Science.gov

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. 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

  3. 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.

  4. 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

  5. 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...

  6. Regional downscaling of global climate runs for Nepal

    NASA Astrophysics Data System (ADS)

    Granerød, M.; Mesquita, M. D.; Basnayake, S.

    2011-12-01

    Nepal is a vulnerable country to changes in climate. This is mainly due to its dependency on water resources from the Himalayas. There is evidence of significant warming in Nepal, with an average trend of around +0.06 degrees Celsius per year. Studies have shown that the warming rates are higher in higher altitudes. Such temperature trend will have an impact on the melting of the glaciers and consequently on Nepal. Precipitation has also been observed to have increased, but not at the same magnitude as temperature. The water supply is affected by more unpredictable precipitation that can lead to droughts and shorter heavy rainfall. Future projections can give an indication whether these factors will affect river runoff, which can have large impacts on agriculture and in other sectors. Global Climate Models (GCMs) have a coarse resolution and limitations in the numerical and in the physical treatment. More detailed climate datasets are needed to produce climate projections for countries like Nepal. In this study, we use the climate version of the Weather Research and Forecasting model (clWRF3.1.1, developed at the University of Cantabria, Spain), which is a regional climate model (RCM), to provide a more detailed description of future climate scenarios in Nepal. The Atmospheric General Circulation Model, ARPEGE, has been used to provide lateral boundary conditions for the model evaluation. A control simulation from 1970 to 2000, and 4 future climate scenario runs from 2030 to 2060 are created based on these data. The parent domain has a horizontal grid resolution of 48 km, covering the area 68 to 100 degrees East and 1 degree South to 38 degree North. The nested domain has a horizontal grid resolution of 12 km, covering the area 79 to 90 degree East and 25 to 32 degree North. Both domains are run with 37 vertical levels reaching up to 50 hPa. In the clWRF setup, the microphysical scheme used is the WRF Single-Moment 3-class scheme and the cumulus option is the Grell

  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. A Demonstration of the Value of Nudging in Downscaled Regional Climate Simulations from a Global Climate Model

    NASA Astrophysics Data System (ADS)

    Otte, T.; Bowden, J.; Herwehe, J. A.; Nolte, C. G.; Pleim, J. E.; Faluvegi, G.; Shindell, D. T.

    2009-12-01

    The WRF Model is being used at the U.S. EPA for dynamical downscaling of the NASA/GISS ModelE fields to assess regional impacts of climate change in the United States. The ModelE fields were included in the IPCC AR4, and updated science in the improved ModelE will contribute toward the IPCC AR5. Downscaling of ModelE with WRF will be performed for multiple Representative Concentration Pathways in AR5. The downscaled fields from WRF ultimately will be used to predict the regional impacts of climate change on water quality and availability, agriculture, ecosystems, human health, air quality resulting from emissions control strategies, and energy demand. To understand the regional impacts, it will be necessary to focus on extreme events (e.g., heat waves, droughts, flooding, stagnation events), in addition to changes in local mean temperatures and precipitation. The use of nudging for downscaled regional climate simulations has been somewhat controversial over the past several years but has recently been gaining popularity. Several recent studies that have used reanalysis (i.e., verifiable) fields as a proxy for GCM input have shown that nudging can be beneficial toward achieving the desired downscaled fields. In this study, the value of nudging will be shown using fields from ModelE that are downscaled using WRF. Several different methods of nudging are explored, and it will be shown that nudging can be used to generate downscaled fields that allow the large-scale features from ModelE to be recast by WRF and still allow WRF to generate plausible mesoscale features. In addition, it will be shown that the method of nudging and the choices made with respect to how nudging is used in WRF are extremely critical to balance the constraint of ModelE against the freedom of WRF to develop its own fields.

  9. 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.

  10. 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. PMID:26293893

  11. Statistical and dynamical downscaling in CORDEX-Africa: differing views on the regional climate

    NASA Astrophysics Data System (ADS)

    Hewitson, Bruce; Lennard, Christopher; Jack, Christopher; Coop, Lisa

    2013-04-01

    The need for credible regional climate change projections for use in adaptation actions and decision making is well recognised. The CORDEX activity has evolved in large part as a response to this need. For the most part, CORDEX has so far been dominated by regional climate modelling (RCM) activities. However, implicit in CORDEX is the use of statistical downscaling (SD) as a complement to RCMs, although the SD activities lag that of the RCMs. For Africa, the CORDEX RCM work is well advanced with the control climate simulations completed, and a number of RCM-based projections also available. The early results indicate the RCMs produce a credible representation of the regional climate when aggregated in time and/or space, and provide an initial multimodal suite of regional climate change projections for Africa. The SD activities are catching up with this process and the emerging challenge is how to integrate and compare the results from the two downscaling methods. The two approaches, SD and RCMs, have respective strengths and weaknesses, but are considered in the literature to be of comparable overall skill. Where climate change stationarity is not considered a major issue, such as on timescales out to perhaps 2050, it is arguable that SD (comprehensively undertaken) may possibly be more skillful. From the perspective of users of regional scale projections, decision makers and policy developers, it is critical to compare, and assess the relative strengths of the methods on a regional basis. To avoid confusion the contradictions and/or robust messages emerging from the two methods needs to be clearly understood and articulated. The inter-comparison between the RCMs is already the subject of a number of papers, and here we present an initial comparison of early results between the SD and the envelope of RCM downscaling for CORDEX-Africa. Using the available SD results, we consider where the overlap and/or marked differences lie between the two methods. The focus is

  12. 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.

  13. 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.

  14. Statistical Downscaling of CMIP5 Global Climate Model Simulations for Use in Regional Impact Studies

    NASA Astrophysics Data System (ADS)

    Thrasher, B.; Duffy, P.; Maurer, E. P.; White, K.; Das, T.; Brekke, L. D.; Girvetz, E. H.

    2009-12-01

    Global climate models encapsulate our best understanding of the physics of the climate system, but at a spatial scale that is too coarse to meet the needs of societal impacts researchers and decision makers. To meet these needs, we are undertaking systematic spatial downscaling of CMIP5 GCM simulations now being performed by modeling groups around the world and archived by the Program for Climate Model Diagnosis and Intercomparison at Lawrence Livermore National Laboratory. A user group of particular interest is researchers contributing to IPCC Working Group II AR5. We are using two empirical downscaling methods, which both add spatial detail based upon fine-scale gridded observations of historical climate: the “Bias-Corrected Constructed Analogs” method and the “Bias-Corrected Spatial Downscaling method.” We will downscale several hundred simulations from all participating models, and from several Representative Concentration Pathways. This effort complements ongoing, related work that produced downscaled versions of CMIP3 global climate model results (http://gdo-dcp.ucllnl.org/downscaled_cmip3_projections/dcpInterface.html). A Google Maps-based user interface will allow at-archive data exploration and visualization, and selection and downloading of desired data subsets. High-resolution monthly results focusing on temperature and precipitation projections for the western United States will be presented.

  15. Development of dynamical downscaling for regional climate modeling and decision aid applications

    NASA Astrophysics Data System (ADS)

    Darmenova, K.; Higgins, G.; Alliss, R.; Kiley, H.; Apling, D.

    2009-12-01

    Current General Circulation Models (GCMs) provide a valuable estimate of both natural and anthropogenic climate changes and variability on global scales. At the same time, future climate projections calculated with GCMs are not of sufficient spatial resolution to address regional needs. There is a growing interest from various industry sectors such as health, energy, agriculture, transportation and water planning in incorporating climate change into their strategic and development plans. To address current deficiencies in local planning and decision making with respect to regional climate change, our research is focused on developing a dynamical downscaling capability with the Weather Research and Forecasting (WRF) model and developing decision aids that translate the regional climate data into actionable information for users. Our methodology involves detailed analysis of ensemble runs performed with the WRF model initialized with the NCEP-NCAR reanalysis data and the ECHAM5 GCM. The WRF model is also run with different physical schemes and spatial resolutions, and compared with ground-based observations to delineate the uncertainties associated with the use of different initial conditions, grid sizes and physical parameterizations.

