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Sample records for regional climate downscaling

  1. Statistical downscaling and dynamical downscaling of regional climate in China: Present climate evaluations and future climate projections

    NASA Astrophysics Data System (ADS)

    Tang, Jianping; Niu, Xiaorui; Wang, Shuyu; Gao, Hongxia; Wang, Xueyuan; Wu, Jian

    2016-03-01

    Statistical downscaling and dynamical downscaling are two approaches to generate high-resolution regional climate models based on the large-scale information from either reanalysis data or global climate models. In this study, these two downscaling methods are used to simulate the surface climate of China and compared. The Statistical Downscaling Model (SDSM) is cross validated and used to downscale the regional climate of China. Then, the downscaled historical climate of 1981-2000 and future climate of 2041-2060 are compared with that from the Weather Research and Forecasting (WRF) model driven by the European Center-Hamburg atmosphere model and the Max Planck Institute Ocean Model (ECHAM5/MPI-OM) and the L'Institut Pierre-Simon Laplace Coupled Model, version 5, coupled with the Nucleus for European Modelling of the ocean, low resolution (IPSL-CM5A-LR). The SDSM can reproduce the surface temperature characteristics of the present climate in China, whereas the WRF tends to underestimate the surface temperature over most of China. Both the SDSM and WRF require further work to improve their ability to downscale precipitation. Both statistical and dynamical downscaling methods produce future surface temperatures for 2041-2060 that are markedly different from the historical climatology. However, the changes in projected precipitation differ between the two downscaling methods. Indeed, large uncertainties remain in terms of the direction and magnitude of future precipitation changes over China.

  2. Use of multi-model ensembles for regional climate downscaling

    NASA Astrophysics Data System (ADS)

    Reichler, Thomas; Andrade, Marcos; Ohara, Noriaki

    2014-05-01

    Dynamic regional downscaling requires use of a regional model driven at its boundaries by the output from coarse-scale global climate models. But individual members from global multi-model ensembles often lead to contradicting answers, and the important question arises of which of the many global models to select for the downscaling work. The perhaps most obvious solution to downscale various models is usually too expensive. Numerous studies have shown that the performance of the multi-model mean of an ensemble is usually superior to that of any individual model. However, it is unclear how to employ the multi-model mean framework for regional downscaling. We propose a simple method that allows use of a multi-model mean for downscaling work. We demonstrate the performance of our method using the WRF regional model system coupled to CMIP5 output. The system is used to perform high-resolution climate change simulations over our prototypical study region of tropical South America. We use objective criteria to select three CMIP5 models that perform best in terms of simulating present day climate. The outcomes from using these three individual global models are contrasted against that from using the CMIP5 multi-model mean. We discuss the advantages and limitations of the new method, and conclude that it represents a promising and computationally inexpensive alternative to the traditional downscaling of individual models.

  3. 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

  4. CORDEX.be: COmbining Regional climate Downscaling EXpertise in Belgium

    NASA Astrophysics Data System (ADS)

    Termonia, Piet; Van Schaeybroeck, Bert; De Ridder, Koen; Fettweis, Xavier; Gobin, Anne; Luyten, Patrick; Marbaix, Philippe; Pottiaux, Eric; Stavrakou, Trissevgeni; Van Lipzig, Nicole; van Ypersele, Jean-Pascal; Willems, Patrick

    2016-04-01

    The main objective of the ongoing project CORDEX.be, "COmbining Regional Downscaling EXpertise in Belgium: CORDEX and Beyond" is to gather existing and ongoing Belgian research activities in the domain of climate modelling to create a coherent scientific basis for future climate services in Belgium. The project regroups eight Belgian Institutes under a single research program of the Belgian Science Policy (BELSPO). The project involves three regional climate models: the ALARO model, the COSMO-CLM model and the MAR model running according to the guidelines of the CORDEX project and at convection permitting resolution on small domains over Belgium. The project creates a framework to address four objectives/challenges. First, this projects aims to contribute to the EURO-CORDEX project. Secondly, RCP simulations are executed at convection-permitting resolutions (3 to 5 km) on small domains. Thirdly, the output of the atmospheric models is used to drive land surface models (the SURFEX model and the Urbclim model) with urban modules, a crop model (REGCROP), a tides and storm model (COHERENS) and the MEGAN-MOHYCAN model that simulates the fluxes emitted by vegetation. Finally, one work package will translate the uncertainty present in the CORDEX database to the high-resolution output of the CORDEX.be project. The organization of the project will be presented and first results will be shown, demonstrating that convection-permitting models can add extra skill to the mesoscale version of the regional climate models, in particular regarding the extreme value statistics and the diurnal cycle.

  5. A standardized framework for evaluating the skill of regional climate downscaling techniques

    NASA Astrophysics Data System (ADS)

    Hayhoe, Katharine Anne

    Regional climate impact assessments require high-resolution projections to resolve local factors that modify the impact of global-scale forcing. To generate these projections, global climate model simulations are commonly downscaled using a variety of statistical and dynamical techniques. Despite the essential role of downscaling in regional assessments, there is no standard approach to evaluating various downscaling methods. Hence, impact communities often have little awareness of limitations and uncertainties associated with downscaled projections. To develop a standardized framework for evaluating and comparing downscaling approaches, I first identify three primary characteristics of a distribution directly relevant to impact analyses that can be used to evaluate a simulated variable such as temperature or precipitation at a given location: (1) annual, seasonal, and monthly mean values; (2) thresholds, extreme values, and accumulated quantities such as 24h precipitation or degree-days; and (3) persistence, reflecting multi-day events such as heat waves, cold spells, and wet periods. Based on a survey of the literature and solicitation of expert opinion, I select a set of ten statistical tests to evaluate these characteristics, including measures of error, skill, and correlation. I apply this framework to evaluate the skill of four downscaling methods, from a simple delta approach to a complex asynchronous quantile regression, in simulating daily temperature at twenty stations across North America. Identical global model fields force each downscaling method, and the historical observational record at each location is randomly divided by year into two equal parts, such that each statistical method is trained on one set of historical observations, and evaluated on an entirely independent set of observations. Biases relative to observations are calculated for the historical evaluation period, and differences between projections for the future. Application of the

  6. Downscaled Regional Climate Information for the Southeastern US

    EPA Science Inventory

    The U.S. Environmental Protection Agency’s Office of Research and Development in Research Triangle Park, NC, has been developing regional climate and air quality fields for North America for current and future periods. Research emphasis has been placed on evaluating near-s...

  7. Regional Climate Simulation with a Variable Resolution Stretched Grid GCM: The Regional Down-Scaling Effects

    NASA Technical Reports Server (NTRS)

    Fox-Rabinovitz, Michael S.; Takacs, Lawrence L.; Suarez, Max; Sawyer, William; Govindaraju, Ravi C.

    1999-01-01

    The results obtained with the variable resolution stretched grid (SG) GEOS GCM (Goddard Earth Observing System General Circulation Models) are discussed, with the emphasis on the regional down-scaling effects and their dependence on the stretched grid design and parameters. A variable resolution SG-GCM and SG-DAS using a global stretched grid with fine resolution over an area of interest, is a viable new approach to REGIONAL and subregional CLIMATE studies and applications. The stretched grid approach is an ideal tool for representing regional to global scale interactions. It is an alternative to the widely used nested grid approach introduced a decade ago as a pioneering step in regional climate modeling. The GEOS SG-GCM is used for simulations of the anomalous U.S. climate events of 1988 drought and 1993 flood, with enhanced regional resolution. The height low level jet, precipitation and other diagnostic patterns are successfully simulated and show the efficient down-scaling over the area of interest the U.S. An imitation of the nested grid approach is performed using the developed SG-DAS (Data Assimilation System) that incorporates the SG-GCM. The SG-DAS is run with withholding data over the area of interest. The design immitates the nested grid framework with boundary conditions provided from analyses. No boundary condition buffer is needed for the case due to the global domain of integration used for the SG-GCM and SG-DAS. The experiments based on the newly developed versions of the GEOS SG-GCM and SG-DAS, with finer 0.5 degree (and higher) regional resolution, are briefly discussed. The major aspects of parallelization of the SG-GCM code are outlined. The KEY OBJECTIVES of the study are: 1) obtaining an efficient DOWN-SCALING over the area of interest with fine and very fine resolution; 2) providing CONSISTENT interactions between regional and global scales including the consistent representation of regional ENERGY and WATER BALANCES; 3) providing a high

  8. Evaluation of Future Precipitation Scenario Using Statistical Downscaling MODEL over Three Climatic Region of Nepal Himalaya

    NASA Astrophysics Data System (ADS)

    Sigdel, M.

    2014-12-01

    Statistical downscaling model (SDSM) was applied in downscaling precipitation in the three climatic regions such as humid, sub-humid and arid region of Nepal Himalaya. The study includes the calibration of the SDSM model by using large-scale atmospheric variables encompassing NCEP reanalysis data, the validation of the model and the outputs of downscaled scenarios A2 (high green house gases emission) and B2 (low green house gases emission) of the HadCM3 model for the future. Under both scenarios H3A2 and H3B2, during the prediction period of 2010-2099, the change of annual mean precipitation in the three climatic regions would present a tendency of surplus of precipitation as compared to the mean values of the base period. On the average for all three climatic regions of Nepal the annual mean precipitation would increase by about 13.75% under scenario H3A2 and increase near about 11.68% under scenario H3B2 in the 2050s. For the 2080s there would be increase of 8.28% and 13.30% under H3A2 and H3B2 respectively compared to the base period.

  9. 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.

  10. Validation of the Regional Climate Model ALARO with different dynamical downscaling approaches and different horizontal resolutions

    NASA Astrophysics Data System (ADS)

    Berckmans, Julie; Hamdi, Rafiq; De Troch, Rozemien; Giot, Olivier

    2015-04-01

    At the Royal Meteorological Institute of Belgium (RMI), climate simulations are performed with the regional climate model (RCM) ALARO, a version of the ALADIN model with improved physical parameterizations. In order to obtain high-resolution information of the regional climate, lateral bounary conditions (LBC) are prescribed from the global climate model (GCM) ARPEGE. Dynamical downscaling is commonly done in a continuous long-term simulation, with the initialisation of the model at the start and driven by the regularly updated LBCs of the GCM. Recently, more interest exists in the dynamical downscaling approach of frequent reinitializations of the climate simulations. For these experiments, the model is initialised daily and driven for 24 hours by the GCM. However, the surface is either initialised daily together with the atmosphere or free to evolve continuously. The surface scheme implemented in ALARO is SURFEX, which can be either run in coupled mode or in stand-alone mode. The regional climate is simulated on different domains, on a 20km horizontal resolution over Western-Europe and a 4km horizontal resolution over Belgium. Besides, SURFEX allows to perform a stand-alone or offline simulation on 1km horizontal resolution over Belgium. This research is in the framework of the project MASC: "Modelling and Assessing Surface Change Impacts on Belgian and Western European Climate", a 4-year project funded by the Belgian Federal Government. The overall aim of the project is to study the feedbacks between climate changes and land surface changes in order to improve regional climate model projections at the decennial scale over Belgium and Western Europe and thus to provide better climate projections and climate change evaluation tools to policy makers, stakeholders and the scientific community.

  11. 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.

  12. Assessment of dynamical downscaling in Japan using an atmosphere-biosphere-river coupling regional climate model

    NASA Astrophysics Data System (ADS)

    Dairaku, K.; Iizuka, S.; Sasaki, W.; Beltran, A.; Pielke, R. A.

    2008-12-01

    The responses of the climate system to increases in carbon dioxide concentrations and to changes in land use/land cover and the subsequent impacts of climatic variability on humans and natural ecosystems are of fundamental concern. Because regional responses of surface hydrological and biogeochemical changes are particularly complex, it is necessary to add spatial resolution to accurately assess critical interactions within the regional climate system for climate change impacts assessments. We investigated the reproducibility of present climate using two regional climate models with 20km horizontal grid spacing, the atmosphere- biosphere-river coupling regional climate model(GEMRAMS) and the Meteorological Research Institute Nonhydrostatic Model(MRI-NHM), both of which used Japanese 25-year ReAnalysis (JRA-25) as lateral boundary conditions. Two key variables for impact studies, surface air temperature and precipitation, were compared with the Japanese high-resolution surface observation, Automated Meteorological Data Acquisition System (AMeDAS) on 78 river basins. Results simulated by the two models were relatively in good agreement with the observation on the basin scale. The differences of surface air temperature between the models and the observation were less than 2K and the ratio of precipitation of the models to the observation was within 0.5-2 on seasonal averages. By adding other two regional climate models, a multi-model ensemble will be applied in climate change impact studies in combination with additional statistical downscaling approaches.

  13. 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

  14. Analysis of climate projections for the Carpathian Region using dynamical downscaling

    NASA Astrophysics Data System (ADS)

    Bartholy, Judit; Pongracz, Rita; Pieczka, Ildiko; Andre, Karolina

    2015-04-01

    Hungarian national climate and adaptation strategies have been recently revised, and a National Adaptation Geo-information System (NAGIS) is currently under development. This platform will serve as a central data collection for various end-users, impact researchers, and decision makers on national level in Hungary. In order to satisfy the demands for climate projection inputs within this framework, RegCM4.3 is one of the regional climate models used to provide results for detailed regional scale analysis and specific impact studies. RegCM is a 3-dimensional, sigma-coordinate, primitive equation model, originally developed by Giorgi et al. Currently, it is available from the ICTP (Abdus Salam International Centre for Theoretical Physics). We have already completed experiments with 50 km horizontal resolution covering both the second half of the past century (1951-2005), and the future (i.e., the 21st century, 2006-2100) using HadGEM2 global model outputs as initial and lateral boundary conditions. The outputs of the 50 km runs drive the further downscaling experiments using 10 km as a horizontal resolution for a smaller domain covering Central Europe with special focus on the Carpathian Region. For the future, RCP4.5 scenario run is analysed in this poster, and moreover, preliminary results of the RCP8.5 scenario run are also presented.

  15. Regional climate simulations over Africa using WRF Model: Sensitivity to the dynamical downscaling methods.

    NASA Astrophysics Data System (ADS)

    Taima-Hernández, D.; Enríquez, A.; Pérez, J. C.; Díaz, J. P.; González, A.; Expósito, F. J.

    2012-04-01

    The main objective of the CORDEX program (COordinated Regional climate Downscaling Experiment) [1] is the production of regional climate change scenarios at a global scale, creating a contribution to the IPCC (Intergovernmental Panel on Climate Change) AR5 (5th Assessment Report). Inside this project, Africa is the key region due to the lack of data at this moment. In this work, a sensitivity study is performed over the CORDEX-AFRICA domain with the same physical parameterizations and using five different WRF configurations: a long-term continuous run, a monthly re-initialized run, a monthly re-initialized run with soil variables fixed, a long-term continuous run with analysis nudging over the planet boundary layer (PBL) and a long-term continuous run with analysis nudging at the whole atmospheric column. These simulations, driven by ERA-Interim data [2] as initial and lateral boundary conditions and with a 50 km spatial resolution, were performed over the 5-year period between December 1990 and December 1995. In order to assess theperformance of the simulations several statistics, such as correlation coefficient (r), bias, root mean square (RMS) and a defined skill score (SS), based on the difference between areas of the probability density functions (PDFs) associated to study parameters [3], were applied using ERA-Interim, CRU-TS 3.1 and University of Delaware database as validation data for some variables, such as near-surface temperature, precipitation and moisture fluxes.

  16. Streamflow estimation using WRF-Hydro with dynamically downscaled climate variables over southern tropical Indian region

    NASA Astrophysics Data System (ADS)

    Davis, S.; Sudheer, K. P.; Gunthe, S. S.

    2015-12-01

    Indian summer monsoon rainfall (ISMR; June to September), which constitutes around 80% of India's annual rainfall, has shown an increasing trend in intensity and frequency of extreme events (Goswami et al., 2006). It is a widely recognized fact that the increasing temperature in association with anthropogenic activities can affect the hydrological cycle, which leads to extreme events. In addition a shift in extremes of the spatial pattern of ISMR has recently been observed (Ghosh et al., 2011). Such changes in rainfall on temporal and spatial scale can further affect the stream flow over a given region subsequently making water resource management a difficult task (Mondal and Mujumdar, 2015). The hydrological models used for the stream flow estimation are dependent on various climate variables as input data. These climate variables could be obtained through either observational networks or climate model outputs. Due to the scarcity of the observational data over the Indian region and the coarse resolution of global climate model output, which is used as input to hydrologic models, large uncertainties are introduced in stream flow output (Overgaard et al., 2007). In the present study we have used the Weather Research and Forecasting (WRF) model (Skamarock et al. 2008) to downscale the essential climate variables (surface temperature, precipitation, relative humidity, etc.) as an input for its coupled hydrological extension, WRF Hydro (NCAR user's guide). We will present the results obtained from the WRF-hydro simulation to estimate the stream flow over the Thamirabarani river basin in Southern Tropical Indian region. Preliminary simulations using WRF to estimate the precipitation showed the reasonable quantitative agreement with observed values. An attempt will be made to demonstrate how these results can further be used for developing flood-forecasting techniques and for local regional water resource management.

  17. Downscaling a Global Climate Model to Simulate Climate Change Impacts on U.S. Regional and Urban Air Quality

    NASA Technical Reports Server (NTRS)

    Trail, M.; Tsimpidi, A. P.; Liu, P.; Tsigaridis, K.; Hu, Y.; Nenes, A.; Russell, A. G.

    2013-01-01

    Climate change can exacerbate future regional air pollution events by making conditions more favorable to form high levels of ozone. In this study, we use spectral nudging with WRF to downscale NASA earth system GISS modelE2 results during the years 2006 to 2010 and 2048 to 2052 over the continental United States in order to compare the resulting meteorological fields from the air quality perspective during the four seasons of five-year historic and future climatological periods. GISS results are used as initial and boundary conditions by the WRF RCM to produce hourly meteorological fields. The downscaling technique and choice of physics parameterizations used are evaluated by comparing them with in situ observations. This study investigates changes of similar regional climate conditions down to a 12km by 12km resolution, as well as the effect of evolving climate conditions on the air quality at major U.S. cities. The high resolution simulations produce somewhat different results than the coarse resolution simulations in some regions. Also, through the analysis of the meteorological variables that most strongly influence air quality, we find consistent changes in regional climate that would enhance ozone levels in four regions of the U.S. during fall (Western U.S., Texas, Northeastern, and Southeastern U.S), one region during summer (Texas), and one region where changes potentially would lead to better air quality during spring (Northeast). We also find that daily peak temperatures tend to increase in most major cities in the U.S. which would increase the risk of health problems associated with heat stress. Future work will address a more comprehensive assessment of emissions and chemistry involved in the formation and removal of air pollutants.

  18. 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.

  19. 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

  20. Dynamical Downscaling NCEP Global Climate Forecast System (CFS) Seasonal Predictions Using Regional Atmospheric Modeling System (RAMS)

    NASA Astrophysics Data System (ADS)

    Lu, L.; Zheng, Y.; Pielke, R. A.

    2009-12-01

    As part of the NOAA CPPA-sponsored MRED project, the state-of-the-art Regional Atmospheric Modeling System (RAMS) version 6.0 is used to dynamically and progressively downscale NCEP global Climate Forecast System (CFS, at 100s-km grid increment) seasonal predictions to a regional domain that covers the conterminous United States at 30-km grid increment. The first set of RCM prediction experiment focuses on the winter seasons, during which the precipitation is largely dependent on synoptic-scale mid-latitude storms and orographic dominant mesoscale processes. Our first suite of numerical experiment includes one ensemble member for each year from 1982 through 2008, with all the simulations starting on December 1 and ending on April 30. Driven by the same atmospheric and SST forcings, RAMS will be compared with other RCMs, and evaluated against observations and reanalysis (NARR) to see if the simulations capture the climatology and interannual variability of temperature and precipitation distributions. The overall strengths and weaknesses of the modeling systems will be identified, as well as the consistent model biases. In addition, we will analyze the changes in kinetic energy spectra before and after the spectral nudging algorithm is implemented. The results show that with the spectral nudging scheme, RAMS can better preserve large-scale kinetic energy than standard boundary forcing method, and allow more large-scale energy to cascade to smaller scales.

  1. Agricultural pests under future climate conditions: downscaling of regional climate scenarios with a stochastic weather generator

    NASA Astrophysics Data System (ADS)

    Hirschi, M.; Stöckli, S.; Dubrovsky, M.; Spirig, C.; Rotach, M. W.; Calanca, P.; Samietz, J.

    2010-09-01

    As a consequence of current and projected climate change in temperate regions of Europe, agricultural pests and diseases are expected to occur more frequently and possibly to extend to previously unaffected regions. Given their economic and ecological relevance, detailed forecasting tools for various pests have been developed, which model the infestation depending on actual weather conditions. Assessing the future risk of pest-related damages therefore requires future weather data at high temporal and spatial resolution. In particular, pest forecast models are often not based on screen temperature and precipitation alone (i.e., the most generally projected climate variables), but might require input variables such as soil temperature, in-canopy net radiation or leaf wetness. Here, we use a stochastic weather and a re-sampling procedure for producing site-specific hourly weather data from regional climate change scenarios for 2050 in Switzerland. The climate change scenarios were derived from multi-model projections and provide probabilistic information on future regional changes in temperature and precipitation. Hourly temperature, precipitation and radiation data were produced by first generating daily weather data for these climate scenarios and then using a nearest neighbor re-sampling approach for creating realistic diurnal cycles. These hourly weather time series were then used for modeling important phases in the lifecycle of codling moth, the major insect pest in apple orchards worldwide. First results indicate a shift in the occurrence and duration of phases relevant for pest disease control for projected as compared to current climate (e.g. the flight of the codling moth starts about ten days earlier in future climate), continuing an already observed trend towards more favorable conditions for this insect during the last 20 years.

  2. Robust Estimation of Precipitation Extremes from Short-Period Regional Climate Downscales

    NASA Astrophysics Data System (ADS)

    Apling, D.; Darmenova, K.; Higgins, G. J.

    2011-12-01

    The US Southwest is likely to experience significant changes in precipitation patterns in coming decades as a result of regional climate change. One serious issue is to better understand extreme precipitation events, which affect infrastructure planning, and human life and safety management. Extreme precipitation events are characterized by the maximum expectation of accumulated precipitation over a short time period, which has a long-period return over some number of years; e.g., the 100-year return of daily precipitation. These measures are statistics drawn from Extreme Value Theory, and can be challenging to accurately and reliably estimate for short data sets. Regional Climate Models (RCM) are often run for shorter decadal periods, both to economize on computational expense, and to characterize specific decadal time bands. In each case, one needs robust statistical estimation algorithms to accurately and reliably retrieve the precipitation recurrence statistics. To produce these important decision-aiding products, we added several processes to an otherwise conventional Peaks Over Threshold technique operating on the combined grid-scale and cumuliform precipitation outputs from our 12 kilometer Weather Research and Forecasting (WRF) downscale of the National Centers for Environmental Prediction (NCEP) reanalysis fields for the ten year period of 2000-2009 over the Southwest US. These processes included interleaved sub-year intermediate aggregations, correlated sample corrections, distributional tail feature extraction, and trimmed set tail fitting with jackknife error estimation. The process resulted in estimated 100-year return 24-hour accumulated precipitation expectations with accompanying error bounds, which compare well to established historical precipitation statistics.

  3. Regional Climate Downscaling Using a High-resolution Global Atmospheric Model

    NASA Astrophysics Data System (ADS)

    Kunhu Bangalath, Hamza; Stenchikov, Georgiy; Osipov, Sergey

    2013-04-01

    In this study, we used HIRAM, a high-resolution atmospheric model [Zhao et al., 2009] for climate downscaling with the horizontal grid spacing of 25 km. Our simulations followed the CORDEX protocol [Giorgi et al., 2009] and were conducted for historic (1975-2006) and future (2005-2050) periods using both RCP 4.5 and RCP 8.5 scenarios. Compared with the Geophysical Fluid Dynamics Laboratory (GFDL) AM2.0 and AM2.1 [Delworth et al., 2006], HIRAM uses enhanced vertical discretization on 32 vertical layers instead of 24 and replaces the relaxed Arakawa-Schubert convective closure with the one developed at the University of Washington. The model retains the surface flux, boundary layer, large-scale cloud microphysics, and radiative transfer modules from the AM2 family [Delworth et al., 2006]. HIRAM also employs a cubed-sphere implementation (here at 25-km resolution) of a finite-volume dynamical core and is coupled to LM3, a new land model with ecosystem dynamics and hydrology. In our simulations, the Sea Surface Temperatures (SSTs) from the GFDL Earth System Model runs, ESM2M and ESM2G, performed for the International Panel for Climate Change AR5 project with a latitude-longitude grid of 2°x2.5° were adopted as the bottom boundary conditions over the sea. We used prescribed time-varying greenhouse gas and stratospheric/tropospheric aerosol distribution datasets to reproduce the observed radiative forcing in the model as described by Delworth et al. [2006]. Here, we present results for the CORDEX Middle East and North Africa domain and compared them with the coarse-resolution ESM2M/ESM2G simulations as well as with the nested regional model projections. Delworth, T. et al. (2006), GFDL's CM2 Global Coupled Models. Part I: Formulation and Simulation Characteristics, J. Climate, 19, 643-674. Giorgi, F., C. Jones, and G. Asrar (2009), Addressing climate information needs at the regional level: The CORDEX framework. WMO Bull., 58, 175-183 Zhao, M., I. M. Held, S-J. Lin

  4. Downscaling 20th century flooding events in complex terrain (Switzerland) using the WRF regional climate model

    NASA Astrophysics Data System (ADS)

    Heikkilä, Ulla; Gómez Navarro, Juan Jose; Franke, Jörg; Brönnimann, Stefan; Cattin, Réne

    2016-04-01

    Switzerland has experienced a number of severe precipitation events during the last few decades, such as during the 14-16 November of 2002 or during the 21-22 August of 2005. Both events, and subsequent extreme floods, caused fatalities and severe financial losses, and have been well studied both in terms of atmospheric conditions leading to extreme precipitation, and their consequences [e.g. Hohenegger et al., 2008, Stucki et al., 2012]. These examples highlight the need to better characterise the frequency and severity of flooding in the Alpine area. In a larger framework we will ultimately produce a high-resolution data set covering the entire 20th century to be used for detailed hydrological studies including all atmospheric parameters relevant for flooding events. In a first step, we downscale the aforementioned two events of 2002 and 2005 to assess the model performance regarding precipitation extremes. The complexity of the topography in the Alpine area demands high resolution datasets. To achieve a sufficient detail in resolution we employ the Weather Research and Forecasting regional climate model (WRF). A set of 4 nested domains is used with a 2-km resolution horizontal resolution over Switzerland. The NCAR 20th century reanalysis (20CR) with a horizontal resolution of 2.5° serves as boundary condition [Compo et al., 2011]. First results of the downscaling the 2002 and 2005 extreme precipitation events show that, compared to station observations provided by the Swiss Meteorological Office MeteoSwiss, the model strongly underestimates the strength of these events. This is mainly due to the coarse resolution of the 20CR data, which underestimates the moisture fluxes during these events. We tested driving WRF with the higher-resolved NCEP reanalysis and found a significant improvement in the amount of precipitation of the 2005 event. In a next step we will downscale the precipitation and wind fields during a 6-year period 2002-2007 to investigate and

  5. Assessment of Mesoscale Dynamical Downscaling Model (MM5) for Regional Climate Simulation in the Tampa Bay region

    NASA Astrophysics Data System (ADS)

    Hwang, S.; Graham, W. D.; Hernandez, J.; Martinez, C. J.; Jones, J. W.

    2009-12-01

    This research analyzes the temporal and spatial variability of historic precipitation in Tampa Bay region and evaluates the ability of the mesoscale downscaling model (MM5, Grell et al., 1994), to reproduce this variability. The long term goal of this effort is to evaluate the utility of using MM5 to downscale GCM forecasts and climate change scenarios for improving water management decisions in the Tampa Bay region. Cumulative probability distributions were constructed using observed daily and monthly rainfall at each station, and the spatial correlations between the 53 stations were analyzed for each month using covariance and variogram analysis for both observed data and MM5 predictions. MM5 was run to predict precipitation at 9x9 and 27x27 km2 spatial resolutions and 6-hour temporal resolution over the 23 year period from 1986 to 2008 using the NCEP/NCAR reanalysis data set as initial and boundary conditions. The raw precipitation predictions were then bias-corrected at each observation station using the cumulative probability distribution mapping approach (Wood et al., 2002). Daily and monthly precipitation totals were estimated over the Alafia and Hillsborough River watersheds using the bias-corrected point precipitation and observed variogram functions. MM5 performance was assessed by cross-validating predicted daily and monthly point and total watershed precipitation for each month. Variograms from the bias-corrected daily precipitation predictions in general indicated that MM5 overestimates the strength of the spatial correlation and underestimates the variance of precipitation compared to the observed data, especially in the summer months when convective storms dominate. The simulations for each month reproduced the daily mean point precipitation values with an average error of -0.0641 in (Jul.) to 0.0214 in (Oct.) with an average RMSE of 0.6834 in (Mar.) to 0.9449 in (Sep.) over the 53 rain stations. Monthly mean point precipitation values were

  6. Very high resolution surface mass balance over Greenland modeled by the regional climate model MAR with a downscaling technique

    NASA Astrophysics Data System (ADS)

    Kittel, Christoph; Lang, Charlotte; Agosta, Cécile; Prignon, Maxime; Fettweis, Xavier; Erpicum, Michel

    2016-04-01

    This study presents surface mass balance (SMB) results at 5 km resolution with the regional climate MAR model over the Greenland ice sheet. Here, we use the last MAR version (v3.6) where the land-ice module (SISVAT) using a high resolution grid (5km) for surface variables is fully coupled while the MAR atmospheric module running at a lower resolution of 10km. This online downscaling technique enables to correct near-surface temperature and humidity from MAR by a gradient based on elevation before forcing SISVAT. The 10 km precipitation is not corrected. Corrections are stronger over the ablation zone where topography presents more variations. The model has been force by ERA-Interim between 1979 and 2014. We will show the advantages of using an online SMB downscaling technique in respect to an offline downscaling extrapolation based on local SMB vertical gradients. Results at 5 km show a better agreement with the PROMICE surface mass balance data base than the extrapolated 10 km MAR SMB results.

  7. The Impact of Incongruous Lake Temperatures on Regional Climate Extremes Downscaled from the CMIP5 Archive Using the WRF Model

    EPA Science Inventory

    The impact of incongruous lake temperatures is demonstrated using the Weather Research and Forecasting (WRF) Model to downscale global climate fields. Unrealistic lake temperatures prescribed by the default WRF configuration cause obvious biases near the lakes and also affect pre...

  8. Recent Regional Climate State and Change - Derived through Downscaling Homogeneous Large-scale Components of Re-analyses

    NASA Astrophysics Data System (ADS)

    Von Storch, H.; Klehmet, K.; Geyer, B.; Li, D.; Schubert-Frisius, M.; Tim, N.; Zorita, E.

    2015-12-01

    Global re-analyses suffer from inhomogeneities, as they process data from networks under development. However, the large-scale component of such re-analyses is mostly homogeneous; additional observational data add in most cases to a better description of regional details and less so on large-scale states. Therefore, the concept of downscaling may be applied to homogeneously complementing the large-scale state of the re-analyses with regional detail - wherever the condition of homogeneity of the large-scales is fulfilled. Technically this can be done by using a regional climate model, or a global climate model, which is constrained on the large scale by spectral nudging. This approach has been developed and tested for the region of Europe, and a skillful representation of regional risks - in particular marine risks - was identified. While the data density in Europe is considerably better than in most other regions of the world, even here insufficient spatial and temporal coverage is limiting risk assessments. Therefore, downscaled data-sets are frequently used by off-shore industries. We have run this system also in regions with reduced or absent data coverage, such as the Lena catchment in Siberia, in the Yellow Sea/Bo Hai region in East Asia, in Namibia and the adjacent Atlantic Ocean. Also a global (large scale constrained) simulation has been. It turns out that spatially detailed reconstruction of the state and change of climate in the three to six decades is doable for any region of the world.The different data sets are archived and may freely by used for scientific purposes. Of course, before application, a careful analysis of the quality for the intended application is needed, as sometimes unexpected changes in the quality of the description of large-scale driving states prevail.

  9. 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.

  10. 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.

  11. Regional climate models downscaling in the Alpine area with multimodel superensemble

    NASA Astrophysics Data System (ADS)

    Cane, D.; Barbarino, S.; Renier, L. A.; Ronchi, C.

    2013-05-01

    The climatic scenarios show a strong signal of warming in the Alpine area already for the mid-XXI century. The climate simulations, however, even when obtained with regional climate models (RCMs), are affected by strong errors when compared with observations, due both to their difficulties in representing the complex orography of the Alps and to limitations in their physical parametrization. Therefore, the aim of this work is to reduce these model biases by using a specific post processing statistic technique, in order to obtain a more suitable projection of climate change scenarios in the Alpine area. For our purposes we used a selection of regional climate models (RCMs) runs which were developed in the framework of the ENSEMBLES project. They were carefully chosen with the aim to maximise the variety of leading global climate models and of the RCMs themselves, calculated on the SRES scenario A1B. The reference observations for the greater Alpine area were extracted from the European dataset E-OBS (produced by the ENSEMBLES project), which have an available resolution of 25 km. For the study area of Piedmont daily temperature and precipitation observations (covering the period from 1957 to the present) were carefully gridded on a 14 km grid over Piedmont region through the use of an optimal interpolation technique. Hence, we applied the multimodel superensemble technique to temperature fields, reducing the high biases of RCMs temperature field compared to observations in the control period. We also proposed the application of a brand new probabilistic multimodel superensemble dressing technique, already applied to weather forecast models successfully, to RCMS: the aim was to estimate precipitation fields, with careful description of precipitation probability density functions conditioned to the model outputs. This technique allowed for reducing the strong precipitation overestimation, arising from the use of RCMs, over the Alpine chain and to reproduce well the

  12. A test for evaluating the downscaling ability of one-way nested regional climate models: The Big-Brother Experiment

    NASA Astrophysics Data System (ADS)

    Denis, Bertrand

    The purpose of this thesis is to evaluate the downscaling ability of one-way nesting regional climate models (RCM). To do this, a rigorous and well-defined experiment for assessing the reliability of the one-way nesting approach is developed. This experiment, baptised the Big-Brother Experiment (BBE), is used for addressing some important one-way nesting issues. The first part of this work is dedicated to the development of a scale decomposition tool employed for the BBE. This tool involves a new spectral analysing technique suitable for two-dimensional fields on limited-area domains, and is based on the discrete cosine transform (DCT). It is used for degrading the spatial resolution of the lateral boundary conditions (LBC) used to drive the Canadian RCM (CRCM), for extracting mesoscale features from the atmospheric fields, and for regional validation, and producing power spectra. The second part of the thesis describes the BBE framework and its first results. The BBE consists in first establishing a reference virtual-reality climate from an RCM simulation using a large and high-resolution domain. This simulation is called the "Big Brother". This big-brother simulation is then degraded toward the resolution of today's global objective analyses (OA) and/or global climate models (GCM) by removing the short scales. The resulting fields are then used as nesting data to drive an RCM (called the "Little Brother") which is integrated at the same high-resolution as the Big Brother, but over a sub-area of the big-brother domain. The climate statistics of the Little Brother are then compared with those of the big-brother simulation over the little-brother domain. Differences between the two climates can thus be unambiguously attributed to errors associated with the dynamical downscaling technique, and not to model errors nor to observation limitations. The results for a February simulation shows that the Canadian RCM, using a factor of 6 between the model and the LBC spatial

  13. Dynamical downscaling of regional climate over eastern China using RSM with multiple physics scheme ensembles

    NASA Astrophysics Data System (ADS)

    Peishu, Zong; Jianping, Tang; Shuyu, Wang; Lingyun, Xie; Jianwei, Yu; Yunqian, Zhu; Xiaorui, Niu; Chao, Li

    2016-06-01

    The parameterization of physical processes is one of the critical elements to properly simulate the regional climate over eastern China. It is essential to conduct detailed analyses on the effect of physical parameterization schemes on regional climate simulation, to provide more reliable regional climate change information. In this paper, we evaluate the 25-year (1983-2007) summer monsoon climate characteristics of precipitation and surface air temperature by using the regional spectral model (RSM) with different physical schemes. The ensemble results using the reliability ensemble averaging (REA) method are also assessed. The result shows that the RSM model has the capacity to reproduce the spatial patterns, the variations, and the temporal tendency of surface air temperature and precipitation over eastern China. And it tends to predict better climatology characteristics over the Yangtze River basin and the South China. The impact of different physical schemes on RSM simulations is also investigated. Generally, the CLD3 cloud water prediction scheme tends to produce larger precipitation because of its overestimation of the low-level moisture. The systematic biases derived from the KF2 cumulus scheme are larger than those from the RAS scheme. The scale-selective bias correction (SSBC) method improves the simulation of the temporal and spatial characteristics of surface air temperature and precipitation and advances the circulation simulation capacity. The REA ensemble results show significant improvement in simulating temperature and precipitation distribution, which have much higher correlation coefficient and lower root mean square error. The REA result of selected experiments is better than that of nonselected experiments, indicating the necessity of choosing better ensemble samples for ensemble.

  14. Regional climate models downscaling in the Alpine area with Multimodel SuperEnsemble

    NASA Astrophysics Data System (ADS)

    Cane, D.; Barbarino, S.; Renier, L. A.; Ronchi, C.

    2012-08-01

    The climatic scenarios show a strong signal of warming in the Alpine area already for the mid XXI century. The climate simulations, however, even when obtained with Regional Climate Models (RCMs), are affected by strong errors where compared with observations, due to their difficulties in representing the complex orography of the Alps and limitations in their physical parametrization. Therefore the aim of this work is reducing these model biases using a specific post processing statistic technique to obtain a more suitable projection of climate change scenarios in the Alpine area. For our purposes we use a selection of RCMs runs from the ENSEMBLES project, carefully chosen in order to maximise the variety of leading Global Climate Models and of the RCMs themselves, calculated on the SRES scenario A1B. The reference observation for the Greater Alpine Area are extracted from the European dataset E-OBS produced by the project ENSEMBLES with an available resolution of 25 km. For the study area of Piedmont daily temperature and precipitation observations (1957-present) were carefully gridded on a 14-km grid over Piedmont Region with an Optimal Interpolation technique. Hence, we applied the Multimodel SuperEnsemble technique to temperature fields, reducing the high biases of RCMs temperature field compared to observations in the control period. We propose also the first application to RCMS of a brand new probabilistic Multimodel SuperEnsemble Dressing technique to estimate precipitation fields, already applied successfully to weather forecast models, with careful description of precipitation Probability Density Functions conditioned to the model outputs. This technique reduces the strong precipitation overestimation by RCMs over the alpine chain and reproduces well the monthly behaviour of precipitation in the control period.

  15. Regional Climate Models Downscaling in the Alpine Area with Multimodel SuperEnsemble

    NASA Astrophysics Data System (ADS)

    Cane, D.; Barbarino, S.; Renier, L.; Ronchi, C.

    2012-04-01

    The climatic scenarios show a strong signal of warming in the Alpine area already for the mid XXI century. The climate simulation, however, even when obtained with Regional Climate Models (RCMs), are affected by strong errors where compared with observations in the control period, due to their difficulties in representing the complex orography of the Alps and limitations in their physical parametrization. In this work we use a selection of RCMs runs from the ENSEMBLES project, carefully chosen in order to maximise the variety of leading Global Climate Models and of the RCMs themselves, calculated on the SRES scenario A1B. The reference observation for the Greater Alpine Area are extracted from the European dataset E-OBS produced by the project ENSEMBLES with an available resolution of 25 km. For the study area of Piemonte daily temperature and precipitation observations (1957-present) were carefully gridded on a 14-km grid over Piemonte Region with an Optimal Interpolation technique. We applied the Multimodel SuperEnsemble technique to temperature fields, reducing the high biases of RCMs temperature field compared to observations in the control period. We propose also the first application to RCMs of a brand new probabilistic Multimodel SuperEnsemble Dressing technique to estimate precipitation fields, already applied successfully to weather forecast models, with careful description of precipitation Probability Density Functions conditioned to the model outputs. This technique reduces the strong precipitation overestimation by RCMs over the alpine chain and reproduces the monthly behaviour of observed precipitation in the control period far better than the direct model outputs.

  16. Two-way against one-way nesting for climate downscaling in Europe and the Mediterranean region using LMDZ4

    NASA Astrophysics Data System (ADS)

    Li, Shan; Li, Laurent; Le Treut, Hervé

    2016-04-01

    In the 21st century, the estimated surface temperature warming projected by General Circulation Models (GCMs) is between 0.3 and 4.8 °C, depending on the scenario considered. GCMs exhibit a good representation of climate on a global scale, but they are not able to reproduce regional climate processes with the same level of accuracy. Society and policymakers need model projections to define climate change adaptation and mitigation policies on a global, regional and local scale. Climate downscaling is mostly conducted with a regional model nested into the outputs of a global model. This one-way nesting approach is generally used in the climate community without feedbacks from Regional Climate Models (RCMs) to GCMs. This lack of interaction between the two models may affect regional modes of variability, in particular those with a boundary conflict. The objective of this study is to evaluate a two-way nesting configuration that makes an interactive coupling between the RCM and the GCM, an approach against the traditional configuration of one-way nesting system. An additional aim of this work is to examine if the two-way nesting system can improve the RCM performance. The atmospheric component of the IPSL integrated climate model (LMDZ) is configured at both regional (LMDZ-regional) and global (LMDZ-global) scales. The two models have the same configuration for the dynamical framework and the physical forcings. The climatology values of sea surface temperature (SST) are prescribed for the two models. The stretched-grid of LMDZ-global is applied to a region defined by Europe, the Mediterranean, North Africa and Western North Atlantic. To ensure a good statistical significance of results, all simulations last at least 80 years. The nesting process of models is performed by a relaxation procedure of a time scale of 90 minutes. In the case of two-way nesting, the exchange between the two models is every two hours. The relaxation procedure induces a boundary conflict

  17. VALUE - Validating and Integrating Downscaling Methods for Climate Change Research

    NASA Astrophysics Data System (ADS)

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

    2013-04-01

    Our understanding of global climate change is mainly based on General Circulation Models (GCMs) with a relatively coarse resolution. Since climate change impacts are mainly experienced on regional scales, high-resolution climate change scenarios need to be derived from GCM simulations by downscaling. Several projects have been carried out over the last years to validate the performance of statistical and dynamical downscaling, yet several aspects have not been systematically addressed: variability on sub-daily, decadal and longer time-scales, extreme events, spatial variability and inter-variable relationships. Different downscaling approaches such as dynamical downscaling, statistical downscaling and bias correction approaches have not been systematically compared. Furthermore, collaboration between different communities, in particular regional climate modellers, statistical downscalers and statisticians has been limited. To address these gaps, the EU Cooperation in Science and Technology (COST) action VALUE (www.value-cost.eu) has been brought into life. VALUE is a research network with participants from currently 23 European countries running from 2012 to 2015. Its main aim is to systematically validate and develop downscaling methods for climate change research in order to improve regional climate change scenarios for use in climate impact studies. Inspired by the co-design idea of the international research initiative "future earth", stakeholders of climate change information have been involved in the definition of research questions to be addressed and are actively participating in the network. The key idea of VALUE is to identify the relevant weather and climate characteristics required as input for a wide range of impact models and to define an open framework to systematically validate these characteristics. Based on a range of benchmark data sets, in principle every downscaling method can be validated and compared with competing methods. The results of

  18. Assessment of climate change downscaling and non-stationarity on the spatial pattern of a mangrove ecosystem in an arid coastal region of southern Iran

    NASA Astrophysics Data System (ADS)

    Etemadi, Halimeh; Samadi, S. Zahra; Sharifikia, Mohammad; Smoak, Joseph M.

    2015-07-01

    Mangrove wetlands exist in the transition zone between terrestrial and marine environments and have remarkable ecological and socio-economic value. This study uses climate change downscaling to address the question of non-stationarity influences on mangrove variations (expansion and contraction) within an arid coastal region. Our two-step approach includes downscaling models and uncertainty assessment, followed by a non-stationary and trend procedure using the Extreme Value Analysis (extRemes code). The Long Ashton Research Station Weather Generator (LARS-WG) model along with two different general circulation model (GCMs) (MIRH and HadCM3) were used to downscale climatic variables during current (1968-2011) and future (2011-2030, 2045-2065, and 2080-2099) periods. Parametric and non-parametric bootstrapping uncertainty tests demonstrated that the LARS-WGS model skillfully downscaled climatic variables at the 95 % significance level. Downscaling results using MIHR model show that minimum and maximum temperatures will increase in the future (2011-2030, 2045-2065, and 2080-2099) during winter and summer in a range of +4.21 and +4.7 °C, and +3.62 and +3.55 °C, respectively. HadCM3 analysis also revealed an increase in minimum (˜+3.03 °C) and maximum (˜+3.3 °C) temperatures during wet and dry seasons. In addition, we examined how much mangrove area has changed during the past decades and, thus, if climate change non-stationarity impacts mangrove ecosystems. Our results using remote sensing techniques and the non-parametric Mann-Whitney two-sample test indicated a sharp decline in mangrove area during 1972,1987, and 1997 periods (p value = 0.002). Non-stationary assessment using the generalized extreme value (GEV) distributions by including mangrove area as a covariate further indicated that the null hypothesis of the stationary climate (no trend) should be rejected due to the very low p values for precipitation (p value = 0.0027), minimum (p value = 0

  19. Assessment of climate change downscaling and non-stationarity on the spatial pattern of a mangrove ecosystem in an arid coastal region of southern Iran

    NASA Astrophysics Data System (ADS)

    Etemadi, Halimeh; Samadi, S. Zahra; Sharifikia, Mohammad; Smoak, Joseph M.

    2016-10-01

    Mangrove wetlands exist in the transition zone between terrestrial and marine environments and have remarkable ecological and socio-economic value. This study uses climate change downscaling to address the question of non-stationarity influences on mangrove variations (expansion and contraction) within an arid coastal region. Our two-step approach includes downscaling models and uncertainty assessment, followed by a non-stationary and trend procedure using the Extreme Value Analysis (extRemes code). The Long Ashton Research Station Weather Generator (LARS-WG) model along with two different general circulation model (GCMs) (MIRH and HadCM3) were used to downscale climatic variables during current (1968-2011) and future (2011-2030, 2045-2065, and 2080-2099) periods. Parametric and non-parametric bootstrapping uncertainty tests demonstrated that the LARS-WGS model skillfully downscaled climatic variables at the 95 % significance level. Downscaling results using MIHR model show that minimum and maximum temperatures will increase in the future (2011-2030, 2045-2065, and 2080-2099) during winter and summer in a range of +4.21 and +4.7 °C, and +3.62 and +3.55 °C, respectively. HadCM3 analysis also revealed an increase in minimum (˜+3.03 °C) and maximum (˜+3.3 °C) temperatures during wet and dry seasons. In addition, we examined how much mangrove area has changed during the past decades and, thus, if climate change non-stationarity impacts mangrove ecosystems. Our results using remote sensing techniques and the non-parametric Mann-Whitney two-sample test indicated a sharp decline in mangrove area during 1972,1987, and 1997 periods ( p value = 0.002). Non-stationary assessment using the generalized extreme value (GEV) distributions by including mangrove area as a covariate further indicated that the null hypothesis of the stationary climate (no trend) should be rejected due to the very low p values for precipitation ( p value = 0.0027), minimum ( p value = 0

  20. A Portable Regional Weather and Climate Downscaling System Using GEOS-5, LIS-6, WRF, and the NASA Workflow Tool

    NASA Astrophysics Data System (ADS)

    Kemp, E. M.; Putman, W. M.; Gurganus, J.; Burns, R. W.; Damon, M. R.; McConaughy, G. R.; Seablom, M. S.; Wojcik, G. S.

    2009-12-01

    We present a regional downscaling system (RDS) suitable for high-resolution weather and climate simulations in multiple supercomputing environments. The RDS is built on the NASA Workflow Tool, a software framework for configuring, running, and managing computer models on multiple platforms with a graphical user interface. The Workflow Tool is used to run the NASA Goddard Earth Observing System Model Version 5 (GEOS-5), a global atmospheric-ocean model for weather and climate simulations down to 1/4 degree resolution; the NASA Land Information System Version 6 (LIS-6), a land surface modeling system that can simulate soil temperature and moisture profiles; and the Weather Research and Forecasting (WRF) community model, a limited-area atmospheric model for weather and climate simulations down to 1-km resolution. The Workflow Tool allows users to customize model settings to user needs; saves and organizes simulation experiments; distributes model runs across different computer clusters (e.g., the DISCOVER cluster at Goddard Space Flight Center, the Cray CX-1 Desktop Supercomputer, etc.); and handles all file transfers and network communications (e.g., scp connections). Together, the RDS is intended to aid researchers by making simulations as easy as possible to generate on the computer resources available. Initial conditions for LIS-6 and GEOS-5 are provided by Modern Era Retrospective-Analysis for Research and Applications (MERRA) reanalysis data stored on DISCOVER. The LIS-6 is first run for 2-4 years forced by MERRA atmospheric analyses, generating initial conditions for the WRF soil physics. GEOS-5 is then initialized from MERRA data and run for the period of interest. Large-scale atmospheric data, sea-surface temperatures, and sea ice coverage from GEOS-5 are used as boundary conditions for WRF, which is run for the same period of interest. Multiply nested grids are used for both LIS-6 and WRF, with the innermost grid run at a resolution sufficient for typical

  1. Downscaling Climate Data from Distributed Archives

    NASA Astrophysics Data System (ADS)

    Radhakrishnan, A.; Guentchev, G.; Cinquini, L.; Schweitzer, R.; Nikonov, S.; Balaji, V.

    2013-12-01

    Model refinement -- numerical estimates of climate change at higher resolution than climate models are currently capable of producing -- is an essential weapon in the arsenal of decision makers and researchers in climate change. We describe here steps toward a general-purpose system for model refinement. We envision a system wherein multiple climate models, alone or in combination, can be used as predictors; multiple refinement methods, alone or in combination, can be deployed and trained, including evaluation within a perfect-model framework, described below; time periods and locations of training can be chosen at will; and providing all of these options as standard web services within the Earth System Grid Federation (ESGF) global data infrastructure for the distribution of climate model output. The perfect-model framework for systematic testing of model refinement using empirical-statistical downscaling (ESD) schemes is being developed at NOAA/GFDL under the National Climate Predictions and Projections Platform (NCPP) project. It uses the approach that Laprise and collaborators call the "big-brother" framework for evaluating dynamical downscaling. High-resolution model output is used as a "nature run" and used in place of observations to train the ESD scheme under testing. The data is interpolated to a coarse grid (the "little brother") and the ESD scheme attempts to downscale and bias-correct the "future", i.e beyond the period of training. The output of ESD can then be rigorously compared to the original nature run on a chosen list of metrics. Initial work was performed in collaboration with Texas Tech University: the high-resolution time-slice models that GFDL submitted to CMIP5 are used as training sets for the downscaling methods developed by Katharine Hayhoe and collaborators. The approach is being extended to using other downscaling schemes, such as BCSD, Delta, quantile mapping, constructed analogs, and machine learning algorithms; and in future to using

  2. Credibility of statistical downscaling under nonstationary climate

    NASA Astrophysics Data System (ADS)

    Salvi, Kaustubh; Ghosh, Subimal; Ganguly, Auroop R.

    2016-03-01

    Statistical downscaling (SD) establishes empirical relationships between coarse-resolution climate model simulations with higher-resolution climate variables of interest to stakeholders. These statistical relations are estimated based on historical observations at the finer resolutions and used for future projections. The implicit assumption is that the SD relations, extracted from data are stationary or remain unaltered, despite non-stationary change in climate. The validity of this assumption relates directly to the credibility of SD. Falsifiability of climate projections is a challenging proposition. Calibration and verification, while necessary for SD, are unlikely to be able to reproduce the full range of behavior that could manifest at decadal to century scale lead times. We propose a design-of-experiments (DOE) strategy to assess SD performance under nonstationary climate and evaluate the strategy via a transfer-function based SD approach. The strategy relies on selection of calibration and validation periods such that they represent contrasting climatic conditions like hot-versus-cold and ENSO-versus-non-ENSO years. The underlying assumption is that conditions such as warming or predominance of El Niño may be more prevalent under climate change. In addition, two different historical time periods are identified, which resemble pre-industrial and the most severe future emissions scenarios. The ability of the empirical relations to generalize under these proxy conditions is considered an indicator of their performance under future nonstationarity. Case studies over two climatologically disjoint study regions, specifically India and Northeast United States, reveal robustness of DOE in identifying the locations where nonstationarity prevails as well as the role of effective predictor selection under nonstationarity.

  3. Physically Based Global Downscaling: Regional Evaluation

    SciTech Connect

    Ghan, Steven J.; Shippert, Timothy R.; Fox, Jared

    2006-02-01

    The climate simulated by a global atmosphere/land model with a physically-based subgrid orography scheme is evaluated in ten selected regions. Climate variables simulated for each of multiple elevation classes within each grid cell are mapped according the high-resolution distribution of surface elevation in each region. Comparison of the simulated annual mean climate with gridded observations leads to the following conclusions. At low to moderate elevations the downscaling scheme correctly simulates increasing precipitation, decreasing temperature, and increasing snow with increasing elevation within regions smaller than 100 km. At high elevations the downscaling scheme correctly simulates a decrease in precipitation with increasing elevation. Too little precipitation is simulated on the windward side of mountain ranges and too much precipitation is simulated on the lee side. The simulated sensitivity of surface air temperature to surface elevation is too strong, particularly in valleys influenced by drainage circulations. Observations show little evidence of a “snow shadow”, so the neglect of the subgrid rainshadow does not produce an unrealistic simulation of the snow distribution. Summertime snow area, which is a proxy for land ice, is much larger than observed. Summertime snow water equivalent is far less than the observed thickness of glaciers because a 1 m upper bound on snow water is applied to the simulations and because snow transport by slides is neglected. The 1 m upper bound on snow water equivalent also causes an underestimate of seasonal snow water during late winter, compared with gridded station measurements. Potential solutions to these problems are discussed.

  4. Downscaling the climate change for oceans around Australia

    NASA Astrophysics Data System (ADS)

    Chamberlain, M. A.; Sun, C.; Matear, R. J.; Feng, M.; Phipps, S. J.

    2012-09-01

    At present, global climate models used to project changes in climate poorly resolve mesoscale ocean features such as boundary currents and eddies. These missing features may be important to realistically project the marine impacts of climate change. Here we present a framework for dynamically downscaling coarse climate change projections utilising a near-global ocean model that resolves these features in the Australasian region, with coarser resolution elsewhere. A time-slice projection for a 2060s ocean was obtained by adding climate change anomalies to initial conditions and surface fluxes of a near-global eddy-resolving ocean model. Climate change anomalies are derived from the differences between present and projected climates from a coarse global climate model. These anomalies are added to observed fields, thereby reducing the effect of model bias from the climate model. The downscaling model used here is ocean-only and does not include the effects that changes in the ocean state will have on the atmosphere and air-sea fluxes. We use restoring of the sea surface temperature and salinity to approximate real-ocean feedback on heat flux and to keep the salinity stable. Extra experiments with different feedback parameterisations are run to test the sensitivity of the projection. Consistent spatial differences emerge in sea surface temperature, salinity, stratification and transport between the downscaled projections and those of the climate model. Also, the spatial differences become established rapidly (< 3 yr), indicating the importance of mesoscale resolution. However, the differences in the magnitude of the difference between experiments show that feedback of the ocean onto the air-sea fluxes is still important in determining the state of the ocean in these projections. Until such a time when it is feasible to regularly run a global climate model with eddy resolution, our framework for ocean climate change downscaling provides an attractive way to explore the

  5. Downscaling approach to develop future sub-daily IDF relations for Canberra Airport Region, Australia

    NASA Astrophysics Data System (ADS)

    Herath, H. M. S. M.; Sarukkalige, P. R.; Nguyen, V. T. V.

    2015-06-01

    Downscaling of climate projections is the most adopted method to assess the impacts of climate change at regional and local scale. In the last decade, downscaling techniques which provide reasonable improvement to resolution of General Circulation Models' (GCMs) output are developed in notable manner. Most of these techniques are limited to spatial downscaling of GCMs' output and still there is a high demand to develop temporal downscaling approaches. As the main objective of this study, combined approach of spatial and temporal downscaling is developed to improve the resolution of rainfall predicted by GCMs. Canberra airport region is subjected to this study and the applicability of proposed downscaling approach is evaluated for Sydney, Melbourne, Brisbane, Adelaide, Perth and Darwin regions. Statistical Downscaling Model (SDSM) is used to spatial downscaling and numerical model based on scaling invariant concept is used to temporal downscaling of rainfalls. National Centre of Environmental Prediction (NCEP) data is used in SDSM model calibration and validation. Regression based bias correction function is used to improve the accuracy of downscaled annual maximum rainfalls using HadCM3-A2. By analysing the non-central moments of observed rainfalls, single time regime (from 30 min to 24 h) is identified which exist scaling behaviour and it is used to estimate the sub daily extreme rainfall depths from daily downscaled rainfalls. Finally, as the major output of this study, Intensity Duration Frequency (IDF) relations are developed for the future periods of 2020s, 2050s and 2080s in the context of climate change.

  6. Downscaling and projection of precipitation from general circulation model predictors in an equatorial climate region by the automated regression-based statistical method

    NASA Astrophysics Data System (ADS)

    Amin, Mohd Zaki M.; Islam, Tanvir; Ishak, Asnor M.

    2014-10-01

    The authors have applied an automated regression-based statistical method, namely, the automated statistical downscaling (ASD) model, to downscale and project the precipitation climatology in an equatorial climate region (Peninsular Malaysia). Five precipitation indices are, principally, downscaled and projected: mean monthly values of precipitation (Mean), standard deviation (STD), 90th percentile of rain day amount, percentage of wet days (Wet-day), and maximum number of consecutive dry days (CDD). The predictors, National Centers for Environmental Prediction (NCEP) products, are taken from the daily series reanalysis data, while the global climate model (GCM) outputs are from the Hadley Centre Coupled Model, version 3 (HadCM3) in A2/B2 emission scenarios and Third-Generation Coupled Global Climate Model (CGCM3) in A2 emission scenario. Meanwhile, the predictand data are taken from the arithmetically averaged rain gauge information and used as a baseline data for the evaluation. The results reveal, from the calibration and validation periods spanning a period of 40 years (1961-2000), the ASD model is capable to downscale the precipitation with reasonable accuracy. Overall, during the validation period, the model simulations with the NCEP predictors produce mean monthly precipitation of 6.18-6.20 mm/day (root mean squared error 0.78 and 0.82 mm/day), interpolated, respectively, on HadCM3 and CGCM3 grids, in contrast to 6.00 mm/day as observation. Nevertheless, the model suffers to perform reasonably well at the time of extreme precipitation and summer time, more specifically to generate the CDD and STD indices. The future projections of precipitation (2011-2099) exhibit that there would be an increase in the precipitation amount and frequency in most of the months. Taking the 1961-2000 timeline as the base period, overall, the annual mean precipitation would indicate a surplus projection by nearly 14~18 % under both GCM output cases (HadCM3 A2/B2 scenarios and

  7. Dynamical Downscaling Technique for Global Climate Model

    NASA Astrophysics Data System (ADS)

    Yoshimura, K.; Kanamitsu, M.

