climate-change responses are to be useful and reproducible.

Calibrating MODIS aerosol optical depth for predicting daily PM2.5 concentrations via statistical downscaling

PubMed Central

Chang, Howard H.; Hu, Xuefei; Liu, Yang

2014-01-01

There has been a growing interest in the use of satellite-retrieved aerosol optical depth (AOD) to estimate ambient concentrations of PM2.5 (particulate matter <2.5 μm in aerodynamic diameter). With their broad spatial coverage, satellite data can increase the spatial–temporal availability of air quality data beyond ground monitoring measurements and potentially improve exposure assessment for population-based health studies. This paper describes a statistical downscaling approach that brings together (1) recent advances in PM2.5 land use regression models utilizing AOD and (2) statistical data fusion techniques for combining air quality data sets that have different spatial resolutions. Statistical downscaling assumes the associations between AOD and PM2.5 concentrations to be spatially and temporally dependent and offers two key advantages. First, it enables us to use gridded AOD data to predict PM2.5 concentrations at spatial point locations. Second, the unified hierarchical framework provides straightforward uncertainty quantification in the predicted PM2.5 concentrations. The proposed methodology is applied to a data set of daily AOD values in southeastern United States during the period 2003–2005. Via cross-validation experiments, our model had an out-of-sample prediction R2 of 0.78 and a root mean-squared error (RMSE) of 3.61 μg/m3 between observed and predicted daily PM2.5 concentrations. This corresponds to a 10% decrease in RMSE compared with the same land use regression model without AOD as a predictor. Prediction performances of spatial–temporal interpolations to locations and on days without monitoring PM2.5 measurements were also examined. PMID:24368510

Calibrating MODIS aerosol optical depth for predicting daily PM2.5 concentrations via statistical downscaling.

PubMed

Chang, Howard H; Hu, Xuefei; Liu, Yang

2014-07-01

There has been a growing interest in the use of satellite-retrieved aerosol optical depth (AOD) to estimate ambient concentrations of PM2.5 (particulate matter <2.5 μm in aerodynamic diameter). With their broad spatial coverage, satellite data can increase the spatial-temporal availability of air quality data beyond ground monitoring measurements and potentially improve exposure assessment for population-based health studies. This paper describes a statistical downscaling approach that brings together (1) recent advances in PM2.5 land use regression models utilizing AOD and (2) statistical data fusion techniques for combining air quality data sets that have different spatial resolutions. Statistical downscaling assumes the associations between AOD and PM2.5 concentrations to be spatially and temporally dependent and offers two key advantages. First, it enables us to use gridded AOD data to predict PM2.5 concentrations at spatial point locations. Second, the unified hierarchical framework provides straightforward uncertainty quantification in the predicted PM2.5 concentrations. The proposed methodology is applied to a data set of daily AOD values in southeastern United States during the period 2003-2005. Via cross-validation experiments, our model had an out-of-sample prediction R(2) of 0.78 and a root mean-squared error (RMSE) of 3.61 μg/m(3) between observed and predicted daily PM2.5 concentrations. This corresponds to a 10% decrease in RMSE compared with the same land use regression model without AOD as a predictor. Prediction performances of spatial-temporal interpolations to locations and on days without monitoring PM2.5 measurements were also examined.

Report from the workshop on climate downscaling and its application in high Hawaiian Islands, September 16–17, 2015

USGS Publications Warehouse

Helweg, David A.; Keener, Victoria; Burgett, Jeff M.

2016-07-14

In the subtropical and tropical Pacific islands, changing climate is predicted to influence precipitation and freshwater availability, and thus is predicted to impact ecosystems goods and services available to ecosystems and human communities. The small size of high Hawaiian Islands, plus their complex microlandscapes, require downscaling of global climate models to provide future projections of greater skill and spatial resolution. Two different climate modeling approaches (physics-based dynamical downscaling and statistics-based downscaling) have produced dissimilar projections. Because of these disparities, natural resource managers and decision makers have low confidence in using the modeling results and are therefore are unwilling to include climate-related projections in their decisions. In September 2015, the Pacific Islands Climate Science Center (PICSC), the Pacific Islands Climate Change Cooperative (PICCC), and the Pacific Regional Integrated Sciences and Assessments (Pacific RISA) program convened a 2-day facilitated workshop in which the two modeling teams, plus key model users and resource managers, were brought together for a comparison of the two approaches, culminating with a discussion of how to provide predictions that are useable by resource managers. The proceedings, discussions, and outcomes of this Workshop are summarized in this Open-File Report.

A statistical adjustment approach for climate projections of snow conditions in mountain regions using energy balance land surface models

NASA Astrophysics Data System (ADS)

Verfaillie, Deborah; Déqué, Michel; Morin, Samuel; Lafaysse, Matthieu

2017-04-01

Projections of future climate change have been increasingly called for lately, as the reality of climate change has been gradually accepted and societies and governments have started to plan upcoming mitigation and adaptation policies. In mountain regions such as the Alps or the Pyrenees, where winter tourism and hydropower production are large contributors to the regional revenue, particular attention is brought to current and future snow availability. The question of the vulnerability of mountain ecosystems as well as the occurrence of climate-related hazards such as avalanches and debris-flows is also under consideration. In order to generate projections of snow conditions, however, downscaling global climate models (GCMs) by using regional climate models (RCMs) is not sufficient to capture the fine-scale processes and thresholds at play. In particular, the altitudinal resolution matters, since the phase of precipitation is mainly controlled by the temperature which is altitude-dependent. Simulations from GCMs and RCMs moreover suffer from biases compared to local observations, due to their rather coarse spatial and altitudinal resolution, and often provide outputs at too coarse time resolution to drive impact models. RCM simulations must therefore be adjusted using empirical-statistical downscaling and error correction methods, before they can be used to drive specific models such as energy balance land surface models. In this study, time series of hourly temperature, precipitation, wind speed, humidity, and short- and longwave radiation were generated over the Pyrenees and the French Alps for the period 1950-2100, by using a new approach (named ADAMONT for ADjustment of RCM outputs to MOuNTain regions) based on quantile mapping applied to daily data, followed by time disaggregation accounting for weather patterns selection. We first introduce a thorough evaluation of the method using using model runs from the ALADIN RCM driven by a global reanalysis over the French Alps. We then illustrate the potential of this method by processing outputs from EURO-CORDEX simulations spanning 6 different RCMs forced by 6 different GCMs under 3 representative concentration pathways scenarios (RCP 2.6, 4.5 and 8.5) over Europe, downscaled at the massif scale and for 300 m elevation bands and statistically adjusted against the extensive SAFRAN reanalysis (1958-2015). These corrected fields were then used to force the SURFEX/ISBA-Crocus land surface model over the Pyrenees and the French Alps. We show the wealth of information, which can be obtained through the systematic application of such a method to a large ensemble of climate projections, in order to capture upcoming trends with an explicit representation of their uncertainty.

Temporal Downscaling of Crop Coefficient and Crop Water Requirement from Growing Stage to Substage Scales

PubMed Central

Shang, Songhao

2012-01-01

Crop water requirement is essential for agricultural water management, which is usually available for crop growing stages. However, crop water requirement values of monthly or weekly scales are more useful for water management. A method was proposed to downscale crop coefficient and water requirement from growing stage to substage scales, which is based on the interpolation of accumulated crop and reference evapotranspiration calculated from their values in growing stages. The proposed method was compared with two straightforward methods, that is, direct interpolation of crop evapotranspiration and crop coefficient by assuming that stage average values occurred in the middle of the stage. These methods were tested with a simulated daily crop evapotranspiration series. Results indicate that the proposed method is more reliable, showing that the downscaled crop evapotranspiration series is very close to the simulated ones. PMID:22619572

Uncertainty Analysis of Downscaled CMIP5 Precipitation Data for Louisiana, USA

NASA Astrophysics Data System (ADS)

Sumi, S. J.; Tamanna, M.; Chivoiu, B.; Habib, E. H.

2014-12-01

The downscaled CMIP3 and CMIP5 Climate and Hydrology Projections dataset contains fine spatial resolution translations of climate projections over the contiguous United States developed using two downscaling techniques (monthly Bias Correction Spatial Disaggregation (BCSD) and daily Bias Correction Constructed Analogs (BCCA)). The objective of this study is to assess the uncertainty of the CMIP5 downscaled general circulation models (GCM). We performed an analysis of the daily, monthly, seasonal and annual variability of precipitation downloaded from the Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections website for the state of Louisiana, USA at 0.125° x 0.125° resolution. A data set of daily gridded observations of precipitation of a rectangular boundary covering Louisiana is used to assess the validity of 21 downscaled GCMs for the 1950-1999 period. The following statistics are computed using the CMIP5 observed dataset with respect to the 21 models: the correlation coefficient, the bias, the normalized bias, the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE). A measure of variability simulated by each model is computed as the ratio of its standard deviation, in both space and time, to the corresponding standard deviation of the observation. The correlation and MAPE statistics are also computed for each of the nine climate divisions of Louisiana. Some of the patterns that we observed are: 1) Average annual precipitation rate shows similar spatial distribution for all the models within a range of 3.27 to 4.75 mm/day from Northwest to Southeast. 2) Standard deviation of summer (JJA) precipitation (mm/day) for the models maintains lower value than the observation whereas they have similar spatial patterns and range of values in winter (NDJ). 3) Correlation coefficients of annual precipitation of models against observation have a range of -0.48 to 0.36 with variable spatial distribution by model. 4) Most of the models show negative correlation coefficients in summer and positive in winter. 5) MAE shows similar spatial distribution for all the models within a range of 5.20 to 7.43 mm/day from Northwest to Southeast of Louisiana. 6) Highest values of correlation coefficients are found at seasonal scale within a range of 0.36 to 0.46.

An extended linear scaling method for downscaling temperature and its implication in the Jhelum River basin, Pakistan, and India, using CMIP5 GCMs

NASA Astrophysics Data System (ADS)

Mahmood, Rashid; JIA, Shaofeng

2017-11-01

In this study, the linear scaling method used for the downscaling of temperature was extended from monthly scaling factors to daily scaling factors (SFs) to improve the daily variations in the corrected temperature. In the original linear scaling (OLS), mean monthly SFs are used to correct the future data, but mean daily SFs are used to correct the future data in the extended linear scaling (ELS) method. The proposed method was evaluated in the Jhelum River basin for the period 1986-2000, using the observed maximum temperature (Tmax) and minimum temperature (Tmin) of 18 climate stations and the simulated Tmax and Tmin of five global climate models (GCMs) (GFDL-ESM2G, NorESM1-ME, HadGEM2-ES, MIROC5, and CanESM2), and the method was also compared with OLS to observe the improvement. Before the evaluation of ELS, these GCMs were also evaluated using their raw data against the observed data for the same period (1986-2000). Four statistical indicators, i.e., error in mean, error in standard deviation, root mean square error, and correlation coefficient, were used for the evaluation process. The evaluation results with GCMs' raw data showed that GFDL-ESM2G and MIROC5 performed better than other GCMs according to all the indicators but with unsatisfactory results that confine their direct application in the basin. Nevertheless, after the correction with ELS, a noticeable improvement was observed in all the indicators except correlation coefficient because this method only adjusts (corrects) the magnitude. It was also noticed that the daily variations of the observed data were better captured by the corrected data with ELS than OLS. Finally, the ELS method was applied for the downscaling of five GCMs' Tmax and Tmin for the period of 2041-2070 under RCP8.5 in the Jhelum basin. The results showed that the basin would face hotter climate in the future relative to the present climate, which may result in increasing water requirements in public, industrial, and agriculture sectors; change in the hydrological cycle and monsoon pattern; and lack of glaciers in the basin.

A Dynamical Downscaling Approach with GCM Bias Corrections and Spectral Nudging

NASA Astrophysics Data System (ADS)

Xu, Z.; Yang, Z.

2013-12-01

To reduce the biases in the regional climate downscaling simulations, a dynamical downscaling approach with GCM bias corrections and spectral nudging is developed and assessed over North America. Regional climate simulations are performed with the Weather Research and Forecasting (WRF) model embedded in the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). To reduce the GCM biases, the GCM climatological means and the variances of interannual variations are adjusted based on the National Centers for Environmental Prediction-NCAR global reanalysis products (NNRP) before using them to drive WRF which is the same as our previous method. In this study, we further introduce spectral nudging to reduce the RCM-based biases. Two sets of WRF experiments are performed with and without spectral nudging. All WRF experiments are identical except that the initial and lateral boundary conditions are derived from the NNRP, the original GCM output, and the bias corrected GCM output, respectively. The GCM-driven RCM simulations with bias corrections and spectral nudging (IDDng) are compared with those without spectral nudging (IDD) and North American Regional Reanalysis (NARR) data to assess the additional reduction in RCM biases relative to the IDD approach. The results show that the spectral nudging introduces the effect of GCM bias correction into the RCM domain, thereby minimizing the climate drift resulting from the RCM biases. The GCM bias corrections and spectral nudging significantly improve the downscaled mean climate and extreme temperature simulations. Our results suggest that both GCM bias corrections or spectral nudging are necessary to reduce the error of downscaled climate. Only one of them does not guarantee better downscaling simulation. The new dynamical downscaling method can be applied to regional projection of future climate or downscaling of GCM sensitivity simulations. Annual mean RMSEs. The RMSEs are computed over the verification region by monthly mean data over 1981-2010. Experimental design

A rank-based approach for correcting systematic biases in spatial disaggregation of coarse-scale climate simulations

NASA Astrophysics Data System (ADS)

Nahar, Jannatun; Johnson, Fiona; Sharma, Ashish

2017-07-01

Use of General Circulation Model (GCM) precipitation and evapotranspiration sequences for hydrologic modelling can result in unrealistic simulations due to the coarse scales at which GCMs operate and the systematic biases they contain. The Bias Correction Spatial Disaggregation (BCSD) method is a popular statistical downscaling and bias correction method developed to address this issue. The advantage of BCSD is its ability to reduce biases in the distribution of precipitation totals at the GCM scale and then introduce more realistic variability at finer scales than simpler spatial interpolation schemes. Although BCSD corrects biases at the GCM scale before disaggregation; at finer spatial scales biases are re-introduced by the assumptions made in the spatial disaggregation process. Our study focuses on this limitation of BCSD and proposes a rank-based approach that aims to reduce the spatial disaggregation bias especially for both low and high precipitation extremes. BCSD requires the specification of a multiplicative bias correction anomaly field that represents the ratio of the fine scale precipitation to the disaggregated precipitation. It is shown that there is significant temporal variation in the anomalies, which is masked when a mean anomaly field is used. This can be improved by modelling the anomalies in rank-space. Results from the application of the rank-BCSD procedure improve the match between the distributions of observed and downscaled precipitation at the fine scale compared to the original BCSD approach. Further improvements in the distribution are identified when a scaling correction to preserve mass in the disaggregation process is implemented. An assessment of the approach using a single GCM over Australia shows clear advantages especially in the simulation of particularly low and high downscaled precipitation amounts.

Intersections of downscaling, the ethics of climate services, and regional research grand challenges.

NASA Astrophysics Data System (ADS)

Hewitson, B.; Jack, C. D.; Gutowski, W. J., Jr.

2014-12-01

Possibly the leading complication for users of climate information for policy and adaptation is the confusing mix of contrasting data sets that offer widely differing (and often times fundamentally contradictory) indications of the magnitude and direction of past and future regional climate change. In this light, the most pressing scientific-societal challenge is the need to find new ways to understand the sources of conflicting messages from multi-model, multi-method and multi-scale disparities, to develop and implement new analytical methodologies to address this difficulty and so to advance the interpretation and communication of robust climate information to decision makers. Compounding this challenge is the growth of climate services which, in view of the confusing mix of climate change messages, raises serious concerns as to the ethics of communication and dissemination of regional climate change data.The Working Group on Regional Climate (WGRC) of the World Climate Research Program (WCRP) oversees the CORDEX downscaling program which offers a systematic approach to compare the CMIP5 GCMs alongside RCMs and Empirical-statistical (ESD) downscaling within a common experimental design, and which facilitates the evaluation and assessment of the relative information content and sources of error. Using results from the CORDEX RCM and ESD evaluation experiment, and set against the regional messages from the CMIP5 GCMs, we examine the differing messages that arise from each data source. These are then considered in terms of the implications of consequence if each data source were to be independently adopted in a real world use-case scenario. This is then cast in the context of the emerging developments on the distillation dilemma - where the pressing need is for multi-method integration - and how this relates to the WCRP regional research grand challenges.

Deriving temporally continuous soil moisture estimations at fine resolution by downscaling remotely sensed product

NASA Astrophysics Data System (ADS)

Jin, Yan; Ge, Yong; Wang, Jianghao; Heuvelink, Gerard B. M.

2018-06-01

Land surface soil moisture (SSM) has important roles in the energy balance of the land surface and in the water cycle. Downscaling of coarse-resolution SSM remote sensing products is an efficient way for producing fine-resolution data. However, the downscaling methods used most widely require full-coverage visible/infrared satellite data as ancillary information. These methods are restricted to cloud-free days, making them unsuitable for continuous monitoring. The purpose of this study is to overcome this limitation to obtain temporally continuous fine-resolution SSM estimations. The local spatial heterogeneities of SSM and multiscale ancillary variables were considered in the downscaling process both to solve the problem of the strong variability of SSM and to benefit from the fusion of ancillary information. The generation of continuous downscaled remote sensing data was achieved via two principal steps. For cloud-free days, a stepwise hybrid geostatistical downscaling approach, based on geographically weighted area-to-area regression kriging (GWATARK), was employed by combining multiscale ancillary variables with passive microwave remote sensing data. Then, the GWATARK-estimated SSM and China Soil Moisture Dataset from Microwave Data Assimilation SSM data were combined to estimate fine-resolution data for cloudy days. The developed methodology was validated by application to the 25-km resolution daily AMSR-E SSM product to produce continuous SSM estimations at 1-km resolution over the Tibetan Plateau. In comparison with ground-based observations, the downscaled estimations showed correlation (R ≥ 0.7) for both ascending and descending overpasses. The analysis indicated the high potential of the proposed approach for producing a temporally continuous SSM product at fine spatial resolution.

Disaggregation of remotely sensed soil moisture under all sky condition using machine learning approach in Northeast Asia

NASA Astrophysics Data System (ADS)

Kim, S.; Kim, H.; Choi, M.; Kim, K.

2016-12-01

Estimating spatiotemporal variation of soil moisture is crucial to hydrological applications such as flood, drought, and near real-time climate forecasting. Recent advances in space-based passive microwave measurements allow the frequent monitoring of the surface soil moisture at a global scale and downscaling approaches have been applied to improve the spatial resolution of passive microwave products available at local scale applications. However, most downscaling methods using optical and thermal dataset, are valid only in cloud-free conditions; thus renewed downscaling method under all sky condition is necessary for the establishment of spatiotemporal continuity of datasets at fine resolution. In present study Support Vector Machine (SVM) technique was utilized to downscale a satellite-based soil moisture retrievals. The 0.1 and 0.25-degree resolution of daily Land Parameter Retrieval Model (LPRM) L3 soil moisture datasets from Advanced Microwave Scanning Radiometer 2 (AMSR2) were disaggregated over Northeast Asia in 2015. Optically derived estimates of surface temperature (LST), normalized difference vegetation index (NDVI), and its cloud products were obtained from MODerate Resolution Imaging Spectroradiometer (MODIS) for the purpose of downscaling soil moisture in finer resolution under all sky condition. Furthermore, a comparison analysis between in situ and downscaled soil moisture products was also conducted for quantitatively assessing its accuracy. Results showed that downscaled soil moisture under all sky condition not only preserves the quality of AMSR2 LPRM soil moisture at 1km resolution, but also attains higher spatial data coverage. From this research we expect that time continuous monitoring of soil moisture at fine scale regardless of weather conditions would be available.

Come rain or shine: Multi-model Projections of Climate Hazards affecting Transportation in the South Central United States

NASA Astrophysics Data System (ADS)

Mullens, E.; Mcpherson, R. A.

2016-12-01

This work develops detailed trends in climate hazards affecting the Department of Transportation's Region 6, in the South Central U.S. Firstly, a survey was developed to gather information regarding weather and climate hazards in the region from the transportation community, identifying key phenomena and thresholds to evaluate. Statistically downscaled datasets were obtained from the Multivariate Adaptive Constructed Analogues (MACA) project, and the Asynchronous Regional Regression Model (ARRM), for a total of 21 model projections, two coupled model intercomparisons (CMIP3, and CMIP5), and four emissions pathways (A1Fi, B1, RCP8.5, RCP4.5). Specific hazards investigated include winter weather, freeze-thaw cycles, hot and cold extremes, and heavy precipitation. Projections for each of these variables were calculated for the region, utilizing spatial mapping, and time series analysis at the climate division level. The results indicate that cold-season phenomena such as winter weather, freeze-thaw, and cold extremes, decrease in intensity and frequency, particularly with the higher emissions pathways. Nonetheless, specific model and downscaling method yields variability in magnitudes, with the most notable decreasing trends late in the 21st century. Hot days show a pronounced increase, particularly with greater emissions, producing annual mean 100oF day frequencies by late 21st century analogous to the 2011 heatwave over the central Southern Plains. Heavy precipitation, evidenced by return period estimates and counts-over-thresholds, also show notable increasing trends, particularly between the recent past through mid-21st Century. Conversely, mean precipitation does not show significant trends and is regionally variable. Precipitation hazards (e.g., winter weather, extremes) diverge between downscaling methods and their associated model samples much more substantially than temperature, suggesting that the choice of global model and downscaled data is particularly important when considering region-specific impacts for precipitation. These results are intended to inform region transportation professionals of the susceptibility of the area to climate extremes, and to be a resource for assessing and incorporating changing risk probabilities into their planning processes.

Modelling uncertainties and possible future trends of precipitation and temperature for 10 sub-basins in Columbia River Basin (CRB)

NASA Astrophysics Data System (ADS)

Ahmadalipour, A.; Rana, A.; Qin, Y.; Moradkhani, H.

2014-12-01

Trends and changes in future climatic parameters, such as, precipitation and temperature have been a central part of climate change studies. In the present work, we have analyzed the seasonal and yearly trends and uncertainties of prediction in all the 10 sub-basins of Columbia River Basin (CRB) for future time period of 2010-2099. The work is carried out using 2 different sets of statistically downscaled Global Climate Model (GCMs) projection datasets i.e. Bias correction and statistical downscaling (BCSD) generated at Portland State University and The Multivariate Adaptive Constructed Analogs (MACA) generated at University of Idaho. The analysis is done for with 10 GCM downscaled products each from CMIP5 daily dataset totaling to 40 different downscaled products for robust analysis. Summer, winter and yearly trend analysis is performed for all the 10 sub-basins using linear regression (significance tested by student t test) and Mann Kendall test (0.05 percent significance level), for precipitation (P), temperature maximum (Tmax) and temperature minimum (Tmin). Thereafter, all the parameters are modelled for uncertainty, across all models, in all the 10 sub-basins and across the CRB for future scenario periods. Results have indicated in varied degree of trends for all the sub-basins, mostly pointing towards a significant increase in all three climatic parameters, for all the seasons and yearly considerations. Uncertainty analysis have reveled very high change in all the parameters across models and sub-basins under consideration. Basin wide uncertainty analysis is performed to corroborate results from smaller, sub-basin scale. Similar trends and uncertainties are reported on the larger scale as well. Interestingly, both trends and uncertainties are higher during winter period than during summer, contributing to large part of the yearly change.

High-resolution projections of 21st century climate over the Athabasca River Basin through an integrated evaluation-classification-downscaling-based climate projection framework

NASA Astrophysics Data System (ADS)

Cheng, Guanhui; Huang, Guohe; Dong, Cong; Zhu, Jinxin; Zhou, Xiong; Yao, Y.

2017-03-01

An evaluation-classification-downscaling-based climate projection (ECDoCP) framework is developed to fill a methodological gap of general circulation models (GCMs)-driven statistical-downscaling-based climate projections. ECDoCP includes four interconnected modules: GCM evaluation, climate classification, statistical downscaling, and climate projection. Monthly averages of daily minimum (Tmin) and maximum (Tmax) temperature and daily cumulative precipitation (Prec) over the Athabasca River Basin (ARB) at a 10 km resolution in the 21st century under four Representative Concentration Pathways (RCPs) are projected through ECDoCP. At the octodecadal scale, temperature and precipitation would increase; after bias correction, temperature would increase with a decreased increment, while precipitation would increase only under RCP 8.5. Interannual variability of climate anomalies would increase from RCPs 4.5, 2.6, 6.0 to 8.5 for temperature and from RCPs 2.6, 4.5, 6.0 to 8.5 for precipitation. Bidecadal averaged climate anomalies would decrease from December-January-February (DJF), March-April-May (MAM), September-October-November (SON) to June-July-August (JJA) for Tmin, from DJF, SON, MAM to JJA for Tmax, and from JJA, MAM, SON to DJF for Prec. Climate projection uncertainties would decrease in May to September for temperature and in November to April for precipitation. Spatial climatic variability would not obviously change with RCPs; climatic anomalies are highly correlated with climate-variable magnitudes. Climate anomalies would decrease from upstream to downstream for temperature, and precipitation would follow an opposite pattern. The north end and the other zones would have colder and warmer days, respectively; precipitation would decrease in the upstream and increase in the remaining region. Climate changes might lead to issues, e.g., accelerated glacier/snow melting, deserving attentions of researchers and the public.

Climate change impacts on the Lehman-Baker Creek drainage in the Great Basin National Park

NASA Astrophysics Data System (ADS)

Volk, J. M.

2013-12-01

Global climate models (GCMs) forced by increased CO2 emissions forecast anomalously dry and warm trends over the southwestern U.S. for the 21st century. The effect of warmer conditions may result in decreased surface water resources within the Great Basin physiographic region critical for ecology, irrigation and municipal water supply. Here we use downscaled GCM output from the A2 and B1 greenhouse gas emission scenarios to force a Precipitation-Runoff Modeling System (PRMS) watershed model developed for the Lehman and Baker Creeks Drainage (LBCD) in the Great Basin National Park, NV for a century long time period. The goal is to quantify the effects of rising temperature to the water budget in the LBCD at monthly and annual timescales. Dynamically downscaled GCM projections are attained from the NSF EPSCoR Nevada Infrastructure for Climate Change Science, Education, and Outreach project and statistically downscaled output is retrieved from the "U.S. Bias Corrected and Downscaled WCRP CMIP3 Climate Projections". Historical daily climate and streamflow data have been collected simultaneously for periods extending 20 years or longer. Mann-Kendal trend test results showed a statistically significant (α= 0.05) long-term rising trend from 1895 to 2012 in annual and monthly average temperatures for the study area. A grid-based, PRMS watershed model of the LBCD has been created within ArcGIS 10, and physical parameters have been estimated at a spatial resolution of 100m. Simulation results will be available soon. Snow cover is expected to decrease and peak runoff to occur earlier in the spring, resulting in increased runoff, decreased infiltration/recharge, decreased baseflows, and decreased evapo-transpiration.

Downscaling of Seasonal Landsat-8 and MODIS Land Surface Temperature (LST) in Kolkata, India

NASA Astrophysics Data System (ADS)

Garg, R. D.; Guha, S.; Mondal, A.; Lakshmi, V.; Kundu, S.

2017-12-01

The quality of life of urban people is affected by urban heat environment. The urban heat studies can be carried out using remotely sensed thermal infrared imagery for retrieving Land Surface Temperature (LST). Currently, high spatial resolution (<200 m) thermal images are limited and their temporal resolution is low (e.g., 17 days of Landsat-8). Coarse spatial resolution (1000 m) and high temporal resolution (daily) thermal images of MODIS (Moderate Resolution Imaging Spectroradiometer) are frequently available. The present study is to downscale spatially coarser resolution of the thermal image to fine resolution thermal image using regression based downscaling technique. This method is based on the relationship between (LST) and vegetation indices (e.g., Normalized Difference Vegetation Index or NDVI) over a heterogeneous landscape. The Kolkata metropolitan city, which experiences a tropical wet-and-dry type of climate has been selected for the study. This study applied different seasonal open source satellite images viz., Landsat-8 and Terra MODIS. The Landsat-8 images are aggregated at 960 m resolution and downscaled into 480, 240 120 and 60 m. Optical and thermal resolution of Landsat-8 and MODIS are 30 m and 60 m; 250 m and 1000 m respectively. The homogeneous land cover areas have shown better accuracy than heterogeneous land cover areas. The downscaling method plays a crucial role while the spatial resolution of thermal band renders it unable for advanced study. Key words: Land Surface Temperature (LST), Downscale, MODIS, Landsat, Kolkata

Evaluation of water productivity under climate change in irrigated areas of the arid Northwest China using an assemble statistical downscaling method and an agro-hydrological model

NASA Astrophysics Data System (ADS)

Liu, Liu; Guo, Zezhong; Huang, Guanhua

2018-06-01

The Heihe River Basin (HRB) is the second largest inland river basin, located in the arid region of Northwest China with a serious water shortage. Evaluation of water productivity will provide scientific implications for agricultural water-saving in irrigated areas of the arid region under climate change. Based on observed meteorological data, 23 GCMs outputs and the ERA-40 reanalysis data, an assemble statistical downscaling model was developed to generate climate change scenarios under RCP2.6, RCP4.5, RCP8.5 respectively, which were then used to drive the SWAP-EPIC model to simulate crop growth in the irrigated areas of the middle HRB for the future period from 2018 to 2047. Crop yield showed an increasing trend, while crop water consumption decreased gradually in Gaotai and Ganzhou irrigated areas. The water productivity in future 30 years showed an increasing trend in both Gaotai and Ganzhou areas, with the most significant increase under RCP4.5 scenario, which were both larger than 2 kg m-3. Compared with that of the period from 2012 to 2015, the water productivity during 2018-2047 under three RCP scenarios increased by 9.2, 14.3 and 11.8 % in the Gaotai area, and 15.4, 21.6, 19.9 % in the Ganzhou area, respectively.

Downscaling Land Surface Temperature in Complex Regions by Using Multiple Scale Factors with Adaptive Thresholds

PubMed Central

Yang, Yingbao; Li, Xiaolong; Pan, Xin; Zhang, Yong; Cao, Chen

2017-01-01

Many downscaling algorithms have been proposed to address the issue of coarse-resolution land surface temperature (LST) derived from available satellite-borne sensors. However, few studies have focused on improving LST downscaling in urban areas with several mixed surface types. In this study, LST was downscaled by a multiple linear regression model between LST and multiple scale factors in mixed areas with three or four surface types. The correlation coefficients (CCs) between LST and the scale factors were used to assess the importance of the scale factors within a moving window. CC thresholds determined which factors participated in the fitting of the regression equation. The proposed downscaling approach, which involves an adaptive selection of the scale factors, was evaluated using the LST derived from four Landsat 8 thermal imageries of Nanjing City in different seasons. Results of the visual and quantitative analyses show that the proposed approach achieves relatively satisfactory downscaling results on 11 August, with coefficient of determination and root-mean-square error of 0.87 and 1.13 °C, respectively. Relative to other approaches, our approach shows the similar accuracy and the availability in all seasons. The best (worst) availability occurred in the region of vegetation (water). Thus, the approach is an efficient and reliable LST downscaling method. Future tasks include reliable LST downscaling in challenging regions and the application of our model in middle and low spatial resolutions. PMID:28368301

Climate change impact on soil erosion in the Mandakini River Basin, North India

NASA Astrophysics Data System (ADS)

Khare, Deepak; Mondal, Arun; Kundu, Sananda; Mishra, Prabhash Kumar

2017-09-01

Correct estimation of soil loss at catchment level helps the land and water resources planners to identify priority areas for soil conservation measures. Soil erosion is one of the major hazards affected by the climate change, particularly the increasing intensity of rainfall resulted in increasing erosion, apart from other factors like landuse change. Changes in climate have an adverse effect with increasing rainfall. It has caused increasing concern for modeling the future rainfall and projecting future soil erosion. In the present study, future rainfall has been generated with the downscaling of GCM (Global Circulation Model) data of Mandakini river basin, a hilly catchment in the state of Uttarakhand, India, to obtain future impact on soil erosion within the basin. The USLE is an erosion prediction model designed to predict the long-term average annual soil loss from specific field slopes in specified landuse and management systems (i.e., crops, rangeland, and recreational areas) using remote sensing and GIS technologies. Future soil erosion has shown increasing trend due to increasing rainfall which has been generated from the statistical-based downscaling method.

Statistical downscaling of GCM simulations to streamflow using relevance vector machine

NASA Astrophysics Data System (ADS)

Ghosh, Subimal; Mujumdar, P. P.

2008-01-01

General circulation models (GCMs), the climate models often used in assessing the impact of climate change, operate on a coarse scale and thus the simulation results obtained from GCMs are not particularly useful in a comparatively smaller river basin scale hydrology. The article presents a methodology of statistical downscaling based on sparse Bayesian learning and Relevance Vector Machine (RVM) to model streamflow at river basin scale for monsoon period (June, July, August, September) using GCM simulated climatic variables. NCEP/NCAR reanalysis data have been used for training the model to establish a statistical relationship between streamflow and climatic variables. The relationship thus obtained is used to project the future streamflow from GCM simulations. The statistical methodology involves principal component analysis, fuzzy clustering and RVM. Different kernel functions are used for comparison purpose. The model is applied to Mahanadi river basin in India. The results obtained using RVM are compared with those of state-of-the-art Support Vector Machine (SVM) to present the advantages of RVMs over SVMs. A decreasing trend is observed for monsoon streamflow of Mahanadi due to high surface warming in future, with the CCSR/NIES GCM and B2 scenario.

Combining Statistics and Physics to Improve Climate Downscaling

NASA Astrophysics Data System (ADS)

Gutmann, E. D.; Eidhammer, T.; Arnold, J.; Nowak, K.; Clark, M. P.

2017-12-01

Getting useful information from climate models is an ongoing problem that has plagued climate science and hydrologic prediction for decades. While it is possible to develop statistical corrections for climate models that mimic current climate almost perfectly, this does not necessarily guarantee that future changes are portrayed correctly. In contrast, convection permitting regional climate models (RCMs) have begun to provide an excellent representation of the regional climate system purely from first principles, providing greater confidence in their change signal. However, the computational cost of such RCMs prohibits the generation of ensembles of simulations or long time periods, thus limiting their applicability for hydrologic applications. Here we discuss a new approach combining statistical corrections with physical relationships for a modest computational cost. We have developed the Intermediate Complexity Atmospheric Research model (ICAR) to provide a climate and weather downscaling option that is based primarily on physics for a fraction of the computational requirements of a traditional regional climate model. ICAR also enables the incorporation of statistical adjustments directly within the model. We demonstrate that applying even simple corrections to precipitation while the model is running can improve the simulation of land atmosphere feedbacks in ICAR. For example, by incorporating statistical corrections earlier in the modeling chain, we permit the model physics to better represent the effect of mountain snowpack on air temperature changes.

Bias-correction and Spatial Disaggregation for Climate Change Impact Assessments at a basin scale

NASA Astrophysics Data System (ADS)

Nyunt, Cho; Koike, Toshio; Yamamoto, Akio; Nemoto, Toshihoro; Kitsuregawa, Masaru

2013-04-01

Basin-scale climate change impact studies mainly rely on general circulation models (GCMs) comprising the related emission scenarios. Realistic and reliable data from GCM is crucial for national scale or basin scale impact and vulnerability assessments to build safety society under climate change. However, GCM fail to simulate regional climate features due to the imprecise parameterization schemes in atmospheric physics and coarse resolution scale. This study describes how to exclude some unsatisfactory GCMs with respect to focused basin, how to minimize the biases of GCM precipitation through statistical bias correction and how to cover spatial disaggregation scheme, a kind of downscaling, within in a basin. GCMs rejection is based on the regional climate features of seasonal evolution as a bench mark and mainly depends on spatial correlation and root mean square error of precipitation and atmospheric variables over the target region. Global Precipitation Climatology Project (GPCP) and Japanese 25-uear Reanalysis Project (JRA-25) are specified as references in figuring spatial pattern and error of GCM. Statistical bias-correction scheme comprises improvements of three main flaws of GCM precipitation such as low intensity drizzled rain days with no dry day, underestimation of heavy rainfall and inter-annual variability of local climate. Biases of heavy rainfall are conducted by generalized Pareto distribution (GPD) fitting over a peak over threshold series. Frequency of rain day error is fixed by rank order statistics and seasonal variation problem is solved by using a gamma distribution fitting in each month against insi-tu stations vs. corresponding GCM grids. By implementing the proposed bias-correction technique to all insi-tu stations and their respective GCM grid, an easy and effective downscaling process for impact studies at the basin scale is accomplished. The proposed method have been examined its applicability to some of the basins in various climate regions all over the world. The biases are controlled very well by using this scheme in all applied basins. After that, bias-corrected and downscaled GCM precipitation are ready to use for simulating the Water and Energy Budget based Distributed Hydrological Model (WEB-DHM) to analyse the stream flow change or water availability of a target basin under the climate change in near future. Furthermore, it can be investigated any inter-disciplinary studies such as drought, flood, food, health and so on.In summary, an effective and comprehensive statistical bias-correction method was established to fulfil the generative applicability of GCM scale to basin scale without difficulty. This gap filling also promotes the sound decision of river management in the basin with more reliable information to build the resilience society.

Designing the Bridge: Perceptions and Use of Downscaled Climate Data by Climate Modelers and Resource Managers in Hawaii

NASA Astrophysics Data System (ADS)

Keener, V. W.; Brewington, L.; Jaspers, K.

2016-12-01

To build an effective bridge from the climate modeling community to natural resource managers, we assessed the existing landscape to see where different groups diverge in their perceptions of climate data and needs. An understanding of a given community's shared knowledge and differences can help design more actionable science. Resource managers in Hawaii are eager to have future climate projections at spatial scales relevant to the islands. National initiatives to downscale climate data often exclude US insular regions, so researchers in Hawaii have generated regional dynamically and statistically downscaled projections. Projections of precipitation diverge, however, leading to difficulties in communication and use. Recently, a two day workshop was held with scientists and managers to evaluate available models and determine a set of best practices for moving forward with decision-relevant downscaling in Hawaii. To seed the discussion, the Pacific Regional Integrated Sciences and Assessments (RISA) program conducted a pre-workshop survey (N=65) of climate modelers and freshwater, ecosystem, and wildfire managers working in Hawaii. Scientists reported spending less than half of their time on operational research, although the majority was eager to partner with managers on specific projects. Resource managers had varying levels of familiarity with downscaled climate projections, but reported needing more information about uncertainty for decision making, and were less interested in the technical model details. There were large differences between groups of managers, with 41.7% of freshwater managers reporting that they used climate projections regularly, while a majority of ecosystem and wildfire managers reported having "no familiarity". Scientists and managers rated which spatial and temporal scales were most relevant to decision making. Finally, when asked to compare how confident they were in projections of specific climate variables between the dynamical and statistical data, 80-90% of managers responded that they had no opinion. Workshop attendees were very interested in the survey results, adding to evidence of a need for sustained engagement between modeler and user groups, as well as different strategies for working with different types of resource managers.

Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa

NASA Technical Reports Server (NTRS)

Roberts, J. Brent; Robertson, Franklin R.; Bosilovich, Michael; Lyon, Bradfield; Funk, Chris

2013-01-01

The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of downscaling methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially downscaled and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available downscaling methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period

Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa

NASA Technical Reports Server (NTRS)

Robertson, Franklin R.; Roberts, J. Brent; Bosilovich, Michael; Lyon, Bradfield

2013-01-01

The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in impact modeling within hub regions including East Africa, the Hindu Kush-Himalayan (HKH) region and Mesoamerica. One of the participating models in NMME is the NASA Goddard Earth Observing System (GEOS5). This work will present an intercomparison of downscaling methods using the GEOS5 seasonal forecasts of temperature and precipitation over East Africa. The current seasonal forecasting system provides monthly averaged forecast anomalies. These anomalies must be spatially downscaled and temporally disaggregated for use in application modeling (e.g. hydrology, agriculture). There are several available downscaling methodologies that can be implemented to accomplish this goal. Selected methods include both a non-homogenous hidden Markov model and an analogue based approach. A particular emphasis will be placed on quantifying the ability of different methods to capture the intermittency of precipitation within both the short and long rain seasons. Further, the ability to capture spatial covariances will be assessed. Both probabilistic and deterministic skill measures will be evaluated over the hindcast period.

Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD

PubMed Central

Lorenz, David J.; Nieto-Lugilde, Diego; Blois, Jessica L.; Fitzpatrick, Matthew C.; Williams, John W.

2016-01-01

Increasingly, ecological modellers are integrating paleodata with future projections to understand climate-driven biodiversity dynamics from the past through the current century. Climate simulations from earth system models are necessary to this effort, but must be debiased and downscaled before they can be used by ecological models. Downscaling methods and observational baselines vary among researchers, which produces confounding biases among downscaled climate simulations. We present unified datasets of debiased and downscaled climate simulations for North America from 21 ka BP to 2100AD, at 0.5° spatial resolution. Temporal resolution is decadal averages of monthly data until 1950AD, average climates for 1950–2005 AD, and monthly data from 2010 to 2100AD, with decadal averages also provided. This downscaling includes two transient paleoclimatic simulations and 12 climate models for the IPCC AR5 (CMIP5) historical (1850–2005), RCP4.5, and RCP8.5 21st-century scenarios. Climate variables include primary variables and derived bioclimatic variables. These datasets provide a common set of climate simulations suitable for seamlessly modelling the effects of past and future climate change on species distributions and diversity. PMID:27377537

Downscaled and debiased climate simulations for North America from 21,000 years ago to 2100AD.

PubMed

Lorenz, David J; Nieto-Lugilde, Diego; Blois, Jessica L; Fitzpatrick, Matthew C; Williams, John W

2016-07-05

Increasingly, ecological modellers are integrating paleodata with future projections to understand climate-driven biodiversity dynamics from the past through the current century. Climate simulations from earth system models are necessary to this effort, but must be debiased and downscaled before they can be used by ecological models. Downscaling methods and observational baselines vary among researchers, which produces confounding biases among downscaled climate simulations. We present unified datasets of debiased and downscaled climate simulations for North America from 21 ka BP to 2100AD, at 0.5° spatial resolution. Temporal resolution is decadal averages of monthly data until 1950AD, average climates for 1950-2005 AD, and monthly data from 2010 to 2100AD, with decadal averages also provided. This downscaling includes two transient paleoclimatic simulations and 12 climate models for the IPCC AR5 (CMIP5) historical (1850-2005), RCP4.5, and RCP8.5 21st-century scenarios. Climate variables include primary variables and derived bioclimatic variables. These datasets provide a common set of climate simulations suitable for seamlessly modelling the effects of past and future climate change on species distributions and diversity.

An Observation-base investigation of nudging in WRF for downscaling surface climate information to 12-km Grid Spacing

EPA Science Inventory

Previous research has demonstrated the ability to use the Weather Research and Forecast (WRF) model and contemporary dynamical downscaling methods to refine global climate modeling results to a horizontal resolution of 36 km. Environmental managers and urban planners have expre...

Satellite-Enhanced Dynamical Downscaling of Extreme Events

NASA Astrophysics Data System (ADS)

Nunes, A.

2015-12-01

Severe weather events can be the triggers of environmental disasters in regions particularly susceptible to changes in hydrometeorological conditions. In that regard, the reconstruction of past extreme weather events can help in the assessment of vulnerability and risk mitigation actions. Using novel modeling approaches, dynamical downscaling of long-term integrations from global circulation models can be useful for risk analysis, providing more accurate climate information at regional scales. Originally developed at the National Centers for Environmental Prediction (NCEP), the Regional Spectral Model (RSM) is being used in the dynamical downscaling of global reanalysis, within the South American Hydroclimate Reconstruction Project. Here, RSM combines scale-selective bias correction with assimilation of satellite-based precipitation estimates to downscale extreme weather occurrences. Scale-selective bias correction is a method employed in the downscaling, similar to the spectral nudging technique, in which the downscaled solution develops in agreement with its coarse boundaries. Precipitation assimilation acts on modeled deep-convection, drives the land-surface variables, and therefore the hydrological cycle. During the downscaling of extreme events that took place in Brazil in recent years, RSM continuously assimilated NCEP Climate Prediction Center morphing technique precipitation rates. As a result, RSM performed better than its global (reanalysis) forcing, showing more consistent hydrometeorological fields compared with more sophisticated global reanalyses. Ultimately, RSM analyses might provide better-quality initial conditions for high-resolution numerical predictions in metropolitan areas, leading to more reliable short-term forecasting of severe local storms.

CORDEX Coordinated Output for Regional Evaluation

NASA Astrophysics Data System (ADS)

Gutowski, William; Giorgi, Filippo; Lake, Irene

2017-04-01

The Science Advisory Team for the Coordinated Regional Downscaling Experiment (CORDEX) has developed a baseline framework of specified regions, resolutions and simulation periods intended to provide a foundation for ongoing regional CORDEX activities: the CORDEX Coordinated Output for Regional Evaluation, or CORDEX-CORE. CORDEX-CORE was conceived in part to be responsive to IPCC needs for coordinated simulations that could provide regional climate downscaling (RCD) that yields fine-scale climate information beyond that resolved by GCMs. For each CORDEX region, a matrix of GCM-RCD experiments is designed based on the need to cover as much as possible different dimensions of the uncertainty space (e.g., different emissions and land-use scenarios, GCMs, RCD models and techniques). An appropriate set of driving GCMs can allow a program of simulations that efficiently addresses key scientific issues within CORDEX, while facilitating comparison and transfer of results and lessons learned across different regions. The CORDEX-CORE program seeks to provide, as much as possible, homogeneity across domains, so it is envisioned that a standard set of regional climate models (RCMs) and empirical statistical downscaling (ESD) methods will downscale a standard set of GCMs over all or at least most CORDEX domains for a minimum set of scenarios (high and low end). The focus is on historical climate simulations for the 20th century and projections for 21st century, implying that data would be needed minimally for the period 1950-2100 (but ideally 1900-2100). This foundational ensemble can be regionally enriched with further contributions (additional GCM-RCD pairs) by individual groups over their selected domains of interest. The RCM model resolution for these core experiments will be in the range of 10-20 km, a resolution that has been shown to provide substantial added value for a variety of climate variables and that represents a significant forward step compared in the CORDEX program. This presentation presents the vision and structure of CORDEX-CORE while also soliciting discussion on plans for implementing the program.

Monitoring Rainfall by Combining Ground-based Observed Precipitation and PERSIANN Satellite Product (Case Study Area: Lake Urmia Basin)

NASA Astrophysics Data System (ADS)

Abrishamchi, A.; Mirshahi, A.

2015-12-01

The global coverage, quick access, and appropriate spatial-temporal resolution of satellite precipitation data renders the data appropriate for hydrologic studies, especially in regions with no sufficient rain-gauge network. On the other hand, satellite precipitation products may have major errors. The present study aims at reduction of estimation error of the PERSIANN satellite precipitation product. Bayesian logic employed to develop a statistical relationship between historical ground-based and satellite precipitation data. This relationship can then be used to reduce satellite precipitation product error in near real time, when there is no ground-based precipitation observation. The method was evaluated in the Lake Urmia basin with a monthly time scale; November to May of 2000- 2008 for the purpose of model development and two years of 2009 and 2010 for the validation of the established relationships. Moreover, Kriging interpolation method was employed to estimate the average rainfall in the basin. Furthermore, to downscale the satellite precipitation product from 0.25o to 0.05o, data-location downscaling algorithm was used. In 76 percent of months, the final product, compared with the satellite precipitation, had less error during the validation period. Additionally, its performance was marginally better than adjusted PERSIANN product.

A downscaling scheme for atmospheric variables to drive soil-vegetation-atmosphere transfer models

NASA Astrophysics Data System (ADS)

Schomburg, A.; Venema, V.; Lindau, R.; Ament, F.; Simmer, C.

2010-09-01

For driving soil-vegetation-transfer models or hydrological models, high-resolution atmospheric forcing data is needed. For most applications the resolution of atmospheric model output is too coarse. To avoid biases due to the non-linear processes, a downscaling system should predict the unresolved variability of the atmospheric forcing. For this purpose we derived a disaggregation system consisting of three steps: (1) a bi-quadratic spline-interpolation of the low-resolution data, (2) a so-called `deterministic' part, based on statistical rules between high-resolution surface variables and the desired atmospheric near-surface variables and (3) an autoregressive noise-generation step. The disaggregation system has been developed and tested based on high-resolution model output (400m horizontal grid spacing). A novel automatic search-algorithm has been developed for deriving the deterministic downscaling rules of step 2. When applied to the atmospheric variables of the lowest layer of the atmospheric COSMO-model, the disaggregation is able to adequately reconstruct the reference fields. Applying downscaling step 1 and 2, root mean square errors are decreased. Step 3 finally leads to a close match of the subgrid variability and temporal autocorrelation with the reference fields. The scheme can be applied to the output of atmospheric models, both for stand-alone offline simulations, and a fully coupled model system.

Accounting for downscaling and model uncertainty in fine-resolution seasonal climate projections over the Columbia River Basin

NASA Astrophysics Data System (ADS)

Ahmadalipour, Ali; Moradkhani, Hamid; Rana, Arun

2018-01-01

Climate change is expected to have severe impacts on natural systems as well as various socio-economic aspects of human life. This has urged scientific communities to improve the understanding of future climate and reduce the uncertainties associated with projections. In the present study, ten statistically downscaled CMIP5 GCMs at 1/16th deg. spatial resolution from two different downscaling procedures are utilized over the Columbia River Basin (CRB) to assess the changes in climate variables and characterize the associated uncertainties. Three climate variables, i.e. precipitation, maximum temperature, and minimum temperature, are studied for the historical period of 1970-2000 as well as future period of 2010-2099, simulated with representative concentration pathways of RCP4.5 and RCP8.5. Bayesian Model Averaging (BMA) is employed to reduce the model uncertainty and develop a probabilistic projection for each variable in each scenario. Historical comparison of long-term attributes of GCMs and observation suggests a more accurate representation for BMA than individual models. Furthermore, BMA projections are used to investigate future seasonal to annual changes of climate variables. Projections indicate significant increase in annual precipitation and temperature, with varied degree of change across different sub-basins of CRB. We then characterized uncertainty of future projections for each season over CRB. Results reveal that model uncertainty is the main source of uncertainty, among others. However, downscaling uncertainty considerably contributes to the total uncertainty of future projections, especially in summer. On the contrary, downscaling uncertainty appears to be higher than scenario uncertainty for precipitation.

High-Resolution Regional Reanalysis in China: Evaluation of 1 Year Period Experiments

NASA Astrophysics Data System (ADS)

Zhang, Qi; Pan, Yinong; Wang, Shuyu; Xu, Jianjun; Tang, Jianping

2017-10-01

Globally, reanalysis data sets are widely used in assessing climate change, validating numerical models, and understanding the interactions between the components of a climate system. However, due to the relatively coarse resolution, most global reanalysis data sets are not suitable to apply at the local and regional scales directly with the inadequate descriptions of mesoscale systems and climatic extreme incidents such as mesoscale convective systems, squall lines, tropical cyclones, regional droughts, and heat waves. In this study, by using a data assimilation system of Gridpoint Statistical Interpolation, and a mesoscale atmospheric model of Weather Research and Forecast model, we build a regional reanalysis system. This is preliminary and the first experimental attempt to construct a high-resolution reanalysis for China main land. Four regional test bed data sets are generated for year 2013 via three widely used methods (classical dynamical downscaling, spectral nudging, and data assimilation) and a hybrid method with data assimilation coupled with spectral nudging. Temperature at 2 m, precipitation, and upper level atmospheric variables are evaluated by comparing against observations for one-year-long tests. It can be concluded that the regional reanalysis with assimilation and nudging methods can better produce the atmospheric variables from surface to upper levels, and regional extreme events such as heat waves, than the classical dynamical downscaling. Compared to the ERA-Interim global reanalysis, the hybrid nudging method performs slightly better in reproducing upper level temperature and low-level moisture over China, which improves regional reanalysis data quality.