  16. 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

  17. Impact of spectral nudging on the downscaling of tropical cyclones in regional climate simulations

    NASA Astrophysics Data System (ADS)

    Choi, Suk-Jin; Lee, Dong-Kyou

    2016-06-01

    This study investigated the simulations of three months of seasonal tropical cyclone (TC) activity over the western North Pacific using the Advanced Research WRF Model. In the control experiment (CTL), the TC frequency was considerably overestimated. Additionally, the tracks of some TCs tended to have larger radii of curvature and were shifted eastward. The large-scale environments of westerly monsoon flows and subtropical Pacific highs were unreasonably simulated. The overestimated frequency of TC formation was attributed to a strengthened westerly wind field in the southern quadrants of the TC center. In comparison with the experiment with the spectral nudging method, the strengthened wind speed was mainly modulated by large-scale flow that was greater than approximately 1000 km in the model domain. The spurious formation and undesirable tracks of TCs in the CTL were considerably improved by reproducing realistic large-scale atmospheric monsoon circulation with substantial adjustment between large-scale flow in the model domain and large-scale boundary forcing modified by the spectral nudging method. The realistic monsoon circulation took a vital role in simulating realistic TCs. It revealed that, in the downscaling from large-scale fields for regional climate simulations, scale interaction between model-generated regional features and forced large-scale fields should be considered, and spectral nudging is a desirable method in the downscaling method.

  18. 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.

  19. Regional climate model downscaling may improve the prediction of alien plant species distributions

    NASA Astrophysics Data System (ADS)

    Liu, Shuyan; Liang, Xin-Zhong; Gao, Wei; Stohlgren, Thomas J.

    2014-12-01

    Distributions of invasive species are commonly predicted with species distribution models that build upon the statistical relationships between observed species presence data and climate data. We used field observations, climate station data, and Maximum Entropy species distribution models for 13 invasive plant species in the United States, and then compared the models with inputs from a General Circulation Model (hereafter GCM-based models) and a downscaled Regional Climate Model (hereafter, RCM-based models).We also compared species distributions based on either GCM-based or RCM-based models for the present (1990-1999) to the future (2046-2055). RCM-based species distribution models replicated observed distributions remarkably better than GCM-based models for all invasive species under the current climate. This was shown for the presence locations of the species, and by using four common statistical metrics to compare modeled distributions. For two widespread invasive taxa ( Bromus tectorum or cheatgrass, and Tamarix spp. or tamarisk), GCM-based models failed miserably to reproduce observed species distributions. In contrast, RCM-based species distribution models closely matched observations. Future species distributions may be significantly affected by using GCM-based inputs. Because invasive plants species often show high resilience and low rates of local extinction, RCM-based species distribution models may perform better than GCM-based species distribution models for planning containment programs for invasive species.

  20. 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.

  1. 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.

  2. Moroccan precipitation in a regional climate change simulation, evaluating a statistical downscaling approach

    NASA Astrophysics Data System (ADS)

    Driouech, F.; Déqué, M.; Sánchez-Gómez, E.

    2009-09-01

    Morocco is located at the extreme north-west of Africa between 20 and 37° N. According to the aridity index of De Martonne classification, Moroccan climate varies from sub-humid in the north to arid in the south. The country has experienced several drought events which have had marked impacts on socio-economic sectors and national economy (1940-1945, 1980-1985, 1994-1995 …). During a dry year, the deficit of rainfall can exceed 60% of the climatological value. Rainfall amounts registered show a negative trend at national and regional scales. The drought seems to become more persistent over time. At the same time, the total number of wet days shows a negative trend revealing an increase in the annual dry day number. Many regions became more arid (According to the aridity index of De Martonne) between 1961 and 2008: namely Oujda, Taza, Kenitra, Rabat, Meknès. In order to evaluate climate change impacts on Moroccan winter precipitation, future climate conditions in Morocco under the SRES scenario A1B, are studied by using two 30-year time-slice simulations performed by the variable resolution configuration of the GCM ARPEGE-Climat. The spatial resolution ranges between 50 and 60 km over the country. This high resolution scenarios exhibit for the period 2021-2050 a change in the precipitation distribution and in extreme events. In particular, the precipitation amounts and the occurrence frequency of wet days will decrease in the scenario on all the fourteen stations considered. In terms of extreme events, the maximum dry spell length increases in nearly all the stations and the frequency of high precipitation events is projected to decrease. The evolution of highest percentiles of precipitation distribution does not go, however, in the same sense everywhere. Assessment of a range of uncertainties due to climate modelling has been done by using present-day and SRES scenario A1B data issued from 10 ENSEMBLES-RCMs. This shows that ARPEGE-Climate results are in the

  3. Regional downscaling of decadal predictions

    NASA Astrophysics Data System (ADS)

    Feldmann, H.

    2014-12-01

    During the last years the research field of decadal predictions gained increased attention. Its intention is to exploit the predictability derived from slowly varying components of the climate system on inter-annual to decadal time-scales. Such predictions are mostly performed using ensembles of global earth system models. The prediction systems are able to achieve a relatively high predictive skill over some oceanic regions, like the North Atlantic sector. But potential users of decadal predictions are often interested in forecasts over land areas and require a higher resolution, too. Therefore, the German research program MiKlip develops a decadal ensemble predictions system with regional downscaling as an additional option. Dynamical downscaling and a statistical-dynamical downscaling approach are applied within the MiKlip regionalization module. The global prediction system consists of the MPI-ESM model. Different RCMs are used for the downscaling, e.g. CCLM and REMO. The focus regions are Europe and Western Africa. Hindcast experiments for the period 1960 - 2013 were performed to assess the general skill of the prediction system. Of special interest is the value added by the regional downscaling. For mean quantities, like annual mean temperature and precipitation, the predictive skill is comparable between the global and the downscaled systems. For extremes on the other hand there seems to be an improvement by the RCM ensemble. The skill strongly varies on sub-continental regions and with the season. The lead time up to which a positive predictive skill can be achieved depends on the parameter and season, too. A further goal is to assess the potential for valuable information, which can be derived from predicting long-term variations of the European climate. The leading mode of decadal variability in the European/Atlantic sector is the Atlantic Multidecadal Variation (AMV). The potential predictability from AMV teleconnections especially for extreme value

  4. 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.

  5. 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

  6. Downscaling large-scale climate variability using a regional climate model: the case of ENSO over Southern Africa

    NASA Astrophysics Data System (ADS)

    Boulard, Damien; Pohl, Benjamin; Crétat, Julien; Vigaud, Nicolas; Pham-Xuan, Thanh

    2013-03-01

    This study documents methodological issues arising when downscaling modes of large-scale atmospheric variability with a regional climate model, over a remote region that is yet under their influence. The retained case study is El Niño Southern Oscillation and its impacts on Southern Africa and the South West Indian Ocean. Regional simulations are performed with WRF model, driven laterally by ERA40 reanalyses over the 1971-1998 period. We document the sensitivity of simulated climate variability to the model physics, the constraint of relaxing the model solutions towards reanalyses, the size of the relaxation buffer zone towards the lateral forcings and the forcing fields through ERA-Interim driven simulations. The model's internal variability is quantified using 15-member ensemble simulations for seasons of interest, single 30-year integrations appearing as inappropriate to investigate the simulated interannual variability properly. The incidence of SST prescription is also assessed through additional integrations using a simple ocean mixed-layer model. Results show a limited skill of the model to reproduce the seasonal droughts associated with El Niño conditions. The model deficiencies are found to result from biased atmospheric forcings and/or biased response to these forcings, whatever the physical package retained. In contrast, regional SST forcing over adjacent oceans favor realistic rainfall anomalies over the continent, although their amplitude remains too weak. These results confirm the significant contribution of nearby ocean SST to the regional effects of ENSO, but also illustrate that regionalizing large-scale climate variability can be a demanding exercise.