    2007-12-01

    Aiming at producing higher resolution global reanalysis datasets from coarse 200 km resolution reanalysis, a global version of the dynamical downscaling using a global spectral model (GSM) is developed. A variant of spectral nudging, the scale-selective bias correction (SSBC) developed for regional models is modified in the following manner to adapt it to the global domain; 1) temperature is nudged in addition to the zonal and meridional components of winds, and 2) humidity is excluded from any nudging or correction. The downscaling was performed using T248L28 (about 50 km resolution) global model for 2001, driven by NCEP/NCAR Reanalysis 2 (T62L28 resolution). Evaluation with high-resolution observations showed that the monthly averaged surface temperature and daily variation of precipitation become better than the Reanalysis over the globe. It was found that humidity plays a significant role for a significant positive bias of global precipitation in the downscaled simulation. Over North America, surface wind speed and temperature become better, and over Japan, the diurnal pattern of surface temperature is much improved, as are wind speed and precipitation, but not humidity. This study suggests that the global downscaling is a viable and economical method to obtain high- resolution reanalysis without re-running a very expensive high-resolution full data assimilation.

  8. Does Dynamical Downscaling Introduce Novel Information in Climate Model Simulations of Recipitation Change over a Complex Topography Region?

    NASA Technical Reports Server (NTRS)

    Tselioudis, George; Douvis, Costas; Zerefos, Christos

    2012-01-01

    Current climate and future climate-warming runs with the RegCM Regional Climate Model (RCM) at 50 and 11 km-resolutions forced by the ECHAM GCM are used to examine whether the increased resolution of the RCM introduces novel information in the precipitation field when the models are run for the mountainous region of the Hellenic peninsula. The model results are inter-compared with the resolution of the RCM output degraded to match that of the GCM, and it is found that in both the present and future climate runs the regional models produce more precipitation than the forcing GCM. At the same time, the RCM runs produce increases in precipitation with climate warming even though they are forced with a GCM that shows no precipitation change in the region. The additional precipitation is mostly concentrated over the mountain ranges, where orographic precipitation formation is expected to be a dominant mechanism. It is found that, when examined at the same resolution, the elevation heights of the GCM are lower than those of the averaged RCM in the areas of the main mountain ranges. It is also found that the majority of the difference in precipitation between the RCM and the GCM can be explained by their difference in topographic height. The study results indicate that, in complex topography regions, GCM predictions of precipitation change with climate warming may be dry biased due to the GCM smoothing of the regional topography.

  9. Simulating expected elevation dependent warming (EDW) mechanisms in a dynamically-downscaled perturbed physics climate model ensemble over the Himalayan region

    NASA Astrophysics Data System (ADS)

    Forsythe, N. D.; Blenkinsop, S.; Fowler, H. J.; Betts, R.; Janes, T.

    2014-12-01

    Current theoretical climatology suggests three key climate processes - snow cover contribution to surface albedo, cloud cover prevalence and near surface water vapour - influencing the surface energy balance are expected to exhibit elevation-gradients in global warming-driven changes. These gradients are in turn expected to act as mechanisms contributing to EDW. This study examines the simulation of these mechanisms and their influence on projections of EDW in a dynamically downscaled transient perturbed physics ensemble (PPE). The downscaling experiment in question is the Hadley Centre Regional Model version 3 PRECIS configuration (HadRM3P) 25km simulation over the South Asian domain driven by the MetOffice 17-member QUMP (Quantifying Uncertainty in Model Projections) ensemble of the Hadley Centre Climate Model version 3 (HadCM3). Use of the multi-member PPE enables quantification of uncertainty in projected changes in climate variables - albedo, cloud cover, water vapour and near surface temperature - while the spatial resolution of a RCM improves insight into the role of elevation in projected rates of change. This work specifically addresses the Regional Climate Model (RCM) representation of expected EDW mechanisms by calculating vertical profiles (relative to modelled surface elevation of downscaled grid cells) for changes in: [1] albedo, i.e. the ratio of future to control period albedo where albedo is calculated as one minus the ratio of absorbed surface solar radiation to incoming surface solar radiation; [2] shortwave cloud radiative effect (CRE), i.e. the ratio of future to present CRE where CRE is calculated as incoming "top of atmosphere" shortwave radiation minus incoming surface shortwave radiation; [3] near surface water vapour -- in terms of specific humidity (Qair) - and related down-welling longwave radiation, but because previous EDW research has shown non-linearity in Qair radiative influence, changes in Qair is evaluated in both delta (additive

  10. Investigating Downscaling Methods and Evaluating Climate Models for Use in Estimating Regional Water Resources in Mountainous Regions under Changing Climatic Conditions

    NASA Technical Reports Server (NTRS)

    Frei, Allan; Nolin, Anne W.; Serreze, Mark C.; Armstrong, Richard L.; McGinnis, David L.; Robinson, David A.

    2004-01-01

    The purpose of this three-year study is to develop and evaluate techniques to estimate the range of potential hydrological impacts of climate change in mountainous areas. Three main objectives are set out in the proposal. (1) To develop and evaluate transfer functions to link tropospheric circulation to regional snowfall. (2) To evaluate a suite of General Circulation Models (GCMs) for use in estimating synoptic scale circulation and the resultant regional snowfall. And (3) to estimate the range of potential hydrological impacts of changing climate in the two case study areas: the Upper Colorado River basin, and the Catskill Mountains of southeastern New York State. Both regions provide water to large populations.

  11. Dynamical downscaling of historical climate over CORDEX East Asia domain: A comparison of regional ocean-atmosphere coupled model to stand-alone RCM simulations

    NASA Astrophysics Data System (ADS)

    Zou, Liwei; Zhou, Tianjun; Peng, Dongdong

    2016-02-01

    The FROALS (flexible regional ocean-atmosphere-land system) model, a regional ocean-atmosphere coupled model, has been applied to the Coordinated Regional Downscaling Experiment (CORDEX) East Asia domain. Driven by historical simulations from a global climate system model, dynamical downscaling for the period from 1980 to 2005 has been conducted at a uniform horizontal resolution of 50 km. The impacts of regional air-sea couplings on the simulations of East Asian summer monsoon rainfall have been investigated, and comparisons have been made to corresponding simulations performed using a stand-alone regional climate model (RCM). The added value of the FROALS model with respect to the driving global climate model was evident in terms of both climatology and the interannual variability of summer rainfall over East China by the contributions of both the high horizontal resolution and the reasonably simulated convergence of the moisture fluxes. Compared with the stand-alone RCM simulations, the spatial pattern of the simulated low-level monsoon flow over East Asia and the western North Pacific was improved in the FROALS model due to its inclusion of regional air-sea coupling. The results indicated that the simulated sea surface temperature (SSTs) resulting from the regional air-sea coupling were lower than those derived directly from the driving global model over the western North Pacific north of 15°N. These colder SSTs had both positive and negative effects. On the one hand, they strengthened the western Pacific subtropical high, which improved the simulation of the summer monsoon circulation over East Asia. On the other hand, the colder SSTs suppressed surface evaporation and favored weaker local interannual variability in the SST, which led to less summer rainfall and weaker interannual rainfall variability over the Korean Peninsula and Japan. Overall, the reference simulation performed using the FROALS model is reasonable in terms of rainfall over the land area of

  12. Climate change scenarios of temperature and precipitation over five Italian regions for the period 2021-2050 obtained by statistical downscaling models

    NASA Astrophysics Data System (ADS)

    Tomozeiu, R.; Tomei, F.; Villani, G.; Pasqui, M.

    2010-09-01

    Climate change scenarios of seasonal maximum, minimum temperature and precipitation in five Italian regions, over the period 2021-2050 against 1961-1990 are assessed. The regions selected by the AGROSCENARI project are important from the local agricultural practises and are situated as follows: in the Northern Italy - Po valley and hilly area of Faenza; in Central part of Italy- Marche, Beneventano and Destra Sele, and in Sardinia Island - Oristano. A statistical downscaling technique applied to the ENSEMBLES global climate simulations, A1B scenario, is used to reach this objective. The method consists of a multivariate regression, based on Canonical Correlation Analysis, using as possible predictors mean sea level pressure, geopotential height at 500hPa and temperature at 850 hPa. The observational data set (predictands) for the selected regions is composed by a reconstruction of minimum, maximum temperature and precipitation daily data on a regular grid with a spatial resolution of 35 km, for 1951-2009 period (managed by the Meteorological and Climatological research unit for agriculture - Agricultural Research Council, CRA - CMA). First, a set-up of statistical model has been made using predictors from ERA40 reanalysis and the seasonal indices of temperature and precipitation from local scale, 1958-2002 period. Then, the statistical downscaling model has been applied to the predictors derived from the ENSEMBLES global climate models, A1B scenario, in order to obtain climate change scenario of temperature and precipitation at local scale, 2021-2050 period. The projections show that increases could be expected to occur under scenario conditions in all seasons, in both minimum and maximum temperature. The magnitude of changes is more intense during summer when the changes could reach values around 2°C for minimum and maximum temperature. In the case of precipitation, the pattern of changes is more complex, different from season to season and over the regions, a

  13. 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.

  14. Downscaling GISS ModelE Boreal Summer Climate over Africa

    NASA Technical Reports Server (NTRS)

    Druyan, Leonard M.; Fulakeza, Matthew

    2015-01-01

    The study examines the perceived added value of downscaling atmosphere-ocean global climate model simulations over Africa and adjacent oceans by a nested regional climate model. NASA/Goddard Institute for Space Studies (GISS) coupled ModelE simulations for June- September 1998-2002 are used to form lateral boundary conditions for synchronous simulations by the GISS RM3 regional climate model. The ModelE computational grid spacing is 2deg latitude by 2.5deg longitude and the RM3 grid spacing is 0.44deg. ModelE precipitation climatology for June-September 1998-2002 is shown to be a good proxy for 30-year means so results based on the 5-year sample are presumed to be generally representative. Comparison with observational evidence shows several discrepancies in ModelE configuration of the boreal summer inter-tropical convergence zone (ITCZ). One glaring shortcoming is that ModelE simulations do not advance the West African rain band northward during the summer to represent monsoon precipitation onset over the Sahel. Results for 1998-2002 show that onset simulation is an important added value produced by downscaling with RM3. ModelE Eastern South Atlantic Ocean computed sea-surface temperatures (SST) are some 4 K warmer than reanalysis, contributing to large positive biases in overlying surface air temperatures (Tsfc). ModelE Tsfc are also too warm over most of Africa. RM3 downscaling somewhat mitigates the magnitude of Tsfc biases over the African continent, it eliminates the ModelE double ITCZ over the Atlantic and it produces more realistic orographic precipitation maxima. Parallel ModelE and RM3 simulations with observed SST forcing (in place of the predicted ocean) lower Tsfc errors but have mixed impacts on circulation and precipitation biases. Downscaling improvements of the meridional movement of the rain band over West Africa and the configuration of orographic precipitation maxima are realized irrespective of the SST biases.

  15. Downscaling GISS ModelE boreal summer climate over Africa

    NASA Astrophysics Data System (ADS)

    Druyan, Leonard M.; Fulakeza, Matthew

    2015-11-01

    The study examines the perceived added value of downscaling atmosphere-ocean global climate model simulations over Africa and adjacent oceans by a nested regional climate model. NASA/Goddard Institute for Space Studies (GISS) coupled ModelE simulations for June-September 1998-2002 are used to form lateral boundary conditions for synchronous simulations by the GISS RM3 regional climate model. The ModelE computational grid spacing is 2° latitude by 2.5° longitude and the RM3 grid spacing is 0.44°. ModelE precipitation climatology for June-September 1998-2002 is shown to be a good proxy for 30-year means so results based on the 5-year sample are presumed to be generally representative. Comparison with observational evidence shows several discrepancies in ModelE configuration of the boreal summer inter-tropical convergence zone (ITCZ). One glaring shortcoming is that ModelE simulations do not advance the West African rain band northward during the summer to represent monsoon precipitation onset over the Sahel. Results for 1998-2002 show that onset simulation is an important added value produced by downscaling with RM3. ModelE Eastern South Atlantic Ocean computed sea-surface temperatures (SST) are some 4 K warmer than reanalysis, contributing to large positive biases in overlying surface air temperatures (Tsfc). ModelE Tsfc are also too warm over most of Africa. RM3 downscaling somewhat mitigates the magnitude of Tsfc biases over the African continent, it eliminates the ModelE double ITCZ over the Atlantic and it produces more realistic orographic precipitation maxima. Parallel ModelE and RM3 simulations with observed SST forcing (in place of the predicted ocean) lower Tsfc errors but have mixed impacts on circulation and precipitation biases. Downscaling improvements of the meridional movement of the rain band over West Africa and the configuration of orographic precipitation maxima are realized irrespective of the SST biases.

  16. 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

  17. Evaluation of near-surface temperature, humidity, and equivalent temperature from regional climate models applied in type II downscaling

    NASA Astrophysics Data System (ADS)

    Pryor, S. C.; Schoof, J. T.

    2016-04-01

    Atmosphere-surface interactions are important components of local and regional climates due to their key roles in dictating the surface energy balance and partitioning of energy transfer between sensible and latent heat. The degree to which regional climate models (RCMs) represent these processes with veracity is incompletely characterized, as is their ability to capture the drivers of, and magnitude of, equivalent temperature (Te). This leads to uncertainty in the simulation of near-surface temperature and humidity regimes and the extreme heat events of relevance to human health, in both the contemporary and possible future climate states. Reanalysis-nested RCM simulations are evaluated to determine the degree to which they represent the probability distributions of temperature (T), dew point temperature (Td), specific humidity (q) and Te over the central U.S., the conditional probabilities of Td|T, and the coupling of T, q, and Te to soil moisture and meridional moisture advection within the boundary layer (adv(Te)). Output from all RCMs exhibits discrepancies relative to observationally derived time series of near-surface T, q, Td, and Te, and use of a single layer for soil moisture by one of the RCMs does not appear to substantially degrade the simulations of near-surface T and q relative to RCMs that employ a four-layer soil model. Output from MM5I exhibits highest fidelity for the majority of skill metrics applied herein, and importantly most realistically simulates both the coupling of T and Td, and the expected relationships of boundary layer adv(Te) and soil moisture with near-surface T and q.

  18. 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.; Goddard, Lisa; Ray, Andrea; Buja, Lawrence; Ammann, Caspar

    2013-01-01

    Suppose you are a city planner, regional water manager, or wildlife conservation specialist who is asked to include the potential impacts of climate variability and change in your risk management and planning efforts. What climate information would you use? The choice is often regional or local climate projections downscaled from global climate models (GCMs; also known as general circulation models) to include detail at spatial and temporal scales that align with those of the decision problem. A few years ago this information was hard to come by. Now there is Web-based access to a proliferation of high-resolution climate projections derived with differing downscaling methods.

  19. 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.

  20. Influences of Regional Climate Change on Air Quality across the Continental U.S. Projected from Downscaling IPCC ARS Simulations

    EPA Science Inventory

    Projecting climate change scenarios to local scales is important for understanding, mitigating, and adapting to the effects of climate change on society and the environment. Many of the global climate models (GCMs) that are participating in the Intergovernmental Panel on Climate ...

  1. 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.

  2. VALUE - A Framework to Validate Downscaling Approaches for Climate Change Studies

    NASA Astrophysics Data System (ADS)

    Maraun, Douglas; Widmann, Martin; Gutiérrez, José M.; Kotlarski, Sven; Chandler, Richard E.; Hertig, Elke; Wibig, Joanna; Huth, Radan; Wilke, Renate A. I.

    2015-04-01

    VALUE is an open European network to validate and compare downscaling methods for climate change research. VALUE aims to foster collaboration and knowledge exchange between climatologists, impact modellers, statisticians, and stakeholders to establish an interdisciplinary downscaling community. 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. Here, we present the key ingredients of this framework. 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. Several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur: what is the isolated downscaling skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Do methods fail in representing regional climate change? How is the overall representation of regional climate, including errors inherited from global climate models? The framework will be the basis for a comprehensive community-open downscaling intercomparison study, but is intended also to provide general guidance for other validation studies.

  3. VALUE: A framework to validate downscaling approaches for climate change studies

    NASA Astrophysics Data System (ADS)

    Maraun, Douglas; Widmann, Martin; Gutiérrez, José M.; Kotlarski, Sven; Chandler, Richard E.; Hertig, Elke; Wibig, Joanna; Huth, Radan; Wilcke, Renate A. I.

    2015-01-01

    VALUE is an open European network to validate and compare downscaling methods for climate change research. VALUE aims to foster collaboration and knowledge exchange between climatologists, impact modellers, statisticians, and stakeholders to establish an interdisciplinary downscaling community. 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. In this paper, we present the key ingredients of this framework. 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. Several experiments have been designed to isolate specific points in the downscaling procedure where problems may occur: what is the isolated downscaling skill? How do statistical and dynamical methods compare? How do methods perform at different spatial scales? Do methods fail in representing regional climate change? How is the overall representation of regional climate, including errors inherited from global climate models? The framework will be the basis for a comprehensive community-open downscaling intercomparison study, but is intended also to provide general guidance for other validation studies.

  4. Evaluation of a Technique for Downscaling Climate-Model Output in Mountainous Terrain Using Local Topographic Lapse Rates

    NASA Astrophysics Data System (ADS)

    Praskievicz, S. J.

    2015-12-01

    One of the challenges in using general circulation model (GCM) output is the need to downscale beyond the model's coarse spatial grid in order to infer climate at any particular location. Traditionally, downscaling has been achieved either dynamically, through regional climate models (RCMs), or statistically, through empirical relationships between predictor variables in the GCM and observed variables. In mountainous terrain, elevation is one of the primary controls on temperature and precipitation at the local scale, which provides the potential for topographic variables to be used to adjust climate-model output. Here, local topographic lapse rates (LTLR) were estimated from gridded climate data for the Pacific Northwest, and those lapse rates were used to downscale RCM output. Skill scores were calculated for the LTLR-downscaled climate-model output relative to an existing set of model output downscaled using the well-established statistical downscaling technique of bias-corrected constructed analogs (BCCA). Spatial and temporal patterns in forecast skill and in bias of the LTLR downscaling method were also examined. The results indicate that the LTLR method performs well in the mountainous study region relative to the BCCA method. There is variability in the forecast skill, however, most notably the LTLR downscaling technique's better performance in the eastern part of the study region for temperature and in the western part of the study region for precipitation. LTLR downscaling offers a promising method for downscaling climate-model output in regions in which elevation is a strong control on climate, particularly for studying impacts of past or future climate change.

  5. Evaluating climate change effects on runoff by statistical downscaling and hydrological model GR2M

    NASA Astrophysics Data System (ADS)

    Okkan, Umut; Fistikoglu, Okan

    2014-07-01

    The main purpose of this study is to evaluate the impacts of climate change on Izmir-Tahtali freshwater basin, which is located in the Aegean Region of Turkey. For this purpose, a developed strategy involving statistical downscaling and hydrological modeling is illustrated through its application to the basin. Prior to statistical downscaling of precipitation and temperature, the explanatory variables are obtained from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis data set. All possible regression approach is used to establish the most parsimonious relationship between precipitation, temperature, and climatic variables. Selected predictors have been used in training of artificial neural networks-based downscaling models and the trained models with the obtained relationships have been operated to produce scenario precipitation and temperature from the simulations of third Generation Coupled Climate Model. Biases from downscaled outputs have been reduced after downscaling process. Finally, the corrected downscaled outputs have been transformed to runoff by means of a monthly parametric hydrological model GR2M to assess the probable impacts of temperature and precipitation changes on runoff. According to the A1B climate scenario results, statistically significant trends are foreseen for precipitation, temperature, and runoff in the study basin.

  6. 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.

  7. Dynamical Downscaling of Climate Change over the Hawaiian Islands

    NASA Astrophysics Data System (ADS)

    Wang, Y.; Zhang, C.; Hamilton, K. P.; Lauer, A.

    2015-12-01

    The pseudo-global-warming (PGW) method was applied to the Hawaii Regional Climate Model (HRCM) to dynamically downscale the projected climate in the late 21st century over the Hawaiian Islands. The initial and boundary conditions were adopted from MERRA reanalysis and NOAA SST data for the present-day simulations. The global warming increments constructed from the CMIP3 multi-model ensemble mean were added to the reanalysis and SST data to perform the future climate simulations. We found that the Hawaiian Islands are vulnerable to global warming effects and the changes are diverse due to the varied topography. The windward side will have more clouds and receive more rainfall. The increase of the moisture in the boundary layer makes the major contribution. On the contrary, the leeward side will have less clouds and rainfall. The clouds and rain can slightly slow down the warming trend over the windward side. The temperature increases almost linearly with the terrain height. Cloud base and top heights will slightly decline in response to the slightly lower trade wind inversion base height, while the trade wind occurrence frequency will increase by about 8% in the future. More extreme rainfall events will occur in the warming climate over the Hawaiian Islands. And the snow cover on the top of Mauna Kea and Mauna Loa will nearly disappear in the future winter.

  8. 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.

  9. A robust framework for probabilistic precipitations downscaling from an ensemble of climate predictions applied to Switzerland

    NASA Astrophysics Data System (ADS)

    Beuchat, X.; Schaefli, B.; Soutter, M.; Mermoud, A.

    2012-04-01

    Rainfall is poorly modeled by general circulation models (GCMs) and has to be downscaled to drive local hydrological impact studies. Such downscaling methods should be robust and accurate (to handle e.g. extreme events), but the non-continuous and highly non-linear nature of rainfall makes this task particularly challenging. Building upon state-of-the-art methods, we propose a robust probabilistic framework to downscale local daily rainfall series from an ensemble of climate simulations. The downscaling is based on Generalized Linear Models (GLMs) that relate monthly GCM-scale atmospheric variables to local daily rainfall series. The other key elements of the framework are i) a cross-validation step to ensure that the fitted models are correctly conditioned by the climate variables, and ii) a statistical procedure to test the stationarity assumption that the statistical relationships identified for the reference period also hold in a future perturbed climate. Additionally, we propose a strategy to downweight poor-performing GCMs-GLMs couples. The methodology is assessed at 27 locations covering Switzerland and is shown to perform well in reproducing historical rainfall statistics, including extremes and inter-annual variability, and their projections are consistent with the simulations of physically-based dynamical models. Although the downscaling models were fitted for each of the 27 sites independently, their projections follow a spatially coherent pattern, exhibiting regions with different climate change impacts, which we identified using an original visualization method based on heatmaps.

  10. 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

  11. 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

    2016-03-01

    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

  12. 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.

  13. Selecting downscaled climate projections for water resource impacts and adaptation

    NASA Astrophysics Data System (ADS)

    Vidal, Jean-Philippe; Hingray, Benoît

    2015-04-01

    Increasingly large ensembles of global and regional climate projections are being produced and delivered to the climate impact community. However, such an enormous amount of information can hardly been dealt with by some impact models due to computational constraints. Strategies for transparently selecting climate projections are therefore urgently needed for informing small-scale impact and adaptation studies and preventing potential pitfalls in interpreting ensemble results from impact models. This work proposes results from a selection approach implemented for an integrated water resource impact and adaptation study in the Durance river basin (Southern French Alps). A large ensemble of 3000 daily transient gridded climate projections was made available for this study. It was built from different runs of 4 ENSEMBLES Stream2 GCMs, statistically downscaled by 3 probabilistic methods based on the K-nearest neighbours resampling approach (Lafaysse et al., 2014). The selection approach considered here exemplifies one of the multiple possible approaches described in a framework for identifying tailored subsets of climate projections for impact and adaptation studies proposed by Vidal & Hingray (2014). It was chosen based on the specificities of both the study objectives and the characteristics of the projection dataset. This selection approach aims at propagating as far as possible the relative contributions of the four different sources of uncertainties considered, namely GCM structure, large-scale natural variability, structure of the downscaling method, and catchment-scale natural variability. Moreover, it took the form of a hierarchical structure to deal with the specific constraints of several types of impact models (hydrological models, irrigation demand models and reservoir management models). The implemented 3-layer selection approach is therefore mainly based on conditioned Latin Hypercube sampling (Christierson et al., 2012). The choice of conditioning

  14. Accounting for Global Climate Model Projection Uncertainty in Modern Statistical Downscaling

    SciTech Connect

    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 the 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

  15. 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.

  16. The Southeast Asia Regional Climate Downscaling (SEACLID) / CORDEX Southeast Asia Project and The Results of Its Sensitivity Experiments of RegCM4 Cumulus and Ocean Fluxes Parameterization Schemes on Temperature and Extremes.

    NASA Astrophysics Data System (ADS)

    Tangang, Fredolin; Juneng, Liew; Cruz, Faye; Narisma, Gemma; Dado, Julie; Van, Tan-Phan; Ngo-Duc, Thanh; Trinh-Tuan, Long; Nguyen-Xuan, Thanh; Santisirisomboon, Jerasorn; Singhruck, Patama; Gunawan, Dodo; Aldrian, Edvin

    2015-04-01

    The Southeast Asia (SEA) region is one of the more vulnerable regions to the impacts of climate change because of the large population exposed to climate-related hazards, mostly living in countries with low adaptive capabilities. In order to adequately prepare and adapt to these future climate change impacts, it is therefore crucial for high-resolution climate projections to be available for this region. The Southeast Asia Regional Climate Downscaling/CORDEX Southeast Asia (SEACLID/CORDEX-SEA) project aims to provide these projections through a collaborative effort in regional climate downscaling. As a first step, model simulations with the 4th version of Regional Climate Model system (RegCM4) developed by International Centre for Theoretical Physics (ICTP) were performed for the SEA domain (80°E-145°E; 15°S-40°N) at 36 km spatial resolution, to determine an optimal configuration of the model for the region. Using the ECMWF ERA Interim data as boundary condition, a total of 18 sensitivity experiments were done with different cumulus parameterization and ocean flux schemes for the period of 1989-2008. In this study, the model's performance in simulating mean temperature is evaluated against APHRODITE, a gridded observed temperature dataset. Initial results showed that RegCM4 tends to enhance the cold bias from the boundary forcing. There is also a consistent cold bias among all simulations over the Tibetan plateau and Indochina, especially during the boreal winter. Consequently, simulations had the smallest biases during boreal summer. The correlation of the model with the observed data is high over the northern half of the region, in contrast with the low correlation over the southern half, which may be due to uncertainties in the APHRODITE dataset over this region. Consistent with the spatial analysis, the analysis of the regional means indicates an overall better performance of the MIT Emanuel scheme, in terms of seasonality and spatial distribution. The

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

    SciTech Connect

    Wood, Andrew W; Leung, Lai R; Sridhar, V; Lettenmaier, D P

    2004-01-01

    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 disaggregation (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

  18. Downscaling transient climate change scenarios for water resource management

    NASA Astrophysics Data System (ADS)

    Blenkinsop, S.; Burton, A.; Fowler, H. J.; Harpham, C.; Goderniaux, P.

    2009-04-01

    The management of hydrological systems in response to climate change requires reliable projections at relevant time horizons and at appropriate spatial scales. Furthermore the robustness of decisions is dependent on both the uncertainty of future climate scenarios and climatic variability. The current generation of climate models do not adequately meet these requirements for hydrological impacts assessments and so new techniques are required to meet the needs of hydrologists and water resource managers. Here, a new methodology is described and implemented which addresses these issues by adopting a hybrid dynamical and stochastic downscaling approach to produce a multi-model ensemble of transient scenarios of daily weather variables. These scenarios will be used to drive hydrological simulations for two groundwater systems in north-west Europe, the Brévilles and the Geer, studied as part of the EU FP6 AQUATERRA project. In so doing, the impact of climate change on the challenges facing these aquifers can be assessed on relevant timescales and provide the means to answer wide-ranging questions relating to water quality and flow. The framework described here integrates two components which use projections of future change derived from regional climate models (RCMs) to generate stochastic climate series. Firstly, a new, transient version of the Neyman Scott Rectangular Pulses (NSRP) stochastic rainfall model is implemented to produce transient rainfall scenarios for the 21st century. Secondly, a novel, transient implementation of the Climatic Research Unit (CRU) daily weather generator is adopted, conditioned with daily rainfall series simulated by the NSRP model. This two-stage process is thus able to produce consistent transient series of rainfall, temperature and other variables. Both of these stages apply monthly change factors (CFs) derived from 13 RCM experiments from the PRUDENCE ensemble to current rainfall and temperature statistics respectively to project

  19. High-resolution climate simulations for Central Europe: An assessment of dynamical and statistical downscaling techniques

    NASA Astrophysics Data System (ADS)

    Miksovsky, J.; Huth, R.; Halenka, T.; Belda, M.; Farda, A.; Skalak, P.; Stepanek, P.

    2009-12-01

    To bridge the resolution gap between the outputs of global climate models (GCMs) and finer-scale data needed for studies of the climate change impacts, two approaches are widely used: dynamical downscaling, based on application of regional climate models (RCMs) embedded into the domain of the GCM simulation, and statistical downscaling (SDS), using empirical transfer functions between the large-scale data generated by the GCM and local measurements. In our contribution, we compare the performance of different variants of both techniques for the region of Central Europe. The dynamical downscaling is represented by the outputs of two regional models run in the 10 km horizontal grid, ALADIN-CLIMATE/CZ (co-developed by the Czech Hydrometeorological Institute and Meteo-France) and RegCM3 (developed by the Abdus Salam Centre for Theoretical Physics). The applied statistical methods were based on multiple linear regression, as well as on several of its nonlinear alternatives, including techniques employing artificial neural networks. Validation of the downscaling outputs was carried out using measured data, gathered from weather stations in the Czech Republic, Slovakia, Austria and Hungary for the end of the 20th century; series of daily values of maximum and minimum temperature, precipitation and relative humidity were analyzed. None of the regional models or statistical downscaling techniques could be identified as the universally best one. For instance, while most statistical methods misrepresented the shape of the statistical distribution of the target variables (especially in the more challenging cases such as estimation of daily precipitation), RCM-generated data often suffered from severe biases. It is also shown that further enhancement of the simulated fields of climate variables can be achieved through a combination of dynamical downscaling and statistical postprocessing. This can not only be used to reduce biases and other systematic flaws in the generated time

  20. Dynamic downscaling of South Asia summer monsoon precipitation over COREDEX South Asia using the regional climate model (RegCM4.3)

    NASA Astrophysics Data System (ADS)

    Hassan, Mujtaba; Pengfei, Du; Iqbal, Waheed; Ba, Wulong

    2015-04-01

    We used the latest version of Abdus Slam International Centre for Theoretical Physics (ICTP) regional climate model (RegCM4.3) in the present study to analyze the multiyear climate simulation over the CORDEX South Asia, which characterize by complex topographical features with extensive mountain ranges of Hindukush-Karakorum-Himalaya (HKH) regions. European Community-Hamburg atmospheric model (ECHAM5) and the European Centre for Medium-Range Weather Forecast (ECMWF) 40 years reanalysis data (ERA-40) are used to provide the initial and lateral boundary conditions. Two experiments hereafter referred as RegCM-ERA40 and RegCM-ECHAM5, are performed for present day climate from 1970-2000 with a horizontal resolution of 50 km. Seasonal mean climatology, Interannual variability and annual cycle are compared against the observational data. The main features of spatial distribution of South Asia Summer Monsoon (SASM) precipitation are reproduced well by RegCM4.3 than ECHAM5 GCM. Regional climate model show wet bias over south peninsular India and dry bias over the central India. However RegCM4.3 improves the simulation results for SASM precipitation as compared to both of the driving fields. Our results show that the performance of RegCM4.3 is sufficient for climate change simulation and impact assessment studies over the complex topographical domain of South Asia. Keywords: Regional climate modeling. South Asia. Summer monsoon precipitation.

  1. Validating the dynamic downscaling ability of WRF for East Asian summer climate

    NASA Astrophysics Data System (ADS)

    Gao, Jiangbo; Hou, Wenjuan; Xue, Yongkang; Wu, Shaohong

    2015-12-01

    To better understand the regional climate model (RCM) performance for East Asian summer climate and the influencing factors, this study evaluated the dynamic downscaling ability of the Weather Research Forecast (WRF) RCM. According to the comprehensive comparison studies on different physical processes and experimental settings, the optimal combination of WRF model setups can be obtained for East Asian precipitation and temperature simulations. Furthermore, based on the optimal combination, when compared with climate observations, WRF shows high ability to downscale NCEP DOE Reanalysis-2, which provided initial and lateral boundary conditions for the WRF, especially for the precipitation simulation due to the better simulated low-level water vapor flux. However, the strengthened Western North Pacific Subtropical High (WPSH) from WRF simulation results in the positive anomaly for summer rainfall.

  2. Statistical downscaling of meteorological time series and climatic projections in a watershed in Turkey

    NASA Astrophysics Data System (ADS)

    Göncü, S.; Albek, E.

    2016-10-01

    In this study, meteorological time series from five meteorological stations in and around a watershed in Turkey were used in the statistical downscaling of global climate model results to be used for future projections. Two general circulation models (GCMs), Canadian Climate Center (CGCM3.1(T63)) and Met Office Hadley Centre (2012) (HadCM3) models, were used with three Special Report Emission Scenarios, A1B, A2, and B2. The statistical downscaling model SDSM was used for the downscaling. The downscaled ensembles were put to validation with GCM predictors against observations using nonparametric statistical tests. The two most important meteorological variables, temperature and precipitation, passed validation statistics, and partial validation was achieved with other time series relevant in hydrological studies, namely, cloudiness, relative humidity, and wind velocity. Heat waves, number of dry days, length of dry and wet spells, and maximum precipitation were derived from the primary time series as annual series. The change in monthly predictor sets used in constructing the multiple regression equations for downscaling was examined over the watershed and over the months in a year. Projections between 1962 and 2100 showed that temperatures and dryness indicators show increasing trends while precipitation, relative humidity, and cloudiness tend to decrease. The spatial changes over the watershed and monthly temporal changes revealed that the western parts of the watershed where water is produced for subsequent downstream use will get drier than the rest and the precipitation distribution over the year will shift. Temperatures showed increasing trends over the whole watershed unparalleled with another period in history. The results emphasize the necessity of mitigation efforts to combat climate change on local and global scales and the introduction of adaptation strategies for the region under study which was shown to be vulnerable to climate change.

  3. Evaluation of the applicability in the future climate of a statistical downscaling method in France

    NASA Astrophysics Data System (ADS)

    Dayon, G.; Boé, J.; Martin, E.

    2013-12-01

    compared to the magnitude of observed trends. Moreover some spurious trends in downscaled precipitation associated with temporal inconsistencies in reanalyses variables as surface humidity are noted. It is therefore difficult to assess the applicability of the downscaling methods in the future climate and their respective skill based on trends. Because of those difficulties, a perfect model approach is developed. In the surrogate world of a regional climate model (RCM), the statistical downscaling relation is established in its present climate and then applied to downscale its future projection. It is finally possible to compare future climate change simulated by the RCM and the result of the SD to test the stationarity hypothesis. To obtain robust results, the perfect model framework is applied to 12 RCMs from the ENSEMBLES project. Several analogs methods using different combination of predictors are tested. Some methods, very skillful for present-day interannual variability, are unable to reproduce correctly changes simulated by the RCMs. Another method with similar skill in the present climate, which only differs by the inclusion of the specific humidity at 850 hPa as predictor, is generally applicable in the future climate.

  4. Dynamically downscaling predictions for deciduous tree leaf emergence in California under current and future climate

    NASA Astrophysics Data System (ADS)

    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.

  5. 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. PMID:26489417

  6. 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

  7. 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.

  8. Lessons learned from the National Climate Predictions and Projections (NCPP) platform Workshop on Quantitative Evaluation of Downscaling 2013

    NASA Astrophysics Data System (ADS)

    Guentchev, G.

    2013-12-01

    The mission of NCPP is to accelerate the provision of climate information on regional and local scale for use in adaptation planning and decision making through collaborative participation of a community of scientists and practitioners. A major focus is the development of a capability for objective and quantitative evaluation of downscaled climate information in support of applications. NCPP recognizes the importance of focusing this evaluation effort on real-world applications and the necessity to work closely with the user community to deliver usable evaluations and guidance. This summer NCPP organized our first workshop on quantitative evaluation of downscaled climate datasets (http://earthsystemcog.org/projects/downscaling-2013/). Workshop participants included representatives from downscaling efforts, applications partners from the health, ecological, agriculture and water resources impacts communities, and people working on data infrastructure, metadata, and standards development. The workshop exemplifies NCPP's approach of collaborative and participatory problem-solving where scientists are working together with practitioners to develop applications related evaluation. The set of observed and downscaled datasets included for evaluation in the workshop were assessed using a variety of metrics to elucidate the statistical characteristics of temperature and precipitation time series. In addition, the downscaled datasets were evaluated in terms of their representation of indices relevant to the participating applications working groups, more specifically related to human health and ecological impacts. The presentation will focus on sharing the lessons we learned from our workshop.

  9. Applying downscaled global climate model data to a hydrodynamic surface-water and groundwater model

    USGS Publications Warehouse

    Swain, Eric; Stefanova, Lydia; Smith, Thomas

    2014-01-01

    Precipitation data from Global Climate Models have been downscaled to smaller regions. Adapting this downscaled precipitation data to a coupled hydrodynamic surface-water/groundwater model of southern Florida allows an examination of future conditions and their effect on groundwater levels, inundation patterns, surface-water stage and flows, and salinity. The downscaled rainfall data include the 1996-2001 time series from the European Center for Medium-Range Weather Forecasting ERA-40 simulation and both the 1996-1999 and 2038-2057 time series from two global climate models: the Community Climate System Model (CCSM) and the Geophysical Fluid Dynamic Laboratory (GFDL). Synthesized surface-water inflow datasets were developed for the 2038-2057 simulations. The resulting hydrologic simulations, with and without a 30-cm sea-level rise, were compared with each other and field data to analyze a range of projected conditions. Simulations predicted generally higher future stage and groundwater levels and surface-water flows, with sea-level rise inducing higher coastal salinities. A coincident rise in sea level, precipitation and surface-water flows resulted in a narrower inland saline/fresh transition zone. The inland areas were affected more by the rainfall difference than the sea-level rise, and the rainfall differences make little difference in coastal inundation, but a larger difference in coastal salinities.

  10. Statistical Downscaling of Climate Data for the River Severn Basin: A Comparative Study

    NASA Astrophysics Data System (ADS)

    Sharifi, Soroosh; Drapier, Christopher

    2016-04-01

    Global Climate Models (GCMs) are the main tools for the assessment of climate change impacts at a global level. However, they have a poor level of resolution, and to assess the impacts of climate change at a local-scale, their outputs need to be downscaled. In this paper, the ability of three well known statistical downscaling methods, namely, K-nn, SDSM and LARS-WG are compared in performing statistical downscaling over the future period 2020 to 2039 within the River Severn Basin in the UK. The GCM outputs were obtained from the Hadley Centre's HadCM3 coupled model. To assess each method's skill at downscaling, observed station data within the River Severn Basin was calibrated and verified with historic GCM data over the period 1960 to 1999 drawn from the 20C3M experiment. In general, downscaling captured the seasonal trend for minimum and maximum temperature within the River Severn Basin. However all methods underestimated the observed weather information by up to 1.5 oC. LARS-WG showed the lowest annual and seasonal variation for temperature downscaling. Provided a sufficiently long period of historic data was available for calibration, this method captured the climate characteristics most effectively. Downscaling for precipitation was poor across all methods, but SDSM and LARS-WG were considered better than K-nn in their skill at downscaling. However, downscaling under SDSM showed a marginally closer match to the observed data.

  11. Trend of climate extremes in North America: A comparison between dynamically downscaled CMIP3 and CMIP5 simulations

    NASA Astrophysics Data System (ADS)

    Castro, C. L.; Chang, H. I.; Mearns, L. O.; Bukovsky, M. S.

    2015-12-01

    Ascertaining the impact of anthropogenically-influenced climate change on climate extremes is of high priority for civil infrastructure and water resource planning. The current future projections based on IPCC models, for example as documented in the recent Climate Change Assessment for the Southwest, indicate a declining trend in precipitation with a warming climate, with associated dramatic reductions in streamflow in the Colorado River basin. However, inconsistent precipitation trends are projected by individual IPCC global climate models (i.e. Sheffield et al. 2013, Bukovsky et al., 2013). The North American Monsoon interannual variability is partly controlled by warm season atmospheric teleconnections emanating from the western tropical Pacific, related to the El Niño Southern Oscillation (ENSO) and Pacific Decadal Variability (PDV). Departure from the ensemble mean approach for long-term climate projection analysis, a physics-based methodology is designed to analyze the relationship between climate extremes and the large scale forcing (Chang et al. 2015). Analysis from the observational record and downscaled CMIP3 regional climate runs has shown intensifying warm season precipitation and temperature extremes following the natural variability of large scale forcing. We will utilize the ongoing community effort in dynamically downscaling the CMIP5 climate projection datasets, part of the North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX), and compare with the previous generation of CMIP3 downscaled products for future climate assessment. We aim to examine the difference in large scale forcing from different generations of the CMIP models, and the related impact on regional scale climate extreme characteristics.

  12. 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

  13. Methodology for Air Quality Forecast Downscaling from Regional- to Street-Scale

    NASA Astrophysics Data System (ADS)

    Baklanov, Alexander; Nuterman, Roman; Mahura, Alexander; Amstrup, Bjarne; Hansen Saas, Bent; Havskov Sørensen, Jens; Lorenzen, Thomas; Weismann, Jakob

    2010-05-01

    The most serious air pollution events occur in cities where there is a combination of high population density and air pollution, e.g. from vehicles. The pollutants can lead to serious human health problems, including asthma, irritation of the lungs, bronchitis, pneumonia, decreased resistance to respiratory infections, and premature death. In particular air pollution is associated with increase in cardiovascular disease and lung cancer. In 2000 WHO estimated that between 2.5 % and 11 % of total annual deaths are caused by exposure to air pollution. However, European-scale air quality models are not suited for local forecasts, as their grid-cell is typically of the order of 5 to 10km and they generally lack detailed representation of urban effects. Two suites are used in the framework of the EC FP7 project MACC (Monitoring of Atmosphere Composition and Climate) to demonstrate how downscaling from the European MACC ensemble to local-scale air quality forecast will be carried out: one will illustrate capabilities for the city of Copenhagen (Denmark); the second will focus on the city of Bucharest (Romania). This work is devoted to the first suite, where methodological aspects of downscaling from regional (European/ Denmark) to urban scale (Copenhagen), and from the urban down to street scale. The first results of downscaling according to the proposed methodology are presented. The potential for downscaling of European air quality forecasts by operating urban and street-level forecast models is evaluated. This will bring a strong support for continuous improvement of the regional forecast modelling systems for air quality in Europe, and underline clear perspectives for the future regional air quality core and downstream services for end-users. At the end of the MACC project, requirements on "how-to-do" downscaling of European air-quality forecasts to the city and street levels with different approaches will be formulated.

  14. Applying downscaled climate data to wildlife areas in Washington State, USA

    NASA Astrophysics Data System (ADS)

    Allan, A.; Shafer, S. L.; Bartlein, P. J.; Helbrecht, L.; Pelltier, R.; Thompson, B.

    2013-12-01

    Conservation and natural resource managers require information about potential climate change effects for the species and ecosystems they manage. We evaluated potential future climate and bioclimate changes for wildlife areas in Washington State (USA) using five climate simulations for the 21st century from the Coupled Model Intercomparison Project phase 3 (CMIP3) dataset run under the A2 greenhouse gases emissions scenario. These data were downscaled to a 30-arc-second (~1-km) grid encompassing the state of Washington by calculating and interpolating future climate anomalies, and then applying the interpolated data to observed historical climate data. This climate data downscaling technique (also referred to as the 'delta' method) is relatively simple and makes a number of assumptions that affect how the downscaled data can be used and interpreted. We used the downscaled climate data to calculate bioclimatic variables (e.g., growing degree days) that represent important physiological and environmental limits for Washington species and habitats of management concern. Multivariate descriptive plots and maps were used to evaluate the direction, magnitude, and spatial patterns of projected future climate and bioclimatic changes. The results indicate which managed areas experience the largest climate and bioclimatic changes under each of the potential future climate simulations. We discuss these changes while accounting for some of the limitations of our downscaling technique and the uncertainties associated with using these downscaled data for conservation and natural resource management applications.

  15. 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.

  16. Downscaling NASA Climatological Data to Produce Detailed Climate Zone Maps

    NASA Technical Reports Server (NTRS)

    Chandler, William S.; Hoell, James M.; Westberg, David J.; Whitlock, Charles H.; Zhang, Taiping; Stackhouse, P. W.

    2011-01-01

    The design of energy efficient sustainable buildings is heavily dependent on accurate long-term and near real-time local weather data. To varying degrees the current meteorological networks over the globe have been used to provide these data albeit often from sites far removed from the desired location. The national need is for access to weather and solar resource data accurate enough to use to develop preliminary building designs within a short proposal time limit, usually within 60 days. The NASA Prediction Of Worldwide Energy Resource (POWER) project was established by NASA to provide industry friendly access to globally distributed solar and meteorological data. As a result, the POWER web site (power.larc.nasa.gov) now provides global information on many renewable energy parameters and several buildings-related items but at a relatively coarse resolution. This paper describes a method of downscaling NASA atmospheric assimilation model results to higher resolution and maps those parameters to produce building climate zone maps using estimates of temperature and precipitation. The distribution of climate zones for North America with an emphasis on the Pacific Northwest for just one year shows very good correspondence to the currently defined distribution. The method has the potential to provide a consistent procedure for deriving climate zone information on a global basis that can be assessed for variability and updated more regularly.

  17. 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.

  18. Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century

    PubMed Central

    Emanuel, Kerry A.

    2013-01-01

    A recently developed technique for simulating large [O(104)] numbers of tropical cyclones in climate states described by global gridded data is applied to simulations of historical and future climate states simulated by six Coupled Model Intercomparison Project 5 (CMIP5) global climate models. Tropical cyclones downscaled from the climate of the period 1950–2005 are compared with those of the 21st century in simulations that stipulate that the radiative forcing from greenhouse gases increases by over preindustrial values. In contrast to storms that appear explicitly in most global models, the frequency of downscaled tropical cyclones increases during the 21st century in most locations. The intensity of such storms, as measured by their maximum wind speeds, also increases, in agreement with previous results. Increases in tropical cyclone activity are most prominent in the western North Pacific, but are evident in other regions except for the southwestern Pacific. The increased frequency of events is consistent with increases in a genesis potential index based on monthly mean global model output. These results are compared and contrasted with other inferences concerning the effect of global warming on tropical cyclones. PMID:23836646

  19. Downscaling CMIP5 climate models shows increased tropical cyclone activity over the 21st century.

    PubMed

    Emanuel, Kerry A

    2013-07-23

    A recently developed technique for simulating large [O(10(4))] numbers of tropical cyclones in climate states described by global gridded data is applied to simulations of historical and future climate states simulated by six Coupled Model Intercomparison Project 5 (CMIP5) global climate models. Tropical cyclones downscaled from the climate of the period 1950-2005 are compared with those of the 21st century in simulations that stipulate that the radiative forcing from greenhouse gases increases by over preindustrial values. In contrast to storms that appear explicitly in most global models, the frequency of downscaled tropical cyclones increases during the 21st century in most locations. The intensity of such storms, as measured by their maximum wind speeds, also increases, in agreement with previous results. Increases in tropical cyclone activity are most prominent in the western North Pacific, but are evident in other regions except for the southwestern Pacific. The increased frequency of events is consistent with increases in a genesis potential index based on monthly mean global model output. These results are compared and contrasted with other inferences concerning the effect of global warming on tropical cyclones.

  20. 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

  1. Enhancing Local Climate Projections of Precipitation: Assets and Limitations of Quantile Mapping Techniques for Statistical Downscaling

    NASA Astrophysics Data System (ADS)

    Ivanov, Martin; Kotlarski, Sven; Schär, Christoph

    2015-04-01

    The Swiss CH2011 scenarios provide a portfolio of climate change scenarios for the region of Switzerland, specifically tailored for use in climate impact research. Although widely applied by a variety of end-users, these scenarios are subject to several limitations related to the underlying delta change methodology. Examples are difficulties to appropriately account for changes in the spatio-temporal variability of meteorological fields and for changes in extreme events. The recently launched ELAPSE project (Enhancing local and regional climate change projections for Switzerland) is connected to the EU COST Action VALUE (www.value-cost.eu) and aims at complementing CH2011 by further scenario products, including a bias-corrected version of daily scenarios at the site scale. For this purpose the well-established empirical quantile mapping (QM) methodology is employed. Here, daily temperature and precipitation output of 15 GCM-RCM model chains of the ENSEMBLES project is downscaled and bias-corrected to match observations at weather stations in Switzerland. We consider established QM techniques based on all empirical quantiles or linear interpolation between the empirical percentiles. In an attempt to improve the downscaling of extreme precipitation events, we also apply a parametric approximation of the daily precipitation distribution by a dynamically weighted mixture of a Gamma distribution for the bulk and a Pareto distribution for the right tail for the first time in the context of QM. All techniques are evaluated and intercompared in a cross-validation framework. The statistical downscaling substantially improves virtually all considered distributional and temporal characteristics as well as their spatial distribution. The empirical methods have in general very similar performances. The parametric method does not show an improvement over the empirical ones. Critical sites and seasons are highlighted and discussed. Special emphasis is placed on investigating the

  2. Coupled Downscaled Climate Models and Ecophysiological Metrics Forecast Habitat Compression for an Endangered Estuarine Fish.

    PubMed

    Brown, Larry R; Komoroske, Lisa M; Wagner, R Wayne; Morgan-King, Tara; May, Jason T; Connon, Richard E; Fangue, Nann A

    2016-01-01

    Climate change is driving rapid changes in environmental conditions and affecting population and species' persistence across spatial and temporal scales. Integrating climate change assessments into biological resource management, such as conserving endangered species, is a substantial challenge, partly due to a mismatch between global climate forecasts and local or regional conservation planning. Here, we demonstrate how outputs of global climate change models can be downscaled to the watershed scale, and then coupled with ecophysiological metrics to assess climate change effects on organisms of conservation concern. We employed models to estimate future water temperatures (2010-2099) under several climate change scenarios within the large heterogeneous San Francisco Estuary. We then assessed the warming effects on the endangered, endemic Delta Smelt, Hypomesus transpacificus, by integrating localized projected water temperatures with thermal sensitivity metrics (tolerance, spawning and maturation windows, and sublethal stress thresholds) across life stages. Lethal temperatures occurred under several scenarios, but sublethal effects resulting from chronic stressful temperatures were more common across the estuary (median >60 days above threshold for >50% locations by the end of the century). Behavioral avoidance of such stressful temperatures would make a large portion of the potential range of Delta Smelt unavailable during the summer and fall. Since Delta Smelt are not likely to migrate to other estuaries, these changes are likely to result in substantial habitat compression. Additionally, the Delta Smelt maturation window was shortened by 18-85 days, revealing cumulative effects of stressful summer and fall temperatures with early initiation of spring spawning that may negatively impact fitness. Our findings highlight the value of integrating sublethal thresholds, life history, and in situ thermal heterogeneity into global change impact assessments. As

  3. Coupled Downscaled Climate Models and Ecophysiological Metrics Forecast Habitat Compression for an Endangered Estuarine Fish

    PubMed Central

    Brown, Larry R.; Komoroske, Lisa M.; Wagner, R. Wayne; Morgan-King, Tara; May, Jason T.; Connon, Richard E.; Fangue, Nann A.

    2016-01-01

    Climate change is driving rapid changes in environmental conditions and affecting population and species’ persistence across spatial and temporal scales. Integrating climate change assessments into biological resource management, such as conserving endangered species, is a substantial challenge, partly due to a mismatch between global climate forecasts and local or regional conservation planning. Here, we demonstrate how outputs of global climate change models can be downscaled to the watershed scale, and then coupled with ecophysiological metrics to assess climate change effects on organisms of conservation concern. We employed models to estimate future water temperatures (2010–2099) under several climate change scenarios within the large heterogeneous San Francisco Estuary. We then assessed the warming effects on the endangered, endemic Delta Smelt, Hypomesus transpacificus, by integrating localized projected water temperatures with thermal sensitivity metrics (tolerance, spawning and maturation windows, and sublethal stress thresholds) across life stages. Lethal temperatures occurred under several scenarios, but sublethal effects resulting from chronic stressful temperatures were more common across the estuary (median >60 days above threshold for >50% locations by the end of the century). Behavioral avoidance of such stressful temperatures would make a large portion of the potential range of Delta Smelt unavailable during the summer and fall. Since Delta Smelt are not likely to migrate to other estuaries, these changes are likely to result in substantial habitat compression. Additionally, the Delta Smelt maturation window was shortened by 18–85 days, revealing cumulative effects of stressful summer and fall temperatures with early initiation of spring spawning that may negatively impact fitness. Our findings highlight the value of integrating sublethal thresholds, life history, and in situ thermal heterogeneity into global change impact assessments. As

  4. Coupled downscaled climate models and ecophysiological metrics forecast habitat compression for an endangered estuarine fish

    USGS Publications Warehouse

    Brown, Larry R.; Komoroske, Lisa M; Wagner, R Wayne; Morgan-King, Tara; May, Jason T.; Connon, Richard E; Fangue, Nann A.

    2016-01-01

    Climate change is driving rapid changes in environmental conditions and affecting population and species’ persistence across spatial and temporal scales. Integrating climate change assessments into biological resource management, such as conserving endangered species, is a substantial challenge, partly due to a mismatch between global climate forecasts and local or regional conservation planning. Here, we demonstrate how outputs of global climate change models can be downscaled to the watershed scale, and then coupled with ecophysiological metrics to assess climate change effects on organisms of conservation concern. We employed models to estimate future water temperatures (2010–2099) under several climate change scenarios within the large heterogeneous San Francisco Estuary. We then assessed the warming effects on the endangered, endemic Delta Smelt, Hypomesus transpacificus, by integrating localized projected water temperatures with thermal sensitivity metrics (tolerance, spawning and maturation windows, and sublethal stress thresholds) across life stages. Lethal temperatures occurred under several scenarios, but sublethal effects resulting from chronic stressful temperatures were more common across the estuary (median >60 days above threshold for >50% locations by the end of the century). Behavioral avoidance of such stressful temperatures would make a large portion of the potential range of Delta Smelt unavailable during the summer and fall. Since Delta Smelt are not likely to migrate to other estuaries, these changes are likely to result in substantial habitat compression. Additionally, the Delta Smelt maturation window was shortened by 18–85 days, revealing cumulative effects of stressful summer and fall temperatures with early initiation of spring spawning that may negatively impact fitness. Our findings highlight the value of integrating sublethal thresholds, life history, and in situ thermal heterogeneity into global change impact assessments. As

  5. Coupled Downscaled Climate Models and Ecophysiological Metrics Forecast Habitat Compression for an Endangered Estuarine Fish.

    PubMed

    Brown, Larry R; Komoroske, Lisa M; Wagner, R Wayne; Morgan-King, Tara; May, Jason T; Connon, Richard E; Fangue, Nann A

    2016-01-01

    Climate change is driving rapid changes in environmental conditions and affecting population and species' persistence across spatial and temporal scales. Integrating climate change assessments into biological resource management, such as conserving endangered species, is a substantial challenge, partly due to a mismatch between global climate forecasts and local or regional conservation planning. Here, we demonstrate how outputs of global climate change models can be downscaled to the watershed scale, and then coupled with ecophysiological metrics to assess climate change effects on organisms of conservation concern. We employed models to estimate future water temperatures (2010-2099) under several climate change scenarios within the large heterogeneous San Francisco Estuary. We then assessed the warming effects on the endangered, endemic Delta Smelt, Hypomesus transpacificus, by integrating localized projected water temperatures with thermal sensitivity metrics (tolerance, spawning and maturation windows, and sublethal stress thresholds) across life stages. Lethal temperatures occurred under several scenarios, but sublethal effects resulting from chronic stressful temperatures were more common across the estuary (median >60 days above threshold for >50% locations by the end of the century). Behavioral avoidance of such stressful temperatures would make a large portion of the potential range of Delta Smelt unavailable during the summer and fall. Since Delta Smelt are not likely to migrate to other estuaries, these changes are likely to result in substantial habitat compression. Additionally, the Delta Smelt maturation window was shortened by 18-85 days, revealing cumulative effects of stressful summer and fall temperatures with early initiation of spring spawning that may negatively impact fitness. Our findings highlight the value of integrating sublethal thresholds, life history, and in situ thermal heterogeneity into global change impact assessments. As

  6. Thermal environment downscaling under the climate chenage in Seto-Inland Sea of Japan

    NASA Astrophysics Data System (ADS)

    Imai, Y.; Mori, N.; Ninomiya, J.; Yasuda, T.; Mase, H.

    2015-12-01

    Introduction There are many studies have been conducted to project future change and assess the impacts. The latest IPCC AR5 WGI reports that there are many impact assessments of large scale changes in coastal and ocean environments but few studies on regional scale changes. We analyzed global and regional near-sea surface physical changes based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) data. The downscaling of regional ocean targeting the semi-enclosed Seto-Inland Sea of Japan by Regional Ocean Modeling System (ROMS) considering the results of CMIP5. We analyzed the future projection of thermal environmental changes of the Seto-Inland Sea based on the downscaling results. Regional analysis of CMIP5 Analysis of CMIP5 was conducted for the historical climate and future climate at the end of 21st century considering two different emission scenarios (RCP4.5 and RCP8.5). All available 61 GCMs in CMIP5 were considered for analysis and the future changes of 11 atmospheric and oceanic variables were computed in detail. Spatial distribution of sea surface temperature (SST) showed a consistent increase overall, with local non-homogeneity. For example, an increase SST more than 4 degrees in the Northwest Pacific against to global mean SST increase of 2.6 degrees. The projection of the Seto-Inland Sea environment Dynamical downscaling for Seto-Inland Sea was calculated for the year 2093 forcing future changes from CMIP5 analysis results to project future regional environmental changes in West-Japan. The results of hindcast were compared with observed results and future climate conditions were added to hindcast results. The SST shows a remarkable increase of about 3.6 degrees in the summer but it is less in the future winter. The major change of water temperature change is increasing trend in upper 20m layer, and thermal e-folding depth in the future climate becomes shallower. The warming tendency decreases with depth in shallow water region but is different

  7. Uncertainties in downscaled relative humidity for a semi-arid region in India

    NASA Astrophysics Data System (ADS)

    Anandhi, Aavudai

    2011-06-01

    Monthly scenarios of relative humidity ( R H) were obtained for the Malaprabha river basin in India using a statistical downscaling technique. Large-scale atmospheric variables (air temperature and specific humidity at 925 mb, surface air temperature and latent heat flux) were chosen as predictors. The predictor variables are extracted from the (1) National Centers for Environmental Prediction reanalysis dataset for the period 1978-2000, and (2) simulations of the third generation Canadian Coupled Global Climate Model for the period 1978-2100. The objective of this study was to investigate the uncertainties in regional scenarios developed for R H due to the choice of emission scenarios (A1B, A2, B1 and COMMIT) and the predictors selected. Multi-linear regression with stepwise screening is the downscaling technique used in this study. To study the uncertainty in the regional scenarios of R H, due to the selected predictors, eight sets of predictors were chosen and a downscaling model was developed for each set. Performance of the downscaling models in the baseline period (1978-2000) was studied using three measures (1) Nash-Sutcliffe error estimate ( E f), (2) mean absolute error (MAE), and (3) product moment correlation ( P). Results show that the performances vary between 0.59 and 0.68, 0.42 and 0.50 and 0.77 and 0.82 for E f, MAE and P. Cumulative distribution functions were prepared from the regional scenarios of R H developed for combinations of predictors and emission scenarios. Results show a variation of 1 to 6% R H in the scenarios developed for combination of predictor sets for baseline period. For a future period (2001-2100), a variation of 6 to 15% R H was observed for the combination of emission scenarios and predictors. The variation was highest for A2 scenario and least for COMMIT and B1 scenario.