Evaluation of statistically downscaled GCM output as input for hydrological and stream temperature simulation in the Apalachicola–Chattahoochee–Flint River Basin (1961–99)

USGS Publications Warehouse

Hay, Lauren E.; LaFontaine, Jacob H.; Markstrom, Steven

2014-01-01

The accuracy of statistically downscaled general circulation model (GCM) simulations of daily surface climate for historical conditions (1961–99) and the implications when they are used to drive hydrologic and stream temperature models were assessed for the Apalachicola–Chattahoochee–Flint River basin (ACFB). The ACFB is a 50 000 km2 basin located in the southeastern United States. Three GCMs were statistically downscaled, using an asynchronous regional regression model (ARRM), to ⅛° grids of daily precipitation and minimum and maximum air temperature. These ARRM-based climate datasets were used as input to the Precipitation-Runoff Modeling System (PRMS), a deterministic, distributed-parameter, physical-process watershed model used to simulate and evaluate the effects of various combinations of climate and land use on watershed response. The ACFB was divided into 258 hydrologic response units (HRUs) in which the components of flow (groundwater, subsurface, and surface) are computed in response to climate, land surface, and subsurface characteristics of the basin. Daily simulations of flow components from PRMS were used with the climate to simulate in-stream water temperatures using the Stream Network Temperature (SNTemp) model, a mechanistic, one-dimensional heat transport model for branched stream networks.The climate, hydrology, and stream temperature for historical conditions were evaluated by comparing model outputs produced from historical climate forcings developed from gridded station data (GSD) versus those produced from the three statistically downscaled GCMs using the ARRM methodology. The PRMS and SNTemp models were forced with the GSD and the outputs produced were treated as “truth.” This allowed for a spatial comparison by HRU of the GSD-based output with ARRM-based output. Distributional similarities between GSD- and ARRM-based model outputs were compared using the two-sample Kolmogorov–Smirnov (KS) test in combination with descriptive metrics such as the mean and variance and an evaluation of rare and sustained events. In general, precipitation and streamflow quantities were negatively biased in the downscaled GCM outputs, and results indicate that the downscaled GCM simulations consistently underestimate the largest precipitation events relative to the GSD. The KS test results indicate that ARRM-based air temperatures are similar to GSD at the daily time step for the majority of the ACFB, with perhaps subweekly averaging for stream temperature. Depending on GCM and spatial location, ARRM-based precipitation and streamflow requires averaging of up to 30 days to become similar to the GSD-based output.Evaluation of the model skill for historical conditions suggests some guidelines for use of future projections; while it seems correct to place greater confidence in evaluation metrics which perform well historically, this does not necessarily mean those metrics will accurately reflect model outputs for future climatic conditions. Results from this study indicate no “best” overall model, but the breadth of analysis can be used to give the product users an indication of the applicability of the results to address their particular problem. Since results for historical conditions indicate that model outputs can have significant biases associated with them, the range in future projections examined in terms of change relative to historical conditions for each individual GCM may be more appropriate.

Impacts of climate change on precipitation and discharge extremes through the use of statistical downscaling approaches in a Mediterranean basin.

PubMed

Piras, Monica; Mascaro, Giuseppe; Deidda, Roberto; Vivoni, Enrique R

2016-02-01

Mediterranean region is characterized by high precipitation variability often enhanced by orography, with strong seasonality and large inter-annual fluctuations, and by high heterogeneity of terrain and land surface properties. As a consequence, catchments in this area are often prone to the occurrence of hydrometeorological extremes, including storms, floods and flash-floods. A number of climate studies focused in the Mediterranean region predict that extreme events will occur with higher intensity and frequency, thus requiring further analyses to assess their effect at the land surface, particularly in small- and medium-sized watersheds. In this study, climate and hydrologic simulations produced within the Climate Induced Changes on the Hydrology of Mediterranean Basins (CLIMB) EU FP7 research project were used to analyze how precipitation extremes propagate into discharge extremes in the Rio Mannu basin (472.5km(2)), located in Sardinia, Italy. The basin hydrologic response to climate forcings in a reference (1971-2000) and a future (2041-2070) period was simulated through the combined use of a set of global and regional climate models, statistical downscaling techniques, and a process based distributed hydrologic model. We analyzed and compared the distribution of annual maxima extracted from hourly and daily precipitation and peak discharge time series, simulated by the hydrologic model under climate forcing. For this aim, yearly maxima were fit by the Generalized Extreme Value (GEV) distribution using a regional approach. Next, we discussed commonality and contrasting behaviors of precipitation and discharge maxima distributions to better understand how hydrological transformations impact propagation of extremes. Finally, we show how rainfall statistical downscaling algorithms produce more reliable forcings for hydrological models than coarse climate model outputs. Copyright © 2015 Elsevier B.V. All rights reserved.

[Downscaling research of spatial distribution of incidence of hand foot and mouth disease based on area-to-area Poisson Kriging method].

PubMed

Wang, J X; Hu, M G; Yu, S C; Xiao, G X

2017-09-10

Objective: To understand the spatial distribution of incidence of hand foot and mouth disease (HFMD) at scale of township and provide evidence for the better prevention and control of HFMD and allocation of medical resources. Methods: The incidence data of HFMD in 108 counties (district) in Shandong province in 2010 were collected. Downscaling interpolation was conducted by using area-to-area Poisson Kriging method. The interpolation results were visualized by using geographic information system (GIS). The county (district) incidence was interpolated into township incidence to get the distribution of spatial distribution of incidence of township. Results: In the downscaling interpolation, the range of the fitting semi-variance equation was 20.38 km. Within the range, the incidence had correlation with each other. The fitting function of scatter diagram of estimated and actual incidence of HFMD at country level was y =1.053 1 x , R (2)=0.99. The incidences at different scale were consistent. Conclusions: The incidence of HFMD had spatial autocorrelation within 20.38 km. When HFMD occurs in one place, it is necessary to strengthen the surveillance and allocation of medical resource in the surrounding area within 20.38 km. Area to area Poisson Kriging method based downscaling research can be used in spatial visualization of HFMD incidence.

THE APPLICATION OF A STATISTICAL DOWNSCALING PROCESS TO DERIVE 21{sup ST} CENTURY RIVER FLOW PREDICTIONS USING A GLOBAL CLIMATE SIMULATION

DOE Office of Scientific and Technical Information (OSTI.GOV)

Werth, D.; Chen, K. F.

2013-08-22

The ability of water managers to maintain adequate supplies in coming decades depends, in part, on future weather conditions, as climate change has the potential to alter river flows from their current values, possibly rendering them unable to meet demand. Reliable climate projections are therefore critical to predicting the future water supply for the United States. These projections cannot be provided solely by global climate models (GCMs), however, as their resolution is too coarse to resolve the small-scale climate changes that can affect hydrology, and hence water supply, at regional to local scales. A process is needed to ‘downscale’ themore » GCM results to the smaller scales and feed this into a surface hydrology model to help determine the ability of rivers to provide adequate flow to meet future needs. We apply a statistical downscaling to GCM projections of precipitation and temperature through the use of a scaling method. This technique involves the correction of the cumulative distribution functions (CDFs) of the GCM-derived temperature and precipitation results for the 20{sup th} century, and the application of the same correction to 21{sup st} century GCM projections. This is done for three meteorological stations located within the Coosa River basin in northern Georgia, and is used to calculate future river flow statistics for the upper Coosa River. Results are compared to the historical Coosa River flow upstream from Georgia Power Company’s Hammond coal-fired power plant and to flows calculated with the original, unscaled GCM results to determine the impact of potential changes in meteorology on future flows.« less

Statistical downscaling for winter streamflow in Douro River

NASA Astrophysics Data System (ADS)

Jesús Esteban Parra, María; Hidalgo Muñoz, José Manuel; García-Valdecasas-Ojeda, Matilde; Raquel Gámiz Fortis, Sonia; Castro Díez, Yolanda

2015-04-01

In this paper we have obtained climate change projections for winter flow of the Douro River in the period 2071-2100 by applying the technique of Partial Regression and various General Circulation Models of CMIP5. The streamflow data base used has been provided by the Center for Studies and Experimentation of Public Works, CEDEX. Series from gauing stations and reservoirs with less than 10% of missing data (filled by regression with well correlated neighboring stations) have been considered. The homogeneity of these series has been evaluated through the Pettit test and degree of human alteration by the Common Area Index. The application of these criteria led to the selection of 42 streamflow time series homogeneously distributed over the basin, covering the period 1951-2011. For these streamflow data, winter seasonal values were obtained by averaging the monthly values from January to March. Statistical downscaling models for the streamflow have been fitted using as predictors the main atmospheric modes of variability over the North Atlantic region. These modes have been obtained using winter sea level pressure data of the NCEP reanalysis, averaged for the months from December to February. Period 1951-1995 was used for calibration, while 1996-2011 period was used in validating the adjusted models. In general, these models are able to reproduce about 70% of the variability of the winter streamflow of the Douro River. Finally, the obtained statistical models have been applied to obtain projections for 2071-2100 period, using outputs from different CMIP5 models under the RPC8.5 scenario. The results for the end of the century show modest declines of winter streamflow in this river for most of the models. Keywords: Statistical downscaling, streamflow, Douro River, climate change. ACKNOWLEDGEMENTS This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).

Climate change impacts on rainfall extremes and urban drainage: state-of-the-art review

NASA Astrophysics Data System (ADS)

Willems, Patrick; Olsson, Jonas; Arnbjerg-Nielsen, Karsten; Beecham, Simon; Pathirana, Assela; Bülow Gregersen, Ida; Madsen, Henrik; Nguyen, Van-Thanh-Van

2013-04-01

Under the umbrella of the IWA/IAHR Joint Committee on Urban Drainage, the International Working Group on Urban Rainfall (IGUR) has reviewed existing methodologies for the analysis of long-term historical and future trends in urban rainfall extremes and their effects on urban drainage systems, due to anthropogenic climate change. Current practises have several limitations and pitfalls, which are important to be considered by trend or climate change impact modellers and users of trend/impact results. The review considers the following aspects: Analysis of long-term historical trends due to anthropogenic climate change: influence of data limitation, instrumental or environmental changes, interannual variations and longer term climate oscillations on trend testing results. Analysis of long-term future trends due to anthropogenic climate change: by complementing empirical historical data with the results from physically-based climate models, dynamic downscaling to the urban scale by means of Limited Area Models (LAMs) including explicitly small-scale cloud processes; validation of RCM/GCM results for local conditions accounting for natural variability, limited length of the available time series, difference in spatial scales, and influence of climate oscillations; statistical downscaling methods combined with bias correction; uncertainties associated with the climate forcing scenarios, the climate models, the initial states and the statistical downscaling step; uncertainties in the impact models (e.g. runoff peak flows, flood or surcharge frequencies, and CSO frequencies and volumes), including the impacts of more extreme conditions than considered during impact model calibration and validation. Implications for urban drainage infrastructure design and management: upgrading of the urban drainage system as part of a program of routine and scheduled replacement and renewal of aging infrastructure; how to account for the uncertainties; flexible and sustainable solutions; adaptive approach that provides inherent flexibility and reversibility and avoids closing off options; importance of active learning. References: Willems, P., Olsson, J., Arnbjerg-Nielsen, K., Beecham, S., Pathirana, A., Bülow Gregersen, I., Madsen, H., Nguyen, V-T-V. (2012). Impacts of climate change on rainfall extremes and urban drainage. IWA Publishing, 252 p., Paperback Print ISBN 9781780401256; Ebook ISBN 9781780401263 Willems, P., Arnbjerg-Nielsen, K., Olsson, J., Nguyen, V.T.V. (2012), 'Climate change impact assessment on urban rainfall extremes and urban drainage: methods and shortcomings', Atmospheric Research, 103, 106-118

Reanalysis Intercomparison on a Surface Wind Statistical Downscaling Exercise over Northeastern North America.

NASA Astrophysics Data System (ADS)

Lucio-Eceiza, Etor E.; Fidel González-Rouco, J.; Navarro, Jorge; García-Bustamante, Elena; Beltrami, Hugo; Rojas-Labanda, Cristina

2017-04-01

The area of North Eastern North America is located in a privileged position for the study of the wind behaviour as it lies within the track of many of the extratropical cyclones that travel that half of the continent. During the winter season the cyclonic activity and wind intensity are higher in the region, offering a great opportunity to analyse the relationships of the surface wind field with various large-scale configurations. The analysis of the wind behaviour is conducted via a statistical downscaling method based on Canonical Correlation Analysis (CCA). This methodology exploits the relationships among the main modes of circulation over the North Atlantic and Pacific Sectors and the behaviour of an observational surface wind database. For this exercise, various predictor variables have been selected (surface wind, SLP, geopotential height at 850 and 500 hPa, and thermal thickness between these two levels), obtained by all the global reanalysis products available to date. Our predictand field consists of an observational surface wind dataset with 525 sites distributed over North Eastern North America that span over a period of about 60 years (1953-2010). These data have been previously subjected to an exhaustive quality control process. A sensitivity analysis of the methodology to different parameter configurations has been carried out, such as reanalysis product, window size, predictor variables, number of retained EOF and CCA modes, and crossvalidation subset (to test the robustness of the method). An evaluation of the predictive skill of the wind estimations has also been conducted. Overall, the methodology offers a good representation of the wind variability, which is very consistent between all the reanalysis products. The wind directly obtained from the reanalyses offer a better temporal correlation but a larger range, and in many cases, worst representation of the local variability. The long observational period has also permitted the study of intra to multidecadal variability as the statistical relationship obtained by this method also allows for the reconstruction of the regional wind behaviour back to the mid 19th century. For this task we have used two 20th century reanalysis products as well as two additional instrumental sea level pressure datasets.

Field significance of performance measures in the context of regional climate model evaluation. Part 2: precipitation

NASA Astrophysics Data System (ADS)

Ivanov, Martin; Warrach-Sagi, Kirsten; Wulfmeyer, Volker

2018-04-01

A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as `field' or `global' significance. The block length for the local resampling tests is precisely determined to adequately account for the time series structure. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 ∘ grid resolution. Daily precipitation climatology for the 1990-2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. While the downscaled precipitation distributions are statistically indistinguishable from the observed ones in most regions in summer, the biases of some distribution characteristics are significant over large areas in winter. WRF-NOAH generates appropriate stationary fine-scale climate features in the daily precipitation field over regions of complex topography in both seasons and appropriate transient fine-scale features almost everywhere in summer. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure.

Downscaling global precipitation for local applications - a case for the Rhine basin

NASA Astrophysics Data System (ADS)

Sperna Weiland, Frederiek; van Verseveld, Willem; Schellekens, Jaap

2017-04-01

Within the EU FP7 project eartH2Observe a global Water Resources Re-analysis (WRR) is being developed. This re-analysis consists of meteorological and hydrological water balance variables with global coverage, spanning the period 1979-2014 at 0.25 degrees resolution (Schellekens et al., 2016). The dataset can be of special interest in regions with limited in-situ data availability, yet for local scale analysis particularly in mountainous regions, a resolution of 0.25 degrees may be too coarse and downscaling the data to a higher resolution may be required. A downscaling toolbox has been made that includes spatial downscaling of precipitation based on the global WorldClim dataset that is available at 1 km resolution as a monthly climatology (Hijmans et al., 2005). The input of the down-scaling tool are either the global eartH2Observe WRR1 and WRR2 datasets based on the WFDEI correction methodology (Weedon et al., 2014) or the global Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset (Beck et al., 2016). Here we present a validation of the datasets over the Rhine catchment by means of a distributed hydrological model (wflow, Schellekens et al., 2014) using a number of precipitation scenarios. (1) We start by running the model using the local reference dataset derived by spatial interpolation of gauge observations. Furthermore we use (2) the MSWEP dataset at the native 0.25-degree resolution followed by (3) MSWEP downscaled with the WorldClim dataset and final (4) MSWEP downscaled with the local reference dataset. The validation will be based on comparison of the modeled river discharges as well as rainfall statistics. We expect that down-scaling the MSWEP dataset with the WorldClim data to higher resolution will increase its performance. To test the performance of the down-scaling routine we have added a run with MSWEP data down-scaled with the local dataset and compare this with the run based on the local dataset itself. - Beck, H. E. et al., 2016. MSWEP: 3-hourly 0.25° global gridded precipitation (1979-2015) by merging gauge, satellite, and reanalysis data, Hydrol. Earth Syst. Sci. Discuss., doi:10.5194/hess-2016-236, accepted for final publication. - Hijmans, R.J. et al., 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978. - Schellekens, J. et al., 2016. A global water resources ensemble of hydrological models: the eartH2Observe Tier-1 dataset, Earth Syst. Sci. Data Discuss., doi:10.5194/essd-2016-55, under review. - Schellekens, J. et al., 2014. Rapid setup of hydrological and hydraulic models using OpenStreetMap and the SRTM derived digital elevation model. Environmental Modelling&Software - Weedon, G.P. et al., 2014. The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data. Water Resources Research, 50, doi:10.1002/2014WR015638.

The Practitioner's Dilemma: How to Assess the Credibility of Downscaled Climate Projections

NASA Technical Reports Server (NTRS)

Barsugli, Joseph J.; Guentchev, Galina; Horton, Radley M.; Wood, Andrew; Mearns, Lindo O.; Liang, Xin-Zhong; Winkler, Julia A.; Dixon, Keith; Hayhoe, Katharine; Rood, Richard B.;

Development of an effective and potentially scalable weather generator for temperature and growing degree days

NASA Astrophysics Data System (ADS)

Rahmani, Elham; Friederichs, Petra; Keller, Jan; Hense, Andreas

2016-05-01

The main purpose of this study is to develop an easy-to-use weather generator (WG) for the downscaling of gridded data to point measurements at regional scale. The WG is applied to daily averaged temperatures and annual growing degree days (GDD) of wheat. This particular choice of variables is motivated by future investigations on temperature impacts as the most important climate variable for wheat cultivation under irrigation in Iran. The proposed statistical downscaling relates large-scale ERA-40 reanalysis to local daily temperature and annual GDD. Long-term local observations in Iran are used at 16 synoptic stations from 1961 to 2001, which is the common period with ERA-40 data. We perform downscaling using two approaches: the first is a linear regression model that uses the ERA-40 fingerprints (FP) defined by the squared correlation with local variability, and the second employs a linear multiple regression (MR) analysis to relate the large-scale information at the neighboring grid points to the station data. Extending the usual downscaling, we implement a WG providing uncertainty information and realizations of the local temperatures and GDD by adding a Gaussian random noise. ERA-40 reanalysis well represents the local daily temperature as well as the annual GDD variability. For 2-m temperature, the FPs are more localized during the warm compared with the cold season. While MR is slightly superior for daily temperature time series, FP seems to perform best for annual GDD. We further assess the quality of the WGs applying probabilistic verification scores like the continuous ranked probability score (CRPS) and the respective skill score. They clearly demonstrate the superiority of WGs compared with a deterministic downscaling.

Climate model biases and statistical downscaling for application in hydrologic model

USDA-ARS?s Scientific Manuscript database

Climate change impact studies use global climate model (GCM) simulations to define future temperature and precipitation. The best available bias-corrected GCM output was obtained from Coupled Model Intercomparison Project phase 5 (CMIP5). CMIP5 data (temperature and precipitation) are available in d...

A daily, 1 km resolution data set of downscaled Greenland ice sheet surface mass balance (1958-2015)

NASA Astrophysics Data System (ADS)

Noël, Brice; van de Berg, Willem Jan; Machguth, Horst; Lhermitte, Stef; Howat, Ian; Fettweis, Xavier; van den Broeke, Michiel R.

2016-10-01

This study presents a data set of daily, 1 km resolution Greenland ice sheet (GrIS) surface mass balance (SMB) covering the period 1958-2015. Applying corrections for elevation, bare ice albedo and accumulation bias, the high-resolution product is statistically downscaled from the native daily output of the polar regional climate model RACMO2.3 at 11 km. The data set includes all individual SMB components projected to a down-sampled version of the Greenland Ice Mapping Project (GIMP) digital elevation model and ice mask. The 1 km mask better resolves narrow ablation zones, valley glaciers, fjords and disconnected ice caps. Relative to the 11 km product, the more detailed representation of isolated glaciated areas leads to increased precipitation over the southeastern GrIS. In addition, the downscaled product shows a significant increase in runoff owing to better resolved low-lying marginal glaciated regions. The combined corrections for elevation and bare ice albedo markedly improve model agreement with a newly compiled data set of ablation measurements.

Evaluation of downscaled, gridded climate data for the conterminous United States

USGS Publications Warehouse

Robert J. Behnke,; Stephen J. Vavrus,; Andrew Allstadt,; Thomas P. Albright,; Thogmartin, Wayne E.; Volker C. Radeloff,

2016-01-01

Weather and climate affect many ecological processes, making spatially continuous yet fine-resolution weather data desirable for ecological research and predictions. Numerous downscaled weather data sets exist, but little attempt has been made to evaluate them systematically. Here we address this shortcoming by focusing on four major questions: (1) How accurate are downscaled, gridded climate data sets in terms of temperature and precipitation estimates?, (2) Are there significant regional differences in accuracy among data sets?, (3) How accurate are their mean values compared with extremes?, and (4) Does their accuracy depend on spatial resolution? We compared eight widely used downscaled data sets that provide gridded daily weather data for recent decades across the United States. We found considerable differences among data sets and between downscaled and weather station data. Temperature is represented more accurately than precipitation, and climate averages are more accurate than weather extremes. The data set exhibiting the best agreement with station data varies among ecoregions. Surprisingly, the accuracy of the data sets does not depend on spatial resolution. Although some inherent differences among data sets and weather station data are to be expected, our findings highlight how much different interpolation methods affect downscaled weather data, even for local comparisons with nearby weather stations located inside a grid cell. More broadly, our results highlight the need for careful consideration among different available data sets in terms of which variables they describe best, where they perform best, and their resolution, when selecting a downscaled weather data set for a given ecological application.

An evaluation of terrain-based downscaling of fractional snow covered area data sets based on LiDAR-derived snow data and orthoimagery

NASA Astrophysics Data System (ADS)

Cristea, Nicoleta C.; Breckheimer, Ian; Raleigh, Mark S.; HilleRisLambers, Janneke; Lundquist, Jessica D.

2017-08-01

Reliable maps of snow-covered areas at scales of meters to tens of meters, with daily temporal resolution, are essential to understanding snow heterogeneity, melt runoff, energy exchange, and ecological processes. Here we develop a parsimonious downscaling routine that can be applied to fractional snow covered area (fSCA) products from satellite platforms such as the Moderate Resolution Imaging Spectroradiometer (MODIS) that provide daily ˜500 m data, to derive higher-resolution snow presence/absence grids. The method uses a composite index combining both the topographic position index (TPI) to represent accumulation effects and the diurnal anisotropic heat (DAH, sun exposure) index to represent ablation effects. The procedure is evaluated and calibrated using airborne-derived high-resolution data sets across the Tuolumne watershed, CA using 11 scenes in 2014 to downscale to 30 m resolution. The average matching F score was 0.83. We then tested our method's transferability in time and space by comparing against the Tuolumne watershed in water years 2013 and 2015, and over an entirely different site, Mt. Rainier, WA in 2009 and 2011, to assess applicability to other topographic and climatic conditions. For application to sites without validation data, we recommend equal weights for the TPI and DAH indices and close TPI neighborhoods (60 and 27 m for downscaling to 30 and 3 m, respectively), which worked well in both our study areas. The method is less effective in forested areas, which still requires site-specific treatment. We demonstrate that the procedure can even be applied to downscale to 3 m resolution, a very fine scale relevant to alpine ecohydrology research.

Spatial Downscaling of Alien Species Presences using Machine Learning

NASA Astrophysics Data System (ADS)

Daliakopoulos, Ioannis N.; Katsanevakis, Stelios; Moustakas, Aristides

2017-07-01

Large scale, high-resolution data on alien species distributions are essential for spatially explicit assessments of their environmental and socio-economic impacts, and management interventions for mitigation. However, these data are often unavailable. This paper presents a method that relies on Random Forest (RF) models to distribute alien species presence counts at a finer resolution grid, thus achieving spatial downscaling. A sufficiently large number of RF models are trained using random subsets of the dataset as predictors, in a bootstrapping approach to account for the uncertainty introduced by the subset selection. The method is tested with an approximately 8×8 km2 grid containing floral alien species presence and several indices of climatic, habitat, land use covariates for the Mediterranean island of Crete, Greece. Alien species presence is aggregated at 16×16 km2 and used as a predictor of presence at the original resolution, thus simulating spatial downscaling. Potential explanatory variables included habitat types, land cover richness, endemic species richness, soil type, temperature, precipitation, and freshwater availability. Uncertainty assessment of the spatial downscaling of alien species’ occurrences was also performed and true/false presences and absences were quantified. The approach is promising for downscaling alien species datasets of larger spatial scale but coarse resolution, where the underlying environmental information is available at a finer resolution than the alien species data. Furthermore, the RF architecture allows for tuning towards operationally optimal sensitivity and specificity, thus providing a decision support tool for designing a resource efficient alien species census.

Projecting Climate Change Impacts on Wildfire Probabilities

NASA Astrophysics Data System (ADS)

Westerling, A. L.; Bryant, B. P.; Preisler, H.

2008-12-01

We present preliminary results of the 2008 Climate Change Impact Assessment for wildfire in California, part of the second biennial science report to the California Climate Action Team organized via the California Climate Change Center by the California Energy Commission's Public Interest Energy Research Program pursuant to Executive Order S-03-05 of Governor Schwarzenegger. In order to support decision making by the State pertaining to mitigation of and adaptation to climate change and its impacts, we model wildfire occurrence monthly from 1950 to 2100 under a range of climate scenarios from the Intergovernmental Panel on Climate Change. We use six climate change models (GFDL CM2.1, NCAR PCM1, CNRM CM3, MPI ECHAM5, MIROC3.2 med, NCAR CCSM3) under two emissions scenarios--A2 (C02 850ppm max atmospheric concentration) and B1(CO2 550ppm max concentration). Climate model output has been downscaled to a 1/8 degree (~12 km) grid using two alternative methods: a Bias Correction and Spatial Donwscaling (BCSD) and a Constructed Analogues (CA) downscaling. Hydrologic variables have been simulated from temperature, precipitation, wind and radiation forcing data using the Variable Infiltration Capacity (VIC) Macroscale Hydrologic Model. We model wildfire as a function of temperature, moisture deficit, and land surface characteristics using nonlinear logistic regression techniques. Previous work on wildfire climatology and seasonal forecasting has demonstrated that these variables account for much of the inter-annual and seasonal variation in wildfire. The results of this study are monthly gridded probabilities of wildfire occurrence by fire size class, and estimates of the number of structures potentially affected by fires. In this presentation we will explore the range of modeled outcomes for wildfire in California, considering the effects of emissions scenarios, climate model sensitivities, downscaling methods, hydrologic simulations, statistical model specifications for california wildfire, and their intersection with a range of development scenarios for California.

Effects of climate change on hydrology and hydraulics of Qu River Basin, East China.

NASA Astrophysics Data System (ADS)

Gao, C.; Zhu, Q.; Zhao, Z.; Pan, S.; Xu, Y. P.

2015-12-01

The impacts of climate change on regional hydrological extreme events have attracted much attention in recent years. This paper aims to provide a general overview of changes on future runoffs and water levels in the Qu River Basin, upper reaches of Qiantang River, East China by combining future climate scenarios, hydrological model and 1D hydraulic model. The outputs of four GCMs BCC, BNU, CanESM and CSIRO under two scenarios RCP4.5 and RCP8.5 for 2021-2050 are chosen to represent future climate change projections. The LARS-WG statistical downscaling method is used to downscale the coarse GCM outputs and generate 50 years of synthetic precipitation and maximum and minimum temperatures to drive the GR4J hydrological model and the 1D hydraulic model for the baseline period 1971-2000 and the future period 2021-2050. Finally the POT (Peaks Over Threshold) method is applied to analyze the change of extreme events in the study area. The results show that design runoffs and water levels all indicate an increasing trend in the future period for Changshangang River, Jiangshangang River and Qu River at most cases, especially for small return periods(≤20), and for Qu River the increase becomes larger, which suggests that the risk of flooding will probably become greater and appropriate adaptation measures need to be taken.

Incremental dynamical downscaling for probabilistic analysis based on multiple GCM projections

NASA Astrophysics Data System (ADS)

Wakazuki, Y.

2015-12-01

A dynamical downscaling method for probabilistic regional scale climate change projections was developed to cover an uncertainty of multiple general circulation model (GCM) climate simulations. The climatological increments (future minus present climate states) estimated by GCM simulation results were statistically analyzed using the singular vector decomposition. Both positive and negative perturbations from the ensemble mean with the magnitudes of their standard deviations were extracted and were added to the ensemble mean of the climatological increments. The analyzed multiple modal increments were utilized to create multiple modal lateral boundary conditions for the future climate regional climate model (RCM) simulations by adding to an objective analysis data. This data handling is regarded to be an advanced method of the pseudo-global-warming (PGW) method previously developed by Kimura and Kitoh (2007). The incremental handling for GCM simulations realized approximated probabilistic climate change projections with the smaller number of RCM simulations. Three values of a climatological variable simulated by RCMs for a mode were used to estimate the response to the perturbation of the mode. For the probabilistic analysis, climatological variables of RCMs were assumed to show linear response to the multiple modal perturbations, although the non-linearity was seen for local scale rainfall. Probability of temperature was able to be estimated within two modes perturbation simulations, where the number of RCM simulations for the future climate is five. On the other hand, local scale rainfalls needed four modes simulations, where the number of the RCM simulations is nine. The probabilistic method is expected to be used for regional scale climate change impact assessment in the future.

Future Climate Change in the Baltic Sea Area

NASA Astrophysics Data System (ADS)

Bøssing Christensen, Ole; Kjellström, Erik; Zorita, Eduardo; Sonnenborg, Torben; Meier, Markus; Grinsted, Aslak

2015-04-01

Regional climate models have been used extensively since the first assessment of climate change in the Baltic Sea region published in 2008, not the least for studies of Europe (and including the Baltic Sea catchment area). Therefore, conclusions regarding climate model results have a better foundation than was the case for the first BACC report of 2008. This presentation will report model results regarding future climate. What is the state of understanding about future human-driven climate change? We will cover regional models, statistical downscaling, hydrological modelling, ocean modelling and sea-level change as it is projected for the Baltic Sea region. Collections of regional model simulations from the ENSEMBLES project for example, financed through the European 5th Framework Programme and the World Climate Research Programme Coordinated Regional Climate Downscaling Experiment, have made it possible to obtain an increasingly robust estimation of model uncertainty. While the first Baltic Sea assessment mainly used four simulations from the European 5th Framework Programme PRUDENCE project, an ensemble of 13 transient regional simulations with twice the horizontal resolution reaching the end of the 21st century has been available from the ENSEMBLES project; therefore it has been possible to obtain more quantitative assessments of model uncertainty. The literature about future climate change in the Baltic Sea region is largely built upon the ENSEMBLES project. Also within statistical downscaling, a considerable number of papers have been published, encompassing now the application of non-linear statistical models, projected changes in extremes and correction of climate model biases. The uncertainty of hydrological change has received increasing attention since the previous Baltic Sea assessment. Several studies on the propagation of uncertainties originating in GCMs, RCMs, and emission scenarios are presented. The number of studies on uncertainties related to downscaling and impact models is relatively small, but more are emerging. A large number of coupled climate-environmental scenario simulations for the Baltic Sea have been performed within the BONUS+ projects (ECOSUPPORT, INFLOW, AMBER and Baltic-C (2009-2011)), using various combinations of output from GCMs, RCMs, hydrological models and scenarios for load and emission of nutrients as forcing for Baltic Sea models. Such a large ensemble of scenario simulations for the Baltic Sea has never before been produced and enables for the first time an estimation of uncertainties.

A downscaled 1 km dataset of daily Greenland ice sheet surface mass balance components (1958-2014)

NASA Astrophysics Data System (ADS)

Noel, B.; Van De Berg, W. J.; Fettweis, X.; Machguth, H.; Howat, I. M.; van den Broeke, M. R.

2015-12-01

The current spatial resolution in regional climate models (RCMs), typically around 5 to 20 km, remains too coarse to accurately reproduce the spatial variability in surface mass balance (SMB) components over the narrow ablation zones, marginal outlet glaciers and neighbouring ice caps of the Greenland ice sheet (GrIS). In these topographically rough terrains, the SMB components are highly dependent on local variations in topography. However, the relatively low-resolution elevation and ice mask prescribed in RCMs contribute to significantly underestimate melt and runoff in these regions due to unresolved valley glaciers and fjords. Therefore, near-km resolution topography is essential to better capture SMB variability in these spatially restricted regions. We present a 1 km resolution dataset of daily GrIS SMB covering the period 1958-2014, which is statistically downscaled from data of the polar regional climate model RACMO2.3 at 11 km, using an elevation dependence. The dataset includes all individual SMB components projected on the elevation and ice mask from the GIMP DEM, down-sampled to 1 km. Daily runoff and sublimation are interpolated to the 1 km topography using a local regression to elevation valid for each day specifically; daily precipitation is bi-linearly downscaled without elevation corrections. The daily SMB dataset is then reconstructed by summing downscaled precipitation, sublimation and runoff. High-resolution elevation and ice mask allow for properly resolving the narrow ablation zones and valley glaciers at the GrIS margins, leading to significant increase in runoff estimate. In these regions, and especially over narrow glaciers tongues, the downscaled products improve on the original RACMO2.3 outputs by better representing local SMB patterns through a gradual ablation increase towards the GrIS margins. We discuss the impact of downscaling on the SMB components in a case study for a spatially restricted region, where large elevation discrepancies are observed between both resolutions. Owing to generally enhanced runoff in the GrIS ablation zone, the evaluation of daily downscaled SMB against ablation measurements, collected at in-situ measuring sites derived from a newly compiled ablation dataset, shows a better agreement with observations relative to native RACMO2.3 SMB at 11 km.

Effects of Climate Change on Flood Frequency in the Pacific Northwest

NASA Astrophysics Data System (ADS)

Gergel, D. R.; Stumbaugh, M. R.; Lee, S. Y.; Nijssen, B.; Lettenmaier, D. P.

2014-12-01

A key concern about climate change as related to water resources is the potential for changes in hydrologic extremes, including flooding. We explore changes in flood frequency in the Pacific Northwest using downscaled output from ten Global Climate Models (GCMs) from the Coupled Model Inter-Comparison Project 5 (CMIP5) for historical forcings (1950-2005) and future Representative Concentration Pathways (RCPs) 4.5 and 8.5 (2006-2100). We use archived output from the Integrated Scenarios Project (ISP) (http://maca.northwestknowledge.net/), which uses the Multivariate Adaptive Constructed Analogs (MACA) method for statistical downscaling. The MACA-downscaled GCM output was then used to force the Variable Infiltration Capacity (VIC) hydrology model with a 1/16th degree spatial resolution and a daily time step. For each of the 238 HUC-08 areas within the Pacific Northwest (USGS Hydrologic Region 15), we computed, from the ISP archive, the series of maximum daily runoff values (surrogate for the annual maximum flood), and then the mean annual flood. Finally, we computed the ratios of the RCP4.5 and RCP8.5 mean annual floods to their corresponding values for the historical period. We evaluate spatial patterns in the results. For snow-dominated watersheds, the changes are dominated by reductions in flood frequency in basins that currently have spring-dominant floods, and increases in snow affected basins with fall-dominant floods. In low elevation basins west of the Cascades, changes in flooding are more directly related to changes in precipitation extremes. We further explore the nature of these effects by evaluating the mean Julian day of the annual maximum flood for each HUC-08 and how this changes between the historical and RCP4.5 and RCP8.5 scenarios.

An automated tool for a daily harmful algal bloom monitoring using MODIS imagery downscaled to 250 meters spatial resolution

NASA Astrophysics Data System (ADS)

El Alem, A.

2016-12-01

Harmful algal bloom (HAB) causes negative impacts to other organisms by producing natural toxins, mechanical damage to other micro-organisms, or simply by degrading waters quality. Contaminated waters could expose several billions of population to serious intoxications problems. Traditionally, HAB monitoring is made with standard methods limited to a restricted network of sampling points. However, rapid evolution of HABs makes it difficult to monitor their variation in time and space, threating then public safety. Daily monitoring is then the best way to control and to mitigate their harmful effect upon population, particularly for sources feeding cities. Recently, an approach for estimating chlorophyll-a (Chl-a) concentration, as a proxy of HAB presence, in inland waters based MODIS imagery downscaled to 250 meters spatial resolution was developed. Statistical evaluation of the developed approach highlighted the accuracy of Chl-a estimate with a R2 = 0.98, a relative RMSE of 15%, a relative BIAS of -2%, and a relative NASH of 0.95. Temporal resolution of MODIS sensor allows then a daily monitoring of HAB spatial distribution for inland waters of more than 2.25 Km2 of surface. Groupe-Hemisphere, a company specialized in environmental and sustainable planning in Quebec, has shown a great interest to the developed approach. Given the complexity of the preprocessing (geometric and atmospheric corrections as well as downscaling spatial resolution) and processing (Chl-a estimate) of images, a standalone application under the MATLAB's GUI environment was developed. The application allows an automated process for all preprocessing and processing steps. Outputs produced by the application for end users, many of whom may be decision makers or policy makers in the public and private sectors, allows a near-real time monitoring of water quality for a more efficient management.

Hydrologic response to multimodel climate output using a physically based model of groundwater/surface water interactions

NASA Astrophysics Data System (ADS)

Sulis, M.; Paniconi, C.; Marrocu, M.; Huard, D.; Chaumont, D.

2012-12-01

General circulation models (GCMs) are the primary instruments for obtaining projections of future global climate change. Outputs from GCMs, aided by dynamical and/or statistical downscaling techniques, have long been used to simulate changes in regional climate systems over wide spatiotemporal scales. Numerous studies have acknowledged the disagreements between the various GCMs and between the different downscaling methods designed to compensate for the mismatch between climate model output and the spatial scale at which hydrological models are applied. Very little is known, however, about the importance of these differences once they have been input or assimilated by a nonlinear hydrological model. This issue is investigated here at the catchment scale using a process-based model of integrated surface and subsurface hydrologic response driven by outputs from 12 members of a multimodel climate ensemble. The data set consists of daily values of precipitation and min/max temperatures obtained by combining four regional climate models and five GCMs. The regional scenarios were downscaled using a quantile scaling bias-correction technique. The hydrologic response was simulated for the 690 km2des Anglais catchment in southwestern Quebec, Canada. The results show that different hydrological components (river discharge, aquifer recharge, and soil moisture storage) respond differently to precipitation and temperature anomalies in the multimodel climate output, with greater variability for annual discharge compared to recharge and soil moisture storage. We also find that runoff generation and extreme event-driven peak hydrograph flows are highly sensitive to any uncertainty in climate data. Finally, the results show the significant impact of changing sequences of rainy days on groundwater recharge fluxes and the influence of longer dry spells in modifying soil moisture spatial variability.

Hydrologic response of Pacific Northwest river to climate change

NASA Astrophysics Data System (ADS)

Su, F.; Cuo, L.; Wu, H.; Mantua, N.; Lettenmaier, D. P.

2009-12-01

The climate of the Pacific Northwest (PNW - which we define as the Columbia River basin and watersheds draining to the Oregon and Washington coasts) is expected to warm by approximately 0.3°C per decade in the next 100 years based on the IPCC the Fourth Assessment Report (AR4) results. PNW hydrology is particularly sensitive to a warming climate because of the dominant role of snowmelt in seasonal streamflow. Timing shifts in seasonality of flows, peak discharge, and base flows will impact water resource management, regional electrical energy production, and freshwater ecosystems. In this work we update previous studies of implications of climate change on PNW hydrology using a macroscale hydrology model driven by simulations of temperature and precipitation downscaled from runs of 20 General Circulation Models (GCMs) under two emissions scenarios (lower B1 and mid-high A1B) in the 21st century. The hydrology model is implemented at 1/16th degree spatial resolution over the entire PNW. A (statistical) bias-correction and spatial disaggregation downscaling approach is used for translating the transient monthly climate model output into continuous daily forcings for the hydrologic analysis. We evaluate projected changes in snow water equivalent, seasonal streamflow, and frequency of peak low flows over a set of case study watersheds in the region. We also compare these hydrologic projections with previous analysis based on delta downscaling method over the PNW. This research is part of a project investigating climate change impacts on the future of wild Pacific salmon, and is a pilot effort to investigate the hydrologic sensitivity of salmon bearing watersheds around the entire North Pacific Rim.

Long-range Prediction of climatic Change in the Eastern Seaboard of Thailand over the 21st Century using various Downscaling Approaches

NASA Astrophysics Data System (ADS)

Bejranonda, Werapol; Koch, Manfred; Koontanakulvong, Sucharit

2010-05-01

Triggered by a long drought, a huge water supply crisis took place at the Eastern Seaboard of Thailand (east of the Gulf of Thailand) in 2005. In that year no rainfall occurred for four months after the beginning of the rainy season which led to the situation that the industrial estates of the Eastern Seaboard were not able to fully operate. Normally, most of the urban and industrial water used in this coastal region along east of the Gulf of Thailand, which is part of the Pacific Ocean, is surface water stored in a large-scale reservoir-network across the main watershed in the region. Thus the three major reservoirs usually gather water from monsoon storms that blow from the South and provide accumulative 80% of the annual rainfall during the 6 months of the rainy season which normally lasts from May-October. During the dry season (November - April) the winds are blowing from northern Indo-China land mass and rain drops only a few days in a month. Because of this typical tropical climate system, surface water resources across most of the southeastern Asia-Pacific region and the Eastern Seaboard of Thailand, in particular, rely on the annual occurrence of the monsoon season. There is now sufficient evidence that the named extreme weather conditions of 2005 occurring in that part of Thailand are not a singularity, but might be another signal of recent ongoing climate change in that country as a whole. Because of this imminent threat to the water resources of the region, and for the set-up of appropriate water management schemes for the near future, a climate impact study is proposed here. More specifically, the water budget of the Khlong Yai basin, which is the main watershed of the Eastern Seaboard, is modeled using the distributed hydrological model SWAT. To that avail the watershed model is coupled sequentially to a global climate model (GCM), whereby the latter provides the input forcing parameters (e.g. precipitation and temperature) to the former. Because of the different scales of the hydrological (local to regional) and of the GCM (global), one is faced with the problem of 'downscaling' the coarse grid resolution output of the GCM to the fine grid of the hydrological model. Although there have been numerous downscaling approaches proposed to that regard over the last decade, the jury is still out about the best method to use in a particular application. The focus here is on the downscaling part of the investigation, i.e. the proper preparation of the GCM's output to serve as input, i.e. the driving force, to the hydrological model (which is not further discussed here). Daily ensembles of climate variables computed by means of the CGCM3 model of the Canadian Climate Center which has a horizontal grid resolution of approximately the size of the whole study basin are used here, indicating clearly the need for downscaling. Daily observations of local climate variables available since 1971 are used as additional input to the various downscaling tools proposed which are, namely, the stochastic weather generator (LARS-WG), the statistical downscaling model (SDSM), and a multiple linear regression model between the observed variables and the CGCM3 predictors. Both the 2D and the 3D versions of the CGCM3 model are employed to predict, 100 years ahead up to year 2100, the monthly rainfall and temperatures, based on the past calibration period (training period) 1971-2000. To investigate the prediction performance, multiple linear regression, autoregressive (AR) and autoregressive integrated moving average (ARIMA) models are applied to the time series of the observation data which are aggregated into monthly time steps to be able compare them with the downscaling results above. Likewise, multiple linear regression and ARIMA models also executed on the CGCM3 predictors and the Pacific / Indian oceans indices as external regressors to predict short-term local climate variations. The results of the various downscaling method are validated for years 2001-2006 at selected meteorological stations in the Khlong Yai basin, assuming the IPCC's A1B and A2 emission scenarios. The performance of the monthly climate prediction has been evaluated by comparison with observed data using the Nash-Sutcliffe model efficiency measure. Among the statistical/stochastical downscaling and the forecasting methods used, the climate prediction by the ARIMA model with ocean indices and CGCM predictor included as external regressors are the most reliable. Thus for the verification period 2001-2006 Nash-Sutcliffe coefficient of 0.84, 0.47 and 0.50 are obtained for the minimum and maximum temperatures and the rainfall, respectively, whereas the corresponding 1-year ahead predictions are 0.77, 0.43 and 0.48, respectively. The best external regressor for the prediction of the minimum temperatures in the basin is, surprisingly, the El Niño 1.2 SST anomaly time series; for the prediction of the maximum temperatures, the minimum surface air temperature predictor in CGCM3 (tasmin); and for the prediction of the rainfall, the 850hPa eastward-wind predictor in CGCM3 (p8_uas). Based on year 2000, the downscaling results show that the average minimum temperature will be higher by 0.4 to 5.9 ° C by year 2100, while the average maximum temperature will be rather stable, with only little change between -0.2 to +0.3° C. As for the rainfall at year 2100, a possible change from +0.2 to 12.0 mm/month is obtained. These climate prediction results mean that, although there will be more rainfall in the future, the much higher temperature will lead to more evapotranspiration, i.e. more agricultural water demand. Besides, the increasing rainfall will most likely lead to unexpected flood events in the future that will require precautionary planning at the watershed-scale. This will be further analyzed during the course of the ongoing study using the SWAT hydrological model mentioned above.

Assessing climate change impacts on water resources in remote mountain regions

NASA Astrophysics Data System (ADS)

Buytaert, Wouter; De Bièvre, Bert

2013-04-01

From a water resources perspective, remote mountain regions are often considered as a basket case. They are often regions where poverty is often interlocked with multiple threats to water supply, data scarcity, and high uncertainties. In these environments, it is paramount to generate locally relevant knowledge about water resources and how they impact local livelihoods. This is often problematic. Existing environmental data collection tends to be geographically biased towards more densely populated regions, and prioritized towards strategic economic activities. Data may also be locked behind institutional and technological barriers. These issues create a "knowledge trap" for data-poor regions, which is especially acute in remote and hard-to-reach mountain regions. We present lessons learned from a decade of water resources research in remote mountain regions of the Andes, Africa and South Asia. We review the entire tool chain of assessing climate change impacts on water resources, including the interrogation and downscaling of global circulation models, translating climate variables in water availability and access, and assessing local vulnerability. In global circulation models, mountain regions often stand out as regions of high uncertainties and lack of agreement of future trends. This is partly a technical artifact because of the different resolution and representation of mountain topography, but it also highlights fundamental uncertainties in climate impacts on mountain climate. This problem also affects downscaling efforts, because regional climate models should be run in very high spatial resolution to resolve local gradients, which is computationally very expensive. At the same time statistical downscaling methods may fail to find significant relations between local climate properties and synoptic processes. Further uncertainties are introduced when downscaled climate variables such as precipitation and temperature are to be translated in hydrologically relevant variables such as streamflow and groundwater recharge. Fundamental limitations in both the understanding of hydrological processes in mountain regions (e.g., glacier melt, wetland attenuation, groundwater flows) and in data availability introduce large uncertainties. Lastly, assessing access to water resources is a major challenge. Topographical gradients and barriers, as well as strong spatiotemporal variations in hydrological processes, makes it particularly difficult to assess which parts of the mountain population is most vulnerable to future perturbations of the water cycle.

Creating Dynamically Downscaled Seasonal Climate Forecast and Climate Change Projection Information for the North American Monsoon Region Suitable for Decision Making Purposes

NASA Astrophysics Data System (ADS)

Castro, C. L.; Dominguez, F.; Chang, H.

2010-12-01

Current seasonal climate forecasts and climate change projections of the North American monsoon are based on the use of course-scale information from a general circulation model. The global models, however, have substantial difficulty in resolving the regional scale forcing mechanisms of precipitation. This is especially true during the period of the North American Monsoon in the warm season. Precipitation is driven primarily due to the diurnal cycle of convection, and this process cannot be resolve in coarse-resolution global models that have a relatively poor representation of terrain. Though statistical downscaling may offer a relatively expedient method to generate information more appropriate for the regional scale, and is already being used in the resource decision making processes in the Southwest U.S., its main drawback is that it cannot account for a non-stationary climate. Here we demonstrate the use of a regional climate model, specifically the Weather Research and Forecast (WRF) model, for dynamical downscaling of the North American Monsoon. To drive the WRF simulations, we use retrospective reforecasts from the Climate Forecast System (CFS) model, the operational model used at the U.S. National Center for Environmental Prediction, and three select “well performing” IPCC AR 4 models for the A2 emission scenario. Though relatively computationally expensive, the use of WRF as a regional climate model in this way adds substantial value in the representation of the North American Monsoon. In both cases, the regional climate model captures a fairly realistic and reasonable monsoon, where none exists in the driving global model, and captures the dominant modes of precipitation anomalies associated with ENSO and the Pacific Decadal Oscillation (PDO). Long-term precipitation variability and trends in these simulations is considered via the standardized precipitation index (SPI), a commonly used metric to characterize long-term drought. Dynamically downscaled climate projection data will be integrated into future water resource projections in the state of Arizona, through a cooperative effort involving numerous water resource stakeholders.

Understanding the joint behavior of temperature and precipitation for climate change impact studies

NASA Astrophysics Data System (ADS)

Rana, Arun; Moradkhani, Hamid; Qin, Yueyue

2017-07-01

The multiple downscaled scenario products allow us to assess the uncertainty of the variations of precipitation and temperature in the current and future periods. Probabilistic assessments of both climatic variables help better understand the interdependence of the two and thus, in turn, help in assessing the future with confidence. In the present study, we use ensemble of statistically downscaled precipitation and temperature from various models. The dataset used is multi-model ensemble of 10 global climate models (GCMs) downscaled product from CMIP5 daily dataset using the Bias Correction and Spatial Downscaling (BCSD) technique, generated at Portland State University. The multi-model ensemble of both precipitation and temperature is evaluated for dry and wet periods for 10 sub-basins across Columbia River Basin (CRB). Thereafter, copula is applied to establish the joint distribution of two variables on multi-model ensemble data. The joint distribution is then used to estimate the change in trends of said variables in future, along with estimation of the probabilities of the given change. The joint distribution trends vary, but certainly positive, for dry and wet periods in sub-basins of CRB. Dry season, generally, is indicating a higher positive change in precipitation than temperature (as compared to historical) across sub-basins with wet season inferring otherwise. Probabilities of changes in future, as estimated from the joint distribution, indicate varied degrees and forms during dry season whereas the wet season is rather constant across all the sub-basins.

Assessing the Added Value of Dynamical Downscaling in the Context of Hydrologic Implication

NASA Astrophysics Data System (ADS)

Lu, M.; IM, E. S.; Lee, M. H.

2017-12-01

There is a scientific consensus that high-resolution climate simulations downscaled by Regional Climate Models (RCMs) can provide valuable refined information over the target region. However, a significant body of hydrologic impact assessment has been performing using the climate information provided by Global Climate Models (GCMs) in spite of a fundamental spatial scale gap. It is probably based on the assumption that the substantial biases and spatial scale gap from GCMs raw data can be simply removed by applying the statistical bias correction and spatial disaggregation. Indeed, many previous studies argue that the benefit of dynamical downscaling using RCMs is minimal when linking climate data with the hydrological model, from the comparison of the impact between bias-corrected GCMs and bias-corrected RCMs on hydrologic simulations. It may be true for long-term averaged climatological pattern, but it is not necessarily the case when looking into variability across various temporal spectrum. In this study, we investigate the added value of dynamical downscaling focusing on the performance in capturing climate variability. For doing this, we evaluate the performance of the distributed hydrological model over the Korean river basin using the raw output from GCM and RCM, and bias-corrected output from GCM and RCM. The impacts of climate input data on streamflow simulation are comprehensively analyzed. [Acknowledgements]This research is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 17AWMP-B083066-04).