  7. Statistical downscaling of regional climate models in Bulgarian mountains and some projections

    NASA Astrophysics Data System (ADS)

    Nojarov, Peter

    2015-01-01

    Air temperature and precipitation data from three high mountainous Bulgarian stations were used as well as outputs from nine regional climate models (RCMs) for air temperatures and eight RCMs for precipitation. Data from 40-year experiments driven by the ERA-40 reanalysis (temporal coverage from 1961 to 2000) of the ECMWF were employed for calibration of statistical downscaling models. Statistical methods were used in this research—Spearman and Pearson correlation, Mann-Whitney test, multiple linear regression, generalized linear models, etc. Projections, based on SRES A1B scenario and RCMs driven by four different GCMs, were made for the following future 30-years periods: 2005-2034, 2035-2064, and 2065-2094. RCMs ETHZ-CLM, DMI-ARPEGE-HIRHAM, HadRM3Q0, and HadRM3Q16 show the best correlation with observed air temperatures in mountain stations. RCMs ETHZ-CLM, HadRM3Q16, and RACMO have the best relationship with precipitation. Constructed monthly multiple linear regression models describe well enough air temperatures throughout the entire year. Monthly GLMs describe better precipitation in January, March, August, and September, as well as peak Musala and Cherni vrah precipitation. Projections for future 30-year periods indicate that air temperatures are expected to rise by 2065-2094 at all of the three investigated stations with 2.8 to 3.2 °C. This increase is mainly due to the summer months. Annual precipitation amounts are expected to decrease by the period 2065-2094 at all the three stations with about 7 to 17 %. Some increase of annual precipitation amounts in the beginning of twenty-first century against the general negative trend could happen at Musala station, which is probably due to the increase in frequency of liquid precipitation.

  8. A Hybrid Framework to Bias Correct and Empirically Downscale Daily Temperature and Precipitation from Regional Climate Models

    NASA Astrophysics Data System (ADS)

    Tan, P.; Abraham, Z.; Winkler, J. A.; Perdinan, P.; Zhong, S. S.; Liszewska, M.

    2013-12-01

    Bias correction and statistical downscaling are widely used approaches for postprocessing climate simulations generated by global and/or regional climate models. The skills of these approaches are typically assessed in terms of their ability to reproduce historical climate conditions as well as the plausibility and consistency of the derived statistical indicators needed by end users. Current bias correction and downscaling approaches often do not adequately satisfy the two criteria of accurate prediction and unbiased estimation. To overcome this limitation, a hybrid regression framework was developed to both minimize prediction errors and preserve the distributional characteristics of climate observations. Specifically, the framework couples the loss functions of standard (linear or nonlinear) regression methods with a regularization term that penalizes for discrepancies between the predicted and observed distributions. The proposed framework can also be extended to generate physically-consistent outputs across multiple response variables, and to incorporate both reanalysis-driven and GCM-driven RCM outputs into a unified learning framework. The effectiveness of the framework is demonstrated using daily temperature and precipitation simulations from the North American Regional Climate Change Program (NARCCAP) . The accuracy of the framework is comparable to standard regression methods, but, unlike the standard regression methods, the proposed framework is able to preserve many of the distribution properties of the response variables, akin to bias correction approaches such as quantile mapping and bivariate geometric quantile mapping.

  9. 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.

  10. 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...

  11. 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.

  12. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2010-12-01

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

  14. Influences of climate change on California and Nevada regions revealed by a high-resolution dynamical downscaling study

    NASA Astrophysics Data System (ADS)

    Pan, Lin-Lin; Chen, Shu-Hua; Cayan, Dan; Lin, Mei-Ying; Hart, Quinn; Zhang, Ming-Hua; Liu, Yubao; Wang, Jianzhong

    2011-11-01

    In this study, the influence of climate change to California and Nevada regions was investigated through high-resolution (4-km grid spacing) dynamical downscaling using the WRF (Weather Research & Forecasting) model. The dynamical downscaling was performed to both the GFS (Global forecast model) reanalysis (called GFS-WRF runs) from 2000-2006 and PCM (Parallel Climate Model) simulations (called PCM-WRF runs) from 1997-2006 and 2047-2056. The downscaling results were first validated by comparing current model outputs with the observational analysis PRISM (Parameter-elevation Regressions on Independent Slopes Model) dataset. In general, the dominant features from GFS-WRF runs and PCM-WRF runs were consistent with each other, as well as with PRISM results. The influences of climate change on the California and Nevada regions can be inferred from the model future runs. The averaged temperature showed a positive trend in the future, as in other studies. The temperature increases by around 1-2°C under the assumption of business as usual over 50 years. This leads to an upward shifting of the freezing level (the contour line of 0°C temperature) and more rain instead of snow in winter (December, January, and February). More hot days (>32.2°C or 90°F) and extreme hot days (>37.8°C or 100°F) are predicted in the Sacramento Valley and the southern parts of California and Nevada during summer (June, July, and August). More precipitation is predicted in northern California but not in southern California. Rainfall frequency slightly increases in the coast regions, but not in the inland area. No obvious trend of the surface wind was indicated. The probability distribution functions (PDF) of daily temperature, wind and precipitation for California and Nevada showed no significant change in shape in either winter or summer. The spatial distributions of precipitation frequency from GFS-WRF and PCM-WRF were highly correlated (r = 0.83). However, overall positive shifts were seen

  15. 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.

  16. The impact of the African Great Lakes on the regional climate in a dynamically downscaled CORDEX simulation (COSMO-CLM)

    NASA Astrophysics Data System (ADS)

    Thiery, W.; Panitz, H.; van Lipzig, N.

    2013-12-01

    Owing to the strong contrast in albedo, roughness and heat capacity between land and water, lakes significantly influence the exchange of moisture, heat and momentum between the surface and the boundary layer. To investigate this two-way interaction, a correct representation of lakes within regional climate models is essential. To this end, the one-dimensional lake parameterisation scheme FLake was recently coupled to the regional climate model COSMO-CLM (CCLM). One region where lakes constitute a key component of the climate system is the African Great Lakes region. In this study, the CCLM CORDEX-Africa evaluation simulation is dynamically downscaled from 0.44° (50 km) to 0.0625° (7 km) over East-Africa. The performance of two lake modules within CCLM are compared for the period 1999-2008: the default FLake scheme and the alternative Community Land Model. Model results are evaluated in a three-step procedure. First, the atmospheric state variables near-surface temperature, precipitation, surface energy fluxes, fractional cloud cover and column precipitable water are evaluated using in-situ based and satellite-derived products. Second, a comprehensive set of in-situ water temperature profile observations serves to evaluate the temporal evolution of water temperatures at three sites: Lake Kivu (Ishungu), Lake Tanganyika's northern basin (Kigoma) and southern basin (Mpulungu). Finally, spatial variability of surface temperatures in Lake Kivu and Lake Tanganyika are evaluated on the basis of satellite-derived lake surface temperatures. Subsequently, the preferred model configuration is used to quantify and understand effects by lakes reported for other regions in the world, such as a dampened diurnal temperature range, enhanced evaporation, modified surface layer stability, increased downwind precipitation, stronger winds, and the formation of local circulation patterns. This is achieved through comparison to a model integration excluding lake effects.