  8. Downscaling Reanalysis over Continental Africa with a Regional Model: NCEP Versus ERA Interim Forcing

    NASA Technical Reports Server (NTRS)

    Druyan, Leonard M.; Fulakeza, Matthew B.

    2013-01-01

    Five annual climate cycles (1998-2002) are simulated for continental Africa and adjacent oceans by a regional atmospheric model (RM3). RM3 horizontal grid spacing is 0.44deg at 28 vertical levels. Each of 2 simulation ensembles is driven by lateral boundary conditions from each of 2 alternative reanalysis data sets. One simulation downs cales National Center for Environmental Prediction reanalysis 2 (NCPR2) and the other the European Centre for Medium Range Weather Forecasts Interim reanalysis (ERA-I). NCPR2 data are archived at 2.5deg grid spacing, while a recent version of ERA-I provides data at 0.75deg spacing. ERA-I-forced simulations are recomrp. ended by the Coordinated Regional Downscaling Experiment (CORDEX). Comparisons of the 2 sets of simulations with each other and with observational evidence assess the relative performance of each downscaling system. A third simulation also uses ERA-I forcing, but degraded to the same horizontal resolution as NCPR2. RM3-simulated pentad and monthly mean precipitation data are compared to Tropical Rainfall Measuring Mission (TRMM) data, gridded at 0.5deg, and RM3-simulated circulation is compared to both reanalyses. Results suggest that each downscaling system provides advantages and disadvantages relative to the other. The RM3/NCPR2 achieves a more realistic northward advance of summer monsoon rains over West Africa, but RM3/ERA-I creates the more realistic monsoon circulation. Both systems recreate some features of JulySeptember 1999 minus 2002 precipitation differences. Degrading the resolution of ERA-I driving data unrealistically slows the monsoon circulation and considerably diminishes summer rainfall rates over West Africa. The high resolution of ERA-I data, therefore, contributes to the quality of the downscaling, but NCPR2laterai boundary conditions nevertheless produce better simulations of some features.

  9. Comparison of a very-fine-resolution GCM with RCM dynamical downscaling in simulating climate in China

    NASA Astrophysics Data System (ADS)

    Guo, Donglin; Wang, Huijun

    2016-05-01

    Regional climate simulation can generally be improved by using an RCM nested within a coarser-resolution GCM. However, whether or not it can also be improved by the direct use of a state-of-the-art GCM with very fine resolution, close to that of an RCM, and, if so, which is the better approach, are open questions. These questions are important for understanding and using these two kinds of simulation approaches, but have not yet been investigated. Accordingly, the present reported work compared simulation results over China from a very-fine-resolution GCM (VFRGCM) and from RCM dynamical downscaling. The results showed that: (1) The VFRGCM reproduces the climatologies and trends of both air temperature and precipitation, as well as inter-monthly variations of air temperature in terms of spatial pattern and amount, closer to observations than the coarse-resolution version of the GCM. This is not the case, however, for the inter-monthly variations of precipitation. (2) The VFRGCM captures the climatology, trend, and inter-monthly variation of air temperature, as well as the trend in precipitation, more reasonably than the RCM dynamical downscaling method. (3) The RCM dynamical downscaling method performs better than the VFRGCM in terms of the climatology and inter-monthly variation of precipitation. Overall, the results suggest that VFRGCMs possess great potential with regard to their application in climate simulation in the future, and the RCM dynamical downscaling method is still dominant in terms of regional precipitation simulation.

  10. 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.

  11. Downscaling climate change scenarios in an urban land use change model.

    PubMed

    Solecki, William D; Oliveri, Charles

    2004-08-01

    The objective of this paper is to describe the process through which climate change scenarios were downscaled in an urban land use model and the results of this experimentation. The land use models (Urban Growth Model [UGM] and the Land Cover Deltatron Model [LCDM]) utilized in the project are part of the SLEUTH program which uses a probabilistic cellular automata protocol. The land use change scenario experiments were developed for the 31-county New York Metropolitan Region (NYMR) of the US Mid-Atlantic Region. The Intergovernmental Panel on Climate Change (IPCC), regional greenhouse gas (GHG) emissions scenarios (Special Report on Emissions Scenarios (SRES) A2 and B2 scenarios) were used to define the narrative scenario conditions of future land use change. The specific research objectives of the land use modeling work involving the SLEUTH program were threefold: (1) Define the projected conversion probabilities and the amount of rural-to-urban land use change for the NYMR as derived by the UGM and LCDM for the years 2020 and 2050, as defined by the pattern of growth for the years 1960-1990; (2) Down-scale the IPCC SRES A2 and B2 scenarios as a narrative that could be translated into alternative growth projections; and, (3) Create two alternative future growth scenarios: A2 scenario which will be associated with more rapid land conversion than found in initial projections, and a B2 scenario which will be associated with a slower level of land conversion. The results of the modeling experiments successfully illustrate the spectrum of possible land use/land cover change scenarios for the years 2020 and 2050. The application of these results into the broader scale climate and health impact study is discussed, as is the general role of land use/land cover change models in climate change studies and associated environmental management strategies.

  12. Estimation methods for monthly humidity from dynamical downscaling data for quantitative assessments of climate change impacts

    NASA Astrophysics Data System (ADS)

    Ueyama, Hideki

    2012-07-01

    Methods are proposed to estimate the monthly relative humidity and wet bulb temperature based on observations from a dynamical downscaling coupled general circulation model with a regional climate model (RCM) for a quantitative assessment of climate change impacts. The water vapor pressure estimation model developed was a regression model with a monthly saturated water vapor pressure that used minimum air temperature as a variable. The monthly minimum air temperature correction model for RCM bias was developed by stepwise multiple regression analysis using the difference in monthly minimum air temperatures between observations and RCM output as a dependent variable and geographic factors as independent variables. The wet bulb temperature was estimated using the estimated water vapor pressure, air temperature, and atmospheric pressure at ground level both corrected for RCM bias. Root mean square errors of the data decreased considerably in August.

  13. 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

  14. 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

  15. Micro climate Simulation in new Town `Hashtgerd' using downscaled climate data

    NASA Astrophysics Data System (ADS)

    Sodoudi, S.

    2010-12-01

    One of the objectives of climatological part of project Young Cities ‘Developing Energy-Efficient Urban Fabric in the Tehran-Karaj Region’ is to simulate the micro climate (with 1m resolution) in 35ha of new town Hashtgerd, which is located 65 km far from mega city Tehran. The Project aims are developing, implementing and evaluating building and planning schemes and technologies which allow to plan and build sustainable, energy-efficient and climate sensible form mass housing settlements in arid and semi-arid regions (energy-efficient fabric). Climate sensitive form also means designing and planning for climate change and its related effects for Hashtgerd New Town. By configuration of buildings and open spaces according to solar radiation, wind and vegetation, climate sensitive urban form can create outdoor thermal comfort. To simulate the climate on small spatial scales, the micro climate model Envi-met has been used to simulate the micro climate in 35 ha. The Eulerian model ENVI-met is a micro-scale climate model which gives information about the influence of architecture and buildings as well as vegetation and green area on the micro climate up to 1 m resolution. Envi-met has been run with information from topography, downscaled climate data with neuro-fuzzy method, meteorological measurements, building height and different vegetation variants (low and high number of trees) The first results were compared with each other and show In semi-arid climates the protection from solar radiation is of major importance. This can be achieved by implementation of vegetation and geometry of buildings. Due to the geographical location and related sun’s orbit the degree of shading in this area is rather low. Technical construction such awnings have to be implemented. A second important factor is wind. The design follows the idea to block the prevailing winds from west and northwest as well as the hot and dusty winds in summer time from the southeast but at the same time

  16. Downscaling the IPCC: The 2008 and 2014 Colorado Climate Assessments

    NASA Astrophysics Data System (ADS)

    Averyt, K.; Lukas, J.; Gordon, E.

    2014-12-01

    The last two years have seen the release of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and the third US National Climate Assessment (NCA). In addition to these high profile climate assessments, many regional, state, and municipal entities are leveraging the emerging climate science and scaling the process and the data to develop climate assessments relevant to decision making at sub-national scales. One such example is the assessment Climate Change in Colorado: A Synthesis to Support Water Resources Management and Adaptation. The report (released in August 2014) expands and updates an earlier Climate Change in Colorado assessment that was published in October 2008. The 2008 Colorado assessment took a hybrid approach to assessment, marrying the IPCC Working Group I focus on physical science, with the adaptation components raditionally embedded within Working Group II. The Colorado assessment approach included the transparency and open review that underpin the credibility of the IPCC and NCA processes. A modest amount of original research was undertaken, which is a slight deviation from the IPCC and NCA processes. The state-based process also used a co-production model that engages users directly at the outset of the process. The 2008 and 2014 reports used the same process, but the newer assesment is vastly expanded in scope. The efficacy of the process is demonstrated by the use of the Colorado assessments by decision makers. The science in the 2008 report supported the Colorado State Drought Plan, Climate Adaptation Plans for several Colorado Counties, and the State Water Supply Initiative. Components of the 2014 assessment have been used as the science basis for Denver's Climate Adaptation Plan. Decisions that involve climate adaptation tend to happen at sub-regional scales. Consequently, IPCC and NCA documents are not as informative for adaptation planning at regional and state scales as localized assessments. This does not

  17. 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

  18. Can dynamically downscaled climate model outputs improve projections of extreme precipitation events?

    NASA Astrophysics Data System (ADS)

    Wobus, C. W.; Reynolds, L.; Jones, R.; Horton, R. M.; Smith, J. B.; Fries, J. S.; Tryby, M.; Spero, T.; Nolte, C. G.

    2015-12-01

    Many of the storms that generate damaging floods are caused by locally intense, sub-daily precipitation, yet the spatial and temporal resolution of the most widely available climate model outputs are both too coarse to simulate these events. Thus there is often a disconnect between the nature of the events that cause damaging floods and the models used to project how climate change might influence their magnitude. This could be a particular problem when developing scenarios to inform future stormwater management options under future climate scenarios. In this study we sought to close this gap, using sub-daily outputs from the Weather Research and Forecasting model (WRF) from each of the nine climate regions in the United States. Specifically, we asked 1) whether WRF outputs projected consistent patterns of change for sub-daily and daily precipitation extremes; and 2) whether this dynamically downscaled model projected different magnitudes of change for 3-hourly vs 24-hourly extreme events. We extracted annual maximum values for 3-hour through 24-hour precipitation totals from an 11-year timeseries of hindcast (1995-2005) and mid-century (2045-2055) climate, and calculated the direction and magnitude of change for 3-hour and 24-hour extreme events over this timeframe. The model results project that the magnitude of both 3-hour and 24-hour events will increase over most regions of the United States, but there was no clear or consistent difference in the relative magnitudes of change for sub-daily vs daily events.

  19. The hydroclimatological response to global warming based on the dynamically downscaled climate change scenario

    NASA Astrophysics Data System (ADS)

    Im, Eun-Soon; Coppola, Erika; Giorgi, Felippo

    2010-05-01

    Given the discernable evidences of climate changes due to human activity, there is a growing demand for the reliable climate change scenario in response to future emission forcing. One of the most significant impacts of climate changes can be that on the hydrological process. Changes in the seasonality and increase in the low and high rainfall extremes can severely influence the water balance of river basin, with serious consequences for societies and ecosystems. In fact, recent studies have reported that East Asia including the Korean peninsula is regarded to be a highly vulnerability region under global warming, in particular for water resources. As an attempt accurately assess the impact of climate change over Korea, we performed a downscaling of the ECAHM5-MPI/OM global projection under the A1B emission scenario for the period 1971-2100 using the RegCM3 one-way double-nested system. Physically based long-term (130 years) fine-scale (20 km) climate information is appropriate for analyzing the detailed structure of the hydroclimatological response to climate change. Changes in temperature and precipitation are translated to the hydrological condition in a direct or indirect way. The change in precipitation shows a distinct seasonal variations and a complicated spatial pattern. While changes in total precipitation do not show any relevant trend, the change patterns in daily precipitation clearly show an enhancement of high intensity precipitation and a reduction of weak intensity precipitation. The increase of temperature enhances the evapotranspiration, and hence the actual water stress becomes more pronounced in the future climate. Precipitation, snow, and runoff changes show the relevant topographical modulation under global warming. This study clearly demonstrates the importance of a refined topography for improving the accuracy of the local climatology. Improved accuracy of regional climate projection could lead to an enhanced reliability of the

  20. Potential for small scale added value of RCM's downscaled climate change signal

    NASA Astrophysics Data System (ADS)

    Di Luca, Alejandro; de Elía, Ramón; Laprise, René

    2013-02-01

    In recent decades, the need of future climate information at local scales have pushed the climate modelling community to perform increasingly higher resolution simulations and to develop alternative approaches to obtain fine-scale climatic information. In this article, various nested regional climate model (RCM) simulations have been used to try to identify regions across North America where high-resolution downscaling generates fine-scale details in the climate projection derived using the "delta method". Two necessary conditions were identified for an RCM to produce added value (AV) over lower resolution atmosphere-ocean general circulation models in the fine-scale component of the climate change (CC) signal. First, the RCM-derived CC signal must contain some non-negligible fine-scale information—independently of the RCM ability to produce AV in the present climate. Second, the uncertainty related with the estimation of this fine-scale information should be relatively small compared with the information itself in order to suggest that RCMs are able to simulate robust fine-scale features in the CC signal. Clearly, considering necessary (but not sufficient) conditions means that we are studying the "potential" of RCMs to add value instead of the AV, which preempts and avoids any discussion of the actual skill and hence the need for hindcast comparisons. The analysis concentrates on the CC signal obtained from the seasonal-averaged temperature and precipitation fields and shows that the fine-scale variability of the CC signal is generally small compared to its large-scale component, suggesting that little AV can be expected for the time-averaged fields. For the temperature variable, the largest potential for fine-scale added value appears in coastal regions mainly related with differential warming in land and oceanic surfaces. Fine-scale features can account for nearly 60 % of the total CC signal in some coastal regions although for most regions the fine scale

  1. A review of downscaling procedures - a contribution to the research on climate change impacts at city scale

    NASA Astrophysics Data System (ADS)

    Smid, Marek; Costa, Ana; Pebesma, Edzer; Granell, Carlos; Bhattacharya, Devanjan

    2016-04-01

    Human kind is currently predominantly urban based, and the majority of ever continuing population growth will take place in urban agglomerations. Urban systems are not only major drivers of climate change, but also the impact hot spots. Furthermore, climate change impacts are commonly managed at city scale. Therefore, assessing climate change impacts on urban systems is a very relevant subject of research. Climate and its impacts on all levels (local, meso and global scale) and also the inter-scale dependencies of those processes should be a subject to detail analysis. While global and regional projections of future climate are currently available, local-scale information is lacking. Hence, statistical downscaling methodologies represent a potentially efficient way to help to close this gap. In general, the methodological reviews of downscaling procedures cover the various methods according to their application (e.g. downscaling for the hydrological modelling). Some of the most recent and comprehensive studies, such as the ESSEM COST Action ES1102 (VALUE), use the concept of Perfect Prog and MOS. Other examples of classification schemes of downscaling techniques consider three main categories: linear methods, weather classifications and weather generators. Downscaling and climate modelling represent a multidisciplinary field, where researchers from various backgrounds intersect their efforts, resulting in specific terminology, which may be somewhat confusing. For instance, the Polynomial Regression (also called the Surface Trend Analysis) is a statistical technique. In the context of the spatial interpolation procedures, it is commonly classified as a deterministic technique, and kriging approaches are classified as stochastic. Furthermore, the terms "statistical" and "stochastic" (frequently used as names of sub-classes in downscaling methodological reviews) are not always considered as synonymous, even though both terms could be seen as identical since they are

  2. Statistical-dynamical downscaling for wind energy potentials: Evaluation and applications to decadal hindcasts and climate change projections

    NASA Astrophysics Data System (ADS)

    Reyers, Mark; Pinto, Joaquim G.; Moemken, Julia

    2015-04-01

    A statistical-dynamical downscaling (SDD) approach for the regionalisation of wind energy output (Eout) over Europe with special focus on Germany is proposed. SDD uses an extended circulation weather type (CWT) analysis on global daily MSLP fields with the central point being located over Germany. 77 weather classes based on the associated circulation weather type and the intensity of the geostrophic flow are identified. Representatives of these classes are dynamical downscaled with the regional climate model COSMO-CLM. By using weather class frequencies of different datasets the simulated representatives are recombined to probability density functions (PDFs) of near-surface wind speed and finally to Eout of a sample wind turbine for present and future climate. This is performed for reanalysis, decadal hindcasts and long-term future projections. For evaluation purposes results of SDD are compared to wind observations and to simulated Eout of purely dynamical downscaling (DD) methods. For the present climate SDD is able to simulate realistic PDFs of 10m-wind speed for most stations in Germany. The resulting spatial Eout patterns are similar to DD simulated Eout. In terms of decadal hindcasts results of SDD are similar to DD simulated Eout over Germany, Poland, Czech Republic, and Benelux, for which high correlations between annual Eout timeseries of SDD and DD are detected for selected hindcasts. Lower correlation is found for other European countries. It is demonstrated that SDD can be used to downscale the full ensemble of the MPI-ESM decadal prediction system. Long-term climate change projections in SRES scenarios of ECHAM5/MPI-OM as obtained by SDD agree well to results of other studies using DD methods, with increasing Eout over Northern Europe and a negative trend over Southern Europe. Despite some biases it is concluded that SDD is an adequate tool to assess regional wind energy changes in large model ensembles.

  3. Comparison of three downscaling methods in simulating the impact of climate change on the hydrology of Mediterranean basins

    NASA Astrophysics Data System (ADS)

    Quintana Seguí, P.; Ribes, A.; Martin, E.; Habets, F.; Boé, J.

    2010-03-01

    SummaryStudies of the impact of climate change on water resources usually follow a top to bottom approach: a scenario of emissions is used to run a GCM simulation, which is downscaled (RCM and/or stastistical methods) and bias-corrected. Then, this data is used to force a hydrological model. Seldom, impact studies take into account all relevant uncertainties. In fact, many published studies only use one climate model and one downscaling technique. In this study, the outputs of an atmosphere-ocean regional climate model are downscaled and bias-corrected using three different techniques: a statistical method based on weather regimes, a quantile-mapping method and the method of the anomaly. The resulting data are used to force a distributed hydrological model to simulate the French Mediterranean basins. These are characterized by water scarcity and an increasing human pressure, which cause a demand in assessments on the impact of climate change hydrological systems. The purpose of the study is mainly methodological: the evaluation of the uncertainty related to the downscaling and bias-correction step. The periods chosen to compare the changes are the end of the 20th century (1970-2000) and the middle of the 21st century (2035-2065). The study shows that the three methods produce similar anomalies of the mean annual precipitation, but there are important differences, mainly in terms of spatial patterns. The study also shows that there are important differences in the anomalies of temperature. These uncertainties are amplified by the hydrological model. In some basins, the simulations do not agree in the sign of the anomalies and, in many others, the differences in amplitude of the anomaly are very important. Therefore, the uncertainty related to the downscaling and bias-correction of the climate simulation must be taken into account in order to better estimate the impact of climate change, with its uncertainty, on a specific basin. The study also shows that according

  4. An evaluation of how downscaled climate data represents historical precipitation characteristics beyond the means and variances

    NASA Astrophysics Data System (ADS)

    Kusangaya, Samuel; Toucher, Michele L. Warburton; van Garderen, Emma Archer; Jewitt, Graham P. W.

    2016-09-01

    Precipitation is the main driver of the hydrological cycle. For climate change impact analysis, use of downscaled precipitation, amongst other factors, determines accuracy of modelled runoff. Precipitation is, however, considerably more difficult to model than temperature, largely due to its high spatial and temporal variability and its nonlinear nature. Due to such qualities of precipitation, a key challenge for water resources management is thus how to incorporate potentially significant but highly uncertain precipitation characteristics when modelling potential changes in climate for water resources management in order to support local management decisions. Research undertaken here was aimed at evaluating how downscaled climate data represented the underlying historical precipitation characteristics beyond the means and variances. Using the uMngeni Catchment in KwaZulu-Natal, South Africa as a case study, the occurrence of rainfall, rainfall threshold events and wet dry sequence was analysed for current climate (1961-1999). The number of rain days with daily rainfall > 1 mm, > 5 mm, > 10 mm, > 20 mm and > 40 mm for each of the 10 selected climate models was, compared to the number of rain days at 15 rain stations. Results from graphical and statistical analysis indicated that on a monthly basis rain days are over estimated for all climate models. Seasonally, the number of rain days were overestimated in autumn and winter and underestimated in summer and spring. The overall conclusion was that despite the advancement in downscaling and the improved spatial scale for a better representation of the climate variables, such as rainfall for use in hydrological impact studies, downscaled rainfall data still does not simulate well some important rainfall characteristics, such as number of rain days and wet-dry sequences. This is particularly critical, since, whilst for climatologists, means and variances might be simulated well in downscaled GCMs, for hydrologists

  5. 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.

  6. Downscaling of precipitation for climate change scenarios: A support vector machine approach

    NASA Astrophysics Data System (ADS)

    Tripathi, Shivam; Srinivas, V. V.; Nanjundiah, Ravi S.

    2006-11-01

    SummaryThe Climate impact studies in hydrology often rely on climate change information at fine spatial resolution. However, general circulation models (GCMs), which are among the most advanced tools for estimating future climate change scenarios, operate on a coarse scale. Therefore the output from a GCM has to be downscaled to obtain the information relevant to hydrologic studies. In this paper, a support vector machine (SVM) approach is proposed for statistical downscaling of precipitation at monthly time scale. The effectiveness of this approach is illustrated through its application to meteorological sub-divisions (MSDs) in India. First, climate variables affecting spatio-temporal variation of precipitation at each MSD in India are identified. Following this, the data pertaining to the identified climate variables (predictors) at each MSD are classified using cluster analysis to form two groups, representing wet and dry seasons. For each MSD, SVM- based downscaling model (DM) is developed for season(s) with significant rainfall using principal components extracted from the predictors as input and the contemporaneous precipitation observed at the MSD as an output. The proposed DM is shown to be superior to conventional downscaling using multi-layer back-propagation artificial neural networks. Subsequently, the SVM-based DM is applied to future climate predictions from the second generation Coupled Global Climate Model (CGCM2) to obtain future projections of precipitation for the MSDs. The results are then analyzed to assess the impact of climate change on precipitation over India. It is shown that SVMs provide a promising alternative to conventional artificial neural networks for statistical downscaling, and are suitable for conducting climate impact studies.

  7. Climate Change in Nicaragua: a dynamical downscaling of precipitation and temperature.

    NASA Astrophysics Data System (ADS)

    Porras, Ignasi; Domingo-Dalmau, Anna; Sole, Josep Maria; Arasa, Raul; Picanyol, Miquel; Ángeles Gonzalez-Serrano, M.°; Masdeu, Marta

    2016-04-01

    Climate Change affects weather patterns and modifies meteorological extreme events like tropical cyclones, heavy rainfalls, dry events, extreme temperatures, etc. The aim of this study is to show the Climate Change projections over Nicaragua for the period 2010-2040 focused on precipitation and temperature. In order to obtain the climate change signal, the results obtained by modelling a past period (1980-2009) were compared with the ones obtained by modelling a future period (2010-2040). The modelling method was based on a dynamical downscaling, coupling global and regional models. The MPI-ESM-MR global climate model was selected due to the better performance over Nicaragua. Moreover, a detailed sensitivity analysis for different parameterizations and schemes of the Weather Research and Forecast (WRF-ARW) model was made to minimize the model uncertainty. To evaluate and validate the methodology, a comparison between model outputs and satellite measurements data was realized. The results show an expected increment of the temperature and an increment of the number of days per year with temperatures higher than 35°C. Monthly precipitation patterns will change although annual total precipitation will be similar. In addition, number of dry days are expected to increase.

  8. Reductions in seasonal climate forecast dependability as a result of downscaling

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This research determines whether NOAA/CPC seasonal climate forecasts are skillful enough to retain utility after they have been downscaled for use in crop models. Utility is assessed using net dependability, the product of the large-scale 3-month forecast dependability and a factor accounting for l...

  9. Uncertainty of downscaling method in quantifying the impact of climate change on hydrology

    NASA Astrophysics Data System (ADS)

    Chen, Jie; Brissette, François P.; Leconte, Robert

    2011-05-01

    SummaryUncertainty estimation of climate change impacts has been given a lot of attention in the recent literature. It is generally assumed that the major sources of uncertainty are linked to General Circulation Models (GCMs) and Greenhouse Gases Emissions Scenarios (GGES). However, other sources of uncertainty such as the choice of a downscaling method have been given less attention. This paper focuses on this issue by comparing six downscaling methods to investigate the uncertainties in quantifying the impacts of climate change on the hydrology of a Canadian (Quebec province) river basin. The downscaling methods regroup dynamical and statistical approaches, including the change factor method and a weather generator-based approach. Future (2070-2099, 2085 horizon) hydrological regimes simulated with a hydrological model are compared to the reference period (1970-1999) using the average hydrograph, annual mean discharge, peak discharge and time to peak discharge as criteria. The results show that all downscaling methods suggest temperature increases over the basin for the 2085 horizon. The regression-based statistical methods predict a larger increase in autumn and winter temperatures. Predicted changes in precipitation are not as unequivocal as those of temperatures, they vary depending on the downscaling methods and seasons. There is a general increase in winter discharge (November-April) while decreases in summer discharge are predicted by most methods. Consistently with the large predicted increases in autumn and winter temperature, regression-based statistical methods show severe increases in winter flows and considerable reductions in peak discharge. Across all variables, a large uncertainty envelope was found to be associated with the choice of a downscaling method. This envelope was compared to the envelope originating from the choice of 28 climate change projections from a combination of seven GCMs and three GGES. Both uncertainty envelopes were similar

  10. Dynamically downscaled climate outputs for estimating hydrological responses for a Wyoming watershed

    NASA Astrophysics Data System (ADS)

    Vithanage, J.; Miller, S. N.; Paige, G. B.; Kelleners, T.

    2014-12-01

    Potential impacts of climate on surface hydrology in western Wyoming were assessed using the Weather Research and Forecasting (WRF) model in conjunction with spatially explicit hydrological models. The study focused on Crow Creek watershed, which is one of the main watersheds providing water to the city of Cheyenne, Wyoming. Pronounced water shortages were occurred between 2011 and 2013, leaving no water in the streams by the end of July each year. We developed climate scenarios by downscaling the predictions from General Circulation Models (GCM's) and Regional Climate Models (RCM's). Therefore, WRF was employed downscale the existing GCM's and RCM's in to local climate conditions and to obtain a higher spatial resolution. The data assimilation system and software architecture enables parallel processing developed by the National Center for Atmospheric Research (NCAR). The Automated Geospatial Water Assessment tool (AGWA) interface was used to parameterize and execute two hydrologic models: the Soil and Water Assessment Tool (SWAT) and the KINEmatic Runoff and EROSion model (KINEROS2). We used freely available data including SSURGO soils, Multi-Resolution Landscape Consortium (MRLC) land cover, and 10m resolution terrain data to derive suitable initial parameters for the models. Observed daily rainfall and temperature inputs as a function of elevation were used for model validation. Cellular Automation used in predicting future land cover. Future scenarios were developed for different global emissions scenarios proposed by the Special Report on Emissions Scenarios (SRES). Daily rainfall and surface temperature series were simulated for Crow Creek watershed for the year 2050 and used as an input to AGWA model. Results were used to find the impacts of the climate on water resources and the flow regimes of the watershed. The results from different data sources were compared for percentage of explained variance, mean bias for temperature and rainfall to produce

  11. Using Dynamically Downscaled Climate Model Outputs to Inform Projections of Extreme Precipitation Events

    NASA Technical Reports Server (NTRS)

    Wobus, Cameron; Reynolds, Lara; Jones, Russell; Horton, Radley; Smith, Joel; Fries, J. Stephen; Tryby, Michael; Spero, Tanya; Nolte, Chris

    2015-01-01

    Many of the storms that generate damaging floods are caused by locally intense, sub-daily precipitation, yet the spatial and temporal resolution of the most widely available climate model outputs are both too coarse to simulate these events. Thus there is often a disconnect between the nature of the events that cause damaging floods and the models used to project how climate change might influence their magnitude. This could be a particular problem when developing scenarios to inform future storm water management options under future climate scenarios. In this study we sought to close this gap, using sub-daily outputs from the Weather Research and Forecasting model (WRF) from each of the nine climate regions in the United States. Specifically, we asked 1) whether WRF outputs projected consistent patterns of change for sub-daily and daily precipitation extremes; and 2) whether this dynamically downscaled model projected different magnitudes of change for 3-hourly vs 24-hourly extreme events. We extracted annual maximum values for 3-hour through 24-hour precipitation totals from an 11-year time series of hindcast (1995-2005) and mid-century (2045-2055) climate, and calculated the direction and magnitude of change for 3-hour and 24-hour extreme events over this timeframe. The model results project that the magnitude of both 3-hour and 24-hour events will increase over most regions of the United States, but there was no clear or consistent difference in the relative magnitudes of change for sub-daily vs daily events.

  12. 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

  13. Intercomparison of statistical and dynamical downscaling models under the EURO- and MED-CORDEX initiative framework: present climate evaluations

    NASA Astrophysics Data System (ADS)

    Vaittinada Ayar, Pradeebane; Vrac, Mathieu; Bastin, Sophie; Carreau, Julie; Déqué, Michel; Gallardo, Clemente

    2016-02-01

    Given the coarse spatial resolution of General Circulation Models, finer scale projections of variables affected by local-scale processes such as precipitation are often needed to drive impacts models, for example in hydrology or ecology among other fields. This need for high-resolution data leads to apply projection techniques called downscaling. Downscaling can be performed according to two approaches: dynamical and statistical models. The latter approach is constituted by various statistical families conceptually different. If several studies have made some intercomparisons of existing downscaling models, none of them included all those families and approaches in a manner that all the models are equally considered. To this end, the present study conducts an intercomparison exercise under the EURO- and MED-CORDEX initiative hindcast framework. Six Statistical Downscaling Models (SDMs) and five Regional Climate Models (RCMs) are compared in terms of precipitation outputs. The downscaled simulations are driven by the ERAinterim reanalyses over the 1989-2008 period over a common area at 0.44° of resolution. The 11 models are evaluated according to four aspects of the precipitation: occurrence, intensity, as well as spatial and temporal properties. For each aspect, one or several indicators are computed to discriminate the models. The results indicate that marginal properties of rain occurrence and intensity are better modelled by stochastic and resampling-based SDMs, while spatial and temporal variability are better modelled by RCMs and resampling-based SDM. These general conclusions have to be considered with caution because they rely on the chosen indicators and could change when considering other specific criteria. The indicators suit specific purpose and therefore the model evaluation results depend on the end-users point of view and how they intend to use with model outputs. Nevertheless, building on previous intercomparison exercises, this study provides a

  14. Regional Climate Modeling: Progress, Challenges, and Prospects

    SciTech Connect

    Wang, Yuqing; Leung, Lai R.; McGregor, John L.; Lee, Dong-Kyou; Wang, Wei-Chyung; Ding, Yihui; Kimura, Fujio

    2004-12-01

    Regional climate modeling with regional climate models (RCMs) has matured over the past decade and allows for meaningful utilization in a broad spectrum of applications. In this paper, latest progresses in regional climate modeling studies are reviewed, including RCM development, applications of RCMs to dynamical downscaling for climate change assessment, seasonal climate predictions and climate process studies, and the study of regional climate predictability. Challenges and potential directions of future research in this important area are discussed, with the focus on those to which less attention has been given previously, such as the importance of ensemble simulations, further development and improvement of regional climate modeling approach, modeling extreme climate events and sub-daily variation of clouds and precipitation, model evaluation and diagnostics, applications of RCMs to climate process studies and seasonal predictions, and development of regional earth system models. It is believed that with both the demonstrated credibility of RCMs’ capability in reproducing not only monthly to seasonal mean climate and interannual variability but also the extreme climate events when driven by good quality reanalysis and the continuous improvements in the skill of global general circulation models (GCMs) in simulating large-scale atmospheric circulation, regional climate modeling will remain an important dynamical downscaling tool for providing the needed information for assessing climate change impacts and seasonal climate predictions, and a powerful tool for improving our understanding of regional climate processes. An internationally coordinated effort can be developed with different focuses by different groups to advance regional climate modeling studies. It is also recognized that since the final quality of the results from nested RCMs depends in part on the realism of the large-scale forcing provided by GCMs, the reduction of errors and improvement in

  15. 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.

  16. 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.

  17. 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

  18. Thirty-four years of Hawaii wave hindcast from downscaling of climate forecast system reanalysis

    NASA Astrophysics Data System (ADS)

    Li, Ning; Cheung, Kwok Fai; Stopa, Justin E.; Hsiao, Feng; Chen, Yi-Leng; Vega, Luis; Cross, Patrick

    2016-04-01

    The complex wave climate of Hawaii includes a mix of seasonal swells and wind waves from all directions across the Pacific. Numerical hindcasting from surface winds provides essential space-time information to complement buoy and satellite observations for studies of the marine environment. We utilize WAVEWATCH III and SWAN (Simulating WAves Nearshore) in a nested grid system to model basin-wide processes as well as high-resolution wave conditions around the Hawaiian Islands from 1979 to 2013. The wind forcing includes the Climate Forecast System Reanalysis (CFSR) for the globe and downscaled regional winds from the Weather Research and Forecasting (WRF) model. Long-term in-situ buoy measurements and remotely-sensed wind speeds and wave heights allow thorough assessment of the modeling approach and data products for practical application. The high-resolution WRF winds, which include orographic and land-surface effects, are validated with QuickSCAT observations from 2000 to 2009. The wave hindcast reproduces the spatial patterns of swell and wind wave events detected by altimeters on multiple platforms between 1991 and 2009 as well as the seasonal variations recorded at 16 offshore and nearshore buoys around the Hawaiian Islands from 1979 to 2013. The hindcast captures heightened seas in interisland channels and around prominent headlands, but tends to overestimate the heights of approaching northwest swells and give lower estimates in sheltered areas. The validated high-resolution hindcast sets a baseline for future improvement of spectral wave models.

  19. Downscaling future climate projections to the watershed scale: a north San Francisco Bay estuary case study

    USGS Publications Warehouse

    Micheli, Elisabeth; Flint, Lorraine; Flint, Alan; Weiss, Stuart; Kennedy, Morgan

    2012-01-01

    We modeled the hydrology of basins draining into the northern portion of the San Francisco Bay Estuary (North San Pablo Bay) using a regional water balance model (Basin Characterization Model; BCM) to estimate potential effects of climate change at the watershed scale. The BCM calculates water balance components, including runoff, recharge, evapotranspiration, soil moisture, and stream flow, based on climate, topography, soils and underlying geology, and the solar-driven energy balance. We downscaled historical and projected precipitation and air temperature values derived from weather stations and global General Circulation Models (GCMs) to a spatial scale of 270 m. We then used the BCM to estimate hydrologic response to climate change for four scenarios spanning this century (2000–2100). Historical climate patterns show that Marin’s coastal regions are typically on the order of 2 °C cooler and receive five percent more precipitation compared to the inland valleys of Sonoma and Napa because of marine influences and local topography. By the last 30 years of this century, North Bay scenarios project average minimum temperatures to increase by 1.0 °C to 3.1 °C and average maximum temperatures to increase by 2.1 °C to 3.4 °C (in comparison to conditions experienced over the last 30 years, 1981–2010). Precipitation projections for the 21st century vary between GCMs (ranging from 2 to 15% wetter than the 20th-century average). Temperature forcing increases the variability of modeled runoff, recharge, and stream discharge, and shifts hydrologic cycle timing. For both high- and low-rainfall scenarios, by the close of this century warming is projected to amplify late-season climatic water deficit (a measure of drought stress on soils) by 8% to 21%. Hydrologic variability within a single river basin demonstrated at the scale of subwatersheds may prove an important consideration for water managers in the face of climate change. Our results suggest that in arid

  20. Downscaling of Minimum Surface Temperature in the Semi-arid Great Basin Region and Implications for Bio-geophysical Processes

    NASA Astrophysics Data System (ADS)

    Hatchett, B. J.; Vellore, R.; Koracin, D.

    2009-12-01

    This study addresses downscaling methodology for monthly surface air temperature from global climate model (GCM) horizontal grid resolutions (> 100 km) to regional scales (< 10 km) appropriate for climate impact studies. Preliminary hindcast analysis for the period 1950-2008 indicated that the minimum temperatures extracted from the GCMs at 46 individual stations in Nevada show correct seasonal trends, but the monthly mean minima are significantly underestimated compared to three observational networks (Western Regional Climate Center (WRCC), DRI), National Climate Data Center (NCDC), and Parameter-elevation Regressions on Independent Slopes Model (PRISM) climate data sets. The daily mean surface air temperature, from the three GCMs (NCAR-CCSM3, ECHAM5, and CSIRO-Mk3.5) and a regional climate model (RCM) using the Weather Research and Forecasting (WRF) model forced by the CCSM3 outputs, is generally under-predicted with root-mean-square errors as large as 6 K on an annual scale. The underlying premise of this study is that changes in minimum temperature are manifested on the landscape via changes in hydrological parameters viz., runoff timing and evapotranspiration rates, ecological parameters viz., rates of invasion of exotic species and fire hazards, and socio-economic parameters viz., urban energy use. The systematic error or bias in surface minimum temperature simulated by the GCMs and their ensembles under designated Intergovernmental Panel on Climate Change (IPCC) climate change scenarios (A1B, A2, and B1) is investigated to assess and substantiate this argument. The present study employs the downscaling technique of bias correction and spatial disaggregation (BCSD) to improve GCM representation of monthly minimum temperature characteristics at local and regional scales which are critical to properly quantify for ecologic, hydrologic, and socio-economic forecasting under future climate change scenarios.

  1. Dynamically downscaled climate simulations over North America: Methods, evaluation, and supporting documentation for users

    USGS Publications Warehouse

    Hostetler, S.W.; Alder, J.R.; Allan, A.M.

    2011-01-01

    We have completed an array of high-resolution simulations of present and future climate over Western North America (WNA) and Eastern North America (ENA) by dynamically downscaling global climate simulations using a regional climate model, RegCM3. The simulations are intended to provide long time series of internally consistent surface and atmospheric variables for use in climate-related research. In addition to providing high-resolution weather and climate data for the past, present, and future, we have developed an integrated data flow and methodology for processing, summarizing, viewing, and delivering the climate datasets to a wide range of potential users. Our simulations were run over 50- and 15-kilometer model grids in an attempt to capture more of the climatic detail associated with processes such as topographic forcing than can be captured by general circulation models (GCMs). The simulations were run using output from four GCMs. All simulations span the present (for example, 1968-1999), common periods of the future (2040-2069), and two simulations continuously cover 2010-2099. The trace gas concentrations in our simulations were the same as those of the GCMs: the IPCC 20th century time series for 1968-1999 and the A2 time series for simulations of the future. We demonstrate that RegCM3 is capable of producing present day annual and seasonal climatologies of air temperature and precipitation that are in good agreement with observations. Important features of the high-resolution climatology of temperature, precipitation, snow water equivalent (SWE), and soil moisture are consistently reproduced in all model runs over WNA and ENA. The simulations provide a potential range of future climate change for selected decades and display common patterns of the direction and magnitude of changes. As expected, there are some model to model differences that limit interpretability and give rise to uncertainties. Here, we provide background information about the GCMs and

  2. High-resolution stochastic downscaling of climate models: simulating wind advection, cloud cover and precipitation

    NASA Astrophysics Data System (ADS)

    Peleg, Nadav; Fatichi, Simone; Burlando, Paolo

    2015-04-01

    A new stochastic approach to generate wind advection, cloud cover and precipitation fields is presented with the aim of formulating a space-time weather generator characterized by fields with high spatial and temporal resolution (e.g., 1 km x 1 km and 5 min). Its use is suitable for stochastic downscaling of climate scenarios in the context of hydrological, ecological and geomorphological applications. The approach is based on concepts from the Advanced WEather GENerator (AWE-GEN) presented by Fatichi et al. (2011, Adv. Water Resour.), the Space-Time Realizations of Areal Precipitation model (STREAP) introduced by Paschalis et al. (2013, Water Resour. Res.), and the High-Resolution Synoptically conditioned Weather Generator (HiReS-WG) presented by Peleg and Morin (2014, Water Resour. Res.). Advection fields are generated on the basis of the 500 hPa u and v wind direction variables derived from global or regional climate models. The advection velocity and direction are parameterized using Kappa and von Mises distributions respectively. A random Gaussian fields is generated using a fast Fourier transform to preserve the spatial correlation of advection. The cloud cover area, total precipitation area and mean advection of the field are coupled using a multi-autoregressive model. The approach is relatively parsimonious in terms of computational demand and, in the context of climate change, allows generating many stochastic realizations of current and projected climate in a fast and efficient way. A preliminary test of the approach is presented with reference to a case study in a complex orography terrain in the Swiss Alps.

  3. 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...

  4. Assessment of dynamical downscaling in Japan using the Regional Atmospheric Modeling System (RAMS)

    NASA Astrophysics Data System (ADS)

    Dairaku, K.; Pielke, R. A.; Beltran-Przekurat, A. B.; Iizuka, S.; Sasaki, W.

    2009-12-01

    The responses of the climate system to increases in carbon dioxide concentrations and to changes in land use/land cover and the subsequent impacts of climatic variability on humans and natural ecosystems are of fundamental concern. Because regional responses of surface hydrological and biogeochemical changes are particularly complex, it is necessary to add spatial resolution to accurately assess critical interactions within the regional climate system for climate change impacts assessments. We quantified the confidence and the uncertainties of Type II dynamical downscaling which the lateral and bottom boundary conditions were obtained from Japanese 25-year ReAnalysis (JRA-25) and assessed the value (skill) added by the downscaling to a climate simulation in Japan. We investigated the reproducibility of present climate using two regional climate models with 20 km horizontal grid spacing, the atmosphere-biosphere-river coupling regional climate model (NIED-RAMS) and the Meteorological Research Institute Nonhydrostatic Model (MRI-NHM), both of which used JRA-25 as boundary conditions. Two key variables for impact studies, surface air temperature and precipitation, were compared with the Japanese high-resolution surface observation, Automated Meteorological Data Acquisition System (AMeDAS) on 78 river basins. Results simulated by the two models were relatively in good agreement with the observation on the basin scale. The NIED-RAMS bias of 2 m air temperature (2mT) were less than 0.5K and the bias of precipitation (P) were around 10% in most of the river basins on annual averages for three years (2002-2004). The biases over 29 years shown in the long term experiment are similar to those of the three year simulation. The model could add some information as to where the larger scale information was obtained. A regional climate model often has sensitivity to model configurations, such as domain size and nudging scheme. We conducted sensitivity experiments to domain size

  5. Assessing the relative effectiveness of statistical downscaling and distribution mapping in reproducing rainfall statistics based on climate model results

    NASA Astrophysics Data System (ADS)

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

    2016-01-01

    To improve the level skill of climate models (CMs) in reproducing the statistics of daily rainfall at a basin level, two types of statistical approaches have been suggested. One is statistical correction of CM rainfall outputs based on historical series of precipitation. The other, usually referred to as statistical rainfall downscaling, is the use of stochastic models to conditionally simulate rainfall series, based on large-scale atmospheric forcing from CMs. While promising, the latter approach attracted reduced attention in recent years, since the developed downscaling schemes involved complex weather identification procedures, while demonstrating limited success in reproducing several statistical features of rainfall. In a recent effort, Langousis and Kaleris () developed a statistical framework for simulation of daily rainfall intensities conditional on upper-air variables, which is simpler to implement and more accurately reproduces several statistical properties of actual rainfall records. Here we study the relative performance of: (a) direct statistical correction of CM rainfall outputs using nonparametric distribution mapping, and (b) the statistical downscaling scheme of Langousis and Kaleris (), in reproducing the historical rainfall statistics, including rainfall extremes, at a regional level. This is done for an intermediate-sized catchment in Italy, i.e., the Flumendosa catchment, using rainfall and atmospheric data from four CMs of the ENSEMBLES project. 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 CM used and the characteristics of the calibration period. This is particularly the case for yearly rainfall maxima.

  6. Does Nudging Squelch the Extremes in Regional Climate Modeling?

    EPA Science Inventory

    An important question in regional climate downscaling is whether to constrain (nudge) the interior of the limited-area domain toward the larger-scale driving fields. Prior research has demonstrated that interior nudging can increase the skill of regional climate predictions origin...

  7. Downscaling of general circulation model outputs: simulation of the snow climatology of the French Alps and sensitivity to climate change

    NASA Astrophysics Data System (ADS)

    Martin, E.; Timbal, B.; Brun, E.

    1996-12-01

    A downscaling method was developed to simulate the seasonal snow cover of the French Alps from general circulation model outputs under various scenarios. It consists of an analogue procedure, which associates a real meteorological situation to a model output. It is based on the comparison between simulated upper air fields and meteorological analyses from the European Centre for Medium-Range Weather Forecasts. The selection uses a nearest neighbour method at a daily time-step. In a second phase, the snow cover is simulated by the snow model CROCUS at several elevations and in the different regions of the French Alps by using data from the real meteorological situations. The method is tested with real data and applied to various ARPEGE/Climat simulations: the present climate and two climate change scenarios.

  8. Modeling responses of large-river fish populations to global climate change through downscaling and incorporation of predictive uncertainty

    USGS Publications Warehouse

    Wildhaber, Mark L.; Wikle, Christopher K.; Anderson, Christopher J.; Franz, Kristie J.; Moran, Edward H.; Dey, Rima; Mader, Helmut; Kraml, Julia

    2012-01-01

    Climate change operates over a broad range of spatial and temporal scales. Understanding its effects on ecosystems requires multi-scale models. For understanding effects on fish populations of riverine ecosystems, climate predicted by coarse-resolution Global Climate Models must be downscaled to Regional Climate Models to watersheds to river hydrology to population response. An additional challenge is quantifying sources of uncertainty given the highly nonlinear nature of interactions between climate variables and community level processes. We present a modeling approach for understanding and accomodating uncertainty by applying multi-scale climate models and a hierarchical Bayesian modeling framework to Midwest fish population dynamics and by linking models for system components together by formal rules of probability. The proposed hierarchical modeling approach will account for sources of uncertainty in forecasts of community or population response. 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. This understanding will aid evaluation of management options for coping with global climate change. In our initial analyses, we found that predicted pallid sturgeon population responses were dependent on the climate scenario considered.

  9. Trend in Surface-Water Balance over the Western United States from Downscaled CMIP5 Climate Projections

    NASA Astrophysics Data System (ADS)

    Xiong, J.; Wang, W.; Melton, F. S.; Milesi, C.; Nemani, R. R.

    2013-12-01

    Trend in Surface-Water Balance over the Western United States from Downscaled CMIP5 Climate Projections Jun Xiong, Weile Wang, Forrest Melton, Cristina Milesi, and Ramakrishna R Nemani The projected changes in Earth's climate will have important consequences on the hydrological cycles over the western United States. Previous studies have suggested that increases in surface temperature can promote the melting of snowpack on the Pacific Coast Ranges, which leads to a shift in water outflow towards earlier spring but subsequently reduces water availability in summer and autumn. However, the uncertainties of the projected hydrological changes remain high in coarse resolution climate projections. In this study, we use NEX-DCP30, a new archive of downscaled CMIP5 climate projections, to evaluate the impact of climate changes on the surface water balance over the western United States between 1950 to 2100 at a 30-arc-second (~800m) spatial resolution. Using the Terrestrial Observational and Prediction System (TOPS), we examine seasonal changes in precipitation, snow water equivalent (SWE), and outflow in thirteen different water resource regions. Declining trends are identified in modeled SWE across the region in response to the warming temperature. Most SWE reductions are associated with increases in winter monthly averaged minimum temperature, in particular when the minimum temperatures increases to -5°C or above. The trends of declining SWE/P ratios are most pronounced in March, corresponding to widespread warming at this time. Correspondingly, significant increases in outflow from the mountainous watersheds are found over the whole western United States. These significant changes in the spatial extent and the timing of the surface hydrological cycles are expected to have important impacts on regional water balance and the associated carbon cycles.

  10. 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.

  11. 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

  12. Downscaling of South America present climate forced by three global models

    NASA Astrophysics Data System (ADS)

    Chou, S. C.; Lyra, A. A.; Sueiro, G.; Mourao, C. F.; Silva, A.; Chagas, G.; Gomes, J. L.; Rodrigues, D. C.; Pilotto, I.; Tavares, P. S.; Campos, D. A.; Dereczynski, C. P.; Bustamante, J. F.; Chagas, D.

    2014-12-01

    The objective of this work is to evaluate the downscaling of three global coupled ocean-atmosphere models by setting up the Regional Climate Model at 20-km resolution over the domain that encompasses South America, Central America and parts of the adjacent oceans. The RCM is the Eta model used at CPTEC since 1997 for weather forecasts, since 2000 for seasonal forecasts and since 2012 for climate change studies. The model has suffered some upgrades and has turn into a finite volume model. Some examples of upgrades are the vertical coordinate refinement, the vertical advection scheme, some physics parameters, etc. To run for the time range of several decades and to synchronize with the global models, the calendar of this version of this model was modified. The global models are: the Hadley Centre model, HadGEM2-ES, the Japanese MIROC5 model, and the Brazilian BESM2.3.1 model. Their resolutions range from about 250 km to about 150 km. The present period simulations started from about 1960 until 2005. This step is the preparation for the future climate change scenarios runs driven by the same global models. Evaluations were based on CRU data. The mean spatial distribution of precipitation and temperature showed agreement against the observations. The simulated precipitation and temperature fields from the Eta showed correct seasonality along the year. The regional model simulations driven by BESM showed the least amount of precipitation over the tropical Pacific and Atlantic oceans, whereas the simulations driven by MIROC5 showed the largest amount in those oceans. The mean seasonal cycle of precipitation for three major regions in Brazil showed underestimate in the Amazon region, but overestimate in Central-South Brazilian region. The mean seasonal cycle of temperature were underestimated along all the year. The frequency distribution of precipitation showed that the regional model reach more intense precipitation rates than the global models, and similarly for

  13. Downscaling Global Land Cover Projections from an Integrated Assessment Model for Use in Regional Analyses: Results and Evaluation for the US from 2005 to 2095

    SciTech Connect

    West, Tristram O.; Le Page, Yannick LB; Huang, Maoyi; Wolf, Julie; Thomson, Allison M.

    2014-06-05

    Projections of land cover change generated from Integrated Assessment Models (IAM) and other economic-based models can be applied for analyses of environmental impacts at subregional and landscape scales. For those IAM and economic models that project land use at the sub-continental or regional scale, these projections must be downscaled and spatially distributed prior to use in climate or ecosystem models. Downscaling efforts to date have been conducted at the national extent with relatively high spatial resolution (30m) and at the global extent with relatively coarse spatial resolution (0.5 degree).

  14. Transient climate rainfall downscaling using a combined dynamic-stochastic methodology

    NASA Astrophysics Data System (ADS)

    Burton, Aidan; Blenkinsop, Stephen; Fowler, Hayley J.; Kilsby, Chris G.

    2010-05-01

    Managers of water resource systems need downscaled climate change projections that are relevant at the catchment scale and at a range of future time horizons. However, the uncertainty in future climate projections and the natural variability of the climate system affect the robustness of their decisions. Dynamic downscaling of discrete future time-slices also limits the analysis of the temporal development of climate change impacts, as only steady state scenarios are widely available. Addressing these issues a new transient (i.e. temporally non-stationary) rainfall simulation methodology has been developed which combines dynamical and statistical downscaling to generate a multi-model ensemble of transient daily point-scale rainfall timeseries. Each timeseries is sampled from a continuous stochastic simulation of the control-future time period and exhibits climatic non-stationarity in accordance with GCM/RCM projections. The ensemble as a whole represents aspects of both climate model uncertainty and natural variability and provides a basis for probabilistic time-horizon analyses such as when a particular impact will occur or when a particular threshold will be reached. The methodology is demonstrated for a case study raingauge located near the Brévilles spring in Northern France. Thirteen RCM projections from the PRUDENCE project for both control (1961-1990) and future (2071-2100) time-slices were obtained to form the basis of a multi-model representation of climate change. Each dynamically downscales the climate from either the ECHAM4/OPYC or the HadCM3 GCM. Multiplicative ‘change factors' were evaluated for a set of statistics of daily rainfall for each RCM. These quantify the future value of each statistic as a multiple of the control value for each calendar month in turn. Multiplying the case study raingauge statistics by the change factors provides future projections with an implicit correction for biases in the RCM control runs and a representation of the

  15. Downscaling of South America present climate driven by 4-member HadCM3 runs

    NASA Astrophysics Data System (ADS)

    Chou, Sin Chan; Marengo, José A.; Lyra, André A.; Sueiro, Gustavo; Pesquero, José F.; Alves, Lincoln M.; Kay, Gillian; Betts, Richard; Chagas, Diego J.; Gomes, Jorge L.; Bustamante, Josiane F.; Tavares, Priscila

    2012-02-01

    The objective of this work is to evaluate climate simulations over South America using the regional Eta Model driven by four members of an ensemble of the UK Met Office Hadley Centre HadCM3 global model. The Eta Model has been modified with the purpose of performing long-term decadal integrations and has shown to reproduce "present climate"—the period 1961-1990—reasonably well when forced by HadCM3. The global model lateral conditions with a resolution of 2.5° latitude × 3.75° longitude were provided at a frequency of 6 h. Each member of the global model ensemble has a different climate sensitivity, and the four members were selected to span the range of uncertainty encompassed by the ensemble. The Eta Model nested in the HadCM3 global model was configured with 40-km horizontal resolution and 38 layers in the vertical. No large-scale internal nudging was applied. Results are shown for austral summer and winter at present climate defined as 1961-90. The upper and low-level circulation patterns produced by the Eta-CPTEC/HadCM3 experiment set-up show good agreement with reanalysis data and the mean precipitation and temperature with CRU observation data. The spread in the downscaled mean precipitation and temperature is small when compared against model errors. On the other hand, the benefits in using an ensemble is clear in the improved representation of the seasonal cycle by the ensemble mean over any one realization. El Niño and La Niña years were identified in the HadCM3 member runs based on the NOAA Climate Prediction Center criterion of sea surface temperature anomalies in the Niño 3.4 area. The frequency of the El Niño and La Niña events in the studied period is underestimated by HadCM3. The precipitation and temperature anomalies typical of these events are reproduced by most of the Eta-CPTEC/HadCM3 ensemble, although small displacements of the positions of the anomalies occur. This experiment configuration is the first step on the implementation

  16. Future summer precipitation changes over CORDEX-East Asia domain downscaled by a regional ocean-atmosphere coupled model: A comparison to the stand-alone RCM

    NASA Astrophysics Data System (ADS)

    Zou, Liwei; Zhou, Tianjun

    2016-03-01

    Climate changes under the RCP8.5 scenario over the Coordinated Regional Downscaling Experiment (CORDEX)-East Asia domain downscaled by a regional ocean-atmosphere coupled model Flexible Regional Ocean-Atmosphere Land System (FROALS) are compared to those downscaled by the corresponding atmosphere-only regional climate model driven by a global climate system model. Changes in the mean and interannual variability of summer rainfall were discussed for the period of 2051-2070 with respect to the present-day period of 1986-2005. Followed by an enhanced western North Pacific subtropical high and an intensified East Asian summer monsoon, an increase in total rainfall over north China, the Korean Peninsula, and Japan but a decrease in total rainfall over southern China are observed in the FROALS projection. Homogeneous increases of extreme rainfall amounts were found over the CORDEX-East Asia domain. A predominant increase in the interannual variability was evident for both total rainfall and the extreme rainfall amount. The spatial patterns of the projected rainfall changes by FROALS were generally consistent with those from the driving global model at a broad scale due to similar projected circulation changes. In both models, the enhanced southerlies over east China increased the moisture divergences over southern China and enhanced the moisture advection over north China. However, the atmosphere-only regional climate model (RCM) exhibited responses to the underlying sea surface temperature (SST) warming anomalies that were too strong, which induced an anomalous cyclone over the north South China Sea, followed by increases (decreases) of total and extreme rainfall over southern China (central China). The differences of the projected changes in both rainfall and circulation between FROALS and the atmosphere-only RCM were partly affected by the differences in the projected SST changes. The results recommend the employment of a regional ocean-atmosphere coupled model in the

  17. Assessing the future of crop yield variability in the United States with downscaled climate projections (Invited)

    NASA Astrophysics Data System (ADS)

    Lobell, D. B.; Urban, D.

    2010-12-01

    One aspect of climate change of particular concern to farmers and food markets is the potential for increased year-to-year variability in crop yields. Recent episodes of food price increases following the Australian drought or Russian heat wave have heightened this concern. Downscaled climate projections that properly capture the magnitude of daily and interannual variability of weather can be useful for projecting future yield variability. Here we examine the potential magnitude and cause of changes in variability of corn yields in the United States up to 2050. Using downscaled climate projections from multiple models, we estimate a distribution of changes in mean and variability of growing season average temperature and precipitation. These projections are then fed into a model of maize yield that explicitly factors in the effect of extremely warm days. Changes in yield variability can result from a shift in mean temperatures coupled with a nonlinear crop response, a shift in climate variability, or a combination of the two. The results are decomposed into these different causes, with implications for future research to reduce uncertainties in projections of future yield variability.

  18. Downscaling transient climate change using a Neyman-Scott Rectangular Pulses stochastic rainfall model

    NASA Astrophysics Data System (ADS)

    Burton, A.; Fowler, H. J.; Blenkinsop, S.; Kilsby, C. G.

    2010-02-01

    SummaryThe future management of hydrological systems must be informed by climate change projections at relevant time horizons and at appropriate spatial scales. Furthermore, the robustness of such management decisions is dependent on both the uncertainty inherent in future climate change scenarios and the natural climate system. Addressing these needs, we present a new transient rainfall simulation methodology which combines dynamical and statistical downscaling techniques to produce transient (i.e. temporally non-stationary) climate change scenarios. This is used to generate a transient multi-model ensemble of simulated point-scale rainfall time series for 1997-2085 for the polluted Brévilles spring in Northern France. The recovery of this previously potable source may be affected by climatic changes and variability over the next few decades. The provision of locally-relevant transient climate change scenarios for use as input to hydrological models of both water quality and quantity will ultimately provide a valuable resource for planning and decision making. Observed rainfall from 1988-2006 was characterised in terms of a set of statistics for each calendar month: the daily mean, variance, probability dry, lag-1 autocorrelation and skew, and the monthly variance. The Neyman-Scott Rectangular Pulses (NSRP) stochastic rainfall model was fitted to these observed statistics and correctly simulated both monthly statistics and extreme rainfall properties. Multiplicative change factors which quantify the change in each statistic between the periods 1961-1990 and 2071-2100 were estimated for each month and for each of 13 Regional Climate Models (RCMs) from the PRUDENCE ensemble. To produce transient climate change scenarios, pattern scaling factors were estimated and interpolated from four time-slice integrations of two General Circulation Models which condition the RCMs, ECHAM4/OPYC and HadCM3. Applying both factors to the observed statistics provided projected

  19. Future risk of global drought from downscaled, bias corrected climate projections

    NASA Astrophysics Data System (ADS)

    Sheffield, Justin; Li, Haibin; Wood, Eric

    2010-05-01

    Understanding how changes in drought conditions will develop in the 21st century, including changes in severity, extent, and duration, is of great importance to many sectors such as water resources management and agricultural activity. There may also be profound implications for the occurrence of wildfires and heat waves that are associated with dry conditions. Recent severe droughts in the Western U.S., southeast Australia, Eastern Africa, Europe and northern China are testament to the impacts that large scale drought can have and are perhaps indicators of things to come. The direct use of climate model outputs for analysis of future drought however is problematic because of known model biases, particularly model simulated precipitation and temperature fields that have first order impact on droughts. Here we present a comprehensive statistical analysis of future drought conditions globally in a multi-model, multi-scenario based framework. The analysis is based on recently completed simulations using the Variable Infiltration Capacity land surface model (LSM), forced by downscaled, bias corrected climate projections using a newly developed equidistant quantile matching method. This improves upon traditional quantile matching methods by taking into account changes in the future projection climate distribution and better represents extreme years that are most associated with the development of drought. We apply this to a suite of climate models for monthly precipitation and temperature but show how this can be extended to radiation, humidity and windspeed to capture associated changes and interplay among these associated drivers, although this is limited to a small set of climate models with available data. Further enhancements include improved temporal downscaling to account for changes in, for example, storm intensities and diurnal temperature range. The bias corrected and downscaled climate forcings are used to drive the LSM to generate future projections of the

  20. Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America

    PubMed Central

    Wang, Tongli; Hamann, Andreas; Spittlehouse, Dave; Carroll, Carlos

    2016-01-01

    Large volumes of gridded climate data have become available in recent years including interpolated historical data from weather stations and future predictions from general circulation models. These datasets, however, are at various spatial resolutions that need to be converted to scales meaningful for applications such as climate change risk and impact assessments or sample-based ecological research. Extracting climate data for specific locations from large datasets is not a trivial task and typically requires advanced GIS and data management skills. In this study, we developed a software package, ClimateNA, that facilitates this task and provides a user-friendly interface suitable for resource managers and decision makers as well as scientists. The software locally downscales historical and future monthly climate data layers into scale-free point estimates of climate values for the entire North American continent. The software also calculates a large number of biologically relevant climate variables that are usually derived from daily weather data. ClimateNA covers 1) 104 years of historical data (1901–2014) in monthly, annual, decadal and 30-year time steps; 2) three paleoclimatic periods (Last Glacial Maximum, Mid Holocene and Last Millennium); 3) three future periods (2020s, 2050s and 2080s); and 4) annual time-series of model projections for 2011–2100. Multiple general circulation models (GCMs) were included for both paleo and future periods, and two representative concentration pathways (RCP4.5 and 8.5) were chosen for future climate data. PMID:27275583

  1. Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America.