High-resolution soil moisture mapping in Afghanistan

NASA Astrophysics Data System (ADS)

Hendrickx, Jan M. H.; Harrison, J. Bruce J.; Borchers, Brian; Kelley, Julie R.; Howington, Stacy; Ballard, Jerry

2011-06-01

Soil moisture conditions have an impact upon virtually all aspects of Army activities and are increasingly affecting its systems and operations. Soil moisture conditions affect operational mobility, detection of landmines and unexploded ordinance, natural material penetration/excavation, military engineering activities, blowing dust and sand, watershed responses, and flooding. This study further explores a method for high-resolution (2.7 m) soil moisture mapping using remote satellite optical imagery that is readily available from Landsat and QuickBird. The soil moisture estimations are needed for the evaluation of IED sensors using the Countermine Simulation Testbed in regions where access is difficult or impossible. The method has been tested in Helmand Province, Afghanistan, using a Landsat7 image and a QuickBird image of April 23 and 24, 2009, respectively. In previous work it was found that Landsat soil moisture can be predicted from the visual and near infra-red Landsat bands1-4. Since QuickBird bands 1-4 are almost identical to Landsat bands 1- 4, a Landsat soil moisture map can be downscaled using QuickBird bands 1-4. However, using this global approach for downscaling from Landsat to QuickBird scale yielded a small number of pixels with erroneous soil moisture values. Therefore, the objective of this study is to examine how the quality of the downscaled soil moisture maps can be improved by using a data stratification approach for the development of downscaling regression equations for each landscape class. It was found that stratification results in a reliable downscaled soil moisture map with a spatial resolution of 2.7 m.

Dynamical Downscaling of Typhoon Vera (1959) and related Storm Surge based on JRA-55 Reanalysis

NASA Astrophysics Data System (ADS)

Ninomiya, J.; Takemi, T.; Mori, N.; Shibutani, Y.; Kim, S.

2015-12-01

Typhoon Vera in 1959 is historical extreme typhoon that caused severest typhoon damage mainly due to the storm surge up to 389 cm in Japan. Vera developed 895 hPa on offshore and landed with 929.2 hPa. There are many studies of the dynamical downscaling of Vera but it is difficult to simulate accurately because of the lack of the accuracy of global reanalysis data. This study carried out dynamical downscaling experiment of Vera using WRF downscaling forced by JRA-55 that are latest atmospheric model and reanalysis data. In this study, the reproducibility of five global reanalysis data for Typhoon Vera were compered. Comparison shows that reanalysis data doesn't have strong typhoon information except for JRA-55, so that downscaling with conventional reanalysis data goes wrong. The dynamical downscaling method for storm surge is studied very much (e.g. choice of physical model, nudging, 4D-VAR, bogus and so on). In this study, domain size and resolution of the coarse domain were considered. The coarse domain size influences the typhoon route and central pressure, and larger domain restrains the typhoon strength. The results of simulations with different domain size show that the threshold of developing restrain is whether the coarse domain fully includes the area of wind speed more than 15 m/s around the typhoon. The results of simulations with different resolution show that the resolution doesn't affect the typhoon route, and higher resolution gives stronger typhoon simulation.

Key Findings from the U.S.-India Partnership for Climate Resilience Workshop on Development and Application of Downscaling Climate Projections

NASA Astrophysics Data System (ADS)

Kunkel, K.; Dissen, J.; Easterling, D. R.; Kulkarni, A.; Akhtar, F. H.; Hayhoe, K.; Stoner, A. M. K.; Swaminathan, R.; Thrasher, B. L.

2017-12-01

s part of the Department of State U.S.-India Partnership for Climate Resilience (PCR), scientists from NOAA NCEI, CICS-NC, Texas Tech University (TTU), Stanford University (SU), and the Indian Institute of Tropical Meteorology (IITM) held a workshop at IITM in Pune, India during 7-9 March 2017 on the development, techniques and applications of downscaled climate projections. Workshop participants from TTU, SU, and IITM presented state-of-the-art climate downscaling techniques using the ARRM method, NASA NEX climate products, CORDEX-South Asia and analysis tools for resilience planning and sustainable development. PCR collaborators in attendance included Indian practitioners, researchers and other NGO including the WRI Partnership for Resilience and Preparedness (PREP), The Energy and Resources Institute (TERI), and NIH. The scientific techniques were provided to workshop participants in a software package written in R by TTU scientists and several sessions were devoted to hands-on experience with the software package. The workshop further examined case studies on the use of downscaled climate data for decision making in a range of sectors, including human health, agriculture, and water resources management as well as to inform the development of the India State Action Plans. This talk will discuss key outcomes including information needs for downscaling climate projections, importance of QA/QC of the data, key findings from select case studies, and the importance of collaborations and partnerships to apply downscaling projections to help inform the development of the India State Action Plans.

Validation of non-stationary precipitation series for site-specific impact assessment: Comparison of two statistical downscaling techniques

USDA-ARS?s Scientific Manuscript database

The generation of realistic future precipitation scenarios is crucial for assessing their impacts on a range of environmental and socio-economic impact sectors. A scale mismatch exists, however, between the coarse spatial resolution at which global climate models (GCMs) output future climate scenari...

Do downscaled general circulation models reliably simulate historical climatic conditions?

USGS Publications Warehouse

Bock, Andrew R.; Hay, Lauren E.; McCabe, Gregory J.; Markstrom, Steven L.; Atkinson, R. Dwight

2018-01-01

The accuracy of statistically downscaled (SD) general circulation model (GCM) simulations of monthly surface climate for historical conditions (1950–2005) was assessed for the conterminous United States (CONUS). The SD monthly precipitation (PPT) and temperature (TAVE) from 95 GCMs from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) were used as inputs to a monthly water balance model (MWBM). Distributions of MWBM input (PPT and TAVE) and output [runoff (RUN)] variables derived from gridded station data (GSD) and historical SD climate were compared using the Kolmogorov–Smirnov (KS) test For all three variables considered, the KS test results showed that variables simulated using CMIP5 generally are more reliable than those derived from CMIP3, likely due to improvements in PPT simulations. At most locations across the CONUS, the largest differences between GSD and SD PPT and RUN occurred in the lowest part of the distributions (i.e., low-flow RUN and low-magnitude PPT). Results indicate that for the majority of the CONUS, there are downscaled GCMs that can reliably simulate historical climatic conditions. But, in some geographic locations, none of the SD GCMs replicated historical conditions for two of the three variables (PPT and RUN) based on the KS test, with a significance level of 0.05. In these locations, improved GCM simulations of PPT are needed to more reliably estimate components of the hydrologic cycle. Simple metrics and statistical tests, such as those described here, can provide an initial set of criteria to help simplify GCM selection.

Processing and Monthly Summaries of Downscaled Climate Data for Knoxville, Tennessee and Surrounding Region

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sylvester, Linda; Omitaomu, Olufemi A.; Parish, Esther S.

2016-09-01

Oak Ridge National Laboratory (ORNL) and the City of Knoxville, Tennessee have partnered to work on a Laboratory Directed Research and Development (LDRD) project towards investigating climate change, mitigation, and adaptation measures in mid-sized cities. ORNL has statistically and dynamically downscaled ten Global Climate Models (GCMs) to both 1 km and 4 km resolutions. The processing and summary of those ten gridded datasets for use in a web-based tool is described. The summaries of each model are shown individually to assist in determining the similarities and differences between the model scenarios. The variables of minimum and maximum daily temperature andmore » total monthly precipitation are summarized for the area of Knoxville, Tennessee for the periods of 1980-2005 and 2025-2050.« less

Climatic change projections for winter streamflow in Guadalquivir river

NASA Astrophysics Data System (ADS)

Jesús Esteban Parra, María; Hidalgo Muñoz, José Manuel; García-Valdecasas-Ojeda, Matilde; Raquel Gámiz Fortis, Sonia; Castro Díez, Yolanda

2015-04-01

In this work we have obtained climate change projections for winter streamflow of the Guadalquivir River in the period 2071-2100 using the Principal Component Regression (PCR) method. The streamflow data base used has been provided by the Center for Studies and Experimentation of Public Works, CEDEX. Series from gauging stations and reservoirs with less than 10% of missing data (filled by regression with well correlated neighboring stations) have been considered. The homogeneity of these series has been evaluated through the Pettit test and degree of human alteration by the Common Area Index. The application of these criteria led to the selection of 13 streamflow time series homogeneously distributed over the basin, covering the period 1952-2011. For this streamflow data, winter seasonal values were obtained by averaging the monthly values from January to March. The PCR method has been applied using the Principal Components of the mean anomalies of sea level pressure (SLP) in winter (December to February averaged) as predictors of streamflow for the development of a downscaled statistical model. The SLP database is the NCEP reanalysis covering the North Atlantic region, and the calibration and validation periods used for fitting and evaluating the ability of the model are 1952-1992 and 1993-2011, respectively. In general, using four Principal Components, regression models are able to explain up to 70% of the variance of the streamflow data. Finally, the statistical model obtained for the observational data was applied to the SLP data for the period 2071-2100, using the outputs of different GCMs of the CMIP5 under the RPC8.5 scenario. The results found for the end of the century show no significant changes or moderate decrease in the streamflow of this river for most GCMs in winter, but for some of them the decrease is very strong. Keywords: Statistical downscaling, streamflow, Guadalquivir River, climate change. ACKNOWLEDGEMENTS This work has been financed by the projects P11-RNM-7941 (Junta de Andalucía-Spain) and CGL2013-48539-R (MINECO-Spain, FEDER).

Which downscaled rainfall data for climate change impact studies in urban areas? Review of current approaches and trends

NASA Astrophysics Data System (ADS)

Gooré Bi, Eustache; Gachon, Philippe; Vrac, Mathieu; Monette, Frédéric

2017-02-01

Changes in extreme precipitation should be one of the primary impacts of climate change (CC) in urban areas. To assess these impacts, rainfall data from climate models are commonly used. The main goal of this paper is to report on the state of knowledge and recent works on the study of CC impacts with a focus on urban areas, in order to produce an integrated review of various approaches to which future studies can then be compared or constructed. Model output statistics (MOS) methods are increasingly used in the literature to study the impacts of CC in urban settings. A review of previous works highlights the non-stationarity nature of future climate data, underscoring the need to revise urban drainage system design criteria. A comparison of these studies is made difficult, however, by the numerous sources of uncertainty arising from a plethora of assumptions, scenarios, and modeling options. All the methods used do, however, predict increased extreme precipitation in the future, suggesting potential risks of combined sewer overflow frequencies, flooding, and back-up in existing sewer systems in urban areas. Future studies must quantify more accurately the different sources of uncertainty by improving downscaling and correction methods. New research is necessary to improve the data validation process, an aspect that is seldom reported in the literature. Finally, the potential application of non-stationarity conditions into generalized extreme value (GEV) distribution should be assessed more closely, which will require close collaboration between engineers, hydrologists, statisticians, and climatologists, thus contributing to the ongoing reflection on this issue of social concern.

Downscaling remotely sensed imagery using area-to-point cokriging and multiple-point geostatistical simulation

NASA Astrophysics Data System (ADS)

Tang, Yunwei; Atkinson, Peter M.; Zhang, Jingxiong

2015-03-01

A cross-scale data integration method was developed and tested based on the theory of geostatistics and multiple-point geostatistics (MPG). The goal was to downscale remotely sensed images while retaining spatial structure by integrating images at different spatial resolutions. During the process of downscaling, a rich spatial correlation model in the form of a training image was incorporated to facilitate reproduction of similar local patterns in the simulated images. Area-to-point cokriging (ATPCK) was used as locally varying mean (LVM) (i.e., soft data) to deal with the change of support problem (COSP) for cross-scale integration, which MPG cannot achieve alone. Several pairs of spectral bands of remotely sensed images were tested for integration within different cross-scale case studies. The experiment shows that MPG can restore the spatial structure of the image at a fine spatial resolution given the training image and conditioning data. The super-resolution image can be predicted using the proposed method, which cannot be realised using most data integration methods. The results show that ATPCK-MPG approach can achieve greater accuracy than methods which do not account for the change of support issue.

MiKlip-PRODEF: Probabilistic Decadal Forecast for Central and Western Europe

NASA Astrophysics Data System (ADS)

Reyers, Mark; Haas, Rabea; Ludwig, Patrick; Pinto, Joaquim

2013-04-01

The demand for skilful climate predictions on time-scales of several years to decades has increased in recent years, in particular for economic, societal and political terms. Within the BMBF MiKlip consortium, a decadal prediction system on the global to local scale is currently being developed. The subproject PRODEF is part of the MiKlip-Module C, which aims at the regionalisation of decadal predictability for Central and Western Europe. In PRODEF, a combined statistical-dynamical downscaling (SDD) and a probabilistic forecast tool are developed and applied to the new Earth system model of the Max-Planck Institute Hamburg (MPI-ESM), which is part of the CMIP5 experiment. Focus is given on the decadal predictability of windstorms, related wind gusts as well as wind energy potentials. SDD combines the benefits of both high resolution dynamical downscaling and purely statistical downscaling of GCM output. Hence, the SDD approach is used to obtain a very large ensemble of highly resolved decadal forecasts. With respect to the focal points of PRODEF, a clustering of temporal evolving atmospheric fields, a circulation weather type (CWT) analysis, and a storm damage indices analysis is applied to the full ensemble of the decadal hindcast experiments of the MPI-ESM in its lower resolution (MPI-ESM-LR). The ensemble consists of up to ten realisations per yearly initialised decadal hindcast experiments for the period 1960-2010 (altogether 287 realisations). Representatives of CWTs / clusters and single storm episodes are dynamical downscaled with the regional climate model COSMO-CLM with a horizontal resolution of 0.22°. For each model grid point, the distributions of the local climate parameters (e.g. surface wind gusts) are determined for different periods (e.g. each decades) by recombining dynamical downscaled episodes weighted with the respective weather type frequencies. The applicability of the SDD approach is illustrated with examples of decadal forecasts of the MPI-ESM. We are able to perform a bias correction of the frequencies of large scale weather types and to quantify the uncertainties of decadal predictability on large and local scale arising from different initial conditions. Further, probability density functions of local parameters like e.g. wind gusts for different periods and decades derived from the SDD approach is compared to observations and reanalysis data. Skill scores are used to quantify the decadal predictability for different leading time periods and to analyse whether the SDD approach shows systematic errors for some regions.

The trend of the multi-scale temporal variability of precipitation in Colorado River Basin

NASA Astrophysics Data System (ADS)

Jiang, P.; Yu, Z.

2011-12-01

Hydrological problems like estimation of flood and drought frequencies under future climate change are not well addressed as a result of the disability of current climate models to provide reliable prediction (especially for precipitation) shorter than 1 month. In order to assess the possible impacts that multi-scale temporal distribution of precipitation may have on the hydrological processes in Colorado River Basin (CRB), a comparative analysis of multi-scale temporal variability of precipitation as well as the trend of extreme precipitation is conducted in four regions controlled by different climate systems. Multi-scale precipitation variability including within-storm patterns and intra-annual, inter-annual and decadal variabilities will be analyzed to explore the possible trends of storm durations, inter-storm periods, average storm precipitation intensities and extremes under both long-term natural climate variability and human-induced warming. Further more, we will examine the ability of current climate models to simulate the multi-scale temporal variability and extremes of precipitation. On the basis of these analyses, a statistical downscaling method will be developed to disaggregate the future precipitation scenarios which will provide a more reliable and finer temporal scale precipitation time series for hydrological modeling. Analysis results and downscaling results will be presented.

Field significance of performance measures in the context of regional climate model evaluation. Part 1: temperature

NASA Astrophysics Data System (ADS)

Ivanov, Martin; Warrach-Sagi, Kirsten; Wulfmeyer, Volker

2018-04-01

A new approach for rigorous spatial analysis of the downscaling performance of regional climate model (RCM) simulations is introduced. It is based on a multiple comparison of the local tests at the grid cells and is also known as "field" or "global" significance. New performance measures for estimating the added value of downscaled data relative to the large-scale forcing fields are developed. The methodology is exemplarily applied to a standard EURO-CORDEX hindcast simulation with the Weather Research and Forecasting (WRF) model coupled with the land surface model NOAH at 0.11 ∘ grid resolution. Monthly temperature climatology for the 1990-2009 period is analysed for Germany for winter and summer in comparison with high-resolution gridded observations from the German Weather Service. The field significance test controls the proportion of falsely rejected local tests in a meaningful way and is robust to spatial dependence. Hence, the spatial patterns of the statistically significant local tests are also meaningful. We interpret them from a process-oriented perspective. In winter and in most regions in summer, the downscaled distributions are statistically indistinguishable from the observed ones. A systematic cold summer bias occurs in deep river valleys due to overestimated elevations, in coastal areas due probably to enhanced sea breeze circulation, and over large lakes due to the interpolation of water temperatures. Urban areas in concave topography forms have a warm summer bias due to the strong heat islands, not reflected in the observations. WRF-NOAH generates appropriate fine-scale features in the monthly temperature field over regions of complex topography, but over spatially homogeneous areas even small biases can lead to significant deteriorations relative to the driving reanalysis. As the added value of global climate model (GCM)-driven simulations cannot be smaller than this perfect-boundary estimate, this work demonstrates in a rigorous manner the clear additional value of dynamical downscaling over global climate simulations. The evaluation methodology has a broad spectrum of applicability as it is distribution-free, robust to spatial dependence, and accounts for time series structure.

Impacts of high resolution model downscaling in coastal regions

NASA Astrophysics Data System (ADS)

Bricheno, Lucy; Wolf, Judith

2013-04-01

With model development and cheaper computational resources ocean forecasts are becoming readily available, high resolution coastal forecasting is now a reality. This can only be achieved, however, by downscaling global or basin-scale products such as the MyOcean reanalyses and forecasts. These model products have resolution ranging from 1/16th - 1/4 degree, which are often insufficient for coastal scales, but can provide initialisation and boundary data. We present applications of downscaling the MyOcean products for use in shelf-seas and the nearshore. We will address the question 'Do coastal predictions improve with higher resolution modelling?' with a few focused examples, while also discussing what is meant by an improved result. Increasing resolution appears to be an obvious route for getting more accurate forecasts in operational coastal models. However, when models resolve finer scales, this may lead to the introduction of high-frequency variability which is not necessarily deterministic. Thus a flow may appear more realistic by generating eddies but the simple statistics like rms error and correlation may become less good because the model variability is not exactly in phase with the observations (Hoffman et al., 1995). By deciding on a specific process to simulate (rather than concentrating on reducing rms error) we can better assess the improvements gained by downscaling. In this work we will select two processes which are dominant in our case-study site: Liverpool Bay. Firstly we consider the magnitude and timing of a peak in tide-surge elevations, by separating out the event into timing (or displacement) and intensity (or amplitude) errors. The model can thus be evaluated on how well it predicts the timing and magnitude of the surge. The second important characteristic of Liverpool Bay is the position of the freshwater front. To evaluate model performance in this case, the location, sharpness, and temperature difference across the front will be considered. We will show that by using intelligent metrics designed with a physical process in mind, we can learn more about model performance than by considering 'bulk' statistics alone. R. M. Hoffman and Z. Liu and J-F. Louic and C. Grassotti (1995) 'Distortion Representation of Forecast Errors' Monthly Weather Review 123: 2758-2770

Multi-year climate variability in the Southwestern United States within a context of a dynamically downscaled twentieth century reanalysis

NASA Astrophysics Data System (ADS)

Carrillo, Carlos M.; Castro, Christopher L.; Chang, Hsin-I.; Luong, Thang M.

2017-12-01

This investigation evaluates whether there is coherency in warm and cool season precipitation at the low-frequency scale that may be responsible for multi-year droughts in the US Southwest. This low-frequency climate variability at the decadal scale and longer is studied within the context of a twentieth-century reanalysis (20CR) and its dynamically-downscaled version (DD-20CR). A spectral domain matrix methods technique (Multiple-Taper-Method Singular Value Decomposition) is applied to these datasets to identify statistically significant spatiotemporal precipitation patterns for the cool (November-April) and warm (July-August) seasons. The low-frequency variability in the 20CR is evaluated by exploring global to continental-scale spatiotemporal variability in moisture flux convergence (MFC) to the occurrence of multiyear droughts and pluvials in Central America, as this region has a demonstrated anti-phase relationship in low-frequency climate variability with northern Mexico and the southwestern US By using the MFC in lieu of precipitation, this study reveals that the 20CR is able to resolve well the low-frequency, multiyear climate variability. In the context of the DD-20CR, multiyear droughts and pluvials in the southwestern US (in the early twentieth century) are significantly related to this low-frequency climate variability. The precipitation anomalies at these low-frequency timescales are in phase between the cool and warm seasons, consistent with the concept of dual-season drought as has been suggested in tree ring studies.

High resolution statistical downscaling of the EUROSIP seasonal prediction. Application for southeastern Romania

NASA Astrophysics Data System (ADS)

Busuioc, Aristita; Dumitrescu, Alexandru; Dumitrache, Rodica; Iriza, Amalia

2017-04-01

Seasonal climate forecasts in Europe are currently issued at the European Centre for Medium-Range Weather Forecasts (ECMWF) in the form of multi-model ensemble predictions available within the "EUROSIP" system. Different statistical techniques to calibrate, downscale and combine the EUROSIP direct model output are used to optimize the quality of the final probabilistic forecasts. In this study, a statistical downscaling model (SDM) based on canonical correlation analysis (CCA) is used to downscale the EUROSIP seasonal forecast at a spatial resolution of 1km x 1km over the Movila farm placed in southeastern Romania. This application is achieved in the framework of the H2020 MOSES project (http://www.moses-project.eu). The combination between monthly standardized values of three climate variables (maximum/minimum temperatures-Tmax/Tmin, total precipitation-Prec) is used as predictand while combinations of various large-scale predictors are tested in terms of their availability as outputs in the seasonal EUROSIP probabilistic forecasting (sea level pressure, temperature at 850 hPa and geopotential height at 500 hPa). The predictors are taken from the ECMWF system considering 15 members of the ensemble, for which the hindcasts since 1991 until present are available. The model was calibrated over the period 1991-2014 and predictions for summers 2015 and 2016 were achieved. The calibration was made for the ensemble average as well as for each ensemble member. The model was developed for each lead time: one month anticipation for June, two months anticipation for July and three months anticipation for August. The main conclusions from these preliminary results are: best predictions (in terms of the anomaly sign) for Tmax (July-2 months anticipation, August-3 months anticipation) for both years (2015, 2016); for Tmin - good predictions only for August (3 months anticipation ) for both years; for precipitation, good predictions for July (2 months anticipation) in 2015 and August (3 months anticipation) in 2016; failed prediction for June (1-month anticipation) for all parameters. To see if the results obtained for 2015 and 2016 summers are in agreement with the general ECMWF model performance in forecast of the three predictors used in the CCA SDM calibration, the mean bias and root mean square errors (RMSE) calculated over the entire period in each grid point, for each ensemble member and ensemble average were computed. The obtained results are confirmed, showing highest ECMWF performance in forecasting of the three predictors for 3 months anticipation (August) and lowest performance for one month anticipation (June). The added value of the CCA SDM in forecasting local Tmax/Tmin and total precipitation was compared to the ECMWF performance using nearest grid point method. Comparisons were performed for the 1991-2014 period, taking into account the forecast made in May for July. An important improvement was found for the CCA SDM predictions in terms of the RMSE value (computed against observations) for Tmax/Tmin and less for precipitation. The tests are in progress for the other summer months (June, July).

Tracking four-decade inundation changes with multi-temporal satellite images in China's largest freshwater lake

NASA Astrophysics Data System (ADS)

Wu, Guiping

2017-04-01

Poyang Lake is the largest freshwater lake in China. The lake has undergone remarkable spatio-temporal changes in both short- and long-term scales since 1970s, resulting in significant hydrological, ecological and economic consequences. Remote sensing techniques have advantages for large-scale studies, by offering images at different spatial and spectral resolutions. However, due to technical difficulties, no single satellite sensor can meet the needs for high spatio-temporal resolution required for such monitoring. In this study, using Landsat Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) images collected between 1973 and 2012, we documented and investigated the short- and long-term characteristics of lake inundation based on Normalized Difference Water Index (NDWI). First, we presented a novel downscaling method based on the NDWI statistical regression algorithm to generate small-scale resolution inundation map (30m) from coarse MODIS data (500m). The downscaling is a linear calibration of the NDWI index from MODIS imagery to Landsat imagery, which is based on the assumption that the relationships between fine resolution and coarse resolution are invariable. Second, Tupu analysis method was further performed to explore the spatial-temporal distribution and changing processes of lake inundation based on downscaling inundation maps. Then, a defined water variation rate (WVR) and inundation frequency (IF) indicator was used to reveal seasonal water surface submersion/exposure processes of lake expansion and shrinkage in different zones. Finally, mathematical statistics methods were utilized to explore the possible driving mechanisms of the revealed change patterns with meteorological data and hydrological data. The results show that, there is a high correlation (mean absolute error of 3.95% and an R2 of 0.97) between the MODIS- and Landsat-derived water surface areas in Poyang Lake. Over the past 40 years, a declining trend to a certain extent for the Poyang Lake's area could be detected. The lake surface displayed comparatively low values ( 2000 km2) in wet periods of 1980, 2006, 2009 and 2011, corresponding to severe hydrological droughts in the lake. In addition, the water surface variation in Poyang Lake had a typical seasonal behavior. It mostly followed a unimodal cycle with area peaks appeared in the wet season. The earliest beginning of the inundation cycle was emerged in 2000 and the latest in 2006. In general, the change of lake area is a synthetic result of climate change, land-cover change and construction of dykes. Our findings should be valuable to a comprehensive understanding of Poyang Lake's decadal and seasonal variation, which is critical for flood/drought prevention, land use planning and lake ecological conservation.

Extended-Range High-Resolution Dynamical Downscaling over a Continental-Scale Domain

NASA Astrophysics Data System (ADS)

Husain, S. Z.; Separovic, L.; Yu, W.; Fernig, D.

2014-12-01

High-resolution mesoscale simulations, when applied for downscaling meteorological fields over large spatial domains and for extended time periods, can provide valuable information for many practical application scenarios including the weather-dependent renewable energy industry. In the present study, a strategy has been proposed to dynamically downscale coarse-resolution meteorological fields from Environment Canada's regional analyses for a period of multiple years over the entire Canadian territory. The study demonstrates that a continuous mesoscale simulation over the entire domain is the most suitable approach in this regard. Large-scale deviations in the different meteorological fields pose the biggest challenge for extended-range simulations over continental scale domains, and the enforcement of the lateral boundary conditions is not sufficient to restrict such deviations. A scheme has therefore been developed to spectrally nudge the simulated high-resolution meteorological fields at the different model vertical levels towards those embedded in the coarse-resolution driving fields derived from the regional analyses. A series of experiments were carried out to determine the optimal nudging strategy including the appropriate nudging length scales, nudging vertical profile and temporal relaxation. A forcing strategy based on grid nudging of the different surface fields, including surface temperature, soil-moisture, and snow conditions, towards their expected values obtained from a high-resolution offline surface scheme was also devised to limit any considerable deviation in the evolving surface fields due to extended-range temporal integrations. The study shows that ensuring large-scale atmospheric similarities helps to deliver near-surface statistical scores for temperature, dew point temperature and horizontal wind speed that are better or comparable to the operational regional forecasts issued by Environment Canada. Furthermore, the meteorological fields resulting from the proposed downscaling strategy have significantly improved spatiotemporal variance compared to those from the operational forecasts, and any time series generated from the downscaled fields do not suffer from discontinuities due to switching between the consecutive forecasts.

Climate change presents increased potential for very large fires in the contiguous United States

Treesearch

R. Barbero; J. T. Abatzoglou; Sim Larkin; C. A. Kolden; B. Stocks

2015-01-01

Very large fires (VLFs) have important implications for communities, ecosystems, air quality and fire suppression expenditures. VLFs over the contiguous US have been strongly linked with meteorological and climatological variability. Building on prior modelling of VLFs (>5000 ha), an ensemble of 17 global climate models were statistically downscaled over the US...

Wild boar mapping using population-density statistics: From polygons to high resolution raster maps.

PubMed

Pittiglio, Claudia; Khomenko, Sergei; Beltran-Alcrudo, Daniel

2018-01-01

The wild boar is an important crop raider as well as a reservoir and agent of spread of swine diseases. Due to increasing densities and expanding ranges worldwide, the related economic losses in livestock and agricultural sectors are significant and on the rise. Its management and control would strongly benefit from accurate and detailed spatial information on species distribution and abundance, which are often available only for small areas. Data are commonly available at aggregated administrative units with little or no information about the distribution of the species within the unit. In this paper, a four-step geostatistical downscaling approach is presented and used to disaggregate wild boar population density statistics from administrative units of different shape and size (polygons) to 5 km resolution raster maps by incorporating auxiliary fine scale environmental variables. 1) First a stratification method was used to define homogeneous bioclimatic regions for the analysis; 2) Under a geostatistical framework, the wild boar densities at administrative units, i.e. subnational areas, were decomposed into trend and residual components for each bioclimatic region. Quantitative relationships between wild boar data and environmental variables were estimated through multiple regression and used to derive trend components at 5 km spatial resolution. Next, the residual components (i.e., the differences between the trend components and the original wild boar data at administrative units) were downscaled at 5 km resolution using area-to-point kriging. The trend and residual components obtained at 5 km resolution were finally added to generate fine scale wild boar estimates for each bioclimatic region. 3) These maps were then mosaicked to produce a final output map of predicted wild boar densities across most of Eurasia. 4) Model accuracy was assessed at each different step using input as well as independent data. We discuss advantages and limits of the method and its potential application in animal health.

Wild boar mapping using population-density statistics: From polygons to high resolution raster maps

PubMed Central

Pittiglio, Claudia; Khomenko, Sergei

2018-01-01

The wild boar is an important crop raider as well as a reservoir and agent of spread of swine diseases. Due to increasing densities and expanding ranges worldwide, the related economic losses in livestock and agricultural sectors are significant and on the rise. Its management and control would strongly benefit from accurate and detailed spatial information on species distribution and abundance, which are often available only for small areas. Data are commonly available at aggregated administrative units with little or no information about the distribution of the species within the unit. In this paper, a four-step geostatistical downscaling approach is presented and used to disaggregate wild boar population density statistics from administrative units of different shape and size (polygons) to 5 km resolution raster maps by incorporating auxiliary fine scale environmental variables. 1) First a stratification method was used to define homogeneous bioclimatic regions for the analysis; 2) Under a geostatistical framework, the wild boar densities at administrative units, i.e. subnational areas, were decomposed into trend and residual components for each bioclimatic region. Quantitative relationships between wild boar data and environmental variables were estimated through multiple regression and used to derive trend components at 5 km spatial resolution. Next, the residual components (i.e., the differences between the trend components and the original wild boar data at administrative units) were downscaled at 5 km resolution using area-to-point kriging. The trend and residual components obtained at 5 km resolution were finally added to generate fine scale wild boar estimates for each bioclimatic region. 3) These maps were then mosaicked to produce a final output map of predicted wild boar densities across most of Eurasia. 4) Model accuracy was assessed at each different step using input as well as independent data. We discuss advantages and limits of the method and its potential application in animal health. PMID:29768413

Parametric vs. non-parametric daily weather generator: validation and comparison

NASA Astrophysics Data System (ADS)

Dubrovsky, Martin

2016-04-01

As the climate models (GCMs and RCMs) fail to satisfactorily reproduce the real-world surface weather regime, various statistical methods are applied to downscale GCM/RCM outputs into site-specific weather series. The stochastic weather generators are among the most favourite downscaling methods capable to produce realistic (observed like) meteorological inputs for agrological, hydrological and other impact models used in assessing sensitivity of various ecosystems to climate change/variability. To name their advantages, the generators may (i) produce arbitrarily long multi-variate synthetic weather series representing both present and changed climates (in the latter case, the generators are commonly modified by GCM/RCM-based climate change scenarios), (ii) be run in various time steps and for multiple weather variables (the generators reproduce the correlations among variables), (iii) be interpolated (and run also for sites where no weather data are available to calibrate the generator). This contribution will compare two stochastic daily weather generators in terms of their ability to reproduce various features of the daily weather series. M&Rfi is a parametric generator: Markov chain model is used to model precipitation occurrence, precipitation amount is modelled by the Gamma distribution, and the 1st order autoregressive model is used to generate non-precipitation surface weather variables. The non-parametric GoMeZ generator is based on the nearest neighbours resampling technique making no assumption on the distribution of the variables being generated. Various settings of both weather generators will be assumed in the present validation tests. The generators will be validated in terms of (a) extreme temperature and precipitation characteristics (annual and 30 years extremes and maxima of duration of hot/cold/dry/wet spells); (b) selected validation statistics developed within the frame of VALUE project. The tests will be based on observational weather series from several European stations available from the ECA&D database.

Downscaling Soil Moisture in the Southern Great Plains Through a Calibrated Multifractal Model for Land Surface Modeling Applications

NASA Technical Reports Server (NTRS)

Mascaro, Giuseppe; Vivoni, Enrique R.; Deidda, Roberto

2010-01-01

Accounting for small-scale spatial heterogeneity of soil moisture (theta) is required to enhance the predictive skill of land surface models. In this paper, we present the results of the development, calibration, and performance evaluation of a downscaling model based on multifractal theory using aircraft!based (800 m) theta estimates collected during the southern Great Plains experiment in 1997 (SGP97).We first demonstrate the presence of scale invariance and multifractality in theta fields of nine square domains of size 25.6 x 25.6 sq km, approximately a satellite footprint. Then, we estimate the downscaling model parameters and evaluate the model performance using a set of different calibration approaches. Results reveal that small-scale theta distributions are adequately reproduced across the entire region when coarse predictors include a dynamic component (i.e., the spatial mean soil moisture ) and a stationary contribution accounting for static features (i.e., topography, soil texture, vegetation). For wet conditions, we found similar multifractal properties of soil moisture across all domains, which we ascribe to the signature of rainfall spatial variability. For drier states, the theta fields in the northern domains are more intermittent than in southern domains, likely because of differences in the distribution of vegetation coverage. Through our analyses, we propose a regional downscaling relation for coarse, satellite-based soil moisture estimates, based on ancillary information (static and dynamic landscape features), which can be used in the study area to characterize statistical properties of small-scale theta distribution required by land surface models and data assimilation systems.

CMIP5 based downscaled temperature over Western Himalayan region

NASA Astrophysics Data System (ADS)

Dutta, M.; Das, L.; Meher, J. K.

2016-12-01

Limited numbers of reliable temperature data is available for assessing warming over the Western Himalayan Region (WHR) of India. India meteorological Department provided many stations having more than 30% missing values. Stations having <30% missing values, were replaced using the Multiple Imputation Chained Equation (MICE) technique. Finally 16 stations having continuous records during 1969-2009 were considered as the "reference stations" for assessing the trends in addition to evaluate the Coupled Model Intercomparison, phase 5 (CMIP5) Global Circulation Model(GCMs). Station data indicates higher and rapid (1.41oC) winter warming than the other seasons and least warming was observed in the post monsoon (0.31oC) season. Mean annual warming is 0.84 oC during 1969-2009 indicating the warming over the WHR is more than double the global warming (0.85oC during 1880-2012). The performance of 34 CMIP5 models was evaluated through three different approaches namely comparison of: i) mean seasonal cycle ii) temporal trends and iii) spatial correlation and a rank was assigned to each GCM. How the better performing GCMs able to reproduce the observed spatial details were verified the ERA-interim reanalysis data. Finally station level future downscaled winter temperature has constructed using Empirical Statistical Downscaling (ESD) technique where 2 meter air temperature (T2m) is considered as predictor and station temperature as predictant. Future range of downscaled temperature change for the stations Dheradun, Manali and Gulmarg are 1.3-6.1OC, 1.1-5.8OC and 0.5-5.8OC respectively at the end of 21st century.

Predicting the Impacts of Climate Change on Runoff and Sediment Processes in Agricultural Watersheds: A Case Study from the Sunflower Watershed in the Lower Mississippi Basin

NASA Astrophysics Data System (ADS)

Elkadiri, R.; Momm, H.; Yasarer, L.; Armour, G. L.

2017-12-01

Climatic conditions play a major role in physical processes impacting soil and agrochemicals detachment and transportation from/in agricultural watersheds. In addition, these climatic conditions are projected to significantly vary spatially and temporally in the 21st century, leading to vast uncertainties about the future of sediment and non-point source pollution transport in agricultural watersheds. In this study, we selected the sunflower basin in the lower Mississippi River basin, USA to contribute in the understanding of how climate change affects watershed processes and the transport of pollutant loads. The climate projections used in this study were retrieved from the archive of World Climate Research Programme's (WCRP) Coupled Model Intercomparison Phase 5 (CMIP5) project. The CMIP5 dataset was selected because it contains the most up-to-date spatially downscaled and bias corrected climate projections. A subset of ten GCMs representing a range in projected climate were spatially downscaled for the sunflower watershed. Statistics derived from downscaled GCM output representing the 2011-2040, 2041-2070 and 2071-2100 time periods were used to generate maximum/minimum temperature and precipitation on a daily time step using the USDA Synthetic Weather Generator, SYNTOR. These downscaled climate data were then utilized as inputs to run in the Annualized Agricultural Non-Point Source (AnnAGNPS) pollution watershed model to estimate time series of runoff, sediment, and nutrient loads produced from the watershed. For baseline conditions a validated simulation of the watershed was created and validated using historical data from 2000 until 2015.

Projecting 21st century snowpack trends in western USA mountains using variable-resolution CESM

NASA Astrophysics Data System (ADS)

Rhoades, Alan M.; Ullrich, Paul A.; Zarzycki, Colin M.

2018-01-01

Climate change will impact western USA water supplies by shifting precipitation from snow to rain and driving snowmelt earlier in the season. However, changes at the regional-to-mountain scale is still a major topic of interest. This study addresses the impacts of climate change on mountain snowpack by assessing historical and projected variable-resolution (VR) climate simulations in the community earth system model (VR-CESM) forced by prescribed sea-surface temperatures along with widely used regional downscaling techniques, the coupled model intercomparison projects phase 5 bias corrected and statistically downscaled (CMIP5-BCSD) and the North American regional climate change assessment program (NARCCAP). The multi-model RCP8.5 scenario analysis of winter season SWE for western USA mountains indicates by 2040-2065 mean SWE could decrease -19% (NARCCAP) to -38% (VR-CESM), with an ensemble median change of -27%. Contrary to CMIP5-BCSD and NARCCAP, VR-CESM highlights a more pessimistic outcome for western USA mountain snowpack in latter-parts of the 21st century. This is related to temperature changes altering the snow-albedo feedback, snowpack storage, and precipitation phase, but may indicate that VR-CESM resolves more physically consistent elevational effects lacking in statistically downscaled datasets and teleconnections that are not captured in limited area models. Overall, VR-CESM projects by 2075-2100 that average western USA mountain snowfall decreases by -30%, snow cover by -44%, SWE by -69%, and average surface temperature increase of +5.0°C. This places pressure on western USA states to preemptively invest in climate adaptation measures such as alternative water storage, water use efficiency, and reassess reservoir storage operations.

Dynamical downscaling inter-comparison for high resolution climate reconstruction

NASA Astrophysics Data System (ADS)

Ferreira, J.; Rocha, A.; Castanheira, J. M.; Carvalho, A. C.

2012-04-01

In the scope of the project: "High-resolution Rainfall EroSivity analysis and fORecasTing - RESORT", an evaluation of various methods of dynamic downscaling is presented. The methods evaluated range from the classic method of nesting a regional model results in a global model, in this case the ECMWF reanalysis, to more recently proposed methods, which consist in using Newtonian relaxation methods in order to nudge the results of the regional model to the reanalysis. The method with better results involves using a system of variational data assimilation to incorporate observational data with results from the regional model. The climatology of a simulation of 5 years using this method is tested against observations on mainland Portugal and the ocean in the area of the Portuguese Continental Shelf, which shows that the method developed is suitable for the reconstruction of high resolution climate over continental Portugal.

Which complexity of regional climate system models is essential for downscaling anthropogenic climate change in the Northwest European Shelf?

NASA Astrophysics Data System (ADS)

Mathis, Moritz; Elizalde, Alberto; Mikolajewicz, Uwe

2018-04-01

Climate change impact studies for the Northwest European Shelf (NWES) make use of various dynamical downscaling strategies in the experimental setup of regional ocean circulation models. Projected change signals from coupled and uncoupled downscalings with different domain sizes and forcing global and regional models show substantial uncertainty. In this paper, we investigate influences of the downscaling strategy on projected changes in the physical and biogeochemical conditions of the NWES. Our results indicate that uncertainties due to different downscaling strategies are similar to uncertainties due to the choice of the parent global model and the downscaling regional model. Downscaled change signals reveal to depend stronger on the downscaling strategy than on the model skills in simulating present-day conditions. Uncoupled downscalings of sea surface temperature (SST) changes are found to be tightly constrained by the atmospheric forcing. The incorporation of coupled air-sea interaction, by contrast, allows the regional model system to develop independently. Changes in salinity show a higher sensitivity to open lateral boundary conditions and river runoff than to coupled or uncoupled atmospheric forcings. Dependencies on the downscaling strategy for changes in SST, salinity, stratification and circulation collectively affect changes in nutrient import and biological primary production.

Development and evaluation of climatologically-downscaled AFWA AGRMET precipitation products over the continental U.S.

NASA Astrophysics Data System (ADS)

Garcia, M.; Peters-Lidard, C. D.; Eylander, J. B.; Daly, C.; Gibson, W.; Tian, Y.; Zeng, J.; Kato, H.

2008-05-01

Collaborations between the Air Force Weather Agency (AFWA), the Hydrological Sciences Branch at NASA-GSFC, and the PRISM Group at Oregon State University have led to improvements in the processing of meteorological forcing inputs for the NASA-GSFC Land Information System (LIS; Kumar et al. 2006), a sophisticated framework for LSM operation and model coupling experiments. Efforts at AFWA toward the production of surface hydrometeorological products are currently in transition from the legacy Agricultural Meteorology modeling system (AGRMET) to use of the LIS framework and procedures. Recent enhancements to meteorological input processing for application to land surface models in LIS include the assimilation of climate-based information for the spatial interpolation and downscaling of precipitation fields. Climatological information included in the LIS- based downscaling procedure for North America is provided by a monthly high-resolution PRISM (Daly et al. 1994, 2002; Daly 2006) dataset based on a 30-year analysis period. The combination of these sources and methods attempts to address the strengths and weaknesses of available legacy products, objective interpolation methods, and the PRISM knowledge-based methodology. All of these efforts are oriented on an operational need for timely estimation of spatial precipitation fields at adequate spatial resolution for customer dissemination and near-real-time simulations in regions of interest. This work focuses on value added to the AGRMET precipitation product by the inclusion of high-quality climatological information on a monthly time scale. The AGRMET method uses microwave-based satellite precipitation estimates from various polar-orbiting platforms (NOAA POES and DMSP), infrared-based estimates from geostationary platforms (GOES, METEOSAT, etc.), related cloud analysis products, and surface gauge observations in a complex and hierarchical blending process. Results from processing of the legacy AGRMET precipitation products over the U.S. using LIS-based methods for downscaling, both with and without climatological factors, are evaluated against high-resolution monthly analyses using the PRISM knowledge- based method (Daly et al. 2002) over a 4-year period. It is demonstrated that the incorporation of climatological information in a downscaling procedure can significantly enhance the accuracy, and potential utility, of AFWA precipitation products for customer applications, especially over mountainous terrain as in the western U.S.

Improved methods for estimating local terrestrial water dynamics from GRACE in the Northern High Plains

NASA Astrophysics Data System (ADS)

Seyoum, Wondwosen M.; Milewski, Adam M.

2017-12-01

Investigating terrestrial water cycle dynamics is vital for understanding the recent climatic variability and human impacts in the hydrologic cycle. In this study, a downscaling approach was developed and tested, to improve the applicability of terrestrial water storage (TWS) anomaly data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission for understanding local terrestrial water cycle dynamics in the Northern High Plains region. A non-parametric, artificial neural network (ANN)-based model, was utilized to downscale GRACE data by integrating it with hydrological variables (e.g. soil moisture) derived from satellite and land surface model data. The downscaling model, constructed through calibration and sensitivity analysis, was used to estimate TWS anomaly for watersheds ranging from 5000 to 20,000 km2 in the study area. The downscaled water storage anomaly data were evaluated using water storage data derived from an (1) integrated hydrologic model, (2) land surface model (e.g. Noah), and (3) storage anomalies calculated from in-situ groundwater level measurements. Results demonstrate the ANN predicts monthly TWS anomaly within the uncertainty (conservative error estimate = 34 mm) for most of the watersheds. Seasonal derived groundwater storage anomaly (GWSA) from the ANN correlated well (r = ∼0.85) with GWSAs calculated from in-situ groundwater level measurements for a watershed size as small as 6000 km2. ANN downscaled TWSA matches closely with Noah-based TWSA compared to standard GRACE extracted TWSA at a local scale. Moreover, the ANN-downscaled change in TWS replicated the water storage variability resulting from the combined effect of climatic and human impacts (e.g. abstraction). The implications of utilizing finer resolution GRACE data for improving local and regional water resources management decisions and applications are clear, particularly in areas lacking in-situ hydrologic monitoring networks.

Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France

NASA Astrophysics Data System (ADS)

Caillouet, Laurie; Vidal, Jean-Philippe; Sauquet, Eric; Graff, Benjamin

2015-04-01

This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the last century built on the NOAA 20th century global extended atmospheric reanalysis (20CR, Compo et al., 2011). It aims at delivering appropriate meteorological forcings for continuous distributed hydrological modelling over the last 140 years. The longer term objective is to improve our knowledge of major historical hydrometeorological events having occurred outside of the last 50-year period, over which comprehensive reconstructions and observations are available. It would constitute a perfect framework for assessing the recent observed events but also future events projected by climate change impact studies. The Sandhy (Stepwise ANalogue Downscaling method for Hydrology) statistical downscaling method (Radanovics et al., 2013), initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between 20CR predictors - temperature, geopotential shape, vertical velocity and relative humidity - and local predictands - precipitation and temperature - relevant for catchment-scale hydrology. Multiple predictor domains for geopotential shape are retained from a local optimisation over France using the Safran near-surface reanalysis (Vidal et al., 2010). Sandhy gives an ensemble of 125 equally plausible gridded precipitation and temperature time series over the whole 1871-2012 period. Previous studies showed that Sandhy precipitation outputs are very slightly biased at the annual time scale. Nevertheless, the seasonal precipitation signal for areas with a high interannual variability is not well simulated. Moreover, winter and summer temperatures are respectively over- and underestimated. Reliable seasonal precipitation and temperature signals are however necessary for hydrological modelling, especially for evapotranspiration and snow accumulation/snowmelt processes. Two different post-processing methods are considered to correct monthly precipitation and temperature time series. The first one applies two new analogy steps, using the sea surface temperature (SST) and the large-scale two-meter temperature. The second method is a calendar selection that keeps the closest analogue dates in the year for each target date. A sensitivity study has been performed to assess the final number of analogues dates to retain for each method. A comparison to Safran over 1958-2010 shows that biases on the interannual cycle of precipitation and temperature are strongly reduced with both methods. Using two supplementary analogy levels moreover leads to a large improvement of correlation in seasonal temperature time series. These two methods have also been validated before 1958 thanks to both raw observations and homogenized time series. The two post-processing methods come with some advantages and drawbacks. The calendar selection allows to slightly better correct for seasonal biases in precipitation and is therefore adapted in a forecasting context. The selection with two supplementary analogy levels would allow for possible season shifts and SST trends and is therefore better suited for climate reconstruction and climate change studies. Compo, G. P. et al. (2011). The Twentieth Century Reanalysis Project. Quarterly Journal of the Royal Meteorological Society, 137:1-28. doi: 10.1002/qj.776 Radanovics, S., Vidal, J.-P., Sauquet, E., Ben Daoud, A., and Bontron, G. (2013). Optimising predictor domains for spatially coherent precipitation downscaling. Hydrology and Earth System Sciences, 17:4189-4208. doi:10.5194/hess-17-4189-2013 Vidal, J.-P ., Martin, E., Franchistéguy, L., Baillon, M., and Soubeyroux, J.-M. (2010). A 50-year high-resolution atmospheric reanalysis over France with the Safran system. International Journal of Climatology, 30:1627-1644. doi:10.1002/joc.2003

Empirical-statistical downscaling of reanalysis data to high-resolution air temperature and specific humidity above a glacier surface (Cordillera Blanca, Peru)

NASA Astrophysics Data System (ADS)

Hofer, Marlis; MöLg, Thomas; Marzeion, Ben; Kaser, Georg

2010-06-01

Recently initiated observation networks in the Cordillera Blanca (Peru) provide temporally high-resolution, yet short-term, atmospheric data. The aim of this study is to extend the existing time series into the past. We present an empirical-statistical downscaling (ESD) model that links 6-hourly National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis data to air temperature and specific humidity, measured at the tropical glacier Artesonraju (northern Cordillera Blanca). The ESD modeling procedure includes combined empirical orthogonal function and multiple regression analyses and a double cross-validation scheme for model evaluation. Apart from the selection of predictor fields, the modeling procedure is automated and does not include subjective choices. We assess the ESD model sensitivity to the predictor choice using both single-field and mixed-field predictors. Statistical transfer functions are derived individually for different months and times of day. The forecast skill largely depends on month and time of day, ranging from 0 to 0.8. The mixed-field predictors perform better than the single-field predictors. The ESD model shows added value, at all time scales, against simpler reference models (e.g., the direct use of reanalysis grid point values). The ESD model forecast 1960-2008 clearly reflects interannual variability related to the El Niño/Southern Oscillation but is sensitive to the chosen predictor type.

Are weather models better than gridded observations for precipitation in the mountains? (Invited)

NASA Astrophysics Data System (ADS)

Gutmann, E. D.; Rasmussen, R.; Liu, C.; Ikeda, K.; Clark, M. P.; Brekke, L. D.; Arnold, J.; Raff, D. A.

2013-12-01

Mountain snowpack is a critical storage component in the water cycle, and it provides drinking water for tens of millions of people in the Western US alone. This water store is susceptible to climate change both because warming temperatures are likely to lead to earlier melt and a temporal shift of the hydrograph, and because changing atmospheric conditions are likely to change the precipitation patterns that produce the snowpack. Current measurements of snowfall in complex terrain are limited in number due in part to the logistics of installing equipment in complex terrain. We show that this limitation leads to statistical artifacts in gridded observations of current climate including errors in precipitation season totals of a factor of two or more, increases in wet day fraction, and decreases in storm intensity. In contrast, a high-resolution numerical weather model (WRF) is able to reproduce observed precipitation patterns, leading to confidence in its predictions for areas without measurements and new observations support this. Running WRF for a future climate scenario shows substantial changes in the spatial patterns of precipitation in the mountains related to the physics of hydrometeor production and detrainment that are not captured by statistical downscaling products. The stationarity in statistical downscaling products is likely to lead to important errors in our estimation of future precipitation in complex terrain.

European extra-tropical storm damage risk from a multi-model ensemble of dynamically-downscaled global climate models

NASA Astrophysics Data System (ADS)

Haylock, M. R.

2011-10-01

Uncertainty in the return levels of insured loss from European wind storms was quantified using storms derived from twenty-two 25 km regional climate model runs driven by either the ERA40 reanalyses or one of four coupled atmosphere-ocean global climate models. Storms were identified using a model-dependent storm severity index based on daily maximum 10 m wind speed. The wind speed from each model was calibrated to a set of 7 km historical storm wind fields using the 70 storms with the highest severity index in the period 1961-2000, employing a two stage calibration methodology. First, the 25 km daily maximum wind speed was downscaled to the 7 km historical model grid using the 7 km surface roughness length and orography, also adopting an empirical gust parameterisation. Secondly, downscaled wind gusts were statistically scaled to the historical storms to match the geographically-dependent cumulative distribution function of wind gust speed. The calibrated wind fields were run through an operational catastrophe reinsurance risk model to determine the return level of loss to a European population density-derived property portfolio. The risk model produced a 50-yr return level of loss of between 0.025% and 0.056% of the total insured value of the portfolio.

On the interest of combining an analog model to a regression model for the adaptation of the downscaling link. Application to probabilistic prediction of precipitation over France.