  17. 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

  18. The impact of the African Great Lakes on the regional climate in a dynamically downscaled CORDEX simulation

    NASA Astrophysics Data System (ADS)

    Thiery, Wim; Panitz, Hans-Jürgen; Davin, Edouard; van Lipzig, Nicole

    2014-05-01

    Owing to the strong contrast in albedo, roughness and heat capacity between land and water, lakes significantly influence the exchange of moisture, heat and momentum between the surface and the boundary layer. To investigate this two-way interaction, a correct representation of lakes within regional climate models is essential. To this end, the one-dimensional lake parameterisation scheme FLake was recently coupled to the regional climate model COSMO-CLM (CCLM). One region where lakes constitute a key component of the climate system is the African Great Lakes region. In this study, the CCLM CORDEX-Africa evaluation simulation is dynamically downscaled from 0.44° (50 km) to 0.0625° (7 km) over East-Africa, an unprecedented resolution for this region. The performance of different CCLM configurations are compared for the period 1999-2008: in particular, CCLM is tested for its sensitivity to the choice of the lake surface temperature description (SST, FLake, an improved version of FLake and Hostetler) and the land surface model (Terra and Community Land Model). Model results are evaluated in a three-step procedure. First, the atmospheric state variables near-surface temperature, precipitation, surface energy fluxes, fractional cloud cover and column precipitable water are evaluated using in-situ based and satellite-derived products. Second, a comprehensive set of in-situ water temperature profile observations serves to evaluate the temporal evolution of water temperatures at three sites: Lake Kivu (Ishungu), Lake Tanganyika's northern basin (Kigoma) and southern basin (Mpulungu). Finally, spatial variability of surface temperatures in Lake Kivu and Lake Tanganyika are evaluated on the basis of satellite-derived lake surface temperatures. Subsequently, the preferred model configuration is used to quantify and understand effects by lakes reported for other regions in the world, such as a dampened diurnal temperature range, enhanced evaporation, modified surface layer

  19. Downscaling Precipitation and Temperature Under Climate Change Over Semi-Arid Regions of Southwestern United States of America.

    NASA Astrophysics Data System (ADS)

    Shrestha, Bijaya Prakash

    Two different space-time models are developed to estimate downscaled precipitation and temperature under the rm 2 times CO_2 scenario of climate change over semi-arid regions of southwestern USA, represented by Arizona and New-Mexico (upper Rio-Grande river basin). Local precipitation and temperature are assumed to be dependent upon two effects: the first one, a global effect, is captured by atmospheric circulation pattern (CP) types and the other, a local effect, is reflected by spatially averaged daily pressure heights of the 500 hPa pressure field (h) within the region. CP classification is performed for the 500 hPa pressure fields of observed data and that obtained from the output of the Max Plank Institute (MPI) general circulation (GCM) model T21 for the rm 1times CO_2 and rm 2 times CO_2 scenarios. The evolution of CP types for different scenarios are modeled by a Markov process. Daily precipitation and temperature conditioned on a CP type are modeled by multivariate autoregressive processes. The daily precipitation probability is linked to h through a parametric regression and daily precipitation amount is modeled by a gamma distribution. The daily temperature is modeled by a two sided normal distribution whose parameters are estimated conditioned on fitted values of h. Models are validated using split sampling. Simulations are performed to generate a series of daily rainfall and temperature (maximum and minimum) both in Arizona and New Mexico stations. Statistical properties of model outputs and statistical significance tests are carried out for current conditions and under climate change using rm 2 times CO_2 scenarios. The results show that precipitation and temperature are increasing significantly with the increase in CO _2 content. Increases in temperature are more prominent in spring and fall. However the actual amounts of increase in precipitation and temperature depend both on the season and station location.

  20. 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

  1. 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

  2. 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

  3. 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

  4. 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.

  5. 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.

  6. Non-Gaussian Winter Daily Minimum and Maximum Temperatures in a Regional Climate Model: Downscaling of Reanalysis, Historical Simulations and Future Projections for the Southeast United States

    NASA Astrophysics Data System (ADS)

    Stefanova, L. B.; Sura, P.; Griffin, M.; Chan, S.; Misra, V.

    2011-12-01

    There is a marked interest in possible changes of the climate variability under future emission scenarios, and, in particular, in the potential for changes of the statistics of extreme weather. One statistical measure of extreme events is the non-Gaussianity of the variable under consideration. For the Southeast US, the non-Gaussianity of the local wintertime temperature distributions is of considerable interest to agriculture, energy, and ecosystem management. Therefore, our goal is to evaluate the expected changes of wintertime daily minimum and maximum temperature distributions in regional climate change projections in response to increased radiative forcing. First, we assess the ability of the regional model that we use - the National Centers for Environmental Prediction (NCEP)/Experimental Climate Prediction Center (ECPC) Regional Spectral Model (RSM) - to represent the observed distributions and their response to the ENSO phase. This analysis is based on the daily minimum and maximum temperatures from the COAPS Land-Atmosphere Regional Reanalysis for the Southeast at 10-km resolution (CLARReS10) obtained by dynamically downscaling the NCEP - Department of Energy (DOE) Reanalysis II (R2) and the European Centre for Medium-Range Weather Forecast (ECMWF) 40-year Reanalysis (ERA40) with the Regional Spectral Model (RSM) over the Southeast United States at a horizontal resolution of 10 km for the period 1979-2001. We demonstrate that with the near-perfect lateral boundary conditions provided by the R2 or ERA40, RSM produces daily min/max temperature distributions and distributions' sensitivity to ENSO in very good agreement with station observations. We then assess the winter daily min/max temperatures distribution generated by dynamically downscaling with RSM the historical (1970-2000) and projected (2040-2070) coupled ocean-atmosphere climate model simulations from select models from the Coupled Model Intercomparison Project Phase 3 (CMIP3).

  7. Consensus between GCM climate change projections with empirical downscaling: precipitation downscaling over South Africa

    NASA Astrophysics Data System (ADS)

    Hewitson, B. C.; Crane, R. G.

    2006-08-01

    This paper discusses issues that surround the development of empirical downscaling techniques as context for presenting a new approach based on self-organizing maps (SOMs). The technique is applied to the downscaling of daily precipitation over South Africa. SOMs are used to characterize the state of the atmosphere on a localized domain surrounding each target location on the basis of NCEP 6-hourly reanalysis data from 1979 to 2002, and using surface and 700-hPa u and v wind vectors, specific and relative humidities, and surface temperature. Each unique atmospheric state is associated with an observed precipitation probability density function (PDF). Future climate states are derived from three global climate models (GCMs): HadAM3, ECHAM4.5, CSIRO Mk2. In each case, the GCM data are mapped to the NCEP SOMs for each target location and a precipitation value is drawn at random from the associated precipitation PDF. The downscaling approach combines the advantages of a direct transfer function and a stochastic weather generator, and provides an indication of the strength of the regional versus stochastic forcing, as well as a measure of stationarity in the atmosphere-precipitation relationship.The methodology is applied to South Africa. The downscaling reveals a similarity in the projected climate change between the models. Each GCM projects similar changes in atmospheric state and they converge on a downscaled solution that points to increased summer rainfall in the interior and the eastern part of the country, and a decrease in winter rainfall in the Western Cape. The actual GCM precipitation projections from the three models show large areas of intermodel disagreement, suggesting that the model differences may be due to their precipitation parameterization schemes, rather than to basic disagreements in their projections of the changing atmospheric state over South Africa.