    PubMed

    Wang, Tongli; Hamann, Andreas; Spittlehouse, Dave; Carroll, Carlos

    2016-01-01

    Large volumes of gridded climate data have become available in recent years including interpolated historical data from weather stations and future predictions from general circulation models. These datasets, however, are at various spatial resolutions that need to be converted to scales meaningful for applications such as climate change risk and impact assessments or sample-based ecological research. Extracting climate data for specific locations from large datasets is not a trivial task and typically requires advanced GIS and data management skills. In this study, we developed a software package, ClimateNA, that facilitates this task and provides a user-friendly interface suitable for resource managers and decision makers as well as scientists. The software locally downscales historical and future monthly climate data layers into scale-free point estimates of climate values for the entire North American continent. The software also calculates a large number of biologically relevant climate variables that are usually derived from daily weather data. ClimateNA covers 1) 104 years of historical data (1901-2014) in monthly, annual, decadal and 30-year time steps; 2) three paleoclimatic periods (Last Glacial Maximum, Mid Holocene and Last Millennium); 3) three future periods (2020s, 2050s and 2080s); and 4) annual time-series of model projections for 2011-2100. Multiple general circulation models (GCMs) were included for both paleo and future periods, and two representative concentration pathways (RCP4.5 and 8.5) were chosen for future climate data. PMID:27275583

  2. Evaluation of a weather generator-based method for statistically downscaling non-stationary climate scenarios for impact assessment at a point scale

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The non-stationarity is a major concern for statistically downscaling climate change scenarios for impact assessment. This study is to evaluate whether a statistical downscaling method is fully applicable to generate daily precipitation under non-stationary conditions in a wide range of climatic zo...

  3. Simulating Regional Climate Change in New Hampshire

    NASA Astrophysics Data System (ADS)

    Komurcu, M.; Acosta, R. P.; Huber, M.

    2014-12-01

    Dynamical downscaling of Global Climate Model (GCM) simulated future projections using smaller scale, higher resolution models is widely used to assess the regional impacts of climate change on weather, ecosystems and economy. In this study, the Weather Research and Forecasting (WRF) model is used to dynamically downscale Community Earth System Model (CESM) future projections using Representative Concentration Pathways (RCP) 4.5 and 8.5 to simulate the possible effects of climate change in New Hampshire (NH). The first step to ensure that the downscaled model output is representative of the NH region is to find the correct WRF model set up for the region. This task is accomplished using CESM simulations of the historical period as forcing for WRF simulations and performing multiple sensitivity tests with different options for WRF physics parameterizations such as boundary layer, cloud microphysics and convection parameterizations. Simulated precipitation, temperature and other variables are compared with observations to obtain the more suitable model setup for NH. WRF simulations are performed on nested grids with 36, 12 and 4 km grid spacing, and the smallest grid sized nest is focused over NH. Furthermore, to prevent the drift of regional model from global model simulated climatology, WRF is reinitialized from GCM output every five days. Previous studies have shown that future regional climate model predictions of precipitation and snow water equivalent depend on the re-initialization interval of WRF from GCM forcing specifically over the western U.S, where topography is high. This problem is mainly because re-initialization erases the simulated memory for certain variables such as soil moisture. To evaluate whether re-initialization time-scale is also important in the Eastern US, in this study, the effects of 5-daily versus monthly re-initialization of WRF using CESM output on model simulated precipitation are also investigated. The obtained WRF model setup is

  4. Probabilistic Downscaling Methods for Developing Categorical Streamflow Forecasts using Climate Forecasts

    NASA Astrophysics Data System (ADS)

    Mazrooei, A. H.

    2015-12-01

    Statistical information from climate forecast ensembles can be utilized in developing probabilistic streamflow forecasts for providing the uncertainty in streamflow forecast potential. This study examines the use of Multinomial Logistic Regression (MLR) in downscaling the probabilistic information from the large-scale climate forecast ensembles into a point-scale categorical streamflow forecasts. Performance of MLR in developing one-month lead categorical forecasts is evaluated for various river basins over the US Sunbelt. Comparison of MLR with the estimated categorical forecasts from Principle Component Regression (PCR) method under both cross-validation and split-sampling validation reveals that in general the forecasts from MLR has better performance and lower Rank Probability Score (RPS) compared to the PCR forecasts. In addition, MLR performs better than PCR method particularly in arid basins that exhibit strong skewness in seasonal flows with records of distinct dry years. A theoretical underpinning for this improved performance of MLR is also provided.

  5. Using Stochastically Downscaled Climate Models and Multiproxy Lake Sediment Data to Connect Climatic Variations Over the Past 1000 Years and the History of Prehistoric Maize Farming in Utah

    NASA Astrophysics Data System (ADS)

    Thomson, M. J.; MacDonald, G. M.

    2015-12-01

    We are investigating the relationship between climatic variations over the past 1000 years and the history of prehistoric maize farming expansion and decline in the American Southwest, with a focus on Utah. We are examining both the downscaled climate models and high resolution analyses of lake cores and dendrochronological data matched with occupation information. We are testing the specific utility of stochastically downscaled general circulation models (viz. ECHO-G) to reconstruct local conditions for sites with documented prehistoric dryland farming through the so-called Medieval Climate Anomaly (MCA) and transition to the Little Ice Age (LIA). We are testing our model-based reconstructions with proxies of temperature and aridity from three subalpine lake sediment cores transecting Utah. We compare the patterns of climate change from the downscaled models and the paleoclimate records to a database of 837 radiocarbon dates over 169 locations of archaeological Native American maize-farmer site occupations in Utah.

  6. Do regional climate models represent regional climate?

    NASA Astrophysics Data System (ADS)

    Maraun, Douglas; Widmann, Martin

    2014-05-01

    When using climate change scenarios - either from global climate models or further downscaled - to assess localised real world impacts, one has to ensure that the local simulation indeed correctly represents the real world local climate. Representativeness has so far mainly been discussed as a scale issue: simulated meteorological variables in general represent grid box averages, whereas real weather is often expressed by means of point values. As a result, in particular simulated extreme values are not directly comparable with observed local extreme values. Here we argue that the issue of representativeness is more general. To illustrate this point, assume the following situations: first, the (GCM or RCM) simulated large scale weather, e.g., the mid-latitude storm track, might be systematically distorted compared to observed weather. If such a distortion at the synoptic scale is strong, the simulated local climate might be completely different from the observed. Second, the orography even of high resolution RCMs is only a coarse model of true orography. In particular in mountain ranges the simulated mesoscale flow might therefore considerably deviate from the observed flow, leading to systematically displaced local weather. In both cases, the simulated local climate does not represent observed local climate. Thus, representativeness also encompasses representing a particular location. We propose to measure this aspect of representativeness for RCMs driven with perfect boundary conditions as the correlation between observations and simulations at the inter-annual scale. In doing so, random variability generated by the RCMs is largely averaged out. As an example, we assess how well KNMIs RACMO2 RCM at 25km horizontal resolution represents winter precipitation in the gridded E-OBS data set over the European domain. At a chosen grid box, RCM precipitation might not be representative of observed precipitation, in particular in the rain shadow of major moutain ranges

  7. Predicting future US water yield and ecosystem productivity by linking an ecohydrological model to WRF dynamically downscaled climate projections

    NASA Astrophysics Data System (ADS)

    Sun, S.; Sun, G.; Cohen, E.; McNulty, S. G.; Caldwell, P.; Duan, K.; Zhang, Y.

    2015-12-01

    Quantifying the potential impacts of climate change on water yield and ecosystem productivity (i.e., carbon balances) is essential to developing sound watershed restoration plans, and climate change adaptation and mitigation strategies. This study links an ecohydrological model (Water Supply and Stress Index, WaSSI) with WRF (Weather Research and Forecasting Model) dynamically downscaled climate projections of the HadCM3 model under the IPCC SRES A2 emission scenario. We evaluated the future (2031-2060) changes in evapotranspiration (ET), water yield (Q) and gross primary productivity (GPP) from the baseline period of 1979-2007 across the 82 773 watersheds (12 digit Hydrologic Unit Code level) in the conterminous US (CONUS), and evaluated the future annual and monthly changes of hydrology and ecosystem productivity for the 18 Water Resource Regions (WRRs) or 2-digit HUCs. Across the CONUS, the future multi-year means show increases in annual precipitation (P) of 45 mm yr-1 (6 %), 1.8 °C increase in temperature (T), 37 mm yr-1 (7 %) increase in ET, 9 mm yr-1 (3 %) increase in Q, and 106 g C m-2 yr-1 (9 %) increase in GPP. Response to climate change was highly variable across the 82, 773 watersheds, but in general, the majority would see consistent increases in all variables evaluated. Over half of the 82 773 watersheds, mostly found in the northeast and the southern part of the southwest would have an increase in annual Q (>100 mm yr-1 or 20 %). This study provides an integrated method and example for comprehensive assessment of the potential impacts of climate change on watershed water balances and ecosystem productivity at high spatial and temporal resolutions. Results will be useful for policy-makers and land managers in formulating appropriate watershed-specific strategies for sustaining water and carbon sources in the face of climate change.

  8. Dynamical downscaling of present climate extremal episodes for the BINGO research site of Cyprus

    NASA Astrophysics Data System (ADS)

    Zittis, George; Hadjinicolaou, Panos; Bruggeman, Adriana; Camera, Corrado; Lelieveld, Jos

    2016-04-01

    Besides global warming, climate change is expected to cause alterations in precipitation amounts and distribution than can be linked to extreme events such as floods or prolonged droughts. This will have a significant impact in strategic societal sectors that base their activities on water resources. While the global climate projections inform us about the long-term and weather forecasts can give useful information only for a few days or weeks, decision-makers and end-users also need guidance on inter-annual to decadal time scales. In this context, the BINGO (Bringing INnovation to onGOing water management - a better future under climate change) H2020 project aims both at reducing the uncertainty of near-term climate predictions and developing response strategies in order to better manage the remaining uncertainty. One of the project's main objectives is to develop improved decadal predictions, in adequate spatiotemporal scales, with a specific focus on extreme precipitation events. The projected rainfall will be eventually used to drive hydrological impact models. BINGO focuses on research sites that encompass river basins, watersheds and urban areas of six European countries including Norway, Cyprus, Germany, Portugal, The Netherlands and Spain. In this study we present the dynamical downscaling of the ERA-Interim dataset for validation purposes and for the research site of Cyprus. Five extreme rainfall periods were identified from the observed precipitation archives and were simulated in very high horizontal resolutions (4~1 km) using the WRF limited area atmospheric model. To optimize the performance of the model we have tested a combination of three cumulus and five microphysics parameterization schemes that resulted in 15 simulations for each extreme precipitation event. The model output was compared with daily or hourly (where available) representative rain gauge data. A set of statistical metrics was applied in order to objectively select the best

  9. 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

    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

  10. Expansion of the On-line Archive "Statistically Downscaled WCRP CMIP3 Climate Projections"

    NASA Astrophysics Data System (ADS)

    Brekke, L. D.; Pruitt, T.; Maurer, E. P.; Das, T.; Duffy, P.; White, K.

    2009-12-01

    Presentation highlights status and plans for a public-access archive of downscaled CMIP3 climate projections. Incorporating climate projection information into long-term evaluations of water and energy resources requires analysts to have access to projections at "basin-relevant" resolution. Such projections would ideally be bias-corrected to account for climate model tendencies to systematically simulate historical conditions different than observed. In 2007, the U.S. Bureau of Reclamation, Santa Clara University and Lawrence Livermore National Laboratory (LLNL) collaborated to develop an archive of 112 bias-corrected and spatially disaggregated (BCSD) CMIP3 temperature and precipitation projections. These projections were generated using 16 CMIP3 models to simulate three emissions pathways (A2, A1b, and B1) from one or more initializations (runs). Projections are specified on a monthly time step from 1950-2099 and at 0.125 degree spatial resolution within the North American Land Data Assimilation System domain (i.e. contiguous U.S., southern Canada and northern Mexico). Archive data are freely accessible at LLNL Green Data Oasis (url). Since being launched, the archive has served over 3500 data requests by nearly 500 users in support of a range of planning, research and educational activities. Archive developers continue to look for ways to improve the archive and respond to user needs. One request has been to serve the intermediate datasets generated during the BCSD procedure, helping users to interpret the relative influences of the bias-correction and spatial disaggregation on the transformed CMIP3 output. This request has been addressed with intermediate datasets now posted at the archive web-site. Another request relates closely to studying hydrologic and ecological impacts under climate change, where users are asking for projected diurnal temperature information (e.g., projected daily minimum and maximum temperature) and daily time step resolution. In

  11. Statistical downscaling of CMIP5 multi-model ensemble for projected changes of climate in the Indus River Basin

    NASA Astrophysics Data System (ADS)

    Su, Buda; Huang, Jinlong; Gemmer, Marco; Jian, Dongnan; Tao, Hui; Jiang, Tong; Zhao, Chengyi

    2016-09-01

    The simulation results of CMIP5 (Coupled Model Inter-comparison Project phase 5) multi-model ensemble in the Indus River Basin (IRB) are compared with the CRU (Climatic Research Unit) and APHRODITE (Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation) datasets. The systematic bias between simulations and observations is corrected by applying the equidistant Cumulative Distribution Functions matching method (EDCDFm) and high-resolution simulations are statistically downscaled. Then precipitation and temperature are projected for the IRB for the mid-21st century (2046-2065) and late 21st century (2081-2100). The results show that the CMIP5 ensemble captures the dominant features of annual and monthly mean temperature and precipitation in the IRB. Based on the downscaling results, it is projected that the annual mean temperature will increase over the entire basin, relative to the 1986-2005 reference period, with greatest changes in the Upper Indus Basin (UIB). Heat waves are more likely to occur. An increase in summer temperature is projected, particularly for regions of higher altitudes in the UIB. The persistent increase of summer temperature might accelerate the melting of glaciers, and has negative impact on the local freshwater availability. Projections under all RCP scenarios show an increase in monsoon precipitation, which will increase the possibility of flood disaster. A decreasing trend in winter and spring precipitation in the IRB is projected except for the RCP2.6 scenario which will cause a lower contribution of winter and spring precipitation to water resources in the mid and high altitude areas of the IRB.

  12. 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

  13. Downscaling Climate Science to the Classroom: Diverse Opportunities for Teaching Climate Science in Diverse Ways to Diverse Undergraduate Populations

    NASA Astrophysics Data System (ADS)

    Jones, R. M.; Gill, T. E.; Quesada, D.; Hedquist, B. C.

    2015-12-01

    Climate literacy and climate education are important topics in current socio-political debate. Despite numerous scientific findings supporting global climate changes and accelerated greenhouse warming, there is a social inertia resisting and slowing the rate at which many of our students understand and absorb these facts. A variety of reasons, including: socio-economic interests, political and ideological biases, misinformation from mass media, inappropriate preparation of science teachers, and lack of numancy have created serious challenges for public awareness of such an important issue. Different agencies and organizations (NASA, NOAA, EPA, AGU, APS, AMS and others) have created training programs for educators, not involved directly in climatology research, in order to learn climate science in a consistent way and then communicate it to the public and students. Different approaches on how to deliver such information to undergraduate students in diverse environments is discussed based on the author's experiences working in different minority-serving institutions across the nation and who have attended AMS Weather and Climate Studies training workshops, MSI-REACH, and the School of Ice. Different parameters are included in the analysis: demographics of students, size of the institutions, geographical locations, target audience, programs students are enrolled in, conceptual units covered, and availability of climate-related courses in the curricula. Additionally, the feasibility of incorporating a laboratory and quantitative analysis is analyzed. As a result of these comparisons it seems that downscaling of climate education experiences do not always work as expected in every institution regardless of the student body demographics. Different geographical areas, student body characteristics and type of institution determine the approach to be adopted as well as the feasibility to introduce different components for weather and climate studies. Some ideas are shared

  14. Marine downscaling of a future climate scenario in the North Sea and possible effects on dinoflagellate harmful algal blooms.

    PubMed

    Friocourt, Y F; Skogen, M; Stolte, W; Albretsen, J

    2012-01-01

    Two hydrodynamic and ecological models were used to investigate the effects of climate change-according to the IPCC A1b emission scenario - on the primary productivity of the North Sea and on harmful algal blooms. Both models were forced with atmospheric fields from a regional downscaling of General Circulation Models to compare two sets of 20-year simulations representative of present climate (1984-2004) conditions and of the 2040s. Both models indicated a general warming of the North Sea by up to 0.8°C and a slight freshening by the 2040s. The models suggested that the eastern North Sea would be subjected to more temperature and salinity changes than the western part. In addition, the ecological modules of the models indicated that the warming up of the sea would result in a slightly earlier spring bloom. The one model that also computes the distribution of four different phytoplankton groups suggests an increase in the abundance of dinoflagellates, whereas the abundance of diatoms, flagellates and Phaeocystis sp. remains comparable to current levels, or decrease. Assuming that Dinophysis spp. would experience a similar increase in abundance as the modelled group of dinoflagellates, it is hypothesised that blooms of Dinophysis spp. may occur more frequently in the North Sea by 2040. However, implications for shellfish toxicity remain unclear.

  15. Investigating climatic drivers of the warming hole through empirical downscaling of eastern U.S. summertime maximum temperatures

    NASA Astrophysics Data System (ADS)

    Wagner, Audrey Romaine

    Global average temperatures have increased over the past century, but not all regions of the world have experienced warming. The central United States has experienced less warming than western and eastern portions of the country, with some stations experiencing cooling trends during the period of 1948-2009, leading researchers to dub the area a "warming hole." The causes of this anomaly have been investigated via general circulation models (GCMs) and regional climate model downscaling of GCM output, but conclusions have been limited. This research identifies important drivers of June, July, and August (JJA) mean maximum daily temperature (Tmax) in the region which includes the "warming hole" by developing a model for Tmax using empirical downscaling of large scale variables and local precipitation. First, robust trend analysis is used to determine temperature trends across the country for two time periods: 1948--2009, and 1978--2009; and to locate stations which have experienced cooling, or minimal warming. Second, 19 surface and upper air variables are investigated to identify the optimal independent predictors of Tmax. Station-by-station models of T max are produced from National Centers for Environmental Prediction/National Center for Atmospheric Research reanalysis sea level pressure, 500 mb geopotential heights, total (meridional and zonal) 850 mb winds in the area of frequent LLJs, as well as station precipitation and assessed for quality. Measures of skill include analysis of error and variance in the modeled time series, as well values of beta-weighted regression coefficients. Third, trends from the modeled time series are calculated and compared with the observed trends. The models show that 500 mb heights have a strong positive correlation with Tmax across the study area, while precipitation widely and uniformly correlates with lower Tmax. Sea level pressure has a negative correlation with Tmax in much of the study area. The LLJ predictor provides novel

  16. A combined dynamical and statistical downscaling technique to reduce biases in climate projections: an example for winter precipitation and snowpack in the western United States

    NASA Astrophysics Data System (ADS)

    Li, R.; Wang, S.-Y.; Gillies, R. R.

    2016-04-01

    Large biases associated with climate projections are problematic when it comes to their regional application in the assessment of water resources and ecosystems. Here, we demonstrate a method that can reduce systematic biases in regional climate projections. The global and regional climate models employed to demonstrate the technique are the Community Climate System Model (CCSM) and the Weather Research and Forecasting (WRF) model. The method first utilized a statistical regression technique and a global reanalysis dataset to correct biases in the CCSM-simulated variables (e.g., temperature, geopotential height, specific humidity, and winds) that are subsequently used to drive the WRF model. The WRF simulations were conducted for the western United States and were driven with (a) global reanalysis, (b) original CCSM, and (c) bias-corrected CCSM data. The bias-corrected CCSM data led to a more realistic regional climate simulation of precipitation and associated atmospheric dynamics, as well as snow water equivalent (SWE), in comparison to the original CCSM-driven WRF simulation. Since most climate applications rely on existing global model output as the forcing data (i.e., they cannot re-run or change the global model), which often contain large biases, this method provides an effective and economical tool to reduce biases in regional climate downscaling simulations of water resource variables.

  17. FY08 LDRD Final Report Regional Climate

    SciTech Connect

    Bader, D C; Chin, H; Caldwell, P M

    2009-05-19

    An integrated, multi-model capability for regional climate change simulation is needed to perform original analyses to understand and prepare for the impacts of climate change on the time and space scales that are critical to California's future environmental quality and economic prosperity. Our intent was to develop a very high resolution regional simulation capability to address consequences of climate change in California to complement the global modeling capability that is supported by DOE at LLNL and other institutions to inform national and international energy policies. The California state government, through the California Energy Commission (CEC), institutionalized the State's climate change assessment process through its biennial climate change reports. The bases for these reports, however, are global climate change simulations for future scenarios designed to inform international policy negotiations, and are primarily focused on the global to continental scale impacts of increasing emissions of greenhouse gases. These simulations do not meet the needs of California public and private officials who will make major decisions in the next decade that require an understanding of climate change in California for the next thirty to fifty years and its effects on energy use, water utilization, air quality, agriculture and natural ecosystems. With the additional development of regional dynamical climate modeling capability, LLNL will be able to design and execute global simulations specifically for scenarios important to the state, then use those results to drive regional simulations of the impacts of the simulated climate change for regions as small as individual cities or watersheds. Through this project, we systematically studied the strengths and weaknesses of downscaling global model results with a regional mesoscale model to guide others, particularly university researchers, who are using the technique based on models with less complete parameterizations or

  18. Regional climate projection of the Maritime Continent using the MIT Regional Climate Model

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

    Given that warming of the climate system is unequivocal (IPCC AR5), accurate assessment of future climate is essential to understand the impact of climate change due to global warming. Modelling the climate change of the Maritime Continent is particularly challenge, showing a high degree of uncertainty. Compared to other regions, model agreement of future projections in response to anthropogenic emission forcings is much less. Furthermore, the spatial and temporal behaviors of climate projections seem to vary significantly due to a complex geographical condition and a wide range of scale interactions. For the fine-scale climate information (27 km) suitable for representing the complexity of climate change over the Maritime Continent, dynamical downscaling is performed using the MIT regional climate model (MRCM) during two thirty-year period for reference (1970-1999) and future (2070-2099) climate. Initial and boundary conditions are provided by Community Earth System Model (CESM) simulations under the emission scenarios projected by MIT Integrated Global System Model (IGSM). Changes in mean climate as well as the frequency and intensity of extreme climate events are investigated at various temporal and spatial scales. Our analysis is primarily centered on the different behavior of changes in convective and large-scale precipitation over land vs. ocean during dry vs. wet season. In addition, we attempt to find the added value to downscaled results over the Maritime Continent through the comparison between MRCM and CESM projection. Acknowledgements.This research was supported by the National Research Foundation Singapore through the Singapore MIT Alliance for Research and Technology's Center for Environmental Sensing and Modeling interdisciplinary research program.

  19. Potential impact of climate change on the Intra-Americas Sea: Part-1. A dynamic downscaling of the CMIP5 model projections

    NASA Astrophysics Data System (ADS)

    Liu, Yanyun; Lee, Sang-Ki; Enfield, David B.; Muhling, Barbara A.; Lamkin, John T.; Muller-Karger, Frank E.; Roffer, Mitchell A.

    2015-08-01

    This study examines the potential impact of anthropogenic greenhouse warming on the Intra-Americas Sea (IAS, Caribbean Sea and Gulf of Mexico) by downscaling the Coupled Model Intercomparison Project phase-5 (CMIP5) model simulations under historical and two future emission scenarios using an eddy-resolving resolution regional ocean model. The simulated volume transport by the western boundary current system in the IAS, including the Caribbean Current, Yucatan Current and Loop Current (LC), is reduced by 20-25% during the 21st century, consistent with a similar rate of reduction in the Atlantic Meridional Overturning Circulation (AMOC). The effect of the LC in the present climate is to warm the Gulf of Mexico (GoM). Therefore, the reduced LC and the associated weakening of the warm transient LC eddies have a cooling impact in the GoM, particularly during boreal spring in the northern deep basin, in agreement with an earlier dynamic downscaling study. In contrast to the reduced warming in the northern deep GoM, the downscaled model predicts an intense warming in the shallow (≤ 200 m) northeastern shelf of the GoM especially during boreal summer since there is no effective mechanism to dissipate the increased surface heating. Potential implications of the regionally distinctive warming trend pattern in the GoM on the marine ecosystems and hurricane intensifications during landfall are discussed. This study also explores the effects of 20th century warming and climate variability in the IAS using the regional ocean model forced with observed surface flux fields. The main modes of sea surface temperature variability in the IAS are linked to the Atlantic Multidecadal Oscillation and a meridional dipole pattern between the GoM and Caribbean Sea. It is also shown that variability of the IAS western boundary current system in the 20th century is largely driven by wind stress curl in the Sverdrup interior and the AMOC.

  20. 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

  1. Climatic Concepts and Regions.

    ERIC Educational Resources Information Center

    Thomas, Paul F.

    Designed for students in grades 7 through 12, this teaching unit presents illustrative resource materials depicting concepts related to climate and geographic regions. Emphasis is on giving students an understanding of climatic elements and factors, not as isolated, disjointed entities, but as a dynamic interplay of forces having a very definite…

  2. Climate downscaling for estimating glacier mass balances in northwestern North America: Validation with a USGS benchmark glacier

    NASA Astrophysics Data System (ADS)

    Zhang, Jing; Bhatt, Uma S.; Tangborn, Wendell V.; Lingle, Craig S.

    2007-11-01

    An atmosphere/glacier modeling system is described for estimating the mass balances of glaciers in both current and future climate in order to estimate their probable future contributions to rising sea level. Dynamically downscaled output from a regional atmospheric model, driven by global atmospheric reanalysis, is used to force a precipitation-temperature-area-altitude (PTAA) glacier mass balance model with daily maximum and minimum temperatures and precipitation. The modeling system is verified by hindcasting the mass balances of Gulkana Glacier, a U.S. Geological Survey (USGS) benchmark glacier in the Alaska Range, U.S.A., during a ten-year period from October 1994 to September 2004. The mass balances simulated with the atmosphere/glacier modeling system are comparable to the USGS measurements, and are also in good agreement with the meteorological station observation-forced PTAA simulations. The results suggest this is a promising approach for realistic estimation of the future mass balances of the glaciers of northwestern North America.

  3. Downscaling precipitation in the Sahara-Sahelian region during the Holocene in order to decipher the paleo-variations of Lake Chad

    NASA Astrophysics Data System (ADS)

    Contoux, Camille; Bondeau, Alberte; Barrier, Nicolas; Sylvestre, Florence

    2016-04-01

    In order to understand the paleo-variability of Saharo-Sahelian paleoprecipitation, which is recorded in the sediments of Lake Chad situated in central Sahel, we use a modelling chain going from global climate to basin-scale hydrological model. Namely, climate model outputs for the Holocene, starting with the mid-Holocene (6ka) available from the IPSL-CM5 global climate model are statistically downscaled with the General Additive Model approach (Levavasseur et al., 2011), then used to feed the LPJmL model (Bondeau et al., 2007) which calculates the equilibrium vegetation and runoff. Climate and runoff are then given to the dynamic routing scheme HYDRA (Coe et al., 2000) in order to calculate the paleo river network and paleo extent of Lake Chad. The results at each step are compared with reconstructions derived from continental proxies on the regional scale in order to assess the robustness of the results. For the mid-Holocene, the downscaled precipitation matches very well precipitation estimations derived from lacustrine pollen data. For the historical period, the LPJmL simulated runoff averaged over the Chad basin depicts the same trend than observations of Lake Chad water level, but the absolute water level is overestimated in HYDRA, which can be attributed to humid biases both in LPJmL and HYDRA. Finally, we will investigate the relative changes in river network and Lake Chad extent between the present and the mid-Holocene.

  4. "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...

  5. Model experiments on climate change in the Tokyo metropolitan area using regional climate scenarios

    NASA Astrophysics Data System (ADS)

    Tsunematsu, N.; Dairaku, K.

    2011-12-01

    There is a possibility that the future atmospheric warming leads to more frequent heavy rainfall in the metropolitan area, thereby increasing the risk of floods. As part of REsearch Program on Climate Change Adaptation (RECCA) funded by Ministry of Education, Culture, Sports, Science and Technology, Japan, we started numerical model experiments for investigating the vulnerability and adaptation to climate change in water hazard assessments in the metropolitan area by the use of regional climate scenarios. The model experiments adopt dynamical downscaling techniques. Future climate projections obtained from regional climate model simulations at 20 km horizontal grid spacing are downscaled into finer grids (less than 5 km resolutions) of Regional Atmospheric Modeling System Version 6.0 modified by National Research Institute for Earth Science and Disaster Prevention (NIED-RAMS). Prior to performing the dynamical downscaling experiments, the NIED-RAMS model biases are evaluated by comparing long-term surface meteorological observations with results of the model simulations that are carried out by using the Japanese Re-Analysis (JRA) data and Japan Meteorological Agency Meso-Scale Model outputs as the initial and boundary conditions.

  6. Physically-Based Global Downscaling Climate Change Projections for a Full Century

    SciTech Connect

    Ghan, Steven J.; Shippert, Timothy R.

    2005-04-15

    A global atmosphere/land model with an embedded subgrid orography scheme is used to simulate the period 1977-2100 using ocean surface conditions and radiative constituent concentrations for a climate change scenario. Climate variables simulated for multiple elevation classes are mapping according to a high-resolution elevation dataset in ten regions with complex terrain. Analysis of changes in the simulated climate leads to the following conclusions. Changes in precipitation vary widely, with precipitation increasing more with increasing altitude in some region, decreasing more with altitude in others, and changing little in still others. In some regions the sign of the precipitation change depends on surface elevation. Changes in surface air temperature are rather uniform, with at most a two-fold difference between the largest and smallest changes within a region; in most cases the warming increases with altitude. Changes in snow water are highly dependent on altitude. Absolute changes usually increase with altitude, while relative changes decrease. In places where snow accumulates, an artificial upper bound on snow water limits the sensitivity of snow water to climate change considerably. The simulated impact of climate change on regional mean snow water varies widely, with little impact in regions in which the upper bound on snow water is the dominant snow water sink, moderate impact in regions with a mixture of seasonal and permanent snow, and profound impacts on regions with little permanent snow.

  7. Physically-Based Global Downscaling: Climate Change Projections for a Full Century

    SciTech Connect

    Ghan, Steven J.; Shippert, Timothy R.

    2006-05-01

    A global atmosphere/land model with an embedded subgrid orography scheme is used to simulate the period 1977-2100 using ocean surface conditions and radiative constituent concentrations for a climate change scenario. Climate variables simulated for multiple elevation classes are mapping according to the high-resolution of topography in ten regions with complex terrain. Analysis of changes in the simulated climate lead to the following conclusions. Changes in precipitation vary widely, with precipitation increasing more with increasing altitude in some region, decreasing more with altitude in others, and changing little in still others. In some regions the sign of the precipitation change depends on surface elevation. Changes in surface air temperature are rather uniform, with at most a two-fold difference between the largest and smallest changes within a region. In most cases the warming increases with altitude. Changes in snow water are highly dependent on altitude. Absolute changes usually increase with altitude, while relative changes decrease. In places where snow accumulates, an artificial upper bound on snow water limits the sensitivity of snow water to climate change considerably. The simulated impact of climate change on regional mean snow water varies widely, with little impact in regions in which the upper bound on snow water is the dominant snow water sink, moderate impact in regions with a mixture of seasonal and permanent snow, and profound impacts on regions with little permanent snow.

  8. 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.

  9. Selecting global climate models for regional climate change studies

    PubMed Central

    Pierce, David W.; Barnett, Tim P.; Santer, Benjamin D.; Gleckler, Peter J.

    2009-01-01

    Regional or local climate change modeling studies currently require starting with a global climate model, then downscaling to the region of interest. How should global models be chosen for such studies, and what effect do such choices have? This question is addressed in the context of a regional climate detection and attribution (D&A) study of January-February-March (JFM) temperature over the western U.S. Models are often selected for a regional D&A analysis based on the quality of the simulated regional climate. Accordingly, 42 performance metrics based on seasonal temperature and precipitation, the El Nino/Southern Oscillation (ENSO), and the Pacific Decadal Oscillation are constructed and applied to 21 global models. However, no strong relationship is found between the score of the models on the metrics and results of the D&A analysis. Instead, the importance of having ensembles of runs with enough realizations to reduce the effects of natural internal climate variability is emphasized. Also, the superiority of the multimodel ensemble average (MM) to any 1 individual model, already found in global studies examining the mean climate, is true in this regional study that includes measures of variability as well. Evidence is shown that this superiority is largely caused by the cancellation of offsetting errors in the individual global models. Results with both the MM and models picked randomly confirm the original D&A results of anthropogenically forced JFM temperature changes in the western U.S. Future projections of temperature do not depend on model performance until the 2080s, after which the better performing models show warmer temperatures. PMID:19439652

  10. Selecting global climate models for regional climate change studies.

    PubMed

    Pierce, David W; Barnett, Tim P; Santer, Benjamin D; Gleckler, Peter J

    2009-05-26

    Regional or local climate change modeling studies currently require starting with a global climate model, then downscaling to the region of interest. How should global models be chosen for such studies, and what effect do such choices have? This question is addressed in the context of a regional climate detection and attribution (D&A) study of January-February-March (JFM) temperature over the western U.S. Models are often selected for a regional D&A analysis based on the quality of the simulated regional climate. Accordingly, 42 performance metrics based on seasonal temperature and precipitation, the El Nino/Southern Oscillation (ENSO), and the Pacific Decadal Oscillation are constructed and applied to 21 global models. However, no strong relationship is found between the score of the models on the metrics and results of the D&A analysis. Instead, the importance of having ensembles of runs with enough realizations to reduce the effects of natural internal climate variability is emphasized. Also, the superiority of the multimodel ensemble average (MM) to any 1 individual model, already found in global studies examining the mean climate, is true in this regional study that includes measures of variability as well. Evidence is shown that this superiority is largely caused by the cancellation of offsetting errors in the individual global models. Results with both the MM and models picked randomly confirm the original D&A results of anthropogenically forced JFM temperature changes in the western U.S. Future projections of temperature do not depend on model performance until the 2080s, after which the better performing models show warmer temperatures.

  11. Downscaled climate change impacts on agricultural water resources in Puerto Rico

    SciTech Connect

    Harmsen, E.W.; Miller, N.L.; Schlegel, N.J.; Gonzalez, J.E.

    2009-04-01

    The purpose of this study is to estimate reference evapotranspiration (ET{sub o}), rainfall deficit (rainfall - ET{sub o}) and relative crop yield reduction for a generic crop under climate change conditions for three locations in Puerto Rico: Adjuntas, Mayaguez, and Lajas. Reference evapotranspiration is estimated by the Penman-Monteith method. Rainfall and temperature data were statistically downscaled and evaluated using the DOE/NCAR PCM global circulation model projections for the B1 (low), A2 (mid-high) and A1fi (high) emission scenarios of the Intergovernmental Panel on Climate Change Special Report on Emission Scenarios. Relative crop yield reductions were estimated from a function dependent water stress factor, which is a function of soil moisture content. Average soil moisture content for the three locations was determined by means of a simple water balance approach. Results from the analysis indicate that the rainy season will become wetter and the dry season will become drier. The 20-year mean 1990-2010 September rainfall excess (i.e., rainfall - ET{sub o} > 0) increased for all scenarios and locations from 149.8 to 356.4 mm for 2080-2100. Similarly, the 20-year average February rainfall deficit (i.e., rainfall - ET{sub o} < 0) decreased from a -26.1 mm for 1990-2010 to -72.1 mm for the year 2080-2100. The results suggest that additional water could be saved during the wet months to offset increased irrigation requirements during the dry months. Relative crop yield reduction did not change significantly under the B1 projected emissions scenario, but increased by approximately 20% during the summer months under the A1fi emissions scenario. Components of the annual water balance for the three climate change scenarios are rainfall, evapotranspiration (adjusted for soil moisture), surface runoff, aquifer recharge and change in soil moisture storage. Under the A1fi scenario, for all locations, annual evapotranspiration decreased owing to lower soil moisture

  12. Northwest Regional Climate Assessment

    NASA Technical Reports Server (NTRS)

    Lipschultz, Fred

    2011-01-01

    Objectives are to establish a continuing, inclusive National process that: 1) synthesizes relevant science and information 2) increases understanding of what is known & not known 3) identifies information needs related to preparing for climate variability and change, and reducing climate impacts and vulnerability 4) evaluates progress of adaptation & mitigation activities 5) informs science priorities 6) builds assessment capacity in regions and sectors 7) builds understanding & skilled use of findings

  13. Providing Western Regional Climate Services - Perspectives from the Western Regional Climate Center

    NASA Astrophysics Data System (ADS)

    Brown, T. J.; Redmond, K. T.

    2014-12-01

    The western United States faces distinct challenges such as persistent drought, dwindling water resources amidst an expanding population, and climate-sensitive alpine environments. The complex terrain of the region compounds these challenges. The Western Regional Climate Center (WRCC), one of six National Oceanic and Atmospheric Administration (NOAA) university-based regional climate centers, has been providing climate services since 1986 that support the unique needs of stakeholders in the mountainous region of the western U.S. This includes meteorological data, tools, and products for thousands of stations across the West, and gridded data products, such as based on PRISM for example, that are used for drought assessment among other needs. WRCC and partners have developed numerous web-based tools and products to support decision-making and research pertinent to the West. Changing climate and variability along with the diverse physical and human geographies of the western U.S. require continuous advancements in climate knowledge and applications development. Examples include the need for tools and model downscaling that support and inform adaptation, mitigation and resiliency planning; web-based analytics that would allow users to interact and explore temporal and spatial data and relationships, and products from new satellite sensors that can provide higher resolution information on soil moisture and vegetation health given the sparseness of in-situ observations for the vastness of the West. This presentation provides an overview of some insights, opportunities and challenges of providing current and future climate services in the West.

  14. Impact of climate change estimated through statistical downscaling on crop productivity and soil water balance in Southern Italy

    NASA Astrophysics Data System (ADS)

    Ventrella, D.; Giglio, L.; Charfeddine, M.; Palatella, L.; Pizzigalli, C.; Vitale, D.; Paradisi, P.; Miglietta, M. M.; Rana, G.

    2010-09-01

    The climatic change induced by the global warming is expected to modify the agricultural activity and consequently the other social and economical sectors. In this context, an efficient management of the water resources is considered very important for Italy and in particular for Southern areas characterized by a typical Mediterranean climate in order to improve the economical and environmental sustainability of the agricultural activity. Climate warming could have a substantial impact on some agronomical practices as the choice of the crops to be included in the rotations, the sowing time and the irrigation scheduling. For a particular zone, the impact of climatic change on agricultural activity will depend also on the continuum "soil-plant-climate" and this continuum has to be included in the analysis for forecasting purposes. The Project CLIMESCO is structured in four workpackages (WP): (1) Identification of homogeneous areas, (2) Climatic change, (3) Optimization of water resources and (4) Scenarios analysis. In this study we applied a statistical downscaling method, Canonical Correlation Analysis after Principal Component Analysis filtering, to two sub-regions of agricultural interest in Sicily and Apulia (respectively, Delia basin and Capitanata). We adopt, as large scale predictors, the sea level pressure from the the EMULATE project dataset and the 1000 hPa temperature obtained from the NCEP reanalyses, while the predictands are monthly time series of maximum and minimum temperature and precipitation. As the crop growth models need daily datasets, a stochastic weather generator (the LARS-WG model) has been applied for this purpose. LARS-WG needs a preliminary calibration with daily time series of meteorological fields, that are available in the framework of CLIMESCO project. Then, the statistical relationships have been applied to two climate change scenarios (SRES A2 and B2), provided by three different GCM's: the Hadley Centre Coupled Model version 3 (Had

  15. Detection and Attribution of Regional Climate Change

    SciTech Connect

    Bala, G; Mirin, A

    2007-01-19

    We developed a high resolution global coupled modeling capability to perform breakthrough studies of the regional climate change. The atmospheric component in our simulation uses a 1{sup o} latitude x 1.25{sup o} longitude grid which is the finest resolution ever used for the NCAR coupled climate model CCSM3. Substantial testing and slight retuning was required to get an acceptable control simulation. The major accomplishment is the validation of this new high resolution configuration of CCSM3. There are major improvements in our simulation of the surface wind stress and sea ice thickness distribution in the Arctic. Surface wind stress and ocean circulation in the Antarctic Circumpolar Current are also improved. Our results demonstrate that the FV version of the CCSM coupled model is a state of the art climate model whose simulation capabilities are in the class of those used for IPCC assessments. We have also provided 1000 years of model data to Scripps Institution of Oceanography to estimate the natural variability of stream flow in California. In the future, our global model simulations will provide boundary data to high-resolution mesoscale model that will be used at LLNL. The mesoscale model would dynamically downscale the GCM climate to regional scale on climate time scales.

  16. Climate change projections over India by a downscaling approach using PRECIS

    NASA Astrophysics Data System (ADS)

    Bal, Prasanta Kumar; Ramachandran, Andimuthu; Palanivelu, Kandasamy; Thirumurugan, Perumal; Geetha, Rajadurai; Bhaskaran, Bhaski

    2016-08-01

    This study presents a comprehensive assessment of the possible regional climate change over India by using Providing REgional Climates for Impacts Studies (PRECIS), a regional climate model (RCM) developed by Met Office Hadley Centre in the United Kingdom. The lateral boundary data for the simulations were taken from a sub-set of six members sampled from the Hadley Centre's 17- member Quantified Uncertainty in Model Projections (QUMP) perturbed physics ensemble. The model was run with 25 km × 25 km resolution from the global climate model (GCM) - HadCM3Q at the emission rate of special report on emission scenarios (SRES) A1B scenarios. Based on the model performance, six member ensembles running over a period of 1970-2100 in each experiment were utilized to predict possible range of variations in the future projections for the periods 2020s (2005-2035), 2050s (2035-2065) and 2080s (2065-2095) with respect to the baseline period (1975-2005). The analyses concentrated on maximum temperature, minimum temperature and rainfall over the region. For the whole India, the projections of maximum temperature from all the six models showed an increase within the range 2.5°C to 4.4°C by end of the century with respect to the present day climate simulations. The annual rainfall projections from all the six models indicated a general increase in rainfall being within the range 15-24%. Mann-Kendall trend test was run on time series data of temperatures and rainfall for the whole India and the results from some of the ensemble members indicated significant increasing trends. Such high resolution climate change information may be useful for the researchers to study the future impacts of climate change in terms of extreme events like floods and droughts and formulate various adaptation strategies for the society to cope with future climate change.

  17. Weather patterns as a downscaling tool - evaluating their skill in stratifying local climate variables

    NASA Astrophysics Data System (ADS)

    Murawski, Aline; Bürger, Gerd; Vorogushyn, Sergiy; Merz, Bruno

    2016-04-01

    The use of a weather pattern based approach for downscaling of coarse, gridded atmospheric data, as usually obtained from the output of general circulation models (GCM), allows for investigating the impact of anthropogenic greenhouse gas emissions on fluxes and state variables of the hydrological cycle such as e.g. on runoff in large river catchments. Here we aim at attributing changes in high flows in the Rhine catchment to anthropogenic climate change. Therefore we run an objective classification scheme (simulated annealing and diversified randomisation - SANDRA, available from the cost733 classification software) on ERA20C reanalyses data and apply the established classification to GCMs from the CMIP5 project. After deriving weather pattern time series from GCM runs using forcing from all greenhouse gases (All-Hist) and using natural greenhouse gas forcing only (Nat-Hist), a weather generator will be employed to obtain climate data time series for the hydrological model. The parameters of the weather pattern classification (i.e. spatial extent, number of patterns, classification variables) need to be selected in a way that allows for good stratification of the meteorological variables that are of interest for the hydrological modelling. We evaluate the skill of the classification in stratifying meteorological data using a multi-variable approach. This allows for estimating the stratification skill for all meteorological variables together, not separately as usually done in existing similar work. The advantage of the multi-variable approach is to properly account for situations where e.g. two patterns are associated with similar mean daily temperature, but one pattern is dry while the other one is related to considerable amounts of precipitation. Thus, the separation of these two patterns would not be justified when considering temperature only, but is perfectly reasonable when accounting for precipitation as well. Besides that, the weather patterns derived from

  18. Effect of downscaling methodology on decision-making

    NASA Astrophysics Data System (ADS)

    McCrary, R. R.; Mearns, L. O.; McGinnis, S. A.; McDaniel, L. R.

    2015-12-01

    There is increasing demand from decision makers for fine scale climate information that is relevant and useful for regional and local adaptation planning. While global climate models (GCMs) are vital for understanding large-scale changes in global circulation patterns, the horizontal resolution of a typical GCM is too coarse for use in local impact studies. A number of methods have been implemented to translate coarse GCM climate projections down to the regional and local scale. These range from the simplest delta approach to complex dynamical downscaling models. With so many diverse methods of downscaling now available, there is a need to perform robust comparisons and evaluations of the different techniques. In this study we explore how the choice of downscaling method may influence the climate change response of important impacts related variables. Our goal is to identify the uncertainty in future climate change associated with different downscaling methods. We then examine how the uncertainty associated with downscaling can affect vulnerability assessments and adaptation planning. We focus on the impact of climate change to extremes in three sectors: forest fire risk management, heat stress and human health, and energy consumption by buildings. For each sector, an impacts relevant index is used to assess current and future risk. The Keetch-Byram Drought Index (KBDI) is used for fire, the Wet Bulb Globe Temperature (WBGT) is used for heat stress, and heating and cooling degree-days are used for energy consumption. Local climate changes have been calculated for each sector using four downscaling techniques: the delta method, a bias correction method (KDDM), the statistical downscaling model (SDSM), and dynamical downscaling with NARCCAP. Climate response surfaces (e.g. response of KBDI to changes in temp. and precip.) are generated at four locations in the United States. Response surfaces are a useful tool to help decision makers estimate the vulnerability to

  19. Regional climate simulations over Vietnam using the WRF model

    NASA Astrophysics Data System (ADS)

    Raghavan, S. V.; Vu, M. T.; Liong, S. Y.

    2015-07-01

    We present an analysis of the present-day (1961-1990) regional climate simulations over Vietnam. The regional climate model Weather Research and Forecasting (WRF) was driven by the global reanalysis ERA40. The performance of the regional climate model in simulating the observed climate is evaluated with a main focus on precipitation and temperature. The regional climate model was able to reproduce the observed spatial patterns of the climate, although with some biases. The model also performed better in reproducing the extreme precipitation and the interannual variability. Overall, the WRF model was able to simulate the main regional signatures of climate variables, seasonal cycles, and frequency distributions. This study is an evaluation of the present-day climate simulations of a regional climate model at a resolution of 25 km. Given that dynamical downscaling has become common for studying climate change and its impacts, the study highlights that much more improvements in modeling might be necessary to yield realistic simulations of climate at high resolutions before they can be used for impact studies at a local scale. The need for a dense network of observations is also realized as observations at high resolutions are needed when it comes to evaluations and validations of models at sub-regional and local scales.

  20. Regional climate simulations over Vietnam using the WRF model

    NASA Astrophysics Data System (ADS)

    Raghavan, S. V.; Vu, M. T.; Liong, S. Y.

    2016-10-01

    We present an analysis of the present-day (1961-1990) regional climate simulations over Vietnam. The regional climate model Weather Research and Forecasting (WRF) was driven by the global reanalysis ERA40. The performance of the regional climate model in simulating the observed climate is evaluated with a main focus on precipitation and temperature. The regional climate model was able to reproduce the observed spatial patterns of the climate, although with some biases. The model also performed better in reproducing the extreme precipitation and the interannual variability. Overall, the WRF model was able to simulate the main regional signatures of climate variables, seasonal cycles, and frequency distributions. This study is an evaluation of the present-day climate simulations of a regional climate model at a resolution of 25 km. Given that dynamical downscaling has become common for studying climate change and its impacts, the study highlights that much more improvements in modeling might be necessary to yield realistic simulations of climate at high resolutions before they can be used for impact studies at a local scale. The need for a dense network of observations is also realized as observations at high resolutions are needed when it comes to evaluations and validations of models at sub-regional and local scales.

  1. Statistical Downscaling of Last Glacial Maximum and mid-Holocene climate simululations over the Continental United States

    NASA Astrophysics Data System (ADS)

    Mondal, Y.; Chiang, J. C. H.; Koo, M.

    2014-12-01

    We document the creation of new high-resolution temperature and precipitation fields over the continental United States during the Last Glacial Maximum (LGM) and mid-Holocene intended for hind-casting species distributions and other biotic scenarios. Global climate simulations do not have the resolution to capture local climate variability that is needed to model ecological and biological variability. To this end, we use a recently developed statistical downscaling method, Equidistant CDF Matching (EDCDFm), developed by Li et al. (2010) [1] to create synthetic high-resolution estimates of the LGM and mid-Holocene climate over the continental United States. We find that this method works well for temperature but performs poorly for precipitation. This required processing over 1.5 billion time series. To do this, we wrote cluster-computing routines in MATLAB and implemented them on Amazon Elastic Compute Cloud.

  2. South America downscaling: using spatial artificial neural network

    NASA Astrophysics Data System (ADS)

    Mendes, David; Marengo, José

    2010-05-01

    The mathematical models used to simulate the present climate and project future climate with forcing by greenhouse gases and aerosols are generally referred to as General Circulation Models or Global Climate Models (GCMs). However, the spatial resolution of GCMs remains quite coarse, in the order of 300 x 300 km, and at scale, the regional and local details of the climate which are influenced by spatial heterogeneities in the regional physiography are lost. Therefore, there is the need to convert the GCM outputs into a reliable data set with higher spatial resolution, with daily rainfall and temperature time series at the scale of the watershed or a region to which the climate impact is going to be investigated. The methods used to convert GCM outputs into local meteorological variables required for reliable climate modeling are usually referred to as downscaling techniques. There are a variety of downscaling techniques in the literature, but two major approaches can be identified at the moment, namely, dynamic downscaling and empirical (statistical) downscaling. The most widely used empirical downscaling methods are the multiple linear regression and stochastic weather generation. However, the interest in nonlinear regression methods, namely, artificial neural network (ANN), is nowadays increasing because of their high potential for complex, nonlinear and time-varying input-output mapping. The main aim of this work is to develop and test a novel type of statistical downscaling technique based on the Artificial Neural Network (ANN), applied of the climate change. This work analyses the performance of the IPCC models in simulate the present and future climate using ANN. The ANN used here are based on a feed forward configuration of the multilayer perception that has been used by a growing number of authors. To carry out statistical downscaling for each meteorological date (grid point), the predictors and predictands were supplied to the models (ANN) and spatial

  3. Challenging some tenets of Regional Climate Modelling

    NASA Astrophysics Data System (ADS)

    Laprise, R.; de Elía, R.; Caya, D.; Biner, S.; Lucas-Picher, P.; Diaconescu, E.; Leduc, M.; Alexandru, A.; Separovic, L.

    2008-08-01

    Nested Regional Climate Models (RCMs) are increasingly used for climate-change projections in order to achieve spatial resolutions that would be computationally prohibitive with coupled global climate models. RCMs are commonly thought to behave as a sort of sophisticated magnifying glass to perform dynamical downscaling, which is to add fine-scale details upon the large-scale flow provided as time-dependent lateral boundary condition. Regional climate modelling is a relatively new approach, initiated less than twenty years ago. The interest for the approach has grown rapidly as it offers a computationally affordable means of entering into appealing applications of timely societal relevance, such as high-resolution climate-change projections and seasonal prediction. There exists however a need for basic research aiming at establishing firmly the strengths and limitations of the technique. This paper synthesises the results of a stream of investigations on the merits and weaknesses of the nested approach, initiated almost a decade ago by some members of our team. This short paper revisits some commonly accepted notions amongst practitioners of Regional Climate Modelling, in the form of four tenets that will be challenged: (1) RCMs are capable of generating small-scale features absent in the driving fields supplied as lateral boundary conditions; (2) The generated small scales have the appropriate amplitudes and statistics; (3) The generated small scales accurately represent those that would be present in the driving data if it were not limited by resolution; (4) In performing dynamical downscaling, RCMs operate as a kind of sophisticated magnifying glass, in the sense that the small scales that are generated are uniquely defined for a given set of lateral boundary conditions (LBC). From the partial failure of the last two tenets emerges the notion of internal variability, which has often been thought to be negligible in one-way nested models due to the control

  4. The Effects of Meteorological Forcings on Hydrologic Model Calibration and Implications for Statistical Downscaling of Climate Projections

    NASA Astrophysics Data System (ADS)

    McGuire Elsner, M.; Gangopadhyay, S.; Pruitt, T.; Brekke, L. D.

    2012-12-01

    Spatially distributed historical meteorological forcings (temperature and precipitation) are commonly used as the basis for statistical downscaling (in time and space) of general circulation model (GCM) projections. A number of such forcing datasets, gridded from station observations, have been developed over the Western U.S. and they all use different methodologies with respect to filtering stations and accounting for temporal inhomogeneities. We compare four historical meteorological datasets at 1/8th degree spatial resolution over a common historical time period (1980-1999) using basic statistical comparisons to better understand their spatial differences. We then employ the Variable Infiltration Capacity hydrologic model (VIC), which has been implemented in numerous studies to evaluate the impacts of climate variability and change on water resources, to explore the sensitivity of hydrologic model response to these forcing datasets. Specifically, we calibrate the VIC model, by means of the automated multiple objective complex evolution method, to reconstructed natural streamflows using each of the four different datasets and compare calibration parameters and water balance variables (eg. streamflow, evapotranspiration, snow water equivalent). We test the sensitivities of the calibrated models by forcing each one with the remaining three datasets. Because multiple sources of downscaled climate change scenarios are currently available for various parts of the Western US from the Coupled Model Intercomparison Project (CMIP) Phase 3 (and many of them use different historical meteorological forcing datasets as their basis) and scenarios from the next CMIP phase (Phase 5) are becoming available, we explore the implications of our findings for statistical downscaling of GCM projections.

  5. 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...

  6. Regional-to-Urban Enviro-HIRLAM Downscaling for Meteorological and Chemical Patterns over Chinese Megacities

    NASA Astrophysics Data System (ADS)

    Mahura, Alexander; Nuterman, Roman; Gonzalez-Aparicio, Iratxe; Amstrup, Bjarne; Baklanov, Alexander; Yang, Xiaohua; Nielsen, Kristian

    2015-04-01

    Due to strong economic growth in the past decades, air pollution became a serious problem in megacities and major industrial agglomerations of China. So, information on air quality in these urbanized areas is important for population. In particular, the metropolitan areas of Shanghai, Beijing, and Pearl River Delta are well known as main regions with serious air pollution issues. One of the aims of the EU FP7 MarcoPolo project is to improve existing regional-meso-urban/city scale air quality forecasts using improved emission inventories and to validate modelling results using satellite and ground-based measurements. The Enviro-HIRLAM (Environment - HIgh Resolution Limited Area Model) adapted for the Shanghai region of China is applied for forecasting. The model is urbanized using the Building Effects Parameterization module, which describes different types of urban districts such as industrial commercial, city center, high density and residential with its own characteristics. For sensitivity studies, the model was run in downscaling chain from regional-to-urban scales at subsequent horizontal resolutions of 15-5-2.5 km for selected dates with elevated pollution levels and unfavorable meteorological conditions. For these dates, the effects of urbanization are analyzed for atmospheric transport, dispersion, deposition, and chemical transformations. The evaluation of formation and development of meteorological and chemical/aerosol patterns due to influence of the urban areas is performed. The impact of selected (in a model domain) megacities of China is estimated on regional-to-urban scales, as well as relationship between air pollution and meteorology are studied.

  7. High-Resolution Subtropical Summer Precipitation Derived from Dynamical Downscaling of the NCEP-DOE Reanalysis: How Much Small-Scale Information Is Added by a Regional Model?

    NASA Technical Reports Server (NTRS)

    Lim, Young-Kwon; Stefanova, Lydia B.; Chan, Steven C.; Schubert, Siegfried D.; OBrien, James J.

    2010-01-01

    This study assesses the regional-scale summer precipitation produced by the dynamical downscaling of analyzed large-scale fields. The main goal of this study is to investigate how much the regional model adds smaller scale precipitation information that the large-scale fields do not resolve. The modeling region for this study covers the southeastern United States (Florida, Georgia, Alabama, South Carolina, and North Carolina) where the summer climate is subtropical in nature, with a heavy influence of regional-scale convection. The coarse resolution (2.5deg latitude/longitude) large-scale atmospheric variables from the National Center for Environmental Prediction (NCEP)/DOE reanalysis (R2) are downscaled using the NCEP Environmental Climate Prediction Center regional spectral model (RSM) to produce precipitation at 20 km resolution for 16 summer seasons (19902005). The RSM produces realistic details in the regional summer precipitation at 20 km resolution. Compared to R2, the RSM-produced monthly precipitation shows better agreement with observations. There is a reduced wet bias and a more realistic spatial pattern of the precipitation climatology compared with the interpolated R2 values. The root mean square errors of the monthly R2 precipitation are reduced over 93 (1,697) of all the grid points in the five states (1,821). The temporal correlation also improves over 92 (1,675) of all grid points such that the domain-averaged correlation increases from 0.38 (R2) to 0.55 (RSM). The RSM accurately reproduces the first two observed eigenmodes, compared with the R2 product for which the second mode is not properly reproduced. The spatial patterns for wet versus dry summer years are also successfully simulated in RSM. For shorter time scales, the RSM resolves heavy rainfall events and their frequency better than R2. Correlation and categorical classification (above/near/below average) for the monthly frequency of heavy precipitation days is also significantly improved

  8. Improving downscaling in South America Sector

    NASA Astrophysics Data System (ADS)

    Mendes, D.; Marengo, J.