NASA Astrophysics Data System (ADS)

Chardon, Jérémy; Hingray, Benoit; Favre, Anne-Catherine

2016-04-01

Scenarios of surface weather required for the impact studies have to be unbiased and adapted to the space and time scales of the considered hydro-systems. Hence, surface weather scenarios obtained from global climate models and/or numerical weather prediction models are not really appropriated. Outputs of these models have to be post-processed, which is often carried out thanks to Statistical Downscaling Methods (SDMs). Among those SDMs, approaches based on regression are often applied. For a given station, a regression link can be established between a set of large scale atmospheric predictors and the surface weather variable. These links are then used for the prediction of the latter. However, physical processes generating surface weather vary in time. This is well known for precipitation for instance. The most relevant predictors and the regression link are also likely to vary in time. A better prediction skill is thus classically obtained with a seasonal stratification of the data. Another strategy is to identify the most relevant predictor set and establish the regression link from dates that are similar - or analog - to the target date. In practice, these dates can be selected thanks to an analog model. In this study, we explore the possibility of improving the local performance of an analog model - where the analogy is applied to the geopotential heights 1000 and 500 hPa - using additional local scale predictors for the probabilistic prediction of the Safran precipitation over France. For each prediction day, the prediction is obtained from two GLM regression models - for both the occurrence and the quantity of precipitation - for which predictors and parameters are estimated from the analog dates. Firstly, the resulting combined model noticeably allows increasing the prediction performance by adapting the downscaling link for each prediction day. Secondly, the selected predictors for a given prediction depend on the large scale situation and on the considered region. Finally, even with such an adaptive predictor identification, the downscaling link appears to be robust: for a same prediction day, predictors selected for different locations of a given region are similar and the regression parameters are consistent within the region of interest.

Improving medium-range and seasonal hydroclimate forecasts in the southeast USA

NASA Astrophysics Data System (ADS)

Tian, Di

Accurate hydro-climate forecasts are important for decision making by water managers, agricultural producers, and other stake holders. Numerical weather prediction models and general circulation models may have potential for improving hydro-climate forecasts at different scales. In this study, forecast analogs of the Global Forecast System (GFS) and Global Ensemble Forecast System (GEFS) based on different approaches were evaluated for medium-range reference evapotranspiration (ETo), irrigation scheduling, and urban water demand forecasts in the southeast United States; the Climate Forecast System version 2 (CFSv2) and the North American national multi-model ensemble (NMME) were statistically downscaled for seasonal forecasts of ETo, precipitation (P) and 2-m temperature (T2M) at the regional level. The GFS mean temperature (Tmean), relative humidity, and wind speed (Wind) reforecasts combined with the climatology of Reanalysis 2 solar radiation (Rs) produced higher skill than using the direct GFS output only. Constructed analogs showed slightly higher skill than natural analogs for deterministic forecasts. Both irrigation scheduling driven by the GEFS-based ETo forecasts and GEFS-based ETo forecast skill were generally positive up to one week throughout the year. The GEFS improved ETo forecast skill compared to the GFS. The GEFS-based analog forecasts for the input variables of an operational urban water demand model were skillful when applied in the Tampa Bay area. The modified operational models driven by GEFS analog forecasts showed higher forecast skill than the operational model based on persistence. The results for CFSv2 seasonal forecasts showed maximum temperature (Tmax) and Rs had the greatest influence on ETo. The downscaled Tmax showed the highest predictability, followed by Tmean, Tmin, Rs, and Wind. The CFSv2 model could better predict ETo in cold seasons during El Nino Southern Oscillation (ENSO) events only when the forecast initial condition was in ENSO. Downscaled P and T2M forecasts were produced by directly downscaling the NMME P and T2M output or indirectly using the NMME forecasts of Nino3.4 sea surface temperatures to predict local-scale P and T2M. The indirect method generally showed the highest forecast skill which occurs in cold seasons. The bias-corrected NMME ensemble forecast skill did not outperform the best single model.

Hydro-meteorological evaluation of downscaled global ensemble rainfall forecasts

NASA Astrophysics Data System (ADS)

Gaborit, Étienne; Anctil, François; Fortin, Vincent; Pelletier, Geneviève

2013-04-01

Ensemble rainfall forecasts are of high interest for decision making, as they provide an explicit and dynamic assessment of the uncertainty in the forecast (Ruiz et al. 2009). However, for hydrological forecasting, their low resolution currently limits their use to large watersheds (Maraun et al. 2010). In order to bridge this gap, various implementations of the statistic-stochastic multi-fractal downscaling technique presented by Perica and Foufoula-Georgiou (1996) were compared, bringing Environment Canada's global ensemble rainfall forecasts from a 100 by 70-km resolution down to 6 by 4-km, while increasing each pixel's rainfall variance and preserving its original mean. For comparison purposes, simpler methods were also implemented such as the bi-linear interpolation, which disaggregates global forecasts without modifying their variance. The downscaled meteorological products were evaluated using different scores and diagrams, from both a meteorological and a hydrological view points. The meteorological evaluation was conducted comparing the forecasted rainfall depths against nine days of observed values taken from Québec City rain gauge database. These 9 days present strong precipitation events occurring during the summer of 2009. For the hydrologic evaluation, the hydrological models SWMM5 and (a modified version of) GR4J were implemented on a small 6 km2 urban catchment located in the Québec City region. Ensemble hydrologic forecasts with a time step of 3 hours were then performed over a 3-months period of the summer of 2010 using the original and downscaled ensemble rainfall forecasts. The most important conclusions of this work are that the overall quality of the forecasts was preserved during the disaggregation procedure and that the disaggregated products using this variance-enhancing method were of similar quality than bi-linear interpolation products. However, variance and dispersion of the different members were, of course, much improved for the variance-enhanced products, compared to the bi-linear interpolation, which is a decisive advantage. The disaggregation technique of Perica and Foufoula-Georgiou (1996) hence represents an interesting way of bridging the gap between the meteorological models' resolution and the high degree of spatial precision sometimes required by hydrological models in their precipitation representation. References Maraun, D., Wetterhall, F., Ireson, A. M., Chandler, R. E., Kendon, E. J., Widmann, M., Brienen, S., Rust, H. W., Sauter, T., Themeßl, M., Venema, V. K. C., Chun, K. P., Goodess, C. M., Jones, R. G., Onof, C., Vrac, M., and Thiele-Eich, I. 2010. Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Reviews of Geophysics, 48 (3): RG3003, [np]. Doi: 10.1029/2009RG000314. Perica, S., and Foufoula-Georgiou, E. 1996. Model for multiscale disaggregation of spatial rainfall based on coupling meteorological and scaling descriptions. Journal Of Geophysical Research, 101(D21): 26347-26361. Ruiz, J., Saulo, C. and Kalnay, E. 2009. Comparison of Methods Used to Generate Probabilistic Quantitative Precipitation Forecasts over South America. Weather and forecasting, 24: 319-336. DOI: 10.1175/2008WAF2007098.1 This work is distributed under the Creative Commons Attribution 3.0 Unported License together with an author copyright. This license does not conflict with the regulations of the Crown Copyright.

Atmospheric Downscaling using Genetic Programming

NASA Astrophysics Data System (ADS)

Zerenner, Tanja; Venema, Victor; Simmer, Clemens

2013-04-01

Coupling models for the different components of the Soil-Vegetation-Atmosphere-System requires up-and downscaling procedures. Subject of our work is the downscaling scheme used to derive high resolution forcing data for land-surface and subsurface models from coarser atmospheric model output. The current downscaling scheme [Schomburg et. al. 2010, 2012] combines a bi-quadratic spline interpolation, deterministic rules and autoregressive noise. For the development of the scheme, training and validation data sets have been created by carrying out high-resolution runs of the atmospheric model. The deterministic rules in this scheme are partly based on known physical relations and partly determined by an automated search for linear relationships between the high resolution fields of the atmospheric model output and high resolution data on surface characteristics. Up to now deterministic rules are available for downscaling surface pressure and partially, depending on the prevailing weather conditions, for near surface temperature and radiation. Aim of our work is to improve those rules and to find deterministic rules for the remaining variables, which require downscaling, e.g. precipitation or near surface specifc humidity. To accomplish that, we broaden the search by allowing for interdependencies between different atmospheric parameters, non-linear relations, non-local and time-lagged relations. To cope with the vast number of possible solutions, we use genetic programming, a method from machine learning, which is based on the principles of natural evolution. We are currently working with GPLAB, a Genetic Programming toolbox for Matlab. At first we have tested the GP system to retrieve the known physical rule for downscaling surface pressure, i.e. the hydrostatic equation, from our training data. We have found this to be a simple task to the GP system. Furthermore we have improved accuracy and efficiency of the GP solution by implementing constant variation and optimization as genetic operators. Next we have worked on an improvement of the downscaling rule for the two-meter-temperature. We have added an if-function with four input arguments to the function set. Since this has shown to increase bloat we have additionally modified our fitness function by including penalty terms for both the size of the solutions and the number intron nodes, i.e program parts that are never evaluated. Starting from the known downscaling rule for the two-meter temperature, which linearly exploits the orography anomalies allowed or disallowed by a certain temperature gradient, our GP system has been able to find an improvement. The rule produced by the GP clearly shows a better performance concerning the reproduced small-scale variability.

Using Four Downscaling Techniques to Characterize Uncertainty in Updating Intensity-Duration-Frequency Curves Under Climate Change

NASA Astrophysics Data System (ADS)

Cook, L. M.; Samaras, C.; McGinnis, S. A.

2017-12-01

Intensity-duration-frequency (IDF) curves are a common input to urban drainage design, and are used to represent extreme rainfall in a region. As rainfall patterns shift into a non-stationary regime as a result of climate change, these curves will need to be updated with future projections of extreme precipitation. Many regions have begun to update these curves to reflect the trends from downscaled climate models; however, few studies have compared the methods for doing so, as well as the uncertainty that results from the selection of the native grid scale and temporal resolution of the climate model. This study examines the variability in updated IDF curves for Pittsburgh using four different methods for adjusting gridded regional climate model (RCM) outputs into station scale precipitation extremes: (1) a simple change factor applied to observed return levels, (2) a naïve adjustment of stationary and non-stationary Generalized Extreme Value (GEV) distribution parameters, (3) a transfer function of the GEV parameters from the annual maximum series, and (4) kernel density distribution mapping bias correction of the RCM time series. Return level estimates (rainfall intensities) and confidence intervals from these methods for the 1-hour to 48-hour duration are tested for sensitivity to the underlying spatial and temporal resolution of the climate ensemble from the NA-CORDEX project, as well as, the future time period for updating. The first goal is to determine if uncertainty is highest for: (i) the downscaling method, (ii) the climate model resolution, (iii) the climate model simulation, (iv) the GEV parameters, or (v) the future time period examined. Initial results of the 6-hour, 10-year return level adjusted with the simple change factor method using four climate model simulations of two different spatial resolutions show that uncertainty is highest in the estimation of the GEV parameters. The second goal is to determine if complex downscaling methods and high-resolution climate models are necessary for updating, or if simpler methods and lower resolution climate models will suffice. The final results can be used to inform the most appropriate method and climate model resolutions to use for updating IDF curves for urban drainage design.

Statistical downscaling of general-circulation-model- simulated average monthly air temperature to the beginning of flowering of the dandelion (Taraxacum officinale) in Slovenia

NASA Astrophysics Data System (ADS)

Bergant, Klemen; Kajfež-Bogataj, Lučka; Črepinšek, Zalika

2002-02-01

Phenological observations are a valuable source of information for investigating the relationship between climate variation and plant development. Potential climate change in the future will shift the occurrence of phenological phases. Information about future climate conditions is needed in order to estimate this shift. General circulation models (GCM) provide the best information about future climate change. They are able to simulate reliably the most important mean features on a large scale, but they fail on a regional scale because of their low spatial resolution. A common approach to bridging the scale gap is statistical downscaling, which was used to relate the beginning of flowering of Taraxacum officinale in Slovenia with the monthly mean near-surface air temperature for January, February and March in Central Europe. Statistical models were developed and tested with NCAR/NCEP Reanalysis predictor data and EARS predictand data for the period 1960-1999. Prior to developing statistical models, empirical orthogonal function (EOF) analysis was employed on the predictor data. Multiple linear regression was used to relate the beginning of flowering with expansion coefficients of the first three EOF for the Janauary, Febrauary and March air temperatures, and a strong correlation was found between them. Developed statistical models were employed on the results of two GCM (HadCM3 and ECHAM4/OPYC3) to estimate the potential shifts in the beginning of flowering for the periods 1990-2019 and 2020-2049 in comparison with the period 1960-1989. The HadCM3 model predicts, on average, 4 days earlier occurrence and ECHAM4/OPYC3 5 days earlier occurrence of flowering in the period 1990-2019. The analogous results for the period 2020-2049 are a 10- and 11-day earlier occurrence.

Application of multi-scale wavelet entropy and multi-resolution Volterra models for climatic downscaling

NASA Astrophysics Data System (ADS)

Sehgal, V.; Lakhanpal, A.; Maheswaran, R.; Khosa, R.; Sridhar, Venkataramana

2018-01-01

This study proposes a wavelet-based multi-resolution modeling approach for statistical downscaling of GCM variables to mean monthly precipitation for five locations at Krishna Basin, India. Climatic dataset from NCEP is used for training the proposed models (Jan.'69 to Dec.'94) and are applied to corresponding CanCM4 GCM variables to simulate precipitation for the validation (Jan.'95-Dec.'05) and forecast (Jan.'06-Dec.'35) periods. The observed precipitation data is obtained from the India Meteorological Department (IMD) gridded precipitation product at 0.25 degree spatial resolution. This paper proposes a novel Multi-Scale Wavelet Entropy (MWE) based approach for clustering climatic variables into suitable clusters using k-means methodology. Principal Component Analysis (PCA) is used to obtain the representative Principal Components (PC) explaining 90-95% variance for each cluster. A multi-resolution non-linear approach combining Discrete Wavelet Transform (DWT) and Second Order Volterra (SoV) is used to model the representative PCs to obtain the downscaled precipitation for each downscaling location (W-P-SoV model). The results establish that wavelet-based multi-resolution SoV models perform significantly better compared to the traditional Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) based frameworks. It is observed that the proposed MWE-based clustering and subsequent PCA, helps reduce the dimensionality of the input climatic variables, while capturing more variability compared to stand-alone k-means (no MWE). The proposed models perform better in estimating the number of precipitation events during the non-monsoon periods whereas the models with clustering without MWE over-estimate the rainfall during the dry season.

Modeling diurnal land temperature cycles over Los Angeles using downscaled GOES imagery

NASA Astrophysics Data System (ADS)

Weng, Qihao; Fu, Peng

2014-11-01

Land surface temperature is a key parameter for monitoring urban heat islands, assessing heat related risks, and estimating building energy consumption. These environmental issues are characterized by high temporal variability. A possible solution from the remote sensing perspective is to utilize geostationary satellites images, for instance, images from Geostationary Operational Environmental System (GOES) and Meteosat Second Generation (MSG). These satellite systems, however, with coarse spatial but high temporal resolution (sub-hourly imagery at 3-10 km resolution), often limit their usage to meteorological forecasting and global climate modeling. Therefore, how to develop efficient and effective methods to disaggregate these coarse resolution images to a proper scale suitable for regional and local studies need be explored. In this study, we propose a least square support vector machine (LSSVM) method to achieve the goal of downscaling of GOES image data to half-hourly 1-km LSTs by fusing it with MODIS data products and Shuttle Radar Topography Mission (SRTM) digital elevation data. The result of downscaling suggests that the proposed method successfully disaggregated GOES images to half-hourly 1-km LSTs with accuracy of approximately 2.5 K when validated against with MODIS LSTs at the same over-passing time. The synthetic LST datasets were further explored for monitoring of surface urban heat island (UHI) in the Los Angeles region by extracting key diurnal temperature cycle (DTC) parameters. It is found that the datasets and DTC derived parameters were more suitable for monitoring of daytime- other than nighttime-UHI. With the downscaled GOES 1-km LSTs, the diurnal temperature variations can well be characterized. An accuracy of about 2.5 K was achieved in terms of the fitted results at both 1 km and 5 km resolutions.

Meeting the Regional Climate Information Needs of Decision Makers: The CORDEX Framework

NASA Astrophysics Data System (ADS)

Asrar, G. R.; Jones, C.; Giorgi, F.

2011-12-01

Regional Climate Downscaling (RCD), both dynamical (e.g. regional climate modeling) and statistical, is an important approach to produce fine scale climate information for use in impact assessment and adaptation/mitigation studies and practices. RCD techniques have evolved significantly over the last decade, however a coherent and wide picture of regional climate change based on RCD products is still not available and the potentials, limitations and uncertainties of RCD methods need to be better understood by the user community. In order to address these issues a new initiative has been launched under the WCRP auspices, referred to as Coordinated Regional climate Downscaling EXperiment, or CORDEX. The aim of CORDEX is to bring together the international RCD community to assess different RCD techniques, recommend best practices and produce a next generation set of RCD-based projections of climate change for regions world-wide. This will involve close interactions between the RCD, global climate modeling, and end users communities. This paper will describe the motivations and design of the first phase of the CORDEX framework, which has a priority focus on Africa, along with the steps that are envisioned to achieve the CORDEX goals within the time framework of the Fifth IPCC assessment report. Some early results for Africa will be presented, together with a short summary of the CORDEX activities in Asia, Americas and other regions of the world.

Downscaling MODIS Land Surface Temperature for Urban Public Health Applications

NASA Technical Reports Server (NTRS)

Al-Hamdan, Mohammad; Crosson, William; Estes, Maurice, Jr.; Estes, Sue; Quattrochi, Dale; Johnson, Daniel

2013-01-01

This study is part of a project funded by the NASA Applied Sciences Public Health Program, which focuses on Earth science applications of remote sensing data for enhancing public health decision-making. Heat related death is currently the number one weather-related killer in the United States. Mortality from these events is expected to increase as a function of climate change. This activity sought to augment current Heat Watch/Warning Systems (HWWS) with NASA remotely sensed data, and models used in conjunction with socioeconomic and heatrelated mortality data. The current HWWS do not take into account intra-urban spatial variation in risk assessment. The purpose of this effort is to evaluate a potential method to improve spatial delineation of risk from extreme heat events in urban environments by integrating sociodemographic risk factors with estimates of land surface temperature (LST) derived from thermal remote sensing data. In order to further improve the consideration of intra-urban variations in risk from extreme heat, we also developed and evaluated a number of spatial statistical techniques for downscaling the 1-km daily MODerate-resolution Imaging Spectroradiometer (MODIS) LST data to 60 m using Landsat-derived LST data, which have finer spatial but coarser temporal resolution than MODIS. In this paper, we will present these techniques, which have been demonstrated and validated for Phoenix, AZ using data from the summers of 2000-2006.

Precipitation Dynamical Downscaling Over the Great Plains

NASA Astrophysics Data System (ADS)

Hu, Xiao-Ming; Xue, Ming; McPherson, Renee A.; Martin, Elinor; Rosendahl, Derek H.; Qiao, Lei

2018-02-01

Detailed, regional climate projections, particularly for precipitation, are critical for many applications. Accurate precipitation downscaling in the United States Great Plains remains a great challenge for most Regional Climate Models, particularly for warm months. Most previous dynamic downscaling simulations significantly underestimate warm-season precipitation in the region. This study aims to achieve a better precipitation downscaling in the Great Plains with the Weather Research and Forecast (WRF) model. To this end, WRF simulations with different physics schemes and nudging strategies are first conducted for a representative warm season. Results show that different cumulus schemes lead to more pronounced difference in simulated precipitation than other tested physics schemes. Simply choosing different physics schemes is not enough to alleviate the dry bias over the southern Great Plains, which is related to an anticyclonic circulation anomaly over the central and western parts of continental U.S. in the simulations. Spectral nudging emerges as an effective solution for alleviating the precipitation bias. Spectral nudging ensures that large and synoptic-scale circulations are faithfully reproduced while still allowing WRF to develop small-scale dynamics, thus effectively suppressing the large-scale circulation anomaly in the downscaling. As a result, a better precipitation downscaling is achieved. With the carefully validated configurations, WRF downscaling is conducted for 1980-2015. The downscaling captures well the spatial distribution of monthly climatology precipitation and the monthly/yearly variability, showing improvement over at least two previously published precipitation downscaling studies. With the improved precipitation downscaling, a better hydrological simulation over the trans-state Oologah watershed is also achieved.

Some Advances in Downscaling Probabilistic Climate Forecasts for Agricultural Decision Support

NASA Astrophysics Data System (ADS)

Han, E.; Ines, A.

2015-12-01

Seasonal climate forecasts, commonly provided in tercile-probabilities format (below-, near- and above-normal), need to be translated into more meaningful information for decision support of practitioners in agriculture. In this paper, we will present two new novel approaches to temporally downscale probabilistic seasonal climate forecasts: one non-parametric and another parametric method. First, the non-parametric downscaling approach called FResampler1 uses the concept of 'conditional block sampling' of weather data to create daily weather realizations of a tercile-based seasonal climate forecasts. FResampler1 randomly draws time series of daily weather parameters (e.g., rainfall, maximum and minimum temperature and solar radiation) from historical records, for the season of interest from years that belong to a certain rainfall tercile category (e.g., being below-, near- and above-normal). In this way, FResampler1 preserves the covariance between rainfall and other weather parameters as if conditionally sampling maximum and minimum temperature and solar radiation if that day is wet or dry. The second approach called predictWTD is a parametric method based on a conditional stochastic weather generator. The tercile-based seasonal climate forecast is converted into a theoretical forecast cumulative probability curve. Then the deviates for each percentile is converted into rainfall amount or frequency or intensity to downscale the 'full' distribution of probabilistic seasonal climate forecasts. Those seasonal deviates are then disaggregated on a monthly basis and used to constrain the downscaling of forecast realizations at different percentile values of the theoretical forecast curve. As well as the theoretical basis of the approaches we will discuss sensitivity analysis (length of data and size of samples) of them. In addition their potential applications for managing climate-related risks in agriculture will be shown through a couple of case studies based on actual seasonal climate forecasts for: rice cropping in the Philippines and maize cropping in India and Kenya.

The climate4impact platform: Providing, tailoring and facilitating climate model data access

NASA Astrophysics Data System (ADS)

Pagé, Christian; Pagani, Andrea; Plieger, Maarten; Som de Cerff, Wim; Mihajlovski, Andrej; de Vreede, Ernst; Spinuso, Alessandro; Hutjes, Ronald; de Jong, Fokke; Bärring, Lars; Vega, Manuel; Cofiño, Antonio; d'Anca, Alessandro; Fiore, Sandro; Kolax, Michael

2017-04-01

One of the main objectives of climate4impact is to provide standardized web services and tools that are reusable in other portals. These services include web processing services, web coverage services and web mapping services (WPS, WCS and WMS). Tailored portals can be targeted to specific communities and/or countries/regions while making use of those services. Easier access to climate data is very important for the climate change impact communities. To fulfill this objective, the climate4impact (http://climate4impact.eu/) web portal and services has been developed, targeting climate change impact modellers, impact and adaptation consultants, as well as other experts using climate change data. It provides to users harmonized access to climate model data through tailored services. It features static and dynamic documentation, Use Cases and best practice examples, an advanced search interface, an integrated authentication and authorization system with the Earth System Grid Federation (ESGF), a visualization interface with ADAGUC web mapping tools. In the latest version, statistical downscaling services, provided by the Santander Meteorology Group Downscaling Portal, were integrated. An innovative interface to integrate statistical downscaling services will be released in the upcoming version. The latter will be a big step in bridging the gap between climate scientists and the climate change impact communities. The climate4impact portal builds on the infrastructure of an international distributed database that has been set to disseminate the results from the global climate model results of the Coupled Model Intercomparison project Phase 5 (CMIP5). This database, the ESGF, is an international collaboration that develops, deploys and maintains software infrastructure for the management, dissemination, and analysis of climate model data. The European FP7 project IS-ENES, Infrastructure for the European Network for Earth System modelling, supports the European contribution to ESGF and contributes to the ESGF open source effort, notably through the development of search, monitoring, quality control, and metadata services. In its second phase, IS-ENES2 supports the implementation of regional climate model results from the international Coordinated Regional Downscaling Experiments (CORDEX). These services were extended within the European FP7 Climate Information Portal for Copernicus (CLIPC) project, and some could be later integrated into the European Copernicus platform.

Anticipating Future Extreme Climate Events for Alaska Using Dynamical Downscaling and Quantile Mapping

NASA Astrophysics Data System (ADS)

Lader, R.; Walsh, J. E.

2016-12-01

Alaska is projected to experience major changes in extreme climate during the 21st century, due to greenhouse warming and exacerbated by polar amplification, wherein the Arctic is warming at twice the rate compared to the Northern Hemisphere. Given its complex topography, Alaska displays extreme gradients of temperature and precipitation. However, global climate models (GCMs), which typically have a spatial resolution on the order of 100km, struggle to replicate these extremes. To help resolve this issue, this study employs dynamically downscaled regional climate simulations and quantile-mapping methodologies to provide a full suite of daily model variables at 20 km spatial resolution for Alaska, from 1970 to 2100. These data include downscaled products of the: ERA-Interim reanalysis from 1979 to 2015, GFDL-CM3 historical from 1970 to 2005, and GFDL-CM3 RCP 8.5 from 2006 to 2100. Due to the limited nature of long-term observations and high-resolution modeling in Alaska, these data enable a broad expansion of extremes analysis. This study uses these data to highlight a subset of the 27 climate extremes indices, previously defined by the Expert Team on Climate Change Detection and Indices, as they pertain to climate change in Alaska. These indices are based on the statistical distributions of daily surface temperature and precipitation and focus on threshold exceedance, and percentiles. For example, the annual number of days with a daily maximum temperature greater than 25°C is anticipated to triple in many locations in Alaska by the end of the century. Climate extremes can also refer to long duration events, such as the record-setting warmth that defined the 2015-16 cold season in Alaska. The downscaled climate model simulations indicate that this past winter will be considered normal by as early as the mid-2040s, if we continue to warm according to the business-as-usual RCP 8.5 emissions scenario. This represents an accelerated warming as compared to projections form the coarse scale GCMs, and this greater rate of change in the downscaled products is noted with other extremes indices as well.

Solar Energy Potential in a Changing Climate: Iberia and Azores Assessment Combining Dynamical and Statistical Downscaling Methods

NASA Astrophysics Data System (ADS)

Magarreiro, Clarisse de Lurdes Chapa

The proper characterization of solar radiation resource is essential for the design of any solar energy harnessing systems which aims its optimal performance. To this end, the solar resource is often quantified through solar radiation measurements at meteorological stations. Unfortunately radiation data recorded on the desired location is often inexistent. Furthermore, the actual existing solar radiation databases have also a limited temporal span and, more frequently than desired, missing values and non-uniform formats. Also, such databases consist almost entirely of global solar radiation; variables such as the nature of the solar energy (direct or diffuse) are rarely recorded. Atmospheric models can add value to solar energy applications by enabling solar resource assessments as they easily overcome the limited spatial and temporal coverage of irradiance measuring networks. Furthermore, climate models can be used for any region of the planet to assess the solar resource for not only present climate conditions but also to analyse its long-term past evolution and future tendency. Nowadays such models are a popular approach on the field of solar radiation forecasting but the quality evaluation of the solar radiation representation by such models is first of all a fundamental step to understand its usefulness. Having this in mind, in this thesis, a dynamical downscaling approach is used to evaluate simulated solar radiation at the Earth’s surface which will then enable the characterization of the solar resource. The model output is also combined with a statistical downscaling approach used in its simplest form to minimize the model biases. The work focuses primarily in the Iberian Peninsula as its large climate gradients are representative of diverse meteorological conditions, enabling therefore the adaptation of the presented methods to other regions. Then, following the same methodology, the solar resource of the Azores archipelago is also addressed. The Azores region is often neglected in solar resource assessments and solar resource maps of the Earth’s surface or even of Europe region. These methods are used to characterize the present climate renewable solar resource and analyse the impact of climate change on its projections for the end of the 21st century for both Iberia Peninsula and Azores archipelago. Atmospheric numerical models are however limited in the sense that they only provide global solar radiation, the direct normal radiation and diffuse components are not common outputs to the user. Given this, the separation of global radiation into its diffuse and direct components is analysed in this thesis through models of diffuse solar radiation fraction. One important characteristic of these models is that they are empirically derived from site-specific measurements and a model developed and validated in a very specific climate type region may not hold its suitability to other regions. This thesis focuses on the assessment of such models only for the Azores region which has not been object of this type of analysis before.

Impact of spectral nudging on the downscaling of tropical cyclones in regional climate simulations

NASA Astrophysics Data System (ADS)

Choi, Suk-Jin; Lee, Dong-Kyou

2016-06-01

This study investigated the simulations of three months of seasonal tropical cyclone (TC) activity over the western North Pacific using the Advanced Research WRF Model. In the control experiment (CTL), the TC frequency was considerably overestimated. Additionally, the tracks of some TCs tended to have larger radii of curvature and were shifted eastward. The large-scale environments of westerly monsoon flows and subtropical Pacific highs were unreasonably simulated. The overestimated frequency of TC formation was attributed to a strengthened westerly wind field in the southern quadrants of the TC center. In comparison with the experiment with the spectral nudging method, the strengthened wind speed was mainly modulated by large-scale flow that was greater than approximately 1000 km in the model domain. The spurious formation and undesirable tracks of TCs in the CTL were considerably improved by reproducing realistic large-scale atmospheric monsoon circulation with substantial adjustment between large-scale flow in the model domain and large-scale boundary forcing modified by the spectral nudging method. The realistic monsoon circulation took a vital role in simulating realistic TCs. It revealed that, in the downscaling from large-scale fields for regional climate simulations, scale interaction between model-generated regional features and forced large-scale fields should be considered, and spectral nudging is a desirable method in the downscaling method.

Mass Balance Modelling of Saskatchewan Glacier, Canada Using Empirically Downscaled Reanalysis Data

NASA Astrophysics Data System (ADS)

Larouche, O.; Kinnard, C.; Demuth, M. N.

2017-12-01

Observations show that glaciers around the world are retreating. As sites with long-term mass balance observations are scarce, models are needed to reconstruct glacier mass balance and assess its sensitivity to climate. In regions with discontinuous and/or sparse meteorological data, high-resolution climate reanalysis data provide a convenient alternative to in situ weather observations, but can also suffer from strong bias due to the spatial and temporal scale mismatch. In this study we used data from the North American Regional Reanalysis (NARR) project with a 30 x 30 km spatial resolution and 3-hour temporal resolution to produce the meteorological forcings needed to drive a physically-based, distributed glacier mass balance model (DEBAM, Hock and Holmgren 2005) for the historical period 1979-2016. A two-year record from an automatic weather station (AWS) operated on Saskatchewan Glacier (2014-2016) was used to downscale air temperature, relative humidity, wind speed and incoming solar radiation from the nearest NARR gridpoint to the glacier AWS site. An homogenized historical precipitation record was produced using data from two nearby, low-elevation weather stations and used to downscale the NARR precipitation data. Three bias correction methods were applied (scaling, delta and empirical quantile mapping - EQM) and evaluated using split sample cross-validation. The EQM method gave better results for precipitation and for air temperature. Only a slight improvement in the relative humidity was obtained using the scaling method, while none of the methods improved the wind speed. The later correlates poorly with AWS observations, probably because the local glacier wind is decoupled from the larger scale NARR wind field. The downscaled data was used to drive the DEBAM model in order to reconstruct the mass balance of Saskatchewan Glacier over the past 30 years. The model was validated using recent snow thickness measurements and previously published geodetic mass balance estimates.

Impact of climate change on Precipitation and temperature under the RCP 8.5 and A1B scenarios in an Alpine Cathment (Alto-Genil Basin,southeast Spain). A comparison of statistical downscaling methods

NASA Astrophysics Data System (ADS)

Pulido-Velazquez, David; Juan Collados-Lara, Antonio; Pardo-Iguzquiza, Eulogio; Jimeno-Saez, Patricia; Fernandez-Chacon, Francisca

2016-04-01

In order to design adaptive strategies to global change we need to assess the future impact of climate change on water resources, which depends on precipitation and temperature series in the systems. The objective of this work is to generate future climate series in the "Alto Genil" Basin (southeast Spain) for the period 2071-2100 by perturbing the historical series using different statistical methods. For this targeted we use information coming from regionals climate model simulations (RCMs) available in two European projects, CORDEX (2013), with a spatial resolution of 12.5 km, and ENSEMBLES (2009), with a spatial resolution of 25 km. The historical climate series used for the period 1971-2000 have been obtained from Spain02 project (2012) which has the same spatial resolution that CORDEX project (both use the EURO-CORDEX grid). Two emission scenarios have been considered: the Representative Concentration Pathways (RCP) 8.5 emissions scenario, which is the most unfavorable scenario considered in the fifth Assessment Report (AR5) by the Intergovernmental Panel on Climate Change (IPCC), and the A1B emission scenario of fourth Assessment Report (AR4). We use the RCM simulations to create an ensemble of predictions weighting their information according to their ability to reproduce the main statistic of the historical climatology. A multi-objective analysis has been performed to identify which models are better in terms of goodness of fit to the cited statistic of the historical series. The ensemble of the CORDEX and the ENSEMBLES projects has been finally created with nine and four models respectively. These ensemble series have been used to assess the anomalies in mean and standard deviation (differences between the control and future RCM series). A "delta-change" method (Pulido-Velazquez et al., 2011) has been applied to define future series by modifying the historical climate series in accordance with the cited anomalies in mean and standard deviation. A comparison between results for scenario A1B and RCP8.5 has been performed. The reduction obtained for the mean rainfall respect to the historical are 24.2 % and 24.4 % respectively, and the increment in the temperature are 46.3 % and 31.2 % respectively. A sensitivity analysis of the results to the statistical downscaling techniques employed has been performed. The next techniques have been explored: Perturbation method or "delta-change"; Regression method (a regression function which relates the RCM and the historic information will be used to generate future climate series for the fixed period); Quantile mapping, (it attempts to find a transformation function which relates the observed variable and the modeled variable maintaining an statistical distribution equals the observed variable); Stochastic weather generator (SWG): They can be uni-site or multi-site (which considers the spatial correlation of climatic series). A comparative analysis of these techniques has been performed identifying the advantages and disadvantages of each of them. Acknowledgments: This research has been partially supported by the GESINHIMPADAPT project (CGL2013-48424-C2-2-R) with Spanish MINECO funds. We would also like to thank Spain02, ENSEMBLES and CORDEX projects for the data provided for this study.

A comparison of methods for assessing power output in non-uniform onshore wind farms

DOE PAGES

Staid, Andrea; VerHulst, Claire; Guikema, Seth D.

2017-10-02

Wind resource assessments are used to estimate a wind farm's power production during the planning process. It is important that these estimates are accurate, as they can impact financing agreements, transmission planning, and environmental targets. Here, we analyze the challenges in wind power estimation for onshore farms. Turbine wake effects are a strong determinant of farm power production. With given input wind conditions, wake losses typically cause downstream turbines to produce significantly less power than upstream turbines. These losses have been modeled extensively and are well understood under certain conditions. Most notably, validation of different model types has favored offshoremore » farms. Models that capture the dynamics of offshore wind conditions do not necessarily perform equally as well for onshore wind farms. We analyze the capabilities of several different methods for estimating wind farm power production in 2 onshore farms with non-uniform layouts. We compare the Jensen model to a number of statistical models, to meteorological downscaling techniques, and to using no model at all. In conclusion, we show that the complexities of some onshore farms result in wind conditions that are not accurately modeled by the Jensen wake decay techniques and that statistical methods have some strong advantages in practice.« less

A comparison of methods for assessing power output in non-uniform onshore wind farms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Staid, Andrea; VerHulst, Claire; Guikema, Seth D.

Wind resource assessments are used to estimate a wind farm's power production during the planning process. It is important that these estimates are accurate, as they can impact financing agreements, transmission planning, and environmental targets. Here, we analyze the challenges in wind power estimation for onshore farms. Turbine wake effects are a strong determinant of farm power production. With given input wind conditions, wake losses typically cause downstream turbines to produce significantly less power than upstream turbines. These losses have been modeled extensively and are well understood under certain conditions. Most notably, validation of different model types has favored offshoremore » farms. Models that capture the dynamics of offshore wind conditions do not necessarily perform equally as well for onshore wind farms. We analyze the capabilities of several different methods for estimating wind farm power production in 2 onshore farms with non-uniform layouts. We compare the Jensen model to a number of statistical models, to meteorological downscaling techniques, and to using no model at all. In conclusion, we show that the complexities of some onshore farms result in wind conditions that are not accurately modeled by the Jensen wake decay techniques and that statistical methods have some strong advantages in practice.« less

Downscaling of inundation extents

NASA Astrophysics Data System (ADS)

Aires, Filipe; Prigent, Catherine; Papa, Fabrice

2014-05-01

The Global Inundation Extent from Multi-Satellite (GIEMS) provides multi-year monthly variations of the global surface water extent at about 25 kmx25 km resolution, from 1993 to 2007. It is derived from multiple satellite observations. Its spatial resolution is usually compatible with climate model outputs and with global land surface model grids but is clearly not adequate for local applications that require the characterization of small individual water bodies. There is today a strong demand for high-resolution inundation extent datasets, for a large variety of applications such as water management, regional hydrological modeling, or for the analysis of mosquitos-related diseases. This paper present three approaches to do downscale GIEMS: The first one is based on a image-processing technique using neighborhood constraints. The third approach uses a PCA representation to perform an algebraic inversion. The PCA-representation is also very convenient to perform temporal and spatial interpolation of complexe inundation fields. The third downscaling method uses topography information from Hydroshed Digital Elevation Model (DEM). Information such as the elevation, distance to river and flow accumulation are used to define a ``flood ability index'' that is used by the downscaling. Three basins will be considered for illustrative purposes: Amazon, Niger and Mekong. Aires, F., F. Papa, C. Prigent, J.-F. Cretaux and M. Berge-Nguyen, Characterization and downscaling of the inundation extent over the Inner Niger delta using a multi-wavelength retrievals and Modis data, J. of Hydrometeorology, in press, 2014. Aires, F., F. Papa and C. Prigent, A long-term, high-resolution wetland dataset over the Amazon basin, downscaled from a multi-wavelength retrieval using SAR, J. of Hydrometeorology, 14, 594-6007, 2013. Prigent, C., F. Papa, F. Aires, C. Jimenez, W.B. Rossow, and E. Matthews. Changes in land surface water dynamics since the 1990s and relation to population pressure. Geophys. Res. Lett., 39(L08403), 2012.

Genetic particle filter application to land surface temperature downscaling

NASA Astrophysics Data System (ADS)

Mechri, Rihab; Ottlé, Catherine; Pannekoucke, Olivier; Kallel, Abdelaziz

2014-03-01

Thermal infrared data are widely used for surface flux estimation giving the possibility to assess water and energy budgets through land surface temperature (LST). Many applications require both high spatial resolution (HSR) and high temporal resolution (HTR), which are not presently available from space. It is therefore necessary to develop methodologies to use the coarse spatial/high temporal resolutions LST remote-sensing products for a better monitoring of fluxes at appropriate scales. For that purpose, a data assimilation method was developed to downscale LST based on particle filtering. The basic tenet of our approach is to constrain LST dynamics simulated at both HSR and HTR, through the optimization of aggregated temperatures at the coarse observation scale. Thus, a genetic particle filter (GPF) data assimilation scheme was implemented and applied to a land surface model which simulates prior subpixel temperatures. First, the GPF downscaling scheme was tested on pseudoobservations generated in the framework of the study area landscape (Crau-Camargue, France) and climate for the year 2006. The GPF performances were evaluated against observation errors and temporal sampling. Results show that GPF outperforms prior model estimations. Finally, the GPF method was applied on Spinning Enhanced Visible and InfraRed Imager time series and evaluated against HSR data provided by an Advanced Spaceborne Thermal Emission and Reflection Radiometer image acquired on 26 July 2006. The temperatures of seven land cover classes present in the study area were estimated with root-mean-square errors less than 2.4 K which is a very promising result for downscaling LST satellite products.

Downscaling Climate Projections to a Mountainous Landscape: A Climate Impact Assessment for the U.S. Northern Rockies Crown of the Continent Ecosystem

NASA Astrophysics Data System (ADS)

Oyler, J.; Anderson, R.; Running, S. W.

2010-12-01

In topographically complex landscapes, there is often a mismatch in scale between global climate model projections and more local climate-forcing factors and related ecological/hydrological processes. To overcome this limitation, the objective of this study was to downscale climate projections to the rugged Crown of the Continent Ecosystem (CCE) within the U.S. Northern Rockies and assess future impacts on water balances, vegetation dynamics, and carbon fluxes. A 40-year (1970-2009) spatial historical climate dataset (800m resolution, daily timestep) was generated for the CCE and modified for terrain influences. Regional climate projections were downscaled by applying them to the fine-scale historical dataset using a modified delta downscaling method and stochastic weather generator. The downscaled projections were used to drive the Biome-BGC ecosystem model. Overall CCE impacts included decreases in April 1 snow water equivalent, less days with snow on the ground, increased vegetation water stress, and increased growing degree days. The relaxing of temperature constraints increased annual net primary productivity (NPP) throughout most of the CCE landscape. However, an increase in water stress seems to have limited the growth in NPP and, in some areas, NPP actually decreased. Thus, CCE vegetation productivity trends under increasing temperatures will likely be determined by local changes in hydrologic function. Given the greater uncertainty in precipitation projections, future work should concentrate on determining thresholds in water constraints that greatly modify the magnitude and direction of carbon accumulation within the CCE under a warming climate.

Seasonal evaluation of evapotranspiration fluxes from MODIS satellite and mesoscale model downscaled global reanalysis datasets

NASA Astrophysics Data System (ADS)

Srivastava, Prashant K.; Han, Dawei; Islam, Tanvir; Petropoulos, George P.; Gupta, Manika; Dai, Qiang

2016-04-01

Reference evapotranspiration (ETo) is an important variable in hydrological modeling, which is not always available, especially for ungauged catchments. Satellite data, such as those available from the MODerate Resolution Imaging Spectroradiometer (MODIS), and global datasets via the European Centre for Medium Range Weather Forecasts (ECMWF) reanalysis (ERA) interim and National Centers for Environmental Prediction (NCEP) reanalysis are important sources of information for ETo. This study explored the seasonal performances of MODIS (MOD16) and Weather Research and Forecasting (WRF) model downscaled global reanalysis datasets, such as ERA interim and NCEP-derived ETo, against ground-based datasets. Overall, on the basis of the statistical metrics computed, ETo derived from ERA interim and MODIS were more accurate in comparison to the estimates from NCEP for all the seasons. The pooled datasets also revealed a similar performance to the seasonal assessment with higher agreement for the ERA interim (r = 0.96, RMSE = 2.76 mm/8 days; bias = 0.24 mm/8 days), followed by MODIS (r = 0.95, RMSE = 7.66 mm/8 days; bias = -7.17 mm/8 days) and NCEP (r = 0.76, RMSE = 11.81 mm/8 days; bias = -10.20 mm/8 days). The only limitation with downscaling ERA interim reanalysis datasets using WRF is that it is time-consuming in contrast to the readily available MODIS operational product for use in mesoscale studies and practical applications.

FORWINE - Statistical Downscaling of Seasonal forecasts for wine

NASA Astrophysics Data System (ADS)

Cardoso, Rita M.; Soares, Pedro M. M.; Miranda, Pedro M. A.

2016-04-01

The most renowned viticulture regions in the Iberian Peninsula have a long standing tradition in winemaking and are considered world-class grapevine (Vitis Vinifera L.) producing regions. Portugal is the 11th wine producer in the world, with internationally acclaimed wines, such as Port wine, and vineyards across the whole territory. Climate is widely acknowledged of one of the most important factors for grapevine development and growth (Fraga et al. 2014a and b; Jackson et al. 1993; Keller 2010). During the growing season (April-October in the Northern Hemisphere) of this perennial and deciduous crop, the climatic conditions are responsible for numerous morphologically and physiological changes. Anomalously low February-March mean temperature, anomalously high May mean temperature and anomalously high March precipitation tend to be favourable to wine production in the Douro Valley. Seasonal forecast of precipitation and temperature tailored to fit critical thresholds, for crucial seasons, can be used to inform management practices (viz. phytosanitary measures, land operations, marketing campaigns) and develop a wine production forecast. Statistical downscaling of precipitation, maximum, minimum temperatures is used to model wine production following Santos et al. (2013) and to calculate bioclimatic indices. The skill of the ensemble forecast is evaluated through anomaly correlation, ROC area, spread-error ratio and CRPS

Utilizing Machine Learning to Downscale SMAP L3_SM_P Brightness Temperatures in Iowa for Agricultural Applications

NASA Astrophysics Data System (ADS)

Chakrabarti, S.; Judge, J.; Bindlish, R.; Bongiovanni, T.; Jackson, T. J.

2016-12-01

The NASA Soil Moisture Active Passive (SMAP) mission provides global observations of brightness temperatures (TB) at 36km. For these observations to be relevant to studies in agricultural regions, the TB values need to be downscaled to finer resolutions. In this study, a machine learning algorithm is introduced for downscaling of TB from 36km to 9km. The algorithm uses image segmentation to cluster the study region based on meteorological and land cover similarity, followed by a support vector machine based regression that computes the value of the disaggregated TB at all pixels. High resolution remote sensing products such as land surface temperature, normalized difference vegetation index, enhanced vegetation index, precipitation, soil texture, and land-cover were used for downscaling. The algorithm was implemented in Iowa, United States, during the growing season from April to July 2015 when the SMAP L3-SM_AP TB product at 9 km was available for comparison. In addition, the downscaled estimates from the algorithm are compared with 9km TB obtained by resampling SMAP L1B_TB product at 36km. It was found that the downscaled TB were very similar to the SMAP-L3_SM _AP TB product, even for vegetated areas with a mean difference ≤ 5K. However, the standard deviation of the downscaled was lower by 7K than that of the AP product. The probability density functions of the downscaled TB were similar to the SMAP- TB. The results indicate that these downscaling algorithms may be used for downscaling TB using complex non-linear correlations on a grid without using active microwave observations.

High-Throughput Effect-Directed Analysis Using Downscaled in Vitro Reporter Gene Assays To Identify Endocrine Disruptors in Surface Water

PubMed Central

2018-01-01

Effect-directed analysis (EDA) is a commonly used approach for effect-based identification of endocrine disruptive chemicals in complex (environmental) mixtures. However, for routine toxicity assessment of, for example, water samples, current EDA approaches are considered time-consuming and laborious. We achieved faster EDA and identification by downscaling of sensitive cell-based hormone reporter gene assays and increasing fractionation resolution to allow testing of smaller fractions with reduced complexity. The high-resolution EDA approach is demonstrated by analysis of four environmental passive sampler extracts. Downscaling of the assays to a 384-well format allowed analysis of 64 fractions in triplicate (or 192 fractions without technical replicates) without affecting sensitivity compared to the standard 96-well format. Through a parallel exposure method, agonistic and antagonistic androgen and estrogen receptor activity could be measured in a single experiment following a single fractionation. From 16 selected candidate compounds, identified through nontargeted analysis, 13 could be confirmed chemically and 10 were found to be biologically active, of which the most potent nonsteroidal estrogens were identified as oxybenzone and piperine. The increased fractionation resolution and the higher throughput that downscaling provides allow for future application in routine high-resolution screening of large numbers of samples in order to accelerate identification of (emerging) endocrine disruptors. PMID:29547277

Long-term Trends and Variability of Eddy Activities in the South China Sea

NASA Astrophysics Data System (ADS)

Zhang, M.; von Storch, H.

2017-12-01

For constructing empirical downscaling models and projecting possible future states of eddy activities in the South China Sea (SCS), long-term statistical characteristics of the SCS eddy are needed. We use a daily global eddy-resolving model product named STORM covering the period of 1950-2010. This simulation has employed the MPI-OM model with a mean horizontal resolution of 10km and been driven by the NCEP reanalysis-1 data set. An eddy detection and tracking algorithm operating on the gridded sea surface height anomaly (SSHA) fields was developed. A set of parameters for the criteria in the SCS are determined through sensitivity tests. Our method detected more than 6000 eddy tracks in the South China Sea. For all of them, eddy diameters, track length, eddy intensity, eddy lifetime and eddy frequency were determined. The long-term trends and variability of those properties also has been derived. Most of the eddies propagate westward. Nearly 100 eddies travel longer than 1000km, and over 800 eddies have a lifespan of more than 2 months. Furthermore, for building the statistical empirical model, the relationship between the SCS eddy statistics and the large-scale atmospheric and oceanic phenomena has been investigated.

Generation of future potential scenarios in an Alpine Catchment by applying bias-correction techniques, delta-change approaches and stochastic Weather Generators at different spatial scale. Analysis of their influence on basic and drought statistics.

NASA Astrophysics Data System (ADS)

Collados-Lara, Antonio-Juan; Pulido-Velazquez, David; Pardo-Iguzquiza, Eulogio

2017-04-01

Assessing impacts of potential future climate change scenarios in precipitation and temperature is essential to design adaptive strategies in water resources systems. The objective of this work is to analyze the possibilities of different statistical downscaling methods to generate future potential scenarios in an Alpine Catchment from historical data and the available climate models simulations performed in the frame of the CORDEX EU project. The initial information employed to define these downscaling approaches are the historical climatic data (taken from the Spain02 project for the period 1971-2000 with a spatial resolution of 12.5 Km) and the future series provided by climatic models in the horizon period 2071-2100 . We have used information coming from nine climate model simulations (obtained from five different Regional climate models (RCM) nested to four different Global Climate Models (GCM)) from the European CORDEX project. In our application we have focused on the Representative Concentration Pathways (RCP) 8.5 emissions scenario, which is the most unfavorable scenario considered in the fifth Assessment Report (AR5) by the Intergovernmental Panel on Climate Change (IPCC). For each RCM we have generated future climate series for the period 2071-2100 by applying two different approaches, bias correction and delta change, and five different transformation techniques (first moment correction, first and second moment correction, regression functions, quantile mapping using distribution derived transformation and quantile mapping using empirical quantiles) for both of them. Ensembles of the obtained series were proposed to obtain more representative potential future climate scenarios to be employed to study potential impacts. In this work we propose a non-equifeaseble combination of the future series giving more weight to those coming from models (delta change approaches) or combination of models and techniques that provides better approximation to the basic and drought statistic of the historical data. A multi-objective analysis using basic statistics (mean, standard deviation and asymmetry coefficient) and droughts statistics (duration, magnitude and intensity) has been performed to identify which models are better in terms of goodness of fit to reproduce the historical series. The drought statistics have been obtained from the Standard Precipitation index (SPI) series using the Theory of Runs. This analysis allows discriminate the best RCM and the best combination of model and correction technique in the bias-correction method. We have also analyzed the possibilities of using different Stochastic Weather Generators to approximate the basic and droughts statistics of the historical series. These analyses have been performed in our case study in a lumped and in a distributed way in order to assess its sensibility to the spatial scale. The statistic of the future temperature series obtained with different ensemble options are quite homogeneous, but the precipitation shows a higher sensibility to the adopted method and spatial scale. The global increment in the mean temperature values are 31.79 %, 31.79 %, 31.03 % and 31.74 % for the distributed bias-correction, distributed delta-change, lumped bias-correction and lumped delta-change ensembles respectively and in the precipitation they are -25.48 %, -28.49 %, -26.42 % and -27.35% respectively. Acknowledgments: This research work has been partially supported by the GESINHIMPADAPT project (CGL2013-48424-C2-2-R) with Spanish MINECO funds. We would also like to thank Spain02 and CORDEX projects for the data provided for this study and the R package qmap.

Daily Estimation of High Resolution PM2.5 Concentrations over BTH area by Fusing MODIS AOD and Ground Observations

NASA Astrophysics Data System (ADS)

Lyu, Baolei; Hu, Yongtao; Chang, Howard; Russell, Armistead; Bai, Yuqi

2017-04-01

The satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) is often used to predict ground-level fine particulate matter (PM2.5) concentrations. The associated estimation accuracy is always reduced by AOD missing values and by insufficiently accounting for the spatio-temporal PM2.5 variations. This study aims to estimate PM2.5 concentrations at a high resolution with enhanced accuracy by fusing MODIS AOD and ground observations in the polluted and populated Beijing-Tianjin-Hebei (BTH) area of China in 2014 and 2015. A Bayesian-based statistical downscaler was employed to model the spatio-temporally varied AOD-PM2.5 relationships. We resampled a 3 km MODIS AOD product to a 4 km resolution in a Lambert conic conformal projection, to assist comparison and fusion with CMAQ predictions. A two-step method was used to fill the missing AOD values to obtain a full AOD dataset with complete spatial coverage. The downscaler has a relatively good performance in the fitting procedure (R2 = 0.75) and in the cross validation procedure (with two evaluation methods, R2 = 0.58 by random method and R2 = 0.47 by city-specific method). The number of missing AOD values was serious and related to elevated PM2.5 concentrations. The gap-filled AOD values corresponded well with our understanding of PM2.5 pollution conditions in BTH. The prediction accuracy of PM2.5 concentrations were improved in terms of their annual and seasonal mean. As a result of its fine spatio-temporal resolution and complete spatial coverage, the daily PM2.5 estimation dataset could provide extensive and insightful benefits to related studies in the BTH area. This may include understanding the formation processes of regional PM2.5 pollution episodes, evaluating daily human exposure, and establishing pollution controlling measures.