  8. Looking for added value in Australian downscaling for climate change studies

    NASA Astrophysics Data System (ADS)

    Grose, Michael

    2015-04-01

    Downscaling gives the prospect of added value in the regional pattern and temporal nature of rainfall change with a warmer climate. However, such value is not guaranteed and the use of downscaling can raise rather than diminish uncertainties. Validation of downscaling methods tends to focus on the ability to simulate current climate statistics, rather than the robustness of simulated future climate change. Here we compare the future climate change signal in average rainfall from various dynamical and statistical downscaling outputs used for all of Australia and in regional climate change studies over smaller domains. We show that downscaling can generate different regional patterns of projected change compared to the global climate models used as input, indicating the potential for added value in projections. These differences often make physical sense in regions of complex topography such as in southeast Australia, the eastern seaboard and Tasmania. However, results from different methods are not always consistent. In addition, downscaling can produce projected changes that are not clearly related to finer resolution and are difficult to interpret. In some cases, each downscaling method gives a different range of results and different messages about projected rainfall change for a region. This shows that downscaling has the potential to add value to projections, but also brings the potential for uncertain or contradictory messages. We conclude that each method has strengths and weaknesses, and these should be clearly communicated.

  9. 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

  10. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  12. Projecting Mid- and End-of-Century Climate Change in the Los Angeles Mountainous Region by a Combination of Dynamical and Statistical Downscaling Techniques

    NASA Astrophysics Data System (ADS)

    Sun, F.; Hall, A. D.; Walton, D.; Capps, S. B.; Reich, K.

    2013-12-01

    Using a combination of dynamical and statistical downscaling techniques, we produced 2-km-resolution regional climate reconstructions and future projections of surface warming and snowfall changes in the Los Angeles region at the middle and end of the 21st century. Projections for both time periods were compared to a validated simulation of a baseline period (1981-2000). We examined outcomes associated with two IPCC-AR5 greenhouse gas emissions scenarios: a "business-as-usual" scenario (RCP8.5) and a "mitigation" scenario (RCP2.6). Output from all available global climate models in the CMIP5 archive was downscaled. We first statistically downscaled surface warming and then applied a statistical model between the surface temperature and snowfall to project the snowfall change. By mid-century, the mountainous areas in the Los Angeles region are likely to receive substantially less snowfall than in the baseline period. In RCP8.5, about 60% of the snowfall is most likely to persist, while in RCP2.6, the likely amount remaining is somewhat higher (about 70%). By end-of-century, however, the two scenarios diverge significantly. In RCP8.5, snowfall sees a dramatic further reduction, with only about a third of baseline snowfall persisting. For RCP2.6, snowfall sees only a negligible further reduction from mid-century. Due to significant differences in climate change outcomes across the global models, we estimated these numbers associated with uncertainty, in the range of 15-30 percentage points. For both scenarios and both time slices, the snowfall loss is consistently greatest at low elevations, and the lower-lying mountain ranges are somewhat more vulnerable to snowfall loss. The similarity in the two scenarios' most likely snowfall outcomes at mid-century illustrates the inevitability of climate change in the coming decades, no matter what mitigation measures are taken. Their stark contrast at century's end reveals that reduction of greenhouse gas emissions will help

  13. 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.

  14. 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

  15. 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.

  16. Accounting for downscaling and model uncertainties in examining the impacts of climate change on hydrological systems

    NASA Astrophysics Data System (ADS)

    Franklin, M.; Yan, E.; Demissie, Y.

    2010-12-01

    Statistical downscaling is a widely used method of transforming global climate model output to a regional or local scale for impact assessment studies. Uncertainties, both in the predictions generated through statistical downscaling and in the climate model simulations themselves, are rarely accounted for in the resultant downscaled climate parameters. Using observational meteorological data from 130 weather stations located in the upper midwest region of the U.S. and the 30-member ensemble of Community Climate System Model forecasts under the A1B SRES scenario, probability distribution functions (PDF) accounting for the aforementioned downscaling and model uncertainties were generated for daily precipitation, maximum and minimum temperature. Two-stage downscaling was performed for each model ensemble member resulting in 30 daily estimates of temperature and precipitation for each weather station. As temperature is a much smoother spatial and temporal process than precipitation, separate downscaling methods were developed for these two parameters. The standard errors from the downscaling stages were retained to quantify uncertainty in the estimates. Combined with the 30 realizations for each day, PDFs were generated that characterize both sources of uncertainty. Repeated samples drawn from the resultant PDFs served as inputs to the Soil and Water Assessment Tool (SWAT) hydrological model. The impact of climate change, accounting for uncertainty in downscaling and the climate model, on the hydrological cycle of the upper Mississippi river basin was assessed. Sensitivity in the SWAT model to uncertainty in the input parameters was also examined.

  17. 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

  18. 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

  19. Statistical Properties of Downscaled CMIP3 Global Climate Model Simulations

    NASA Astrophysics Data System (ADS)

    Duffy, P.; Tyan, S.; Thrasher, B.; Maurer, E. P.; Tebaldi, C.

    2009-12-01

    Spatial downscaling of global climate model projections adds physically meaningful spatial detail, and brings the results down to a scale that is more relevant to human and ecological systems. Statistical/empirical downscaling methods are computationally inexpensive, and thus can be applied to large ensembles of global climate model projections. Here we examine some of the statistical properties of a large ensemble of empirically downscale global climate projections. The projections are the CMIP3 global climate model projections that were performed by modeling groups around the world and archived by the Program for Climate Model Diagnosis and Intercomparison at Lawrence Livermore National Laboratory. Downscaled versions of 112 of these simulations were created on 2007 and are archived at http://gdo-dcp.ucllnl.org/downscaled_cmip3_projections/dcpInterface.html. The downscaling methodology employed, “Bias Correction/Spatial Downscaling” (BCSD), includes a correction of GCM biases relative to observations during a historical reference period, as well as empirical downscaling to grid scale of ~12 km. We analyzed these downscaled projections and some of the original global model results to assess effects of the bias correction and downscaling on the statistical properties of the ensemble. We also assessed uncertainty in the climate response to increased greenhouse gases from initial conditions relative to the uncertainty introduced by choice of global climate model.

  20. 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.

  1. 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.

  2. Assessing the effect of spatial resolution of regional climate downscaling on the productivity and distribution of three widespread tree species over France

    NASA Astrophysics Data System (ADS)

    Martin-StPaul, Nicolas K.; Stephanon, Marc; Francois, Christophe; Soudani, Kamel; Dufrêne, Eric; Drobinski, Phillipe; Cheaib, Alissar; Ruffault, Julien; Rambal, Serge; Mouillot, Florent; Leadley, Paul

    2013-04-01

    The recent increases in temperature and water deficit as a result of climate changes have already impaired forest functioning and might trigger tree dieback worldwide in the near future. The assessment of future forest conditions relies on mechanistic models that predict changes in trees and forest functioning as a function of meteorological drivers. Currently, global and regional models (GCM and RCM) are the main providers of climate forcing in impact studies. One large uncertainty when forecasting the forest functioning is associated with the coarse spatial resolution of climate scenarii. In this study we assessed how the spatial resolution in climate forcing provided by the RCM WRF impacted the simulated productivity and distribution of three species (Fagus sylvatica, Quercus ilex) over France. We ran the forest model CASTANEA over France (that simulates fluxes of carbon and water and forest growth) using the output of WRF at different spatial scales (50 km, 20km, 8km and 1km) as forcing climate entries. The productivity simulated by CASTANEA was used as a surrogate of beech persistence for the reference period of WRF (1988-2008). Because climate variables simulated by WRF exhibited large bias compared to surface observations, WRF was first corrected using a reference dataset (SAFRAN database) upscaled at the WRF resolution (50km and 20 km). Additionally, on 2 specific limited areas (the Languedoc Roussillon and the Bourgogne region) we used a statistical downscaling of the WRF forcing entries in order to increase the spatial resolution up to 1km. Our results showed that simulations at finer resolution had relatively little impact on the mean and variance of beech productivity over France compared to coarser resolutions. However, at the finest resolutions, we observed strong local gradients with important variations in the mean and the variance of forest productivity (up to 60%). These results are particularly noticeable in regions characterized by complex

  3. 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

  4. Downscaling climate variability associated with quasi-periodic climate signals: A new statistical approach using MSSA

    NASA Astrophysics Data System (ADS)

    Cañón, Julio; Domínguez, Francina; Valdés, Juan B.