    2009-04-01

    The mathematical models used to simulate the present climate and project future climate with forcing by greenhouse gases and aerosols are generally referred to as General Circulation Models or Global Climate Models (GCMs). However, the spatial resolution of GCMs remains quite coarse, in the order of 300 x 300 km, and at scale, the regional and local details of the climate which are influenced by spatial heterogeneities in the regional physiography are lost. Therefore, there is the need to convert the GCM outputs into a reliable data set with higher spatial resolution, with daily rainfall and temperature time series at the scale of the watershed or a region to which the climate impact is going to be investigated. The methods used to convert GCM outputs into local meteorological variables required for reliable climate modeling are usually referred to as downscaling techniques. There are a variety of downscaling techniques in the literature, but two major approaches can be identified at the moment, namely, dynamic downscaling and empirical (statistical) downscaling. The most widely used empirical downscaling methods are the multiple linear regression and stochastic weather generation. However, the interest in nonlinear regression methods, namely, artificial neural network (ANN), is nowadays increasing because of their high potential for complex, nonlinear and time-varying input-output mapping. The main aim of this work is to develop and test a novel type of statistical downscaling technique based on the Artificial Neural Network (ANN), applied of the climate change. This work analyses the performance of the IPCC models (CGCM3.1, CSIRO-MK3.5, ECHAM5-MPI, GFDL-CM2.1, and MIROC3.2-MEDRES ) in simulate the present and future climate using ANN. The ANN used here are based on a feed forward configuration of the multilayer perception that has been used by a growing number of authors. To carry out statistical downscaling for each meteorological date (grid point), the

  9. Assimilation of Downscaled SMOS Soil Moisture for Quantifying Drought Impacts on Crop Yield in Agricultural Regions in Brazil

    NASA Astrophysics Data System (ADS)

    Chakrabarti, S.; Bongiovanni, T. E.; Judge, J.; Principe, J. C.; Fraisse, C.

    2013-12-01

    Reliable soil moisture (SM) information in the root zone (RZSM) is critical for quantification of agricultural drought impacts on crop yields and for recommending management and adaptation strategies for crop management, commodity trading and food security.The recently launched European Space Agency-Soil Moisture and Ocean Salinity (ESA-SMOS) and the near-future National Aeronautics and Space Administration-Soil Moisture Active Passive (NASA-SMAP) missions provide SM at unprecedented spatial resolutions of 10-25 km, but these resolutions are still too coarse for agricultural applications in heterogeneous landscapes, making downscaling a necessity. This downscaled near-surface SM can be merged with crop growth models in a data assimilation framework to provide optimal estimates of RZSM and crop yield. The objectives of the study include: 1) to implement a novel downscalingalgorithm based on the Information theoretical learning principlesto downscale SMOS soil moisture at 25 km to 1km in the Brazilian La Plata Basin region and2) to assimilate the 1km-soil moisture in the crop model for a normal and a drought year to understand the impact on crop yield. In this study, a novel downscaling algorithm based on the Principle of Relevant Information (PRI) was applied to in-situ and remotely sensed precipitation, SM, land surface temperature and leaf area index in the Brazilian Lower La Plata region in South America. An Ensemble Kalman Filter (EnKF) based assimilation algorithm was used to assimilate the downscaled soil moisture to update both states and parameters. The downscaled soil moisture for two growing seasons in2010-2011 and 2011-2012 was assimilated into the Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model over 161 km2 rain-fed region in the Brazilian LPB regionto improve the estimates of soybean yield. The first season experienced normal precipitation, while the second season was impacted by drought. Assimilation improved yield

  10. Future Variations of Wave Climate in Winter Japan: Application of Pseudo Global Warming Dynamic Downscaling Method for CMIP5 output and a Wave Model Simulation

    NASA Astrophysics Data System (ADS)

    Taniguchi, K.

    2014-12-01

    Ocean waves change alongshore topographies and their effects reach to ecological systems and coastal infrastructures. Ocean waves are mainly formed by sea surface wind, then variations in sea surface winds can cause changes in ocean waves. Under the condition of global warming, atmospheric motion will change in future climate and they will give changes in wave climates. However, effects of global warming on wave climate have not yet investigated in detail. In this study, a dynamic downscaling method by a weather prediction model (WRF developed by NCAR) is applied to obtain detail information of sea surface wind in the present and future climates. The ocean wave is simulated by a wave model (WaveWatch-III developed by NOAA) with the downscaled wind in the present and future climate, then variations in wave climate in future are investigated. For future climate, climate projections in Coupled Model Intercomparison Project phase 5 (CMIP5) are used. For future scenario, the representative concentrate pathways 4.5 (RCP4.5) is selected. Variations of ocean waves in the Sea of Japan is focused in this work. Over the Sea of Japan, the strong northwesterly blows from the Eurasian Continent to the Japan islands in winter and causes high waves, and sometimes it causes marine accidents and breaks in coastal civil structures. The results of WaveWatch-III showed that the maximum value of significant wave height becomes smaller in the northern and central part of the Sea of Japan, however, clear increases are found in the southern part in future. In some regions, the maximum wave heights in future become 4 meter larger than in present. On the other hand, there are variations in frequency distribution of significant wave height. The frequency of modestly high waves increases and the low wave frequency decreases mainly in January and February (even in areas with smaller maximum significant wave height in future). Without extremely high ocean waves, variations in frequency

  11. Impact of climate change on fish population dynamics in the Baltic sea: a dynamical downscaling investigation.

    PubMed

    Mackenzie, Brian R; Meier, H E Markus; Lindegren, Martin; Neuenfeldt, Stefan; Eero, Margit; Blenckner, Thorsten; Tomczak, Maciej T; Niiranen, Susa

    2012-09-01

    Understanding how climate change, exploitation and eutrophication will affect populations and ecosystems of the Baltic Sea can be facilitated with models which realistically combine these forcings into common frameworks. Here, we evaluate sensitivity of fish recruitment and population dynamics to past and future environmental forcings provided by three ocean-biogeochemical models of the Baltic Sea. Modeled temperature explained nearly as much variability in reproductive success of sprat (Sprattus sprattus; Clupeidae) as measured temperatures during 1973-2005, and both the spawner biomass and the temperature have influenced recruitment for at least 50 years. The three Baltic Sea models estimate relatively similar developments (increases) in biomass and fishery yield during twenty-first century climate change (ca. 28 % range among models). However, this uncertainty is exceeded by the one associated with the fish population model, and by the source of global climate data used by regional models. Knowledge of processes and biases could reduce these uncertainties.

  12. Climate change projections for Tamil Nadu, India: deriving high-resolution climate data by a downscaling approach using PRECIS

    NASA Astrophysics Data System (ADS)

    Bal, Prasanta Kumar; Ramachandran, A.; Geetha, R.; Bhaskaran, B.; Thirumurugan, P.; Indumathi, J.; Jayanthi, N.

    2016-02-01

    In this paper, we present regional climate change projections for the Tamil Nadu state of India, simulated by the Met Office Hadley Centre regional climate model. The model is run at 25 km horizontal resolution driven by lateral boundary conditions generated by a perturbed physical ensemble of 17 simulations produced by a version of Hadley Centre coupled climate model, known as HadCM3Q under A1B scenario. The large scale features of these 17 simulations were evaluated for the target region to choose lateral boundary conditions from six members that represent a range of climate variations over the study region. The regional climate, known as PRECIS, was then run 130 years from 1970. The analyses primarily focus on maximum and minimum temperatures and rainfall over the region. For the Tamil Nadu as a whole, the projections of maximum temperature show an increase of 1.0, 2.2 and 3.1 °C for the periods 2020s (2005-2035), 2050s (2035-2065) and 2080s (2065-2095), respectively, with respect to baseline period (1970-2000). Similarly, the projections of minimum temperature show an increase of 1.1, 2.4 and 3.5 °C, respectively. This increasing trend is statistically significant (Mann-Kendall trend test). The annual rainfall projections for the same periods indicate a general decrease in rainfall of about 2-7, 1-4 and 4-9 %, respectively. However, significant exceptions are noticed over some pockets of western hilly areas and high rainfall areas where increases in rainfall are seen. There are also indications of increasing heavy rainfall events during the northeast monsoon season and a slight decrease during the southwest monsoon season. Such an approach of using climate models may maximize the utility of high-resolution climate change information for impact-adaptation-vulnerability assessments.

  13. Downscaling climate change scenarios for apple pest and disease modeling in Switzerland

    NASA Astrophysics Data System (ADS)

    Hirschi, M.; Stoeckli, S.; Dubrovsky, M.; Spirig, C.; Calanca, P.; Rotach, M. W.; Fischer, A. M.; Duffy, B.; Samietz, J.

    2011-08-01

    As a consequence of current and projected climate change in temperate regions of Europe, agricultural pests and diseases are expected to occur more frequently and possibly to extend to previously not affected regions. Given their economic and ecological relevance, detailed forecasting tools for various pests and diseases have been developed, which model their phenology depending on actual weather conditions and suggest management decisions on that basis. Assessing the future risk of pest-related damages requires future weather data at high temporal and spatial resolution. Here, we use a combined stochastic weather generator and re-sampling procedure for producing site-specific hourly weather series representing present and future (1980-2009 and 2045-2074 time periods) climate conditions in Switzerland. The climate change scenarios originate from the ENSEMBLES multi-model projections and provide probabilistic information on future regional changes in temperature and precipitation. Hourly weather series are produced by first generating daily weather data for these climate scenarios and then using a nearest neighbor re-sampling approach for creating realistic diurnal cycles. These hourly weather series are then used for modeling the impact of climate change on important life phases of the codling moth and on the number of predicted infection days of fire blight. Codling moth (Cydia pomonella) and fire blight (Erwinia amylovora) are two major pest and disease threats to apple, one of the most important commercial and rural crops across Europe. Results for the codling moth indicate a shift in the occurrence and duration of life phases relevant for pest control. In southern Switzerland, a 3rd generation per season occurs only very rarely under today's climate conditions but is projected to become normal in the 2045-2074 time period. While the potential risk for a 3rd generation is also significantly increasing in northern Switzerland (for most stations from roughly 1

  14. Downscaling climate change scenarios for apple pest and disease modeling in Switzerland

    NASA Astrophysics Data System (ADS)

    Hirschi, M.; Stoeckli, S.; Dubrovsky, M.; Spirig, C.; Calanca, P.; Rotach, M. W.; Fischer, A. M.; Duffy, B.; Samietz, J.

    2012-02-01

    As a consequence of current and projected climate change in temperate regions of Europe, agricultural pests and diseases are expected to occur more frequently and possibly to extend to previously non-affected regions. Given their economic and ecological relevance, detailed forecasting tools for various pests and diseases have been developed, which model their phenology, depending on actual weather conditions, and suggest management decisions on that basis. Assessing the future risk of pest-related damages requires future weather data at high temporal and spatial resolution. Here, we use a combined stochastic weather generator and re-sampling procedure for producing site-specific hourly weather series representing present and future (1980-2009 and 2045-2074 time periods) climate conditions in Switzerland. The climate change scenarios originate from the ENSEMBLES multi-model projections and provide probabilistic information on future regional changes in temperature and precipitation. Hourly weather series are produced by first generating daily weather data for these climate scenarios and then using a nearest neighbor re-sampling approach for creating realistic diurnal cycles. These hourly weather series are then used for modeling the impact of climate change on important life phases of the codling moth and on the number of predicted infection days of fire blight. Codling moth (Cydia pomonella) and fire blight (Erwinia amylovora) are two major pest and disease threats to apple, one of the most important commercial and rural crops across Europe. Results for the codling moth indicate a shift in the occurrence and duration of life phases relevant for pest control. In southern Switzerland, a 3rd generation per season occurs only very rarely under today's climate conditions but is projected to become normal in the 2045-2074 time period. While the potential risk for a 3rd generation is also significantly increasing in northern Switzerland (for most stations from roughly 1

  15. Climate change in the Iberian Upwelling System: a numerical study using GCM downscaling

    NASA Astrophysics Data System (ADS)

    Cordeiro Pires, Ana; Nolasco, Rita; Rocha, Alfredo; Ramos, Alexandre M.; Dubert, Jesus

    2016-07-01

    The present work aims at evaluating the impacts of a climate change scenario on the hydrography and dynamics of the Iberian Upwelling System. Using regional ocean model configurations, the study domain is forced with three different sets of surface fields: a climatological dataset to provide the control run; a dataset obtained from averaging several global climate models (GCM) that integrate the Intergovernmental Panel for Climate Change (IPCC) models used in climate scenarios, for the same period as the climatological dataset; and this same dataset but for a future period, retrieved from the IPCC A2 climate scenario. After ascertaining that the ocean run forced with the GCM dataset for the present compared reasonably well with the climatologically forced run, the results for the future run (relative to the respective present run) show a general temperature increase (from +0.5 to +3 °C) and salinity decrease (from -0.1 to -0.3), particularly in the upper 100-200 m, although these differences depend strongly on season and distance to the coast. There is also strengthening of the SST cross-shore gradient associated to upwelling, which causes narrowing and shallowing of the upwelling jet. This effect is contrary to the meridional wind stress intensification that is also observed, which would tend to strengthen the upwelling jet.

  16. Downscaling of Bulgarian chemical weather forecast from Bulgaria region to Sofia city

    NASA Astrophysics Data System (ADS)

    Syrakov, D.; Etropolska, I.; Prodanova, M.; Slavov, K.; Ganev, K.; Miloshev, N.; Ljubenov, T.

    2013-10-01

    In the paper, Bulgarian Chemical Weather Forecast System (BgCWFS), version 3, will be described end the respective end-user products will be demonstrated. Chemical Weather is understood as concentration distribution of some key pollutants in a particular area and its changes during some forecast period. In Bulgaria, a prototype of such a system was built in the frame of a project with the National Science fund. It covers a relatively small domain including Bulgaria that requires the use of chemical boundary conditions (CBC) from similar foreign systems. The last version of the System is built in the frame of EU FP7 project PASODOBLE. Following its requirements, concentration data (CBC) for the region of Bulgaria are provided by SILAM System of Finish Meteorological Institute. It operates over the whole European region but is able to provide data for any European sub-domain by its THREDDS service. The customer makes an Internet request containing all necessary parameters - sub-region dimensions, pollutants, period of forecast etc. In a few minutes, the request is proceeded and all required data is downloaded as a single NetCDF file. This file is post-processed as to obtain the necessary boundary conditions. The new version of the system is built on the base of the nesting approach - two other domains with increasing resolution are nested in the Bulgaria one downscaling to 1 km space resolution over Sofia city. The System is fully atomized. Computations start at 00 UTC every day and the forecast period is 72 hours. It is based on the well known models WRF (Mesometeorological Model) and US EPA dispersion model CMAQ (Chemical Transport Model). As emission input the 2010 inventory data prepared by Bulgarian environmental authorities is exploited. The results are presented in the System's web-site (http://www.niggg.bas.bg/cw3/).

  17. 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...

  18. Development and application of downscaled hydroclimatic predictor variables for use in climate vulnerability and assessment studies

    USGS Publications Warehouse

    Thorne, James; Boynton, Ryan; Flint, Lorraine; Flint, Alan; N'goc Le, Thuy

    2012-01-01

    This paper outlines the production of 270-meter grid-scale maps for 14 climate and derivative hydrologic variables for a region that encompasses the State of California and all the streams that flow into it. The paper describes the Basin Characterization Model (BCM), a map-based, mechanistic model used to process the hydrological variables. Three historic and three future time periods of 30 years (1911–1940, 1941–1970, 1971–2000, 2010–2039, 2040–2069, and 2070–2099) were developed that summarize 180 years of monthly historic and future climate values. These comprise a standardized set of fine-scale climate data that were shared with 14 research groups, including the U.S. National Park Service and several University of California groups as part of this project. We present three analyses done with the outputs from the Basin Characterization Model: trends in hydrologic variables over baseline, the most recent 30-year period; a calibration and validation effort that uses measured discharge values from 139 streamgages and compares those to Basin Characterization Model-derived projections of discharge for the same basins; and an assessment of the trends of specific hydrological variables that links historical trend to projected future change under four future climate projections. Overall, increases in potential evapotranspiration dominate other influences in future hydrologic cycles. Increased potential evapotranspiration drives decreasing runoff even under forecasts with increased precipitation, and drives increased climatic water deficit, which may lead to conversion of dominant vegetation types across large parts of the study region as well as have implications for rain-fed agriculture. The potential evapotranspiration is driven by air temperatures, and the Basin Characterization Model permits it to be integrated with a water balance model that can be derived for landscapes and summarized by watershed. These results show the utility of using a process

  19. 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.

  20. Portuguese wine regions under a changing climate

    NASA Astrophysics Data System (ADS)

    Santos, João A.; Fraga, Helder; Malheiro, Aureliano C.; Moutinho-Pereira, José; Jones, Gregory V.; Pinto, Joaquim G.

    2014-05-01

    Viticulture and wine production are among the most important sectors of the Portuguese economy. However, as grapevines are strongly affected by weather and climate, climate change may represent an important threat to wine production. The current (1950-2000) and future (2041-2070) bioclimatic conditions in Portugal are discussed by analyzing a number of indices suitable for viticultural zoning, including a categorized bioclimatic index. A two-step method of spatial pattern downscaling is applied in order to achieve a very high spatial resolution (of approximately 1 km) throughout Portugal. Future projections are based on an ensemble of 13 climate model transient experiments, forced by the SRES A1B emission scenario. Results for the recent past are in clear agreement with the current distribution of vineyards and of the established Denomination of Origin regions. Furthermore, the typical climatic conditions associated with each grapevine variety that are currently grown in Portugal are assessed. Under future scenarios, nevertheless, the current conditions are projected to change significantly towards a lower bioclimatic diversity. This can be explained by the projected warming and drying in future decades. The resulting changes in varietal suitability and wine characteristics of each region may thereby bring important challenges for the Portuguese winemaking sector. As such, new measures need to be timely implemented to adapt to these climate change projections and to mitigate their likely detrimental impacts on the Portuguese economy. Acknowledgments: this work is supported by European Union Funds (FEDER/COMPETE - Operational Competitiveness Programme) and by national funds (FCT - Portuguese Foundation for Science and Technology) under the project ClimVineSafe (PTDC/AGR-ALI/110877/2009).

  1. Evaluation of regional climate model simulations of rainfall over the Upper Blue Nile basin

    NASA Astrophysics Data System (ADS)

    Alemseged, Tamiru Haile; Tom, Rientjes

    2015-07-01

    Climate change impact and adaptation studies can benefit from an enhanced understanding about the performance of individual as well as ensemble simulations of climate models. Studies that evaluate downscaled simulations of General Circulation Models (GCMs) by Regional Climate Models (RCMs) for African basins are noticeably missing. Recently, the Coordinated Regional Climate Downscaling Experiment (CORDEX) initiative has made multiple RCMs' outputs available for end users across the African continent. Before climate simulations receive applications in impact and adaptation studies, accuracy of the simulation results has to be evaluated. In this study, the rainfall accuracy of eight independent GCMs at a wide range of time scales over the Upper Blue Nile Basin (UBN) in Ethiopia is evaluated. The reference data for performance assessment was obtained from the rain gauge network of the National Meteorological Agency of Ethiopia (http://www.ethiomet.gov.et/)

  2. Regional Climate Change Projections over Northeast Brazil

    NASA Astrophysics Data System (ADS)

    Cassain Sales, Domingo; Araújo Costa, Alexandre; Mariano da Silva, Emerson; Cavalcante, Arnóbio M. B.; das Chagas Vasconcelos Júnior, Francisco; Martins de Araújo Junior, Luiz; Oliveira Guimarães, Sullyandro

    2013-04-01

    Climate change and climate change impact studies often require a spatial resolution beyond the horizontal grid spacing of the data generated by Global Climate Models (GCMs). Dynamical Downscaling is one of techniques that allow regionalization of information from such models, in which the GCM data drive a Regional Climate Model (RCM) that in turn, at least theoretically, presents the climatological fields in more detail and can add value to climatic analysis. In this context, CORDEX is a coordinated experiment that standardizes dynamical downscaling simulations over continental regions, to provide a contribution from the regional climate modeling community to the IPCC/AR5 and beyond. Because computer resources are limited, a modeling group involved in CORDEX typically chooses one or few of the suggested domains, and use one or a few CMIP5 GCM data to drive its regional model. At the State University of Ceará (UECE), in Brazil, we used RAMS6.0 (Regional Atmospheric Modeling System Version 6.0), driven by HadGEM2-ES (Hadley Centre Global Enviroment Model Version 2 - Earth System) data, over a extended CORDEX Central America domain (longitude: 124.5W to 24.5W, latitude: 33.5N to 17.5S). This work presents the evaluation of climatological features of precipitation and temperature over Northeast Brazil region (longitude: 47W to 34.5W, latitude: 2.5S to 17.5S) for 20 years of the historical period (1985-2005) evaluating short-term (2015-2035), mid-term (2045-2065) and long-term (2079-2099) changes, under the RCP4.5 e RCP8.5 scenarios. For the historical period, the results were compared against several observed data sets, in order to evaluate the performance of RAMS6.0 nested to HadGEM2-ES. The correlation between the simulated and observed annual cycle of precipitation is high (above 0.93). RAMS6.0 shows a wet bias of 0.706 mm/day that is larger than HadGEM2-ES bias (0.197 mm/day), however the regional model corrects the month of maximum precipitation (the global model

  3. Two-Way Integration of WRF and CCSM for Regional Climate Simulations

    SciTech Connect

    Lin, Wuyin; Zhang, Minghua; He, Juanxiong; Jiao, Xiangmin; Chen, Ying; Colle, Brian; Vogelmann, Andrew M.; Liu, Ping; Khairoutdinov, Marat; Leung, Ruby

    2013-07-12

    Under the support of the DOE award DE-SC0004670, we have successfully developed an integrated climate modeling system by nesting Weather Research and Forecasting (WRF) model within the Community Climate System Model (CCSM) and the ensuing new generation Community Earth System Model (CESM). The integrated WRF/CESM system is intended as one method of global climate modeling with regional simulation capabilities. It allows interactive dynamical regional downscaling in the computational flow of present or future global climate simulations. This capability substantially simplifies the process of dynamical downscaling by avoiding massive intermediate model outputs at high frequency that are typically required for offline regional downscaling. The inline coupling also has the advantage of higher temporal resolution for the interaction between regional and global model components. With the aid of the inline coupling, a capability has also been developed to ingest other global climate simulations (by CESM or other models), which otherwise may not have necessary intermediate outputs for regional downscaling, to realize their embedded regional details. It is accomplished by relaxing the global atmospheric state of the integrated model to that of the source simulations with an appropriate time scale. This capability has the potential to open a new venue for ensemble regional climate simulations using a single modeling system. Furthermore, this new modeling system provides an effective modeling framework for the studies of physical and dynamical feedbacks of regional weather phenomena to the large scale circulation. The projected uses of this capability include the research of up-scaling effect of regional weather system, and its use as an alternative physical representation of sub-scale processes in coarser-resolution climate models.

  4. Correlating CCM upper atmosphere parameters to surface observations for regional climate change predictions

    SciTech Connect

    Li, Xiangshang; Sailor, D.J.

    1997-11-01

    This paper explores the use of statistical downscaling of General Circulation Model (GCM) results for the purpose of regional climate change analysis. The strong correlation between surface observations and GCM upper air predictions is used in an approach very similar to the Model Output Statistics approach used in numerical weather prediction. The primary assumption in this analysis is that the statistical relationships remain unchanged under conditions of climatic change. These relations are applied to GCM upper atmosphere predictions for future (2*CO{sub 2}) climate predictions. The result is a set of regional climate change predictions conceptually valid at the scale of cities. The downscaling for specific cities within a GCM grid cell reveals some of the anticipated variability within the grid cell. In addition, multiple linear regression analysis may indicate warming that is significantly higher or lower for a particular region than the raw data from the GCM runs. 3 refs., 3 figs., 2 tabs.

  5. Gridded climate data from 5 GCMs of the Last Glacial Maximum downscaled to 30 arc s for Europe

    NASA Astrophysics Data System (ADS)

    Schmatz, D. R.; Luterbacher, J.; Zimmermann, N. E.; Pearman, P. B.

    2015-06-01

    Studies of the impacts of historical, current and future global change require very high-resolution climate data (≤ 1 km) as a basis for modelled responses, meaning that data from digital climate models generally require substantial rescaling. Another shortcoming of available datasets on past climate is that the effects of sea level rise and fall are not considered. Without such information, the study of glacial refugia or early Holocene plant and animal migration are incomplete if not impossible. Sea level at the last glacial maximum (LGM) was approximately 125 m lower, creating substantial additional terrestrial area for which no current baseline data exist. Here, we introduce the development of a novel, gridded climate dataset for LGM that is both very high resolution (1 km) and extends to the LGM sea and land mask. We developed two methods to extend current terrestrial precipitation and temperature data to areas between the current and LGM coastlines. The absolute interpolation error is less than 1 and 0.5 °C for 98.9 and 87.8 %, respectively, of all pixels within two arc degrees of the current coastline. We use the change factor method with these newly assembled baseline data to downscale five global circulation models of LGM climate to a resolution of 1 km for Europe. As additional variables we calculate 19 "bioclimatic" variables, which are often used in climate change impact studies on biological diversity. The new LGM climate maps are well suited for analysing refugia and migration during Holocene warming following the LGM.

  6. Joint Variable Spatial Downscaling (JVSD): A New Downscaling Method with Application to the Southeast US

    NASA Astrophysics Data System (ADS)

    Zhang, F.; Georgakakos, A. P.

    2011-12-01

    Joint Variable Spatial Downscaling (JVSD) is a new downscaling method developed to produce high resolution gridded hydrological datasets suitable for regional watershed modeling and assessments. JVSD differs from other statistical downscaling methods in that multiple climatic variables are downscaled simultaneously to produce realistic and consistent climate fields. JVSD includes two major steps: bias correction and spatial downscaling. In the bias correction step, JVSD uses a differencing process to create stationary joint cumulative frequency statistics of the variables being downscaled. Bias correction is then based on quantile-to-quantile mapping of these stationary frequency distributions probability space. The functional relationship between these statistics and those of the historical observation period is subsequently used to remove GCM bias. The original variables are recovered through summation of bias corrected differenced sequences. In the spatial disaggregation step, JVSD uses a historical analogue approach, with historical analogues identified simultaneously for all atmospheric fields and over all areas of the basin under study. Analysis and comparisons with 20th Century Climate in Coupled Models (20C3M) data show that JVSD reproduces the sub-grid climatic features as well as their temporal/spatial variability in the historical periods. Comparisons are also performed for precipitation and temperature with the North American regional climate change assessment program (NARCCAP) and other statistical downscaling methods over the southeastern US. The results show that JVSD performs favorably. JVSD is applied for all A1B and A2 CMIP3 GCM scenarios in the Apalachicola-Chattahoochee-Flint River Basin (southeast US) with the following general findings: (i) Mean monthly temperature exhibits increasing trends over the ACF basin for all seasons and all A1B and A2 scenarios; Most significant are the A2 temperature increases in the 2050 - 2099 time periods; (ii

  7. Future drought scenarios for the Greater Alpine Region based on dynamical downscaling experiments.

    NASA Astrophysics Data System (ADS)

    Haslinger, Klaus; Anders, Ivonne; Schöner, Wolfgang

    2014-05-01

    Large scale droughts have major ecologic, agricultural, economic as well as societal impacts by reducing crop yield, producing low flows in river systems or by limiting the public water supply. Under the perspective of rising temperatures and possibly altered precipitation regimes in the upcoming decades due to global climate change, we accomplish an assessment of future drought characteristics for the Greater Alpine Region (GAR) with regional climate model simulations. This study consists of two parts: First, the ability of the Regional Climate Model COSMO-CLM (CCLM) to simulate drought conditions in the past in space and time is evaluated. Second, an analysis of future drought scenarios for the GAR is conducted. As a drought index the Standardized Precipitation Evapotranspiration Index (SPEI) is used. For the evaluation of the Regional Climate Model in the past, simulations driven by ERA-40 are compared to observations. The gridded observational datasets of the HISTALP-database are used for evaluation in the first place. To assess the skill of CCLM, correlation coefficients between the SPEI of model simulations and gridded observations stratified by seasons and time scales are accomplished. For the analysis of future changes in the drought characteristics, four scenario runs are investigated. These are ECHAM5 and HadCM3 driven CCLM runs for the SRES scenarios A1B, A2 and B1. The SPEI is calculated spanning both the C20 and the scenario runs and are therefore regarded as transient simulations. Generally, trends to dryer annual mean conditions are apparent in each of the scenario runs, whereas the signal is rather strong in summer, contradicted by winter which shows a slight increase in precipitation north of the Alps. This in turn leads to higher variability of the SPEI in the future, as differences between winter (wetter or no change) and summer (considerably dryer) grow larger.

  8. Climate Projection Data base for Roads - CliPDaR: Design a guideline for a transnational database of downscaled climate projection data for road impact models - within the Conference's of European Directors of Roads (CEDR) TRANSNATIONAL ROAD RESEARCH PROG

    NASA Astrophysics Data System (ADS)

    Matulla, Christoph; Namyslo, Joachim; Fuchs, Tobias; Türk, Konrad

    2013-04-01

    The European road sector is vulnerable to extreme weather phenomena, which can cause large socio-economic losses. Almost every year there occur several weather triggered events (like heavy precipitation, floods, landslides, high winds, snow and ice, heat or cold waves, etc.), that disrupt transportation, knock out power lines, cut off populated regions from the outside and so on. So, in order to avoid imbalances in the supply of vital goods to people as well as to prevent negative impacts on health and life of people travelling by car it is essential to know present and future threats to roads. Climate change might increase future threats to roads. CliPDaR focuses on parts of the European road network and contributes, based on the current body of knowledge, to the establishment of guidelines helping to decide which methods and scenarios to apply for the estimation of future climate change based challenges in the field of road maintenance. Based on regional scale climate change projections specific road-impact models are applied in order to support protection measures. In recent years, it has been recognised that it is essential to assess the uncertainty and reliability of given climate projections by using ensemble approaches and downscaling methods. A huge amount of scientific work has been done to evaluate these approaches with regard to reliability and usefulness for investigations on possible impacts of climate changes. CliPDaR is going to collect the existing approaches and methodologies in European countries, discuss their differences and - in close cooperation with the road owners - develops a common line on future applications of climate projection data to road impact models. As such, the project will focus on reviewing and assessing existing regional climate change projections regarding transnational highway transport needs. The final project report will include recommendations how the findings of CliPDaR may support the decision processes of European

  9. Projecting water yield and ecosystem productivity across the United States by linking an ecohydrological model to WRF dynamically downscaled climate data

    NASA Astrophysics Data System (ADS)

    Sun, Shanlei; Sun, Ge; Cohen, Erika; McNulty, Steven G.; Caldwell, Peter V.; Duan, Kai; Zhang, Yang

    2016-03-01

    Quantifying the potential impacts of climate change on water yield and ecosystem productivity is essential to developing sound watershed restoration plans, and ecosystem adaptation and mitigation strategies. This study links an ecohydrological model (Water Supply and Stress Index, WaSSI) with WRF (Weather Research and Forecasting Model) using dynamically downscaled climate data of the HadCM3 model under the IPCC SRES A2 emission scenario. We evaluated the future (2031-2060) changes in evapotranspiration (ET), water yield (Q) and gross primary productivity (GPP) from the baseline period of 1979-2007 across the 82 773 watersheds (12-digit Hydrologic Unit Code level) in the coterminous US (CONUS). Across the CONUS, the future multi-year means show increases in annual precipitation (P) of 45 mm yr-1 (6 %), 1.8° C increase in temperature (T), 37 mm yr-1 (7 %) increase in ET, 9 mm yr-1 (3 %) increase in Q, and 106 gC m-2 yr-1 (9 %) increase in GPP. We found a large spatial variability in response to climate change across the CONUS 12-digit HUC watersheds, but in general, the majority would see consistent increases all variables evaluated. Over half of the watersheds, mostly found in the northeast and the southern part of the southwest, would see an increase in annual Q (> 100 mm yr-1 or 20 %). In addition, we also evaluated the future annual and monthly changes of hydrology and ecosystem productivity for the 18 Water Resource Regions (WRRs) or two-digit HUCs. The study provides an integrated method and example for comprehensive assessment of the potential impacts of climate change on watershed water balances and ecosystem productivity at high spatial and temporal resolutions. Results may be useful for policy-makers and land managers to formulate appropriate watershed-specific strategies for sustaining water and carbon sources in the face of climate change.

  10. Alternative ways to evaluate the NARCCAP regional climate forecasts

    NASA Astrophysics Data System (ADS)

    Shin, D.; Cocke, S.; O'Brien, J. J.

    2011-12-01

    Values added by the NARCCAP regional climate models, compared to corresponding global climate models, are explicitly unveiled by using non-traditional skill evaluation statistics. The conventional model evaluation methods, such as temporal correlation of seasonal average rainfall, cannot explain the values of dynamically downscaled data. The hidden values of the NARCCAP regional models can be better exposed by high frequency statistics, e.g., the time series of accumulated rainfall and Lawn-and-Garden Moisture Index. Application (e.g., crop, hydrological) models can also be used to uncover the value because the outcomes of application models include the high-frequency variability of seasonal climate (e.g., dry/wet spell sequences).

  11. Spatial random downscaling of rainfall signals in Andean heterogeneous terrain

    NASA Astrophysics Data System (ADS)

    Posadas, A.; Duffaut Espinosa, L. A.; Yarlequé, C.; Carbajal, M.; Heidinger, H.; Carvalho, L.; Jones, C.; Quiroz, R.

    2015-07-01

    Remotely sensed data are often used as proxies for indirect precipitation measures over data-scarce and complex-terrain areas such as the Peruvian Andes. Although this information might be appropriate for some research requirements, the extent at which local sites could be related to such information is very limited because of the resolution of the available satellite data. Downscaling techniques are used to bridge the gap between what climate modelers (global and regional) are able to provide and what decision-makers require (local). Precipitation downscaling improves the poor local representation of satellite data and helps end-users acquire more accurate estimates of water availability. Thus, a multifractal downscaling technique complemented by a heterogeneity filter was applied to TRMM (Tropical Rainfall Measuring Mission) 3B42 gridded data (spatial resolution ~ 28 km) from the Peruvian Andean high plateau or Altiplano to generate downscaled rainfall fields that are relevant at an agricultural scale (spatial resolution ~ 1 km).

  12. Regional Collaborations to Combat Climate Change: The Climate Science Centers as Strategies for Climate Adaptation

    NASA Astrophysics Data System (ADS)

    Morelli, T. L.; Palmer, R. N.

    2014-12-01

    The Department of Interior Northeast Climate Science Center (NE CSC) is part of a federal network of eight Climate Science Centers created to provide scientific information, tools, and techniques that managers and other parties interested in land, water, wildlife and cultural resources can use to anticipate, monitor, and adapt to climate change. The consortium approach taken by the CSCs allows the academic side of the Centers to gather expertise across departments, disciplines, and even institutions. This interdisciplinary approach is needed for successfully meeting regional needs for climate impact assessment, adaptive management, education, and stakeholder outreach. Partnership with the federal government facilitates interactions with the key on-the-ground stakeholders who are able to operationalize the results and conclusions of that research, monitor the progress of management actions, and provide feedback to refine future methodology and decisions as new information on climate impacts is discovered. For example, NE CSC researchers are analyzing the effect of climate change on the timing and volume of seasonal and annual streamflows and the concomitant effects on ecological and cultural resources; developing techniques to monitor tree range dynamics as affected by natural disturbances which can enable adaptation of projected climate impacts; studying the effects of changes in the frequency and magnitude of drought and stream temperature on brook trout habitats, spatial distribution and population persistence; and conducting assessments of northeastern regional climate projections and high-resolution downscaling. Project methods are being developed in collaboration with stakeholders and results are being shared broadly with federal, state, and other partners to implement and refine effective and adaptive management actions.

  13. Regional Climate Change and Development of Public Health Decision Aids

    NASA Astrophysics Data System (ADS)

    Hegedus, A. M.; Darmenova, K.; Grant, F.; Kiley, H.; Higgins, G. J.; Apling, D.

    2011-12-01

    According to the World Heath Organization (WHO) climate change is a significant and emerging threat to public health, and changes the way we must look at protecting vulnerable populations. Worldwide, the occurrence of some diseases and other threats to human health depend predominantly on local climate patterns. Rising average temperatures, in combination with changing rainfall patterns and humidity levels, alter the lifecycle and regional distribution of certain disease-carrying vectors, such as mosquitoes, ticks and rodents. In addition, higher surface temperatures will bring heat waves and heat stress to urban regions worldwide and will likely increase heat-related health risks. A growing body of scientific evidence also suggests an increase in extreme weather events such as floods, droughts and hurricanes that can be destructive to human health and well-being. Therefore, climate adaptation and health decision aids are urgently needed by city planners and health officials to determine high risk areas, evaluate vulnerable populations and develop public health infrastructure and surveillance systems. To address current deficiencies in local planning and decision making with respect to regional climate change and its effect on human health, our research is focused on performing a dynamical downscaling with the Weather Research and Forecasting (WRF) model to develop decision aids that translate the regional climate data into actionable information for users. WRF model is initialized with the Max Planck Institute European Center/Hamburg Model version 5 (ECHAM5) General Circulation Model simulations forced with the Special Report on Emissions (SRES) A1B emissions scenario. Our methodology involves development of climatological indices of extreme weather, quantifying the risk of occurrence of water/rodent/vector-borne diseases as well as developing various heat stress related decision aids. Our results indicate that the downscale simulations provide the necessary

  14. RegCM4 Dynamical Downscaling of Seasonal Climate Predictions over the Southeast of Brazil

    NASA Astrophysics Data System (ADS)

    Reboita, Michelle S.; Dutra, Lívia M. M.; da Rocha, Rosmeri P.

    2013-04-01

    In this study the Regional Climate Model version 4 (RegCM4) was nested in the General Circulation Model from the Brazilian Center for Weather Forecasts and Climate Studies (CPTEC) to produce three month (seasonal) predictions to the southeast of Brazil (SB). The predictions for MAM (March-April-May), AMJ (April-May-June) and SON (September-October-November) of 2012 used six different parameterizations of convection: 1) Grell with Arakawa-Schubert (GAS) closure, 2) Grell with Fritsch-Chappell (GFC) closure, 3) Kuo, 4) Emanuel (EM), 5) Mixed-1 with GFC and Emanuel schemes over the land and ocean, respectively and 6) Mixed-2 with Emanuel and GFC over the land and ocean, respectively. The simulations started 48 days before the seasons of interest to permit a spin-up period. The predicted precipitation was compared with observation from Climate Prediction Center (CPC). For MAM/2012, the experiment with Kuo scheme presented the seasonal precipitation similar to CPC, while GAS, GFC and Mixed-1 experiments underestimated the precipitation (~1-2 mm/day) over the center of SB and EM and Mixed-2 schemes overestimated it (~4 mm/day) over most part of SB. For AMJ/2012 all experiments underestimated the precipitation (~2-3 mm/day) in the central-south part of SB, but they simulated the precipitation intensity close to the observation over the center-north SB (except the EM which shows overestimation in this area). For this season, Mixed-1 presented the smaller bias compared to the other convective schemes. For AMJ/2012, the experiments underestimated the precipitation (~2-4 mm/day) over the center-north of SB and overestimated it (~2-4 mm/day) in the eastern sector. In this period, the EM and Mixed-1 predictions presented the smaller bias compared to CPC. Considering the three seasons, this study suggests that the best convective scheme to seasonal predictions for SB is Mixed-1, while GFC, GAS and Kuo also produce satisfactory seasonal precipitation.

  15. 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.

  16. Regional Scale Analyses of Climate Change Impacts on Agriculture

    NASA Astrophysics Data System (ADS)

    Wolfe, D. W.; Hayhoe, K.

    2006-12-01

    New statistically downscaled climate modeling techniques provide an opportunity for improved regional analysis of climate change impacts on agriculture. Climate modeling outputs can often simultaneously meet the needs of those studying impacts on natural as well as managed ecosystems. Climate outputs can be used to drive existing forest or crop models, or livestock models (e.g., temperature-humidity index model predicting dairy milk production) for improved information on regional impact. High spatial resolution climate forecasts, combined with knowledge of seasonal temperatures or rainfall constraining species ranges, can be used to predict shifts in suitable habitat for invasive weeds, insects, and pathogens, as well as cash crops. Examples of climate thresholds affecting species range and species composition include: minimum winter temperature, duration of winter chilling (vernalization) hours (e.g., hours below 7.2 C), frost-free period, and frequency of high temperature stress days in summer. High resolution climate outputs can also be used to drive existing integrated pest management models predicting crop insect and disease pressure. Collectively, these analyses can be used to test hypotheses or provide insight into the impact of future climate change scenarios on species range shifts and threat from invasives, shifts in crop production zones, and timing and regional variation in economic impacts.

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

    SciTech Connect

    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’ the 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.

  18. The Added Value to Global Model Projections of Climate Change by Dynamical Downscaling: A Case Study over the Continental U.S. using the GISS-ModelE2 and WRF Models

    NASA Technical Reports Server (NTRS)

    Racherla, P. N.; Shindell, D. T.; Faluvegi, G. S.

    2012-01-01

    Dynamical downscaling is being increasingly used for climate change studies, wherein the climates simulated by a coupled atmosphere-ocean general circulation model (AOGCM) for a historical and a future (projected) decade are used to drive a regional climate model (RCM) over a specific area. While previous studies have demonstrated that RCMs can add value to AOGCM-simulated climatologies over different world regions, it is unclear as to whether or not this translates to a better reproduction of the observed climate change therein. We address this issue over the continental U.S. using the GISS-ModelE2 and WRF models, a state-of-the-science AOGCM and RCM, respectively. As configured here, the RCM does not effect holistic improvement in the seasonally and regionally averaged surface air temperature or precipitation for the individual historical decades. Insofar as the climate change between the two decades is concerned, the RCM does improve upon the AOGCM when nudged in the domain proper, but only modestly so. Further, the analysis indicates that there is not a strong relationship between skill in capturing climatological means and skill in capturing climate change. Though additional research would be needed to demonstrate the robustness of this finding in AOGCM/RCM models generally, the evidence indicates that, for climate change studies, the most important factor is the skill of the driving global model itself, suggesting that highest priority should be given to improving the long-range climate skill of AOGCMs.

  19. Predictor selection for downscaling GCM data with LASSO

    NASA Astrophysics Data System (ADS)

    Hammami, Dorra; Lee, Tae Sam; Ouarda, Taha B. M. J.; Lee, Jonghyun

    2012-09-01

    Over the last 10 years, downscaling techniques, including both dynamical (i.e., the regional climate model) and statistical methods, have been widely developed to provide climate change information at a finer resolution than that provided by global climate models (GCMs). Because one of the major aims of downscaling techniques is to provide the most accurate information possible, data analysts have tried a number of approaches to improve predictor selection, which is one of the most important steps in downscaling techniques. Classical methods such as regression techniques, particularly stepwise regression (SWR), have been employed for downscaling. However, SWR presents some limits, such as deficiencies in dealing with collinearity problems, while also providing overly complex models. Thus, the least absolute shrinkage and selection operator (LASSO) technique, which is a penalized regression method, is presented as another alternative for predictor selection in downscaling GCM data. It may allow for more accurate and clear models that can properly deal with collinearity problems. Therefore, the objective of the current study is to compare the performances of a classical regression method (SWR) and the LASSO technique for predictor selection. A data set from 9 stations located in the southern region of Québec that includes 25 predictors measured over 29 years (from 1961 to 1990) is employed. The results indicate that, due to its computational advantages and its ease of implementation, the LASSO technique performs better than SWR and gives better results according to the determination coefficient and the RMSE as parameters for comparison.

  20. Future changes in Australian midlatitude cyclones using a regional climate model ensemble

    NASA Astrophysics Data System (ADS)

    Pepler, Acacia; Di Luca, Alejandro; Ji, Fei; Alexander, Lisa; Evans, Jason; Sherwood, Steven

    2016-04-01

    Midlatitude cyclones cause the majority of strong winds, high seas and coastal flooding along the east coast of Australia, and are also an important contributor to annual rainfall variability and water security. For this reason, there is substantial interest in how the frequency or behaviour of these cyclones may change during the 21st century. A recent regional downscaling project in southeastern Australia (NARCliM) provides an ensemble of climate model projections at both 50km and 10km resolutions for the 20-year periods 1990-2009 and 2060-2079. This allows us to analyse and assess the projections of midlatitude cyclones in significantly more detail than previous studies. NARCliM is an ensemble of 4 CMIP3 Global Climate Models (GCMs) which have been downscaled using three different configurations of the Weather Research and Forecasting model, with both GCMs and regional downscaling parameters chosen to optimise both model skill and independence of errors during the current climate. In addition to the ensemble of regional climate projections, we also employ three different cyclone identification and tracking methods which have been recently evaluated in the study region. This provides the most robust assessment to date of future changes in cyclone activity in this region, drawing attention to both areas of consistency and seasons and locations of high inter-model or inter-method uncertainty. The high resolution regional models also allow the first assessment of future changes in the frequency of heavy rainfall and strong winds associated with these systems.

  1. Regional climate model projections of the South Pacific Convergence Zone

    NASA Astrophysics Data System (ADS)

    Evans, J. P.; Bormann, K.; Katzfey, J.; Dean, S.; Arritt, R.

    2016-08-01

    This study presents results from regional climate model (RCM) projections for the south-west Pacific Ocean. The regional models used bias corrected sea surface temperatures. Six global climate models (GCMs) were used to drive a global variable resolution model on a quasi-uniform 60 km grid. One of these simulations was used to drive three limited area regional models. Thus a four member ensemble was produced by different RCMs downscaling the same GCM (GFDL2.1), and a six member ensemble was produced by the same RCM (Conformal Cubic Atmospheric Model—CCAM) downscaling six different GCMs. Comparison of the model results with precipitation observations shows the differences to be dominated by the choice of RCM, with all the CCAM simulations performing similarly and generally having lower error than the other RCMs. However, evaluating aspects of the model representation of the South Pacific Convergence Zone (SPCZ) does not show CCAM to perform better in this regard. In terms of the future projections of the SPCZ for the December-January-February season, the ensemble showed no consensus change in most characteristics though a majority of the ensemble members project a decrease in the SPCZ strength. Thus, similar to GCM based studies, there is large uncertainty concerning future changes in the SPCZ and there is no evidence to suggest that future changes will be outside the natural variability. These RCM simulations do not support an increase in the frequency of zonal SPCZ events.

  2. Identification of robust statistical downscaling methods based on a comprehensive suite of performance metrics for South Korea

    NASA Astrophysics Data System (ADS)

    Eum, H. I.; Cannon, A. J.

    2015-12-01

    Climate models are a key provider to investigate impacts of projected future climate conditions on regional hydrologic systems. However, there is a considerable mismatch of spatial resolution between GCMs and regional applications, in particular a region characterized by complex terrain such as Korean peninsula. Therefore, a downscaling procedure is an essential to assess regional impacts of climate change. Numerous statistical downscaling methods have been used mainly due to the computational efficiency and simplicity. In this study, four statistical downscaling methods [Bias-Correction/Spatial Disaggregation (BCSD), Bias-Correction/Constructed Analogue (BCCA), Multivariate Adaptive Constructed Analogs (MACA), and Bias-Correction/Climate Imprint (BCCI)] are applied to downscale the latest Climate Forecast System Reanalysis data to stations for precipitation, maximum temperature, and minimum temperature over South Korea. By split sampling scheme, all methods are calibrated with observational station data for 19 years from 1973 to 1991 are and tested for the recent 19 years from 1992 to 2010. To assess skill of the downscaling methods, we construct a comprehensive suite of performance metrics that measure an ability of reproducing temporal correlation, distribution, spatial correlation, and extreme events. In addition, we employ Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to identify robust statistical downscaling methods based on the performance metrics for each season. The results show that downscaling skill is considerably affected by the skill of CFSR and all methods lead to large improvements in representing all performance metrics. According to seasonal performance metrics evaluated, when TOPSIS is applied, MACA is identified as the most reliable and robust method for all variables and seasons. Note that such result is derived from CFSR output which is recognized as near perfect climate data in climate studies. Therefore, the

  3. Assessing the impact of a downscaled climate change simulation on the fish fauna in an Inner-Alpine River.

    PubMed

    Matulla, C; Schmutz, S; Melcher, A; Gerersdorfer, T; Haas, P

    2007-12-01

    This study assesses the impact of a changing climate on fish fauna by comparing the past mean state of fish assemblage to a possible future mean state. It is based on (1) local scale observations along an Inner-Alpine river called Mur, (2) an IPCC emission scenario (IS92a), implemented by atmosphere-ocean global circulation model (AOGCM) ECHAM4/OPYC3, and (3) a model-chain that links climate research to hydrobiology. The Mur River is still in a near-natural condition and water temperature in summer is the most important aquatic ecological constraint for fish distribution. The methodological strategy is (1) to use downscaled air temperature and precipitation scenarios for the first half of the twenty-first century, (2) to establish a model that simulates water temperature by means of air temperature and flow rate in order to generate water temperature scenarios, and (3) to evaluate the impact on fish communities using an ecological model that is driven by water temperature. This methodology links the response of fish fauna to an IPCC emission scenario and is to our knowledge an unprecedented approach. The downscaled IS92a scenarios show increased mean air temperatures during the whole year and increased precipitation totals during summer, but reduced totals for the rest of the annual cycle. These changes result in scenarios of increased water temperatures, an altered annual cycle of flow rate, and, in turn, a 70 m displacement in elevation of fish communities towards the river's head. This would enhance stress on species that rely on low water temperatures and coerce cyprinid species into advancing against retreating salmonids. Hyporhithral river sectors would turn into epipotamal sectors. Grayling (Thymallus thymallus) and Danube salmon (Hucho hucho), presently characteristic for the Mur River, would be superceded by other species. Native brown trout (Salmo trutta), already now under pressure of competition, may be at risk of losing its habitat in favour of

  4. Reductions in seasonal climate forecast dependability as a result of downscaling.

    Technology Transfer Automated Retrieval System (TEKTRAN)

    NOAA's Climate Prediction Center issues seasonal climate forecasts predicting total precipitation and average air temperature for three-month periods out to a year in advance. The utility of seasonal forecasts for agricultural applications depends on several forecast characteristics, including depe...

  5. Climate model biases and statistical downscaling for application in hydrologic model

    Technology Transfer Automated Retrieval System (TEKTRAN)

    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...

  6. 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

    2015-10-01

    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.

  7. Linking the Weather Generator with Regional Climate Model

    NASA Astrophysics Data System (ADS)

    Dubrovsky, Martin; Farda, Ales; Skalak, Petr; Huth, Radan

    2013-04-01

    One of the downscaling approaches, which transform the raw outputs from the climate models (GCMs or RCMs) into data with more realistic structure, is based on linking the stochastic weather generator with the climate model output. The present contribution, in which the parametric daily surface weather generator (WG) M&Rfi is linked to the RCM output, follows two aims: (1) Validation of the new simulations of the present climate (1961-1990) made by the ALADIN-Climate Regional Climate Model at 25 km resolution. The WG parameters are derived from the RCM-simulated surface weather series and compared to those derived from weather series observed in 125 Czech meteorological stations. The set of WG parameters will include statistics of the surface temperature and precipitation series (including probability of wet day occurrence). (2) Presenting a methodology for linking the WG with RCM output. This methodology, which is based on merging information from observations and RCM, may be interpreted as a downscaling procedure, whose product is a gridded WG capable of producing realistic synthetic multivariate weather series for weather-ungauged locations. In this procedure, WG is calibrated with RCM-simulated multi-variate weather series in the first step, and the grid specific WG parameters are then de-biased by spatially interpolated correction factors based on comparison of WG parameters calibrated with gridded RCM weather series and spatially scarcer observations. The quality of the weather series produced by the resultant gridded WG will be assessed in terms of selected climatic characteristics (focusing on characteristics related to variability and extremes of surface temperature and precipitation). Acknowledgements: The present experiment is made within the frame of projects ALARO-Climate (project P209/11/2405 sponsored by the Czech Science Foundation), WG4VALUE (project LD12029 sponsored by the Ministry of Education, Youth and Sports of CR) and VALUE (COST ES 1102

  8. Evaluation and intercomparison of downscaled daily precipitation indices over Japan in present-day climate: Strengths and weaknesses of dynamical and bias correction-type statistical downscaling methods

    NASA Astrophysics Data System (ADS)

    Iizumi, Toshichika; Nishimori, Motoki; Dairaku, Koji; Adachi, Sachiho A.; Yokozawa, Masayuki

    2011-01-01

    In this study, we evaluate the accuracy of four regional climate models (NHRCM, NRAMS, TRAMS, and TWRF) and one bias correction-type statistical model (CDFDM) for daily precipitation indices under the present-day climate (1985-2004) over Japan on a 20 km grid interval. The evaluated indices are (1) mean precipitation, (2) number of days with precipitation ≥1 mm/d (corresponds to number of wet days), (3) mean amount per wet day, (4) 90th percentile of daily precipitation, and (5) number of days with precipitation ≥90th percentile of daily precipitation. The boundary conditions of the dynamical models and the predictors of the statistical model are given from the single reanalysis data, i.e., JRA25. Both types of models successfully improved the accuracy of the indices relative to the reanalysis data in terms of bias, seasonal cycle, geographical pattern, cumulative distribution function of wet-day amount, and interannual variation pattern. In most aspects, NHRCM is the best model of all indices. Through the intercomparison between the dynamical and statistical models, respective strengths and weaknesses emerged. Briefly, (1) many dynamical models simulate too many wet days with a small amount of precipitation in humid climate zones, such as summer in Japan, relative to the statistical model, unless the cumulus convection scheme improved for such a condition is incorporated; (2) a few dynamical models can derive a better high-order percentile of daily precipitation (e.g., 90th percentile) than the statistical model; (3) both the dynamical and statistical models are still insufficient in the representation of the interannual variation pattern of the number of days with precipitation ≥90th percentile of daily precipitation; (4) the statistical model is comparable to the dynamical models in the long-term mean geographical pattern of the indices even on a 20 km grid interval if a dense observation network is applicable; (5) the statistical model is less accurate

  9. Regional Impacts of Climate Change in the Caribou Chilcotin Region, Fraser River Basin, BC, Canada

    NASA Astrophysics Data System (ADS)

    Bennett, K. E.; Werner, A. T.; Salathé, E. P.; Schnorbus, M.; Nelitz, M.; David, R. R.

    2009-05-01

    The terrain and climate of British Columbia (BC) is some of the most complex in the country, and is likely going to face unprecedented changes in hydrology due to the impacts of climate change. The Pacific Climate Impacts Consortium (PCIC) was formed in 2005 to produce tools to determine how water resources in BC and its surrounding provinces, territories and states are being affected by climate change. PCIC's first large-scale watershed modelling project implemented, in collaboration with the River Forecast Centre and the University of Washington, the Variable Infiltration Capacity (VIC) model in several major BC watersheds. Future scenarios were developed to analyse the impacts of climate change on snowpack, streamflow and soil moisture in these basins. The current study focuses on the methods to develop future scenarios and the results of the hydrologic modelling. Six different GCM emissions scenarios were selected for BC from the AR4 scenarios. A modified bias correction and statistical downscaling (BCSD) technique created at the University of Washington was used to downscale GCM results to the scale of gridded historical forcings data to generate transient-daily time step, regional-scale projections of future climate change. These forcings were then used to drive the VIC macro-scale hydrologic model. A comparison of forcings for the historical period (1961-1990) from the downscaled GCM data to the forcings created from the observed records on the monthly-timescale demonstrated that the downscaled data captured the range of variability present in the 1961-1990 period in large and medium sized basins quite well. Accurately downscaling data for application in small basins was more difficult. Daily results created with the original BCSD technique were unrealistic in places and problematic for application in hydrologic models, such as VIC that depend on an accurate daily temperature range to model evaporation and snowpack. Results for the Fraser Basin study include

  10. Statistical downscaling of hourly and daily climate scenarios for various meteorological variables in South-central Canada

    NASA Astrophysics Data System (ADS)

    Cheng, C. S.; Li, G.; Li, Q.; Auld, H.

    2008-02-01

    A regression-based methodology was used to downscale hourly and daily station-scale meteorological variables from outputs of large-scale general circulation models (GCMs). Meteorological variables include air temperature, dew point, and west east and south north wind velocities at the surface and three upper atmospheric levels (925, 850, and 500 hPa), as well as mean sea-level air pressure and total cloud cover. Different regression methods were used to construct downscaling transfer functions for different weather variables. Multiple stepwise regression analysis was used for all weather variables, except total cloud cover. Cumulative logit regression was employed for analysis of cloud cover, since cloud cover is an ordered categorical data format. For both regression procedures, to avoid multicollinearity between explanatory variables, principal components analysis was used to convert inter-correlated weather variables into uncorrelated principal components that were used as predictors. The results demonstrated that the downscaling method was able to capture the relationship between the premises and the response; for example, most hourly downscaling transfer functions could explain over 95% of the total variance for several variables (e.g. surface air temperature, dew point, and air pressure). Downscaling transfer functions were validated using a cross-validation scheme, and it was concluded that the functions for all weather variables used in the study are reliable. Performance of the downscaling method was also evaluated by comparing data distributions and extreme weather characteristics of downscaled GCM historical runs and observations during the period 1961 2000. The results showed that data distributions of downscaled GCM historical runs for all weather variables are significantly similar to those of observations. In addition, extreme characteristics of the downscaled meteorological variables (e.g. temperature, dew point, air pressure, and total cloud cover

  11. Evaluating the utility of dynamical downscaling in agricultural impacts projections

    NASA Astrophysics Data System (ADS)

    Glotter, M.; Elliott, J. W.; McInerney, D. J.; Moyer, E. J.

    2013-12-01

    The need to understand the future impacts of climate change has driven the increasing use of dynamical downscaling to produce fine-spatial-scale climate projections for impacts models. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield. Our results suggest that it does not. We simulate U.S. maize yields under current and future CO2 concentrations with the widely-used DSSAT crop model, driven by a variety of climate inputs including two general circulation models (GCMs), each in turn downscaled by two regional climate models (RCMs). We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied, GCM- and RCM-driven yields are essentially indistinguishable in all scenarios (<10% discrepancy in national yield, equivalent to error from observations). While RCMs correct some GCM biases related to fine-scale geographic features, errors in yield are dominated by broad-scale (100s of kms) GCM systematic errors that RCMs cannot compensate for. These results support previous suggestions that the added value of dynamically downscaling raw GCM output for impacts assessments may not justify its computational demands, and that some rethinking of downscaling methods is warranted.

  12. Technical Challenges and Solutions in Representing Lakes when using WRF in Downscaling Applications

    EPA Science Inventory

    The Weather Research and Forecasting (WRF) model is commonly used to make high resolution future projections of regional climate by downscaling global climate model (GCM) outputs. Because the GCM fields are typically at a much coarser spatial resolution than the target regional ...

  13. Downscaling biogeochemistry in the Benguela eastern boundary current

    NASA Astrophysics Data System (ADS)

    Machu, E.; Goubanova, K.; Le Vu, B.; Gutknecht, E.; Garçon, V.