Possible future changes in South East Australian frost frequency: an inter-comparison of statistical downscaling approaches

NASA Astrophysics Data System (ADS)

Crimp, Steven; Jin, Huidong; Kokic, Philip; Bakar, Shuvo; Nicholls, Neville

2018-04-01

Anthropogenic climate change has already been shown to effect the frequency, intensity, spatial extent, duration and seasonality of extreme climate events. Understanding these changes is an important step in determining exposure, vulnerability and focus for adaptation. In an attempt to support adaptation decision-making we have examined statistical modelling techniques to improve the representation of global climate model (GCM) derived projections of minimum temperature extremes (frosts) in Australia. We examine the spatial changes in minimum temperature extreme metrics (e.g. monthly and seasonal frost frequency etc.), for a region exhibiting the strongest station trends in Australia, and compare these changes with minimum temperature extreme metrics derived from 10 GCMs, from the Coupled Model Inter-comparison Project Phase 5 (CMIP 5) datasets, and via statistical downscaling. We compare the observed trends with those derived from the "raw" GCM minimum temperature data as well as examine whether quantile matching (QM) or spatio-temporal (spTimerQM) modelling with Quantile Matching can be used to improve the correlation between observed and simulated extreme minimum temperatures. We demonstrate, that the spTimerQM modelling approach provides correlations with observed daily minimum temperatures for the period August to November of 0.22. This represents an almost fourfold improvement over either the "raw" GCM or QM results. The spTimerQM modelling approach also improves correlations with observed monthly frost frequency statistics to 0.84 as opposed to 0.37 and 0.81 for the "raw" GCM and QM results respectively. We apply the spatio-temporal model to examine future extreme minimum temperature projections for the period 2016 to 2048. The spTimerQM modelling results suggest the persistence of current levels of frost risk out to 2030, with the evidence of continuing decadal variation.

Optimal selection of MULTI-model downscaled ensembles for interannual and seasonal climate prediction in the eastern seaboard of Thailand

NASA Astrophysics Data System (ADS)

Bejranonda, W.; Koch, M.

2010-12-01

Because of the imminent threat of the water resources of the eastern seaboard of Thailand, a climate impact study has been carried out there. To that avail, a hydrological watershed model is being used to simulate the future water availability in the wake of possible climate change in the region. The hydrological model is forced by predictions from global climate models (GCMs) that are to be downscaled in an appropriate manner. The challenge at that stage of the climate impact analysis lies then the in the choice of the best GCM and the (statistical) downscaling method. In this study the selection of coarse grid resolution output of the GCMs, transferring information to the fine grid of local climate-hydrology is achieved by cross-correlation and multiple linear regression using meteorological data in the eastern seaboard of Thailand observed between 1970-1999. The grids of 20 atmosphere/ocean global climate models (AOGCM), covering latitude 12.5-15.0 N and longitude 100.0-102.5 E were examined using the Climate-Change Scenario Generator (SCENGEN). With that tool the model efficiency of the prediction of daily precipitation and mean temperature was calculated by comparing the 1980-1999 ECMWF reanalysis predictions with the observed data during that time period. The root means square errors of the predictions were considered and ranked to select the top 5 models, namely, BCCR-BCM2.0, GISS-ER, ECHO-G, ECHAM5/MPI-OM and PCM. The daily time-series of 338 predictors in 9 runs of the 5 selected models were gathered from the CMIP3 multi-model database. Monthly time-serial cross-correlations between the climate predictors and the meteorological measurements from 25 rainfall, 4 minimum and maximum temperature, 4 humidity and 2 solar radiation stations in the study area were then computed and ranked. Using the ranked predictors, a multiple-linear regression model (downscaling transfer model) to forecast the local climate was set up. To improve the prediction power of this GCM downscaling approach, the regression equations were considered as a dynamic regression model that can alter the predictor by seasonal variation. The possible seasonal effect was examined for the 1974-1999 period which was equally divided into a calibration and verification sub-period. The calibrated model using the whole observed time-series was compared with the models separated into 2 seasons; dry and wet, 3 seasons; winter, summer and rainy, and 4 seasons; dry, pre-monsoon, first monsoon and second monsoon. The verification power of the various model variants was measured considering Akaike's information criterion (AIC) and the Nash-Sutcliffe coefficient of the corresponding model fit. The results show that the 4-seasons-variation prediction works best. The highest efficiency for the prediction of rainfall is achieved for the dry season, Oct-Mar, whereas the smallest efficiency is obtained in the monsoon seasons. The overall number of predictor giving top efficiency lies between 3 and 20 in the regression models. In the next, still ongoing stage of the climate impact study the predictions from this new, seasonally optimized downscaling transfer model are being used in the simulations of the future hydrological water budget in that region of Thailand.

Downscaling the Local Weather Above Glaciers in Complex Topography

NASA Astrophysics Data System (ADS)

Horak, Johannes; Hofer, Marlis; Gutmann, Ethan; Gohm, Alexander; Rotach, Mathias

2017-04-01

Glaciers have experienced a substantial ice-volume loss during the 20th century. To study their response to climate change, process-based glacier mass-balance models (PBGMs) are employed, which require a faithful representation of the state of the atmosphere above the glacier at high spatial and temporal resolution. Glaciers are usually located in complex topography where weather stations are scarce or not existent at all due to the remoteness of such sites and the associated high cost of maintenance. Furthermore. the effective resolution of global circulation models is too large to adequately capture the local topography and represent local weather, which is prerequisite for atmospheric input used by PBGMs. Dynamical downscaling is a physically consistent but computationally expensive approach to bridge the scale gap between GCM output and input needed by PBGMs, while statistical downscaling is faster but requires measurements for training. Both methods have their merits, however, a computationally frugal approach that does not rely on measurements is desirable, especially for long term studies of glacier response to future climate. In this study the intermediate complexity atmospheric research model (ICAR) is employed (Gutmann et al., 2016). It simplifies the wind field physics by relying on analytical solutions derived with linear theory. ICAR then advects atmospheric quantities within this wind field. This allows for computationally fast downscaling and yields a physically consistent set of atmospheric variables. First results obtained from downscaling air temperature, precipitation amount, relative humidity and wind speed to 4 × 4 km2 are presented. Preliminary ICAR is applied for a six month simulation period during five years and evaluated for three domains located in very distinct climates, namely the Southern Alps of New Zealand, the Cordillera Blanca in Peru and the European Alps using ERA Interim reanalysis data (ERAI) as forcing data set. The evaluation is based on determining the added value of the ICAR simulations - with ERAI output as a reference - in representing the local-scale weather measured at several automatic weather stations. For precipitation amount in particular, data by the Global Precipitation Measurement project are used in a fuzzy verification approach. The results indicate that ICAR provides added value for the Southern Alps of New Zealand in the case of precipitation and relative humidity, for the Cordillera Blanca and the European Alps for wind speed and, at certain locations in the European Alps, for precipitation. In order to more comprehensively investigate the physical plausibility of skill obtained for specific weather situations, the spatio-temporal evolution of the wind field resulting from the ICAR dynamics is analysed for individual case studies. To the authors knowledge this is the first study that specifically investigates the multi-variable consistency of ICAR for different climates, an important prerequisite for all applications which require multi-variable or multi-site input. References: Gutmann, E., Barstad, I., Clark, M., Arnold, J., and Rasmussen, R. (2016). The Intermediate Complexity Atmospheric Research Model (ICAR). Journal of Hydrometeorology, 17(3), 957-973.

Interannual variability of the frequency and intensity of tropical cyclones striking the California coast

NASA Astrophysics Data System (ADS)

Mendez, F. J.; Rueda, A.; Barnard, P.; Mori, N.; Nakajo, S.; Albuquerque, J.

2016-12-01

Hurricanes hitting California have a very low ocurrence probability due to typically cool ocean temperature and westward tracks. However, damages associated to these improbable events would be dramatic in Southern California and understanding the oceanographic and atmospheric drivers is of paramount importance for coastal risk management for present and future climates. A statistical analysis of the historical events is very difficult due to the limited resolution of atmospheric and oceanographic forcing data available. In this work, we propose a combination of: (a) climate-based statistical downscaling methods (Espejo et al, 2015); and (b) a synthetic stochastic tropical cyclone (TC) model (Nakajo et al, 2014). To build the statistical downscaling model, Y=f(X), we apply a combination of principal component analysis and the k-means classification algorithm to find representative patterns from large-scale may-to-november averaged monthly anomalies of SST and thermocline depth fields in Tropical Pacific (predictor X) and the associated historical tropical cyclones in Eastern North Pacific basin (predictand Y). As data for the historical occurrence and paths of tropical cyclones are scarce, we apply a stochastic TC model which is based on a Monte Carlo simulation of the joint distribution of track, minimum sea level pressure and translation speed of the historical events in the Eastern Central Pacific Ocean. Results will show the ability of the approach to explain the interannual variability of the frequency and intensity of TCs in Southern California, which is clearly related to post El Niño Eastern Pacific and El Niño Central Pacific. References Espejo, A., Méndez, F.J., Diez, J., Medina, R., Al-Yahyai, S. (2015) Seasonal probabilistic forecasting of tropical cyclone activity in the North Indian Ocean, Journal of Flood Risk Management, DOI: 10.1111/jfr3.12197 Nakajo, S., N. Mori, T. Yasuda, and H. Mase (2014) Global Stochastic Tropical Cyclone Model Based on Principal Component Analysis and Cluster Analysis, Journal of Applied Meteorology and Climatology, DOI: 10.1175/JAMC-D-13-08.1

Statistical downscaling of CMIP5 outputs for projecting future maximum and minimum temperature over the Haihe River Bain, China

NASA Astrophysics Data System (ADS)

Yan, Tiezhu; Shen, Zhenyao; Heng, Lee; Dercon, Gerd

2016-04-01

Future climate change information is important to formulate adaptation and mitigation strategies for climate change. In this study, a statistical downscaling model (SDSM) was established using both NCEP reanalysis data and ground observations (daily maximum and minimum temperature) during the period 1971-2010, and then calibrated model was applied to generate the future maximum and minimum temperature projections using predictors from the two CMIP5 models (MPI-ESM-LR and CNRM-CM5) under two Representative Concentration Pathway (RCP2.6 and RCP8.5) during the period 2011-2100 for the Haihe River Basin, China. Compared to the baseline period, future change in annual and seasonal maximum and minimum temperature was computed after bias correction. The spatial distribution and trend change of annual maximum and minimum temperature were also analyzed using ensemble projections. The results shows that: (1)The downscaling model had a good applicability on reproducing daily and monthly mean maximum and minimum temperature over the whole basin. (2) Bias was observed when using historical predictors from CMIP5 models and the performance of CNRM-CM5 was a little worse than that of MPI-ESM-LR. (3) The change in annual mean maximum and minimum temperature under the two scenarios in 2020s, 2050s and 2070s will increase and magnitude of maximum temperature will be higher than minimum temperature. (4) The increase in temperature in the mountains and along the coastline is remarkably high than the other parts of the studies basin. (5) For annual maximum and minimum temperature, the significant upward trend will be obtained under RCP 8.5 scenario and the magnitude will be 0.37 and 0.39 ℃ per decade, respectively; the increase in magnitude under RCP 2.6 scenario will be upward in 2020s and then decrease in 2050s and 2070s, and the magnitude will be 0.01 and 0.01℃ per decade, respectively.

Statistical downscaling of regional climate scenarios for the French Alps : Impacts on snow cover

NASA Astrophysics Data System (ADS)

Rousselot, M.; Durand, Y.; Giraud, G.; Mérindol, L.; Déqué, M.; Sanchez, E.; Pagé, C.; Hasan, A.

2010-12-01

Mountain areas are particularly vulnerable to climate change. Owing to the complexity of mountain terrain, climate research at scales relevant for impacts studies and decisive for stakeholders is challenging. A possible way to bridge the gap between these fine scales and those of the general circulation models (GCMs) consists of combining high-resolution simulations of Regional Climate Models (RCMs) to statistical downscaling methods. The present work is based on such an approach. It aims at investigating the impacts of climate change on snow cover in the French Alps for the periods 2021-2050 and 2071-2100 under several IPCC hypotheses. An analogue method based on high resolution atmospheric fields from various RCMs and climate reanalyses is used to simulate local climate scenarios. These scenarios, which provide meteorological parameters relevant for snowpack evolution, subsequently feed the CROCUS snow model. In these simulations, various sources of uncertainties are thus considered (several greenhouse gases emission scenarios and RCMs). Results are obtained for different regions of the French Alps at various altitudes. For all scenarios, temperature increase is relatively uniform over the Alps. This regional warming is larger than that generally modeled at the global scale (IPCC, 2007), and particularly strong in summer. Annual precipitation amounts seem to decrease, mainly as a result of decreasing precipitation trends in summer and fall. As a result of these climatic evolutions, there is a general decrease of the mean winter snow depth and seasonal snow duration for all massifs. Winter snow depths are particularly reduced in the Northern Alps. However, the impact on seasonal snow duration is more significant in the Southern and Extreme Southern Alps, since these regions are already characterized by small winter snow depths at low elevations. Reference : IPCC (2007a). Climate change 2007 : The physical science basis. Contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change. In : Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Averyt, M. Tignor, and H.L. Miller (eds.). Cambridge University Press, Cambridge, UK and New York, NY, USA. This work is performed in the framework of the SCAMPEI ANR (French research project).

Validation of two (parametric vs non-parametric) daily weather generators

NASA Astrophysics Data System (ADS)

Dubrovsky, M.; Skalak, P.

2015-12-01

As the climate models (GCMs and RCMs) fail to satisfactorily reproduce the real-world surface weather regime, various statistical methods are applied to downscale GCM/RCM outputs into site-specific weather series. The stochastic weather generators are among the most favourite downscaling methods capable to produce realistic (observed-like) meteorological inputs for agrological, hydrological and other impact models used in assessing sensitivity of various ecosystems to climate change/variability. To name their advantages, the generators may (i) produce arbitrarily long multi-variate synthetic weather series representing both present and changed climates (in the latter case, the generators are commonly modified by GCM/RCM-based climate change scenarios), (ii) be run in various time steps and for multiple weather variables (the generators reproduce the correlations among variables), (iii) be interpolated (and run also for sites where no weather data are available to calibrate the generator). This contribution will compare two stochastic daily weather generators in terms of their ability to reproduce various features of the daily weather series. M&Rfi is a parametric generator: Markov chain model is used to model precipitation occurrence, precipitation amount is modelled by the Gamma distribution, and the 1st order autoregressive model is used to generate non-precipitation surface weather variables. The non-parametric GoMeZ generator is based on the nearest neighbours resampling technique making no assumption on the distribution of the variables being generated. Various settings of both weather generators will be assumed in the present validation tests. The generators will be validated in terms of (a) extreme temperature and precipitation characteristics (annual and 30-years extremes and maxima of duration of hot/cold/dry/wet spells); (b) selected validation statistics developed within the frame of VALUE project. The tests will be based on observational weather series from several European stations available from the ECA&D database. Acknowledgements: The weather generator is developed and validated within the frame of projects WG4VALUE (sponsored by the Ministry of Education, Youth and Sports of CR), and VALUE (COST ES 1102 action).

Use of a Weather Generator for analysis of projections of future daily temperature and its validation with climate change indices

NASA Astrophysics Data System (ADS)

Di Piazza, A.; Cordano, E.; Eccel, E.

2012-04-01

The issue of climate change detection is considered a major challenge. In particular, high temporal resolution climate change scenarios are required in the evaluation of the effects of climate change on agricultural management (crop suitability, yields, risk assessment, etc.) energy production and water management. In this work, a "Weather Generator" technique was used for downscaling climate change scenarios for temperature. An R package (RMAWGEN, Cordano and Eccel, 2011 - available on http://cran.r-project.org) was developed aiming to generate synthetic daily weather conditions by using the theory of vectorial auto-regressive models (VAR). The VAR model was chosen for its ability in maintaining the temporal and spatial correlations among variables. In particular, observed time series of daily maximum and minimum temperature are transformed into "new" normally-distributed variable time series which are used to calibrate the parameters of a VAR model by using ordinary least square methods. Therefore the implemented algorithm, applied to monthly mean climatic values downscaled by Global Climate Model predictions, can generate several stochastic daily scenarios where the statistical consistency among series is saved. Further details are present in RMAWGEN documentation. An application is presented here by using a dataset with daily temperature time series recorded in 41 different sites of Trentino region for the period 1958-2010. Temperature time series were pre-processed to fill missing values (by a site-specific calibrated Inverse Distance Weighting algorithm, corrected with elevation) and to remove inhomogeneities. Several climatic indices were taken into account, useful for several impact assessment applications, and their time trends within the time series were analyzed. The indices go from the more classical ones, as annual mean temperatures, seasonal mean temperatures and their anomalies (from the reference period 1961-1990) to the climate change indices selected from the list recommended by the World Meteorological Organization Commission for Climatology (WMO-CCL) and the Research Programme on Climate Variability and Predictability (CLIVAR) project's Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI). Each index was applied to both observed (and processed) data and to synthetic time series produced by the Weather Generator, over the thirty year reference period 1981-2010, in order to validate the procedure. Climate projections were statistically downscaled for a selection of sites for the two 30-year periods 2021-2050 and 2071-2099 of the European project "Ensembles" multi-model output (scenario A1B). The use of several climatic indices strengthens the trend analysis of both the generated synthetic series and future climate projections.

Reanalysis of the 1893 heat wave in France through offline data assimilation in a downscaled ensemble meteorological reconstruction

NASA Astrophysics Data System (ADS)

Devers, Alexandre; Vidal, Jean-Philippe; Lauvernet, Claire; Graff, Benjamin

2017-04-01

The knowledge of historical French weather has recently been improved through the development of the SCOPE (Spatially COherent Probabilistic Extended) Climate reconstruction, a probabilistic high-resolution daily reconstruction of precipitation and temperature covering the period 1871-2012 and based on the statistical downscaling of the Twentieth Century Reanalysis (Caillouet et al., 2016). However, historical surface observations - even though rather scarce and sparse - do exist from at least the beginning of the period considered, and this information does not currently feed SCOPE Climate reconstructions. The goal of this study is therefore to assimilate these historical observations into SCOPE Climate reconstructions in order to build a 150-year meteorological reanalysis over France. This study considers "offline" data assimilation methods - Kalman filtering methods like the Ensemble Square Root Filter - that have successfully been used in recent paleoclimate studies, i.e. at much larger temporal and spatial scales (see e.g. Bhend et al., 2012). These methods are here applied for reconstructing the 8-24 August 1893 heat wave in France, using all available daily temperature observations from that period. Temperatures reached that summer were indeed compared at the time to those of Senegal (Garnier, 2012). Results show a spatially coherent view of the heat wave at the national scale as well as a reduced uncertainty compared to initial meteorological reconstructions, thus demonstrating the added value of data assimilation. In order to assess the performance of assimilation methods in a more recent context, these methods are also used to reconstruct the well-known 3-14 August 2003 heat wave by using (1) all available stations, and (2) the same station density as in August 1893, the rest of the observations being saved for validation. This analysis allows comparing two heat waves having occurred 100 years apart in France with different associated uncertainties, in terms of dynamics and intensity. Bhend, J., Franke, J., Folini, D., Wild, M., and Brönnimann, S.: An ensemble-based approach to climate reconstructions, Clim. Past, 8, 963-976, doi: 10.5194/cp-8-963-2012, 2012 Caillouet, L., Vidal, J-P., Sauquet, E., and Graff, B.: Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France, Clim. Past, 12, 635-662, doi: 10.5194/cp-12-635-2016, 2016. Garnier, E.: Sécheresses et canicules avant le Global Warming - 1500-1950. In: Canicules et froids extrêmes. L'Événement climatique et ses représentations (II) Histoire, littérature, peinture (Berchtlod, J., Le Roy ladurie, E., Sermain, J.-P., and Vasak, A., Eds.), 297-325, Hermann, 2012.

Downscaling catchment scale flood risk to contributing sub-catchments to determine the optimum location for flood management.

NASA Astrophysics Data System (ADS)

Pattison, Ian; Lane, Stuart; Hardy, Richard; Reaney, Sim

2010-05-01

The recent increase in flood frequency and magnitude has been hypothesised to have been caused by either climate change or land management. Field scale studies have found that changing land management practices does affect local runoff and streamflow, but upscaling these effects to the catchment scale continues to be problematic, both conceptually and more importantly methodologically. The impact on downstream flood risk is highly dependent upon where the changes are in the catchment, indicating that some areas of the catchment are more important in determining downstream flood risk than others. This is a major flaw in the traditional approach to studying the effect of land use on downstream flood risk: catchment scale hydrological models, which treat every cell in the model equally. We are proposing an alternative ideological approach for doing flood management research, which is underpinned by downscaling the downstream effect (problem i.e. flooding) to the upstream causes (contributing sub-catchments). It is hoped that this approach could have several benefits over the traditional upscaling approach. Firstly, it provides an efficient method to prioritise areas for land use management changes to be implemented to reduce downstream flood risk. Secondly, targets for sub-catchment hydrograph change can be determined which will deliver the required downstream effect. Thirdly, it may be possible to detect the effect of land use changes in upstream areas on downstream flood risk, by weighting the areas of most importance in hydrological models. Two methods for doing this downscaling are proposed; 1) data-based statistical analysis; and 2) hydraulic modelling-based downscaling. These will be outlined using the case study of the River Eden, Cumbria, NW England. The data-based methodology uses the timing and magnitude of floods for each sub-catchment. Principal components analysis (PCA) is used to simplify sub-catchment interactions and optimising stepwise regression is used to predict downstream flood magnitude from the significant principal components. Two particular sub-catchments, the Eamont and the Upper Eden were highlighted as explaining the highest proportion of downstream flood risk, with 21.0% and 19.6% respectively. This approach uses the concept of data mining, whereby commonly available discharge data is used in an innovative way to learn about catchment behaviour. An alternative downscaling approach is hydraulic modelling whereby the input hydrographs from each tributary are changed in turn, both in terms of the magnitudes and the timing of the flows. This basic scenario testing approach can be used to assess the sensitivity of downstream flood risk to upstream contributing tributaries. This approach also highlighted the Upper Eden and Eamont as the most sensitive sub-catchments. A 25% reduction in the flows from these sub-catchments resulted in a 33.1cm and 21.9cm stage reduction downstream respectively, while an 8 hour delay of the peak flow caused a 32.3cm and 27.4cm decrease in downstream stage respectively. This alternative flood management approach is not a replacement to traditional hydrological modelling (upscaling), but a pre-step which allows for more focussed and informed investigation of land management scenarios, in the area where they are most likely to have beneficial impacts on downstream flooding.

Observational uncertainty and regional climate model evaluation: A pan-European perspective

NASA Astrophysics Data System (ADS)

Kotlarski, Sven; Szabó, Péter; Herrera, Sixto; Räty, Olle; Keuler, Klaus; Soares, Pedro M.; Cardoso, Rita M.; Bosshard, Thomas; Pagé, Christian; Boberg, Fredrik; Gutiérrez, José M.; Jaczewski, Adam; Kreienkamp, Frank; Liniger, Mark. A.; Lussana, Cristian; Szepszo, Gabriella

2017-04-01

Local and regional climate change assessments based on downscaling methods crucially depend on the existence of accurate and reliable observational reference data. In dynamical downscaling via regional climate models (RCMs) observational data can influence model development itself and, later on, model evaluation, parameter calibration and added value assessment. In empirical-statistical downscaling, observations serve as predictand data and directly influence model calibration with corresponding effects on downscaled climate change projections. Focusing on the evaluation of RCMs, we here analyze the influence of uncertainties in observational reference data on evaluation results in a well-defined performance assessment framework and on a European scale. For this purpose we employ three different gridded observational reference grids, namely (1) the well-established EOBS dataset (2) the recently developed EURO4M-MESAN regional re-analysis, and (3) several national high-resolution and quality-controlled gridded datasets that recently became available. In terms of climate models five reanalysis-driven experiments carried out by five different RCMs within the EURO-CORDEX framework are used. Two variables (temperature and precipitation) and a range of evaluation metrics that reflect different aspects of RCM performance are considered. We furthermore include an illustrative model ranking exercise and relate observational spread to RCM spread. The results obtained indicate a varying influence of observational uncertainty on model evaluation depending on the variable, the season, the region and the specific performance metric considered. Over most parts of the continent, the influence of the choice of the reference dataset for temperature is rather small for seasonal mean values and inter-annual variability. Here, model uncertainty (as measured by the spread between the five RCM simulations considered) is typically much larger than reference data uncertainty. For parameters of the daily temperature distribution and for the spatial pattern correlation, however, important dependencies on the reference dataset can arise. The related evaluation uncertainties can be as large or even larger than model uncertainty. For precipitation the influence of observational uncertainty is, in general, larger than for temperature. It often dominates model uncertainty especially for the evaluation of the wet day frequency, the spatial correlation and the shape and location of the distribution of daily values. But even the evaluation of large-scale seasonal mean values can be considerably affected by the choice of the reference. When employing a simple and illustrative model ranking scheme on these results it is found that RCM ranking in many cases depends on the reference dataset employed.

Examining Extreme Events Using Dynamically Downscaled 12-km WRF Simulations

EPA Science Inventory

Continued improvements in the speed and availability of computational resources have allowed dynamical downscaling of global climate model (GCM) projections to be conducted at increasingly finer grid scales and over extended time periods. The implementation of dynamical downscal...

Empirical downscaling of atmospheric key variables above a tropical glacier surface (Cordillera Blanca, Peru)

NASA Astrophysics Data System (ADS)

Hofer, M.; Kaser, G.; Mölg, T.; Juen, I.; Wagnon, P.

2009-04-01

Glaciers in the outer tropical Cordillera Blanca (Peru, South America) are of major socio-economic importance, since glacier runoff represents the primary water source during the dry season, when little or no rainfall occurs. Due to their location at high elevations, the glaciers moreover provide important information about climate change in the tropical troposphere, where measurements are sparse. This study targets the local reconstruction of air temperature, specific humidity and wind speed above the surface of an outer tropical glacier from NCEP/NCAR reanalysis data as large scale predictors. Since a farther scope is to provide input data for process based glacier mass balance modelling, the reconstruction pursues a high temporal resolution. Hence an empirical downscaling scheme is developed, based on a few years' time series of hourly observations from automatic weather stations, located at the glacier Artesonraju and nearby moraines (Northern Cordillera Blanca). Principal component and multiple regression analyses are applied to define the appropriate spatial downscaling domain, suitable predictor variables, and the statistical transfer functions. The model performance is verified using an independent data set. The best predictors are lower tropospheric air temperature and specific humidity, at reanalysis model grid points that represent the Bolivian Altiplano, located in the South of the Cordillera Blanca. The developed downscaling model explaines a considerable portion (more than 60%) of the diurnal variance of air temperature and specific humidity at the moraine stations, and air temperature above the glacier surface. Specific humidity above the glacier surface, however, can be reconstructed well in the seasonal, but not in the required diurnal time resolution. Wind speed can only be poorly determined by the large scale predictors (r² lower than 0.3) at both sites. We assume a complex local interaction between valley and glacier wind system to be the main cause for the differences between model and observations.

Assessing and mapping spatial associations among oral cancer mortality rates, concentrations of heavy metals in soil, and land use types based on multiple scale data.

PubMed

Lin, Wei-Chih; Lin, Yu-Pin; Wang, Yung-Chieh; Chang, Tsun-Kuo; Chiang, Li-Chi

2014-02-21

In this study, a deconvolution procedure was used to create a variogram of oral cancer (OC) rates. Based on the variogram, area-to-point (ATP) Poisson kriging and p-field simulation were used to downscale and simulate, respectively, the OC rate data for Taiwan from the district scale to a 1 km × 1 km grid scale. Local cluster analysis (LCA) of OC mortality rates was then performed to identify OC mortality rate hot spots based on the downscaled and the p-field-simulated OC mortality maps. The relationship between OC mortality and land use was studied by overlapping the maps of the downscaled OC mortality, the LCA results, and the land uses. One thousand simulations were performed to quantify local and spatial uncertainties in the LCA to identify OC mortality hot spots. The scatter plots and Spearman's rank correlation yielded the relationship between OC mortality and concentrations of the seven metals in the 1 km cell grid. The correlation analysis results for the 1 km scale revealed a weak correlation between OC mortality rate and concentrations of the seven studied heavy metals in soil. Accordingly, the heavy metal concentrations in soil are not major determinants of OC mortality rates at the 1 km scale at which soils were sampled. The LCA statistical results for local indicator of spatial association (LISA) revealed that the sites with high probability of high-high (high value surrounded by high values) OC mortality at the 1 km grid scale were clustered in southern, eastern, and mid-western Taiwan. The number of such sites was also significantly higher on agricultural land and in urban regions than on land with other uses. The proposed approach can be used to downscale and evaluate uncertainty in mortality data from a coarse scale to a fine scale at which useful additional information can be obtained for assessing and managing land use and risk.

Spatial analysis and statistical modelling of snow cover dynamics in the Central Himalayas, Nepal

NASA Astrophysics Data System (ADS)

Weidinger, Johannes; Gerlitz, Lars; Böhner, Jürgen

2017-04-01

General circulation models are able to predict large scale climate variations in global dimensions, however small scale dynamic characteristics, such as snow cover and its temporal variations in high mountain regions, are not represented sufficiently. Detailed knowledge about shifts in seasonal ablation times and spatial distribution of snow cover are crucial for various research interests. Since high mountain areas, for instance the Central Himalayas in Nepal, are generally remote, it is difficult to obtain data in high spatio-temporal resolutions. Regional climate models and downscaling techniques are implemented to compensate coarse resolution. Furthermore earth observation systems, such as MODIS, also permit bridging this gap to a certain extent. They offer snow (cover) data in daily temporal and medium spatial resolution of around 500 m, which can be applied as evaluation and training data for dynamical hydrological and statistical analyses. Within this approach two snow distribution models (binary snow cover and fractional snow cover) as well as one snow recession model were implemented for a research domain in the Rolwaling Himal in Nepal, employing the random forest technique, which represents a state of the art machine learning algorithm. Both bottom-up strategies provide inductive reasoning to derive rules for snow related processes out of climate (temperature, precipitation and irradiance) and climate-related topographic data sets (elevation, aspect and convergence index) obtained by meteorological network stations, remote sensing products (snow cover - MOD10-A1 and land surface temperatures - MOD11-A1) along with GIS. Snow distribution is predicted reliably on a daily basis in the research area, whereas further effort is necessary for predicting daily snow cover recession processes adequately. Swift changes induced by clear sky conditions with high insolation rates are well represented, whereas steady snow loss still needs continuing effort. All approaches underline the technical difficulties of snow cover modelling during the monsoon season, in accordance with previous studies. The developed methods in combination with continuous in situ measurements provide a basis for further downscaling approaches.

Climatological Downscaling and Evaluation of AGRMET Precipitation Analyses Over the Continental U.S.

NASA Astrophysics Data System (ADS)

Garcia, M.; Peters-Lidard, C. D.; Eylander, J. B.; Daly, C.; Tian, Y.; Zeng, J.

2007-05-01

The spatially distributed application of a land surface model (LSM) over a region of interest requires the application of similarly distributed precipitation fields that can be derived from various sources, including surface gauge networks, surface-based radar, and orbital platforms. The spatial variability of precipitation influences the spatial organization of soil temperature and moisture states and, consequently, the spatial variability of land- atmosphere fluxes. The accuracy of spatially-distributed precipitation fields can contribute significantly to the uncertainty of model-based hydrological states and fluxes at the land surface. Collaborations between the Air Force Weather Agency (AFWA), NASA, and Oregon State University have led to improvements in the processing of meteorological forcing inputs for the NASA-GSFC Land Information System (LIS; Kumar et al. 2006), a sophisticated framework for LSM operation and model coupling experiments. Efforts at AFWA toward the production of surface hydrometeorological products are currently in transition from the legacy Agricultural Meteorology modeling system (AGRMET) to use of the LIS framework and procedures. Recent enhancements to meteorological input processing for application to land surface models in LIS include the assimilation of climate-based information for the spatial interpolation and downscaling of precipitation fields. Climatological information included in the LIS-based downscaling procedure for North America is provided by a monthly high-resolution PRISM (Daly et al. 1994, 2002; Daly 2006) dataset based on a 30-year analysis period. The combination of these sources and methods attempts to address the strengths and weaknesses of available legacy products, objective interpolation methods, and the PRISM knowledge-based methodology. All of these efforts are oriented on an operational need for timely estimation of spatial precipitation fields at adequate spatial resolution for customer dissemination and near-real-time simulations in regions of interest. This work focuses on value added to the AGRMET precipitation product by the inclusion of high-quality climatological information on a monthly time scale. The AGRMET method uses microwave-based satellite precipitation estimates from various polar-orbiting platforms (NOAA POES and DMSP), infrared-based estimates from geostationary platforms (GOES, METEOSAT, etc.), related cloud analysis products, and surface gauge observations in a complex and hierarchical blending process. Results from processing of the legacy AGRMET precipitation products over the U.S. using LIS-based methods for downscaling, both with and without climatological factors, are evaluated against high-resolution monthly analyses using the PRISM knowledge- based method (Daly et al. 2002). It is demonstrated that the incorporation of climatological information in a downscaling procedure can significantly enhance the accuracy, and potential utility, of AFWA precipitation products for military and civilian customer applications.

Utilization of an Enhanced Canonical Correlation Analysis (ECCA) to Predict Daily Precipitation and Temperature in a Semi-Arid Environment

NASA Astrophysics Data System (ADS)

Lopez, S. R.; Hogue, T. S.

2011-12-01

Global climate models (GCMs) are primarily used to generate historical and future large-scale circulation patterns at a coarse resolution (typical order of 50,000 km2) and fail to capture climate variability at the ground level due to localized surface influences (i.e topography, marine, layer, land cover, etc). Their inability to accurately resolve these processes has led to the development of numerous 'downscaling' techniques. The goal of this study is to enhance statistical downscaling of daily precipitation and temperature for regions with heterogeneous land cover and topography. Our analysis was divided into two periods, historical (1961-2000) and contemporary (1980-2000), and tested using sixteen predictand combinations from four GCMs (GFDL CM2.0, GFDL CM2.1, CNRM-CM3 and MRI-CGCM2 3.2a. The Southern California area was separated into five county regions: Santa Barbara, Ventura, Los Angeles, Orange and San Diego. Principle component analysis (PCA) was performed on ground-based observations in order to (1) reduce the number of redundant gauges and minimize dimensionality and (2) cluster gauges that behave statistically similarly for post-analysis. Post-PCA analysis included extensive testing of predictor-predictand relationships using an enhanced canonical correlation analysis (ECCA). The ECCA includes obtaining the optimal predictand sets for all models within each spatial domain (county) as governed by daily and monthly overall statistics. Results show all models maintain mean annual and monthly behavior within each county and daily statistics are improved. The level of improvement highly depends on the vegetation extent within each county and the land-to-ocean ratio within the GCM spatial grid. The utilization of the entire historical period also leads to better statistical representation of observed daily precipitation. The validated ECCA technique is being applied to future climate scenarios distributed by the IPCC in order to provide forcing data for regional hydrologic models and assess future water resources in the Southern California region.

Evaluation of climate change effects on the hydrology of a medium-sized Mediterranean basin affected by data sparseness

NASA Astrophysics Data System (ADS)

Piras, Monica; Mascaro, Giuseppe; Deidda, Roberto; Vivoni, Enrique R.

2014-05-01

Many studies based on global and regional climate models agree on the prediction that the Mediterranean area will be most likely affected by climate changes with consequent reduced water availability and intensified hydrologic extremes. This study evaluates the effects of climate changes on the hydrologic response of a medium-sized Mediterranean basin through downscaling techniques and hydrologic simulations. The watershed is the Rio Mannu at Monastir basin (473 km2), located in an agricultural area of southern Sardinia, Italy, which has suffered drought issues in the last decades. It is one of the seven study cases of a multidisciplinary European research project, CLIMB (Climate Induced Changes on the Hydrology of Mediterranean Basins). In such basins, characterized by strong climate variability and by a complex hydrologic response, process based distributed hydrologic models, DHMs, combined with regional climate models, RCMs, and downscaling techniques can help in the evaluation of the local impacts of climate change on water resources decreasing the uncertainty. Since the Rio Mannu basin is affected by data sparseness (meteorological and streamflow data are collected in non overlapping time periods and at diverse time resolutions), two statistical downscaling strategies for precipitation and potential evapotranspiration have been designed which allow to obtain the high-resolution input data required for the calibration of our hydrologic model, the TIN-based Real time Integrated Basin Simulator (tRIBS). We show how the DHM has been calibrated and validated with reasonable accuracy using the disaggregation tools. Next, the same downscaling algorithms have been used to fill the resolution discrepancy between RCMs and the hydrologic model. The outputs of four RCMs, selected as the best performing and bias corrected within the CLIMB project, have been downscaled and used to force the tRIBS during a reference (1971-2000) and a future (2041-2070) period. Several hydro-climatic indicators have been computed based on the time series and spatial maps produced by the DHM to assess the variation in Rio Mannu water resources budget and hydrologic extremes in the future period as compared to the reference one. Our results confirms what is generally predicted for the Mediterranean area, showing a basin future condition of more water shortages due to both reduced precipitations and increased temperatures.

Downscaling SSPs in Bangladesh - Integrating Science, Modelling and Stakeholders Through Qualitative and Quantitative Scenarios

NASA Astrophysics Data System (ADS)

Allan, A.; Barbour, E.; Salehin, M.; Hutton, C.; Lázár, A. N.; Nicholls, R. J.; Rahman, M. M.

2015-12-01

A downscaled scenario development process was adopted in the context of a project seeking to understand relationships between ecosystem services and human well-being in the Ganges-Brahmaputra delta. The aim was to link the concerns and priorities of relevant stakeholders with the integrated biophysical and poverty models used in the project. A 2-stage process was used to facilitate the connection between stakeholders concerns and available modelling capacity: the first to qualitatively describe what the future might look like in 2050; the second to translate these qualitative descriptions into the quantitative form required by the numerical models. An extended, modified SSP approach was adopted, with stakeholders downscaling issues identified through interviews as being priorities for the southwest of Bangladesh. Detailed qualitative futures were produced, before modellable elements were quantified in conjunction with an expert stakeholder cadre. Stakeholder input, using the methods adopted here, allows the top-down focus of the RCPs to be aligned with the bottom-up approach needed to make the SSPs appropriate at the more local scale, and also facilitates the translation of qualitative narrative scenarios into a quantitative form that lends itself to incorporation of biophysical and socio-economic indicators. The presentation will describe the downscaling process in detail, and conclude with findings regarding the importance of stakeholder involvement (and logistical considerations), balancing model capacity with expectations and recommendations on SSP refinement at local levels.

Downscaling SSPs in the GBM Delta - Integrating Science, Modelling and Stakeholders Through Qualitative and Quantitative Scenarios

NASA Astrophysics Data System (ADS)

Allan, Andrew; Barbour, Emily; Salehin, Mashfiqus; Munsur Rahman, Md.; Hutton, Craig; Lazar, Attila

2016-04-01

A downscaled scenario development process was adopted in the context of a project seeking to understand relationships between ecosystem services and human well-being in the Ganges-Brahmaputra delta. The aim was to link the concerns and priorities of relevant stakeholders with the integrated biophysical and poverty models used in the project. A 2-stage process was used to facilitate the connection between stakeholders concerns and available modelling capacity: the first to qualitatively describe what the future might look like in 2050; the second to translate these qualitative descriptions into the quantitative form required by the numerical models. An extended, modified SSP approach was adopted, with stakeholders downscaling issues identified through interviews as being priorities for the southwest of Bangladesh. Detailed qualitative futures were produced, before modellable elements were quantified in conjunction with an expert stakeholder cadre. Stakeholder input, using the methods adopted here, allows the top-down focus of the RCPs to be aligned with the bottom-up approach needed to make the SSPs appropriate at the more local scale, and also facilitates the translation of qualitative narrative scenarios into a quantitative form that lends itself to incorporation of biophysical and socio-economic indicators. The presentation will describe the downscaling process in detail, and conclude with findings regarding the importance of stakeholder involvement (and logistical considerations), balancing model capacity with expectations and recommendations on SSP refinement at local levels.

Simulation of tropical cyclone activity over the western North Pacific based on CMIP5 models

NASA Astrophysics Data System (ADS)

Shen, Haibo; Zhou, Weican; Zhao, Haikun

2017-09-01

Based on the Coupled Model Inter-comparison Project 5 (CMIP5) models, the tropical cyclone (TC) activity in the summers of 1965-2005 over the western North Pacific (WNP) is simulated by a TC dynamically downscaling system. In consideration of diversity among climate models, Bayesian model averaging (BMA) and equal-weighed model averaging (EMA) methods are applied to produce the ensemble large-scale environmental factors of the CMIP5 model outputs. The environmental factors generated by BMA and EMA methods are compared, as well as the corresponding TC simulations by the downscaling system. Results indicate that BMA method shows a significant advantage over the EMA. In addition, impacts of model selections on BMA method are examined. To each factor, ten models with better performance are selected from 30 CMIP5 models and then conduct BMA, respectively. As a consequence, the ensemble environmental factors and simulated TC activity are similar with the results from the 30 models' BMA, which verifies the BMA method can afford corresponding weight for each model in the ensemble based on the model's predictive skill. Thereby, the existence of poor performance models will not particularly affect the BMA effectiveness and the ensemble outcomes are improved. Finally, based upon the BMA method and downscaling system, we analyze the sensitivity of TC activity to three important environmental factors, i.e., sea surface temperature (SST), large-scale steering flow, and vertical wind shear. Among three factors, SST and large-scale steering flow greatly affect TC tracks, while average intensity distribution is sensitive to all three environmental factors. Moreover, SST and vertical wind shear jointly play a critical role in the inter-annual variability of TC lifetime maximum intensity and frequency of intense TCs.

Exploring Land Use and Land Cover Change and Feedbacks in the Global Change Assessment Model

NASA Astrophysics Data System (ADS)

Chen, M.; Vernon, C. R.; Huang, M.; Calvin, K. V.; Le Page, Y.; Kraucunas, I.

2017-12-01

Land Use and Land Cover Change (LULCC) is a major driver of global and regional environmental change. Projections of land use change are thus an essential component in Integrated Assessment Models (IAMs) to study feedbacks between transformation of energy systems and land productivity under the context of climate change. However, the spatial scale of IAMs, e.g., the Global Change Assessment Model (GCAM), is typically larger than the scale of terrestrial processes in the human-Earth system, LULCC downscaling therefore becomes a critical linkage among these multi-scale and multi-sector processes. Parametric uncertainties in LULCC downscaling algorithms, however, have been under explored, especially in the context of how such uncertainties could propagate to affect energy systems in a changing climate. In this study, we use a LULCC downscaling model, Demeter, to downscale GCAM-based future land use scenarios into fine spatial scales, and explore the sensitivity of downscaled land allocations to key parameters. Land productivity estimates (e.g., biomass production and crop yield) based on the downscaled LULCC scenarios are then fed to GCAM to evaluate how energy systems might change due to altered water and carbon cycle dynamics and their interactions with the human system, , which would in turn affect future land use projections. We demonstrate that uncertainties in LULCC downscaling can result in significant differences in simulated scenarios, indicating the importance of quantifying parametric uncertainties in LULCC downscaling models for integrated assessment studies.

Quantifying Information Gain from Dynamic Downscaling Experiments

NASA Astrophysics Data System (ADS)

Tian, Y.; Peters-Lidard, C. D.

2015-12-01

Dynamic climate downscaling experiments are designed to produce information at higher spatial and temporal resolutions. Such additional information is generated from the low-resolution initial and boundary conditions via the predictive power of the physical laws. However, errors and uncertainties in the initial and boundary conditions can be propagated and even amplified to the downscaled simulations. Additionally, the limit of predictability in nonlinear dynamical systems will also damper the information gain, even if the initial and boundary conditions were error-free. Thus it is critical to quantitatively define and measure the amount of information increase from dynamic downscaling experiments, to better understand and appreciate their potentials and limitations. We present a scheme to objectively measure the information gain from such experiments. The scheme is based on information theory, and we argue that if a downscaling experiment is to exhibit value, it has to produce more information than what can be simply inferred from information sources already available. These information sources include the initial and boundary conditions, the coarse resolution model in which the higher-resolution models are embedded, and the same set of physical laws. These existing information sources define an "information threshold" as a function of the spatial and temporal resolution, and this threshold serves as a benchmark to quantify the information gain from the downscaling experiments, or any other approaches. For a downscaling experiment to shown any value, the information has to be above this threshold. A recent NASA-supported downscaling experiment is used as an example to illustrate the application of this scheme.

"Going the Extra Mile in Downscaling: Why Downscaling is not jut "Plug-and-Play"

EPA Science Inventory

This presentation provides an example of doing additional work for preprocessing global climate model data for use in regional climate modeling simulations with the Weather Research and Forecasting (WRF) model. In this presentation, results from 15 months of downscaling the Comm...

Fast downscaled inverses for images compressed with M-channel lapped transforms.

PubMed

de Queiroz, R L; Eschbach, R

1997-01-01

Compressed images may be decompressed and displayed or printed using different devices at different resolutions. Full decompression and rescaling in space domain is a very expensive method. We studied downscaled inverses where the image is decompressed partially, and a reduced inverse transform is used to recover the image. In this fashion, fewer transform coefficients are used and the synthesis process is simplified. We studied the design of fast inverses, for a given forward transform. General solutions are presented for M-channel finite impulse response (FIR) filterbanks, of which block and lapped transforms are a subset. Designs of faster inverses are presented for popular block and lapped transforms.

Two Methods to Derive Ground-level Concentrations of PM2.5 with Improved Accuracy in the North China, Calibrating MODIS AOD and CMAQ Model Predictions

NASA Astrophysics Data System (ADS)

Lyu, Baolei; Hu, Yongtao; Chang, Howard; Russell, Armistead; Bai, Yuqi

2016-04-01

Reliable and accurate characterizations of ground-level PM2.5 concentrations are essential to understand pollution sources and evaluate human exposures etc. Monitoring network could only provide direct point-level observations at limited locations. At the locations without monitors, there are generally two ways to estimate the pollution levels of PM2.5. One is observations of aerosol properties from the satellite-based remote sensing, such as Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD). The other one is from deterministic atmospheric chemistry models, such as the Community Multi-Scale Air Quality Model (CMAQ). In this study, we used a statistical spatio-temporal downscaler to calibrate the two datasets to monitor observations to derive fine-scale ground-level concentrations of PM2.5 with improved accuracy. We treated both MODIS AOD and CMAQ model predictions as biased proxy estimations of PM2.5 pollution levels. The downscaler proposed a Bayesian framework to model the spatially and temporally varying coefficients of the two types of estimations in the linear regression setting, in order to correct biases. Especially for calibrating MODIS AOD, a city-specific linear model was established to fill the missing AOD values, and a novel interpolation-based variable, i.e. PM2.5 Spatial Interpolator, was introduced to account for the spatial dependence among grid cells. We selected the heavy polluted and populated North China as our study area, in a grid setting of 81×81 12-km cells. For the evaluation of calibration performance for retrieved MODIS AOD, the R2 was 0.61 by the full model with PM2.5 Spatial Interpolator being presented, and was 0.48 with PM2.5 Spatial Interpolator not being presented. The constructed AOD values effectively predicted PM2.5 concentrations under our model structure, with R2=0.78. For the evaluation of calibrated CMAQ predictions, the R2 was 0.51, a little less than that of calibrated AOD. Finally we obtained two sets of calibrated estimations of ground-level PM2.5 concentrations with complete spatial coverage. By comparing the two datasets, we found that the prediction from AOD have a little smoother texture than that from CMAQ. The former also predicted larger heavy pollution area in the southern Hebei province than the latter, but in a small margin. In general, they have pretty similar spatial patterns, indicating the reliability of our data fusion method. In summary, the statistical spatio-temporal downscaler could provide improvements on MODIS AOD and CMAQ's predictions on PM2.5 pollution levels. Future work would focus on fusing three datasets, as aforementioned monitor observations, MODIS AOD and CMAQ predictions, to derive predictions of ground-level PM2.5 pollution levels with even increased accuracy.

Assessing the hydrological response from an ensemble of CMIP5 climate projections in the transition zone of the Atlantic region (Bay of Biscay)

NASA Astrophysics Data System (ADS)

Meaurio, Maite; Zabaleta, Ane; Boithias, Laurie; Epelde, Ane Miren; Sauvage, Sabine; Sánchez-Pérez, Jose-Miguel; Srinivasan, Raghavan; Antiguedad, Iñaki

2017-05-01

The climate changes projected for the 21st century will have consequences on the hydrological response of catchments. These changes, and their consequences, are most uncertain in the transition zones. The study area, in the Bay of Biscay, is located in the transition zone of the European Atlantic region, where hydrological impact of climate change was scarcely studied. In order to address this scarcity, the hydrological impacts of climate change on river discharge were assessed. To do so, a hydrological modelling was carried out considering 16 climate scenarios that include 5 General Circulation Models (GCM) from the 5th report of the Coupled Model Intercomparison Project (CMIP5), 2 statistical downscaling methods and 2 Representative Concentration Pathways. Projections for future discharge (2011-2100) were divided into three 30-year horizons (2030s, 2060s and 2090s) and a comparison was made between these time horizons and the baseline (1961-2000). The results show that the downscaling method used resulted in a higher source of uncertainty than GCM itself. In addition, the uncertainties inherent to the methods used at all the levels do not affect the results equally along the year. In spite of those uncertainties, general trends for the 2090s predict seasonal discharge decreases by around -17% in autumn, -16% in spring, -11% in winter and -7% in summer. These results are in line with those predicted for the Atlantic region (France and the Iberian Peninsula). Trends for extreme flows were also analysed: the most significant show an increase in the duration (days) of low flows. From an environmental point of view, and considering the need to meet the objectives established by the Water Framework Directive (WFD), this will be a major challenge for the future planning on water management.

Famine Early Warning Systems Network (FEWS NET) Contributions to Strengthening Resilience and Sustainability for the East African Community

NASA Astrophysics Data System (ADS)

Budde, M. E.; Galu, G.; Funk, C. C.; Verdin, J. P.; Rowland, J.

2014-12-01

The Planning for Resilience in East Africa through Policy, Adaptation, Research, and Economic Development (PREPARED) is a multi-organizational project aimed at mainstreaming climate-resilient development planning and program implementation into the East African Community (EAC). The Famine Early Warning Systems Network (FEWS NET) has partnered with the PREPARED project to address three key development challenges for the EAC; 1) increasing resiliency to climate change, 2) managing trans-boundary freshwater biodiversity and conservation and 3) improving access to drinking water supply and sanitation services. USGS FEWS NET has been instrumental in the development of gridded climate data sets that are the fundamental building blocks for climate change adaptation studies in the region. Tools such as the Geospatial Climate Tool (GeoCLIM) have been developed to interpolate time-series grids of precipitation and temperature values from station observations and associated satellite imagery, elevation data, and other spatially continuous fields. The GeoCLIM tool also allows the identification of anomalies and assessments of both their frequency of occurrence and directional trends. A major effort has been put forth to build the capacities of local and regional institutions to use GeoCLIM to integrate their station data (which is not typically available to the public) into improved national and regional gridded climate data sets. In addition to the improvements and capacity building activities related to geospatial analysis tools, FEWS NET will assist in two other areas; 1) downscaling of climate change scenarios and 2) vulnerability impact assessments. FEWS NET will provide expertise in statistical downscaling of Global Climate Model output fields and work with regional institutions to assess results of other downscaling methods. Completion of a vulnerability impact assessment (VIA) involves the examination of sectoral consequences in identified climate "hot spots". FEWS NET will lead the VIA for the agriculture and food security sector, but will also provide key geospatial layers needed by multiple sectors in the areas of exposure, sensitivity, and adaptive capacity. Project implementation will strengthen regional coordination in policy-making, planning, and response to climate change issues.