    2011-02-01

    SummaryA statistical method is introduced to downscale hydroclimatic variables while incorporating the variability associated with quasi-periodic global climate signals. The method extracts statistical information of distributed variables from historic time series available at high resolution and uses Multichannel Singular Spectrum Analysis (MSSA) to reconstruct, on a cell-by-cell basis, specific frequency signatures associated with both the variable at a coarse scale and the global climate signals. Historical information is divided in two sets: a reconstruction set to identify the dominant modes of variability of the series for each cell and a validation set to compare the downscaling relative to the observed patterns. After validation, the coarse projections from Global Climate Models (GCMs) are disaggregated to higher spatial resolutions by using an iterative gap-filling MSSA algorithm to downscale the projected values of the variable, using the distributed series statistics and the MSSA analysis. The method is data adaptive and useful for downscaling short-term forecasts as well as long-term climate projections. The method is applied to the downscaling of temperature and precipitation from observed records and GCM projections over a region located in the US Southwest, taking into account the seasonal variability associated with ENSO.

  5. 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.

  6. 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.

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

    NASA Astrophysics Data System (ADS)

    Barsugli, Joseph J.; Guentchev, Galina; Horton, Radley M.; Wood, Andrew; Mearns, Linda O.; Liang, Xin-Zhong; Winkler, Julie A.; Dixon, Keith; Hayhoe, Katharine; Rood, Richard B.; Goddard, Lisa; Ray, Andrea; Buja, Lawrence; Ammann, Caspar

    2013-11-01

    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.

  8. Hydrological responses to dynamically and statistically downscaled climate model output

    USGS Publications Warehouse

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

    2000-01-01

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

  9. 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.

  10. 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 ...

  11. 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.

  12. 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.

  13. 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.

  14. Combining Global Climate Model Outputs and Insights from Downscaling for Australian Climate Projections

    NASA Astrophysics Data System (ADS)

    Grose, M. R.; Timbal, B.; Katzfey, J. J.; Moise, A. F.; Eksrtrom, M.; Whetton, P.

    2013-12-01

    Dynamical and statistical downscaling of global climate model (GCM) outputs has the potential to provide valuable insights when making regional climate projections. It may reveal regional detail in the projected climate change signal through higher resolution and accounting for local influences such as topography and coastlines. However, climate change adaptation research and planning desires a coherent view of possible future climate that accounts for the various sources of uncertainty and at a relevant spatial scale. This means there is value in combining the most useful insights from all available downscaling with a more comprehensive set of designed global climate model (GCM) projections (e.g. the CMIP5 archive), and this is done for the next set of national climate projections products in Australia. There are several practical considerations in this process that affect the process, primarily because downscaling is done using various disparate methods for a limited set of models and scenarios. There is no objective framework to combine different sets of ad hoc downscaling simulations with a set of GCMs, so some degree of expert judgment is used. We emphasize cases where there is the most apparent ';added value' and report these insights in complement, and in some cases in preference to, GCM projections. Confidence in such insights first requires understanding of what input data is used from the host model, what biases are reduced and what new biases are potentially introduced. We then seek an understanding of how the climate change signal differs from that of the host model, and an attribution of the cause of this difference. Several case studies within Australia are discussed.

  15. Dynamical downscaling inter-comparison for high resolution climate reconstruction

    NASA Astrophysics Data System (ADS)

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

    2012-04-01

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

  16. Effects of climate change on daily minimum and maximum temperatures and cloudiness in the Shikoku region: a statistical downscaling model approach

    NASA Astrophysics Data System (ADS)

    Tatsumi, Kenichi; Oizumi, Tsutao; Yamashiki, Yosuke

    2015-04-01

    In this study, we present a detailed analysis of the effect of changes in cloudiness (CLD) between a future period (2071-2099) and the base period (1961-1990) on daily minimum temperature (TMIN) and maximum temperature (TMAX) in the same period for the Shikoku region, Japan. This analysis was performed using climate data obtained with the use of the Statistical DownScaling Model (SDSM). We calibrated the SDSM using the National Center for Environmental Prediction (NCEP) reanalysis dataset for the SDSM input and daily time series of temperature and CLD from 10 surface data points (SDP) in Shikoku. Subsequently, we validated the SDSM outputs, specifically, TMIN, TMAX, and CLD, obtained with the use of the NCEP reanalysis dataset and general circulation model (GCM) data against the SDP. The GCM data used in the validation procedure were those from the Hadley Centre Coupled Model, version 3 (HadCM3) for the Special Report on Emission Scenarios (SRES) A2 and B2 scenarios and from the third generation Coupled Global Climate Model (CGCM3) for the SRES A2 and A1B scenarios. Finally, the validated SDSM was run to study the effect of future changes in CLD on TMIN and TMAX. Our analysis showed that (1) the negative linear fit between changes in TMAX and those in CLD was statistically significant in winter while the relationship between the two changes was not evident in summer, (2) the dependency of future changes in TMAX and TMIN on future changes in CLD were more evident in winter than in other seasons with the present SDSM, (3) the diurnal temperature range (DTR) decreased in the southern part of Shikoku in summer in all the SDSM projections while DTR increased in the northern part of Shikoku in the same season in these projections, (4) the dependencies of changes in DTR on changes in CLD were unclear in summer and winter. Results of the SDSM simulations performed for climate change scenarios such as those from this study contribute to local-scale agricultural and

  17. Sensitivity of systematic biases in South Asian summer monsoon simulations to regional climate model domain size and implications for downscaled regional process studies

    NASA Astrophysics Data System (ADS)

    Karmacharya, J.; Levine, R. C.; Jones, R.; Moufouma-Okia, W.; New, M.

    2015-07-01

    Global climate models (GCMs) have good skill in simulating climate at the global scale yet they show significant systematic errors at regional scale. For example, many GCMs exhibit significant biases in South Asian summer monsoon (SASM) simulations. Those errors not only limit application of such GCM output in driving regional climate models (RCMs) over these regions but also raise questions on the usefulness of RCMs derived from those GCMs. We focus on process studies where the RCM is driven by realistic lateral boundary conditions from atmospheric re-analysis which prevents remote systematic errors from influencing the regional simulation. In this context it is pertinent to investigate whether RCMs also suffer from similar errors when run over regions where their parent models show large systematic errors. Furthermore, the general sensitivity of the RCM simulation to domain size is informative in understanding remote drivers of systematic errors in the GCM and in choosing a suitable RCM domain that minimizes those errors. We investigate Met Office Unified Model systematic errors in SASM by comparing global and regional model simulations with targeted changes to the domain and forced with atmospheric re-analysis. We show that excluding remote drivers of systematic errors from the direct area of interest allows the application of RCMs for process studies of the SASM, despite the large errors in the parent global model. The findings in this study are also relevant to other models, many of which suffer from a similar pattern of systematic errors in global model simulations of the SASM.

  18. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2010-12-01

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

  20. 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.

  1. 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.