    2015-06-01

    Dynamical downscaling is developed to better predict the regional impact of global changes in the framework of scenarios. As an intermediary step towards this objective we used the Regional Ocean Modeling System (ROMS) to downscale a low resolution coupled atmosphere-ocean global circulation model (AOGCM; IPSL-CM4) for simulating the recent-past dynamics and biogeochemistry of the Benguela eastern boundary current. Both physical and biogeochemical improvements are discussed over the present climate scenario (1980-1999) under the light of downscaling. Despite biases introduced through boundary conditions (atmospheric and oceanic), the physical and biogeochemical processes in the Benguela Upwelling System (BUS) have been improved by the ROMS model, relative to the IPSL-CM4 simulation. Nevertheless, using coarse-resolution AOGCM daily atmospheric forcing interpolated on ROMS grids resulted in a shifted SST seasonality in the southern BUS, a deterioration of the northern Benguela region and a very shallow mixed layer depth over the whole regional domain. We then investigated the effect of wind downscaling on ROMS solution. Together with a finer resolution of dynamical processes and of bathymetric features (continental shelf and Walvis Ridge), wind downscaling allowed correction of the seasonality, the mixed layer depth, and provided a better circulation over the domain and substantial modifications of subsurface biogeochemical properties. It has also changed the structure of the lower trophic levels by shifting large offshore areas from autotrophic to heterotrophic regimes with potential important consequences on ecosystem functioning. The regional downscaling also improved the phytoplankton distribution and the southward extension of low oxygen waters in the Northern Benguela. It allowed simulating low oxygen events in the northern BUS and highlighted a potential upscaling effect related to the nitrogen irrigation from the productive BUS towards the tropical

  14. A holistic, multi-scale dynamic downscaling framework for climate impact assessments and challenges of addressing finer-scale watershed dynamics

    NASA Astrophysics Data System (ADS)

    Kim, Jongho; Ivanov, Valeriy Y.

    2015-03-01

    We present a state-of-the-art holistic, multi-scale dynamic downscaling approach suited to address climate change impacts on hydrologic metrics and hydraulic regime of surface flow at the "scale of human decisions" in ungauged basins. The framework rests on stochastic and physical downscaling techniques that permit one-way crossing 106-100 m scales, with a specific emphasis on 'nesting' hydraulic assessments within a coarser-scale hydrologic model. Future climate projections for the location of Manchester watershed (MI) are obtained from an ensemble of General Circulation Models of the 3rd phase of the Coupled Model Intercomparison Project database and downscaled to a "point" scale using a weather generator. To represent the natural variability of historic and future climates, we generated continuous time series of 300 years for the locations of 3 meteorological stations located in the vicinity of the ungauged basin. To make such a multi-scale approach computationally feasible, we identified the months of May and August as the periods of specific interest based on ecohydrologic considerations. Analyses of historic and future simulation results for the identified periods show that the same median rainfall obtained by accounting for climate natural variability triggers hydrologically-mediated non-uniqueness in flow variables resolved at the hydraulic scale. An emerging challenge is that uncertainty initiated at the hydrologic scale is not necessarily preserved at smaller-scale flow variables, because of non-linearity of underlying physical processes, which ultimately can mask climate uncertainty. We stress the necessity of augmenting climate-level uncertainties of emission scenario, multi-model, and natural variability with uncertainties arising due to non-linearities in smaller-scale processes.

  15. 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

  16. Can dynamically downscaled climate model outputs improve pojections of extreme precipitation events?

    EPA Science Inventory

    Many of the storms that generate damaging floods are caused by locally intense, sub-daily precipitation, yet the spatial and temporal resolution of the most widely available climate model outputs are both too coarse to simulate these events. Thus there is often a disconnect betwe...

  17. 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

  18. A proxy for high-resolution regional reanalysis for the Southeast United States: assessment of precipitation variability in dynamically downscaled reanalyses

    USGS Publications Warehouse

    Stefanova, Lydia; Misra, Vasubandhu; Chan, Steven; Griffin, Melissa; O'Brien, James J.; Smith, Thomas J.

    2012-01-01

    We present an analysis of the seasonal, subseasonal, and diurnal variability of rainfall from COAPS Land- Atmosphere Regional Reanalysis for the Southeast at 10-km resolution (CLARReS10). Most of our assessment focuses on the representation of summertime subseasonal and diurnal variability.Summer precipitation in the Southeast United States is a particularly challenging modeling problem because of the variety of regional-scale phenomena, such as sea breeze, thunderstorms and squall lines, which are not adequately resolved in coarse atmospheric reanalyses but contribute significantly to the hydrological budget over the region. We find that the dynamically downscaled reanalyses are in good agreement with station and gridded observations in terms of both the relative seasonal distribution and the diurnal structure of precipitation, although total precipitation amounts tend to be systematically overestimated. The diurnal cycle of summer precipitation in the downscaled reanalyses is in very good agreement with station observations and a clear improvement both over their "parent" reanalyses and over newer-generation reanalyses. The seasonal cycle of precipitation is particularly well simulated in the Florida; this we attribute to the ability of the regional model to provide a more accurate representation of the spatial and temporal structure of finer-scale phenomena such as fronts and sea breezes. Over the northern portion of the domain summer precipitation in the downscaled reanalyses remains, as in the "parent" reanalyses, overestimated. Given the degree of success that dynamical downscaling of reanalyses demonstrates in the simulation of the characteristics of regional precipitation, its favorable comparison to conventional newer-generation reanalyses and its cost-effectiveness, we conclude that for the Southeast United states such downscaling is a viable proxy for high-resolution conventional reanalysis.

  19. Regional Climate Model Projections for the State of Washington

    SciTech Connect

    Salathe, E.; Leung, Lai-Yung R.; Qian, Yun; Zhang, Yongxin

    2010-05-05

    Global climate models do not have sufficient spatial resolution to represent the atmospheric and land surface processes that determine the unique regional heterogeneity of the climate of the State of Washington. If future large-scale weather patterns interact differently with the local terrain and coastlines than current weather patterns, local changes in temperature and precipitation could be quite different from the coarse-scale changes projected by global models. Regional climate models explicitly simulate the interactions between the large-scale weather patterns simulated by a global model and the local terrain. We have performed two 100-year climate simulations using the Weather and Research Forecasting (WRF) model developed at the National Center for Atmospheric Research (NCAR). One simulation is forced by the NCAR Community Climate System Model version 3 (CCSM3) and the second is forced by a simulation of the Max Plank Institute, Hamburg, global model (ECHAM5). The mesoscale simulations produce regional changes in snow cover, cloudiness, and circulation patterns associated with interactions between the large-scale climate change and the regional topography and land-water contrasts. These changes substantially alter the temperature and precipitation trends over the region relative to the global model result or statistical downscaling. To illustrate this effect, we analyze the changes from the current climate (1970-1999) to the mid 21st century (2030-2059). Changes in seasonal-mean temperature, precipitation, and snowpack are presented. Several climatological indices of extreme daily weather are also presented: precipitation intensity, fraction of precipitation occurring in extreme daily events, heat wave frequency, growing season length, and frequency of warm nights. Despite somewhat different changes in seasonal precipitation and temperature from the two regional simulations, consistent results for changes in snowpack and extreme precipitation are found in

  20. Regional climate projections in two alpine river basins: Upper Danube and Upper Brahmaputra

    NASA Astrophysics Data System (ADS)

    Dobler, A.; Yaoming, M.; Sharma, N.; Kienberger, S.; Ahrens, B.

    2011-04-01

    Projections from coarse-grid global circulation models are not suitable for regional estimates of water balance or trends of extreme precipitation and temperature, especially not in complex terrain. Thus, downscaling of global to regionally resolved projections is necessary to provide input to integrated water resources management approaches for river basins like the Upper Danube River Basin (UDRB) and the Upper Brahmaputra River Basin (UBRB). This paper discusses the application of the regional climate model COSMO-CLM as a dynamical downscaling tool. To provide accurate data the COSMO-CLM model output was post-processed by statistical means. This downscaling chain performs well in the baseline period 1971 to 2000. However, COSMO-CLM performs better in the UDRB than in the UBRB because of a longer application experience and a less complex climate in Europe. Different climate change scenarios were downscaled for the time period 1960-2100. The projections show an increase of temperature in both basins and for all seasons. The values are generally higher in the UBRB with the highest values occurring in the region of the Tibetan Plateau. Annual precipitation shows no substantial change. However, seasonal amounts show clear trends, for instance an increasing amount of spring precipitation in the UDRB. Again, the largest trends for different precipitation statistics are projected in the region of the Tibetan Plateau. Here, the projections show up to 50% longer dry periods in the months June to September with a simultaneous increase of about 10% for the maximum amount of precipitation on five consecutive days. For the Assam region in India, the projections also show an increase of 25% in the number of consecutive dry days during the monsoon season leading to prolonged monsoon breaks.

  1. A new generation of regional climate simulations for Europe: The EURO-CORDEX Initiative

    NASA Astrophysics Data System (ADS)

    Gobiet, A.; Jacob, D.; Euro-Cordex Community

    2012-04-01

    The Coordinated Regional Downscaling Experiment (CORDEX) aims to provide an internationally coordinated framework within which various regional climate downscaling (RCD) methodologies can be compared, improved, standardized and, where possible, best-practices recommended. The specific aims of CORDEX are to provide a framework to coordinate model evaluation and improvement, produce a new generation of RCD projections for land-regions worldwide based on new CMIP5 GCM projections, to foster the dialogue between the RCD communities and the impact, adaptation and vulnerability communities, and to engage developing nation scientists in the generation, evaluation and use of CORDEX data. Within this framework, regional initiatives are formed. In Europe, regional climate downscaling can build on wide experience from previous RCD projects like STARDEX, PRUDENCE, and ENSEMBLES. An ensemble of rather high resolution regional climate simulations (25 km x 25 km grid) is already available. This led to the decision that EURO-CORDEX focuses, other than other regions, on simulations at very high resolution (about 12 km x 12 km grid). In its first phase, EURO-CORDEX focuses on the evaluation of the high resolution simulations and on the construction of a simulation matrix that covers both the uncertainty induced by the driving global climate models and the uncertainty induced by the RCD methods in the best affordable manner. Further future activities include the analysis future climate simulations, the joint analysis of dynamical and empirical-statistical methods, and the design and application of suitable bias correction techniques to provide EURO-CORDEX results that are directly applicable in climate change impact research. This presentation will give an overview of the current status and activities of the EURO-CORDEX community.

  2. Projections of African drought extremes in CORDEX regional climate simulations

    NASA Astrophysics Data System (ADS)

    Gbobaniyi, Emiola; Nikulin, Grigory; Jones, Colin; Kjellström, Erik

    2013-04-01

    We investigate trends in drought extremes for different climate regions of the African continent over a combined historical and future period 1951-2100. Eight CMIP5 coupled atmospheric global climate models (CanESM2, CNRM-CM5, HadGEM2-ES, NorESM1-M, EC-EARTH, MIROC5, GFDL-ESM2M and MPI-ESM-LR) under two forcing scenarios, the relative concentration pathways (RCP) 4.5 and 8.5, with spatial resolution varying from about 1° to 3° are downscaled to 0.44° resolution by the Rossby Centre (SMHI) regional climate model RCA4. We use data from the ensuing ensembles of CORDEX-Africa regional climate simulations to explore three drought indices namely: standardized precipitation index (SPI), moisture index (MI) and difference in precipitation and evaporation (P-E). Meteorological and agricultural drought conditions are assessed in our analyses and a climate change signal is obtained for the SPI by calculating gamma functions for future SPI with respect to a baseline present climate. Results for the RCP4.5 and RCP8.5 scenarios are inter-compared to assess uncertainties in the future projections. We show that there is a pronounced sensitivity to the choice of forcing GCM which indicates that assessments of future drought conditions in Africa would benefit from large model ensembles. We also note that the results are sensitive to the choice of drought index. We discuss both spatial and temporal variability of drought extremes for different climate zones of Africa and the importance of the ensemble mean. Our study highlights the usefulness of CORDEX simulations in identifying possible future impacts of climate at local and regional scales.

  3. Regional Climate Simulation Experiments with a Variable Resolution Stretched Grid GCM

    NASA Technical Reports Server (NTRS)

    Takacs, Lawrence L.; Stein, Uri; Govindaraju, Ravi C.

    1999-01-01

    The variable resolution stretched grid (SG) version of the Goddard Earth Observing System (GEOS) GCM has been recently developed and tested in a regional climate simulation mode. The SG-approach is an alternative to the widely used nested grid approach introduced a decade ago as a pioneering step to regional climate modeling. The region of interest with a uniform about 60 km resolution used in experiments is a rectangle over the U.S. The results of one annual as well as two-month simulations for the anomalous climate event of the U.S. drought of 1988, are validated against data analysis fields and diagnostics. The efficient regional down-scaling as well as the positive impact of fine regional resolution, are obtained. The SG-concept appeared to be a promising candidate for regional and subregional climate studies and applications.

  4. 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.

  5. Evaluating the utility of dynamical downscaling in agricultural impacts projections.

    PubMed

    Glotter, Michael; Elliott, Joshua; McInerney, David; Best, Neil; Foster, Ian; Moyer, Elisabeth J

    2014-06-17

    Interest in estimating the potential socioeconomic costs of climate change has led to the increasing use of dynamical downscaling--nested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output--to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield, one of the greatest concerns under climate change. Our results suggest that it does not. We simulate US maize yields under current and future CO2 concentrations with the widely used Decision Support System for Agrotechnology Transfer crop model, driven by a variety of climate inputs including two GCMs, each in turn downscaled by two RCMs. We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied, GCM- and RCM-driven US maize yields are essentially indistinguishable in all scenarios (<10% discrepancy, equivalent to error from observations). Although RCMs correct some GCM biases related to fine-scale geographic features, errors in yield are dominated by broad-scale (100s of kilometers) GCM systematic errors that RCMs cannot compensate for. These results support previous suggestions that the benefits for impacts assessments of dynamically downscaling raw GCM output may not be sufficient to justify its computational demands. Progress on fidelity of yield projections may benefit more from continuing efforts to understand and minimize systematic error in underlying climate projections.

  6. Continental-Scale Convection-Permitting Regional Climate Modeling

    NASA Astrophysics Data System (ADS)

    Prein, A. F.; Rasmussen, R.; Clark, M. P.; Ikeda, K.; Liu, C.

    2015-12-01

    Convection-permitting regional climate models (CPCMs) have proven to be useful for down scaling large-scale climate information to regional and local scales. They add value to the representation of impact relevant parameters such as near surface temperature, precipitation, and the representation of extremes by improving local scale processes such as soil atmosphere interactions, snowpack dynamics, or the representation of deep convection. Due to their high computational costs most CPCM simulations have been restricted to small domains on the order of a few 100 km. On such small domains CPCMs might not reach their full potential because they are restricted by the lateral boundary forcing and may not be able to spin up properly. In this study we investigate the ability of a continental scale CPCM to simulate climate conditions in the Contiguous United States within the period October 2000 to December 2010. We downscale ERA-Interim reanalysis data to a horizontal grid spacing of 4 km with the Weather Research and Forecasting (WRF) Model that allows an explicit treatment of deep convection. The model performance is analyzed in different synoptic-scale weather regimes, which enables a process-oriented evaluation. The significance of model biases in simulated precipitation and temperature is investigated by including observational uncertainties in the analysis. Significant biases are further investigated and possible error sources are discussed. The goal of this study is to provide a benchmark on the state-of-the-art convection-permitting regional climate modeling and to give guidance for future model development.

  7. Great Lakes' regional climate regimes

    NASA Astrophysics Data System (ADS)

    Kravtsov, Sergey; Sugiyama, Noriyuki; Roebber, Paul

    2016-04-01

    We simulate the seasonal cycle of the Great Lakes' water temperature and lake ice using an idealized coupled lake-atmosphere-ice model. Under identical seasonally varying boundary conditions, this model exhibits more than one seasonally varying equilibrium solutions, which we associate with distinct regional climate regimes. Colder/warmer regimes are characterized by abundant/scarce amounts of wintertime ice and cooler/warmer summer temperatures, respectively. These regimes are also evident in the observations of the Great Lakes' climate variability over recent few decades, and are found to be most pronounced for Lake Superior, the deepest of the Great Lakes, consistent with model predictions. Multiple climate regimes of the Great Lakes also play a crucial role in the accelerated warming of the lakes relative to the surrounding land regions in response to larger-scale global warming. We discuss the physical origin and characteristics of multiple climate regimes over the lakes, as well as their implications for a longer-term regional climate variability.

  8. 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

  9. Climate change trend in the tropical and Caribbean regions and its hydrological impacts

    NASA Astrophysics Data System (ADS)

    Setegn, S. G.; Melesse, A. M.

    2010-12-01

    Climate variability and climate change pose significant economic and environmental risks worldwide. There are limited studies in the Caribbean islands in terms of trends in climate change and its impact on hydrology and environmental problems. The study focused in Caribbean watersheds of Puerto Rico, Dominican Republic, Jamaica and south Florida. Drought, heavy rainfall, high winds, and flooding cause losses to the agricultural and natural resources sectors locally in Florida and in the Caribbean islands. Projected changes in precipitation and temperature in the regions for three seasons (2011-2040, 2041-2070 and 2071-2100) were analyzed using outputs from fifteen global climate models (GCMs). Comparison of projected changes in precipitation and temperature across different models for the three future seasons was carried out to get an indication of the consistency of the projected changes in the region. Different downscaling methods were used to downscale the large scale GCM into watershed level climate data. We interpret the different aspects of the hydrological response to imply that changes in runoff and other hydrological variables in the region could be significant, even though the GCMs do not agree on the direction of the change. This implies that climate change may well impact on the surface and ground water resources of the region.

  10. 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. PMID:25833698

  11. 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.

  12. Influence of the African Great Lakes on the regional climate

    NASA Astrophysics Data System (ADS)

    Thiery, Wim; Davin, Edouard; Panitz, Hans-Jürgen; Demuzere, Matthias; Lhermitte, Stef; van Lipzig, Nicole

    2015-04-01

    Although the African Great Lakes are important regulators for the East-African climate, their influence on atmospheric dynamics and the regional hydrological cycle remains poorly understood. We aim to assess this impact by conducting a regional climate model simulation which resolves individual lakes and explicitly computes lake temperatures. The regional climate model COSMO-CLM, coupled to a state-of-the-art lake parameterization scheme and land surface model, is used to dynamically downscale the COSMO-CLM CORDEX-Africa evaluation simulation to 7 km grid spacing for the period 1999-2008. Evaluation of the model reveals good performance compared to both in-situ and satellite observations, especially for spatio-temporal variability of lake surface temperatures and precipitation. Model integrations indicate that the four major African Great Lakes almost double precipitation amounts over their surface relative to a simulation without lakes, but hardly exert any influence on precipitation beyond their shores. The largest lakes also cool their near-surface air, this time with pronounced downwind influence. The lake-induced cooling happens during daytime, when the lakes absorb incoming solar radiation and inhibit upward turbulent heat transport. At night, when this heat is released, the lakes warm the near-surface air. Furthermore, Lake Victoria has profound influence on atmospheric dynamics and stability as it induces cellular motion with over-lake convective inhibition during daytime, and the reversed pattern at night. Overall, this study shows the added value of resolving individual lakes and realistically representing lake surface temperatures for climate studies in this region. Thiery, W., Davin, E., Panitz, H.-J., Demuzere, M., Lhermitte, S., van Lipzig, N.P.M., The impact of the African Great Lakes on the regional climate, J. Climate (in review).

  13. 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.

  14. Refinement of horizontal resolution in dynamical downscaling of climate information using WRF: Costs, benefits, and lessons learned

    EPA Science Inventory

    Dynamical downscaling techniques have previously been developed by the U.S. Environmental Protection Agency (EPA) using a nested WRF at 108- and 36-km. Subsequent work extended one-way nesting down to 12-km resolution. Recently, the EPA Office of Research and Development used com...

  15. Evaluating global reanalysis datasets for provision of boundary conditions in regional climate modelling

    NASA Astrophysics Data System (ADS)

    Moalafhi, Ditiro B.; Evans, Jason P.; Sharma, Ashish

    2016-01-01

    Regional climate modelling studies often begin by downscaling a reanalysis dataset in order to simulate the observed climate, allowing the investigation of regional climate processes and quantification of the errors associated with the regional model. To date choice of reanalysis to perform such downscaling has been made based either on convenience or on performance of the reanalyses within the regional domain for relevant variables such as near-surface air temperature and precipitation. However, the only information passed from the reanalysis to the regional model are the atmospheric temperature, moisture and winds at the location of the boundaries of the regional domain. Here we present a methodology to evaluate reanalyses derived lateral boundary conditions for an example domain over southern Africa using satellite data. This study focusses on atmospheric temperature and moisture which are easily available. Five commonly used global reanalyses (NCEP1, NCEP2, ERA-I, 20CRv2, and MERRA) are evaluated against the Atmospheric Infrared Sounder satellite temperature and relative humidity over boundaries of two domains centred on southern Africa for the years 2003-2012 inclusive. The study reveals that MERRA is the most suitable for climate mean with NCEP1 the next most suitable. For climate variability, ERA-I is the best followed by MERRA. Overall, MERRA is preferred for generating lateral boundary conditions for this domain, followed by ERA-I. While a "better" LBC specification is not the sole precursor to an improved downscaling outcome, any reduction in uncertainty associated with the specification of LBCs is a step in the right direction.

  16. Climate change velocity underestimates climate change exposure in mountainous regions.

    PubMed

    Dobrowski, Solomon Z; Parks, Sean A

    2016-01-01

    Climate change velocity is a vector depiction of the rate of climate displacement used for assessing climate change impacts. Interpreting velocity requires an assumption that climate trajectory length is proportional to climate change exposure; longer paths suggest greater exposure. However, distance is an imperfect measure of exposure because it does not quantify the extent to which trajectories traverse areas of dissimilar climate. Here we calculate velocity and minimum cumulative exposure (MCE) in degrees Celsius along climate trajectories for North America. We find that velocity is weakly related to MCE; each metric identifies contrasting areas of vulnerability to climate change. Notably, velocity underestimates exposure in mountainous regions where climate trajectories traverse dissimilar climates, resulting in high MCE. In contrast, in flat regions velocity is high where MCE is low, as these areas have negligible climatic resistance to movement. Our results suggest that mountainous regions are more climatically isolated than previously reported. PMID:27476545

  17. Climate change velocity underestimates climate change exposure in mountainous regions

    NASA Astrophysics Data System (ADS)

    Dobrowski, Solomon Z.; Parks, Sean A.

    2016-08-01

    Climate change velocity is a vector depiction of the rate of climate displacement used for assessing climate change impacts. Interpreting velocity requires an assumption that climate trajectory length is proportional to climate change exposure; longer paths suggest greater exposure. However, distance is an imperfect measure of exposure because it does not quantify the extent to which trajectories traverse areas of dissimilar climate. Here we calculate velocity and minimum cumulative exposure (MCE) in degrees Celsius along climate trajectories for North America. We find that velocity is weakly related to MCE; each metric identifies contrasting areas of vulnerability to climate change. Notably, velocity underestimates exposure in mountainous regions where climate trajectories traverse dissimilar climates, resulting in high MCE. In contrast, in flat regions velocity is high where MCE is low, as these areas have negligible climatic resistance to movement. Our results suggest that mountainous regions are more climatically isolated than previously reported.

  18. Climate change velocity underestimates climate change exposure in mountainous regions

    PubMed Central

    Dobrowski, Solomon Z.; Parks, Sean A.

    2016-01-01

    Climate change velocity is a vector depiction of the rate of climate displacement used for assessing climate change impacts. Interpreting velocity requires an assumption that climate trajectory length is proportional to climate change exposure; longer paths suggest greater exposure. However, distance is an imperfect measure of exposure because it does not quantify the extent to which trajectories traverse areas of dissimilar climate. Here we calculate velocity and minimum cumulative exposure (MCE) in degrees Celsius along climate trajectories for North America. We find that velocity is weakly related to MCE; each metric identifies contrasting areas of vulnerability to climate change. Notably, velocity underestimates exposure in mountainous regions where climate trajectories traverse dissimilar climates, resulting in high MCE. In contrast, in flat regions velocity is high where MCE is low, as these areas have negligible climatic resistance to movement. Our results suggest that mountainous regions are more climatically isolated than previously reported. PMID:27476545

  19. Climate change velocity underestimates climate change exposure in mountainous regions.

    PubMed

    Dobrowski, Solomon Z; Parks, Sean A

    2016-08-01

    Climate change velocity is a vector depiction of the rate of climate displacement used for assessing climate change impacts. Interpreting velocity requires an assumption that climate trajectory length is proportional to climate change exposure; longer paths suggest greater exposure. However, distance is an imperfect measure of exposure because it does not quantify the extent to which trajectories traverse areas of dissimilar climate. Here we calculate velocity and minimum cumulative exposure (MCE) in degrees Celsius along climate trajectories for North America. We find that velocity is weakly related to MCE; each metric identifies contrasting areas of vulnerability to climate change. Notably, velocity underestimates exposure in mountainous regions where climate trajectories traverse dissimilar climates, resulting in high MCE. In contrast, in flat regions velocity is high where MCE is low, as these areas have negligible climatic resistance to movement. Our results suggest that mountainous regions are more climatically isolated than previously reported.

  20. Using a Coupled Lake Model with WRF to Improve High-Resolution Regional Climate Simulations

    NASA Astrophysics Data System (ADS)

    Mallard, M.; Bullock, R.; Nolte, C. G.; Alapaty, K.; Otte, T.; Gula, J.

    2012-12-01

    Lakes can play a significant role in regional climate by modifying air masses through fluxes of heat and moisture and by modulating inland extremes in temperature. Representing these effects becomes more important as regional climate modeling efforts employ finer grid spacing in order to simulate smaller scales. The Weather Research and Forecasting (WRF) model does not simulate lakes explicitly. Instead, lake points are treated as ocean points, with sea surface temperatures (SSTs) interpolated from the nearest neighboring ocean point in the driving coarse-scale fields. This can result in substantial errors for inland lakes such as the Great Lakes. Although prescribed lake surface temperatures (LSTs) can be used for retrospective modeling applications, this may not be desirable for applications involving downscaling future climate scenarios from a global climate model (GCM). In such downscaling simulations, lakes that impact the regional climate in the area of interest may not be resolved by the coarser global input fields. Explicitly simulating the LST would allow WRF to better represent interannual variability in regions significantly affected by lakes, and the influence of such variability on temperature and precipitation patterns. Therefore, coupling a lake model to WRF may lead to more reliable assessments of the impacts of extreme events on human health and the environment. We employ a version of WRF coupled to the Freshwater Lake model, FLake (Gula and Peltier 2012). FLake is a 1D bulk lake model which provides updated LSTs and ice coverage throughout the integration. This two-layer model uses a temperature-depth profile which includes a homogeneous mixed layer at the surface and a thermocline below. The shape of the thermocline is assumed, based on past theoretical and observational studies. Therefore, additional variables required for FLake to run are minimal, and it does not require tuning for individual lakes. These characteristics are advantageous for a

  1. Stochastic Cascade Dynamical Downscaling of Precipitation over Complex Terrain

    NASA Astrophysics Data System (ADS)

    Posadas, A.; Duffaut, L. E.; Jones, C.; Carvalho, L. V.; Carbajal, M.; Heidinger, H.; Quiroz, R.

    2013-12-01

    Global Climate Models (GCMs) suggest that rising concentrations of greenhouse gases will have significant implications for climate at global and regional scales. Less certain is the extent to which meteorological processes at individual sites will be affected. So-called downscaling techniques are used to bridge the spatial and temporal resolution gaps between what climate modelers are currently able to provide and what decision-makers require. Among the most important impacts of regional-scale prediction of climate change is to assess how food production and security will be affected. Regional scale precipitation and temperature simulations are crucial to understand how global warming will affect fresh water storage and the ability to grow agricultural crops. Precipitation and temperature downscaling improve the coarse resolution and poor local representation of global climate models and help decision-makers to assess the likely hydrological impacts of climate change, and it would also help crop modelers to generate more realistic climatic-change scenarios. Thus, a spatial downscaling method was developed based on the multiplicative random cascade disaggregation theory, considering a β-lognormal model describing the rainfall precipitation distribution and using the Mandelbrot-Kahane-Peyriere (MKP) function. In this paper, gridded 15 km resolution rainfall data over a 220 x 220 km section of the Andean Plateau and surroundings, generated by the Weather Research and Forecasting model (WRF), were downscaled to gridded 1 km layers with the Multifractal downscaling technique, complemented by a local heterogeneity filter. The process was tested for daily data over a period of five years (01/01/2001 - 12/31/2005). Specifically, The model parameters were estimated from 5 years of observed daily rainfall data from 18 rain gauges located in the region. A detailed testing of the model was undertaken on the basis of a comparison of the statistical characteristics of the

  2. Simulating the link between ENSO and summer drought in Southern Africa using regional climate models

    NASA Astrophysics Data System (ADS)

    Meque, Arlindo; Abiodun, Babatunde J.

    2015-04-01

    This study evaluates the capability of regional climate models (RCMs) in simulating the link between El Niño Southern Oscillation (ENSO) and Southern African droughts. It uses the Standardized Precipitation-Evapotranspiration Index (SPEI, computed using rainfall and temperature data) to identify 3-month drought over Southern Africa, and compares the observed and simulated correlation between ENSO and SPEI. The observation data are from the Climate Research Unit, while the simulation data are from ten RCMs (ARPEGE, CCLM, HIRHAM, RACMO, REMO, PRECIS, RegCM3, RCA, WRF, and CRCM) that participated in the regional climate downscaling experiment (CORDEX) project. The study analysed the rainy season (December-February) data for 19 years (1989-2008). The results show a strong link between ENSO and droughts (SPEI) over Southern Africa. The link is owing to the influence of ENSO on both rainfall and temperature fields, but the correlation between ENSO and temperature is stronger than the correlation between ENSO and rainfall. Hence, using only rainfall to monitor droughts in Southern Africa may underestimate the influence of ENSO on the droughts. Only few CORDEX RCMs simulate the influence of ENSO on Southern African drought as observed. In this regard, the ARPEGE model shows the best simulation, while CRCM shows the worst. The different in the performance may be due to their lateral boundary conditions. The RCA-simulated link between ENSO and Southern African droughts is sensitive to the global dataset used as the lateral boundary conditions. In some cases, using RCA to downscale global circulation models (GCM) simulations adds value to the simulated link between ENSO and the droughts, but in other cases the downscaling adds no value to the link. The added value of RCA to the simulated link decreases as the capability of the GCM to simulate the link increases. This study suggests that downscaling GCM simulations with RCMs over Southern Africa may improve or depreciate the

  3. Downscaled climate change projections with uncertainty assessment over India using a high resolution multi-model approach.

    PubMed

    Kumar, Pankaj; Wiltshire, Andrew; Mathison, Camilla; Asharaf, Shakeel; Ahrens, Bodo; Lucas-Picher, Philippe; Christensen, Jens H; Gobiet, Andreas; Saeed, Fahad; Hagemann, Stefan; Jacob, Daniela

    2013-12-01

    This study presents the possible regional climate change over South Asia with a focus over India as simulated by three very high resolution regional climate models (RCMs). One of the most striking results is a robust increase in monsoon precipitation by the end of the 21st century but regional differences in strength. First the ability of RCMs to simulate the monsoon climate is analyzed. For this purpose all three RCMs are forced with ECMWF reanalysis data for the period 1989-2008 at a horizontal resolution of ~25 km. The results are compared against independent observations. In order to simulate future climate the models are driven by lateral boundary conditions from two global climate models (GCMs: ECHAM5-MPIOM and HadCM3) using the SRES A1B scenario, except for one RCM, which only used data from one GCM. The results are presented for the full transient simulation period 1970-2099 and also for several time slices. The analysis concentrates on precipitation and temperature over land. All models show a clear signal of gradually wide-spread warming throughout the 21st century. The ensemble-mean warming over India is 1.5°C at the end of 2050, whereas it is 3.9°C at the end of century with respect to 1970-1999. The pattern of projected precipitation changes shows considerable spatial variability, with an increase in precipitation over the peninsular of India and coastal areas and, either no change or decrease further inland. From the analysis of a larger ensemble of global climate models using the A1B scenario a wide spread warming (~3.2°C) and an overall increase (~8.5%) in mean monsoon precipitation by the end of the 21st century is very likely. The influence of the driving GCM on the projected precipitation change simulated with each RCM is as strong as the variability among the RCMs driven with one.

  4. 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.

  5. Simulated Future Air Temperature and Precipitation Climatology and Variability in the Mediterranean Basin by Using Downscaled Global Climate Model Outputs

    NASA Astrophysics Data System (ADS)

    Ozturk, Tugba; Pelin Ceber, Zeynep; Türkeş, Murat; Kurnaz, M. Levent

    2014-05-01

    The Mediterranean Basin is one of the regions that shall be affected most by the impacts of the future climate changes on temperature regime including changes in heat waves intensity and frequency, seasonal and interannual precipitation variability including changes in summer dryness and drought events, and hydrology and water resources. In this study, projected future changes in mean air temperature and precipitation climatology and inter-annual variability over the Mediterranean region were simulated. For performing this aim, the future changes in annual and seasonal averages for the future period of 2070-2100 with respect to the period from 1970 to 2000 were investigated. Global climate model outputs of the World Climate Research Program's (WCRP's) Coupled Model Intercomparison Project Phase 3 (CMIP3) multi-model dataset were used. SRES A2, A1B and B1 emission scenarios' outputs of the Intergovernmental Panel on Climate Change (IPCC) were used in future climate model projections. Future surface mean air temperatures of the larger Mediterranean basin increase mostly in summer and least in winter, and precipitation amounts decreases in all seasons at almost all parts of the basin. Future climate signals for surface air temperatures and precipitation totals will be much larger than the inter-model standard deviation. Inter-annual temperature variability increases evidently in summer season and decreases in the northern part of the domain in the winter season, while precipitation variability increases in almost all parts of domain. Probability distribution functions are found to be shifted and flattened for future period compared to reference period. This indicates that occurrence frequency and intensity of extreme weather conditions will increase in the future period. This work has been supported by Bogazici University BAP under project number 7362. One of the authors (MLK) was partially supported by Mercator-IPC Fellowship Program.

  6. The Influence of Downscaling Models and Observations on Future Hydrochemistry Reponses of Forest Watersheds

    NASA Astrophysics Data System (ADS)

    Pourmokhtarian, A.; Driscoll, C. T.; Campbell, J. L.; Hayhoe, K.; Stoner, A. M. K.

    2014-12-01

    Most projections of climate change impacts on ecosystems rely on multiple climate model projections, but 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 in the complex mountainous terrain of the Hubbard Brook Experimental Forest, New Hampshire, USA. We evaluated three different downscaling methods: the monthly 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 AOGCMs (CCSM3, HadCM3, PCM, and GFDL-CM2) driven by higher (A1fi) and lower (B1) future emission scenarios, on two sets of observations (1/8th degree resolution grid vs. individual weather station) to generate the high-resolution climate input for the hydrochemical model PnET-BGC (ensemble of 48 runs). The choice of downscaling approach and spatial resolution of the observations used to train the downscaling model both had a major impact on modeled soil moisture and streamflow which in turn affected forest growth, net nitrification and stream chemistry. Specifically, the delta method, the simplest downscaling approach evaluated, was highly sensitive to the observations used, resulting in projections that were significantly different than those produced with the BCSD and ARRM methods. 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 poor results in model applications run at higher temporal and/or spatial resolutions. These results underscore the importance of carefully considering the observations and downscaling method used to generate climate change projections for smaller scale modeling

  7. Using Different Spatial Scales of Climate Data for Regional Climate Impact Assessment: Effect on Crop Modeling Analysis

    NASA Astrophysics Data System (ADS)

    Mereu, V.; Gallo, A.; Trabucco, A.; Montesarchio, M.; Mercogliano, P.; Spano, D.

    2015-12-01

    The high vulnerability of the agricultural sector to climate conditions causes serious concern regarding climate change impacts on crop development and production, particularly in vulnerable areas like the Mediterranean Basin. Crop simulation models are the most common tools applied for the assessment of such impacts on crop development and yields, both at local and regional scales. However, the use of these models in regional impact studies requires spatial input data for weather, soil, management, etc, whose resolution could affect simulation results. Indeed, the uncertainty in projecting climate change impacts on crop phenology and yield at the regional scale is affected not only by the uncertainty related to climate models and scenarios, but also by the downscaling methods and the resolution of climate data. The aim of this study was the evaluation of the effects of spatial resolutions of climate projections in estimating maturity date and grain yield for different varieties of durum wheat, common wheat and maize in Italy. The simulations were carried out using the CSM-CERES-Wheat and CSM-CERES-Maize crop models included in the DSSAT-CSM (Decision Support System for Agrotechnology Transfer - Cropping System Model) software, parameterized and evaluated in different experimental sites located in Italy. Dynamically downscaled climate data at different resolutions and different RCP scenarios were used as input in the crop models. A spatial platform, DSSAT-CSM based, developed in R programming language was applied to perform the simulation of maturity date and grain yield for durum wheat, common wheat and maize in each grid cell. Results, analyzed at the national and regional level, will be discussed.

  8. Future meteorological drought: projections of regional climate models for Europe

    NASA Astrophysics Data System (ADS)

    Stagge, James; Tallaksen, Lena; Rizzi, Jonathan

    2015-04-01

    In response to the major European drought events of the last decade, projecting future drought frequency and severity in a non-stationary climate is a major concern for Europe. Prior drought studies have identified regional hotspots in the Mediterranean and Eastern European regions, but have otherwise produced conflicting results with regard to future drought severity. Some of this disagreement is likely related to the relatively coarse resolution of Global Climate Models (GCMs) and regional averaging, which tends to smooth extremes. This study makes use of the most current Regional Climate Models (RCMs) forced with CMIP5 climate projections to quantify the projected change in meteorological drought for Europe during the next century at a fine, gridded scale. Meteorological drought is quantified using the Standardized Precipitation Index (SPI) and the Standardized Precipitation-Evapotranspiration Index (SPEI), which normalize accumulated precipitation and climatic water balance anomaly, respectively, for a specific location and time of year. By comparing projections for these two indices, the importance of precipitation deficits can be contrasted with the importance of evapotranspiration increases related to temperature changes. Climate projections are based on output from CORDEX (the Coordinated Regional Climate Downscaling Experiment), which provides high resolution regional downscaled climate scenarios that have been extensively tested for numerous regions around the globe, including Europe. SPI and SPEI are then calculated on a gridded scale at a spatial resolution of either 0.44 degrees (~50 km) or 0.11 degrees (~12.5km) for the three projected emission pathways (rcp26, rcp45, rcp85). Analysis is divided into two major sections: first validating the models with respect to observed historical trends in meteorological drought from 1970-2005 and then comparing drought severity and frequency during three future time periods (2011-2040, 2041-2070, 2071-2100) to the

  9. Use of beta regression for statistical downscaling of precipitation in the Campbell River basin, British Columbia, Canada

    NASA Astrophysics Data System (ADS)

    Mandal, Sohom; Srivastav, Roshan K.; Simonovic, Slobodan P.

    2016-07-01

    Impacts of global climate change on water resources systems are assessed by downscaling coarse scale climate variables into regional scale hydro-climate variables. In this study, a new multisite statistical downscaling method based on beta regression (BR) is developed for generating synthetic precipitation series, which can preserve temporal and spatial dependence along with other historical statistics. The beta regression based downscaling method includes two main steps: (1) prediction of precipitation states for the study area using classification and regression trees, and (2) generation of precipitation at different stations in the study area conditioned on the precipitation states. Daily precipitation data for 53 years from the ANUSPLIN data set is used to predict precipitation states of the study area where predictor variables are extracted from the NCEP/NCAR reanalysis data set for the same interval. The proposed model is applied to downscaling daily precipitation at ten different stations in the Campbell River basin, British Columbia, Canada. Results show that the proposed downscaling model can capture spatial and temporal variability of local precipitation very well at various locations. The performance of the model is compared with a recently developed non-parametric kernel regression based downscaling model. The BR model performs better regarding extrapolation compared to the non-parametric kernel regression model. Future precipitation changes under different GHG (greenhouse gas) emission scenarios also projected with the developed downscaling model that reveals a significant amount of changes in future seasonal precipitation and number of wet days in the river basin.

  10. 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

  11. Regional climate modeling of heat stress, frost, and water stress events in the agricultural region of Southwest Western Australia under the current climate and future climate scenarios.

    NASA Astrophysics Data System (ADS)

    Kala, Jatin; Lyons, Tom J.; Abbs, Deborah J.; Foster, Ian J.

    2010-05-01

    Heat stress, frost, and water stress events have significant impacts on grain quality and production within the agricultural region (wheat-belt) of Southwest Western Australia (SWWA) (Cramb, 2000) and understanding how the frequency and intensity of these events will change in the future is crucial for management purposes. Hence, the Regional Atmospheric Modeling System (Pielke et al, 1992) (RAMS Version 6.0) is used to simulate the past 10 years of the climate of SWWA at a 20 km grid resolution by down-scaling the 6-hourly 1.0 by 1.0 degree National Center for Environmental Prediction Final Analyses from December 1999 to Present. Daily minimum and maximum temperatures, as well as daily rainfall are validated against observations. Simulations of future climate are carried out by down-scaling the Commonwealth Scientific and Industrial Research Organization (CSIRO) Mark 3.5 General Circulation Model (Gordon et al, 2002) for 10 years (2046-2055) under the SRES A2 scenario using the Cubic Conformal Atmospheric Model (CCAM) (McGregor and Dix, 2008). The 6-hourly CCAM output is then downscaled to a 20 km resolution using RAMS. Changes in extreme events are discussed within the context of the continued viability of agriculture in SWWA. Cramb, J. (2000) Climate in relation to agriculture in south-western Australia. In: The Wheat Book (Eds W. K. Anderson and J. R. Garlinge). Bulletin 4443. Department of Agriculture, Western Australia. Gordon, H. B., Rotstayn, L. D., McGregor, J. L., Dix, M. R., Kowalczyk, E. A., O'Farrell, S. P., Waterman, L. J., Hirst, A. C., Wilson, S. G., Collier, M. A., Watterson, I. G., and Elliott, T. I. (2002). The CSIRO Mk3 Climate System Model [Electronic publication]. Aspendale: CSIRO Atmospheric Research. (CSIRO Atmospheric Research technical paper; no. 60). 130 p McGregor, J. L., and Dix, M. R., (2008) An updated description of the conformal-cubic atmospheric model. High Resolution Simulation of the Atmosphere and Ocean, Hamilton, K. and Ohfuchi

  12. A combined statistical and dynamical approach for downscaling large-scale footprints of European windstorms

    NASA Astrophysics Data System (ADS)

    Haas, R.; Pinto, J. G.

    2012-12-01

    The occurrence of mid-latitude windstorms is related to strong socio-economic effects. For detailed and reliable regional impact studies, large datasets of high-resolution wind fields are required. In this study, a statistical downscaling approach in combination with dynamical downscaling is introduced to derive storm related gust speeds on a high-resolution grid over Europe. Multiple linear regression models are trained using reanalysis data and wind gusts from regional climate model simulations for a sample of 100 top ranking windstorm events. The method is computationally inexpensive and reproduces individual windstorm footprints adequately. Compared to observations, the results for Germany are at least as good as pure dynamical downscaling. This new tool can be easily applied to large ensembles of general circulation model simulations and thus contribute to a better understanding of the regional impact of windstorms based on decadal and climate change projections.

  13. Climate change and projections for the Barents region: what is expected to change and what will stay the same?

    NASA Astrophysics Data System (ADS)

    Benestad, Rasmus E.; Parding, Kajsa M.; Isaksen, Ketil; Mezghani, Abdelkader

    2016-05-01

    We present an outlook for a number of climate parameters for temperature, precipitation, and storm statistics in the Barents region. Projected temperatures exhibited strongest increase over northern Fennoscandia and the high Arctic, exceeding 7 °C by 2099 for a typical ‘warm winter’ under the RCP4.5 scenario. More extreme temperatures may be expected with the RCP8.5, with an increase exceeding 18 °C in some places. The magnitude of the day-to-day variability in temperature is likely to decrease with higher temperatures. The skill of the downscaling models was moderate for the wet-day frequency for which the projections indicated both increases and decreases within the range of -5-+10% by 2099. The downscaled results for the wet-day mean precipitation was poor, but for the warming associated with RCP 4.5, it could result in wet-day mean precipitation being intensified by as much as 70% in 2099. The number of synoptic storms over the Barents Sea was found to increase with a warming in the Arctic, however, other climate parameters may not change much, such as the persistence of the temperature and precipitation. These climate change projections were derived using a new strategy for empirical-statistical downscaling, making use of principal component analysis to represent the local climate parameters and large ensembles of global climate model (GCM) simulations to provide information about the large scales. The method and analysis were validated on three different levels: (a) the representativeness of the GCMs, (b) traditional validation of the downscaling method, and (c) assessment of the ensembles of downscaled results in terms of past trends and interannual variability.

  14. Downscaled climate change projections with uncertainty assessment over India using a high resolution multi-model approach.

    PubMed

    Kumar, Pankaj; Wiltshire, Andrew; Mathison, Camilla; Asharaf, Shakeel; Ahrens, Bodo; Lucas-Picher, Philippe; Christensen, Jens H; Gobiet, Andreas; Saeed, Fahad; Hagemann, Stefan; Jacob, Daniela

    2013-12-01

    This study presents the possible regional climate change over South Asia with a focus over India as simulated by three very high resolution regional climate models (RCMs). One of the most striking results is a robust increase in monsoon precipitation by the end of the 21st century but regional differences in strength. First the ability of RCMs to simulate the monsoon climate is analyzed. For this purpose all three RCMs are forced with ECMWF reanalysis data for the period 1989-2008 at a horizontal resolution of ~25 km. The results are compared against independent observations. In order to simulate future climate the models are driven by lateral boundary conditions from two global climate models (GCMs: ECHAM5-MPIOM and HadCM3) using the SRES A1B scenario, except for one RCM, which only used data from one GCM. The results are presented for the full transient simulation period 1970-2099 and also for several time slices. The analysis concentrates on precipitation and temperature over land. All models show a clear signal of gradually wide-spread warming throughout the 21st century. The ensemble-mean warming over India is 1.5°C at the end of 2050, whereas it is 3.9°C at the end of century with respect to 1970-1999. The pattern of projected precipitation changes shows considerable spatial variability, with an increase in precipitation over the peninsular of India and coastal areas and, either no change or decrease further inland. From the analysis of a larger ensemble of global climate models using the A1B scenario a wide spread warming (~3.2°C) and an overall increase (~8.5%) in mean monsoon precipitation by the end of the 21st century is very likely. The influence of the driving GCM on the projected precipitation change simulated with each RCM is as strong as the variability among the RCMs driven with one. PMID:23541400

  15. Downscaling IPCC control run and future scenario with focus on the Barents Sea

    NASA Astrophysics Data System (ADS)

    Sandø, Anne Britt; Melsom, Arne; Budgell, William Paul

    2014-07-01

    Global atmosphere-ocean general circulation models are the tool by which projections for climate changes due to radiative forcing scenarios have been produced. Further, regional atmospheric downscaling of the global models may be applied in order to evaluate the details in, e.g., temperature and precipitation patterns. Similarly, detailed regional information is needed in order to assess the implications of future climate change for the marine ecosystems. However, regional results for climate change in the ocean are sparse. We present the results for the circulation and hydrography of the Barents Sea from the ocean component of two global models and from a corresponding pair of regional model configurations. The global models used are the GISS AOM and the NCAR CCSM3. The ROMS ocean model is used for the regional downscaling of these results (ROMS-G and ROMS-N configurations, respectively). This investigation was undertaken in order to shed light on two questions that are essential in the context of regional ocean projections: (1) How should a regional model be set up in order to take advantage of the results from global projections; (2) What limits to quality in the results of regional models are imposed by the quality of global models? We approached the first question by initializing the ocean model in the control simulation by a realistic ocean analysis and specifying air-sea fluxes according to the results from the global models. For the projection simulation, the global models' oceanic anomalies from their control simulation results were added upon initialization. Regarding the second question, the present set of simulations includes regional downscalings of the present-day climate as well as projected climate change. Thus, we study separately how downscaling changes the results in the control climate case, and how scenario results are changed. For the present-day climate, we find that downscaling reduces the differences in the Barents Sea between the original

  16. Downscaling and extrapolating dynamic seasonal marine forecasts for coastal ocean users

    NASA Astrophysics Data System (ADS)

    Vanhatalo, Jarno; Hobday, Alistair J.; Little, L. Richard; Spillman, Claire M.

    2016-04-01

    Marine weather and climate forecasts are essential in planning strategies and activities on a range of temporal and spatial scales. However, seasonal dynamical forecast models, that provide forecasts in monthly scale, often have low offshore resolution and limited information for inshore coastal areas. Hence, there is increasing demand for methods capable of fine scale seasonal forecasts covering coastal waters. Here, we have developed a method to combine observational data with dynamical forecasts from POAMA (Predictive Ocean Atmosphere Model for Australia; Australian Bureau of Meteorology) in order to produce seasonal downscaled, corrected forecasts, extrapolated to include inshore regions that POAMA does not cover. We demonstrate the method in forecasting the monthly sea surface temperature anomalies in the Great Australian Bight (GAB) region. The resolution of POAMA in the GAB is approximately 2° × 1° (lon. × lat.) and the resolution of our downscaled forecast is approximately 1° × 0.25°. We use data and model hindcasts for the period 1994-2010 for forecast validation. The predictive performance of our statistical downscaling model improves on the original POAMA forecast. Additionally, this statistical downscaling model extrapolates forecasts to coastal regions not covered by POAMA and its forecasts are probabilistic which allows straightforward assessment of uncertainty in downscaling and prediction. A range of marine users will benefit from access to downscaled and nearshore forecasts at seasonal timescales.

  17. A spatial hybrid approach for downscaling of extreme precipitation fields

    NASA Astrophysics Data System (ADS)

    Bechler, Aurélien; Vrac, Mathieu; Bel, Liliane

    2015-05-01

    For a few decades, climate models are used to provide future scenarios of precipitation with increasingly higher spatial resolution. However, this resolution is not yet sufficient to describe efficiently what happens at local scale. Dynamical and statistical methods of downscaling have been developed and allow us to make the link between two levels of resolution and enable us to get values at a local scale based on large-scale information from global or regional climate models. Nevertheless, both the extreme behavior and the spatial structures are not well described by these downscaling methods. We propose a two-step methodology, called spatial hybrid downscaling (SHD), to solve this problem. The first step consists in applying a univariate (i.e., one-dimensional) statistical downscaling to link the high- and low-resolution variables at some given locations. Once this 1d-link is performed, a conditional simulation algorithm of max-stable processes adapted to the extremal t process enables us to get conditional distributions of extreme precipitation at any point of the region. An application is performed on precipitation data in the south of France where extreme (Cevenol) events have major impacts (e.g., floods). Different versions of the SHD approach are tested. Most of them show particularly good results regarding univariate and multivariate criteria and overcome classical downscaling techniques tested in comparison. Furthermore, these conclusions are robust to the choice of the 1d-link functions tested and to the choice of the conditioning points to drive the conditional local-scale simulations performed by the SHD approach.

  18. Stochastic downscaling of precipitation: From dry events to heavy rainfalls

    NASA Astrophysics Data System (ADS)

    Vrac, M.; Naveau, P.

    2007-07-01

    Downscaling precipitation is a difficult challenge for the climate community. We propose and study a new stochastic weather typing approach to perform such a task. In addition to providing accurate small and medium precipitation, our procedure possesses built-in features that allow us to model adequately extreme precipitation distributions. First, we propose a new distribution for local precipitation via a probability mixture model of Gamma and Generalized Pareto (GP) distributions. The latter one stems from Extreme Value Theory (EVT). The performance of this mixture is tested on real and simulated data, and also compared to classical rainfall densities. Then our downscaling method, extending the recently developed nonhomogeneous stochastic weather typing approach, is presented. It can be summarized as a three-step program. First, regional weather precipitation patterns are constructed through a hierarchical ascending clustering method. Second, daily transitions among our precipitation patterns are represented by a nonhomogeneous Markov model influenced by large-scale atmospheric variables like NCEP reanalyses. Third, conditionally on these regional patterns, precipitation occurrence and intensity distributions are modeled as statistical mixtures. Precipitation amplitudes are assumed to follow our mixture of Gamma and GP densities. The proposed downscaling approach is applied to 37 weather stations in Illinois and compared to various possible parameterizations and to a direct modeling. Model selection procedures show that choosing one GP distribution shape parameter per pattern for all stations provides the best rainfall representation amongst all tested models. This work highlights the importance of EVT distributions to improve the modeling and downscaling of local extreme precipitations.

  19. Impact of climate Change on Groundwater Recharge in the Tiber River Basin (Central Italy) Using Regional Climate model Outputs

    NASA Astrophysics Data System (ADS)

    Muluneh, F. B.; Setegn, S. G.; Melesse, A. M.; Fiori, A.

    2011-12-01

    Quantification of the various components of hydrological processes in a watershed remains a challenging topic as the hydrological system is altered by many internal and external drivers. Changes in climate variables can affect the quantity and quality of various components of hydrological cycle. Among others, the local effects of climate change on groundwater resources were not fully studied in different part of the world as compared to the surface water. Moreover, understanding the potential impact of climate change on groundwater is more complex than surface water. The main objective of this study is to analyze the potential impact of climate change on Groundwater recharge in the Tiber River Basin using outputs from Regional Climate model. In this study, a physically-based watershed model called Soil Water Assessment Tool (SWAT) was used to estimate recharge characteristics and its response to climate change in Tiber River Basin (central Italy). The SWAT model was successfully calibrated and validated using observed weather and flow data for the period of 1963-1970 and 1971-1978 respectively. During calibration, the model was highly sensitivity to groundwater flow parameters. Dynamically downscaled rainfall and temperature datasets from ten Regional Climate Models (RCM) archived in 'Prediction of Regional scenarios and Uncertainties for Defining EuropeaN Climate change risks and Effects (PRUDENCE)' were used to force the model to assess the climate change impact on the study area. A quantile-mapping statistical correction procedure was applied to the RCM dataset to correct the inherent systematic biases. The climate change analysis indicated that by the end of 2080s the rainfall was found to decrease nearly up to 40% in dry period and there was an increase in temperature that could reach as high as 3 to 5 oC. By the end of 2080s the ground water recharge shows a decreasing trend as a response to changes in rainfall. However as the timing of both precipitation and

  20. Dynamical downscaling: Fundamental issues from an NWP point of view and recommendations

    NASA Astrophysics Data System (ADS)

    Hong, Song-You; Kanamitsu, Masao

    2014-01-01

    Dynamical downscaling has been recognized as a useful tool not only for the climate community, but also for associated application communities such as the environmental and hydrological societies. Although climate projection data are available in lower-resolution general circulation models (GCMs), higher-resolution climate projections using regional climate models (RCMs) have been obtained over various regions of the globe. Various model outputs from RCMs with a high resolution of even as high as a few km have become available with heavy weight on applications. However, from a scientific point of view in numerical atmospheric modeling, it is not clear how to objectively judge the degree of added value in the RCM output against the corresponding GCM results. A key factor responsible for skepticism is based on the fundamental limitations in the nesting approach between GCMs and RCMs. In this article, we review the current status of the dynamical downscaling for climate prediction, focusing on basic assumptions that are scrutinized from a numerical weather prediction (NWP) point of view. Uncertainties in downscaling due to the inconsistencies in the physics packages between GCMs and RCMs were revealed. Recommendations on how to tackle the ultimate goal of dynamical downscaling were also described.

  1. Evaluating the utility of dynamical downscaling in agricultural impacts projections

    PubMed Central

    Glotter, Michael; Elliott, Joshua; McInerney, David; Best, Neil; Foster, Ian; Moyer, Elisabeth J.

    2014-01-01

    Interest in estimating the potential socioeconomic costs of climate change has led to the increasing use of dynamical downscaling—nested modeling in which regional climate models (RCMs) are driven with general circulation model (GCM) output—to produce fine-spatial-scale climate projections for impacts assessments. We evaluate here whether this computationally intensive approach significantly alters projections of agricultural yield, one of the greatest concerns under climate change. Our results suggest that it does not. We simulate US maize yields under current and future CO2 concentrations with the widely used Decision Support System for Agrotechnology Transfer crop model, driven by a variety of climate inputs including two GCMs, each in turn downscaled by two RCMs. We find that no climate model output can reproduce yields driven by observed climate unless a bias correction is first applied. Once a bias correction is applied, GCM- and RCM-driven US maize yields are essentially indistinguishable in all scenarios (<10% discrepancy, equivalent to error from observations). Although RCMs correct some GCM biases related to fine-scale geographic features, errors in yield are dominated by broad-scale (100s of kilometers) GCM systematic errors that RCMs cannot compensate for. These results support previous suggestions that the benefits for impacts assessments of dynamically downscaling raw GCM output may not be sufficient to justify its computational demands. Progress on fidelity of yield projections may benefit more from continuing efforts to understand and minimize systematic error in underlying climate projections. PMID:24872455

  2. Use of NARCCAP data to characterize regional climate uncertainty in the impact of global climate change on large river fish population: Missouri River sturgeon example

    NASA Astrophysics Data System (ADS)

    Anderson, C. J.; Wildhaber, M. L.; Wikle, C. K.; Moran, E. H.; Franz, K. J.; Dey, R.

    2012-12-01

    Climate change operates over a broad range of spatial and temporal scales. Understanding the effects of change on ecosystems requires accounting for the propagation of information and uncertainty across these scales. For example, to understand potential climate change effects on fish populations in riverine ecosystems, climate conditions predicted by course-resolution atmosphere-ocean global climate models must first be translated to the regional climate scale. In turn, this regional information is used to force watershed models, which are used to force river condition models, which impact the population response. A critical challenge in such a multiscale modeling environment is to quantify sources of uncertainty given the highly nonlinear nature of interactions between climate variables and the individual organism. We use a hierarchical modeling approach for accommodating uncertainty in multiscale ecological impact studies. This framework allows for uncertainty due to system models, model parameter settings, and stochastic parameterizations. This approach is a hybrid between physical (deterministic) downscaling and statistical downscaling, recognizing that there is uncertainty in both. We use NARCCAP data to determine confidence the capability of climate models to simulate relevant processes and to quantify regional climate variability within the context of the hierarchical model of uncertainty quantification. By confidence, we mean the ability of the regional climate model to replicate observed mechanisms. We use the NCEP-driven simulations for this analysis. This provides a base from which regional change can be categorized as either a modification of previously observed mechanisms or emergence of new processes. The management implications for these categories of change are significantly different in that procedures to address impacts from existing processes may already be known and need adjustment; whereas, an emergent processes may require new management

  3. Understanding the Propagation of GCM and Downscaling Uncertainty for Projecting Crop Yield: A Nationwide Analysis over India

    NASA Astrophysics Data System (ADS)

    Sharma, T.; Murari, H. V.; H, V.; Karmakar, S.; Ghosh, S.; Soora, N. K.

    2015-12-01

    General Circulation Models (GCM) play an important role in assessing the impacts of climate change at global scale; however, coarser resolution limits their direct application at regional scale. To understand the climate variability at regional scale, different downscaling techniques (such as dynamical and statistical) have been developed which use the GCM outputs as boundary condition to produce finer resolution climate projections. Although, both dynamical and statistical downscaling techniques have proven to be able to capture the climate variability at regional scale; there are certain uncertainties lying in their projections especially for a region like India which have complex terrain and climatic pattern. Here, the uncertainties, resulting from the use of multiple GCM and downscaling models, are quantified with the assessment of impacts on regional crop yield. Two crop models with different complexity-Decision Support System for Agro-technology Transfer (DSSAT) and Infocrop, are used, forced by dynamically (CORDEX, COordinated Regional climate Downscaling EXperiment) and statistically (Kannan and Ghosh, 2011; Salvi et al., 2013) downscaled data derived from multiple GCM's. Advantage of these crop models is their ability to capture complexity of Indian condition. Yields of major crops in India, such as, rice, wheat and maize have been considered in the crop model and the impacts of climate change are assessed on their yields. The uncertainties in projected crop yields are also quantified, which must be incorporated for deriving vulnerability and risk maps for crop-climate assessments. This may further help to determine different crop management practices in order to reduce adverse impacts of climate change in future.