Regional climate model downscaling may improve the prediction of alien plant species distributions

NASA Astrophysics Data System (ADS)

Liu, Shuyan; Liang, Xin-Zhong; Gao, Wei; Stohlgren, Thomas J.

2014-12-01

Distributions of invasive species are commonly predicted with species distribution models that build upon the statistical relationships between observed species presence data and climate data. We used field observations, climate station data, and Maximum Entropy species distribution models for 13 invasive plant species in the United States, and then compared the models with inputs from a General Circulation Model (hereafter GCM-based models) and a downscaled Regional Climate Model (hereafter, RCM-based models).We also compared species distributions based on either GCM-based or RCM-based models for the present (1990-1999) to the future (2046-2055). RCM-based species distribution models replicated observed distributions remarkably better than GCM-based models for all invasive species under the current climate. This was shown for the presence locations of the species, and by using four common statistical metrics to compare modeled distributions. For two widespread invasive taxa ( Bromus tectorum or cheatgrass, and Tamarix spp. or tamarisk), GCM-based models failed miserably to reproduce observed species distributions. In contrast, RCM-based species distribution models closely matched observations. Future species distributions may be significantly affected by using GCM-based inputs. Because invasive plants species often show high resilience and low rates of local extinction, RCM-based species distribution models may perform better than GCM-based species distribution models for planning containment programs for invasive species.

Assessing the Added Value of Dynamical Downscaling Using the Standardized Precipitation Index

EPA Science Inventory

In this study, the Standardized Precipitation Index (SPI) is used to ascertain the added value of dynamical downscaling over the contiguous United States. WRF is used as a regional climate model (RCM) to dynamically downscale reanalysis fields to compare values of SPI over drough...

Smart Climatology System

DTIC Science & Technology

2010-09-24

12 2.1 Downscaling /Reanalysis Data ................................................................................ 12 2.2 Downscaling of...Comparison of Resolutions of Maximum Significant Wave heights for La Niña >= 8 ft >= 6 ft 12 2 Data Production Issues 2.1 Downscaling /Reanalysis...numerical weather prediction systems. The usage of satellite data , for example, is markedly different than the past practice. This played havoc with

Characterizing and Addressing the Need for Statistical Adjustment of Global Climate Model Data

NASA Astrophysics Data System (ADS)

White, K. D.; Baker, B.; Mueller, C.; Villarini, G.; Foley, P.; Friedman, D.

2017-12-01

As part of its mission to research and measure the effects of the changing climate, the U. S. Army Corps of Engineers (USACE) regularly uses the World Climate Research Programme's Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model dataset. However, these data are generated at a global level and are not fine-tuned for specific watersheds. This often causes CMIP5 output to vary from locally observed patterns in the climate. Several downscaling methods have been developed to increase the resolution of the CMIP5 data and decrease systemic differences to support decision-makers as they evaluate results at the watershed scale. Evaluating preliminary comparisons of observed and projected flow frequency curves over the US revealed a simple framework for water resources decision makers to plan and design water resources management measures under changing conditions using standard tools. Using this framework as a basis, USACE has begun to explore to use of statistical adjustment to alter global climate model data to better match the locally observed patterns while preserving the general structure and behavior of the model data. When paired with careful measurement and hypothesis testing, statistical adjustment can be particularly effective at navigating the compromise between the locally observed patterns and the global climate model structures for decision makers.

Scenarios of land use and land cover change in the conterminous United States: Utilizing the special report on emission scenarios at ecoregional scales

USGS Publications Warehouse

Sleeter, Benjamin M.; Sohl, Terry L.; Bouchard, Michelle A.; Reker, Ryan R.; Soulard, Christopher E.; Acevedo, William; Griffith, Glenn E.; Sleeter, Rachel R.; Auch, Roger F.; Sayler, Kristi L.; Prisley, Stephen; Zhu, Zhi-Liang

2012-01-01

Global environmental change scenarios have typically provided projections of land use and land cover for a relatively small number of regions or using a relatively coarse resolution spatial grid, and for only a few major sectors. The coarseness of global projections, in both spatial and thematic dimensions, often limits their direct utility at scales useful for environmental management. This paper describes methods to downscale projections of land-use and land-cover change from the Intergovernmental Panel on Climate Change's Special Report on Emission Scenarios to ecological regions of the conterminous United States, using an integrated assessment model, land-use histories, and expert knowledge. Downscaled projections span a wide range of future potential conditions across sixteen land use/land cover sectors and 84 ecological regions, and are logically consistent with both historical measurements and SRES characteristics. Results appear to provide a credible solution for connecting regionalized projections of land use and land cover with existing downscaled climate scenarios, under a common set of scenario-based socioeconomic assumptions.

Inevitable changes in snowpack and water resources over California's Sierra Nevada

NASA Astrophysics Data System (ADS)

Hall, A. D.; Sun, F.; Walton, D.; Berg, N.; Schwartz, M. A.

2015-12-01

Here we use a downscaling technique incorporating both dynamical and statistical methods to project end-of-century changes in spring snow water equivalent in California's Sierra Nevada. The technique produces outcomes for all Global Climate Models (GCMs) and the four greenhouse gas forcing scenarios adopted by the Intergovernmental Panel on Climate Change (IPCC). For all GCMs and forcing scenarios, significant snow loss occurs at elevations below 2500 meters, despite increasing precipitation in many GCMs. The loss is significantly enhanced by snow albedo feedback. The approximate intermodel range in percent of total snow remaining in the entire region is 60-85% for a likely "mitigation" scenario, and 35-55% for the "business-as-usual" scenario. Thus significant snowpack decrease by century's end is inevitable, even if the loss can be cushioned through greenhouse gas emissions reductions over the coming decades. The snowpack loss also leads to significant changes in runoff timing, which are also inevitable.

Impacts of climate change on the hydrological cycle over France and associated uncertainties

NASA Astrophysics Data System (ADS)

Dayon, Gildas; Boé, Julien; Martin, Éric; Gailhard, Joël

2018-05-01

This study deals with the evolution of the hydrological cycle over France during the 21st century. A large multi-member, multi-scenario, and multi-model ensemble of climate projections is downscaled with a new statistical method to drive a physically-based hydrological model with recent improvements. For a business-as-usual scenario, annual precipitation changes generally remain small, except over southern France, where decreases close to 20% are projected. Annual streamflows roughly decrease by 10% (±20%) on the Seine, by 20% (±20%) on the Loire, by 20% (±15%) on the Rhone and by 40% (±15%) on the Garonne. Attenuation measures, as implied by the other scenarios analyzed, lead to less severe changes. However, even with a scenario generally compatible with a limitation of global warming to two degrees, some notable impacts may still occur, with for example a decrease in summer river flows close to 25% for the Garonne.

Assessing the implementation of bias correction in the climate prediction

NASA Astrophysics Data System (ADS)

Nadrah Aqilah Tukimat, Nurul

2018-04-01

An issue of the climate changes nowadays becomes trigger and irregular. The increment of the greenhouse gases (GHGs) emission into the atmospheric system day by day gives huge impact to the fluctuated weather and global warming. It becomes significant to analyse the changes of climate parameters in the long term. However, the accuracy in the climate simulation is always be questioned to control the reliability of the projection results. Thus, the Linear Scaling (LS) as a bias correction method (BC) had been applied to treat the gaps between observed and simulated results. About two rainfall stations were selected in Pahang state there are Station Lubuk Paku and Station Temerloh. Statistical Downscaling Model (SDSM) used to perform the relationship between local weather and atmospheric parameters in projecting the long term rainfall trend. The result revealed the LS was successfully to reduce the error up to 3% and produced better climate simulated results.

Validation of extremes within the Perfect-Predictor Experiment of the COST Action VALUE

NASA Astrophysics Data System (ADS)

Hertig, Elke; Maraun, Douglas; Wibig, Joanna; Vrac, Mathieu; Soares, Pedro; Bartholy, Judith; Pongracz, Rita; Mares, Ileana; Gutierrez, Jose Manuel; Casanueva, Ana; Alzbutas, Robertas

2016-04-01

Extreme events are of widespread concern due to their damaging consequences on natural and anthropogenic systems. From science to applications the statistical attributes of rare and infrequent occurrence and low probability become connected with the socio-economic aspect of strong impact. Specific end-user needs regarding information about extreme events depend on the type of application, but as a joining element there is always the request for easily accessible climate change information with a clear description of their uncertainties and limitations. Within the Perfect-Predictor Experiment of the COST Action VALUE extreme indices modelled from a wide range of downscaling methods are compared to reference indices calculated from observational data. The experiment uses reference data from a selection of 86 weather stations representative of the different climates in Europe. Results are presented for temperature and precipitation extremes and include aspects of the marginal distribution as well as spell-length related aspects.

A Bayesian hierarchical model with spatial variable selection: the effect of weather on insurance claims

PubMed Central

Scheel, Ida; Ferkingstad, Egil; Frigessi, Arnoldo; Haug, Ola; Hinnerichsen, Mikkel; Meze-Hausken, Elisabeth

2013-01-01

Climate change will affect the insurance industry. We develop a Bayesian hierarchical statistical approach to explain and predict insurance losses due to weather events at a local geographic scale. The number of weather-related insurance claims is modelled by combining generalized linear models with spatially smoothed variable selection. Using Gibbs sampling and reversible jump Markov chain Monte Carlo methods, this model is fitted on daily weather and insurance data from each of the 319 municipalities which constitute southern and central Norway for the period 1997–2006. Precise out-of-sample predictions validate the model. Our results show interesting regional patterns in the effect of different weather covariates. In addition to being useful for insurance pricing, our model can be used for short-term predictions based on weather forecasts and for long-term predictions based on downscaled climate models. PMID:23396890

Inter-comparison of precipitable water among reanalyses and its effect on downscaling in the tropics

NASA Astrophysics Data System (ADS)

Takahashi, H. G.; Fujita, M.; Hara, M.

2012-12-01

This paper compared precipitable water (PW) among four major reanalyses. In addition, we also investigated the effect of the boundary conditions on downscaling in the tropics, using a regional climate model. The spatial pattern of PW in the reanalyses agreed closely with observations. However, the absolute amounts of PW in some reanalyses were very small compared to observations. The discrepancies of the 12-year mean PW in July over the Southeast Asian monsoon region exceeded the inter-annual standard deviation of the PW. There was also a discrepancy in tropical PWs throughout the year, an indication that the problem is not regional, but global. The downscaling experiments were conducted, which were forced by the different four reanalyses. The atmospheric circulation, including monsoon westerlies and various disturbances, was very small among the reanalyses. However, simulated precipitation was only 60 % of observed precipitation, although the dry bias in the boundary conditions was only 6 %. This result indicates that dry bias has large effects on precipitation in downscaling over the tropics. This suggests that a simulated regional climate downscaled from ensemble-mean boundary conditions is quite different from an ensemble-mean regional climate averaged over the several regional ones downscaled from boundary conditions of the ensemble members in the tropics. Downscaled models can provide realistic simulations of regional tropical climates only if the boundary conditions include realistic absolute amounts of PW. Use of boundary conditions that include realistic absolute amounts of PW in downscaling in the tropics is imperative at the present time. This work was partly supported by the Global Environment Research Fund (RFa-1101) of the Ministry of the Environment, Japan.

Evaluation of cool season precipitation event characteristics over the Northeast US in a suite of downscaled climate model hindcasts

NASA Astrophysics Data System (ADS)

Loikith, Paul C.; Waliser, Duane E.; Kim, Jinwon; Ferraro, Robert

2017-08-01

Cool season precipitation event characteristics are evaluated across a suite of downscaled climate models over the northeastern US. Downscaled hindcast simulations are produced by dynamically downscaling the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2) using the National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (WRF) regional climate model (RCM) and the Goddard Earth Observing System Model, Version 5 (GEOS-5) global climate model. NU-WRF RCM simulations are produced at 24, 12, and 4-km horizontal resolutions using a range of spectral nudging schemes while the MERRA2 global downscaled run is provided at 12.5-km. All model runs are evaluated using four metrics designed to capture key features of precipitation events: event frequency, event intensity, even total, and event duration. Overall, the downscaling approaches result in a reasonable representation of many of the key features of precipitation events over the region, however considerable biases exist in the magnitude of each metric. Based on this evaluation there is no clear indication that higher resolution simulations result in more realistic results in general, however many small-scale features such as orographic enhancement of precipitation are only captured at higher resolutions suggesting some added value over coarser resolution. While the differences between simulations produced using nudging and no nudging are small, there is some improvement in model fidelity when nudging is introduced, especially at a cutoff wavelength of 600 km compared to 2000 km. Based on the results of this evaluation, dynamical regional downscaling using NU-WRF results in a more realistic representation of precipitation event climatology than the global downscaling of MERRA2 using GEOS-5.

A framework for identifying tailored subsets of climate projections for impact and adaptation studies

NASA Astrophysics Data System (ADS)

Vidal, Jean-Philippe; Hingray, Benoît

2014-05-01

In order to better understand the uncertainties in the climate of the next decades, an increasingly large number of increasingly diverse climate projections is being produced by the climate research community through coordinated initiatives (e.g., CMIP5, CORDEX), but also through more specific experiments at both the global scale (perturbed parameter ensembles) and the regional-to-local scale (empirical statistical downscaling ensembles). When significant efforts are put into making such projections available online, very few works focus on how to make such an enormous amount of information actually usable by the impact and adaptation community. Climate services should therefore include guidelines and recommendations for identifying subsets of climate projections that would have (1) a size manageable by downstream modelling approaches and (2) the relevant properties for informing adaptation strategies. This works proposes a generic framework for identifying tailored subsets of climate projections that would meet both the objectives and the constraints of a specific impact / adaptation study in a typical top-down approach. This decision framework builds on two main preliminary tasks that lead to critical choices in the selection strategy: (1) understanding the requirements of the specific impact / adaptation study, and (2) characterizing the (downscaled) climate projections dataset available. An impact / adaptation study has two types of requirements. First, the study may aim at various outcomes for a given climate-related feature: the best estimate of the future, the range of possible futures, a set of representative futures, or a statistically interpretable ensemble of futures. Second, impact models may come with specific constraints on climate input variables, like spatio-temporal and between-variables coherence. Additionally, when concurrent impact models are used, the most restrictive constraints have to be considered in order to be able to assess the uncertainty associated from this modelling step. Besides, the climate projection dataset available for a given study has several characteristics that will heavily condition the type of conclusions that can be reached. Indeed, the dataset at hand may or not sample different types of uncertainty (socio-economic, structural, parametric, along with internal variability). Moreover, these types are present at different steps in the well-known cascade of uncertainty, from the emission / concentration scenarios and the global climate to the regional-to-local climate. Critical choices for the selection are therefore conditioned on all features above. The type of selection (picking out, culling, or statistical sampling) is closely related to the study objectives and the uncertainty types present in the dataset. Moreover, grounds for picking out or culling projections may stem from global, regional or feature-specific present-day performance, representativeness, or covered range. An example use of this framework is a hierarchical selection for 3 classes of impact models among 3000 transient climate projections from different runs of 4 GCMs, statistically downscaled by 3 probabilistic methods, and made available for an integrated water resource adaptation study in the Durance catchment (southern French Alps). This work is part of the GICC R2D2-20501 project (Risk, water Resources and sustainable Development of the Durance catchment in 2050) and the EU FP7 COMPLEX2 project (Knowledge Based Climate Mitigation Systems for a Low Carbon Economy).

New Physical Algorithms for Downscaling SMAP Soil Moisture

NASA Astrophysics Data System (ADS)

Sadeghi, M.; Ghafari, E.; Babaeian, E.; Davary, K.; Farid, A.; Jones, S. B.; Tuller, M.

2017-12-01

The NASA Soil Moisture Active Passive (SMAP) mission provides new means for estimation of surface soil moisture at the global scale. However, for many hydrological and agricultural applications the spatial SMAP resolution is too low. To address this scale issue we fused SMAP data with MODIS observations to generate soil moisture maps at 1-km spatial resolution. In course of this study we have improved several existing empirical algorithms and introduced a new physical approach for downscaling SMAP data. The universal triangle/trapezoid model was applied to relate soil moisture to optical/thermal observations such as NDVI, land surface temperature and surface reflectance. These algorithms were evaluated with in situ data measured at 5-cm depth. Our results demonstrate that downscaling SMAP soil moisture data based on physical indicators of soil moisture derived from the MODIS satellite leads to higher accuracy than that achievable with empirical downscaling algorithms. Keywords: Soil moisture, microwave data, downscaling, MODIS, triangle/trapezoid model.

A parametric approach for simultaneous bias correction and high-resolution downscaling of climate model rainfall

NASA Astrophysics Data System (ADS)

Mamalakis, Antonios; Langousis, Andreas; Deidda, Roberto; Marrocu, Marino

2017-03-01

Distribution mapping has been identified as the most efficient approach to bias-correct climate model rainfall, while reproducing its statistics at spatial and temporal resolutions suitable to run hydrologic models. Yet its implementation based on empirical distributions derived from control samples (referred to as nonparametric distribution mapping) makes the method's performance sensitive to sample length variations, the presence of outliers, the spatial resolution of climate model results, and may lead to biases, especially in extreme rainfall estimation. To address these shortcomings, we propose a methodology for simultaneous bias correction and high-resolution downscaling of climate model rainfall products that uses: (a) a two-component theoretical distribution model (i.e., a generalized Pareto (GP) model for rainfall intensities above a specified threshold u*, and an exponential model for lower rainrates), and (b) proper interpolation of the corresponding distribution parameters on a user-defined high-resolution grid, using kriging for uncertain data. We assess the performance of the suggested parametric approach relative to the nonparametric one, using daily raingauge measurements from a dense network in the island of Sardinia (Italy), and rainfall data from four GCM/RCM model chains of the ENSEMBLES project. The obtained results shed light on the competitive advantages of the parametric approach, which is proved more accurate and considerably less sensitive to the characteristics of the calibration period, independent of the GCM/RCM combination used. This is especially the case for extreme rainfall estimation, where the GP assumption allows for more accurate and robust estimates, also beyond the range of the available data.

Development of predictive weather scenarios for early prediction of rice yield in South Korea

NASA Astrophysics Data System (ADS)

Shin, Y.; Cho, J.; Jung, I.

2017-12-01

International grain prices are becoming unstable due to frequent occurrence of abnormal weather phenomena caused by climate change. Early prediction of grain yield using weather forecast data is important for stabilization of international grain prices. The APEC Climate Center (APCC) is providing seasonal forecast data based on monthly climate prediction models for global seasonal forecasting services. The 3-month and 6-month seasonal forecast data using the multi-model ensemble (MME) technique are provided in their own website, ADSS (APCC Data Service System, http://adss.apcc21.org/). The spatial resolution of seasonal forecast data for each individual model is 2.5°×2.5°(about 250km) and the time scale is created as monthly. In this study, we developed customized weather forecast scenarios that are combined seasonal forecast data and observational data apply to early rice yield prediction model. Statistical downscale method was applied to produce meteorological input data of crop model because field scale crop model (ORYZA2000) requires daily weather data. In order to determine whether the forecasting data is suitable for the crop model, we produced spatio-temporal downscaled weather scenarios and evaluated the predictability by comparison with observed weather data at 57 ASOS stations in South Korea. The customized weather forecast scenarios can be applied to various application fields not only early rice yield prediction. Acknowledgement This work was carried out with the support of "Cooperative Research Program for Agriculture Science and Technology Development (Project No: PJ012855022017)" Rural Development Administration, Republic of Korea.

Reconstructing decades of glacial mass loss in the Canadian Arctic Archipelago

NASA Astrophysics Data System (ADS)

Noël, Brice; van de Berg, Willem Jan; Lhermitte, Stef; Wouters, Bert; van den Broeke, Michiel

2017-04-01

The Canadian Arctic Archipelago (CAA) comprises multiple small glaciers and ice caps mostly concentrated on Ellesmere and Baffin Islands situated in the north (NCAA) and south (SCAA) of the archipelago, respectively. Because they cover a relatively small area and show complex geometries, current regional climate models, generally running at 5 to 20 km horizontal resolution, struggle to accurately resolve surface mass change patterns. Here, we present a 58-year (1958-2015) reconstruction of daily, 1 km surface mass balance (SMB) of the CAA, statistically downscaled from the output of the regional climate model RACMO2.3 at 11 km. By correcting for biases in elevation and ice albedo, the downscaling method significantly improves mass loss estimates over narrow outlet glaciers and isolated ice fields through better resolved marginal meltwater runoff. During the last two decades, CAA glaciers have experienced warmer conditions (+1.1°C) resulting in continued mass loss. NCAA and SCAA mass loss accounted for -24.7 ± 18.0 Gt yr-1 and -21.9 ± 8.2 Gt yr-1 respectively, almost tripling (-8.4 Gt yr-1) and doubling (-11.8 Gt yr-1) the 1958-1995 average. Following the recent warming, enhanced meltwater production reduced the refreezing capacity of inland firn layers by about 6%. While the interior of NCAA ice caps can still buffer most of the additional melt, the lack of a perennial firn area over low-lying SCAA glaciers caused uninterrupted mass loss since the 1980s, which, in the absence of significant refreezing capacity, indicates inevitable disappearance of these highly sensitive glaciers.

Climate Projections from the NARCliM Project: Bayesian Model Averaging of Maximum Temperature Projections

NASA Astrophysics Data System (ADS)

Olson, R.; Evans, J. P.; Fan, Y.

2015-12-01

NARCliM (NSW/ACT Regional Climate Modelling Project) is a regional climate project for Australia and the surrounding region. It dynamically downscales 4 General Circulation Models (GCMs) using three Regional Climate Models (RCMs) to provide climate projections for the CORDEX-AustralAsia region at 50 km resolution, and for south-east Australia at 10 km resolution. The project differs from previous work in the level of sophistication of model selection. Specifically, the selection process for GCMs included (i) conducting literature review to evaluate model performance, (ii) analysing model independence, and (iii) selecting models that span future temperature and precipitation change space. RCMs for downscaling the GCMs were chosen based on their performance for several precipitation events over South-East Australia, and on model independence.Bayesian Model Averaging (BMA) provides a statistically consistent framework for weighing the models based on their likelihood given the available observations. These weights are used to provide probability distribution functions (pdfs) for model projections. We develop a BMA framework for constructing probabilistic climate projections for spatially-averaged variables from the NARCliM project. The first step in the procedure is smoothing model output in order to exclude the influence of internal climate variability. Our statistical model for model-observations residuals is a homoskedastic iid process. Comparing RCMs with Australian Water Availability Project (AWAP) observations is used to determine model weights through Monte Carlo integration. Posterior pdfs of statistical parameters of model-data residuals are obtained using Markov Chain Monte Carlo. The uncertainty in the properties of the model-data residuals is fully accounted for when constructing the projections. We present the preliminary results of the BMA analysis for yearly maximum temperature for New South Wales state planning regions for the period 2060-2079.

Statistical downscaling forecast of Chinese winter temperature based on the autumn SST anomalies

NASA Astrophysics Data System (ADS)

Lu, J.

2017-12-01

This study investigates the impacts of the autumn sea surface temperature anomalies (SSTA) on interannual variations of Chinese winter temperature, and discusses the potential predictability of December-January-February (DJF) 2-m air temperature anomalies (TSA) over China based on the intimate linkage between the DJF TSA and autumn SSTA. According to the Empirical Orthogonal Function (EOF) analysis, three leading EOF modes jointly account for 80% of the total TSA variances and are characterized by a homogeneous spatial pattern, a north-south seesaw and a cross structure. The first three EOFs exhibit a stable feature revealed by cross-validation, suggesting the potential predictability of the DJF TSA. The EOF1 mode is influenced by changes in the intensities of the Siberian High (SH), East Asian winter monsoon (EAWM) and East Asian Trough related to an Eurasian pattern teleconnection, which can be tracked back to September-October-November (SON) SSTA associated with two SSTA tripole patterns in the North Pacific and North Atlantic, a dipole mode in the Indian Ocean and an ENSO-like mode in the equatorial and subtropical Pacific. However, the Arctic Oscillation plays an important role in the second mode. The teleconnection connecting the atmospheric circulation anomalies in two hemispheres indicates that the configuration of global SON SSTA induces the two annular modes and causes a TSA oscillation between the northern and southern parts of China. The third mode is related to the westward shift of the SH and western pathway EAWM, which are attributed to two dipole modes in the North Pacific and South Pacific, Atlantic Multidecadal Oscillation and Indian Ocean Basin Mode. Therefore a physically-based statistical model is established based on autumn SSTA indices. Cross-validation suggests that this statistical downscaling forecast model shows a good performance in predicting the DJF TSA.

Downscaling SMAP Radiometer Soil Moisture over the CONUS using Soil-Climate Information and Ensemble Learning

NASA Astrophysics Data System (ADS)

Abbaszadeh, P.; Moradkhani, H.

2017-12-01

Soil moisture contributes significantly towards the improvement of weather and climate forecast and understanding terrestrial ecosystem processes. It is known as a key hydrologic variable in the agricultural drought monitoring, flood modeling and irrigation management. While satellite retrievals can provide an unprecedented information on soil moisture at global-scale, the products are generally at coarse spatial resolutions (25-50 km2). This often hampers their use in regional or local studies, which normally require a finer resolution of the data set. This work presents a new framework based on an ensemble learning method while using soil-climate information derived from remote-sensing and ground-based observations to downscale the level 3 daily composite version (L3_SM_P) of SMAP radiometer soil moisture over the Continental U.S. (CONUS) at 1 km spatial resolution. In the proposed method, a suite of remotely sensed and in situ data sets in addition to soil texture information and topography data among others were used. The downscaled product was validated against in situ soil moisture measurements collected from a limited number of core validation sites and several hundred sparse soil moisture networks throughout the CONUS. The obtained results indicated a great potential of the proposed methodology to derive the fine resolution soil moisture information applicable for fine resolution hydrologic modeling, data assimilation and other regional studies.

Downscaled rainfall projections in south Florida using self-organizing maps.

PubMed

Sinha, Palash; Mann, Michael E; Fuentes, Jose D; Mejia, Alfonso; Ning, Liang; Sun, Weiyi; He, Tao; Obeysekera, Jayantha

2018-04-20

We make future projections of seasonal precipitation characteristics in southern Florida using a statistical downscaling approach based on Self Organized Maps. Our approach is applied separately to each three-month season: September-November; December-February; March-May; and June-August. We make use of 19 different simulations from the Coupled Model Inter-comparison Project, phase 5 (CMIP5) and generate an ensemble of 1500 independent daily precipitation surrogates for each model simulation, yielding a grand ensemble of 28,500 total realizations for each season. The center and moments (25%ile and 75%ile) of this distribution are used to characterize most likely scenarios and their associated uncertainties. This approach is applied to 30-year windows of daily mean precipitation for both the CMIP5 historical simulations (1976-2005) and the CMIP5 future (RCP 4.5) projections. For the latter case, we examine both the "near future" (2021-2050) and "far future" (2071-2100) periods for three scenarios (RCP2.6, RCP4.5, and RCP8.5). Copyright © 2018 Elsevier B.V. All rights reserved.

Climatologies at high resolution for the earth’s land surface areas

PubMed Central

Karger, Dirk Nikolaus; Conrad, Olaf; Böhner, Jürgen; Kawohl, Tobias; Kreft, Holger; Soria-Auza, Rodrigo Wilber; Zimmermann, Niklaus E.; Linder, H. Peter; Kessler, Michael

2017-01-01

High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth’s land surface areas) data of downscaled model output temperature and precipitation estimates of the ERA-Interim climatic reanalysis to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. The resulting data consist of a monthly temperature and precipitation climatology for the years 1979–2013. We compare the data derived from the CHELSA algorithm with other standard gridded products and station data from the Global Historical Climate Network. We compare the performance of the new climatologies in species distribution modelling and show that we can increase the accuracy of species range predictions. We further show that CHELSA climatological data has a similar accuracy as other products for temperature, but that its predictions of precipitation patterns are better. PMID:28872642

Climatologies at high resolution for the earth's land surface areas

NASA Astrophysics Data System (ADS)

Karger, Dirk Nikolaus; Conrad, Olaf; Böhner, Jürgen; Kawohl, Tobias; Kreft, Holger; Soria-Auza, Rodrigo Wilber; Zimmermann, Niklaus E.; Linder, H. Peter; Kessler, Michael

2017-09-01

High-resolution information on climatic conditions is essential to many applications in environmental and ecological sciences. Here we present the CHELSA (Climatologies at high resolution for the earth's land surface areas) data of downscaled model output temperature and precipitation estimates of the ERA-Interim climatic reanalysis to a high resolution of 30 arc sec. The temperature algorithm is based on statistical downscaling of atmospheric temperatures. The precipitation algorithm incorporates orographic predictors including wind fields, valley exposition, and boundary layer height, with a subsequent bias correction. The resulting data consist of a monthly temperature and precipitation climatology for the years 1979-2013. We compare the data derived from the CHELSA algorithm with other standard gridded products and station data from the Global Historical Climate Network. We compare the performance of the new climatologies in species distribution modelling and show that we can increase the accuracy of species range predictions. We further show that CHELSA climatological data has a similar accuracy as other products for temperature, but that its predictions of precipitation patterns are better.

Potential impact of climate change to the future streamflow of Yellow River Basin based on CMIP5 data

NASA Astrophysics Data System (ADS)

Yang, Xiaoli; Zheng, Weifei; Ren, Liliang; Zhang, Mengru; Wang, Yuqian; Liu, Yi; Yuan, Fei; Jiang, Shanhu

2018-02-01

The Yellow River Basin (YRB) is the largest river basin in northern China, which has suffering water scarcity and drought hazard for many years. Therefore, assessments the potential impacts of climate change on the future streamflow in this basin is very important for local policy and planning on food security. In this study, based on the observations of 101 meteorological stations in YRB, equidistant CDF matching (EDCDFm) statistical downscaling approach was applied to eight climate models under two emissions scenarios (RCP4.5 and RCP8.5) from phase five of the Coupled Model Intercomparison Project (CMIP5). Variable infiltration capacity (VIC) model with 0.25° × 0.25° spatial resolution was developed based on downscaled fields for simulating streamflow in the future period over YRB. The results show that with the global warming trend, the annual streamflow will reduced about 10 % during the period of 2021-2050, compared to the base period of 1961-1990 in YRB. There should be suitable water resources planning to meet the demands of growing populations and future climate changing in this region.

Future projections of extreme precipitation using Advanced Weather Generator (AWE-GEN) over Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Syafrina, A. H.; Zalina, M. D.; Juneng, L.

2014-09-01

A stochastic downscaling methodology known as the Advanced Weather Generator, AWE-GEN, has been tested at four stations in Peninsular Malaysia using observations available from 1975 to 2005. The methodology involves a stochastic downscaling procedure based on a Bayesian approach. Climate statistics from a multi-model ensemble of General Circulation Model (GCM) outputs were calculated and factors of change were derived to produce the probability distribution functions (PDF). New parameters were obtained to project future climate time series. A multi-model ensemble was used in this study. The projections of extreme precipitation were based on the RCP 6.0 scenario (2081-2100). The model was able to simulate both hourly and 24-h extreme precipitation, as well as wet spell durations quite well for almost all regions. However, the performance of GCM models varies significantly in all regions showing high variability of monthly precipitation for both observed and future periods. The extreme precipitation for both hourly and 24-h seems to increase in future, while extreme of wet spells remain unchanged, up to the return periods of 10-40 years.

Weather types in the South Shetlands (Antarctica) using a circulation type approach

NASA Astrophysics Data System (ADS)

Mora, Carla; João Rocha, Maria; Dutra, Emanuel; Trigo, Isabel; Vieira, Gonçalo; Fragoso, Marcelo; Ramos, Miguel

2010-05-01

Weather types in the South Shetlands (Antarctica) were defined using an automated method based on the Lamb Weather Type classification scheme (Jones et al. 1993). This is an objective classification originally developed for the British Isles (Jones et al., 1993) and also applied to southeast (Goodess and Palutikof 1998) and northwest Spain (Lorenzo et al, 2009), Portugal (Trigo and DaCamara 2000) and Greece (Maheras et al. 2004) with good results. Daily atmospheric circulation in the South Shetlands region from 1989 to 2009 was classified using a 16-node grid of sea level pressure data from the ERA Interim. The classification is obtained through the comparison of the magnitudes of the directional and rotational components of the geostrophic flow. Basic circulation types were combined into 10 groups of weather types: four directional types (NW, N, S and SW), three anticyclonic types (A, ASW and ANW), and three cyclonic types (C, CSW and CNW). Westerly flow and cyclonic circulation are the most frequent events throughout the year. The sea level pressure field for each weather type is presented and the synoptic characteristics are described. The analysis is based on ERA-Interim fields, including mean sea level pressure, precipitation, cloud cover, humidity and air temperature. Snow thickess modelled using HTESSEL is also considered. Analysis of variance (anova) and multivariate analysis (principal component analysis) are applied to evaluate the characteristics of each weather type. This circulation-type approach showed good results in the past for the downscaling of precipitation in other regions, and we are interested in evaluating the possibilities that the classification offers for downscaling precipitation, but also for snow and air temperature. For this we will be using observational data at test sites in Livingston and Deception islands. We are also motivated by the possibility of using the circulation-type approach as a predictor in statistical downscaling. References: Goodess CM, Palutikof JP.1998. Development of daily rainfall scenarios for southeast Spain using a Circulation-type approach to downscaling. International Journal of Climatology. 10: 1051-1083. JonesPD, Hulme M, Briffa KR. 1993. A comparison of Lamb circulation types with an objective classification scheme. International Journal of Climatology, 13:655-663. Lorenzo M N, Iglesias I , Taboada JJ , Gómez-Gesteira M. 2009. Relationship between monthly rainfall in northwest Iberian Peninsula and North Atlantic sea surface temperature. International Journal of Climatology. Maheras P, Tolika K, Anagnostopoulou C, Vafiadis M, Patrikas I, Flocas H. 2004. On the relationship between circulation types and changes in rainfall variability in Grece. International Journal of Climatology 24: 1695-1712. Trigo RM, DaCamara C. 2000. Circulation weather types and their influence on the precipitation regime in Portugal. International Journal of Climatology. 20: 1559-1581.

Utilizing Multi-Ensemble of Downscaled CMIP5 GCMs to Investigate Trends and Spatial and Temporal Extent of Drought in Willamette Basin

NASA Astrophysics Data System (ADS)

Ahmadalipour, A.; Beal, B.; Moradkhani, H.

2015-12-01

Changing climate and potential future increases in global temperature are likely to have impacts on drought characteristics and hydrologic cylce. In this study, we analyze changes in temporal and spatial extent of meteorological and hydrological droughts in future, and their trends. Three statistically downscaled datasets from NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP), Multivariate Adaptive Constructed Analogs (MACA), and Bias Correction and Spatial Disagregation (BCSD-PSU) each consisting of 10 CMIP5 Global Climate Models (GCM) are utilized for RCP4.5 and RCP8.5 scenarios. Further, Precipitation Runoff Modeling System (PRMS) hydrologic model is used to simulate streamflow from GCM inputs and assess the hydrological drought characteristics. Standard Precipitation Index (SPI) and Streamflow Drought Index (SDI) are the two indexes used to investigate meteorological and hydrological drought, respectively. Study is done for Willamette Basin with a drainage area of 29,700 km2 accommodating more than 3 million inhabitants and 25 dams. We analyze our study for annual time scale as well as three future periods of near future (2010-2039), intermediate future (2040-2069), and far future (2070-2099). Large uncertainty is found from GCM predictions. Results reveal that meteorological drought events are expected to increase in near future. Severe to extreme drought with large areal coverage and several years of occurance is predicted around year 2030 with the likelihood of exceptional drought for both drought types. SPI is usually showing positive trends, while SDI indicates negative trends in most cases.

Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling.

PubMed

Stoy, Paul C; Quaife, Tristan

2015-01-01

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes.

Probabilistic Downscaling of Remote Sensing Data with Applications for Multi-Scale Biogeochemical Flux Modeling

PubMed Central

Stoy, Paul C.; Quaife, Tristan

2015-01-01

Upscaling ecological information to larger scales in space and downscaling remote sensing observations or model simulations to finer scales remain grand challenges in Earth system science. Downscaling often involves inferring subgrid information from coarse-scale data, and such ill-posed problems are classically addressed using regularization. Here, we apply two-dimensional Tikhonov Regularization (2DTR) to simulate subgrid surface patterns for ecological applications. Specifically, we test the ability of 2DTR to simulate the spatial statistics of high-resolution (4 m) remote sensing observations of the normalized difference vegetation index (NDVI) in a tundra landscape. We find that the 2DTR approach as applied here can capture the major mode of spatial variability of the high-resolution information, but not multiple modes of spatial variability, and that the Lagrange multiplier (γ) used to impose the condition of smoothness across space is related to the range of the experimental semivariogram. We used observed and 2DTR-simulated maps of NDVI to estimate landscape-level leaf area index (LAI) and gross primary productivity (GPP). NDVI maps simulated using a γ value that approximates the range of observed NDVI result in a landscape-level GPP estimate that differs by ca 2% from those created using observed NDVI. Following findings that GPP per unit LAI is lower near vegetation patch edges, we simulated vegetation patch edges using multiple approaches and found that simulated GPP declined by up to 12% as a result. 2DTR can generate random landscapes rapidly and can be applied to disaggregate ecological information and compare of spatial observations against simulated landscapes. PMID:26067835

Multifractal Downscaling of Rainfall Using Normalized Difference Vegetation Index (NDVI) in the Andes Plateau.

PubMed

Duffaut Espinosa, L A; Posadas, A N; Carbajal, M; Quiroz, R

2017-01-01

In this paper, a multifractal downscaling technique is applied to adequately transformed and lag corrected normalized difference vegetation index (NDVI) in order to obtain daily estimates of rainfall in an area of the Peruvian Andean high plateau. This downscaling procedure is temporal in nature since the original NDVI information is provided at an irregular temporal sampling period between 8 and 11 days, and the desired final scale is 1 day. The spatial resolution of approximately 1 km remains the same throughout the downscaling process. The results were validated against on-site measurements of meteorological stations distributed in the area under study.

Multifractal Downscaling of Rainfall Using Normalized Difference Vegetation Index (NDVI) in the Andes Plateau

PubMed Central

Posadas, A. N.; Carbajal, M.; Quiroz, R.

2017-01-01

In this paper, a multifractal downscaling technique is applied to adequately transformed and lag corrected normalized difference vegetation index (NDVI) in order to obtain daily estimates of rainfall in an area of the Peruvian Andean high plateau. This downscaling procedure is temporal in nature since the original NDVI information is provided at an irregular temporal sampling period between 8 and 11 days, and the desired final scale is 1 day. The spatial resolution of approximately 1 km remains the same throughout the downscaling process. The results were validated against on-site measurements of meteorological stations distributed in the area under study. PMID:28125607

Satellite-enhanced dynamical downscaling for the analysis of extreme events

NASA Astrophysics Data System (ADS)

Nunes, Ana M. B.

2016-09-01

The use of regional models in the downscaling of general circulation models provides a strategy to generate more detailed climate information. In that case, boundary-forcing techniques can be useful to maintain the large-scale features from the coarse-resolution global models in agreement with the inner modes of the higher-resolution regional models. Although those procedures might improve dynamics, downscaling via regional modeling still aims for better representation of physical processes. With the purpose of improving dynamics and physical processes in regional downscaling of global reanalysis, the Regional Spectral Model—originally developed at the National Centers for Environmental Prediction—employs a newly reformulated scale-selective bias correction, together with the 3-hourly assimilation of the satellite-based precipitation estimates constructed from the Climate Prediction Center morphing technique. The two-scheme technique for the dynamical downscaling of global reanalysis can be applied in analyses of environmental disasters and risk assessment, with hourly outputs, and resolution of about 25 km. Here the satellite-enhanced dynamical downscaling added value is demonstrated in simulations of the first reported hurricane in the western South Atlantic Ocean basin through comparisons with global reanalyses and satellite products available in ocean areas.

Using High Resolution Model Data to Improve Lightning Forecasts across Southern California

NASA Astrophysics Data System (ADS)

Capps, S. B.; Rolinski, T.

2014-12-01

Dry lightning often results in a significant amount of fire starts in areas where the vegetation is dry and continuous. Meteorologists from the USDA Forest Service Predictive Services' program in Riverside, California are tasked to provide southern and central California's fire agencies with fire potential outlooks. Logistic regression equations were developed by these meteorologists several years ago, which forecast probabilities of lightning as well as lightning amounts, out to seven days across southern California. These regression equations were developed using ten years of historical gridded data from the Global Forecast System (GFS) model on a coarse scale (0.5 degree resolution), correlated with historical lightning strike data. These equations do a reasonably good job of capturing a lightning episode (3-5 consecutive days or greater of lightning), but perform poorly regarding more detailed information such as exact location and amounts. It is postulated that the inadequacies in resolving the finer details of episodic lightning events is due to the coarse resolution of the GFS data, along with limited predictors. Stability parameters, such as the Lifted Index (LI), the Total Totals index (TT), Convective Available Potential Energy (CAPE), along with Precipitable Water (PW) are the only parameters being considered as predictors. It is hypothesized that the statistical forecasts will benefit from higher resolution data both in training and implementing the statistical model. We have dynamically downscaled NCEP FNL (Final) reanalysis data using the Weather Research and Forecasting model (WRF) to 3km spatial and hourly temporal resolution across a decade. This dataset will be used to evaluate the contribution to the success of the statistical model of additional predictors in higher vertical, spatial and temporal resolution. If successful, we will implement an operational dynamically downscaled GFS forecast product to generate predictors for the resulting statistical lightning model. This data will help fire agencies be better prepared to pre-deploy resources in advance of these events. Specific information regarding duration, amount, and location will be especially valuable.

Spatial downscaling of soil prediction models based on weighted generalized additive models in smallholder farm settings.

PubMed

Xu, Yiming; Smith, Scot E; Grunwald, Sabine; Abd-Elrahman, Amr; Wani, Suhas P; Nair, Vimala D

2017-09-11

Digital soil mapping (DSM) is gaining momentum as a technique to help smallholder farmers secure soil security and food security in developing regions. However, communications of the digital soil mapping information between diverse audiences become problematic due to the inconsistent scale of DSM information. Spatial downscaling can make use of accessible soil information at relatively coarse spatial resolution to provide valuable soil information at relatively fine spatial resolution. The objective of this research was to disaggregate the coarse spatial resolution soil exchangeable potassium (K ex ) and soil total nitrogen (TN) base map into fine spatial resolution soil downscaled map using weighted generalized additive models (GAMs) in two smallholder villages in South India. By incorporating fine spatial resolution spectral indices in the downscaling process, the soil downscaled maps not only conserve the spatial information of coarse spatial resolution soil maps but also depict the spatial details of soil properties at fine spatial resolution. The results of this study demonstrated difference between the fine spatial resolution downscaled maps and fine spatial resolution base maps is smaller than the difference between coarse spatial resolution base maps and fine spatial resolution base maps. The appropriate and economical strategy to promote the DSM technique in smallholder farms is to develop the relatively coarse spatial resolution soil prediction maps or utilize available coarse spatial resolution soil maps at the regional scale and to disaggregate these maps to the fine spatial resolution downscaled soil maps at farm scale.

A comparison of daily precipitation metrics downscaled using SDSM and WRF + WRFDA models over the Iberian Peninsula.

NASA Astrophysics Data System (ADS)

José González-Rojí, Santos; Wilby, Robert L.; Sáenz, Jon; Ibarra-Berastegi, Gabriel

2017-04-01

Downscaling via the Statistical DownScaling Model (SDSM) version 5.2 and two different configurations of the dynamical WRF model (with and without 3DVAR data assimilation) was evaluated for the estimation of daily precipitation over 21 sites across the Iberian Peninsula during the period 2010-2014. Six different strategies were used to calibrate the SDSM model. These options cover (1) use of NCEP/NCAR R1 Reanalysis and (2) ERA Interim data for downscaling predictor variables calibrated with data from periods (3) 1948-2009 (NCEP/NCAR R1) and (4) 1979-2009 (NCEP/NCAR R1 and ERA Interim). Additionally, for the ERA Interim case, two different grid resolutions have been used, (5) 2.5° and (6) 0.75°. On the other side, for the NCEP/NCAR R1 case, only the 2.5° resolution has been used. Configuring the SDSM model in this way allows testing the sensitivity of the results to different origins of the predictors, fit to different calibration periods and use of different reanalysis resolutions. On the other hand, ERA Interim data at the highest resolution was used as the initial/boundary conditions to run WRF simulations with a 15 km x 15 km horizontal resolution over the Iberian Peninsula, for two different configurations. The first experiment (N) was run using the same configuration typically used for numerical downscaling, with information being fed through the boundaries of the domain. The second experiment (D) was run using 3DVAR data assimilation at 00UTC, 06UTC, 12UTC and 18UTC. In both cases, WRF simulations were run over the period 2009-2014, using the first year (2009) as spin-up for the soil model. Results from the WRF N and D runs and comparable SDSM set up for the period 2010-2014 were evaluated using observations from ECA and E-OBS datasets. In each case, model skill was assessed using seven daily precipitation metrics (absolute mean, wet-day intensity, 90th percentile, maximum 5-day total, maximum number of consecutive dry days, fraction of total from heavy events and number of heavy events defined here as values over the threshold of 90th percentile. Our results show that the SDSM model improves its behaviour when using predictors from the ERA Interim Reanalysis. Improvements are even more impressive when using the 0.75° resolution for ERA Interim. Better results than using WRF D are obtained with this configuration of the SDSM model for mean precipitation and precipitation intensity. Overall, the analysis reveals the extent to which the skill of SDSM can be improved through judicious choice of downscaling predictor source, grid resolution and calibration period. Moreover, the computationally efficient SDSM tool can achieve comparable skill to WRF over a range of precipitation metrics and the contrasting rainfall regimes of the Iberian Peninsula.

Downscaling Pest Risk Analyses: Identifying Current and Future Potentially Suitable Habitats for Parthenium hysterophorus with Particular Reference to Europe and North Africa

PubMed Central

Kriticos, Darren J.; Brunel, Sarah; Ota, Noboru; Fried, Guillaume; Oude Lansink, Alfons G. J. M.; Panetta, F. Dane; Prasad, T. V. Ramachandra; Shabbir, Asad; Yaacoby, Tuvia

2015-01-01

Pest Risk Assessments (PRAs) routinely employ climatic niche models to identify endangered areas. Typically, these models consider only climatic factors, ignoring the ‘Swiss Cheese’ nature of species ranges due to the interplay of climatic and habitat factors. As part of a PRA conducted for the European and Mediterranean Plant Protection Organization, we developed a climatic niche model for Parthenium hysterophorus, explicitly including the effects of irrigation where it was known to be practiced. We then downscaled the climatic risk model using two different methods to identify the suitable habitat types: expert opinion (following the EPPO PRA guidelines) and inferred from the global spatial distribution. The PRA revealed a substantial risk to the EPPO region and Central and Western Africa, highlighting the desirability of avoiding an invasion by P. hysterophorus. We also consider the effects of climate change on the modelled risks. The climate change scenario indicated the risk of substantial further spread of P. hysterophorus in temperate northern hemisphere regions (North America, Europe and the northern Middle East), and also high elevation equatorial regions (Western Brazil, Central Africa, and South East Asia) if minimum temperatures increase substantially. Downscaling the climate model using habitat factors resulted in substantial (approximately 22–53%) reductions in the areas estimated to be endangered. Applying expert assessments as to suitable habitat classes resulted in the greatest reduction in the estimated endangered area, whereas inferring suitable habitats factors from distribution data identified more land use classes and a larger endangered area. Despite some scaling issues with using a globally conformal Land Use Systems dataset, the inferential downscaling method shows promise as a routine addition to the PRA toolkit, as either a direct model component, or simply as a means of better informing an expert assessment of the suitable habitat types. PMID:26325680

Uncertainties in Hydrologic and Climate Change Impact Analysis in Major Watersheds in British Columbia, Canada

NASA Astrophysics Data System (ADS)

Bennett, K. E.; Schnorbus, M.; Werner, A. T.; Music, B.; Caya, D.; Rodenhuis, D. R.

2009-12-01

Uncertainties in the projections of future hydrologic change can be assessed using a suite of tools, thereby allowing researchers to focus on improvement to identifiable sources of uncertainty. A pareto set of optimal hydrologic parameterizations was run for three BC watersheds (Fraser, Peace and Columbia) for a range of downscaled Global Climate Model (GCM) emission scenarios to illustrate the uncertainty in hydrologic response to climate change. Results show varying responses of hydrologic regimes across geographic landscapes. Uncertainties in streamflow and water balance (runoff, evapo-transpiration, snow water equivalent, soil moisture) were analysed by forcing the Variable Infiltration Capacity (VIC) hydrologic model, run under twenty-five optimal parameter solution sets using six Bias-Corrected Statistically Downscaled (BCSD) GCM emission scenario projections for the 2050s and the 2080s. Projected changes by the 2050s include increased winter flows, increases and decreases in freshet magnitude depending on the scenario, and decreases in summer flows persisting until September. Winter runoff had the greatest range between GCM emission scenarios, while the hydrologic parameters within individual GCM emission scenarios had a winter runoff range an order of magnitude smaller. Evapo-transpiration, snow water equivalent and soil moisture exhibited a spread of ~10% or less. Streamflow changes by the 2080s lie outside the natural range of historic variability over the winter and spring. Results indicate that the changes projected between GCM emission scenarios are greater than the differences between the hydrologic model parameterizations. An alternate tool, the Canadian Regional Climate Model (CRCM) has been set up for these watersheds and various runs have been analysed to determine the range and variability present and to examine these results in comparison to the hydrologic model projections. The CRCM range and variability is an improvement over the Canadian GCM and thus requires less bias correction. However, without downscaling the CRCM results are still coarser than what is required to drive macroscale hydrologic models, such as VIC. Applying these tools has illustrated the importance of focusing on improved downscaling efforts, including downscaling CRCM results rather than CGCM data. Tools for decision-making in the face of uncertainty are emerging as a priority for the climate change impacts community, and there is a need to focus on incorporating uncertainty information along with the projection of impacts. Assessing uncertainty across a range of regimes and geographic regions can assist to identify the main sources of uncertainty and allow researchers to focus on improving those sources using more robust methodological approaches and tools.

21st century projections of snowfall and winter severity across central-eastern North America

NASA Astrophysics Data System (ADS)

Notaro, M.; Lorenz, D. J.; Hoving, C.; Schummer, M.

2014-12-01

Statistically downscaled climate projections from nine global climate models (GCMs) are used to force a snow accumulation and ablation model (SNOW-17) across the central-eastern North American Landscape Conservation Cooperatives (LCCs) to develop high-resolution projections of snowfall, snow depth, and winter severity index (WSI) by the mid- and late 21st century. Here, we use projections of a cumulative WSI (CWSI) known to influence autumn-winter waterfowl migration to demonstrate the utility of SNOW-17 results. The application of statistically downscaled climate data and a snow model leads to a better representation of lake processes in the Great Lakes Basin, topographic effects in the Appalachian Mountains, and spatial patterns of climatological snowfall, compared to the original GCMs. Annual mean snowfall is simulated to decline across the region, particularly in early winter (December-January), leading to a delay in the mean onset of the snow season. Due to a warming-induced acceleration of snowmelt, the percentage loss in snow depth exceeds that of snowfall. Across the Plains and Prairie Potholes LCC and Upper Midwest and Great Lakes LCC, daily snowfall events are projected to become less common, but more intense. The greatest reductions in the number of days per year with a present snowpack are expected close to the historical position of the -5°C isotherm in DJFM, around 44°N. The CWSI is projected to decline substantially during December-January, leading to increased likelihood of delays in timing and intensity of autumn-winter waterfowl migrations.