    2012-02-01

    Rainfall is poorly modeled by general circulation models (GCMs) and requires appropriate downscaling for local-scale hydrological impact studies. Such downscaling methods should be robust and accurate (to handle, e.g., extreme events and uncertainties), but the noncontinuous and highly nonlinear nature of rainfall makes this task particularly challenging. This paper brings together and extends state-of-the-art methods into an integrated and robust probabilistic methodology 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-scale daily rainfall series. A cross-validation step ensures that the fitted models are correctly conditioned by the climate variables, and a statistical procedure is proposed to test whether the statistical relationships identified for the reference period also hold in a future perturbed climate (i.e., to test the stationarity assumption). Additionally, we propose a strategy to downweigh poorly performing GCM-GLM couples. The methodology is assessed at 27 locations covering Switzerland and is shown to perform well in reproducing historical rainfall statistics including extremes and interannual variability. Furthermore, the projections are consistent with the simulations of physically based dynamical models. Using an original visualization method based on heat maps, we show that although the downscaling models were fitted at each of the 27 sites independently, their projections follow a spatially coherent pattern and that regions exhibiting different climate change impacts can be identified.

  2. 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

  3. 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

  4. 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.

  5. 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

  6. 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

  7. 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

  8. 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.

  9. Calibrating regionally downscaled precipitation over Norway through quantile-based approaches

    NASA Astrophysics Data System (ADS)

    Bolin, David; Frigessi, Arnoldo; Guttorp, Peter; Haug, Ola; Orskaug, Elisabeth; Scheel, Ida; Wallin, Jonas

    2016-06-01

    Dynamical downscaling of earth system models is intended to produce high-resolution climate information at regional to local scales. Current models, while adequate for describing temperature distributions at relatively small scales, struggle when it comes to describing precipitation distributions. In order to better match the distribution of observed precipitation over Norway, we consider approaches to statistical adjustment of the output from a regional climate model when forced with ERA-40 reanalysis boundary conditions. As a second step, we try to correct downscalings of historical climate model runs using these transformations built from downscaled ERA-40 data. Unless such calibrations are successful, it is difficult to argue that scenario-based downscaled climate projections are realistic and useful for decision makers. We study both full quantile calibrations and several different methods that correct individual quantiles separately using random field models. Results based on cross-validation show that while a full quantile calibration is not very effective in this case, one can correct individual quantiles satisfactorily if the spatial structure in the data are accounted for. Interestingly, different methods are favoured depending on whether ERA-40 data or historical climate model runs are adjusted.

  10. Multilayer perceptron neural network for downscaling rainfall in arid region: A case study of Baluchistan, Pakistan

    NASA Astrophysics Data System (ADS)

    Ahmed, Kamal; Shahid, Shamsuddin; Haroon, Sobri Bin; Xiao-jun, Wang

    2015-08-01

    Downscaling rainfall in an arid region is much challenging compared to wet region due to erratic and infrequent behaviour of rainfall in the arid region. The complexity is further aggregated due to scarcity of data in such regions. A multilayer perceptron (MLP) neural network has been proposed in the present study for the downscaling of rainfall in the data scarce arid region of Baluchistan province of Pakistan, which is considered as one of the most vulnerable areas of Pakistan to climate change. The National Center for Environmental Prediction (NCEP) reanalysis datasets from 20 grid points surrounding the study area were used to select the predictors using principal component analysis. Monthly rainfall data for the time periods 1961-1990 and 1991-2001 were used for the calibration and validation of the MLP model, respectively. The performance of the model was assessed using various statistics including mean, variance, quartiles, root mean square error (RMSE), mean bias error (MBE), coefficient of determination (R 2) and Nash-Sutcliffe efficiency (NSE). Comparisons of mean monthly time series of observed and downscaled rainfall showed good agreement during both calibration and validation periods, while the downscaling model was found to underpredict rainfall variance in both periods. Other statistical parameters also revealed good agreement between observed and downscaled rainfall during both calibration and validation periods in most of the stations.

  11. California Reanalysis Downscaling at 10km: Implication to regional reanalysis over south Asia.

    NASA Astrophysics Data System (ADS)

    Kanamaru, H.; Kanamitsu, M.

    2006-12-01

    We have completed 57 year dynamical downscaling of NCEP/NCAR Reanalysis over California at 10km resolution (CaRD10) for the purpose of regional climate research and application. The unique feature of the downscaling method by the Regional Spectral Model is the use of Scale Selective Bias Correction (Kanamaru and Kanamitsu 2006a) which preserves the reanalysis of the scale greater than 1000km within the regional domain. The detailed validation of the analysis with station observation and comparison with the North American Regional Reanalysis (NARR) have been performed and submitted for publication (Kanamitsu and Kanamaru, 2006; Kanamaru and Kanamitsu, 2006b). The study indicated that the CaRD10 generally produce analysis better fit with observation than NARR over land, due to higher spatial resolution. Precipitation suffers from wet bias, but their temporal variation agrees well with observation on time scales ranging from hourly to decadal. Comparison of moisture budget with NARR indicated that large budget residual exists for both analyses, which makes it difficult to use them in some hydrological studies. High resolution downscaling well simulates large-scale forced meso-scale features, such as Catalina eddies and Santa Ana events. The quality of the simulations strongly depends on the model resolution. These results suggest that 1) the spatial resolution as high as 10km is desirable particularly for hydrological application, 2) our downscaling technique is an economical alternative to full regional data assimilation. When combined with assimilation of observed precipitation, it has a potential of producing analysis as good and as useful as regional data assimilation product and 3) regional data assimilation technique is not mature enough to fully utilize surface observation where spatial inhomogeneity dominates. Thus, regional data assimilation near the surface tends to give higher weight to model forecast guess and the resulting analysis becomes very similar to

  12. 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

  13. 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.

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

    NASA Astrophysics Data System (ADS)

    Göncü, S.; Albek, E.

    2015-07-01

    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.

  15. New Daily Downscaled Information at the "Bias-Corrected Downscaled WCRP CMIP3 Climate Projections" online archive

    NASA Astrophysics Data System (ADS)

    Pruitt, T.; Thrasher, B.; Das, T.; Maurer, E. P.; Duffy, P.; Long, J.; Brekke, L. D.

    2010-12-01

    Recent efforts have generated a new empirical downscaling technique that is well-positioned to inform climate change vulnerability assessments for ecosystems as well as studies on future storm and flood frequency. The technique combines bias-correction (BC) of general circulation model (GCM) outputs with a constructed analogs approach (CA) for spatially downscale the daily solutions from GCM simulations. These combined steps are referred to as BCCA. A recent methods intercomparison (Maurer et al. 2010, HESS, 14:1125-1139) shows that BCCA outperforms CA and the archive's current underlying methodology (BCSD, Wood et al. 2002) when applied to NCEP/NCAR Reanalysis. Given how BCCA is designed to translate daily sequences from GCM simulations, it offers the opportunity to provide downscaled projection information on diurnal temperature range (relevant to ecohydrological investigations) and interarrival frequencies of daily to multi-day precipitation events. The information on diurnal temperature range also has significance to watershed hydrologic studies in arid to semi-arid regions, where evapotranspiration (ET) is the dominant fate of precipitation and simulation of ET processes is sensitive to diurnal temperature range. Recognizing these benefits, archive collaborators initiated an effort to develop a daily BCCA CMIP3 data archive that complements the archive's existing monthly BCSD CMIP3 dataset. The two datasets' have the following attributes: -- Space: BCSD coverage = NLDAS domain), resolution = 1/8°; BCCA has same attributes -- Time: BCSD period = GCM-simulated 1950-2099, BCCA has three nested periods based on common availability of daily GCM outputs at PCMDI (1961-2000, 2045-2064, and 2080-2099) -- Variables: BCSD has been performed for monthly mean temperature and precipitation; BCCA has been performed for daily minimum and maximum temperature and precipitation. Presentation highlights BCCA implementation for archive expansions, illustrates key differences in

  16. 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

  17. 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.