  4. 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

    2015-08-01

    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.

  5. A comparison of Gridded Quantile Mapping vs. Station Based Downscaling Approaches on Potential Hydrochemical Responses of Forested Watersheds to Climate Change Using a Dynamic Biogeochemical Model (PnET-BGC)

    NASA Astrophysics Data System (ADS)

    Pourmokhtarian, A.; Driscoll, C. T.; Campbell, J. L.; Hayhoe, K.

    2012-12-01

    Dynamic hydrochemical models are useful tools to understand and predict the interactive effects of climate change, atmospheric CO2, and atmospheric deposition on the hydrology and water quality of forested watersheds. Although application of these models for climate projections necessitates the use of climatic variables simulated by atmosphere-ocean general circulation models (AOGCMs) to determine inputs to drive model projections. Due to the coarse resolution of AOGCMs, outputs need to be downscaled to bridge the gap between coarse spatial resolution and higher resolution required for hydrochemical models. This research compares two different statistical downscaling approaches; Gridded Quantile Mapping (BCSD) and Station-based Daily Asynchronous Regression, and their effects on potential biogeochemical responses of forested watershed. In this study, we used the biogeochemical model, PnET-BGC, to assess, compare and contrast the effects of these two downscaling approaches on potential future changes in temperature, precipitation, solar radiation and atmospheric CO2 and their effects in projections of pools, concentrations, and fluxes of major elements at Hubbard Brook Experimental Forest in New Hampshire, U.S. Future emissions scenarios were developed from monthly output from three AOGCMs (HadCM3, GFDL, PCM) in conjunction with potential lower and upper bounds of projected atmospheric CO2 (550 and 970 ppm by 2099, respectively). The climate projections from both downscaling approaches indicate that over the 21st century, average air temperature will increase with simultaneous increases in annual average precipitation. The modeling results from both downscaling approaches suggest that climate change is projected to cause substantial temporal shifts in hydrologic and hydrochemistry patterns. The choice of downscaling approach had a major impact on the streamflow simulations, which was directly related to the ability of the downscaling approach to mimic observed

  6. A Hierarchical Evaluation of Regional Climate Simulations

    SciTech Connect

    Leung, Lai-Yung R.; Ringler, Todd; Collins, William D.; Taylor, Mark; Ashfaq, Moetasim

    2013-08-20

    Global climate models (GCMs) are the primary tools for predicting the evolution of the climate system. Through decades of development, GCMs have demonstrated useful skill in simulating climate at continental to global scales. However, large uncertainties remain in projecting climate change at regional scales, which limit our ability to inform decisions on climate change adaptation and mitigation. To bridge this gap, different modeling approaches including nested regional climate models (RCMs), global stretch-grid models, and global high-resolution atmospheric models have been used to provide regional climate simulations (Leung et al. 2003). In previous efforts to evaluate these approaches, isolating their relative merits was not possible because factors such as dynamical frameworks, physics parameterizations, and model resolutions were not systematically constrained. With advances in high performance computing, it is now feasible to run coupled atmosphere-ocean GCMs at horizontal resolution comparable to what RCMs use today. Global models with local refinement using unstructured grids have become available for modeling regional climate (e.g., Rauscher et al. 2012; Ringler et al. 2013). While they offer opportunities to improve climate simulations, significant efforts are needed to test their veracity for regional-scale climate simulations.

  7. Integrated Assessment of Climate Change, Land-Use Changes, and Regional Carbon Dynamics in United States

    NASA Astrophysics Data System (ADS)

    Mu, J. E.; Sleeter, B. M.; Abatzoglou, J. T.

    2015-12-01

    The fact that climate change is likely to accelerate throughout this century means that climate-sensitive sectors such as agriculture will need to adapt increasingly to climate change. This fact also means that understanding the potential for agricultural adaptation, and how it could come about, is important for ongoing technology investments in the public and private sectors, for infrastructure investments, and for the various policies that address agriculture directly or indirectly. This paper is an interdisciplinary study by collaborating with climate scientist, agronomists, economists, and ecologists. We first use statistical models to estimate impacts of climate change on major crop yields (wheat, corn, soybeans, sorghum, and cotton) and predict changes in crop yields under future climate condition using downscaled climate projections from CMIP5. Then, we feed the predicted yield changes to a partial equilibrium economic model (FASOM-GHG) to evaluate economic and environmental outcomes including changes in land uses (i.e., cropland, pastureland, forest land, urban land and land for conservation) in United States. Finally, we use outputs from FASOM-GHG as inputs for the ST-SIM ecological model to simulate future carbon dynamics through changes in land use under future climate conditions and discuss the rate of adaptation through land-use changes. Findings in this paper have several merits compared to previous findings in the literature. First, we add economic components to the carbon calculation. It is important to include socio-economic conditions when calculating carbon emission and/or carbon sequestration because human activities are the major contribution to atmosphere GHG emissions. Second, we use the most recent downscaled climate projections from CMIP5 to capture uncertainties from climate model projections. Instead of using all GCMs, we select five GCMs to represent the ensemble. Third, we use a bottom-up approach because we start from micro-level data

  8. Errors in Climatological Variation of Mean Areal Precipitation based on Satellite Observations and Implications for Downscaling of Climate Model Outputs

    NASA Astrophysics Data System (ADS)

    Zhang, Y.; Seo, D. J.; Habib, E. H.

    2015-12-01

    This study compares the scale-dependent variation in hourly Mean Areal Precipitation (MAP) derived from a satellite (S) and a radar-gauge (R) quantitative precipitation estimate (QPE), and seeks to explain the S-R differences on the basis of errors in the satellite QPE. This study employs an analytical framework to estimate the coefficient of variation (CV) of MAP for window sizes ranging from 4 to 512 km, using the rainfall fields of the CPC Morphing (CMORPH) satellite QPE and a radar-gauge multisensor QPE (MQPE) over five domains centered in Texas, Oklahoma and New Mexico. Our analyses reveal that CMORPH-based CV tends to plateau at larger window sizes (referred to as critical window size, or CWS), and is broadly higher in magnitude than that based on MQPE. The mechanisms underlying the CV differences differ between winter and summer. Over the winter, CMORPH suffers from severe underdetection, which yields suppressed fractional coverage (FC) across window sizes. This underestimation of FC, together with the lack of resolution of internal rainfall structure by CMORPH, leads to an magnification of both CWS and the magnitude of CV. By contrast, over the summer, widespread false precipitation detections in CMORPH lead to inflated FC, which tends to suppress CWS but this effect is outweighed by the opposing impacts of inflated outer and inner scales (i.e., distance parameters of indicator and conditional correlograms). Synthetic experiment shows that downscaling using the CMORPH-based CV tends to produce overly suppressed variance at finer spatial scales.

  9. Application of regional climate models to the Indian winter monsoon over the western Himalayas.

    PubMed

    Dimri, A P; Yasunari, T; Wiltshire, A; Kumar, P; Mathison, C; Ridley, J; Jacob, D

    2013-12-01

    The Himalayan region is characterized by pronounced topographic heterogeneity and land use variability from west to east, with a large variation in regional climate patterns. Over the western part of the region, almost one-third of the annual precipitation is received in winter during cyclonic storms embedded in westerlies, known locally as the western disturbance. In the present paper, the regional winter climate over the western Himalayas is analyzed from simulations produced by two regional climate models (RCMs) forced with large-scale fields from ERA-Interim. The analysis was conducted by the composition of contrasting (wet and dry) winter precipitation years. The findings showed that RCMs could simulate the regional climate of the western Himalayas and represent the atmospheric circulation during extreme precipitation years in accordance with observations. The results suggest the important role of topography in moisture fluxes, transport and vertical flows. Dynamical downscaling with RCMs represented regional climates at the mountain or even event scale. However, uncertainties of precipitation scale and liquid-solid precipitation ratios within RCMs are still large for the purposes of hydrological and glaciological studies.

  10. The ENSEMBLES Statistical Downscaling Portal

    NASA Astrophysics Data System (ADS)

    Cofino, Antonio S.; San-Martín, Daniel; Gutiérrez, Jose M.

    2010-05-01

    The demand for high-resolution seasonal and ACC predictions is continuously increasing due to the multiple end-user applications in a variety of sectors (hydrology, agronomy, energy, etc.) which require regional meteorological inputs. To fill the gap between the coarse-resolution grids used by global weather models and the regional needs of applications, a number of statistical downscaling techniques have been proposed. Statistical downscaling is a complex multi-disciplinary problem which requires a cascade of different scientific tools to access and process different sources of data, from GCM outputs to local observations and to run complex statistical algorithms. Thus, an end-to-end approach is needed in order to link the outputs of the ensemble prediction systems to a range of impact applications. To accomplish this task in an interactive and user-friendly form, a Web portal has been developed within the European ENSEMBLES project, integrating the necessary tools and providing the appropriate technology for distributed data access and computing. In this form, users can obtain their downscaled data testing and validating different statistical methods (from the categories weather typing, regression or weather generators) in a transparent form, not worrying about the details of the downscaling techniques and the data formats and access.

  11. Diagnosing the drivers of rain on snow events in Alaska using dynamical downscaling

    NASA Astrophysics Data System (ADS)

    Bieniek, P.; Bhatt, U. S.; Lader, R.; Walsh, J. E.; Rupp, S. T.

    2015-12-01

    Rain on snow (ROS) events are fairly rare in Alaska but have broad impacts ranging from economic losses to hazardous driving conditions to difficult caribou foraging due to ice formation on the snow. While rare, these events have recently increased in frequency in Alaska and may continue to increase under the projected warming climate. Dynamically downscaled data are now available for Alaska based on historical reanalysis for 1979-2013, while CMIP5 historical and future scenario downscaling are in progress. These new data offer a detailed, gridded product of rain and snowfall not previously possible in the spatially and temporally coarser reanalysis and GCM output currently available. Preliminary analysis shows that the dynamical downscaled data can identify extreme ROS events in Interior Alaska. The ROS events in the dynamically downscaled data are analyzed against observations and the ERA-Interim reanalysis data used to force the historical downscaling simulations. Additionally, the synoptic atmospheric circulations conditions that correspond to major ROS events in various regions of Alaska are identified with Self-Organizing Map (SOM) analysis. Such analysis is beneficial for operational forecasters with the National Weather Service and for diagnosing the mechanisms of change in future climate projections.

  12. Dynamical Downscaling of GCM Simulations: Toward the Improvement of Forecast Bias over California

    SciTech Connect

    Chin, H S

    2008-09-24

    The effects of climate change will mostly be felt on local to regional scales. However, global climate models (GCMs) are unable to produce reliable climate information on the scale needed to assess regional climate-change impacts and variability as a result of coarse grid resolution and inadequate model physics though their capability is improving. Therefore, dynamical and statistical downscaling (SD) methods have become popular methods for filling the gap between global and local-to-regional climate applications. Recent inter-comparison studies of these downscaling techniques show that both downscaling methods have similar skill in simulating the mean and variability of present climate conditions while they show significant differences for future climate conditions (Leung et al., 2003). One difficulty with the SD method is that it relies on predictor-predict and relationships, which may not hold in future climate conditions. In addition, it is now commonly accepted that the dynamical downscaling with the regional climate model (RCM) is more skillful at the resolving orographic climate effect than the driving coarser-grid GCM simulations. To assess the possible societal impacts of climate changes, many RCMs have been developed and used to provide a better projection of future regional-scale climates for guiding policies in economy, ecosystem, water supply, agriculture, human health, and air quality (Giorgi et al., 1994; Leung and Ghan, 1999; Leung et al., 2003; Liang et al., 2004; Kim, 2004; Duffy et al., 2006). Although many regional climate features, such as seasonal mean and extreme precipitation have been successfully captured in these RCMs, obvious biases of simulated precipitation remain, particularly the winter wet bias commonly seen in mountain regions of the Western United States. The importance of regional climate research over California is not only because California has the largest population in the nation, but California has one of the most

  13. Final Technical Report for Collaborative Research: Regional climate-change projections through next-generation empirical and dynamical models, DE-FG02-07ER64429

    SciTech Connect

    Smyth, Padhraic

    2013-07-22

    This is the final report for a DOE-funded research project describing the outcome of research on non-homogeneous hidden Markov models (NHMMs) and coupled ocean-atmosphere (O-A) intermediate-complexity models (ICMs) to identify the potentially predictable modes of climate variability, and to investigate their impacts on the regional-scale. The main results consist of extensive development of the hidden Markov models for rainfall simulation and downscaling specifically within the non-stationary climate change context together with the development of parallelized software; application of NHMMs to downscaling of rainfall projections over India; identification and analysis of decadal climate signals in data and models; and, studies of climate variability in terms of the dynamics of atmospheric flow regimes.

  14. The impacts of land use, radiative forcing, and biological changes on regional climate in Japan

    NASA Astrophysics Data System (ADS)

    Dairaku, K.; Pielke, R. A., Sr.

    2013-12-01

    Because regional responses of surface hydrological and biogeochemical changes are particularly complex, it is necessary to develop assessment tools for regional scale adaptation to climate. We developed a dynamical downscaling method using the regional climate model (NIED-RAMS) over Japan. The NIED-RAMS model includes a plant model that considers biological processes, the General Energy and Mass Transfer Model (GEMTM) which adds spatial resolution to accurately assess critical interactions within the regional climate system for vulnerability assessments to climate change. We digitalized a potential vegetation map that formerly existed only on paper into Geographic Information System data. It quantified information on the reduction of green spaces and the expansion of urban and agricultural areas in Japan. We conducted regional climate sensitivity experiments of land use and land cover (LULC) change, radiative forcing, and biological effects by using the NIED-RAMS with horizontal grid spacing of 20 km. We investigated regional climate responses in Japan for three experimental scenarios: 1. land use and land cover is changed from current to potential vegetation; 2. radiative forcing is changed from 1 x CO2 to 2 x CO2; and 3. biological CO2 partial pressures in plants are doubled. The experiments show good accuracy in reproducing the surface air temperature and precipitation. The experiments indicate the distinct change of hydrological cycles in various aspects due to anthropogenic LULC change, radiative forcing, and biological effects. The relative impacts of those changes are discussed and compared. Acknowledgments This study was conducted as part of the research subject "Vulnerability and Adaptation to Climate Change in Water Hazard Assessed Using Regional Climate Scenarios in the Tokyo Region' (National Research Institute for Earth Science and Disaster Prevention; PI: Koji Dairaku) of Research Program on Climate Change Adaptation (RECCA), and was supported by the

  15. NCAR Global Climate Four-Dimensional Data Assimilation (CFDDA) Hourly 40 km Reanalysis: a high-resolution dynamically downscaled climatography

    NASA Astrophysics Data System (ADS)

    Peng, G. S.; Hou, C. Y.; Rife, D. L.; Dattore, R.

    2014-12-01

    Wind energy cost models incur inaccuracies from uncertainty in ambient wind measurements and estimates. This inhibits the best possible investment in wind energy infrastructure and management systems. High-resolution temporal and spatial wind data needed for wind availability analysis—usually created with regional-scale models—have traditionally been proprietary and costly to obtain. Freely available global model data suffers from either lower spatial or temporal resolution, or both. Low spatial resolution fails to realistically represent wind speeds in complex terrain. Low temporal resolution fails to capture the full diurnal cycle of wind behavior. The NCAR Global Climate Four-Dimensional Data Assimilation (CFDDA) Hourly 40 km Reanalysis was developed in 2009-2010 by the Research Applications Laboratory (RAL) to provide the most accurate boundary layer wind estimates available at that time. CFDDA used 28 sigma levels, with 19 between the surface and 700 hPa, a four-fold improvement over the contemporary NWP models. The dataset spans 21 years, 1985-2005, providing hourly atmospheric parameters, including winds, on 28 vertical levels on a global 40 km grid. This presentation will introduce the modeling and assimilation strategy, highlight the available data content including the parameter set, and review the data access options available from the RDA. CFDDA project partners, Defense Threat Reduction Agency (DTRA), NCAR RAL and NCAR Mesoscale & Microscale Meteorology (MMM) divisions are offering this dataset to the public for free with minor restrictions. NCAR Research Data Archive (RDA), hosted by the Computational and Information Systems Laboratory, provides data support. It is available at http://rda.ucar.edu/datasets/ds604.0/

  16. Coupled terrestrial and aquatic regional responses to land use change and climate variability in a temperate New England watershed

    NASA Astrophysics Data System (ADS)

    Wollheim, W. M.; Samal, N. R.; Zhou, Z.; Zuidema, S.; Stewart, R. J.; Mineau, M.

    2015-12-01

    Climate change and land use interact to alter hydrology, biogeochemistry, and ecosystem function. Regional scale analyses that link terrestrial and aquatic ecosystems across spatial scales are needed to understand the mechanisms of response and tradeoffs among different ecosystem services. We coupled the terrestrial ecosystem model, PnET, with a river network model, FrAMES, to explore how terrestrial and aquatic conditions simultaneously respond to variation and changes in climate and land use. We applied the coupled model to the Merrimack R. watershed, NH/MA, USA, to understand how impacts vary at different nested basin scales. The coupled PnET-FrAMES predicts variables relevant to key ecosystem services including snow pack, runoff, woody biomass accumulation, net carbon sequestration, nitrogen runoff, discharge, conductivity, water temperature, aquatic denitrification, and nitrogen flux. We used statistically downscaled high and low emission scenarios of GCMs (GFDL CM2.1) to explore projected future responses to 2100. Some variables were more sensitive (snowpack, runoff, net carbon sequestration, water temperature) than others (woody biomass, conductivity, nitrogen concentration) to interannual climate variability. Water quality and terrestrial ecosystem responses were more sensitive to land use changes. Water quality responses are buffered in large rivers due to the dilution capacity of forested areas of the watersheds, but this dilution capacity is altered by future climate changes. Coupled terrestrial-aquatic models at regional scales using downscale climate projections will be essential for planning adaption and mitigation strategies in response to future climate and land use change.

  17. Simulation of Anomalous Regional Climate Events with a Variable Resolution Stretched Grid GCM

    NASA Technical Reports Server (NTRS)

    Fox-Rabinovitz, Michael S.

    1999-01-01

    The stretched-grid approach provides an efficient down-scaling and consistent interactions between global and regional scales due to using one variable-resolution model for integrations. It is a workable alternative to the widely used nested-grid approach introduced over a decade ago as a pioneering step in regional climate modeling. A variable-resolution General Circulation Model (GCM) employing a stretched grid, with enhanced resolution over the US as the area of interest, is used for simulating two anomalous regional climate events, the US summer drought of 1988 and flood of 1993. The special mode of integration using a stretched-grid GCM and data assimilation system is developed that allows for imitating the nested-grid framework. The mode is useful for inter-comparison purposes and for underlining the differences between these two approaches. The 1988 and 1993 integrations are performed for the two month period starting from mid May. Regional resolutions used in most of the experiments is 60 km. The major goal and the result of the study is obtaining the efficient down-scaling over the area of interest. The monthly mean prognostic regional fields for the stretched-grid integrations are remarkably close to those of the verifying analyses. Simulated precipitation patterns are successfully verified against gauge precipitation observations. The impact of finer 40 km regional resolution is investigated for the 1993 integration and an example of recovering subregional precipitation is presented. The obtained results show that the global variable-resolution stretched-grid approach is a viable candidate for regional and subregional climate studies and applications.

  18. 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.

  19. Towards predictive understanding of regional climate change

    NASA Astrophysics Data System (ADS)

    Xie, Shang-Ping; Deser, Clara; Vecchi, Gabriel A.; Collins, Matthew; Delworth, Thomas L.; Hall, Alex; Hawkins, Ed; Johnson, Nathaniel C.; Cassou, Christophe; Giannini, Alessandra; Watanabe, Masahiro

    2015-10-01

    Regional information on climate change is urgently needed but often deemed unreliable. To achieve credible regional climate projections, it is essential to understand underlying physical processes, reduce model biases and evaluate their impact on projections, and adequately account for internal variability. In the tropics, where atmospheric internal variability is small compared with the forced change, advancing our understanding of the coupling between long-term changes in upper-ocean temperature and the atmospheric circulation will help most to narrow the uncertainty. In the extratropics, relatively large internal variability introduces substantial uncertainty, while exacerbating risks associated with extreme events. Large ensemble simulations are essential to estimate the probabilistic distribution of climate change on regional scales. Regional models inherit atmospheric circulation uncertainty from global models and do not automatically solve the problem of regional climate change. We conclude that the current priority is to understand and reduce uncertainties on scales greater than 100 km to aid assessments at finer scales.

  20. Modeling the Impacts of Global Climate and Regional Land Use Change on Regional Climate, Air Quality and Public Health in the New York Metropolitan Region

    NASA Astrophysics Data System (ADS)

    Rosenthal, J. E.; Knowlton, K. M.; Kinney, P. L.

    2002-12-01

    There is an imminent need to downscale the global climate models used by international consortiums like the IPCC (Intergovernmental Panel on Climate Change) to predict the future regional impacts of climate change. To meet this need, a "place-based" climate model that makes specific regional projections about future environmental conditions local inhabitants could face is being created by the Mailman School of Public Health at Columbia University, in collaboration with other researchers and universities, for New York City and the 31 surrounding counties. This presentation describes the design and initial results of this modeling study, aimed at simulating the effects of global climate change and regional land use change on climate and air quality over the northeastern United States in order to project the associated public health impacts in the region. Heat waves and elevated concentrations of ozone and fine particles are significant current public health stressors in the New York metropolitan area. The New York Climate and Health Project is linking human dimension and natural sciences models to assess the potential for future public health impacts from heat stress and air quality, and yield improved tools for assessing climate change impacts. The model will be applied to the NY metropolitan east coast region. The following questions will be addressed: 1. What changes in the frequency and severity of extreme heat events are likely to occur over the next 80 years due to a range of possible scenarios of land use and land cover (LU/LC) and climate change in the region? 2. How might the frequency and severity of episodic concentrations of ozone (O3) and airborne particulate matter smaller than 2.5 æm in diameter (PM2.5) change over the next 80 years due to a range of possible scenarios of land use and climate change in the metropolitan region? 3. What is the range of possible human health impacts of these changes in the region? 4. How might projected future human

  1. Climate change and probabilistic scenario of streamflow extremes in a cryospheric alpine region

    NASA Astrophysics Data System (ADS)

    Yang, Tao; Gao, Cheng

    2015-04-01

    Future projections of streamflow extremes are of paramount significance in assessing the climate impacts on social and natural systems, particularly for the Himalayan alpine region in the Tibetan Plateau known as the Asian Water Tower. This study strives to quantify the uncertainties from different sources in simulating future extreme flows and seeks to construct reliable scenarios of future extreme flows for the headwater catchment of the Yellow River Basin in the 21st century. The results can be formulated as follows: (1) The revised snow model based on a daily active temperature method is superior to the commonly used degree-day method in simulating snowmelt processes. (2) In general, hydrological models contribute more uncertainties than the downscaling methods in high flow and low flow over the cryospheric alpine regions characterized by the snow-rainfall induced runoff processes under most scenarios. Meanwhile, impacts to uncertainty vary with time. (3) The ultimate probability of high-flow exhibits a downward trend in future by using an unconditional method, whereas positive changes in probability of low-flow are projected. The method in the work includes a variety of influence from different contributing factors (e.g. downscaling models, hydrological models, model parameters, and their simulation skills) on streamflow projection, therefore can offer more information (i.e. different percentiles of flow and uncertainty ranges) for future water resources planning compared with the purely deterministic approaches. Hence, the results are beneficial to boost our current methodologies of climate impact research in the Himalayan alpine zone.

  2. Climate change and probabilistic scenario of streamflow extremes in an alpine region

    NASA Astrophysics Data System (ADS)

    Yang, Tao; Wang, Xiaoyan; Yu, Zhongbo; Krysanova, Valentina; Chen, Xi; Schwartz, Franklin W.; Sudicky, Edward A.

    2014-07-01

    Future projections of streamflow extremes are of paramount significance in assessing the climate impacts on social and natural systems, particularly for the Himalayan alpine region in the Tibetan Plateau known as the Asian water tower. This study strives to quantify the uncertainties from different sources in simulating future extreme flows and seeks to construct reliable scenarios of future extreme flows for the headwater catchment of the Yellow River Basin in the 21st century. The results can be formulated as follows: (1) The revised snow model based on a daily active temperature method is superior to the commonly used degree-day method in simulating snowmelt processes. (2) In general, hydrological models contribute more uncertainties than the downscaling methods in high flow and low flow over the cryospheric alpine regions characterized by the snow-rainfall-induced runoff processes under most scenarios. Meanwhile, impacts to uncertainty vary with time. (3) The ultimate probability of high flow exhibits a downward trend in future by using an unconditional method, whereas positive changes in the probability of low flow are projected. The method in the work includes a variety of influence from different contributing factors (e.g., downscaling models, hydrological models, model parameters, and their simulation skills) on streamflow projection, therefore can offer more information (i.e., different percentiles of flow and uncertainty ranges) for future water resource planning compared with the purely deterministic approaches. Hence, the results are beneficial to boost our current methodologies of climate impact research in the Himalayan alpine zone.

  3. Regional Climate Simulation with a Variable Resolution Stretch Grid GCM: The 1998 Summer Drought

    NASA Technical Reports Server (NTRS)

    Fox-Rabinovitz, Michael; Stein, Uri; Takacs, Lawrence; Govindaraju, Ravi; Suarez, Max

    1999-01-01

    The variable resolution stretched grid(SG) GCM based on the Goddard Earth Observing System (GEOS) GCM, has been developed and tested in a regional climate simulation mode. The GEOS SG-GCM is used for simulation of the 1988 summer drought over the U.S. Midwest. Within the stretched grid, the region of interest with a uniform about 60 km resolution is a rectangle over the U.S. Outside the region, the grid intervals increase or stretch with a constant stretching factor (as a geometric progression). The results of two-month simulation for the anomalous climate event of the U.S. drought of 1988, are validated against data analysis fields and diagnostics. The event has been chosen by the Project to Inter-compare Regional Climate Simulations(PIRCS). The efficient regional down-scaling as well as the positive impact of fine regional resolution, are obtained. More specifically, the precipitation, 500 hPa, and low-level jet patterns and characteristics are well represented in the simulation. The SG-concept appeared to be a promising candidate for regional and subregional climate studies and applications.

  4. Impacts on Water Management and Crop Production of Regional Cropping System Adaptation to Climate Change

    NASA Astrophysics Data System (ADS)

    Zhong, H.; Sun, L.; Tian, Z.; Liang, Z.; Fischer, G.

    2014-12-01

    China is one of the most populous and fast developing countries, also faces a great pressure on grain production and food security. Multi-cropping system is widely applied in China to fully utilize agro-climatic resources and increase land productivity. As the heat resource keep improving under climate warming, multi-cropping system will also shifting northward, and benefit crop production. But water shortage in North China Plain will constrain the adoption of new multi-cropping system. Effectiveness of multi-cropping system adaptation to climate change will greatly depend on future hydrological change and agriculture water management. So it is necessary to quantitatively express the water demand of different multi-cropping systems under climate change. In this paper, we proposed an integrated climate-cropping system-crops adaptation framework, and specifically focused on: 1) precipitation and hydrological change under future climate change in China; 2) the best multi-cropping system and correspondent crop rotation sequence, and water demand under future agro-climatic resources; 3) attainable crop production with water constraint; and 4) future water management. In order to obtain climate projection and precipitation distribution, global climate change scenario from HADCAM3 is downscaled with regional climate model (PRECIS), historical climate data (1960-1990) was interpolated from more than 700 meteorological observation stations. The regional Agro-ecological Zone (AEZ) model is applied to simulate the best multi-cropping system and crop rotation sequence under projected climate change scenario. Finally, we use the site process-based DSSAT model to estimate attainable crop production and the water deficiency. Our findings indicate that annual land productivity may increase and China can gain benefit from climate change if multi-cropping system would be adopted. This study provides a macro-scale view of agriculture adaptation, and gives suggestions to national

  5. Regional Climate and Streamflow Projections in North America Under IPCC CMIP5 Scenarios

    NASA Astrophysics Data System (ADS)

    Chang, H. I.; Castro, C. L.; Troch, P. A. A.; Mukherjee, R.

    2014-12-01

    The Colorado River system is the predominant source of water supply for the Southwest U.S. and is already fully allocated, making the region's environmental and economic health particularly sensitive to annual and multi-year streamflow variability. Observed streamflow declines in the Colorado Basin in recent years are likely due to synergistic combination of anthropogenic global warming and natural climate variability, which are creating an overall warmer and more extreme climate. IPCC assessment reports have projected warmer and drier conditions in arid to semi-arid regions (e.g. Solomon et al. 2007). The NAM-related precipitation contributes to substantial Colorado streamflows. Recent climate change studies for the Southwest U.S. region project a dire future, with chronic drought, and substantially reduced Colorado River flows. These regional effects reflect the general observation that climate is being more extreme globally, with areas climatologically favored to be wet getting wetter and areas favored to be dry getting drier (Wang et al. 2012). Multi-scale downscaling modeling experiments are designed using recent IPCC AR5 global climate projections, which incorporate regional climate and hydrologic modeling components. The Weather Research and Forecasting model (WRF) has been selected as the main regional modeling tool; the Variable Infiltration Capacity model (VIC) will be used to generate streamflow projections for the Colorado River Basin. The WRF domain is set up to follow the CORDEX-North America guideline with 25km grid spacing, and VIC model is individually calibrated for upper and lower Colorado River basins in 1/8° resolution. The multi-scale climate and hydrology study aims to characterize how the combination of climate change and natural climate variability is changing cool and warm season precipitation. Further, to preserve the downscaled RCM sensitivity and maintain a reasonable climatology mean based on observed record, a new bias correction

  6. The Regional Impacts of Climate Change

    NASA Astrophysics Data System (ADS)

    Watson, Robert T.; Zinyowera, Marufu C.; Moss, Richard H.

    1998-01-01

    The degree to which human conditions and the natural environment are vulnerable to the potential effects of climate change is a key concern for governments and the environmental science community worldwide. This book from the Intergovernmental Panel on Climate Change (IPCC) provides the best available base of scientific information for policymakers and public use. The Regional Impacts of Climate Change: An Assessment of Vulnerability reviews state-of-the-art information on potential impacts of climate change for ecological systems, water supply, food production, coastal infrastructure, human health, and other resources for ten global regions. It also illustrates that the increasing costs of climate and climate variability, in terms of loss of human life and capital due to floods, storms, and droughts, are a result of the lack of adjustment and response in society's policies and use of resources. This book points to management options that would make many sectors more resilient to current variability in climate and thus help these sectors adapt to future changes in climate. This book will become the primary source of information on regional aspects of climate change for policymakers, the scientific community, and students.

  7. The Regional Impacts of Climate Change

    NASA Astrophysics Data System (ADS)

    Watson, Robert T.; Zinyowera, Marufu C.; Moss, Richard H.

    1997-12-01

    The degree to which human conditions and the natural environment are vulnerable to the potential effects of climate change is a key concern for governments and the environmental science community worldwide. This book from the Intergovernmental Panel on Climate Change (IPCC) provides the best available base of scientific information for policymakers and public use. The Regional Impacts of Climate Change: An Assessment of Vulnerability reviews state-of-the-art information on potential impacts of climate change for ecological systems, water supply, food production, coastal infrastructure, human health, and other resources for ten global regions. It also illustrates that the increasing costs of climate and climate variability, in terms of loss of human life and capital due to floods, storms, and droughts, are a result of the lack of adjustment and response in society's policies and use of resources. This book points to management options that would make many sectors more resilient to current variability in climate and thus help these sectors adapt to future changes in climate. This book will become the primary source of information on regional aspects of climate change for policymakers, the scientific community, and students.

  8. Great plains regional climate assessment technical report

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The Great Plains region (GP) plays important role in providing food and energy to the economy of the United States. Multiple climatic and non-climatic stressors put multiple sectors, livelihoods and communities at risk, including agriculture, water, ecosystems and rural and tribal communities. The G...

  9. It's Not Just the Heat, It's the Humidity: Downscaled Wet-Bulb Temperature Projections and Implication for Future Summer Experiences from the American Climate Prospectus

    NASA Astrophysics Data System (ADS)

    Rasmussen, D.; Kopp, R. E., III

    2014-12-01

    The health impacts of extreme heat are significantly aggravated when combined with high humidity [1]. Wet-bulb temperature (TwT_w), measured by wrapping a thermometer in a wetted cloth and fully ventilating it, provides a physical metric of the combined effect of both heat and humidity. TwT_w in excess of 30∘^circC is extremely dangerous and has been observed in the US only during the peak of the 1995 Midwest heat wave. Historically unprecedented TwT_w in excess of 33∘^circC represents an extreme threat to human health, with heat stroke likely for fit individuals after less than one hour of shaded activity [2,3]. We present an empirical method for generating downscaled probability distributions of daily maximum TwT_w conditional on dry-bulb temperature. The approach is based upon the statistical relationship between these two parameters, as estimated from reanalysis data. Using statistically downscaled temperature projections for Representative Concentration Pathways (RCPs) 8.5, 4.5 and 2.6, we project changes in TwT_w for the next two centuries. We find that dangerously humid days (TwT_w > 27∘^circC) will become increasingly common in the eastern U.S. under RCP 8.5, with the expected number of days per summer surpassing those of Louisiana today in Chicago in 25 years, Washington, D.C. in 30 years, New York City in 50 years and Portland in 60 years. By the end of the century under RCP 8.5, one extraordinarily dangerous (TwT_w > 33∘^circC) day per year is expected in counties currently home to about one-third of the U.S. population. Mitigation can significantly the expected number of extreme wet-bulb temperature days, with only one-eighth of the U.S. population in counties with a 1-in-10 chance per year of an extraordinarily dangerous day by the end of the century under RCP 4.5. References: [1] Liang et al. (2011), Building and Environment 46: 2472-2479, doi:10.1016/j.buildenv.2011.06.013. [2] T. Houser et al. (2014), American Climate Prospectus, www

  10. Regional climate service in Southern Germany

    NASA Astrophysics Data System (ADS)

    Schipper, Janus; Hackenbruch, Julia

    2013-04-01

    Climate change challenges science, politics, business and society at the international, national and regional level. The South German Climate Office at the Karlsruhe Institute of Technology (KIT) is a contact for the structuring and dissemination of information on climate and climate change in the South German region. It provides scientifically based and user-oriented climate information. Thereby it builds a bridge between the climate sciences and society and provides scientific information on climate change in an understandable way. The expertise of KIT, in which several institutions operate on fundamental and applied climate research, and of partner institutions is the basis for the work in the climate office. The regional focus is on the south of Germany. Thematic focuses are e.g. regional climate modeling, trends in extreme weather events such as heavy rain and hail event, and issues for energy and water management. The South German Climate Office is one of four Regional Helmholtz Climate Offices, of which each has a regional and thematic focus. The users of the Climate Office can be summarized into three categories. First, there is the general public. This category consists mainly of non-professionals. Here, special attention is on an understandable translation of climate information. Attention is paid to application-related aspects, because each individual is affected in a different way by climate change. Typical examples of this category are school groups, citizens and the media. The second category consists of experts of other disciplines. Unlike the first category they are mainly interested in the exchange of results and data. It is important to the climate office to provide support for the use of climatological results. Typical representatives of this category are ministries, state offices, and companies. In the third and final category are scientists. In addition to the climatologists, this category also holds representatives from other scientific

  11. Sensitivity of the Regional Arctic System Model surface climate to ice-ocean state

    NASA Astrophysics Data System (ADS)

    Roberts, A.; Maslowski, W.; Osinski, R.; Cassano, J. J.; Craig, A.; Duvivier, A.; Fisel, B. J.; Gutowski, W. J.; Higgins, M.; Hughes, M. R.; Lettenmaier, D. P.; Nijssen, B.

    2012-12-01

    The Regional Arctic System Model (RASM) is a high-resolution Earth System model extending across the Arctic Ocean, its marginal seas, the Arctic drainage basin, and including the Coordinated Regional Downscaling Experiment (CORDEX) Arctic domain. RASM uses the flux coupler (CPL7) within the Community Earth System Model framework to couple regional configurations of the Weather Research and Forecasting model (WRF), Parallel Ocean Program (POP), Los Alamos sea ice model (CICE), and Variable Infiltration Capacity land hydrology model (VIC). Work is also underway to incorporate the Community Ice Sheet Model (CISM) as well as glacier, ice cap and dynamic vegetation models. As part of RASM development, coupled simulations are being prepared for the CORDEX Arctic domain, which is unique among CORDEX regions by being centered over the ocean. Up to this point, there has been uncertainty over how much initial and surface conditions in the ice-ocean boundary layer influence the surface climate of the Arctic in RASM, relative to regional atmospheric model constraints, such as spectral nudging and boundary conditions. We present results that suggest there is a significant dependency on the initial sea ice conditions on decadal timescales within RASM. This has important implications for (i) how results from different regional artic models may be combined and compared in CORDEX and (ii) appropriate methods for ensemble generation in regional polar models. We will also present results illustrating the influence of sub-hourly sea ice deformation on decadal climate in RASM, highlighting an important reason why fully coupled and high-resolution regional models are essential for regional Arctic downscaling.

  12. Final Technical Report for "Collaborative Research: Regional climate-change projections through next-generation empirical and dynamical models"

    SciTech Connect

    Robertson, A.W.; Ghil, M.; Kravtsov, K.; Smyth, P.J.

    2011-04-08

    This project was a continuation of previous work under DOE CCPP funding in which we developed a twin approach of non-homogeneous hidden Markov models (NHMMs) and coupled ocean-atmosphere (O-A) intermediate-complexity models (ICMs) to identify the potentially predictable modes of climate variability, and to investigate their impacts on the regional-scale. We have developed a family of latent-variable NHMMs to simulate historical records of daily rainfall, and used them to downscale seasonal predictions. We have also developed empirical mode reduction (EMR) models for gaining insight into the underlying dynamics in observational data and general circulation model (GCM) simulations. Using coupled O-A ICMs, we have identified a new mechanism of interdecadal climate variability, involving the midlatitude oceans mesoscale eddy field and nonlinear, persistent atmospheric response to the oceanic anomalies. A related decadal mode is also identified, associated with the oceans thermohaline circulation. The goal of the continuation was to build on these ICM results and NHMM/EMR model developments and software to strengthen two key pillars of support for the development and application of climate models for climate change projections on time scales of decades to centuries, namely: (a) dynamical and theoretical understanding of decadal-to-interdecadal oscillations and their predictability; and (b) an interface from climate models to applications, in order to inform societal adaptation strategies to climate change at the regional scale, including model calibration, correction, downscaling and, most importantly, assessment and interpretation of spread and uncertainties in multi-model ensembles. Our main results from the grant consist of extensive further development of the hidden Markov models for rainfall simulation and downscaling specifically within the non-stationary climate change context together with the development of parallelized software; application of NHMMs to

  13. Final Technical Report for "Collaborative Research. Regional climate-change projections through next-generation empirical and dynamical models"

    SciTech Connect

    Kravtsov, S.; Robertson, Andrew W.; Ghil, Michael; Smyth, Padhraic J.

    2011-04-08

    This project was a continuation of previous work under DOE CCPP funding in which we developed a twin approach of non-homogeneous hidden Markov models (NHMMs) and coupled ocean-atmosphere (O-A) intermediate-complexity models (ICMs) to identify the potentially predictable modes of climate variability, and to investigate their impacts on the regional-scale. We have developed a family of latent-variable NHMMs to simulate historical records of daily rainfall, and used them to downscale seasonal predictions. We have also developed empirical mode reduction (EMR) models for gaining insight into the underlying dynamics in observational data and general circulation model (GCM) simulations. Using coupled O-A ICMs, we have identified a new mechanism of interdecadal climate variability, involving the midlatitude oceans mesoscale eddy field and nonlinear, persistent atmospheric response to the oceanic anomalies. A related decadal mode is also identified, associated with the oceans thermohaline circulation. The goal of the continuation was to build on these ICM results and NHMM/EMR model developments and software to strengthen two key pillars of support for the development and application of climate models for climate change projections on time scales of decades to centuries, namely: (a) dynamical and theoretical understanding of decadal-to-interdecadal oscillations and their predictability; and (b) an interface from climate models to applications, in order to inform societal adaptation strategies to climate change at the regional scale, including model calibration, correction, downscaling and, most importantly, assessment and interpretation of spread and uncertainties in multi-model ensembles. Our main results from the grant consist of extensive further development of the hidden Markov models for rainfall simulation and downscaling specifically within the non-stationary climate change context together with the development of parallelized software; application of NHMMs to

  14. Physical processes mediating climate change impacts on regional sea ecosystems

    NASA Astrophysics Data System (ADS)

    Holt, J.; Schrum, C.; Cannaby, H.; Daewel, U.; Allen, I.; Artioli, Y.; Bopp, L.; Butenschon, M.; Fach, B. A.; Harle, J.; Pushpadas, D.; Salihoglu, B.; Wakelin, S.

    2014-02-01

    Regional seas are exceptionally vulnerable to climate change, yet are the most directly societally important regions of the marine environment. The combination of widely varying conditions of mixing, forcing, geography (coastline and bathymetry) and exposure to the open-ocean makes these seas subject to a wide range of physical processes that mediates how large scale climate change impacts on these seas' ecosystems. In this paper we explore these physical processes and their biophysical interactions, and the effects of atmospheric, oceanic and terrestrial change on them. Our aim is to elucidate the controlling dynamical processes and how these vary between and within regional seas. We focus on primary production and consider the potential climatic impacts: on long term changes in elemental budgets, on seasonal and mesoscale processes that control phytoplankton's exposure to light and nutrients, and briefly on direct temperature response. We draw examples from the MEECE FP7 project and five regional models systems using ECOSMO, POLCOMS-ERSEM and BIMS_ECO. These cover the Barents Sea, Black Sea, Baltic Sea, North Sea, Celtic Seas, and a region of the Northeast Atlantic, using a common global ocean-atmosphere model as forcing. We consider a common analysis approach, and a more detailed analysis of the POLCOMS-ERSEM model. Comparing projections for the end of the 21st century with mean present day conditions, these simulations generally show an increase in seasonal and permanent stratification (where present). However, the first order (low- and mid-latitude) effect in the open ocean projections of increased permanent stratification leading to reduced nutrient levels, and so to reduced primary production, is largely absent, except in the NE Atlantic. Instead, results show a highly heterogeneous picture of positive and negative change arising from the varying mixing and circulation conditions. Even in the two highly stratified, deep water seas (Black and Baltic Seas) the

  15. Climate change signal over Northern Adriatic Region

    NASA Astrophysics Data System (ADS)

    Zampieri, M.; Lionello, P.; Nikulin, G.

    2009-04-01

    In this study we collect the results of several regional climate models from various projects (PRUDENCE, ENSEMBLES, and others). Temperature, precipitation, Sea Level Pressure, 500hPa geopotential height and wind speed are analyzed for Venetia and the Northern Adriatic region. The agreement among models is analyzed for the control runs and for the scenario simulations, and the dispersion among models is evaluated. The importance of resolutions is addressed as well as the changes of climate extremes in future scenarios.

  16. Errors and uncertainties introduced by a regional climate model in climate impact assessments: example of crop yield simulations in West Africa

    NASA Astrophysics Data System (ADS)

    Ramarohetra, Johanna; Pohl, Benjamin; Sultan, Benjamin

    2015-12-01

    The challenge of estimating the potential impacts of climate change has led to an increasing use of dynamical downscaling to produce fine spatial-scale climate projections for impact assessments. In this work, we analyze if and to what extent the bias in the simulated crop yield can be reduced by using the Weather Research and Forecasting (WRF) regional climate model to downscale ERA-Interim (European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis) rainfall and radiation data. Then, we evaluate the uncertainties resulting from both the choice of the physical parameterizations of the WRF model and its internal variability. Impact assessments were performed at two sites in Sub-Saharan Africa and by using two crop models to simulate Niger pearl millet and Benin maize yields. We find that the use of the WRF model to downscale ERA-Interim climate data generally reduces the bias in the simulated crop yield, yet this reduction in bias strongly depends on the choices in the model setup. Among the physical parameterizations considered, we show that the choice of the land surface model (LSM) is of primary importance. When there is no coupling with a LSM, or when the LSM is too simplistic, the simulated precipitation and then the simulated yield are null, or respectively very low; therefore, coupling with a LSM is necessary. The convective scheme is the second most influential scheme for yield simulation, followed by the shortwave radiation scheme. The uncertainties related to the internal variability of the WRF model are also significant and reach up to 30% of the simulated yields. These results suggest that regional models need to be used more carefully in order to improve the reliability of impact assessments.

  17. Joint Applications Pilot of the National Climate Predictions and Projections Platform and the North Central Climate Science Center: Delivering climate projections on regional scales to support adaptation planning

    NASA Astrophysics Data System (ADS)

    Ray, A. J.; Ojima, D. S.; Morisette, J. T.

    2012-12-01

    The DOI North Central Climate Science Center (NC CSC) and the NOAA/NCAR National Climate Predictions and Projections (NCPP) Platform and have initiated a joint pilot study to collaboratively explore the "best available climate information" to support key land management questions and how to provide this information. NCPP's mission is to support state of the art approaches to develop and deliver comprehensive regional climate information and facilitate its use in decision making and adaptation planning. This presentation will describe the evolving joint pilot as a tangible, real-world demonstration of linkages between climate science, ecosystem science and resource management. Our joint pilot is developing a deliberate, ongoing interaction to prototype how NCPP will work with CSCs to develop and deliver needed climate information products, including translational information to support climate data understanding and use. This pilot also will build capacity in the North Central CSC by working with NCPP to use climate information used as input to ecological modeling. We will discuss lessons to date on developing and delivering needed climate information products based on this strategic partnership. Four projects have been funded to collaborate to incorporate climate information as part of an ecological modeling project, which in turn will address key DOI stakeholder priorities in the region: Riparian Corridors: Projecting climate change effects on cottonwood and willow seed dispersal phenology, flood timing, and seedling recruitment in western riparian forests. Sage Grouse & Habitats: Integrating climate and biological data into land management decision models to assess species and habitat vulnerability Grasslands & Forests: Projecting future effects of land management, natural disturbance, and CO2 on woody encroachment in the Northern Great Plains The value of climate information: Supporting management decisions in the Plains and Prairie Potholes LCC. NCCSC's role in

  18. One regional ARM guide for climatic evaluation

    SciTech Connect

    Brown, R.M.

    1990-04-01

    One of the early tasks of the Atmospheric Radiation Measurements (ARM) Program is to provide climatic guides for site selection purposes including possible continental, regional, local and on-site locations. The first guide ``A Preliminary ARM Guide for Climatic Evaluations`` provided some climate data on a continental scale; this one is an attempt to show the variability that exists over a region. Kansas was chosen for this particular guide because it satisfies most of the requirements given in the ARM Program Plan, i.e., climatic significance, potential for synergism with other programs and scientific and logistical viability. Kansas has extreme climatic variations, is centrally located, is compatible with other large scale programs (Fife), has good airfields and accommodations to minimize time and effort in planning and operating an ARM site for continuous use and special campaigns.

  19. One regional ARM guide for climatic evaluation

    SciTech Connect

    Brown, R.M.

    1990-04-01

    One of the early tasks of the Atmospheric Radiation Measurements (ARM) Program is to provide climatic guides for site selection purposes including possible continental, regional, local and on-site locations. The first guide A Preliminary ARM Guide for Climatic Evaluations'' provided some climate data on a continental scale; this one is an attempt to show the variability that exists over a region. Kansas was chosen for this particular guide because it satisfies most of the requirements given in the ARM Program Plan, i.e., climatic significance, potential for synergism with other programs and scientific and logistical viability. Kansas has extreme climatic variations, is centrally located, is compatible with other large scale programs (Fife), has good airfields and accommodations to minimize time and effort in planning and operating an ARM site for continuous use and special campaigns.

  20. "Climate Matters Documoments": Enabling Regionally-Specific Climate Awareness

    NASA Astrophysics Data System (ADS)

    Keener, V. W.; Finucane, M.

    2012-12-01

    The Pacific Regional Integrated Sciences & Assessments (RISA) is a multidisciplinary program that enhances the ability of Pacific Island communities to understand, plan for, and adapt to climate-induced change. Using both social and physical science research methods, the Pacific RISA engages a network of regional decision-makers and stakeholders to help solve climate-related issues. Pacific RISA has a broad audience of local and regional decision-makers (i.e. natural resource managers, community planners, state and federal government agencies) and stakeholders (i.e. farmers and ranchers, fishermen, community and native islander groups). The RISA program engages with this audience through a mixed-method approach of two-way communication, including one-on-one interviews, workshops, consensus discussions and public presentations that allow us to tailor our efforts to the needs of specific stakeholders. A recent Pacific RISA project was the creation and production of four short, educational "documoment" videos that explore the different ways in which climate change in Hawaii affects stakeholders from different sectors. The documoments, generally titled "Climate Matters", start with a quote about why climate matters to each stakeholder: a rancher, a coastal hotel owner, the manager of a landfill, and the local branch of the National Weather Service. The narratives then have each stakeholder discussing how climate impacts their professional and personal lives, and describing the types of climate change they have experienced in the islands. Each video ends with a technical fact about how different climate variables in Hawaii (sea level, precipitation, ENSO) have actually changed within the last century of observational data. Freely available on www.PacificRISA.org, the Documoments have been viewed over 350 times, and have inspired similar video projects and received positive attention from different audiences of stakeholders and scientists. In other assessment work the

  1. CLIMATE IMPACTS ON REGIONAL WATER

    EPA Science Inventory

    The New England region (including the 6 New England
    states plus upstate New York) offers a very diverse geography,
    matched by an equally diverse economy and human
    population. Livelihoods throughout the region are based
    on service industries that depend heavily on comm...

  2. Delivering Climate Projections at Regional Scales to Support Decisionmakers: a new NOAA effort

    NASA Astrophysics Data System (ADS)

    Anderson, D. E.; Ray, A. J.; MacDonald, A. E.; Rood, R. B.; Schneider, J. P.

    2010-12-01

    NOAA is developing a pilot effort for a capability to deliver climate projections at regional scales across the nation, in order to support a wide range of public policy and planning decisionmaking, from urban planning to ecosystems sustainability and management. The initial pilot effort will utilize model output and analyses from previous IPCC studies, such as those available from the DOE LLNL PCMDI archive and the NARCCAP datasets. New global model datasets applicable to US decision support will be generated through access to IPCC-vetted, publically available and documented models. Application of downscaling approaches will be evaluated through community interaction in order to support decisions at regional scales. Over the longer-term, this effort will evolve into a capability to support state-of-the-art approaches and applications of downscaled climate projection information to support regional decision making, including facilitating better connectivity of high resolution data with decision processes and models. This effort addresses the need articulated by the White House Interagency Climate Change Adaptation Task Force for science inputs to adaptation decisions and policy. The effort has considerable science challenges as well as challenges in meeting the needs of the end user community. This talk will discuss plans for addressing near-term and longer-term needs for regional climate information, defined for this effort as decision-scale climate projections over time scales ranging from seasonal to inter-annual out to a century or so. Initially, this effort will engage three key user communities through collaborative efforts: the Regional Integrated Science and Assessment network and other NOAA regional networks, the National Assessment, and the Department of Interior (DOI) via a recently signed DOI-Department of Commerce (DOC) Memorandum of Understanding to cooperate on climate-related activities. In summary, this effort is envisioned as an intellectual

  3. Development of Crop Yield Estimation Method by Applying Seasonal Climate Prediction in Asia-Pacific Region

    NASA Astrophysics Data System (ADS)

    Shin, Y.; Lee, E.

    2015-12-01

    Under the influence of recent climate change, abnormal weather condition such as floods and droughts has issued frequently all over the world. The occurrence of abnormal weather in major crop production areas leads to soaring world grain prices because it influence the reduction of crop yield. Development of crop yield estimation method is important means to accommodate the global food crisis caused by abnormal weather. However, due to problems with the reliability of the seasonal climate prediction, application research on agricultural productivity has not been much progress yet. In this study, it is an object to develop long-term crop yield estimation method in major crop production countries worldwide using multi seasonal climate prediction data collected by APEC Climate Center. There are 6-month lead seasonal predictions produced by six state-of-the-art global coupled ocean-atmosphere models(MSC_CANCM3, MSC_CANCM4, NASA, NCEP, PNU, POAMA). First of all, we produce a customized climate data through temporal and spatial downscaling methods for use as a climatic input data to the global scale crop model. Next, we evaluate the uncertainty of climate prediction by applying multi seasonal climate prediction in the crop model. Because rice is the most important staple food crop in the Asia-Pacific region, we assess the reliability of the rice yields using seasonal climate prediction for main rice production countries. RMSE(Root Mean Squire Error) and TCC(Temporal Correlation Coefficient) analysis is performed in Asia-Pacific countries, major 14 rice production countries, to evaluate the reliability of the rice yield according to the climate prediction models. We compare the rice yield data obtained from FAOSTAT and estimated using the seasonal climate prediction data in Asia-Pacific countries. In addition, we show that the reliability of seasonal climate prediction according to the climate models in Asia-Pacific countries where rice cultivation is being carried out.

  4. Projecting changes in future heavy rainfall events for Oahu, Hawaii: A statistical downscaling approach

    NASA Astrophysics Data System (ADS)

    Norton, Chase W.; Chu, Pao-Shin; Schroeder, Thomas A.

    2011-09-01

    A statistical model based on nonlinear artificial neural networks is used to downscale daily extreme precipitation events in Oahu, Hawaii, from general circulation model (GCM) outputs and projected into the future. From a suite of GCMs and their emission scenarios, two tests recommended by the International Panel on Climate Change are conducted and the ECHAM5 A2 is selected as the most appropriate one for downscaling precipitation extremes for Oahu. The skill of the neural network model is highest in drier, leeward regions where orographic uplifting has less influence on daily extreme precipitation. The trained model is used with the ECHAM5 forced by emissions from the A2 scenario to simulate future daily precipitation on Oahu. A BCa bootstrap resampling method is used to provide 95% confidence intervals of the storm frequency and intensity for all three data sets (actual observations, downscaled GCM output from the present-day climate, and downscaled GCM output for future climate). Results suggest a tendency for increased frequency of heavy rainfall events but a decrease in rainfall intensity during the next 30 years (2011-2040) for the southern shoreline of Oahu.

  5. Spatial analysis of future East Asian seasonal temperature using two regional climate model simulations

    NASA Astrophysics Data System (ADS)

    Kim, Yura; Jun, Mikyoung; Min, Seung-Ki; Suh, Myoung-Seok; Kang, Hyun-Suk

    2016-05-01

    CORDEX-East Asia, a branch of the coordinated regional climate downscaling experiment (CORDEX) initiative, provides high-resolution climate simulations for the domain covering East Asia. This study analyzes temperature data from regional climate models (RCMs) participating in the CORDEX - East Asia region, accounting for the spatial dependence structure of the data. In particular, we assess similarities and dissimilarities of the outputs from two RCMs, HadGEM3-RA and RegCM4, over the region and over time. A Bayesian functional analysis of variance (ANOVA) approach is used to simultaneously model the temperature patterns from the two RCMs for the current and future climate. We exploit nonstationary spatial models to handle the spatial dependence structure of the temperature variable, which depends heavily on latitude and altitude. For a seasonal comparison, we examine changes in the winter temperature in addition to the summer temperature data. We find that the temperature increase projected by RegCM4 tends to be smaller than the projection of HadGEM3-RA for summers, and that the future warming projected by HadGEM3-RA tends to be weaker for winters. Also, the results show that there will be a warming of 1-3°C over the region in 45 years. More specifically, the warming pattern clearly depends on the latitude, with greater temperature increases in higher latitude areas, which implies that warming may be more severe in the northern part of the domain.

  6. A Variable Resolution Stretched Grid General Circulation Model: Regional Climate Simulation

    NASA Technical Reports Server (NTRS)

    Fox-Rabinovitz, Michael S.; Takacs, Lawrence L.; Govindaraju, Ravi C.; Suarez, Max J.

    2000-01-01

    The development of and results obtained with a variable resolution stretched-grid GCM for the regional climate simulation mode, are presented. A global variable resolution stretched- grid used in the study has enhanced horizontal resolution over the U.S. as the area of interest The stretched-grid approach is an ideal tool for representing regional to global scale interaction& It is an alternative to the widely used nested grid approach introduced over a decade ago as a pioneering step in regional climate modeling. The major results of the study are presented for the successful stretched-grid GCM simulation of the anomalous climate event of the 1988 U.S. summer drought- The straightforward (with no updates) two month simulation is performed with 60 km regional resolution- The major drought fields, patterns and characteristics such as the time averaged 500 hPa heights precipitation and the low level jet over the drought area. appear to be close to the verifying analyses for the stretched-grid simulation- In other words, the stretched-grid GCM provides an efficient down-scaling over the area of interest with enhanced horizontal resolution. It is also shown that the GCM skill is sustained throughout the simulation extended to one year. The developed and tested in a simulation mode stretched-grid GCM is a viable tool for regional and subregional climate studies and applications.

  7. GFDL's unified regional-global weather-climate modeling system with variable resolution capability for severe weather predictions and regional climate simulations

    NASA Astrophysics Data System (ADS)

    Lin, S. J.

    2015-12-01

    The NOAA/Geophysical Fluid Dynamics Laboratory has been developing a unified regional-global modeling system with variable resolution capabilities that can be used for severe weather predictions (e.g., tornado outbreak events and cat-5 hurricanes) and ultra-high-resolution (1-km) regional climate simulations within a consistent global modeling framework. The fundation of this flexible regional-global modeling system is the non-hydrostatic extension of the vertically Lagrangian dynamical core (Lin 2004, Monthly Weather Review) known in the community as FV3 (finite-volume on the cubed-sphere). Because of its flexability and computational efficiency, the FV3 is one of the final candidates of NOAA's Next Generation Global Prediction System (NGGPS). We have built into the modeling system a stretched (single) grid capability, a two-way (regional-global) multiple nested grid capability, and the combination of the stretched and two-way nests, so as to make convection-resolving regional climate simulation within a consistent global modeling system feasible using today's High Performance Computing System. One of our main scientific goals is to enable simulations of high impact weather phenomena (such as tornadoes, thunderstorms, category-5 hurricanes) within an IPCC-class climate modeling system previously regarded as impossible. In this presentation I will demonstrate that it is computationally feasible to simulate not only super-cell thunderstorms, but also the subsequent genesis of tornadoes using a global model that was originally designed for century long climate simulations. As a unified weather-climate modeling system, we evaluated the performance of the model with horizontal resolution ranging from 1 km to as low as 200 km. In particular, for downscaling studies, we have developed various tests to ensure that the large-scale circulation within the global varaible resolution system is well simulated while at the same time the small-scale can be accurately captured

  8. Variable-Resolution GCMs for Regional Climate Modeling: Stretched-Grid Model Intercomparison Project (SGMIP)

    NASA Astrophysics Data System (ADS)

    Fox-Rabinovitz, M.; Cote, J.; Dugas, B.; Deque, M.; McGregor, J.