A Multiplicative Cascade Model for High-Resolution Space-Time Downscaling of Rainfall

NASA Astrophysics Data System (ADS)

Raut, Bhupendra A.; Seed, Alan W.; Reeder, Michael J.; Jakob, Christian

2018-02-01

Distributions of rainfall with the time and space resolutions of minutes and kilometers, respectively, are often needed to drive the hydrological models used in a range of engineering, environmental, and urban design applications. The work described here is the first step in constructing a model capable of downscaling rainfall to scales of minutes and kilometers from time and space resolutions of several hours and a hundred kilometers. A multiplicative random cascade model known as the Short-Term Ensemble Prediction System is run with parameters from the radar observations at Melbourne (Australia). The orographic effects are added through multiplicative correction factor after the model is run. In the first set of model calculations, 112 significant rain events over Melbourne are simulated 100 times. Because of the stochastic nature of the cascade model, the simulations represent 100 possible realizations of the same rain event. The cascade model produces realistic spatial and temporal patterns of rainfall at 6 min and 1 km resolution (the resolution of the radar data), the statistical properties of which are in close agreement with observation. In the second set of calculations, the cascade model is run continuously for all days from January 2008 to August 2015 and the rainfall accumulations are compared at 12 locations in the greater Melbourne area. The statistical properties of the observations lie with envelope of the 100 ensemble members. The model successfully reproduces the frequency distribution of the 6 min rainfall intensities, storm durations, interarrival times, and autocorrelation function.

An adaptive two-stage analog/regression model for probabilistic prediction of small-scale precipitation in France

NASA Astrophysics Data System (ADS)

Chardon, Jérémy; Hingray, Benoit; Favre, Anne-Catherine

2018-01-01

Statistical downscaling models (SDMs) are often used to produce local weather scenarios from large-scale atmospheric information. SDMs include transfer functions which are based on a statistical link identified from observations between local weather and a set of large-scale predictors. As physical processes driving surface weather vary in time, the most relevant predictors and the regression link are likely to vary in time too. This is well known for precipitation for instance and the link is thus often estimated after some seasonal stratification of the data. In this study, we present a two-stage analog/regression model where the regression link is estimated from atmospheric analogs of the current prediction day. Atmospheric analogs are identified from fields of geopotential heights at 1000 and 500 hPa. For the regression stage, two generalized linear models are further used to model the probability of precipitation occurrence and the distribution of non-zero precipitation amounts, respectively. The two-stage model is evaluated for the probabilistic prediction of small-scale precipitation over France. It noticeably improves the skill of the prediction for both precipitation occurrence and amount. As the analog days vary from one prediction day to another, the atmospheric predictors selected in the regression stage and the value of the corresponding regression coefficients can vary from one prediction day to another. The model allows thus for a day-to-day adaptive and tailored downscaling. It can also reveal specific predictors for peculiar and non-frequent weather configurations.

Projected Changes in Hydrological Extremes in a Cold Region Watershed: Sensitivity of Results to Statistical Methods of Analysis

NASA Astrophysics Data System (ADS)

Dibike, Y. B.; Eum, H. I.; Prowse, T. D.

2017-12-01

Flows originating from alpine dominated cold region watersheds typically experience extended winter low flows followed by spring snowmelt and summer rainfall driven high flows. In a warmer climate, there will be temperature- induced shift in precipitation from snow towards rain as well as changes in snowmelt timing affecting the frequency of extreme high and low flow events which could significantly alter ecosystem services. This study examines the potential changes in the frequency and severity of hydrologic extremes in the Athabasca River watershed in Alberta, Canada based on the Variable Infiltration Capacity (VIC) hydrologic model and selected and statistically downscaled climate change scenario data from the latest Coupled Model Intercomparison Project (CMIP5). The sensitivity of these projected changes is also examined by applying different extreme flow analysis methods. The hydrological model projections show an overall increase in mean annual streamflow in the watershed and a corresponding shift in the freshet timing to earlier period. Most of the streams are projected to experience increases during the winter and spring seasons and decreases during the summer and early fall seasons, with an overall projected increases in extreme high flows, especially for low frequency events. While the middle and lower parts of the watershed are characterised by projected increases in extreme high flows, the high elevation alpine region is mainly characterised by corresponding decreases in extreme low flow events. However, the magnitude of projected changes in extreme flow varies over a wide range, especially for low frequent events, depending on the climate scenario and period of analysis, and sometimes in a nonlinear way. Nonetheless, the sensitivity of the projected changes to the statistical method of analysis is found to be relatively small compared to the inter-model variability.

Land surface temperature downscaling using random forest regression: primary result and sensitivity analysis

NASA Astrophysics Data System (ADS)

Pan, Xin; Cao, Chen; Yang, Yingbao; Li, Xiaolong; Shan, Liangliang; Zhu, Xi

2018-04-01

The land surface temperature (LST) derived from thermal infrared satellite images is a meaningful variable in many remote sensing applications. However, at present, the spatial resolution of the satellite thermal infrared remote sensing sensor is coarser, which cannot meet the needs. In this study, LST image was downscaled by a random forest model between LST and multiple predictors in an arid region with an oasis-desert ecotone. The proposed downscaling approach was evaluated using LST derived from the MODIS LST product of Zhangye City in Heihe Basin. The primary result of LST downscaling has been shown that the distribution of downscaled LST matched with that of the ecosystem of oasis and desert. By the way of sensitivity analysis, the most sensitive factors to LST downscaling were modified normalized difference water index (MNDWI)/normalized multi-band drought index (NMDI), soil adjusted vegetation index (SAVI)/ shortwave infrared reflectance (SWIR)/normalized difference vegetation index (NDVI), normalized difference building index (NDBI)/SAVI and SWIR/NDBI/MNDWI/NDWI for the region of water, vegetation, building and desert, with LST variation (at most) of 0.20/-0.22 K, 0.92/0.62/0.46 K, 0.28/-0.29 K and 3.87/-1.53/-0.64/-0.25 K in the situation of +/-0.02 predictor perturbances, respectively.

An intercomparison of GCM and RCM dynamical downscaling for characterizing the hydroclimatology of California and Nevada

NASA Astrophysics Data System (ADS)

Xu, Z.; Rhoades, A.; Johansen, H.; Ullrich, P. A.; Collins, W. D.

2017-12-01

Dynamical downscaling is widely used to properly characterize regional surface heterogeneities that shape the local hydroclimatology. However, the factors in dynamical downscaling, including the refinement of model horizontal resolution, large-scale forcing datasets and dynamical cores, have not been fully evaluated. Two cutting-edge global-to-regional downscaling methods are used to assess these, specifically the variable-resolution Community Earth System Model (VR-CESM) and the Weather Research & Forecasting (WRF) regional climate model, under different horizontal resolutions (28, 14, and 7 km). Two groups of WRF simulations are driven by either the NCEP reanalysis dataset (WRF_NCEP) or VR-CESM outputs (WRF_VRCESM) to evaluate the effects of the large-scale forcing datasets. The impacts of dynamical core are assessed by comparing the VR-CESM simulations to the coupled WRF_VRCESM simulations with the same physical parameterizations and similar grid domains. The simulated hydroclimatology (i.e., total precipitation, snow cover, snow water equivalent and surface temperature) are compared with the reference datasets. The large-scale forcing datasets are critical to the WRF simulations in more accurately simulating total precipitation, SWE and snow cover, but not surface temperature. Both the WRF and VR-CESM results highlight that no significant benefit is found in the simulated hydroclimatology by just increasing horizontal resolution refinement from 28 to 7 km. Simulated surface temperature is sensitive to the choice of dynamical core. WRF generally simulates higher temperatures than VR-CESM, alleviates the systematic cold bias of DJF temperatures over the California mountain region, but overestimates the JJA temperature in California's Central Valley.

Using sea surface temperatures to improve performance of single dynamical downscaling model in flood simulation under climate change

NASA Astrophysics Data System (ADS)

Chao, Y.; Cheng, C. T.; Hsiao, Y. H.; Hsu, C. T.; Yeh, K. C.; Liu, P. L.

2017-12-01

There are 5.3 typhoons hit Taiwan per year on average in last decade. Typhoon Morakot in 2009, the most severe typhoon, causes huge damage in Taiwan, including 677 casualties and roughly NT 110 billion (3.3 billion USD) in economic loss. Some researches documented that typhoon frequency will decrease but increase in intensity in western North Pacific region. It is usually preferred to use high resolution dynamical model to get better projection of extreme events; because coarse resolution models cannot simulate intense extreme events. Under that consideration, dynamical downscaling climate data was chosen to describe typhoon satisfactorily, this research used the simulation data from AGCM of Meteorological Research Institute (MRI-AGCM). Considering dynamical downscaling methods consume massive computing power, and typhoon number is very limited in a single model simulation, using dynamical downscaling data could cause uncertainty in disaster risk assessment. In order to improve the problem, this research used four sea surfaces temperatures (SSTs) to increase the climate change scenarios under RCP 8.5. In this way, MRI-AGCMs project 191 extreme typhoons in Taiwan (when typhoon center touches 300 km sea area of Taiwan) in late 21th century. SOBEK, a two dimensions flood simulation model, was used to assess the flood risk under four SSTs climate change scenarios in Tainan, Taiwan. The results show the uncertainty of future flood risk assessment is significantly decreased in Tainan, Taiwan in late 21th century. Four SSTs could efficiently improve the problems of limited typhoon numbers in single model simulation.

Detailed climate-change projections for urban land-use change and green-house gas increases for Belgium with COSMO-CLM coupled to TERRA_URB

NASA Astrophysics Data System (ADS)

Wouters, Hendrik; Vanden Broucke, Sam; van Lipzig, Nicole; Demuzere, Matthias

2016-04-01

Recent research clearly show that climate modelling at high resolution - which resolve the deep convection, the detailed orography and land-use including urbanization - leads to better modelling performance with respect to temperatures, the boundary-layer, clouds and precipitation. The increasing computational power enables the climate research community to address climate-change projections with higher accuracy and much more detail. In the framework of the CORDEX.be project aiming for coherent high-resolution micro-ensemble projections for Belgium employing different GCMs and RCMs, the KU Leuven contributes by means of the downscaling of EC-EARTH global climate model projections (provided by the Royal Meteorological Institute of the Netherlands) to the Belgian domain. The downscaling is obtained with regional climate simulations at 12.5km resolution over Europe (CORDEX-EU domain) and at 2.8km resolution over Belgium (CORDEX.be domain) using COSMO-CLM coupled to urban land-surface parametrization TERRA_URB. This is done for the present-day (1975-2005) and future (2040 → 2070 and 2070 → 2100). In these high-resolution runs, both GHG changes (in accordance to RCP8.5) and urban land-use changes (in accordance to a business-as-usual urban expansion scenario) are taken into account. Based on these simulations, it is shown how climate-change statistics are modified when going from coarse resolution modelling to high-resolution modelling. The climate-change statistics of particular interest are the changes in number of extreme precipitation events and extreme heat waves in cities. Hereby, it is futher investigated for the robustness of the signal change between the course and high-resolution and whether a (statistical) translation is possible. The different simulations also allow to address the relative impact and synergy between the urban expansion and increased GHG on the climate-change statistics. Hereby, it is investigated for which climate-change statistics the urban heat island and urban expansion is relevant, and to what extent the urban expansion can be included in the coarse-to-high resolution translation.

Impact of bias-corrected reanalysis-derived lateral boundary conditions on WRF simulations

NASA Astrophysics Data System (ADS)

Moalafhi, Ditiro Benson; Sharma, Ashish; Evans, Jason Peter; Mehrotra, Rajeshwar; Rocheta, Eytan

2017-08-01

Lateral and lower boundary conditions derived from a suitable global reanalysis data set form the basis for deriving a dynamically consistent finer resolution downscaled product for climate and hydrological assessment studies. A problem with this, however, is that systematic biases have been noted to be present in the global reanalysis data sets that form these boundaries, biases which can be carried into the downscaled simulations thereby reducing their accuracy or efficacy. In this work, three Weather Research and Forecasting (WRF) model downscaling experiments are undertaken to investigate the impact of bias correcting European Centre for Medium range Weather Forecasting Reanalysis ERA-Interim (ERA-I) atmospheric temperature and relative humidity using Atmospheric Infrared Sounder (AIRS) satellite data. The downscaling is performed over a domain centered over southern Africa between the years 2003 and 2012. The sample mean and the mean as well as standard deviation at each grid cell for each variable are used for bias correction. The resultant WRF simulations of near-surface temperature and precipitation are evaluated seasonally and annually against global gridded observational data sets and compared with ERA-I reanalysis driving field. The study reveals inconsistencies between the impact of the bias correction prior to downscaling and the resultant model simulations after downscaling. Mean and standard deviation bias-corrected WRF simulations are, however, found to be marginally better than mean only bias-corrected WRF simulations and raw ERA-I reanalysis-driven WRF simulations. Performances, however, differ when assessing different attributes in the downscaled field. This raises questions about the efficacy of the correction procedures adopted.

Use of dynamical downscaling to improve the simulation of Central U.S. warm season precipitation in CMIP5 models

NASA Astrophysics Data System (ADS)

Harding, Keith J.; Snyder, Peter K.; Liess, Stefan

2013-11-01

supporting exceptionally productive agricultural lands, the Central U.S. is susceptible to severe droughts and floods. Such precipitation extremes are expected to worsen with climate change. However, future projections are highly uncertain as global climate models (GCMs) generally fail to resolve precipitation extremes. In this study, we assess how well models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulate summer means, variability, extremes, and the diurnal cycle of Central U.S. summer rainfall. Output from a subset of historical CMIP5 simulations are used to drive the Weather Research and Forecasting model to determine whether dynamical downscaling improves the representation of Central U.S. rainfall. We investigate which boundary conditions influence dynamically downscaled precipitation estimates and identify GCMs that can reasonably simulate precipitation when downscaled. The CMIP5 models simulate the seasonal mean and variability of summer rainfall reasonably well but fail to resolve extremes, the diurnal cycle, and the dynamic forcing of precipitation. Downscaling to 30 km improves these characteristics of precipitation, with the greatest improvement in the representation of extremes. Additionally, sizeable diurnal cycle improvements occur with higher (10 km) resolution and convective parameterization disabled, as the daily rainfall peak shifts 4 h closer to observations than 30 km resolution simulations. This lends greater confidence that the mechanisms responsible for producing rainfall are better simulated. Because dynamical downscaling can more accurately simulate these aspects of Central U.S. summer rainfall, policymakers can have added confidence in dynamically downscaled rainfall projections, allowing for more targeted adaptation and mitigation.

The Change of First-flowering Date over South Korea Projected from Downscaled IPCC AR5 Simulation: Peach and Pear

NASA Astrophysics Data System (ADS)

Ahn, J. B.; Hur, J.

2014-12-01

The variations in the first-flowering date (FFD) of peach (Prunus persica) and pear (Pyrus pyrifolia) under future climate change in South Korea are investigated using simulations obtained from five models of the fifth Coupled Model Intercomparison Project. For the study, daily temperature simulations with Historical (1986-2005), and RCP (2071-2090) 4.5 and 8.5 scenarios are statistically downscaled to 50 peach and pear FFD (FFDpeach and FFDpear, respectively) observation sites over South Korea. The number of days transformed to standard temperature (DTS) method is selected as the phenological model and applied to simulations for estimating FFDpeach and FFDpear over South Korea, due to its superior performance on the target plants and region compared to the growing degree days (GDD) and chill days (CD) methods. In the analysis, mean temperatures for early spring (February to April) over South Korea in 2090 under RCP4.5 and 8.5 scenarios are expected to have increased by 1.9K and 3.3K, respectively. Among the early spring months of February to April, February shows the largest temperature increase of 2.1K and 3.7K for RCP4.5 and 8.5 scenarios, respectively. The increased temperature during February and March accelerates the plant growth rate and thereby advances FFDpeach by 7.0 and 12.7 days and FFDpear by 6.1 and 10.7 days, respectively. These results imply that the present flowering of peach and pear in the middle of April will have advanced to late March or early April by the end of this century. Acknowledgements This work was carried out with the support of the Rural Development Administration Cooperative Research Program for Agriculture Science and Technology Development under Grant Project No. PJ009953, Republic of Korea.

Multi-year downscaling application of two-way coupled WRF v3.4 and CMAQ v5.0.2 over east Asia for regional climate and air quality modeling: model evaluation and aerosol direct effects

NASA Astrophysics Data System (ADS)

Hong, Chaopeng; Zhang, Qiang; Zhang, Yang; Tang, Youhua; Tong, Daniel; He, Kebin

2017-06-01

In this study, a regional coupled climate-chemistry modeling system using the dynamical downscaling technique was established by linking the global Community Earth System Model (CESM) and the regional two-way coupled Weather Research and Forecasting - Community Multi-scale Air Quality (WRF-CMAQ) model for the purpose of comprehensive assessments of regional climate change and air quality and their interactions within one modeling framework. The modeling system was applied over east Asia for a multi-year climatological application during 2006-2010, driven with CESM downscaling data under Representative Concentration Pathways 4.5 (RCP4.5), along with a short-term air quality application in representative months in 2013 that was driven with a reanalysis dataset. A comprehensive model evaluation was conducted against observations from surface networks and satellite observations to assess the model's performance. This study presents the first application and evaluation of the two-way coupled WRF-CMAQ model for climatological simulations using the dynamical downscaling technique. The model was able to satisfactorily predict major meteorological variables. The improved statistical performance for the 2 m temperature (T2) in this study (with a mean bias of -0.6 °C) compared with the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-models might be related to the use of the regional model WRF and the bias-correction technique applied for CESM downscaling. The model showed good ability to predict PM2. 5 in winter (with a normalized mean bias (NMB) of 6.4 % in 2013) and O3 in summer (with an NMB of 18.2 % in 2013) in terms of statistical performance and spatial distributions. Compared with global models that tend to underpredict PM2. 5 concentrations in China, WRF-CMAQ was able to capture the high PM2. 5 concentrations in urban areas. In general, the two-way coupled WRF-CMAQ model performed well for both climatological and air quality applications. The coupled modeling system with direct aerosol feedbacks predicted aerosol optical depth relatively well and significantly reduced the overprediction in downward shortwave radiation at the surface (SWDOWN) over polluted regions in China. The performance of cloud variables was not as good as other meteorological variables, and underpredictions of cloud fraction resulted in overpredictions of SWDOWN and underpredictions of shortwave and longwave cloud forcing. The importance of climate-chemistry interactions was demonstrated via the impacts of aerosol direct effects on climate and air quality. The aerosol effects on climate and air quality in east Asia (e.g., SWDOWN and T2 decreased by 21.8 W m-2 and 0.45 °C, respectively, and most pollutant concentrations increased by 4.8-9.5 % in January over China's major cities) were more significant than in other regions because of higher aerosol loadings that resulted from severe regional pollution, which indicates the need for applying online-coupled models over east Asia for regional climate and air quality modeling and to study the important climate-chemistry interactions. This work established a baseline for WRF-CMAQ simulations for a future period under the RCP4.5 climate scenario, which will be presented in a future paper.

Application of hierarchical clustering method to classify of space-time rainfall patterns

NASA Astrophysics Data System (ADS)

Yu, Hwa-Lung; Chang, Tu-Je

2010-05-01

Understanding the local precipitation patterns is essential to the water resources management and flooding mitigation. The precipitation patterns can vary in space and time depending upon the factors from different spatial scales such as local topological changes and macroscopic atmospheric circulation. The spatiotemporal variation of precipitation in Taiwan is significant due to its complex terrain and its location at west pacific and subtropical area, where is the boundary between the pacific ocean and Asia continent with the complex interactions among the climatic processes. This study characterizes local-scale precipitation patterns by classifying the historical space-time precipitation records. We applied the hierarchical ascending clustering method to analyze the precipitation records from 1960 to 2008 at the six rainfall stations located in Lan-yang catchment at the northeast of the island. Our results identify the four primary space-time precipitation types which may result from distinct driving forces from the changes of atmospheric variables and topology at different space-time scales. This study also presents an important application of the statistical downscaling to combine large-scale upper-air circulation with local space-time precipitation patterns.

Possible explanation of the atmospheric kinetic and potential energy spectra.

PubMed

Vallgren, Andreas; Deusebio, Enrico; Lindborg, Erik

2011-12-23

We hypothesize that the observed wave number spectra of kinetic and potential energy in the atmosphere can be explained by assuming that there are two related cascade processes emanating from the same large-scale energy source, a downscale cascade of potential enstrophy, giving rise to the k(-3) spectrum at synoptic scales and a downscale energy cascade giving rise to the k(-5/3) spectrum at mesoscales. The amount of energy which is going into the downscale energy cascade is determined by the rate of system rotation, with negligible energy going downscale in the limit of very fast rotation. We present a set of simulations of a system with strong rotation and stratification, supporting these hypotheses and showing good agreement with observations.

Effect of climate data on simulated carbon and nitrogen balances for Europe

NASA Astrophysics Data System (ADS)

Blanke, Jan Hendrik; Lindeskog, Mats; Lindström, Johan; Lehsten, Veiko

2016-05-01

In this study, we systematically assess the spatial variability in carbon and nitrogen balance simulations related to the choice of global circulation models (GCMs), representative concentration pathways (RCPs), spatial resolutions, and the downscaling methods used as calculated with LPJ-GUESS. We employed a complete factorial design and performed 24 simulations for Europe with different climate input data sets and different combinations of these four factors. Our results reveal that the variability in simulated output in Europe is moderate with 35.6%-93.5% of the total variability being common among all combinations of factors. The spatial resolution is the most important factor among the examined factors, explaining 1.5%-10.7% of the total variability followed by GCMs (0.3%-7.6%), RCPs (0%-6.3%), and downscaling methods (0.1%-4.6%). The higher-order interactions effect that captures nonlinear relations between the factors and random effects is pronounced and accounts for 1.6%-45.8% to the total variability. The most distinct hot spots of variability include the mountain ranges in North Scandinavia and the Alps, and the Iberian Peninsula. Based on our findings, we advise to conduct the application of models such as LPJ-GUESS at a reasonably high spatial resolution which is supported by the model structure. There is no notable gain in simulations of ecosystem carbon and nitrogen stocks and fluxes from using regionally downscaled climate in preference to bias-corrected, bilinearly interpolated CMIP5 projections.

High Resolution Hydro-climatological Projections for Western Canada

NASA Astrophysics Data System (ADS)

Erler, Andre Richard

Accurate identification of the impact of global warming on water resources and hydro-climatic extremes represents a significant challenge to the understanding of climate change on the regional scale. Here an analysis of hydro-climatic changes in western Canada is presented, with specific focus on the Fraser and Athabasca River basins and on changes in hydro-climatic extremes. The analysis is based on a suite of simulations designed to characterize internal variability, as well as model uncertainty. A small ensemble of Community Earth System Model version 1 (CESM1) simulations was employed to generate global climate projections, which were downscaled to 10 km resolution using the Weather Research and Forecasting model (WRF V3.4.1) with several sets of physical parameterizations. Downscaling was performed for a historical validation period and a mid- and end-21st-century projection period, using the RCP8.5 greenhouse gas trajectory. Daily station observations and monthly gridded datasets were used for validation. Changes in hydro-climatic extremes are characterized using Extreme Value Analysis. A novel method of aggregating data from climatologically similar stations was employed to increase the statistical power of the analysis. Changes in mean and extreme precipitation are found to differ strongly between seasons and regions, but (relative) changes in extremes generally follow changes in the (seasonal) mean. At the end of the 21st century, precipitation and precipitation extremes are projected to increase by 30% at the coast in fall and land-inwards in winter, while the projected increase in summer precipitation is smaller and changes in extremes are often not statistically significant. Reasons for the differences between seasons, the role of precipitation recycling in atmospheric water transport, and the sensitivity to physics parameterizations are discussed. Major changes are projected for the Fraser River basin, including earlier snowmelt and a 50% reduction in peak runoff. Combined with higher evapotranspiration, a significant increase in late summer drought risk is likely, but increasing fall precipitation might also increase the risk of moderate flooding. In the Athabasca River basin, increasing winter precipitation and snowmelt is balanced by increasing evapotranspiration in summer and no significant change in flood or drought risk is projected.

An improved bias correction method of daily rainfall data using a sliding window technique for climate change impact assessment

NASA Astrophysics Data System (ADS)

Smitha, P. S.; Narasimhan, B.; Sudheer, K. P.; Annamalai, H.

2018-01-01

Regional climate models (RCMs) are used to downscale the coarse resolution General Circulation Model (GCM) outputs to a finer resolution for hydrological impact studies. However, RCM outputs often deviate from the observed climatological data, and therefore need bias correction before they are used for hydrological simulations. While there are a number of methods for bias correction, most of them use monthly statistics to derive correction factors, which may cause errors in the rainfall magnitude when applied on a daily scale. This study proposes a sliding window based daily correction factor derivations that help build reliable daily rainfall data from climate models. The procedure is applied to five existing bias correction methods, and is tested on six watersheds in different climatic zones of India for assessing the effectiveness of the corrected rainfall and the consequent hydrological simulations. The bias correction was performed on rainfall data downscaled using Conformal Cubic Atmospheric Model (CCAM) to 0.5° × 0.5° from two different CMIP5 models (CNRM-CM5.0, GFDL-CM3.0). The India Meteorological Department (IMD) gridded (0.25° × 0.25°) observed rainfall data was considered to test the effectiveness of the proposed bias correction method. The quantile-quantile (Q-Q) plots and Nash Sutcliffe efficiency (NSE) were employed for evaluation of different methods of bias correction. The analysis suggested that the proposed method effectively corrects the daily bias in rainfall as compared to using monthly factors. The methods such as local intensity scaling, modified power transformation and distribution mapping, which adjusted the wet day frequencies, performed superior compared to the other methods, which did not consider adjustment of wet day frequencies. The distribution mapping method with daily correction factors was able to replicate the daily rainfall pattern of observed data with NSE value above 0.81 over most parts of India. Hydrological simulations forced using the bias corrected rainfall (distribution mapping and modified power transformation methods that used the proposed daily correction factors) was similar to those simulated by the IMD rainfall. The results demonstrate that the methods and the time scales used for bias correction of RCM rainfall data have a larger impact on the accuracy of the daily rainfall and consequently the simulated streamflow. The analysis suggests that the distribution mapping with daily correction factors can be preferred for adjusting RCM rainfall data irrespective of seasons or climate zones for realistic simulation of streamflow.

A spectral method for spatial downscaling

EPA Science Inventory

Complex computer models play a crucial role in air quality research. These models are used to evaluate potential regulatory impacts of emission control strategies and to estimate air quality in areas without monitoring data. For both of these purposes, it is important to calibrat...

Impact of bias correction and downscaling through quantile mapping on simulated climate change signal: a case study over Central Italy

NASA Astrophysics Data System (ADS)

Sangelantoni, Lorenzo; Russo, Aniello; Gennaretti, Fabio

2018-02-01

Quantile mapping (QM) represents a common post-processing technique used to connect climate simulations to impact studies at different spatial scales. Depending on the simulation-observation spatial scale mismatch, QM can be used for two different applications. The first application uses only the bias correction component, establishing transfer functions between observations and simulations at similar spatial scales. The second application includes a statistical downscaling component when point-scale observations are considered. However, knowledge of alterations to climate change signal (CCS) resulting from these two applications is limited. This study investigates QM impacts on the original temperature and precipitation CCSs when applied according to a bias correction only (BC-only) and a bias correction plus downscaling (BC + DS) application over reference stations in Central Italy. BC-only application is used to adjust regional climate model (RCM) simulations having the same resolution as the observation grid. QM BC + DS application adjusts the same simulations to point-wise observations. QM applications alter CCS mainly for temperature. BC-only application produces a CCS of the median 1 °C lower than the original ( 4.5 °C). BC + DS application produces CCS closer to the original, except over the summer 95th percentile, where substantial amplification of the original CCS resulted. The impacts of the two applications are connected to the ratio between the observed and the simulated standard deviation (STD) of the calibration period. For the precipitation, original CCS is essentially preserved in both applications. Yet, calibration period STD ratio cannot predict QM impact on the precipitation CCS when simulated STD and mean are similarly misrepresented.

Analysis of reference evapotranspiration (ET0) trends under climate change in Bangladesh using observed and CMIP5 data sets

NASA Astrophysics Data System (ADS)

Rahman, Mohammad Atiqur; Yunsheng, Lou; Sultana, Nahid; Ongoma, Victor

2018-03-01

ET0 is an important hydro-meteorological phenomenon, which is influenced by changing climate like other climatic parameters. This study investigates the present and future trends of ET0 in Bangladesh using 39 years' historical and downscaled CMIP5 daily climatic data for the twenty-first century. Statistical Downscaling Model (SDSM) was used to downscale the climate data required to calculate ET0. Penman-Monteith formula was applied in ET0 calculation for both the historical and modelled data. To analyse ET0 trends and trend changing patterns, modified Mann-Kendall and Sequential Mann-Kendall tests were, respectively, done. Spatial variations of ET0 trends are presented by inverse distance weighting interpolation using ArcGIS 10.2.2. Results show that RCP8.5 (2061-2099) will experience the highest amount of ET0 totals in comparison to the historical and all other scenarios in the same time span of 39 years. Though significant positive trends were observed in the mid and last months of year from month-wise trend analysis of representative concentration pathways, significant negative trends were also found for some months using historical data in similar analysis. From long-term annual trend analysis, it was found that major part of the country represents decreasing trends using historical data, but increasing trends were observed for modelled data. Theil-Sen estimations of ET0 trends in the study depict a good consistency with the Mann-Kendall test results. The findings of the study would contribute in irrigation water management and planning of the country and also in furthering the climate change study using modelled data in the context of Bangladesh.

Coupling the WRF model with a temperature index model based on remote sensing for snowmelt simulations in a river basin in the Altay Mountains, northwest China

NASA Astrophysics Data System (ADS)

Wu, X.; Shen, Y.; Wang, N.; Pan, X.; Zhang, W.; He, J.; Wang, G.

2017-12-01

Snowmelt water is an important freshwater resource in the Altay Mountains in northwest China, and it is also crucial for local ecological system, economic and social sustainable development; however, warming climate and rapid spring snowmelt can cause floods that endanger both eco-environment and public and personal property and safety. This study simulates snowmelt in the Kayiertesi River catchment using a temperature-index model based on remote sensing coupled with high-resolution meteorological data obtained from NCEP reanalysis fields that were downscaled using Weather Research Forecasting model, then bias-corrected using a statistical downscaled model. Validation of the forcing data revealed that the high-resolution meteorological fields derived from downscaled NCEP reanalysis were reliable for driving the snowmelt model. Parameters of temperature-index model based on remote sensing were calibrated for spring 2014, and model performance was validated using MODIS snow cover and snow observations from spring 2012. The results show that the temperature-index model based on remote sensing performed well, with a simulation mean relative error of 6.7% and a Nash-Sutchliffe efficiency of 0.98 in spring 2012 in the river of Altay Mountains. Based on the reliable distributed snow water equivalent simulation, daily snowmelt runoff was calculated for spring 2012 in the basin. In the study catchment, spring snowmelt runoff accounts for 72% of spring runoff and 21% of annual runoff. Snowmelt is the main source of runoff for the catchment and should be managed and utilized effectively. The results provide a basis for snowmelt runoff predictions, so as to prevent snowmelt-induced floods, and also provide a generalizable approach that can be applied to other remote locations where high-density, long-term observational data is lacking.

Decomposition of Sources of Errors in Seasonal Streamflow Forecasting over the U.S. Sunbelt

NASA Technical Reports Server (NTRS)

Mazrooei, Amirhossein; Sinah, Tusshar; Sankarasubramanian, A.; Kumar, Sujay V.; Peters-Lidard, Christa D.

2015-01-01

Seasonal streamflow forecasts, contingent on climate information, can be utilized to ensure water supply for multiple uses including municipal demands, hydroelectric power generation, and for planning agricultural operations. However, uncertainties in the streamflow forecasts pose significant challenges in their utilization in real-time operations. In this study, we systematically decompose various sources of errors in developing seasonal streamflow forecasts from two Land Surface Models (LSMs) (Noah3.2 and CLM2), which are forced with downscaled and disaggregated climate forecasts. In particular, the study quantifies the relative contributions of the sources of errors from LSMs, climate forecasts, and downscaling/disaggregation techniques in developing seasonal streamflow forecast. For this purpose, three month ahead seasonal precipitation forecasts from the ECHAM4.5 general circulation model (GCM) were statistically downscaled from 2.8deg to 1/8deg spatial resolution using principal component regression (PCR) and then temporally disaggregated from monthly to daily time step using kernel-nearest neighbor (K-NN) approach. For other climatic forcings, excluding precipitation, we considered the North American Land Data Assimilation System version 2 (NLDAS-2) hourly climatology over the years 1979 to 2010. Then the selected LSMs were forced with precipitation forecasts and NLDAS-2 hourly climatology to develop retrospective seasonal streamflow forecasts over a period of 20 years (1991-2010). Finally, the performance of LSMs in forecasting streamflow under different schemes was analyzed to quantify the relative contribution of various sources of errors in developing seasonal streamflow forecast. Our results indicate that the most dominant source of errors during winter and fall seasons is the errors due to ECHAM4.5 precipitation forecasts, while temporal disaggregation scheme contributes to maximum errors during summer season.

NASA Earth Exchange (NEX) Supporting Analyses for National Climate Assessments

NASA Astrophysics Data System (ADS)

Nemani, R. R.; Thrasher, B. L.; Wang, W.; Lee, T. J.; Melton, F. S.; Dungan, J. L.; Michaelis, A.

2015-12-01

The NASA Earth Exchange (NEX) is a collaborative computing platform that has been developed with the objective of bringing scientists together with the software tools, massive global datasets, and supercomputing resources necessary to accelerate research in Earth systems science and global change. NEX supports several research projects that are closely related with the National Climate Assessment including the generation of high-resolution climate projections, identification of trends and extremes in climate variables and the evaluation of their impacts on regional carbon/water cycles and biodiversity, the development of land-use management and adaptation strategies for climate-change scenarios, and even the exploration of climate mitigation through geo-engineering. Scientists also use the large collection of satellite data on NEX to conduct research on quantifying spatial and temporal changes in land surface processes in response to climate and land-cover-land-use changes. Researchers, leveraging NEX's massive compute/storage resources, have used statistical techniques to downscale the coarse-resolution CMIP5 projections to fulfill the demands of the community for a wide range of climate change impact analyses. The DCP-30 (Downscaled Climate Projections at 30 arcsecond) for the conterminous US at monthly, ~1km resolution and the GDDP (Global Daily Downscaled Projections) for the entire world at daily, 25km resolution are now widely used in climate research and applications, as well as for communicating climate change. In order to serve a broader community, the NEX team in collaboration with Amazon, Inc, created the OpenNEX platform. OpenNEX provides ready access to NEX data holdings, including the NEX-DCP30 and GDDP datasets along with a number of pertinent analysis tools and workflows on the AWS infrastructure in the form of publicly available, self contained, fully functional Amazon Machine Images (AMI's) for anyone interested in global climate change.

Assessing data assimilation and model boundary error strategies for high resolution ocean model downscaling in the Northern North Sea

NASA Astrophysics Data System (ADS)

Sandvig Mariegaard, Jesper; Huiban, Méven Robin; Tornfeldt Sørensen, Jacob; Andersson, Henrik

2017-04-01

Determining the optimal domain size and associated position of open boundaries in local high-resolution downscaling ocean models is often difficult. As an important input data set for downscaling ocean modelling, the European Copernicus Marine Environment Monitoring Service (CMEMS) provides baroclinic initial and boundary conditions for local ocean models. Tidal dynamics is often neglected in CMEMS services at large scale but tides are generally crucial for coastal ocean dynamics. To address this need, tides can be superposed via Flather (1976) boundary conditions and the combined flow downscaled using unstructured mesh. The surge component is also only partially represented in selected CMEMS products and must be modelled inside the domain and modelled independently and superposed if the domain becomes too small to model the effect in the downscaling model. The tide and surge components can generally be improved by assimilating water level from tide gauge and altimetry data. An intrinsic part of the problem is to find the limitations of local scale data assimilation and the requirement for consistency between the larger scale ocean models and the local scale assimilation methodologies. This contribution investigates the impact of domain size and associated positions of open boundaries with and without data assimilation of water level. We have used the baroclinic ocean model, MIKE 3 FM, and its newly re-factored built-in data assimilation package. We consider boundary conditions of salinity, temperature, water level and depth varying currents from the Global CMEMS 1/4 degree resolution model from 2011, where in situ ADCP velocity data is available for validation. We apply data assimilation of in-situ tide gauge water levels and along track altimetry surface elevation data from selected satellites. The MIKE 3 FM data assimilation model which use the Ensemble Kalman filter have recently been parallelized with MPI allowing for much larger applications running on HPC. The success of the downscaling is to a large degree determined by the ability to realistically describe and dynamically model the errors on the open boundaries. Three different sizes of downscaling model domains in the Northern North Sea have been examined and two different strategies for modelling the uncertainties on the open Flather boundaries are investigated. The combined downscaling and local data assimilation skill is assessed and the impact on recommended domain size is compared to pure downscaling.

Prediction of hydrological responds to climate changes in the Upper Yangtze River Basin, China

NASA Astrophysics Data System (ADS)

Yang, X.; Ren, L.; Wang, Y.; Zhang, M.; Liu, Y.; Jiang, S.; Yuan, F.

2017-12-01

Climate changes have direct effects on hydrological cycle, with the increasing temperature and seasonal shift of precipitation. Therefore, understanding of how climate change may affect the population and water resources and economic development is critical to the water and food security for China. This study aims to evaluate the potential impacts of future climate changes on water resources of the upper basin of Yangtze River (the area controlled by the Yichang hydrological station) using the variable infiltration capacity (VIC) model driven by composite observations (1961-2005) and projections of eight CMIP5 models under scenarios RCP4.5 and RCP8.5 from 2006 to 2099. The raw eight CMIP5 models have been downscaled by the equidistant cumulative distribution functions (EDCDF) statistical downscaling approach from 1961 to 2099. The assessment of the performance of model simulated precipitation and temperature were calculated by comparing to the observations during the historical period (1961-2005). For the same variables, eight CMIP5 models for RCP 4.5 and RCP 8.5 downscaled by EDCDF method were generated during the future period (2006-2099). Overall, the VIC model performed well in monthly streamflow simulation, with the Nash-Sutcliffe coefficient of efficiency (NSCE) 0.92 and 0.97 for calibration and validation, respectively. The annual precipitation is projected to increase by 6.3mm and 8.6mm per decade and the annual temperature will increase by 0.22 °C and 0.53°C per decade (2006-2099) for RCP4.5 and RCP8.5, respectively. In the future period, The total runoff of the study basins would either remain stable or moderately increase by 2.7% and 22.4% per decade, the evapotranspiration increase by 2mm and 13mm per decade, and the soil moisture will reduce by -0.1% and -7.4% per decade under RCP4.5 and RCP8.5, respectively. The changes of model-simulated soil moisture, runoff, and evapotranspiration suggest that there probably be an increasing risk of drought in the twenty-first century in UYRB and the policy maker and managers need to pay more attention to the adaption actions of implement robust water management in UYRB. Keywords: EDCDF; Bias correction; Climate changes; Water and food security; Upper Yangtze River Basin

Dynamically downscaling predictions for deciduous tree leaf emergence in California under current and future climate.

PubMed

Medvigy, David; Kim, Seung Hee; Kim, Jinwon; Kafatos, Menas C

2016-07-01

Models that predict the timing of deciduous tree leaf emergence are typically very sensitive to temperature. However, many temperature data products, including those from climate models, have been developed at a very coarse spatial resolution. Such coarse-resolution temperature products can lead to highly biased predictions of leaf emergence. This study investigates how dynamical downscaling of climate models impacts simulations of deciduous tree leaf emergence in California. Models for leaf emergence are forced with temperatures simulated by a general circulation model (GCM) at ~200-km resolution for 1981-2000 and 2031-2050 conditions. GCM simulations are then dynamically downscaled to 32- and 8-km resolution, and leaf emergence is again simulated. For 1981-2000, the regional average leaf emergence date is 30.8 days earlier in 32-km simulations than in ~200-km simulations. Differences between the 32 and 8 km simulations are small and mostly local. The impact of downscaling from 200 to 8 km is ~15 % smaller in 2031-2050 than in 1981-2000, indicating that the impacts of downscaling are unlikely to be stationary.

Applications of Machine Learning to Downscaling and Verification

NASA Astrophysics Data System (ADS)

Prudden, R.

2017-12-01

Downscaling, sometimes known as super-resolution, means converting model data into a more detailed local forecast. It is a problem which could be highly amenable to machine learning approaches, provided that sufficient historical forecast data and observations are available. It is also closely linked to the subject of verification, since improving a forecast requires a way to measure that improvement. This talk will describe some early work towards downscaling Met Office ensemble forecasts, and discuss how the output may be usefully evaluated.

Uncertainty of future projections of species distributions in mountainous regions.

PubMed

Tang, Ying; Winkler, Julie A; Viña, Andrés; Liu, Jianguo; Zhang, Yuanbin; Zhang, Xiaofeng; Li, Xiaohong; Wang, Fang; Zhang, Jindong; Zhao, Zhiqiang

2018-01-01

Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution.

Uncertainty of future projections of species distributions in mountainous regions

PubMed Central

Tang, Ying; Viña, Andrés; Liu, Jianguo; Zhang, Yuanbin; Zhang, Xiaofeng; Li, Xiaohong; Wang, Fang; Zhang, Jindong; Zhao, Zhiqiang

2018-01-01

Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution. PMID:29320501

Developing perturbations for Climate Change Impact Assessments

NASA Astrophysics Data System (ADS)

Hewitson, Bruce

Following the 2001 Intergovernmental Panel on Climate Change (IPCC) Third Assessment Report [TAR; IPCC, 2001], and the paucity of climate change impact assessments from developing nations, there has been a significant growth in activities to redress this shortcoming. However, undertaking impact assessments (in relation to malaria, crop stress, regional water supply, etc.) is contingent on available climate-scale scenarios at time and space scales of relevance to the regional issues of importance. These scales are commonly far finer than even the native resolution of the Global Climate Models (GCMs) (the principal tools for climate change research), let alone the skillful resolution (scales of aggregation at which GCM observational error is acceptable for a given application) of GCMs.Consequently, there is a growing demand for regional-scale scenarios, which in turn are reliant on techniques to downscale from GCMs, such as empirical downscaling or nested Regional Climate Models (RCMs). These methods require significant skill, experiential knowledge, and computational infrastructure in order to derive credible regional-scale scenarios. In contrast, it is often the case that impact assessment researchers in developing nations have inadequate resources with limited access to scientists in the broader international scientific community who have the time and expertise to assist. However, where developing effective downscaled scenarios is problematic, it is possible that much useful information can still be obtained for impact assessments by examining the system sensitivity to largerscale climate perturbations. Consequently, one may argue that the early phase of assessing sensitivity and vulnerability should first be characterized by evaluation of the first-order impacts, rather than immediately addressing the finer, secondary factors that are dependant on scenarios derived through downscaling.

High-resolution dynamical downscaling of the future Alpine climate

NASA Astrophysics Data System (ADS)

Bozhinova, Denica; José Gómez-Navarro, Juan; Raible, Christoph

2017-04-01

The Alpine region and Switzerland is a challenging area for simulating and analysing Global Climate Model (GCM) results. This is mostly due to the combination of a very complex topography and the still rather coarse horizontal resolution of current GCMs, in which not all of the many-scale processes that drive the local weather and climate can be resolved. In our study, the Weather Research and Forecasting (WRF) model is used to dynamically downscale a GCM simulation to a resolution as high as 2 km x 2 km. WRF is driven by initial and boundary conditions produced with the Community Earth System Model (CESM) for the recent past (control run) and until 2100 using the RCP8.5 climate scenario (future run). The control run downscaled with WRF covers the period 1976-2005, while the future run investigates a 20-year-slice simulated for the 2080-2099. We compare the control WRF-CESM simulations to an observational product provided by MeteoSwiss and an additional WRF simulation driven by the ERA-Interim reanalysis, to estimate the bias that is introduced by the extra modelling step of our framework. Several bias-correction methods are evaluated, including a quantile mapping technique, to ameliorate the bias in the control WRF-CESM simulation. In the next step of our study these corrections are applied to our future WRF-CESM run. The resulting downscaled and bias-corrected data is analysed for the properties of precipitation and wind speed in the future climate. Our special interest focuses on the absolute quantities simulated for these meteorological variables as these are used to identify extreme events, such as wind storms and situations that can lead to floods.

The added value of dynamical downscaling in a climate change scenario simulation:A case study for European Alps and East Asia

NASA Astrophysics Data System (ADS)

Im, Eun-Soon; Coppola, Erika; Giorgi, Filippo

2010-05-01

Since anthropogenic climate change is a rather important factor for the future human life all over the planet and its effects are not globally uniform, climate information at regional or local scales become more and more important for an accurate assessment of the potential impact of climate change on societies and ecosystems. High resolution information with suitably fine-scale for resolving complex geographical features could be a critical factor for successful linkage between climate models and impact assessment studies. However, scale mismatch between them still remains major problem. One method for overcoming the resolution limitations of global climate models and for adding regional details to coarse-grid global projections is to use dynamical downscaling by means of a regional climate model. In this study, the ECHAM5/MPI-OM (1.875 degree) A1B scenario simulation has been dynamically downscaled by using two different approaches within the framework of RegCM3 modeling system. First, a mosaic-type parameterization of subgrid-scale topography and land use (Sub-BATS) is applied over the European Alpine region. The Sub-BATS system is composed of 15 km coarse-grid cell and 3 km sub-grid cell. Second, we developed the RegCM3 one-way double-nested system, with the mother domain encompassing the eastern regions of Asia at 60 km grid spacing and the nested domain covering the Korean Peninsula at 20 km grid spacing. By comparing the regional climate model output and the driving global model ECHAM5/MPI-OM output, it is possible to estimate the added value of physically-based dynamical downscaling when for example impact studies at hydrological scale are performed.

Temporal downscaling of crop coefficients for winter wheat in the North China Plain: A case study at the Gucheng ecological-meteorological experimental station

USDA-ARS?s Scientific Manuscript database

The crop coefficient (Kc) method is widely used for operational estimation of actual evapotranspiration (ETa) and crop water requirements. The standard method for obtaining Kc is via a lookup table from FAO-56 (Food and Agriculture Organization of the United Nations Irrigation and Drainage Paper No....

WRF dynamically downscaling PCM data for climate change impacts in California & application of a signal technique to the source-receptor relationship in WRF

NASA Astrophysics Data System (ADS)

Zhao, Zhan

2009-12-01

My dissertation consists of three parts. Parts I and II are focused on the climate change impacts on meteorology and air quality conditions in California (CA), while Part III is focused on the source-receptor relationship. The WRF model is applied to dynamically downscaled PCM data, with a horizontal resolution of approximately 2.8°x2.8°, to 4km resolution under the Business as Usual (BAU) scenario. The dynamical downscaling method could retain the large-scale features of the global simulations with more meso-scale details. A seven year simulation is conducted for both present (2000˜2006) and future (2047˜2053) in order to avoid the El Nino related inter-annual variation. In order to assess the PCM data quality and estimate the simulation error inherited from the PCM data bias, the present seven year simulations are driven by NCEP's Global Forecast System (GFS) data with the same model configuration. Part I is focused on the comparisons of the present time climatology from the two sets of simulations and the driving global datasets (i.e., PCM vs. GFS), which illustrate that the biases of the downscaling results are mostly inherited from the driving GCM. The imprecise prediction for the location and strength of the Pacific Subtropical High (PSH) is a main source of the PCM data bias. The analysis also implies that using the simulation results driven by PCM data as the input of the air quality model will underrate the air pollution problems in CA. The regional averaged statistics of the downscaling results compared to observational data show that both the surface temperature and wind speed were overestimate for most times of the year, and WRF preformed better during summer than winter. The low summer PBLH in the San Joaquin Valley (SJV) is addressed, and two reasons causing this are the dominance of a high pressure system over the valley and, to a lesser extent, the valley wind at daytime during summer. Part II is focused on the future change of meteorology and air quality in CA and comparisons are made between future and present simulations driven by the PCM data. Both the duration and strength of stagnant events, during which most air pollution problems occur in SJV, are increased during summer and winter. The seven-year averaged spatial distribution of the air-pollution related meteorological variables, such as surface wind, temperature, PBLH, etc., indicate that the future summer ozone problem would be mitigated in the coast region of Los Angeles County (LAC), while both the summer ozone and winter particulate matter (PM) problem in SJV and other parts of the Southern California Air Basin (SoCAB) will be exacerbated in the future. The impact on the land-sea breeze, which plays a big role in California's climate, is also explored in this part. Part III of the thesis is to investigate the potential of applying a signal technique on the source-receptor relationship. This approach is more economical in terms of computational time and memory than the conventional tracer method. The signal technique was implemented into the WRF model, and an idealized supercell case and a real case in Turkey were used to investigate the potential of the technique. Emissions from different source locations were tagged with different frequencies, which were added onto the emitted pollutants, with a specific frequency from each location. The time series of the pollutant concentration collected at receptors were then projected onto the frequency space using the Fourier transform and short-time Fourier transform methods to identify the source locations. During the model integration, a particular constant tracer was also emitted from each pollutant source location to validate and evaluate the signal technique. Results show that the frequencies could be slightly shifted after signals were transported over a long distance and evident secondary frequencies (i.e., beats) could be generated due to nonlinear effects. Although these could potentially confuse the identification of signals released from source points, signals were still distinguishable in this study.

A probabilistic method for streamflow projection and associated uncertainty analysis in a data sparse alpine region

NASA Astrophysics Data System (ADS)

Ren, Weiwei; Yang, Tao; Shi, Pengfei; Xu, Chong-yu; Zhang, Ke; Zhou, Xudong; Shao, Quanxi; Ciais, Philippe

2018-06-01

Climate change imposes profound influence on regional hydrological cycle and water security in many alpine regions worldwide. Investigating regional climate impacts using watershed scale hydrological models requires a large number of input data such as topography, meteorological and hydrological data. However, data scarcity in alpine regions seriously restricts evaluation of climate change impacts on water cycle using conventional approaches based on global or regional climate models, statistical downscaling methods and hydrological models. Therefore, this study is dedicated to development of a probabilistic model to replace the conventional approaches for streamflow projection. The probabilistic model was built upon an advanced Bayesian Neural Network (BNN) approach directly fed by the large-scale climate predictor variables and tested in a typical data sparse alpine region, the Kaidu River basin in Central Asia. Results show that BNN model performs better than the general methods across a number of statistical measures. The BNN method with flexible model structures by active indicator functions, which reduce the dependence on the initial specification for the input variables and the number of hidden units, can work well in a data limited region. Moreover, it can provide more reliable streamflow projections with a robust generalization ability. Forced by the latest bias-corrected GCM scenarios, streamflow projections for the 21st century under three RCP emission pathways were constructed and analyzed. Briefly, the proposed probabilistic projection approach could improve runoff predictive ability over conventional methods and provide better support to water resources planning and management under data limited conditions as well as enable a facilitated climate change impact analysis on runoff and water resources in alpine regions worldwide.

Evaluating climate change impacts on streamflow variability based on a multisite multivariate GCM downscaling method in the Jing River of China

NASA Astrophysics Data System (ADS)

Li, Zhi; Jin, Jiming

2017-11-01

Projected hydrological variability is important for future resource and hazard management of water supplies because changes in hydrological variability can cause more disasters than changes in the mean state. However, climate change scenarios downscaled from Earth System Models (ESMs) at single sites cannot meet the requirements of distributed hydrologic models for simulating hydrological variability. This study developed multisite multivariate climate change scenarios via three steps: (i) spatial downscaling of ESMs using a transfer function method, (ii) temporal downscaling of ESMs using a single-site weather generator, and (iii) reconstruction of spatiotemporal correlations using a distribution-free shuffle procedure. Multisite precipitation and temperature change scenarios for 2011-2040 were generated from five ESMs under four representative concentration pathways to project changes in streamflow variability using the Soil and Water Assessment Tool (SWAT) for the Jing River, China. The correlation reconstruction method performed realistically for intersite and intervariable correlation reproduction and hydrological modeling. The SWAT model was found to be well calibrated with monthly streamflow with a model efficiency coefficient of 0.78. It was projected that the annual mean precipitation would not change, while the mean maximum and minimum temperatures would increase significantly by 1.6 ± 0.3 and 1.3 ± 0.2 °C; the variance ratios of 2011-2040 to 1961-2005 were 1.15 ± 0.13 for precipitation, 1.15 ± 0.14 for mean maximum temperature, and 1.04 ± 0.10 for mean minimum temperature. A warmer climate was predicted for the flood season, while the dry season was projected to become wetter and warmer; the findings indicated that the intra-annual and interannual variations in the future climate would be greater than in the current climate. The total annual streamflow was found to change insignificantly but its variance ratios of 2011-2040 to 1961-2005 increased by 1.25 ± 0.55. Streamflow variability was predicted to become greater over most months on the seasonal scale because of the increased monthly maximum streamflow and decreased monthly minimum streamflow. The increase in streamflow variability was attributed mainly to larger positive contributions from increased precipitation variances rather than negative contributions from increased mean temperatures.

Projecting Range Limits with Coupled Thermal Tolerance - Climate Change Models: An Example Based on Gray Snapper (Lutjanus griseus) along the U.S. East Coast

PubMed Central

Hare, Jonathan A.; Wuenschel, Mark J.; Kimball, Matthew E.

2012-01-01

We couple a species range limit hypothesis with the output of an ensemble of general circulation models to project the poleward range limit of gray snapper. Using laboratory-derived thermal limits and statistical downscaling from IPCC AR4 general circulation models, we project that gray snapper will shift northwards; the magnitude of this shift is dependent on the magnitude of climate change. We also evaluate the uncertainty in our projection and find that statistical uncertainty associated with the experimentally-derived thermal limits is the largest contributor (∼ 65%) to overall quantified uncertainty. This finding argues for more experimental work aimed at understanding and parameterizing the effects of climate change and variability on marine species. PMID:23284974

A globally calibrated scheme for generating daily meteorology from monthly statistics: Global-WGEN (GWGEN) v1.0

NASA Astrophysics Data System (ADS)

Sommer, Philipp S.; Kaplan, Jed O.

2017-10-01

While a wide range of Earth system processes occur at daily and even subdaily timescales, many global vegetation and other terrestrial dynamics models historically used monthly meteorological forcing both to reduce computational demand and because global datasets were lacking. Recently, dynamic land surface modeling has moved towards resolving daily and subdaily processes, and global datasets containing daily and subdaily meteorology have become available. These meteorological datasets, however, cover only the instrumental era of the last approximately 120 years at best, are subject to considerable uncertainty, and represent extremely large data files with associated computational costs of data input/output and file transfer. For periods before the recent past or in the future, global meteorological forcing can be provided by climate model output, but the quality of these data at high temporal resolution is low, particularly for daily precipitation frequency and amount. Here, we present GWGEN, a globally applicable statistical weather generator for the temporal downscaling of monthly climatology to daily meteorology. Our weather generator is parameterized using a global meteorological database and simulates daily values of five common variables: minimum and maximum temperature, precipitation, cloud cover, and wind speed. GWGEN is lightweight, modular, and requires a minimal set of monthly mean variables as input. The weather generator may be used in a range of applications, for example, in global vegetation, crop, soil erosion, or hydrological models. While GWGEN does not currently perform spatially autocorrelated multi-point downscaling of daily weather, this additional functionality could be implemented in future versions.

Multiscale climate emulator of multimodal wave spectra: MUSCLE-spectra

NASA Astrophysics Data System (ADS)

Rueda, Ana; Hegermiller, Christie A.; Antolinez, Jose A. A.; Camus, Paula; Vitousek, Sean; Ruggiero, Peter; Barnard, Patrick L.; Erikson, Li H.; Tomás, Antonio; Mendez, Fernando J.

2017-02-01

Characterization of multimodal directional wave spectra is important for many offshore and coastal applications, such as marine forecasting, coastal hazard assessment, and design of offshore wave energy farms and coastal structures. However, the multivariate and multiscale nature of wave climate variability makes this complex problem tractable using computationally expensive numerical models. So far, the skill of statistical-downscaling model-based parametric (unimodal) wave conditions is limited in large ocean basins such as the Pacific. The recent availability of long-term directional spectral data from buoys and wave hindcast models allows for development of stochastic models that include multimodal sea-state parameters. This work introduces a statistical downscaling framework based on weather types to predict multimodal wave spectra (e.g., significant wave height, mean wave period, and mean wave direction from different storm systems, including sea and swells) from large-scale atmospheric pressure fields. For each weather type, variables of interest are modeled using the categorical distribution for the sea-state type, the Generalized Extreme Value (GEV) distribution for wave height and wave period, a multivariate Gaussian copula for the interdependence between variables, and a Markov chain model for the chronology of daily weather types. We apply the model to the southern California coast, where local seas and swells from both the Northern and Southern Hemispheres contribute to the multimodal wave spectrum. This work allows attribution of particular extreme multimodal wave events to specific atmospheric conditions, expanding knowledge of time-dependent, climate-driven offshore and coastal sea-state conditions that have a significant influence on local nearshore processes, coastal morphology, and flood hazards.

Multiscale Climate Emulator of Multimodal Wave Spectra: MUSCLE-spectra

NASA Astrophysics Data System (ADS)

Rueda, A.; Hegermiller, C.; Alvarez Antolinez, J. A.; Camus, P.; Vitousek, S.; Ruggiero, P.; Barnard, P.; Erikson, L. H.; Tomas, A.; Mendez, F. J.

2016-12-01

Characterization of multimodal directional wave spectra is important for many offshore and coastal applications, such as marine forecasting, coastal hazard assessment, and design of offshore wave energy farms and coastal structures. However, the multivariate and multiscale nature of wave climate variability makes this problem complex yet tractable using computationally-expensive numerical models. So far, the skill of statistical-downscaling models based parametric (unimodal) wave conditions is limited in large ocean basins such as the Pacific. The recent availability of long-term directional spectral data from buoys and wave hindcast models allows for development of stochastic models that include multimodal sea-state parameters. This work introduces a statistical-downscaling framework based on weather types to predict multimodal wave spectra (e.g., significant wave height, mean wave period, and mean wave direction from different storm systems, including sea and swells) from large-scale atmospheric pressure fields. For each weather type, variables of interest are modeled using the categorical distribution for the sea-state type, the Generalized Extreme Value (GEV) distribution for wave height and wave period, a multivariate Gaussian copula for the interdependence between variables, and a Markov chain model for the chronology of daily weather types. We apply the model to the Southern California coast, where local seas and swells from both the Northern and Southern Hemispheres contribute to the multimodal wave spectrum. This work allows attribution of particular extreme multimodal wave events to specific atmospheric conditions, expanding knowledge of time-dependent, climate-driven offshore and coastal sea-state conditions that have a significant influence on local nearshore processes, coastal morphology, and flood hazards.

An effective drift correction for dynamical downscaling of decadal global climate predictions

NASA Astrophysics Data System (ADS)

Paeth, Heiko; Li, Jingmin; Pollinger, Felix; Müller, Wolfgang A.; Pohlmann, Holger; Feldmann, Hendrik; Panitz, Hans-Jürgen

2018-04-01

Initialized decadal climate predictions with coupled climate models are often marked by substantial climate drifts that emanate from a mismatch between the climatology of the coupled model system and the data set used for initialization. While such drifts may be easily removed from the prediction system when analyzing individual variables, a major problem prevails for multivariate issues and, especially, when the output of the global prediction system shall be used for dynamical downscaling. In this study, we present a statistical approach to remove climate drifts in a multivariate context and demonstrate the effect of this drift correction on regional climate model simulations over the Euro-Atlantic sector. The statistical approach is based on an empirical orthogonal function (EOF) analysis adapted to a very large data matrix. The climate drift emerges as a dramatic cooling trend in North Atlantic sea surface temperatures (SSTs) and is captured by the leading EOF of the multivariate output from the global prediction system, accounting for 7.7% of total variability. The SST cooling pattern also imposes drifts in various atmospheric variables and levels. The removal of the first EOF effectuates the drift correction while retaining other components of intra-annual, inter-annual and decadal variability. In the regional climate model, the multivariate drift correction of the input data removes the cooling trends in most western European land regions and systematically reduces the discrepancy between the output of the regional climate model and observational data. In contrast, removing the drift only in the SST field from the global model has hardly any positive effect on the regional climate model.

Desertification of forest, range and desert in Tehran province, affected by climate change

NASA Astrophysics Data System (ADS)

Eskandari, Hadi; Borji, Moslem; Khosravi, Hassan; Mesbahzadeh, Tayebeh

2016-06-01

Climate change has been identified as a leading human and environmental crisis of the twenty-first century. Drylands throughout the world have always undergone periods of degradation due to naturally occurring fluctuation in climate. Persistence of widespread degradation in arid and semiarid regions of Iran necessitates monitoring and evaluation. This paper aims to monitor the desertification trend in three types of land use, including range, forest and desert, affected by climate change in Tehran province for the 2000s and 2030s. For assessing climate change at Mehrabad synoptic station, the data of two emission scenarios, including A2 and B2, were used, utilizing statistical downscaling techniques and data generated by the Statistical DownScaling Model (SDSM). The index of net primary production (NPP) resulting from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images was employed as an indicator of destruction from 2001 to 2010. The results showed that temperature is the most significant driving force which alters the net primary production in rangeland, forest and desert land use in Tehran province. On the basis of monitoring findings under real conditions, in the 2000s, over 60 % of rangelands and 80 % of the forest were below the average production in the province. On the other hand, the long-term average changes of NPP in the rangeland and forests indicated the presence of relatively large areas of these land uses with a production rate lower than the desert. The results also showed that, assuming the existence of circumstances of each emission scenarios, the desertification status will not improve significantly in the rangelands and forests of Tehran province.

Theoretical electron scattering amplitudes and spin polarizations. Electron energies 100 to 1500 eV Part II. Be, N, O, Al, Cl, V, Co, Cu, As, Nb, Ag, Sn, Sb, I, and Ta targets

NASA Astrophysics Data System (ADS)

Wildhaber, M. L.; Wikle, C. K.; Anderson, C. J.; Franz, K. J.; Moran, E. H.; Dey, R.

2012-12-01

Recent decades have brought substantive changes in land use and climate across the earth, prompting a need to think of population and community ecology not as a static entity, but as a dynamic process. Increasingly there is evidence of ecological changes due to climate change. Although much of this evidence comes from ground-truth observations of biogeographic data, there is increasing reliance on models that relate climate variables to biological systems. Such models can then be used to explore potential changes to population and community level ecological systems in response to climate scenarios as obtained from global climate models (GCMs). A key issue associated with modeling ecosystem response to climate is GCM downscaling to regional and local ecological/biological response models that can be used in vulnerability and risk assessments of the potential effects of climate change. The need is for an explicit means for scaling results up or down multiple hierarchical levels and an effective assessment of the level of uncertainty surrounding current knowledge, data, and data collection methods with these goals identified as in need of acceleration in the U.S. Climate Change Science Program FY2009 Implementation Priorities. In the end, such work should provide the information needed to develop adaptation and mitigation methodologies to minimize the effects of directional and nonlinear climate change on the Nation's land, water, ecosystems, and biological populations. We are working to develop an approach that includes multi-scale and hierarchical Bayesian modeling of Missouri River sturgeon population dynamics. Statistical linkages are defined to quantify implications of climate on fish populations of the Missouri River ecosystem. This approach is a hybrid between physical (deterministic) downscaling and statistical downscaling, recognizing that there is uncertainty in both. The model must include linkages between climate and habitat, and between habitat and population. A key advantage of the hierarchical approach used in this study is that it incorporates various sources of observations and includes established scientific knowledge, and associated uncertainties. The goal is to evaluate the potential distributional changes in an ecological system, given distributional changes implied by a series of linked climate and system models under various emissions/use scenarios. The predictive modeling system being developed will be a powerful tool for evaluating management options for coping with global change consequences and assessing uncertainty of those evaluations. Specifically for the endangered pallid sturgeon (Scaphirhynchus albus), we are already able to assess potential effects of any climate scenario on growth and population size distribution. Future models will incorporate survival and reproduction. Ultimately, these models provide guidance for successful recovery and conservation of the pallid sturgeon. Here we present a basic outline of the approach we are developing and a simple pallid sturgeon example to demonstrate how multiple scales and parameter uncertainty are incorporated.

What model resolution is required in climatological downscaling over complex terrain?

NASA Astrophysics Data System (ADS)

El-Samra, Renalda; Bou-Zeid, Elie; El-Fadel, Mutasem

2018-05-01

This study presents results from the Weather Research and Forecasting (WRF) model applied for climatological downscaling simulations over highly complex terrain along the Eastern Mediterranean. We sequentially downscale general circulation model results, for a mild and wet year (2003) and a hot and dry year (2010), to three local horizontal resolutions of 9, 3 and 1 km. Simulated near-surface hydrometeorological variables are compared at different time scales against data from an observational network over the study area comprising rain gauges, anemometers, and thermometers. The overall performance of WRF at 1 and 3 km horizontal resolution was satisfactory, with significant improvement over the 9 km downscaling simulation. The total yearly precipitation from WRF's 1 km and 3 km domains exhibited < 10% bias with respect to observational data. The errors in minimum and maximum temperatures were reduced by the downscaling, along with a high-quality delineation of temperature variability and extremes for both the 1 and 3 km resolution runs. Wind speeds, on the other hand, are generally overestimated for all model resolutions, in comparison with observational data, particularly on the coast (up to 50%) compared to inland stations (up to 40%). The findings therefore indicate that a 3 km resolution is sufficient for the downscaling, especially that it would allow more years and scenarios to be investigated compared to the higher 1 km resolution at the same computational effort. In addition, the results provide a quantitative measure of the potential errors for various hydrometeorological variables.

High Spatial Resolution Forecasting of Long-Term Monthly Precipitation and Mean Temperature Trends in Data Scarce Regions

NASA Astrophysics Data System (ADS)

Mosier, T. M.; Hill, D. F.; Sharp, K. V.

2013-12-01

High spatial resolution time-series data are critical for many hydrological and earth science studies. Multiple groups have developed historical and forecast datasets of high-resolution monthly time-series for regions of the world such as the United States (e.g. PRISM for hindcast data and MACA for long-term forecasts); however, analogous datasets have not been available for most data scarce regions. The current work fills this data need by producing and freely distributing hindcast and forecast time-series datasets of monthly precipitation and mean temperature for all global land surfaces, gridded at a 30 arc-second resolution. The hindcast data are constructed through a Delta downscaling method, using as inputs 0.5 degree monthly time-series and 30 arc-second climatology global weather datasets developed by Willmott & Matsuura and WorldClim, respectively. The forecast data are formulated using a similar downscaling method, but with an additional step to remove bias from the climate variable's probability distribution over each region of interest. The downscaling package is designed to be compatible with a number of general circulation models (GCM) (e.g. with GCMs developed for the IPCC AR4 report and CMIP5), and is presently implemented using time-series data from the NCAR CESM1 model in conjunction with 30 arc-second future decadal climatologies distributed by the Consultative Group on International Agricultural Research. The resulting downscaled datasets are 30 arc-second time-series forecasts of monthly precipitation and mean temperature available for all global land areas. As an example of these data, historical and forecast 30 arc-second monthly time-series from 1950 through 2070 are created and analyzed for the region encompassing Pakistan. For this case study, forecast datasets corresponding to the future representative concentration pathways 45 and 85 scenarios developed by the IPCC are presented and compared. This exercise highlights a range of potential meteorological trends for the Pakistan region and more broadly serves to demonstrate the utility of the presented 30 arc-second monthly precipitation and mean temperature datasets for use in data scarce regions.

Development of Spatiotemporal Bias-Correction Techniques for Downscaling GCM Predictions

NASA Astrophysics Data System (ADS)

Hwang, S.; Graham, W. D.; Geurink, J.; Adams, A.; Martinez, C. J.

2010-12-01

Accurately representing the spatial variability of precipitation is an important factor for predicting watershed response to climatic forcing, particularly in small, low-relief watersheds affected by convective storm systems. Although Global Circulation Models (GCMs) generally preserve spatial relationships between large-scale and local-scale mean precipitation trends, most GCM downscaling techniques focus on preserving only observed temporal variability on point by point basis, not spatial patterns of events. Downscaled GCM results (e.g., CMIP3 ensembles) have been widely used to predict hydrologic implications of climate variability and climate change in large snow-dominated river basins in the western United States (Diffenbaugh et al., 2008; Adam et al., 2009). However fewer applications to smaller rain-driven river basins in the southeastern US (where preserving spatial variability of rainfall patterns may be more important) have been reported. In this study a new method was developed to bias-correct GCMs to preserve both the long term temporal mean and variance of the precipitation data, and the spatial structure of daily precipitation fields. Forty-year retrospective simulations (1960-1999) from 16 GCMs were collected (IPCC, 2007; WCRP CMIP3 multi-model database: https://esg.llnl.gov:8443/), and the daily precipitation data at coarse resolution (i.e., 280km) were interpolated to 12km spatial resolution and bias corrected using gridded observations over the state of Florida (Maurer et al., 2002; Wood et al, 2002; Wood et al, 2004). In this method spatial random fields which preserved the observed spatial correlation structure of the historic gridded observations and the spatial mean corresponding to the coarse scale GCM daily rainfall were generated. The spatiotemporal variability of the spatio-temporally bias-corrected GCMs were evaluated against gridded observations, and compared to the original temporally bias-corrected and downscaled CMIP3 data for the central Florida. The hydrologic response of two southwest Florida watersheds to the gridded observation data, the original bias corrected CMIP3 data, and the new spatiotemporally corrected CMIP3 predictions was compared using an integrated surface-subsurface hydrologic model developed by Tampa Bay Water.

Improving Evapotranspiration Estimates Using Multi-Platform Remote Sensing

NASA Astrophysics Data System (ADS)

Knipper, Kyle; Hogue, Terri; Franz, Kristie; Scott, Russell

2016-04-01

Understanding the linkages between energy and water cycles through evapotranspiration (ET) is uniquely challenging given its dependence on a range of climatological parameters and surface/atmospheric heterogeneity. A number of methods have been developed to estimate ET either from primarily remote-sensing observations, in-situ measurements, or a combination of the two. However, the scale of many of these methods may be too large to provide needed information about the spatial and temporal variability of ET that can occur over regions with acute or chronic land cover change and precipitation driven fluxes. The current study aims to improve the spatial and temporal variability of ET utilizing only satellite-based observations by incorporating a potential evapotranspiration (PET) methodology with satellite-based down-scaled soil moisture estimates in southern Arizona, USA. Initially, soil moisture estimates from AMSR2 and SMOS are downscaled to 1km through a triangular relationship between MODIS land surface temperature (MYD11A1), vegetation indices (MOD13Q1/MYD13Q1), and brightness temperature. Downscaled soil moisture values are then used to scale PET to actual ET (AET) at a daily, 1km resolution. Derived AET estimates are compared to observed flux tower estimates, the North American Land Data Assimilation System (NLDAS) model output (i.e. Variable Infiltration Capacity (VIC) Macroscale Hydrologic Model, Mosiac Model, and Noah Model simulations), the Operational Simplified Surface Energy Balance Model (SSEBop), and a calibrated empirical ET model created specifically for the region. Preliminary results indicate a strong increase in correlation when incorporating the downscaling technique to original AMSR2 and SMOS soil moisture values, with the added benefit of being able to decipher small scale heterogeneity in soil moisture (riparian versus desert grassland). AET results show strong correlations with relatively low error and bias when compared to flux tower estimates. In addition, AET results show improved bias to those reported by SSEBop, with similar correlations and errors when compared to the empirical ET model. Spatial patterns of estimated AET display patterns representative of the basin's elevation and vegetation characteristics, with improved spatial resolution and temporal heterogeneity when compared to previous models.

Daily Reservoir Inflow Forecasting using Deep Learning with Downscaled Multi-General Circulation Models (GCMs) Platform

NASA Astrophysics Data System (ADS)

Li, D.; Fang, N. Z.

2017-12-01

Dallas-Fort Worth Metroplex (DFW) has a population of over 7 million depending on many water supply reservoirs. The reservoir inflow plays a vital role in water supply decision making process and long-term strategic planning for the region. This paper demonstrates a method of utilizing deep learning algorithms and multi-general circulation model (GCM) platform to forecast reservoir inflow for three reservoirs within the DFW: Eagle Mountain Lake, Lake Benbrook and Lake Arlington. Ensemble empirical mode decomposition was firstly employed to extract the features, which were then represented by the deep belief networks (DBNs). The first 75 years of the historical data (1940 -2015) were used to train the model, while the last 2 years of the data (2016-2017) were used for the model validation. The weights of each DBN gained from the training process were then applied to establish a neural network (NN) that was able to forecast reservoir inflow. Feature predictors used for the forecasting model were generated from weather forecast results of the downscaled multi-GCM platform for the North Texas region. By comparing root mean square error (RMSE) and mean bias error (MBE) with the observed data, the authors found that the deep learning with downscaled multi-GCM platform is an effective approach in the reservoir inflow forecasting.

Mid-Century Warming in the Los Angeles Region and its Uncertainty using Dynamical and Statistical Downscaling

NASA Astrophysics Data System (ADS)

Sun, F.; Hall, A. D.; Walton, D.; Capps, S. B.; Qu, X.; Huang, H. J.; Berg, N.; Jousse, A.; Schwartz, M.; Nakamura, M.; Cerezo-Mota, R.

2012-12-01

Using a combination of dynamical and statistical downscaling techniques, we projected mid-21st century warming in the Los Angeles region at 2-km resolution. To account for uncertainty associated with the trajectory of future greenhouse gas emissions, we examined projections for both "business-as-usual" (RCP8.5) and "mitigation" (RCP2.6) emissions scenarios from the Fifth Coupled Model Intercomparison Project (CMIP5). To account for the considerable uncertainty associated with choice of global climate model, we downscaled results for all available global climate models in CMIP5. For the business-as-usual scenario, we find that by the mid-21st century, the most likely warming is roughly 2.6°C averaged over the region's land areas, with a 95% confidence that the warming lies between 0.9 and 4.2°C. The high resolution of the projections reveals a pronounced spatial pattern in the warming: High elevations and inland areas separated from the coast by at least one mountain complex warm 20 to 50% more than the areas near the coast or within the Los Angeles basin. This warming pattern is especially apparent in summertime. The summertime warming contrast between the inland and coastal zones has a large effect on the most likely expected number of extremely hot days per year. Coastal locations and areas within the Los Angeles basin see roughly two to three times the number of extremely hot days, while high elevations and inland areas typically experience approximately three to five times the number of extremely hot days. Under the mitigation emissions scenario, the most likely warming and increase in heat extremes are somewhat smaller. However, the majority of the warming seen in the business-as-usual scenario still occurs at all locations in the most likely case under the mitigation scenario, and heat extremes still increase significantly. This warming study is the first part of a series studies of our project. More climate change impacts on the Santa Ana wind, rainfall, snowfall and snowmelt, cloud and surface hydrology are forthcoming and could be found in www.atmos.ucla.edu/csrl.he ensemble-mean, annual-mean surface air temperature change and its uncertainty from the available CMIP5 GCMs under the RCP8.5 (left) and RCP2.6 (right) emissions scenarios, unit: °C.

Are We Telling Decision-makers the Wrong Things - and with Too Much Confidence?

NASA Astrophysics Data System (ADS)

Arnold, J.; Nowak, K. C.; Vano, J. A.; Newman, A. J.; Mizukami, N.; Mendoza, P. A.; Nijssen, B.; Wood, A.; Gutmann, E. D.; Clark, M. P.; Rasmussen, R.

2016-12-01

Water-resource management relies on decision-making over a wide range of space-time scales, nearly none of which maps cleanly onto the scales of current hydroclimatic scenarios of anthropogenic change. Myriad choices are made during vulnerability and impact assessments to quantify the changed-climate sensitivities of models used in that decision-making, including choices of hydrologic models, parameters, and parameterizations; their input forcings determined with various climate downscaling approaches; selected GCMs and output variables to be downscaled; and the forcing emissions scenarios, to name a few. Choosing alternative methods for producing gridded meteorological fields, for examples, can produce very different effects on the projected hydrologic outcomes they drive, with uncertainties across those methods revealed to be as large or larger than the climate change signal itself in some cases. Additionally, many popular climate downscaling methods simply rescale GCM precipitation, producing hydroclimatic projections with too much drizzle, incorrect representations of extreme events, and improper spatial scaling of variables crucial to water-resource vulnerability assessments and, importantly, the decisions they seek to inform. Real-world water-resource vulnerability and impacts assessments can be highly time-sensitive and resource limited, though, so they typically do not confront or even fully represent uncertainties associated with all choices. That deficiency results in assessments built on only partially revealed uncertainties which can misrepresent significant sensitivities and impacts in the final assessments of climate threats and hydrologic vulnerabilities. This talk will describe recent work by the U.S. Army Corps of Engineers, Bureau of Reclamation, University of Washington, and National Center for Atmospheric Research to develop and test methods to characterize more fully the uncertainties in the modeling chain for real-world uses. Examples will illustrate new implementations for communicating that fuller characterization in the ways most useful to inform water-resource management across multiple space-time scales under climate-changed futures.

Scaling and stochastic cascade properties of NEMO oceanic simulations and their potential value for GCM evaluation and downscaling

NASA Astrophysics Data System (ADS)

Verrier, Sébastien; Crépon, Michel; Thiria, Sylvie

2014-09-01

Spectral scaling properties have already been evidenced on oceanic numerical simulations and have been subject to several interpretations. They can be used to evaluate classical turbulence theories that predict scaling with specific exponents and to evaluate the quality of GCM outputs from a statistical and multiscale point of view. However, a more complete framework based on multifractal cascades is able to generalize the classical but restrictive second-order spectral framework to other moment orders, providing an accurate description of probability distributions of the fields at multiple scales. The predictions of this formalism still needed systematic verification in oceanic GCM while they have been confirmed recently for their atmospheric counterparts by several papers. The present paper is devoted to a systematic analysis of several oceanic fields produced by the NEMO oceanic GCM. Attention is focused to regional, idealized configurations that permit to evaluate the NEMO engine core from a scaling point of view regardless of limitations involved by land masks. Based on classical multifractal analysis tools, multifractal properties were evidenced for several oceanic state variables (sea surface temperature and salinity, velocity components, etc.). While first-order structure functions estimated a different nonconservativity parameter H in two scaling ranges, the multiorder statistics of turbulent fluxes were scaling over almost the whole available scaling range. This multifractal scaling was then parameterized with the help of the universal multifractal framework, providing parameters that are coherent with existing empirical literature. Finally, we argue that the knowledge of these properties may be useful for oceanographers. The framework seems very well suited for the statistical evaluation of OGCM outputs. Moreover, it also provides practical solutions to simulate subpixel variability stochastically for GCM downscaling purposes. As an independent perspective, the existence of multifractal properties in oceanic flows seems also interesting for investigating scale dependencies in remote sensing inversion algorithms.

Stochastic Analysis and Probabilistic Downscaling of Soil Moisture

NASA Astrophysics Data System (ADS)

Deshon, J. P.; Niemann, J. D.; Green, T. R.; Jones, A. S.

2017-12-01

Soil moisture is a key variable for rainfall-runoff response estimation, ecological and biogeochemical flux estimation, and biodiversity characterization, each of which is useful for watershed condition assessment. These applications require not only accurate, fine-resolution soil-moisture estimates but also confidence limits on those estimates and soil-moisture patterns that exhibit realistic statistical properties (e.g., variance and spatial correlation structure). The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales coarse-resolution (9-40 km) soil moisture from satellite remote sensing or land-surface models to produce fine-resolution (10-30 m) estimates. The model was designed to produce accurate deterministic soil-moisture estimates at multiple points, but the resulting patterns do not reproduce the variance or spatial correlation of observed soil-moisture patterns. The primary objective of this research is to generalize the EMT+VS model to produce a probability density function (pdf) for soil moisture at each fine-resolution location and time. Each pdf has a mean that is equal to the deterministic soil-moisture estimate, and the pdf can be used to quantify the uncertainty in the soil-moisture estimates and to simulate soil-moisture patterns. Different versions of the generalized model are hypothesized based on how uncertainty enters the model, whether the uncertainty is additive or multiplicative, and which distributions describe the uncertainty. These versions are then tested by application to four catchments with detailed soil-moisture observations (Tarrawarra, Satellite Station, Cache la Poudre, and Nerrigundah). The performance of the generalized models is evaluated by comparing the statistical properties of the simulated soil-moisture patterns to those of the observations and the deterministic EMT+VS model. The versions of the generalized EMT+VS model with normally distributed stochastic components produce soil-moisture patterns with more realistic statistical properties than the deterministic model. Additionally, the results suggest that the variance and spatial correlation of the stochastic soil-moisture variations do not vary consistently with the spatial-average soil moisture.

The Impact of Spring Subsurface Soil Temperature Anomaly in the Western U.S. on North American Summer Precipitation: A Case Study Using Regional Climate Model Downscaling

DTIC Science & Technology

2012-06-02

regional climate model downscaling , J. Geophys. Res., 117, D11103, doi:10.1029/2012JD017692. 1. Introduction [2] Modeling studies and data analyses...based on ground and satellite data have demonstrated that the land surface state variables, such as soil moisture, snow, vegetation, and soil temperature... downscaling rather than simply applying reanal- ysis data as LBC for both Eta control and sensitivity experiments as done in many RCM sensitivity studies

Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis

USGS Publications Warehouse

Flint, Lorraine E.; Flint, Alan L.

2012-01-01

The methodology, which includes a sequence of rigorous analyses and calculations, is intended to reduce the addition of uncertainty to the climate data as a result of the downscaling while providing the fine-scale climate information necessary for ecological analyses. It results in new but consistent data sets for the US at 4 km, the southwest US at 270 m, and California at 90 m and illustrates the utility of fine-scale downscaling to analyses of ecological processes influenced by topographic complexity.

Projections of Flood Risk using Credible Climate Signals in the Ohio River Basin

NASA Astrophysics Data System (ADS)

Schlef, K.; Robertson, A. W.; Brown, C.

2017-12-01

Estimating future hydrologic flood risk under non-stationary climate is a key challenge to the design of long-term water resources infrastructure and flood management strategies. In this work, we demonstrate how projections of large-scale climate patterns can be credibly used to create projections of long-term flood risk. Our study area is the northwest region of the Ohio River Basin in the United States Midwest. In the region, three major teleconnections have been previously demonstrated to affect synoptic patterns that influence extreme precipitation and streamflow: the El Nino Southern Oscillation, the Pacific North American pattern, and the Pacific Decadal Oscillation. These teleconnections are strongest during the winter season (January-March), which also experiences the greatest number of peak flow events. For this reason, flood events are defined as the maximum daily streamflow to occur in the winter season. For each gage in the region, the location parameter of a log Pearson type 3 distribution is conditioned on the first principal component of the three teleconnections to create a statistical model of flood events. Future projections of flood risk are created by forcing the statistical model with projections of the teleconnections from general circulation models selected for skill. We compare the results of our method to the results of two other methods: the traditional model chain (i.e., general circulation model projections to downscaling method to hydrologic model to flood frequency analysis) and that of using the historic trend. We also discuss the potential for developing credible projections of flood events for the continental United States.

75 FR 54403 - U.S. National Climate Assessment Objectives, Proposed Topics, and Next Steps

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-07

..., methods and design, tools for assessing climate change and impacts, dealing with uncertainty, sources of..., coordination with other Federal climate-related programs, design of documents and tailored communications with... methodological perspectives related to selecting model and downscaling outputs and approaches for their use in...

Potential of bias correction for downscaling passive microwave and soil moisture data

USDA-ARS?s Scientific Manuscript database

Passive microwave satellites such as SMOS (Soil Moisture and Ocean Salinity) or SMAP (Soil Moisture Active Passive) observe brightness temperature (TB) and retrieve soil moisture at a spatial resolution greater than most hydrological processes. Bias correction is proposed as a simple method to disag...

Landslide susceptibility mapping using downscaled AMSR-E soil moisture: A case study from Cleveland Corral, California, US

USDA-ARS?s Scientific Manuscript database

As soil moisture increases, slope stability decreases. Remotely sensed soil moisture data can provide routine updates of slope conditions necessary for landslide predictions. For regional scale landslide investigations, only remote sensing methods have the spatial and temporal resolution required to...

A simple technique for obtaining future climate data inputs for natural resource models

USDA-ARS?s Scientific Manuscript database

Those conducting impact studies using natural resource models need to be able to quickly and easily obtain downscaled future climate data from multiple models, scenarios, and timescales for multiple locations. This paper describes a method of quickly obtaining future climate data over a wide range o...

Reconstruction of a Three Hourly 1-km Land Surface Air Temperature Dataset in the Qinghai-Tibet Plateau

NASA Astrophysics Data System (ADS)

Zhou, J.; Ding, L.

2017-12-01

Land surface air temperature (SAT) is an important parameter in the modeling of radiation balance and energy budget of the earth surface. Generally, SAT is measured at ground meteorological stations; then SAT mapping is possible though a spatial interpolation process. The interpolated SAT map relies on the spatial distribution of ground stations, the terrain, and many other factors; thus, it has great uncertainties in regions with complicated terrain. Instead, SAT map can also be obtained through physical modeling of interactions between the land surface and the atmosphere. Such dataset generally has coarse spatial resolution (e.g. coarser than 0.1°) and cannot satisfy the applications at fine scales, e.g. 1 km. This presentation reports the reconstruction of a three hourly 1-km SAT dataset from 2001 to 2015 over the Qinghai-Tibet Plateau. The terrain in the Qinghai-Tibet Plateau, especially in the eastern part, is extremely complicated. Two SAT datasets with good qualities are used in this study. The first one is from the 3h China Meteorological Forcing Dataset with a 0.1° resolution released by the Institute of Tibetan Plateau Research, Chinese Academy of Sciences (Yang et al., 2010); the second one is from the ERA-Interim product with the same temporal resolution and a 0.125° resolution. A statistical approach is developed to downscale the spatial resolution of the derived SAT to 1-km. The elevation and the normalized difference vegetation index (NDVI) are selected as two scaling factors in the downscaling approach. Results demonstrate there is significantly negative correlation between the SAT and elevation in all seasons; there is also significantly negative correlation between the SAT and NDVI in the vegetation growth seasons, while the correlation decreases in the other seasons. Therefore, a temporally dynamic downscaling approach is feasible to enhance the spatial resolution of the SAT. Compared with the SAT at the 0.1° or 0.125°, the reconstructed 1-km SAT can provide much more spatial details in areas with complicated terrain. Additionally, the 1-km SAT agrees well with the ground measured air temperatures as well as the SAT before downscaling. The reconstructed SAT will be beneficial for the modeling of surface radiation balance and energy budget over the Qinghai-Tibet Plateau.

Impact of the climate change to shallow groundwater in Baltic artesian basin

NASA Astrophysics Data System (ADS)

Lauva, D.; Bethers, P.; Timuhins, A.; Sennikovs, J.

2012-04-01

The purpose of our work was to find the long term pattern of annual shallow ground water changes in region of Latvia, ground water level modelling for the contemporary climate and future climate scenarios and the model generalization to the Baltic artesian basin (BAB) region. Latvia is located in the middle part of BAB. It occupies about 65'000 square kilometers. BAB territory (480'000 square kilometres) also includes Lithuania, Estonia as well as parts of Poland, Russia, Belarus and the Baltic Sea. Territory of BAB is more than seven times bigger than Latvia. Precipitation and spring snow melt are the main sources of the ground water recharge in BAB territory. The long term pattern of annual shallow ground water changes was extracted from the data of 25 monitoring wells in the territory of Latvia. The main Latvian groundwater level fluctuation regime can be described as a function with two maximums (in spring and late autumn) and two minimums (in winter and late summer). The mathematical model METUL (developed by Latvian University of Agriculture) was chosen for the ground water modelling. It was calibrated on the observations in 25 gauging wells around Latvia. After the calibration we made calculations using data provided by an ensemble of regional climate models, yielding a continuous groundwater table time-series from 1961 to 2100, which were analysed and split into 3 time windows for further analysis: contemporary climate (1961-1990), near future (2021-2050) and far future (2071-2100). The daily average temperature, precipitation and humidity time series were used as METUL forcing parameters. The statistical downscaling method (Sennikovs and Bethers, 2009) was applied for the bias correction of RCM calculated and measured variables. The qualitative differences in future and contemporary annual groundwater regime are expected. The future Latvian annual groundwater cycle according to the RCM climate projection changes to curve with one peak and one drought point. Acknowledgements. This research was supported by the European Social Fund project "Establishment of interdisciplinary scientist group and modelling system for groundwater research" (Project Nr. 2009/0212/1DP/1.1.1.2.0/09/APIA/VIAA/060). Regional climate model data was provided through the ENSEMBLES data archive, funded by the EU FP6 Integrated Project ENSEMBLES (Contract number 505539). Reference: Sennikovs, J., Bethers, U. 2009. Statistical downscaling method of regional climate model results for hydrological modelling. In: Proceedings of 18th World IMACS / MODSIM Congress.

Downscaling GCM Output with Genetic Programming Model

NASA Astrophysics Data System (ADS)

Shi, X.; Dibike, Y. B.; Coulibaly, P.

2004-05-01

Climate change impact studies on watershed hydrology require reliable data at appropriate spatial and temporal resolution. However, the outputs of the current global climate models (GCMs) cannot be used directly because GCM do not provide hourly or daily precipitation and temperature reliable enough for hydrological modeling. Nevertheless, we can get more reliable data corresponding to future climate scenarios derived from GCM outputs using the so called 'downscaling techniques'. This study applies Genetic Programming (GP) based technique to downscale daily precipitation and temperature values at the Chute-du-Diable basin of the Saguenay watershed in Canada. In applying GP downscaling technique, the objective is to find a relationship between the large-scale predictor variables (NCEP data which provide daily information concerning the observed large-scale state of the atmosphere) and the predictand (meteorological data which describes conditions at the site scale). The selection of the most relevant predictor variables is achieved using the Pearson's coefficient of determination ( R2) (between the large-scale predictor variables and the daily meteorological data). In this case, the period (1961 - 2000) is identified to represent the current climate condition. For the forty years of data, the first 30 years (1961-1990) are considered for calibrating the models while the remaining ten years of data (1991-2000) are used to validate those models. In general, the R2 between the predictor variables and each predictand is very low in case of precipitation compared to that of maximum and minimum temperature. Moreover, the strength of individual predictors varies for every month and for each GP grammar. Therefore, the most appropriate combination of predictors has to be chosen by looking at the output analysis of all the twelve months and the different GP grammars. During the calibration of the GP model for precipitation downscaling, in addition to the mean daily precipitation and daily precipitation variability for each month, monthly average dry and wet-spell lengths are also considered as performance criteria. For the cases of Tmax and Tmin, means and variances of these variables corresponding to each month were considered as performance criteria. The GP downscaling results show satisfactory agreement between the observed daily temperature (Tmax and Tmin) and the simulated temperature. However, the downscaling results for the daily precipitation still require some improvement - suggesting further investigation of other grammars. KEY WORDS: Climate change; GP downscaling; GCM.

Indirect downscaling of global circulation model data based on atmospheric circulation and temperature for projections of future precipitation in hourly resolution

NASA Astrophysics Data System (ADS)

Beck, F.; Bárdossy, A.

2013-07-01

Many hydraulic applications like the design of urban sewage systems require projections of future precipitation in high temporal resolution. We developed a method to predict the regional distribution of hourly precipitation sums based on daily mean sea level pressure and temperature data from a Global Circulation Model. It is an indirect downscaling method avoiding uncertain precipitation data from the model. It is based on a fuzzy-logic classification of atmospheric circulation patterns (CPs) that is further subdivided by means of the average daily temperature. The observed empirical distributions at 30 rain gauges to each CP-temperature class are assumed as constant and used for projections of the hourly precipitation sums in the future. The method was applied to the CP-temperature sequence derived from the 20th century run and the scenario A1B run of ECHAM5. According to ECHAM5, the summers in southwest Germany will become progressively drier. Nevertheless, the frequency of the highest hourly precipitation sums will increase. According to the predictions, estival water stress and the risk of extreme hourly precipitation will both increase simultaneously during the next decades.

Towards a High-Resolution Global Inundation Delineation Dataset

NASA Astrophysics Data System (ADS)

Fluet-Chouinard, E.; Lehner, B.

2011-12-01

Although their importance for biodiversity, flow regulation and ecosystem service provision is widely recognized, wetlands and temporarily inundated landscapes remain poorly mapped globally because of their inherent elusive nature. Inventorying of wetland resources has been identified in international agreements as an essential component of appropriate conservation efforts and management initiatives of these threatened ecosystems. However, despite recent advances in remote sensing surface water monitoring, current inventories of surface water variations remain incomplete at the regional-to-global scale due to methodological limitations restricting truly global application. Remote sensing wetland applications such as SAR L-band are particularly constrained by image availability and heterogeneity of acquisition dates, while coarse resolution passive microwave and multi-sensor methods cannot discriminate distinct surface water bodies. As a result, the most popular global wetland dataset remains to this day the Global Lake & Wetland Database (Lehner and Doll, 2004) a spatially inconsistent database assembled from various existing data sources. The approach taken in this project circumvents the limitations of current global wetland monitoring methods by combining globally available topographic and hydrographic data to downscale coarse resolution global inundation data (Prigent et al., 2007) and thus create a superior inundation delineation map product. The developed procedure downscales inundation data from the coarse resolution (~27km) of current passive microwave sensors to the finer spatial resolution (~500m) of the topographic and hydrographic layers of HydroSHEDS' data suite (Lehner et al., 2006), while retaining the high temporal resolution of the multi-sensor inundation dataset. From the downscaling process emerges new information on the specific location of inundation, but also on its frequency and duration. The downscaling algorithm employs a decision tree classifier trained on regional remote sensing wetland maps, to derive inundation probability followed by a seeded region growing segmentation process to redistribute the inundated area at the finer resolution. Assessment of the algorithm's performance is accomplished by evaluating the level of agreement between its outputted downscaled inundation maps and existing regional remote sensing inundation delineation. Upon completion, this project's will offer a dynamic globally seamless inundation map at an unprecedented spatial and temporal scale, which will provide the baseline inventory long requested by the research community, and will open the door to a wide array of possible conservation and hydrological modeling applications which were until now data-restricted. Literature Lehner, B., K. Verdin, and A. Jarvis. 2008. New global hydrography derived from spaceborne elevation data. Eos 89, no. 10. Lehner, B, and P Doll. 2004. Development and validation of a global database of lakes, reservoirs and wetlands. Journal of Hydrology 296, no. 1-4: 1-22. Prigent, C., F. Papa, F. Aires, W. B. Rossow, and E. Matthews. 2007. Global inundation dynamics inferred from multiple satellite observations, 1993-2000. Journal of Geophysical Research 112, no. D12: 1-13.

Assessing the Added Value of Dynamical Downscaling Using ...

EPA Pesticide Factsheets

In this study, the Standardized Precipitation Index (SPI) is used to ascertain the added value of dynamical downscaling over the contiguous United States. WRF is used as a regional climate model (RCM) to dynamically downscale reanalysis fields to compare values of SPI over drought timescales that have implications for agriculture and water resources planning. The regional climate generated by WRF has the largest improvement over reanalysis for SPI correlation with observations as the drought timescale increases. This suggests that dynamically downscaled fields may be more reliable than larger-scale fields for water resource applications (e.g., water storage within reservoirs). WRF improves the timing and intensity of moderate to extreme wet and dry periods, even in regions with homogenous terrain. This study also examines changes in SPI from the extreme drought of 1988 and three “drought busting” tropical storms. Each of those events illustrates the importance of using downscaling to resolve the spatial extent of droughts. The analysis of the “drought busting” tropical storms demonstrates that while the impact of these storms on ending prolonged droughts is improved by the RCM relative to the reanalysis, it remains underestimated. These results illustrate the importance and some limitations of using RCMs to project drought. The National Exposure Research Laboratory’s Atmospheric Modeling Division (AMAD) conducts research in support of EPA’s mission t

Projection of Summer Climate on Tokyo Metropolitan Area using Pseudo Global Warming Method

NASA Astrophysics Data System (ADS)

Adachi, S. A.; Kimura, F.; Kusaka, H.; Hara, M.

2010-12-01

Recent surface air temperature observations in most of urban areas show the remarkable increasing trend affected by the global warming and the heat island effects. There are many populous areas in Japan. In such areas, the effects of land-use change and urbanization on the local climate are not negligible (Fujibe, 2010). The heat stress for citizen there is concerned to swell moreover in the future. Therefore, spatially detailed climate projection is required for making adaptation and mitigation plans. This study focuses on the Tokyo metropolitan area (TMA) in summer and aims to estimate the local climate change over the TMA in 2070s using a regional climate model. The Regional Atmospheric Modeling System (RAMS) was used for downscaling. A single layer urban canopy model (Kusaka et al., 2001) is built into RAMS as a parameterization expressing the features of urban surface. We performed two experiments for estimating present and future climate. In the present climate simulation, the initial and boundary conditions for RAMS are provided from the JRA-25/JCDAS. On the other hand, the Pseudo Global Warming (PGW) method (Sato et al., 2007) is applied to estimate the future climate, instead of the conventional dynamical downscaling method. The PGW method is expected to reduce the model biases in the future projection estimated by Atmosphere-Ocean General Circulation Models (AOGCM). The boundary conditions used in the PGW method is given by the PGW data, which are obtained by adding the climate monthly difference between 1990s and 2070s estimated by AOGCMs to the 6-hourly reanalysis data. In addition, the uncertainty in the regional climate projection depending on the AOGCM projections is estimated from additional downscaling experiments using the different PGW data obtained from five AOGCMs. Acknowledgment: This work was supported by the Global Environment Research Fund (S-5-3) of the Ministry of the Environment, Japan. References: 1. Fujibe, F., Int. J. Climatol., doi:10.1002/joc.2142 (2010). 2. Kusaka, H., H. Kondo, Y. Kikegawa, and F. Kimura, Bound.-Layer Meteor., 101, 329-358 (2001). 3. Sato, T., F. Kimura, and A. Kitoh, J. Hydrology, 144-154 (2007).

Climate Change Impacts on the Upper Indus Hydrology: Sources, Shifts and Extremes

PubMed Central

Immerzeel, W. W.; Kraaijenbrink, P. D. A.; Shrestha, A. B.; Bierkens, M. F. P.

2016-01-01

The Indus basin heavily depends on its upstream mountainous part for the downstream supply of water while downstream demands are high. Since downstream demands will likely continue to increase, accurate hydrological projections for the future supply are important. We use an ensemble of statistically downscaled CMIP5 General Circulation Model outputs for RCP4.5 and RCP8.5 to force a cryospheric-hydrological model and generate transient hydrological projections for the entire 21st century for the upper Indus basin. Three methodological advances are introduced: (i) A new precipitation dataset that corrects for the underestimation of high-altitude precipitation is used. (ii) The model is calibrated using data on river runoff, snow cover and geodetic glacier mass balance. (iii) An advanced statistical downscaling technique is used that accounts for changes in precipitation extremes. The analysis of the results focuses on changes in sources of runoff, seasonality and hydrological extremes. We conclude that the future of the upper Indus basin’s water availability is highly uncertain in the long run, mainly due to the large spread in the future precipitation projections. Despite large uncertainties in the future climate and long-term water availability, basin-wide patterns and trends of seasonal shifts in water availability are consistent across climate change scenarios. Most prominent is the attenuation of the annual hydrograph and shift from summer peak flow towards the other seasons for most ensemble members. In addition there are distinct spatial patterns in the response that relate to monsoon influence and the importance of meltwater. Analysis of future hydrological extremes reveals that increases in intensity and frequency of extreme discharges are very likely for most of the upper Indus basin and most ensemble members. PMID:27828994

Climate Change Impacts on the Upper Indus Hydrology: Sources, Shifts and Extremes.

PubMed

Lutz, A F; Immerzeel, W W; Kraaijenbrink, P D A; Shrestha, A B; Bierkens, M F P

2016-01-01

The Indus basin heavily depends on its upstream mountainous part for the downstream supply of water while downstream demands are high. Since downstream demands will likely continue to increase, accurate hydrological projections for the future supply are important. We use an ensemble of statistically downscaled CMIP5 General Circulation Model outputs for RCP4.5 and RCP8.5 to force a cryospheric-hydrological model and generate transient hydrological projections for the entire 21st century for the upper Indus basin. Three methodological advances are introduced: (i) A new precipitation dataset that corrects for the underestimation of high-altitude precipitation is used. (ii) The model is calibrated using data on river runoff, snow cover and geodetic glacier mass balance. (iii) An advanced statistical downscaling technique is used that accounts for changes in precipitation extremes. The analysis of the results focuses on changes in sources of runoff, seasonality and hydrological extremes. We conclude that the future of the upper Indus basin's water availability is highly uncertain in the long run, mainly due to the large spread in the future precipitation projections. Despite large uncertainties in the future climate and long-term water availability, basin-wide patterns and trends of seasonal shifts in water availability are consistent across climate change scenarios. Most prominent is the attenuation of the annual hydrograph and shift from summer peak flow towards the other seasons for most ensemble members. In addition there are distinct spatial patterns in the response that relate to monsoon influence and the importance of meltwater. Analysis of future hydrological extremes reveals that increases in intensity and frequency of extreme discharges are very likely for most of the upper Indus basin and most ensemble members.

Influence of spatial temperature estimation method in ecohydrologic modeling in the western Oregon Cascades

Treesearch

E. Garcia; C.L. Tague; J. Choate

2013-01-01

Most spatially explicit hydrologic models require estimates of air temperature patterns. For these models, empirical relationships between elevation and air temperature are frequently used to upscale point measurements or downscale regional and global climate model estimates of air temperature. Mountainous environments are particularly sensitive to air temperature...

Downscaling soil moisture over regions that include multiple coarse-resolution grid cells

USDA-ARS?s Scientific Manuscript database

Many applications require soil moisture estimates over large spatial extents (30-300 km) and at fine-resolutions (10-30 m). Remote-sensing methods can provide soil moisture estimates over very large spatial extents (continental to global) at coarse resolutions (10-40 km), but their output must be d...

Using a Coupled Lake Model with WRF for Dynamical Downscaling

EPA Science Inventory

The Weather Research and Forecasting (WRF) model is used to downscale a coarse reanalysis (National Centers for Environmental Prediction–Department of Energy Atmospheric Model Intercomparison Project reanalysis, hereafter R2) as a proxy for a global climate model (GCM) to examine...

Examining Projected Changes in Weather & Air Quality Extremes Between 2000 & 2030 using Dynamical Downscaling

EPA Science Inventory

Climate change may alter regional weather extremes resulting in a range of environmental impacts including changes in air quality, water quality and availability, energy demands, agriculture, and ecology. Dynamical downscaling simulations were conducted with the Weather Research...

From cyclone tracks to the costs of European winter storms: A probabilistic loss assessment model

NASA Astrophysics Data System (ADS)

Renggli, Dominik; Corti, Thierry; Reese, Stefan; Wueest, Marc; Viktor, Elisabeth; Zimmerli, Peter

2014-05-01

The quantitative assessment of the potential losses of European winter storms is essential for the economic viability of a global reinsurance company. For this purpose, reinsurance companies generally use probabilistic loss assessment models. This work presents an innovative approach to develop physically meaningful probabilistic events for Swiss Re's new European winter storm loss model. The meteorological hazard component of the new model is based on cyclone and windstorm tracks identified in the 20th Century Reanalysis data. The knowledge of the evolution of winter storms both in time and space allows the physically meaningful perturbation of properties of historical events (e.g. track, intensity). The perturbation includes a random element but also takes the local climatology and the evolution of the historical event into account. The low-resolution wind footprints taken from 20th Century Reanalysis are processed by a statistical-dynamical downscaling to generate high-resolution footprints of the historical and probabilistic winter storm events. Downscaling transfer functions are generated using ENSEMBLES regional climate model data. The result is a set of reliable probabilistic events representing thousands of years. The event set is then combined with country- and risk-specific vulnerability functions and detailed market- or client-specific exposure information to compute (re-)insurance risk premiums.