    2006-05-01

    Variable-resolution GCMs using a global stretched grid (SG) with enhanced resolution over the region(s) of interest have proven to be an established approach to regional climate modeling providing an efficient regional down-scaling to mesoscales. This approach has been used since the early-mid 90s by the French, U.S., Canadian, Australian and other climate modeling groups along with the widely-used nested-grid approach. The important advantages of SG-GCMs are that they do not require any lateral boundary conditions/forcing and are free of the associated undesirable computational problems. SG-GCMs provide self-consistent interactions between global and regional scales, while a high quality of global circulation is preserved. The international SGMIP-1 (Stretched-Grid Model Intercomparison Project, phase-1), using SG-GCMs developed at the major centers/groups in Australia, Canada, France, and the U.S., has been successfully conducted in 2002-2005. The results of the 12-year (1987-1998) climate simulations for a major part of North America are available at the SGMIP web site: http://essic.umd.edu/~foxrab/sgmip.html, and are described in [1]. The multi-model SGMIP-1 regional climate simulations were conducted with enhanced 0.45 - 0.5 degree regional resolution for SG-GCMs, with the same or a similar number of global grid points as in a 1 x 1 degree global grid. The SGMIP-1 SG-GCM simulations were analyzed in terms of studying the impact of high regional resolution on efficient downscaling to realistic mesoscales and regional climate variability. We focused mostly on studying the quality of the multi-model ensemble results. The SGMIP-1 multi-model ensemble results for the region compare well with reanalysis and observations, in terms of spatial and temporal diagnostics. The next SGMIP phase, SGMIP-2 (phase-2), includes comparisons of high resolution stretched and uniform grid GCMs. These SGMIP-2 experiments provide the possibility for a comprehensive analysis of enhanced

  9. Integrating Climate Information and Decision Processes for Regional Climate Resilience

    NASA Astrophysics Data System (ADS)

    Buizer, James; Goddard, Lisa; Guido, Zackry

    2015-04-01

    An integrated multi-disciplinary team of researchers from the University of Arizona and the International Research Institute for Climate and Society at Columbia University have joined forces with communities and institutions in the Caribbean, South Asia and West Africa to develop relevant, usable climate information and connect it to real decisions and development challenges. The overall objective of the "Integrating Climate Information and Decision Processes for Regional Climate Resilience" program is to build community resilience to negative impacts of climate variability and change. We produce and provide science-based climate tools and information to vulnerable peoples and the public, private, and civil society organizations that serve them. We face significant institutional challenges because of the geographical and cultural distance between the locale of climate tool-makers and the locale of climate tool-users and because of the complicated, often-inefficient networks that link them. To use an accepted metaphor, there is great institutional difficulty in coordinating the supply of and the demand for useful climate products that can be put to the task of building local resilience and reducing climate vulnerability. Our program is designed to reduce the information constraint and to initiate a linkage that is more demand driven, and which provides a set of priorities for further climate tool generation. A demand-driven approach to the co-production of appropriate and relevant climate tools seeks to meet the direct needs of vulnerable peoples as these needs have been canvassed empirically and as the benefits of application have been adequately evaluated. We first investigate how climate variability and climate change affect the livelihoods of vulnerable peoples. In so doing we assess the complex institutional web within which these peoples live -- the public agencies that serve them, their forms of access to necessary information, the structural constraints

  10. CARICOF - The Caribbean Regional Climate Outlook Forum

    NASA Astrophysics Data System (ADS)

    Van Meerbeeck, Cedric

    2013-04-01

    Regional Climate Outlook Forums (RCOFs) are viewed as a critical building block in the Global Framework for Climate Services (GFCS) of the World Meteorological Organization (WMO). The GFCS seeks to extend RCOFs to all vulnerable regions of the world such as the Caribbean, of which the entire population is exposed to water- and heat-related natural hazards. An RCOF is initially intended to identify gaps in information and technical capability; facilitate research cooperation and data exchange within and between regions, and improve coordination within the climate forecasting community. A focus is given on variations in climate conditions on a seasonal timescale. In this view, the relevance of a Caribbean RCOF (CARICOF) is the following: while the seasonality of the climate in the Caribbean has been well documented, major gaps in knowledge exist in terms of the drivers in the shifts of amplitude and phase of seasons (as evidenced from the worst region-wide drought period in recent history during 2009-2010). To address those gaps, CARICOF has brought together National Weather Services (NWSs) from 18 territories under the coordination of the Caribbean Institute for Meteorology and Hydrology (CIMH), to produce region-wide, consensus, seasonal climate outlooks since March 2012. These outlooks include tercile rainfall forecasts, sea and air surface temperature forecasts as well as the likely evolution of the drivers of seasonal climate variability in the region, being amongst others the El Niño Southern Oscillation or tropical Atlantic and Caribbean Sea temperatures. Forecasts for both the national-scale forecasts made by the NWSs and CIMH's regional-scale forecast amalgamate output from several forecasting tools. These currently include: (1) statistical models such as Canonical Correlation Analysis run with the Climate Predictability Tool, providing tercile rainfall forecasts at weather station scale; (2) a global outlooks published by the WMO appointed Global Producing

  11. 2. Wind speed change in central Europe: the projections based on regional climate models

    NASA Astrophysics Data System (ADS)

    Siedlecki, M.

    2010-09-01

    This work presents dynamically downscaled near-surface wind speed fields and examines the impact of climate changes on wind speed across central Europe. The analysis is based on regional model simulation (5 RCM simulations taken from the project PRUDENCE and CLM regional climate model from M&D group) forced by IPCC emission scenario SRES - A2. Each model provided data from two 30-year simulations: a control run under present day climate conditions for the period 1961-90 and a simulation under conditions projected for the period 2021-2050. The research domain covered region from 42°N to 62°N and from 6°E to 36°E. The model ensemble shows a possible increase in future mean wind speed during winter season, especially over zonal belt from North German to North Poland where the future mean wind speed is 0.4 m/s higher than in the control period. The projected climate change in summer over most research domain shows a decrease of mean wind speed (about 0.2 m/s). The Jutland Peninsula and North German is the region with the highest simulated wind speed. The simulated changes are more pronounced in 95th percentile than in the mean. In winter, the values of the 95th percentile will increase over the North German, Jutland Peninsula and North Poland but the highest change is projected over east coast of Baltic Sea (1m/s).

  12. Implications of climate change for water surplus and scarcity and how that affects agricultural sustainability in Hungary

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Projected impacts of climate change have included, in addition to warmer temperatures, regionally variable effects on precipitation amounts, intensities, and seasonal distribution. Projections downscaled to Hungary and surrounding region were identified and their effects on streamflow, other water r...

  13. A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States

    USGS Publications Warehouse

    Hay, L.E.; Wilby, R.L.; Leavesley, G.H.

    2000-01-01

    Simulated daily precipitation, temperature, and runoff time series were compared in three mountainous basins in the United States: (1) the Animas River basin in Colorado, (2) the East Fork of the Carson River basin in Nevada and California, and (3) the Cle Elum River basin in Washington State. Two methods of climate scenario generation were compared: delta change and statistical downscaling. The delta change method uses differences between simulated current and future climate conditions from the Hadley Centre for Climate Prediction and Research (HadCM2) General Circulation Model (GCM) added to observed time series of climate variables. A statistical downscaling (SDS) model was developed for each basin using station data and output from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis regridded to the scale of HadCM2. The SDS model was then used to simulate local climate variables using HadCM2 output for current and future conditions. Surface climate variables from each scenario were used in a precipitation-runoff model. Results from this study show that, in the basins tested, a precipitation-runoff model can simulate realistic runoff series for current conditions using statistically downscaled NCEP output. But, use of downscaled HadCM2 output for current or future climate assessments are questionable because the GCM does not produce accurate estimates of the surface variables needed for runoff in these regions. Given the uncertainties in the GCMs ability to simulate current conditions based on either the delta change or downscaling approaches, future climate assessments based on either of these approaches must be treated with caution.

  14. Large Ensembles of Regional Climate Projections

    NASA Astrophysics Data System (ADS)

    Massey, Neil; Allen, Myles; Hall, Jim

    2016-04-01

    Projections of regional climate change have great utility for impact assessment at a local scale. The CORDEX climate projection framework presents a method of providing these regional projections by driving a regional climate model (RCM) with output from CMIP5 climate projection runs of global climate models (GCM). This produces an ensemble of regional climate projections, sampling the model uncertainty, the forcing uncertainty and the uncertainty of the response of the climate system to the increase in greenhouse gas (GHG) concentrations. Using the weather@home project to compute large ensembles of RCMs via volunteer distributed computing presents another method of generating projections of climate variables and also allows the sampling of the uncertainty due to internal variability. weather@home runs both a RCM and GCM on volunteer's home computers, with the free-running GCM driving the boundaries of the RCM. The GCM is an atmosphere only model and requires forcing at the lower boundary with sea-surface temperature (SST) and sea-ice concentration (SIC) data. By constructing SST and SIC projections, using projections of GHG and other atmospheric gases, and running the weather@home RCM and GCM with these forcings, large ensembles of projections of climate variables at regional scales can be made. To construct the SSTs and SICs, a statistical model is built to represent the response of SST and SIC to increases in GHG concentrations in the CMIP5 ensemble, for both the RCP4.5 and RCP8.5 scenarios. This statistical model uses empirical orthogonal functions (EOFs) to represent the change in the long term trend of SSTs in the CMIP5 projections. A multivariate distribution of the leading principle components (PC) is produced using a copula and sampled to produce a timeseries of PCs which are recombined with the EOFs to generate a timeseries of SSTs, with internal variability added from observations. Hence, a large ensemble of SST projections is generated, with each SST

  15. Gene movement and genetic association with regional climate gradients in California valley oak (Quercus lobata Née) in the face of climate change

    USGS Publications Warehouse

    Sork, Victoria L.; Davis, Frank W.; Westfall, Robert; Flint, Alan L.; Ikegami, Makihiko; Wang, Hongfang; Grivet, Delphine

    2010-01-01

    Rapid climate change jeopardizes tree populations by shifting current climate zones. To avoid extinction, tree populations must tolerate, adapt, or migrate. Here we investigate geographic patterns of genetic variation in valley oak, Quercus lobata N??e, to assess how underlying genetic structure of populations might influence this species' ability to survive climate change. First, to understand how genetic lineages shape spatial genetic patterns, we examine historical patterns of colonization. Second, we examine the correlation between multivariate nuclear genetic variation and climatic variation. Third, to illustrate how geographic genetic variation could interact with regional patterns of 21st Century climate change, we produce region-specific bioclimatic distributions of valley oak using Maximum Entropy (MAXENT) models based on downscaled historical (1971-2000) and future (2070-2100) climate grids. Future climatologies are based on a moderate-high (A2) carbon emission scenario and two different global climate models. Chloroplast markers indicate historical range-wide connectivity via colonization, especially in the north. Multivariate nuclear genotypes show a strong association with climate variation that provides opportunity for local adaptation to the conditions within their climatic envelope. Comparison of regional current and projected patterns of climate suitability indicates that valley oaks grow in distinctly different climate conditions in different parts of their range. Our models predict widely different regional outcomes from local displacement of a few kilometres to hundreds of kilometres. We conclude that the relative importance of migration, adaptation, and tolerance are likely to vary widely for populations among regions, and that late 21st Century conditions could lead to regional extinctions. ?? 2010 Blackwell Publishing Ltd.

  16. 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

  17. A coupled regional climate-biosphere model for climate studies

    SciTech Connect

    Bossert, J.; Winterkamp, J.; Barnes, F.; Roads, J.

    1996-04-01

    This is the final report of a three-year, Laboratory-Directed Research and Development (LDRD) project at the Los Alamos National Laboratory (LANL). The objective of this project has been to develop and test a regional climate modeling system that couples a limited-area atmospheric code to a biosphere scheme that properly represents surface processes. The development phase has included investigations of the impact of variations in surface forcing parameters, meteorological input data resolution, and model grid resolution. The testing phase has included a multi-year simulation of the summer climate over the Southwest United States at higher resolution than previous studies. Averaged results from a nine summer month simulation demonstrate the capability of the regional climate model to produce a representative climatology of the Southwest. The results also show the importance of strong summertime thermal forcing of the surface in defining this climatology. These simulations allow us to observe the climate at much higher temporal and spatial resolutions than existing observational networks. The model also allows us to see the full three-dimensional state of the climate and thereby deduce the dominant physical processes at any particular time.

  18. Regional Climate Studies with Variable-Resolution Stretched-Grid GCMs

    NASA Technical Reports Server (NTRS)

    Fox-Rabinovitz, Michael; Einaudi, Franco (Technical Monitor)

    2001-01-01

    A variable resolution GCM using a global stretched grid with fine resolution over the area(s) of interest, is a viable new approach to regional and subregional climate studies and applications. It is an alternative to the widely used nested grid approach introduced a decade ago as a pioneering step in regional climate modeling. The first version of the SG-GCM based on the GEOS (Goddard Earth Observing System) GCM using a finite-difference approximation, has been developed and thoroughly tested during the last few years. Successful simulations have been performed with the SG-GCM for the anomalous regional climate events of the U.S. 1988 summer drought and 1993 summer flood. They have shown the practical feasibility of the SG-approach for regional climate modeling. The GEOS SG-DAS (Data Assimilation System) incorporating the SG-GCM has also been developed and tested. The assimilated regional fields and diagnostics are used for validating the SG-GCM regional simulations. Two new SG-GCMs are being developed. The first is the SG-version of the new NASANCAR FV-GCM (with the finite-volume (FV) dynamics), and the second is the SG-version of the new GCM with spectral-element dynamics. Both GCMs use the WAR CCM4 physics. Using these advanced numerics will provide increased computational efficiency for the new the SG-GCMs, and will allow us to employ more flexible stretching strategies beneficial for the efficient regional down-scaling. The major current developments are focused on: simulating the 1997-1999 (and beyond) ENSO cycle and related monsoonal circulations, with enhanced regional resolution; studying intraseasonal and interannual regional climate variability for the extended multiyear (AMIP-type) SG-GCM simulations; and studying the impact of ensemble integrations.

  19. Regional climate change scenarios applied to viticultural zoning in Mendoza, Argentina.

    PubMed

    Cabré, María Fernanda; Quénol, Hervé; Nuñez, Mario

    2016-09-01

    Due to the importance of the winemaking sector in Mendoza, Argentina, the assessment of future scenarios for viticulture is of foremost relevance. In this context, it is important to understand how temperature increase and precipitation changes will impact on grapes, because of changes in grapevine phenology and suitability wine-growing regions must be understood as an indicator of climate change. The general objective is to classify the suitable areas of viticulture in Argentina for the current and future climate using the MM5 regional climate change simulations. The spatial distribution of annual mean temperature, annual rainfall, and some bioclimatic indices has been analyzed for the present (1970-1989) and future (2080-2099) climate under SRES A2 emission scenario. In general, according to projected average growing season temperature and Winkler index classification, the regional model estimates (i) a reduction of cool areas, (ii) a westward and southward displacement of intermediate and warm suitability areas, and (iii) the arise of new suitability regions (hot and very hot areas) over Argentina. In addition, an increase of annual accumulated precipitation is projected over the center-west of Argentina. Similar pattern of change is modeled for growing season, but with lower intensity. Furthermore, the evaluation of projected seasonal precipitation shows a little precipitation increase over Cuyo and center of Argentina in summer and a little precipitation decrease over Cuyo and northern Patagonia in winter. Results show that Argentina has a great potential for expansion into new suitable vineyard areas by the end of twenty-first century, particularly due to projected displacement to higher latitudes for most present suitability winegrowing regions. Even though main conclusions are based on one global-regional model downscaling, this approach provides valuable information for implementing proper and diverse adaptation measures in the Argentinean viticultural

  20. Regional climate change scenarios applied to viticultural zoning in Mendoza, Argentina

    NASA Astrophysics Data System (ADS)

    Cabré, María Fernanda; Quénol, Hervé; Nuñez, Mario

    2016-09-01

    Due to the importance of the winemaking sector in Mendoza, Argentina, the assessment of future scenarios for viticulture is of foremost relevance. In this context, it is important to understand how temperature increase and precipitation changes will impact on grapes, because of changes in grapevine phenology and suitability wine-growing regions must be understood as an indicator of climate change. The general objective is to classify the suitable areas of viticulture in Argentina for the current and future climate using the MM5 regional climate change simulations. The spatial distribution of annual mean temperature, annual rainfall, and some bioclimatic indices has been analyzed for the present (1970-1989) and future (2080-2099) climate under SRES A2 emission scenario. In general, according to projected average growing season temperature and Winkler index classification, the regional model estimates (i) a reduction of cool areas, (ii) a westward and southward displacement of intermediate and warm suitability areas, and (iii) the arise of new suitability regions (hot and very hot areas) over Argentina. In addition, an increase of annual accumulated precipitation is projected over the center-west of Argentina. Similar pattern of change is modeled for growing season, but with lower intensity. Furthermore, the evaluation of projected seasonal precipitation shows a little precipitation increase over Cuyo and center of Argentina in summer and a little precipitation decrease over Cuyo and northern Patagonia in winter. Results show that Argentina has a great potential for expansion into new suitable vineyard areas by the end of twenty-first century, particularly due to projected displacement to higher latitudes for most present suitability winegrowing regions. Even though main conclusions are based on one global-regional model downscaling, this approach provides valuable information for implementing proper and diverse adaptation measures in the Argentinean viticultural

  1. Regional climate change scenarios applied to viticultural zoning in Mendoza, Argentina

    NASA Astrophysics Data System (ADS)

    Cabré, María Fernanda; Quénol, Hervé; Nuñez, Mario

    2016-01-01

    Due to the importance of the winemaking sector in Mendoza, Argentina, the assessment of future scenarios for viticulture is of foremost relevance. In this context, it is important to understand how temperature increase and precipitation changes will impact on grapes, because of changes in grapevine phenology and suitability wine-growing regions must be understood as an indicator of climate change. The general objective is to classify the suitable areas of viticulture in Argentina for the current and future climate using the MM5 regional climate change simulations. The spatial distribution of annual mean temperature, annual rainfall, and some bioclimatic indices has been analyzed for the present (1970-1989) and future (2080-2099) climate under SRES A2 emission scenario. In general, according to projected average growing season temperature and Winkler index classification, the regional model estimates (i) a reduction of cool areas, (ii) a westward and southward displacement of intermediate and warm suitability areas, and (iii) the arise of new suitability regions (hot and very hot areas) over Argentina. In addition, an increase of annual accumulated precipitation is projected over the center-west of Argentina. Similar pattern of change is modeled for growing season, but with lower intensity. Furthermore, the evaluation of projected seasonal precipitation shows a little precipitation increase over Cuyo and center of Argentina in summer and a little precipitation decrease over Cuyo and northern Patagonia in winter. Results show that Argentina has a great potential for expansion into new suitable vineyard areas by the end of twenty-first century, particularly due to projected displacement to higher latitudes for most present suitability winegrowing regions. Even though main conclusions are based on one global-regional model downscaling, this approach provides valuable information for implementing proper and diverse adaptation measures in the Argentinean viticultural

  2. Future hub-height wind speed distributions from statistically downscaled CMIP5 simulations

    NASA Astrophysics Data System (ADS)

    Devis, A.; Demuzere, M.; van Lipzig, N.

    2013-12-01

    In order to realistically estimate wind-power yields, we need to know the hub-height wind speed under future climate conditions. Climate conditions of the upper atmosphere are commonly simulated using general circulation models (GCMs). However their typical resolutions are too coarse to assess the climate at the height of a wind turbine. This study simulates the hub-height wind speed probability distributions (PDFs) over Europe under future climate conditions. The analysis is based on the simulations of the CMIP5 earth system models, which are the latest development of GCMs. They include more components and feedbacks and their runs are performed at higher resolutions. In a first step, the ensemble of GCMs is evaluated on their representation of the wind speed PDFs in the lower atmosphere using ERA-Interim data. The evaluation indicates that GCMs are skillful down to their lowest model levels apart for the south of Europe, which is affected by a large scale winter bias and for certain coastal and orographical regions. Secondly, a statistical approach is developed which downscales the GCM output to the wind speed PDF at the height of the wind turbine hub. Since the evaluation analysis shows that GCMs are also skillful at the lower model levels, the statistical downscaling uses GCM variables describing the lower atmosphere, instead of the commonly used large scale circulation variables of the upper atmosphere. By doing so less uncertainty will be added trough the downscaling implementation. The downscaling methodology is developed for an observational site in the Netherlands, using hub-height wind speed observations and ERA-Interim data for the period 1989-2009. The statistical approach is based on a regression analysis of the parameters of the PDFs. Results indicate that the predictor selection is very much defined by the stability conditions of the atmospheric boundary layer. During convective summer-day conditions, the observed hub-height wind speed can skillfully

  3. Dam management and multifractal downscaling

    NASA Astrophysics Data System (ADS)

    Biaou, A.; Hubert, P.; Schertzer, D.; Hendrickx, F.; Tchiguirinskaia, I.

    2003-04-01

    In order to get a more efficient production management of reservoirs, it would be helpful to apply long-term meteorological forecasts to hydrological models. Unfortunately, the explicit scales of present meteorological models are quite larger than those of hydrological models. Therefore it is indispensable to proceed to a downscaling of the output of the former in order to obtain an input for the latter. In this paper, we discuss a multifractal downscaling procedure. This type of procedure was motivated because it deals with scaling variability of the fields. The site of the study is the region of the Doubs, but we make an extension on the whole France for the multifractale analysis to take into account well the spatial variabilities. We first present the results of a detailed multifractal analysis of various data bases. Concerning the development of our downscaling model, we show how to develop a scaling space-time cascade, which takes into account the distinct space and time scaling. We will present it first in the framework of the pedagogical b-model and a-model, then in the framework of universal multifractal models. The obtained results can be the object of an relief and microclimate conditioning before being compared with the real values.

  4. Downscaling land use and land cover from the Global Change Assessment Model for coupling with Earth system models

    NASA Astrophysics Data System (ADS)

    Le Page, Yannick; West, Tris O.; Link, Robert; Patel, Pralit

    2016-09-01

    The Global Change Assessment Model (GCAM) is a global integrated assessment model used to project future societal and environmental scenarios, based on economic modeling and on a detailed representation of food and energy production systems. The terrestrial module in GCAM represents agricultural activities and ecosystems dynamics at the subregional scale, and must be downscaled to be used for impact assessments in gridded models (e.g., climate models). In this study, we present the downscaling algorithm of the GCAM model, which generates gridded time series of global land use and land cover (LULC) from any GCAM scenario. The downscaling is based on a number of user-defined rules and drivers, including transition priorities (e.g., crop expansion preferentially into grasslands rather than forests) and spatial constraints (e.g., nutrient availability). The default parameterization is evaluated using historical LULC change data, and a sensitivity experiment provides insights on the most critical parameters and how their influence changes regionally and in time. Finally, a reference scenario and a climate mitigation scenario are downscaled to illustrate the gridded land use outcomes of different policies on agricultural expansion and forest management. Several features of the downscaling can be modified by providing new input data or changing the parameterization, without any edits to the code. Those features include spatial resolution as well as the number and type of land classes being downscaled, thereby providing flexibility to adapt GCAM LULC scenarios to the requirements of a wide range of models and applications. The downscaling system is version controlled and freely available.

  5. Early results from an effort to downscale a global dissolved inorganic nitrogen model to achieve a regional assessment of nitrogen dynamics in the Columbia River Basin

    NASA Astrophysics Data System (ADS)

    Miller, C. C.; Harrison, J.

    2013-12-01

    Excessive nitrogen (N) export to coastal systems has increased dramatically since the early 20th century. The increase in N has been linked to significant environmental impacts such as eutrophication, fish kills, and harmful algal blooms and is caused in part by the increasing use and quantity of synthetic fertilizer on farmland. Significant portions of both the Willamette River Valley in Oregon and the Palouse region of eastern Washington are agricultural land, approximately 20% and 57% respectively. Nitrogen in the form of dissolved inorganic nitrogen (DIN) can leach from farms and pasture land into ground and surface water systems. This leaching, combined with DIN in runoff, contributes to the environmental degradation of both waterways (i.e. streams, rivers) and coastal estuaries. Because of this it is important to understand what effects changes in DIN application will have on water quality and DIN export to the coast. DIN export data, retrieved from the U.S. Geological Survey National Water Information System, was analyzed for 23 major subbasins in the Columbia River Basin (CRB) and estimates DIN export (per area yield) ranging from 5.0 to 883.1 kg N km-2 yr-1. Here we present early results from our effort to downscale the Global Nutrient Export from WaterSheds (Global NEWS) DIN model for application within the Columbia River Basin (CRB). This first attempt at downscaling Global NEWS is missing some key higher-resolution N inputs for the model as well as accurate dam retention and runoff factors which could account for the low correlation between model output and observed data (R2 = 0.21).Our regional model predicts DIN yields ranging from 7.9 to 1146.6 kg N km-2 yr-1. Both the model output and observed data predict the highest per area DIN yields occurring in the Willamette river subbasin. Total DIN export to the coast was modeled as 0.06 Tg N yr-1 compared to 0.07 Tg N yr-1 calculated from the measured data. Based on current model inputs biological N2

  6. Regional climate change and national responsibilities

    NASA Astrophysics Data System (ADS)

    Hansen, James; Sato, Makiko

    2016-03-01

    Global warming over the past several decades is now large enough that regional climate change is emerging above the noise of natural variability, especially in the summer at middle latitudes and year-round at low latitudes. Despite the small magnitude of warming relative to weather fluctuations, effects of the warming already have notable social and economic impacts. Global warming of 2 °C relative to preindustrial would shift the ‘bell curve’ defining temperature anomalies a factor of three larger than observed changes since the middle of the 20th century, with highly deleterious consequences. There is striking incongruity between the global distribution of nations principally responsible for fossil fuel CO2 emissions, known to be the main cause of climate change, and the regions suffering the greatest consequences from the warming, a fact with substantial implications for global energy and climate policies.

  7. Characterization and Quantification of Uncertainty in the NARCCAP Regional Climate Model Ensemble and Application to Impacts on Water Systems

    NASA Astrophysics Data System (ADS)

    Mearns, L. O.; Sain, S. R.; McGinnis, S. A.; Steinschneider, S.; Brown, C. M.

    2015-12-01

    In this talk we present the development of a joint Bayesian Probabilistic Model for the climate change results of the North American Regional Climate Change Assessment Program (NARCCAP) that uses a unique prior in the model formulation. We use the climate change results (joint distribution of seasonal temperature and precipitation changes (future vs. current)) from the global climate models (GCMs) that provided boundary conditions for the six different regional climate models used in the program as informative priors for the bivariate Bayesian Model. The two variables involved are seasonal temperature and precipitation over sub-regions (i.e., Bukovsky Regions) of the full NARCCAP domain. The basic approach to the joint Bayesian hierarchical model follows the approach of Tebaldi and Sansó (2009). We compare model results using informative (i.e., GCM information) as well as uninformative priors. We apply these results to the Water Evaluation and Planning System (WEAP) model for the Colorado Springs Utility in Colorado. We investigate the layout of the joint pdfs in the context of the water model sensitivities to ranges of temperature and precipitation results to determine the likelihoods of future climate conditions that cannot be accommodated by possible adaptation options. Comparisons may also be made with joint pdfs formed from the CMIP5 collection of global climate models and empirically downscaled to the region of interest.

  8. Historical Climate Change Analysis over American River Watershed by Means of 137-Year Long-Term Dynamic Downscaling

    NASA Astrophysics Data System (ADS)

    Ishida, K.; Kavvas, M. L.; Chen, Z. R.; Ohara, N.; Jang, S.

    2014-12-01

    Historical precipitation over Northern California during 137 water years from October, 1872 to September, 2008 is reconstructed by means of a physically-based regional atmospheric model. NOAA Twentieth Century Reanalysis version 2 (20CR) is utilized as the initial and boundary conditions. The reconstructed precipitation is validated by comparing with Parameter-elevation Regressions on Independent Slopes Model (PRISM) monthly precipitation over the American River watershed (ARW) in Northern California. The correlation coefficient and the Nash-Sutcliffe coefficient were 0.925 and 0.793, respectively. Then, the basin-average precipitation over ARW is analyzed. The analysis shows that the annual basin-average precipitation increased during the reconstructed period. The mean monthly precipitation of the late period (wy1872-1940) is higher for every month than that of the early period (wy1940-2008). Moreover, frequency analyses of 6-, 12-, 24-, 48-, 72-, and 96-hour accumulated basin-average precipitation were conducted. The results show that the peak value of basin-average precipitation also increased.

  9. The Swedish Regional Climate Modelling Programme, SWECLIM: a review.

    PubMed

    Rummukainen, Markku; Bergström, Sten; Persson, Gunn; Rodhe, Johan; Tjernström, Michael

    2004-06-01

    The Swedish Regional Climate Modelling Programme, SWECLIM, was a 6.5-year national research network for regional climate modeling, regional climate change projections and hydrological impact assessment and information to a wide range of stakeholders. Most of the program activities focussed on the regional climate system of Northern Europe. This led to the establishment of an advanced, coupled atmosphere-ocean-hydrology regional climate model system, a suite of regional climate change projections and progress on relevant data and process studies. These were, in turn, used for information and educational purposes, as a starting point for impact analyses on different societal sectors and provided contributions also to international climate research. PMID:15264594

  10. High-resolution ensemble projections of near-term regional climate over the continental United States

    DOE PAGESBeta

    Ashfaq, Moetasim; Rastogi, Deeksha; Mei, Rui; Kao, Shih -Chieh; Gangrade, Sudershan; Naz, Bibi S.; Touma, Danielle

    2016-09-01

    We present high-resolution near-term ensemble projections of hydro-climatic changes over the contiguous U.S. using a regional climate model (RegCM4) that dynamically downscales 11 Global Climate Models from the 5th phase of Coupled Model Inter-comparison Project at 18km horizontal grid spacing. All model integrations span 41 years in the historical period (1965 – 2005) and 41 years in the near-term future period (2010 – 2050) under Representative Concentration Pathway 8.5 and cover a domain that includes the contiguous U.S. and parts of Canada and Mexico. Should emissions continue to rise, surface temperatures in every region within the U.S. will reach amore » new climate norm well before mid 21st century regardless of the magnitudes of regional warming. Significant warming will likely intensify the regional hydrological cycle through the acceleration of the historical trends in cold, warm and wet extremes. The future temperature response will be partly regulated by changes in snow hydrology over the regions that historically receive a major portion of cold season precipitation in the form of snow. Our results indicate the existence of the Clausius-Clapeyron scaling at regional scales where per degree centigrade rise in surface temperature will lead to a 7.4% increase in precipitation from extremes. More importantly, both winter (snow) and summer (liquid) extremes are projected to increase across the U.S. These changes in precipitation characteristics will be driven by a shift towards shorter and wetter seasons. Altogether, projected changes in the regional hydro-climate can have substantial impacts on the natural and human systems across the U.S.« less

  11. 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

  12. 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

  13. Regional Climate Modeling of Possible Changes in Precipitation, Soil Moisture and Surface Fluxes over the Amazon

    NASA Astrophysics Data System (ADS)

    Costa, Alexandre; Sales, Domingo; das Chagas Vasconcelos, Francisco, Jr.; Souza, Samuel; Guimarães, Sullyandro; Wender Marinho, Marcos; Araújo, Luiz, Jr.

    2013-04-01

    Dynamical downscaling simulations were performed over the Coordinated Regional Climate Downscaling Experiment (CORDEX) "Central America" domain (which also comprises southern North America, the Caribbean and northern South America), using the Regional Atmospheric Modeling System (RAMS), forced by data from the Hadley Centre Global Environmental Model, Earth System (HadGEM2-ES) for both the current climate and the Representative Concentration Pathway 8.5 (RCP 8.5). Projected changes include a generalized warming, more intense over land areas, as well as changes in precipitation patterns. Projected warming increases along the 21st century, being especially pronounced over the Amazon. Over South America, by the end of the century, the regional model projects changes in the annual cycle, with enhanced precipitation in most months over Southern Amazon (except for the SON season) and April-August over Northern Amazon (with rainfall reduction in the rest of the year). The relationships between temperature and precipitation changes and moisture flux, radiative and turbulence fluxes were also investigated. Over Eastern Amazon, especially in the 2079-2099 period, under the heavy-emission scenario, a coherent springtime (SON) reduction of soil moisture, evaporation and precipitation was found, along with increased sensible heat flux, and an increase in temperature that is greater than in the rest of the year. Such pattern is also accompanied by changes in the distribution of daily precipitation, with a reduction of the number of rainfall events below 20 mm and an increase in precipitation events with 24h total greater than 30 mm, as well as longer periods with consecutive dry days.

  14. Projected changes of extreme precipitation over Contiguous United States with Nested regional climate model (NRCM)

    NASA Astrophysics Data System (ADS)

    Wang, J.

    2013-12-01

    Extreme weather events have already significantly influenced North America. During 2005-2011, the extreme events have increased by 250 %, from four or fewer events occurring in 2005, while 14 events occurring in 2011 (www.ncdc.noaa.gov/billions/). In addition, extreme rainfall amounts, frequency, and intensity were all expected to increase under greenhouse warming scenarios (Wehner 2005; Kharin et al. 2007; Tebaldi et al. 2006). Global models are powerful tools to investigate the climate and climate change on large scales. However, such models do not represent local terrain and mesoscale weather systems well owing to their coarse horizontal resolution (150-300 km). To capture the fine-scale features of extreme weather events, regional climate models (RCMs) with a more realistic representation of the complex terrain and heterogeneous land surfaces are needed (Mass et al. 2002). This study uses the Nested Regional Climate model (NRCM) to perform regional scale climate simulations on a 12-km × 12-km high resolution scale over North America (including Alaska; with 600 × 515 grid cells at longitude and latitude), known as CORDEX_North America, instead of small regions as studied previously (eg., Dominguez et al. 2012; Gao et al. 2012). The performance and the biases of the NRCM extreme precipitation calculations (2000-2010) have been evaluated with PRISM precipitation (Daly et al. 1997) by Wang and Kotamarthi (2013): the NRCM replicated very well the monthly amount of extreme precipitation with less than 3% overestimation over East CONUS, and the frequency of extremes over West CONUS and upper Mississippi River Basin. The Representative Concentration Pathway (RCP) 8.5 and RCP 4.5 from the new Community Earth System Model version 1.0 (CESM v1.0) are dynamically downscaled to predict the extreme rainfall events at the end-of-century (2085-2095) and to explore the uncertainties of future extreme precipitation induced by different scenarios over distinct regions. We have

  15. Climate Variability, Andean Livelihood Strategies, Development and Adaptation in the Andean Region

    NASA Astrophysics Data System (ADS)

    Valdivia, C.; Quiroz, R.; Zorogastua, P.; Baigorrea, G.

    2002-05-01

    Development programs in the Andes have failed to recognize climate variability as an element that is crucial to the adoption of new alternatives. Dairy, potatoes, improved sheep, forages are all part of the history of development in this region. A combination of climate variability, changes in the economy, the political environment, and land tenure reform shape rural livelihoods and welfare. Diversification, linking to markets, and networking are some elements that contribute to the resilience of families in the Andes. Strategies change, are flexible, and may incorporate non-agricultural activities. While some farmers are able to improve their welfare through the life cycle, others become poorer. Climate variability increases the vulnerability of some groups; in other cases, because of diversification and assets, households build economic portfolios that are more resilient to the elements. The many projects provide insights into how in the long run households improve their environment, hinting at mechanisms to adapt to climate change. In order to understand changing composition of portfolios in future scenarios of spatial heterogeneous areas such as mountains (Andes), estimates of models predicting climate change at a global scale are not useful because their resolution. Therefore, downscaling tools are useful. Spatial heterogeneity is assessed through agroecozoning. Both production and the impact on some environmental indicators are simulated through process-based models, for the Ilave-Huenque watershed in Peru that help in discussing scenarios of adaptation.

  16. [Evaluating the performance of the UCLA method for spatially downscaling soil moisture products using three Ts/VI indices].

    PubMed

    Ling, Zi-Wei; He, Long-Bin; Zeng, Hui

    2014-02-01

    Soil moisture products derived from microwave remote sensing data are commonly used in the studies of large-scale water resources or climate change. However, the spatial resolutions of these products are usually too coarse to be used in regional- or watershed-scale studies. Therefore, it is necessary to spatially downscale the coarse-resolution soil moisture products for use in regional- or watershed-scale studies. The UCLA method is one of the methods for spatially downscaling soil moisture products. In this method, the spatial indices (Ts/VI indices) calculated from land surface temperature and vegetation index are used as auxiliary variables for spatial downscaling. In this paper, we compared the performance of the UCLA method for spatially downscaling the coarse-resolution AMSR-E soil moisture products, using three Ts/VI indices as auxiliary variables, i. e., the soil wetness index (SW), temperature vegetation dryness index (TVDI), and vegetation temperature condition index (VTCI). These auxiliary variables were calculated from the products of MODIS land surface temperature (MYD11A1) and MODIS vegetation index (MYD13A2). The downscaled results using the three Ts/VI indices were all reasonable. However, the downscaled results using TVDI and VTCI were better than using SW. Therefore, we concluded that TVDI and VTCI are more suitable than SW to be used as the auxiliary variable when applying the UCLA method for downscaling soil moisture products. Finally, we discussed the error sources of applying the UCLA method, such as measurement errors of coarse resolution soil products, calculation errors from spatial indices, and errors from the UCLA method itself, and we also discussed the potential improvements of future research.

  17. [Evaluating the performance of the UCLA method for spatially downscaling soil moisture products using three Ts/VI indices].

    PubMed

    Ling, Zi-Wei; He, Long-Bin; Zeng, Hui

    2014-02-01

    Soil moisture products derived from microwave remote sensing data are commonly used in the studies of large-scale water resources or climate change. However, the spatial resolutions of these products are usually too coarse to be used in regional- or watershed-scale studies. Therefore, it is necessary to spatially downscale the coarse-resolution soil moisture products for use in regional- or watershed-scale studies. The UCLA method is one of the methods for spatially downscaling soil moisture products. In this method, the spatial indices (Ts/VI indices) calculated from land surface temperature and vegetation index are used as auxiliary variables for spatial downscaling. In this paper, we compared the performance of the UCLA method for spatially downscaling the coarse-resolution AMSR-E soil moisture products, using three Ts/VI indices as auxiliary variables, i. e., the soil wetness index (SW), temperature vegetation dryness index (TVDI), and vegetation temperature condition index (VTCI). These auxiliary variables were calculated from the products of MODIS land surface temperature (MYD11A1) and MODIS vegetation index (MYD13A2). The downscaled results using the three Ts/VI indices were all reasonable. However, the downscaled results using TVDI and VTCI were better than using SW. Therefore, we concluded that TVDI and VTCI are more suitable than SW to be used as the auxiliary variable when applying the UCLA method for downscaling soil moisture products. Finally, we discussed the error sources of applying the UCLA method, such as measurement errors of coarse resolution soil products, calculation errors from spatial indices, and errors from the UCLA method itself, and we also discussed the potential improvements of future research. PMID:24830256

  18. Climate change impacts utilizing regional models for agriculture, hydrology and natural ecosystems

    NASA Astrophysics Data System (ADS)

    Kafatos, M.; Asrar, G. R.; El-Askary, H. M.; Hatzopoulos, N.; Kim, J.; Kim, S.; Medvigy, D.; Prasad, A. K.; Smith, E.; Stack, D. H.; Tremback, C.; Walko, R. L.

    2012-12-01

    Climate change impacts the entire Earth but with crucial and often catastrophic impacts at local and regional levels. Extreme phenomena such as fires, dust storms, droughts and other natural hazards present immediate risks and challenges. Such phenomena will become more extreme as climate change and anthropogenic activities accelerate in the future. We describe a major project funded by NIFA (Grant # 2011-67004-30224), under the joint NSF-DOE-USDA Earth System Models (EaSM) program, to investigate the impacts of climate variability and change on the agricultural and natural (i.e. rangeland) ecosystems in the Southwest USA using a combination of historical and present observations together with climate, and ecosystem models, both in hind-cast and forecast modes. The applicability of the methodology to other regions is relevant (for similar geographic regions as well as other parts of the world with different agriculture and ecosystems) and should advance the state of knowledge for regional impacts of climate change. A combination of multi-model global climate projections from the decadal predictability simulations, to downscale dynamically these projections using three regional climate models, combined with remote sensing MODIS and other data, in order to obtain high-resolution climate data that can be used with hydrological and ecosystem models for impacts analysis, is described in this presentation. Such analysis is needed to assess the future risks and potential impacts of projected changes on these natural and managed ecosystems. The results from our analysis can be used by scientists to assist extended communities to determine agricultural coping strategies, and is, therefore, of interest to wide communities of stakeholders. In future work we will be including surface hydrologic modeling and water resources, extend modeling to higher resolutions and include significantly more crops and geographical regions with different weather and climate conditions

  19. 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.

  20. Downscaling of Extreme Precipitation: Proposing a New Statistical Approach and Investigating a Taken-for-Granted Assumption

    NASA Astrophysics Data System (ADS)

    Elshorbagy, Amin; Alam, Shahabul

    2015-04-01

    In spite of the ability of General Circulation Models (GCMs) to predict and generate atmospheric variables under pre-identified climate change scenarios, their coarse horizontal scale is an obstacle for impact studies. Therefore, downscaling of variables (e.g., precipitation) from coarse spatial and temporal scales to finer ones is inevitable. Downscaling methods are classified into various types ranging from applications related to short term numerical weather prediction to multidecadal global climate prediction. For engineering applications of impact assessment of climate change on infrastructure, the multidecadal global climate projection, is the most widely used type. One of the important engineering applications of climate change impact assessment is the development and reconstruction of intensity-duration-frequency (IDF) curves under possible climate change. IDF curves are widely used for design and management of urban hydrosystems. Their construction requires accurate information about intense short duration rainfall, including sub-hourly, extremes. Previous attempts were made to construct IDF curves in various places under climate change using dynamical and statistical downscaling. The deficiency of GCMs, and even RCMs, in representing local surface conditions, especially extreme weather and convective precipitation in many areas, necessitates the use of statistical downscaling for IDF-related applications. In statistical downscaling methods, and in particular regression-based methods, the search is always for the optimum set of inputs at a coarser scale that act as predictors for the desired surface weather variable (predictand) at the local finer scale. The grid box nearest to the local site may not provide the optimum predictor-predictand relationship. In fact, even the set of predictors varies from one region to another. In this study, a novel approach using genetic programming (GP) for specific application of downscaling annual maximum precipitation

  1. Combined effects of global climate change and regional ecosystem drivers on an exploited marine food web.

    PubMed

    Niiranen, Susa; Yletyinen, Johanna; Tomczak, Maciej T; Blenckner, Thorsten; Hjerne, Olle; Mackenzie, Brian R; Müller-Karulis, Bärbel; Neumann, Thomas; Meier, H E Markus

    2013-11-01

    Changes in climate, in combination with intensive exploitation of marine resources, have caused large-scale reorganizations in many of the world's marine ecosystems during the past decades. The Baltic Sea in Northern Europe is one of the systems most affected. In addition to being exposed to persistent eutrophication, intensive fishing, and one of the world's fastest rates of warming in the last two decades of the 20th century, accelerated climate change including atmospheric warming and changes in precipitation is projected for this region during the 21st century. Here, we used a new multimodel approach to project how the interaction of climate, nutrient loads, and cod fishing may affect the future of the open Central Baltic Sea food web. Regionally downscaled global climate scenarios were, in combination with three nutrient load scenarios, used to drive an ensemble of three regional biogeochemical models (BGMs). An Ecopath with Ecosim food web model was then forced with the BGM results from different nutrient-climate scenarios in combination with two different cod fishing scenarios. The results showed that regional management is likely to play a major role in determining the future of the Baltic Sea ecosystem. By the end of the 21st century, for example, the combination of intensive cod fishing and high nutrient loads projected a strongly eutrophicated and sprat-dominated ecosystem, whereas low cod fishing in combination with low nutrient loads resulted in a cod-dominated ecosystem with eutrophication levels close to present. Also, nonlinearities were observed in the sensitivity of different trophic groups to nutrient loads or fishing depending on the combination of the two. Finally, many climate variables and species biomasses were projected to levels unseen in the past. Hence, the risk for ecological surprises needs to be addressed, particularly when the results are discussed in the ecosystem-based management context.

  2. Satellite-based climate information within the WMO RA VI Regional Climate Centre on Climate Monitoring

    NASA Astrophysics Data System (ADS)

    Obregón, A.; Nitsche, H.; Körber, M.; Kreis, A.; Bissolli, P.; Friedrich, K.; Rösner, S.

    2014-05-01

    The World Meteorological Organization (WMO) established Regional Climate Centres (RCCs) around the world to create science-based climate information on a regional scale within the Global Framework for Climate Services (GFCS). The paper introduces the satellite component of the WMO Regional Climate Centre on Climate Monitoring (RCC-CM) for Europe and the Middle East. The RCC-CM product portfolio is based on essential climate variables (ECVs) as defined by the Global Climate Observing System (GCOS), spanning the atmospheric (radiation, clouds, water vapour) and terrestrial domains (snow cover, soil moisture). In the first part, the input data sets are briefly described, which are provided by the EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) Satellite Application Facilities (SAF), in particular CM SAF, and by the ESA (European Space Agency) Climate Change Initiative (CCI). In the second part, the derived RCC-CM products are presented, which are divided into two groups: (i) operational monitoring products (e.g. monthly means and anomalies) based on near-real-time environmental data records (EDRs) and (ii) climate information records (e.g. climatologies, time series, trend maps) based on long-term thematic climate data records (TCDRs) with adequate stability, accuracy and homogeneity. The products are provided as maps, statistical plots and gridded data, which are made available through the RCC-CM website (www.dwd.de/rcc-cm).

  3. Climatic Effects of Regional Nuclear War

    NASA Technical Reports Server (NTRS)

    Oman, Luke D.

    2011-01-01

    We use a modern climate model and new estimates of smoke generated by fires in contemporary cities to calculate the response of the climate system to a regional nuclear war between emerging third world nuclear powers using 100 Hiroshima-size bombs (less than 0.03% of the explosive yield of the current global nuclear arsenal) on cities in the subtropics. We find significant cooling and reductions of precipitation lasting years, which would impact the global food supply. The climate changes are large and longlasting because the fuel loadings in modern cities are quite high and the subtropical solar insolation heats the resulting smoke cloud and lofts it into the high stratosphere, where removal mechanisms are slow. While the climate changes are less dramatic than found in previous "nuclear winter" simulations of a massive nuclear exchange between the superpowers, because less smoke is emitted, the changes seem to be more persistent because of improvements in representing aerosol processes and microphysical/dynamical interactions, including radiative heating effects, in newer global climate system models. The assumptions and calculations that go into these conclusions will be described.

  4. Incorporating cold-air pooling into downscaled climate models increases potential refugia for snow-dependent species within the Sierra Nevada Ecoregion, CA.

    PubMed

    Curtis, Jennifer A; Flint, Lorraine E; Flint, Alan L; Lundquist, Jessica D; Hudgens, Brian; Boydston, Erin E; Young, Julie K

    2014-01-01

    We present a unique water-balance approach for modeling snowpack under historic, current and future climates throughout the Sierra Nevada Ecoregion. Our methodology uses a finer scale (270 m) than previous regional studies and incorporates cold-air pooling, an atmospheric process that sustains cooler temperatures in topographic depressions thereby mitigating snowmelt. Our results are intended to support management and conservation of snow-dependent species, which requires characterization of suitable habitat under current and future climates. We use the wolverine (Gulo gulo) as an example species and investigate potential habitat based on the depth and extent of spring snowpack within four National Park units with proposed wolverine reintroduction programs. Our estimates of change in spring snowpack conditions under current and future climates are consistent with recent studies that generally predict declining snowpack. However, model development at a finer scale and incorporation of cold-air pooling increased the persistence of April 1st snowpack. More specifically, incorporation of cold-air pooling into future climate projections increased April 1st snowpack by 6.5% when spatially averaged over the study region and the trajectory of declining April 1st snowpack reverses at mid-elevations where snow pack losses are mitigated by topographic shading and cold-air pooling. Under future climates with sustained or increased precipitation, our results indicate a high likelihood for the persistence of late spring snowpack at elevations above approximately 2,800 m and identify potential climate refugia sites for snow-dependent species at mid-elevations, where significant topographic shading and cold-air pooling potential exist. PMID:25188379

  5. Incorporating Cold-Air Pooling into Downscaled Climate Models Increases Potential Refugia for Snow-Dependent Species within the Sierra Nevada Ecoregion, CA

    PubMed Central

    Curtis, Jennifer A.; Flint, Lorraine E.; Flint, Alan L.; Lundquist, Jessica D.; Hudgens, Brian; Boydston, Erin E.; Young, Julie K.

    2014-01-01

    We present a unique water-balance approach for modeling snowpack under historic, current and future climates throughout the Sierra Nevada Ecoregion. Our methodology uses a finer scale (270 m) than previous regional studies and incorporates cold-air pooling, an atmospheric process that sustains cooler temperatures in topographic depressions thereby mitigating snowmelt. Our results are intended to support management and conservation of snow-dependent species, which requires characterization of suitable habitat under current and future climates. We use the wolverine (Gulo gulo) as an example species and investigate potential habitat based on the depth and extent of spring snowpack within four National Park units with proposed wolverine reintroduction programs. Our estimates of change in spring snowpack conditions under current and future climates are consistent with recent studies that generally predict declining snowpack. However, model development at a finer scale and incorporation of cold-air pooling increased the persistence of April 1st snowpack. More specifically, incorporation of cold-air pooling into future climate projections increased April 1st snowpack by 6.5% when spatially averaged over the study region and the trajectory of declining April 1st snowpack reverses at mid-elevations where snow pack losses are mitigated by topographic shading and cold-air pooling. Under future climates with sustained or increased precipitation, our results indicate a high likelihood for the persistence of late spring snowpack at elevations above approximately 2,800 m and identify potential climate refugia sites for snow-dependent species at mid-elevations, where significant topographic shading and cold-air pooling potential exist. PMID:25188379

  6. Incorporating cold-air pooling into downscaled climate models increases potential refugia for snow-dependent species within the Sierra Nevada Ecoregion, CA

    USGS Publications Warehouse

    Curtis, Jennifer A.; Flint, Lorraine E.; Flint, Alan L.; Lundquist, Jessica D.; Hudgens, Brian; Boydston, Erin E.; Young, Julie K.

    2014-01-01

    We present a unique water-balance approach for modeling snowpack under historic, current and future climates throughout the Sierra Nevada Ecoregion. Our methodology uses a finer scale (270 m) than previous regional studies and incorporates cold-air pooling, an atmospheric process that sustains cooler temperatures in topographic depressions thereby mitigating snowmelt. Our results are intended to support management and conservation of snow-dependent species, which requires characterization of suitable habitat under current and future climates. We use the wolverine (Gulo gulo) as an example species and investigate potential habitat based on the depth and extent of spring snowpack within four National Park units with proposed wolverine reintroduction programs. Our estimates of change in spring snowpack conditions under current and future climates are consistent with recent studies that generally predict declining snowpack. However, model development at a finer scale and incorporation of cold-air pooling increased the persistence of April 1st snowpack. More specifically, incorporation of cold-air pooling into future climate projections increased April 1st snowpack by 6.5% when spatially averaged over the study region and the trajectory of declining April 1st snowpack reverses at mid-elevations where snow pack losses are mitigated by topographic shading and cold-air pooling. Under future climates with sustained or increased precipitation, our results indicate a high likelihood for the persistence of late spring snowpack at elevations above approximately 2,800 m and identify potential climate refugia sites for snow-dependent species at mid-elevations, where significant topographic shading and cold-air pooling potential exist.

  7. Evaluation of Downscaled CMIP5 Model Skill in Simulating Daily Maximum Temperature Over the Southeastern United States

    NASA Astrophysics Data System (ADS)

    Keellings, D.

    2015-12-01

    Downscaled CMIP5 climate projections of maximum daily temperature from the Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections archive are examined regionally over the southeastern U.S. Three measures of model skill (means-based, distribution-based, extreme-based) are utilized to assess the ability of 15 downscaled models to simulate daily maximum temperature observations. A new test is proposed to determine statistical significance of the probability density function based skill measures. Skill scores are found to be generally high for all three measures throughout the study region, but lower scores are present in coastal and mountainous areas. Application of the significance test shows that while the skill scores may be high they are not significantly higher than could be expected at random in some areas. The distribution-based skill scores are not significant in much of Florida and the Appalachians. The extreme-based skill scores are not significant in more than 90% of the region for all models investigated. The findings suggest that although the downscaled models have simulated observed means well and are a good match to the entire distribution of observations, they are not simulating the occurrence of extreme (above 90th percentile) maximum daily temperatures.

  8. Impacts of Climate Change on Energy Consumption and Peak Demand in Buildings: A Detailed Regional Approach

    SciTech Connect

    Dirks, James A.; Gorrissen, Willy J.; Hathaway, John E.; Skorski, Daniel C.; Scott, Michael J.; Pulsipher, Trenton C.; Huang, Maoyi; Liu, Ying; Rice, Jennie S.

    2015-01-01

    This paper presents the results of numerous commercial and residential building simulations, with the purpose of examining the impact of climate change on peak and annual building energy consumption over the portion of the Eastern Interconnection (EIC) located in the United States. The climate change scenario considered (IPCC A2 scenario as downscaled from the CASCaDE data set) has changes in mean climate characteristics as well as changes in the frequency and duration of intense weather events. This investigation examines building energy demand for three annual periods representative of climate trends in the CASCaDE data set at the beginning, middle, and end of the century--2004, 2052, and 2089. Simulations were performed using the Building ENergy Demand (BEND) model which is a detailed simulation platform built around EnergyPlus. BEND was developed in collaboration with the Platform for Regional Integrated Modeling and Analysis (PRIMA), a modeling framework designed to simulate the complex interactions among climate, energy, water, and land at decision-relevant spatial scales. Over 26,000 building configurations of different types, sizes, vintages, and, characteristics which represent the population of buildings within the EIC, are modeled across the 3 EIC time zones using the future climate from 100 locations within the target region, resulting in nearly 180,000 spatially relevant simulated demand profiles for each of the 3 years. In this study, the building stock characteristics are held constant based on the 2005 building stock in order to isolate and present results that highlight the impact of the climate signal on commercial and residential energy demand. Results of this analysis compare well with other analyses at their finest level of specificity. This approach, however, provides a heretofore unprecedented level of specificity across multiple spectrums including spatial, temporal, and building characteristics. This capability enables the ability to

  9. Climatic controls on ecosystem resilience: Postfire regeneration in the Cape Floristic Region of South Africa.

    PubMed

    Wilson, Adam M; Latimer, Andrew M; Silander, John A

    2015-07-21

    Conservation of biodiversity and natural resources in a changing climate requires understanding what controls ecosystem resilience to disturbance. This understanding is especially important in the fire-prone Mediterranean systems of the world. The fire frequency in these systems is sensitive to climate, and recent climate change has resulted in more frequent fires over the last few decades. However, the sensitivity of postfire recovery and biomass/fuel load accumulation to climate is less well understood than fire frequency despite its importance in driving the fire regime. In this study, we develop a hierarchical statistical framework to model postfire ecosystem recovery using satellite-derived observations of vegetation as a function of stand age, topography, and climate. In the Cape Floristic Region (CFR) of South Africa, a fire-prone biodiversity hotspot, we found strong postfire recovery gradients associated with climate resulting in faster recovery in regions with higher soil fertility, minimum July (winter) temperature, and mean January (summer) precipitation. Projections using an ensemble of 11 downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) suggest that warmer winter temperatures in 2080-2100 will encourage faster postfire recovery across the region, which could further increase fire frequency due to faster fuel accumulation. However, some models project decreasing precipitation in the western CFR, which would slow recovery rates there, likely reducing fire frequency through lack of fuel and potentially driving local biome shifts from fynbos shrubland to nonburning semidesert vegetation. This simple yet powerful approach to making inferences from large, remotely sensed datasets has potential for wide application to modeling ecosystem resilience in disturbance-prone ecosystems globally.

  10. Assessment of production risks for winter wheat in different German regions under climate change conditions

    NASA Astrophysics Data System (ADS)

    Kersebaum, K. C.; Gandorfer, M.; Wegehenkel, M.

    2012-04-01

    The study shows climate change impacts on wheat production in selected regions across Germany. To estimate yield and economic effects the agro-ecosystem model HERMES was used. The model performed runs using 2 different releases of the model WETTREG providing statistically downscaled climate change scenarios for the weather station network of the German Weather Service. Simulations were done using intersected GIS information on soil types and land use identifying the most relevant sites for wheat production. The production risks for wheat yields at the middle of this century were compared to a reference of the present climate. The irrigation demand was determined by the model using an automatic irrigation mode. Production risks with and without irrigation were assessed and the economic feasibility to reduce production risks by irrigation was evaluated. Costs and benefits were compared. Additionally, environmental effects, e.g. groundwater recharge and nitrogen emissions were assessed for irrigated and rain fed systems. Results show that positive and negative effects of climate change occur within most regions depending on the site conditions. Water holding capacity and groundwater distance were the most important factors which determined the vulnerability of sites. Under climate change condition in the middle of the next century we can expect especially at sites with low water holding capacity decreasing average gross margins, higher production risks and a reduced nitrogen use efficiency under rainfed conditions. Irrigation seems to be profitable and risk reducing at those sites, provided that water for irrigation is available. Additionally, the use of irrigation can also increase nitrogen use efficiency which reduced emissions by leaching. Despite the site conditions results depend strongly on the used regional climate scenario and the model approach to consider the effect of elevated CO2 in the atmosphere.

  11. Climatic controls on ecosystem resilience: Postfire regeneration in the Cape Floristic Region of South Africa

    PubMed Central

    Wilson, Adam M.; Latimer, Andrew M.; Silander, John A.

    2015-01-01

    Conservation of biodiversity and natural resources in a changing climate requires understanding what controls ecosystem resilience to disturbance. This understanding is especially important in the fire-prone Mediterranean systems of the world. The fire frequency in these systems is sensitive to climate, and recent climate change has resulted in more frequent fires over the last few decades. However, the sensitivity of postfire recovery and biomass/fuel load accumulation to climate is less well understood than fire frequency despite its importance in driving the fire regime. In this study, we develop a hierarchical statistical framework to model postfire ecosystem recovery using satellite-derived observations of vegetation as a function of stand age, topography, and climate. In the Cape Floristic Region (CFR) of South Africa, a fire-prone biodiversity hotspot, we found strong postfire recovery gradients associated with climate resulting in faster recovery in regions with higher soil fertility, minimum July (winter) temperature, and mean January (summer) precipitation. Projections using an ensemble of 11 downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) suggest that warmer winter temperatures in 2080–2100 will encourage faster postfire recovery across the region, which could further increase fire frequency due to faster fuel accumulation. However, some models project decreasing precipitation in the western CFR, which would slow recovery rates there, likely reducing fire frequency through lack of fuel and potentially driving local biome shifts from fynbos shrubland to nonburning semidesert vegetation. This simple yet powerful approach to making inferences from large, remotely sensed datasets has potential for wide application to modeling ecosystem resilience in disturbance-prone ecosystems globally. PMID:26150521

  12. Climatic controls on ecosystem resilience: Postfire regeneration in the Cape Floristic Region of South Africa.

    PubMed

    Wilson, Adam M; Latimer, Andrew M; Silander, John A

    2015-07-21

    Conservation of biodiversity and natural resources in a changing climate requires understanding what controls ecosystem resilience to disturbance. This understanding is especially important in the fire-prone Mediterranean systems of the world. The fire frequency in these systems is sensitive to climate, and recent climate change has resulted in more frequent fires over the last few decades. However, the sensitivity of postfire recovery and biomass/fuel load accumulation to climate is less well understood than fire frequency despite its importance in driving the fire regime. In this study, we develop a hierarchical statistical framework to model postfire ecosystem recovery using satellite-derived observations of vegetation as a function of stand age, topography, and climate. In the Cape Floristic Region (CFR) of South Africa, a fire-prone biodiversity hotspot, we found strong postfire recovery gradients associated with climate resulting in faster recovery in regions with higher soil fertility, minimum July (winter) temperature, and mean January (summer) precipitation. Projections using an ensemble of 11 downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) suggest that warmer winter temperatures in 2080-2100 will encourage faster postfire recovery across the