  18. Dynamical downscaling of short-term climate fluctuations: On the benefits of precipitation assimilation

    NASA Astrophysics Data System (ADS)

    Nunes, Ana M. B.; Roads, John O.

    2009-06-01

    Regional downscaling has proven useful in adding details to the global solution. However, the parameterized physical processes can systematically deviate the large-scale features in the regional solution. To demonstrate the precipitation assimilation beneficial impact on the dynamical downscaling, a regional spectral model driven by the National Centers for Environmental Prediction/Department of Energy Atmospheric Model Intercomparison Project II (NCEP/DOE AMIP-II) Reanalysis was used to downscale the large-scale features over most of North America. The North American Regional Reanalysis provided the 3-hourly precipitation rates that the regional model employed to simulate two opposite extreme climate events: the upper Mississippi River Basin 1988 drought and 1993 floods. In addition to these two cases, the 1990 summer anomalous precipitation over the same area was also investigated. Precipitation assimilation positively influences the dynamical downscaling of these extreme climate events. The regional model when assimilating precipitation was particularly successful in reproducing the observed precipitation patterns over the central United States, where the large-scale circulation affects the precipitation variability. Particularly for the flood year, the intensity and location of the subtropical upper-level westerly jet and its associated transverse circulations were noticeably improved in the regional simulations, where the heavy precipitation core was found. This also suggests that the cumulus convection scheme, in this case the Relaxed Arakawa-Schubert parameterization scheme, can cause the large-scale features to drift during the regional simulation, and precipitation assimilation reduces this departure from the global solution. These changes in the upper-level winds were also followed by better characterization of the drought of 1988 as well as the 1990 summer heavy precipitation simulation, in comparison to regional control simulations, where precipitation

  19. Present and LGM permafrost from climate simulations: contribution of statistical downscaling

    NASA Astrophysics Data System (ADS)

    Levavasseur, G.; Vrac, M.; Roche, D. M.; Paillard, D.; Martin, A.; Vandenberghe, J.

    2010-10-01

    We quantify the agreement between permafrost distributions from PMIP2 (Paleoclimate Modeling Intercomparison Project) climate models and permafrost data. We evaluate the ability of several climate models to represent permafrost and assess the inter-variability between them. Studying an heterogeneous variable such as permafrost implies to conduct analysis at a smaller spatial scale compared with climate models resolution. Our approach consists in applying statistical downscaling methods (SDMs) on large- or regional-scale atmospheric variables provided by climate models, leading to local permafrost modelling. Among the SDMs, we first choose a transfer function approach based on Generalized Additive Models (GAMs) to produce high-resolution climatology of surface air temperature (SAT). Then, we define permafrost distribution over Eurasia by SAT conditions. In a first validation step on present climate (CTRL period), GAM shows some limitations with non-systemic improvements in comparison with the large-scale fields. So, we develop an alternative method of statistical downscaling based on a stochastic generator approach through a Multinomial Logistic Regression (MLR), which directly models the probabilities of local permafrost indices. The obtained permafrost distributions appear in a better agreement with data. In both cases, the provided local information reduces the inter-variability between climate models. Nevertheless, this also proves that a simple relationship between permafrost and the SAT only is not always sufficient to represent local permafrost. Finally, we apply each method on a very different climate, the Last Glacial Maximum (LGM) time period, in order to quantify the ability of climate models to represent LGM permafrost. Our SDMs do not significantly improve permafrost distribution and do not reduce the inter-variability between climate models, at this period. We show that LGM permafrost distribution from climate models strongly depends on large-scale SAT

  20. 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

  1. 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.

  2. 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.

  3. Biases and improvements in three dynamical downscaling climate simulations over China

    NASA Astrophysics Data System (ADS)

    Yang, Hao; Jiang, Zhihong; Li, Laurent

    2016-02-01

    A dynamical downscaling is performed to improve the regional climate simulation in China. It consists of using a variable resolution model LMDZ4 nested into three global climate models (GCMs): BCC-csm1-1-m, FGOALS-g2 and IPSL-CM5A-MR, respectively. The regional climate from different simulations is assessed in terms of surface air temperature and rainfalls through a comparison to observations (both station data and gridded data). The comparison includes climatic trends during the last 40 years, statistical distribution of sub-regional climate, and the seasonal cycle. For surface air temperature, a significant part of the improvement provided by LMDZ4 is related to the effect of surface elevation which is more realistic in high-resolution simulations; the rest is related to changes in regional or local atmospheric general circulation. All GCMs and the downscaling model LMDZ4 are, more or less, able to describe the spatial distribution of surface air temperature and precipitation in China. LMDZ4 does show its superiority, compared to GCMs, in depicting a good regional terrain including the Tibetan Plateau, the Sichuan Basin and the Qilian Mountains.

  4. Statistical Downscaling: Lessons Learned

    NASA Astrophysics Data System (ADS)

    Walton, D.; Hall, A. D.; Sun, F.

    2013-12-01

    In this study, we examine ways to improve statistical downscaling of general circulation model (GCM) output. Why do we downscale GCM output? GCMs have low resolution, so they cannot represent local dynamics and topographic effects that cause spatial heterogeneity in the regional climate change signal. Statistical downscaling recovers fine-scale information by utilizing relationships between the large-scale and fine-scale signals to bridge this gap. In theory, the downscaled climate change signal is more credible and accurate than its GCM counterpart, but in practice, there may be little improvement. Here, we tackle the practical problems that arise in statistical downscaling, using temperature change over the Los Angeles region as a test case. This region is an ideal place to apply downscaling since its complex topography and shoreline are poorly simulated by GCMs. By comparing two popular statistical downscaling methods and one dynamical downscaling method, we identify issues with statistically downscaled climate change signals and develop ways to fix them. We focus on scale mismatch, domain of influence, and other problems - many of which users may be unaware of - and discuss practical solutions.

  5. 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.

  6. Climate change projection in the Northwest Pacific marginal seas through dynamic downscaling

    NASA Astrophysics Data System (ADS)

    Seo, Gwang-Ho; Cho, Yang-Ki; Choi, Byoung-Ju; Kim, Kwang-Yul; Kim, Bong-guk; Tak, Yong-jin

    2014-06-01

    This study presents future climate change projections in the Northwest Pacific (NWP) marginal seas using dynamic downscaling from global climate models (GCMs). A regional climate model (RCM) for the Northwest Pacific Ocean was setup and integrated over the period from 2001 to 2100. The model used forcing fields from three different GCM simulations to downscale the effect of global climate change. MIROC, ECHAM, and HADCM were selected to provide climate change signals for the RCM. These signals were calculated from the GCMs using Cyclostationary Empirical Orthogonal Function analysis and added to the present lateral open boundary and the surface forcing. The RCM was validated by comparing hindcast result with the observation. It was able to project detailed regional climate change processes that GCMs were not able to resolve. A relatively large increases of water temperature were found in the marginal seas. However, only a marginal change was found along the Kuroshio path. Heat supply to the atmosphere decreases in most study areas due to a slower warming of the sea surface compared to the atmosphere. The RCM projection suggests that the temperature of the Yellow Sea Bottom Cold Water will gradually increase by 2100. Volume transports through major straits except the Taiwan Strait in the marginal seas are projected to increase slightly in future. Increased northeasterly wind stress in the East China Sea may also result in the transport change.

  7. The regional MiKlip decadal forecast ensemble for Europe: the added value of downscaling

    NASA Astrophysics Data System (ADS)

    Mieruch, S.; Feldmann, H.; Schädler, G.; Lenz, C.-J.; Kothe, S.; Kottmeier, C.

    2014-12-01

    The prediction of climate on time scales of years to decades is attracting the interest of both climate researchers and stakeholders. The German Ministry for Education and Research (BMBF) has launched a major research programme on decadal climate prediction called MiKlip (Mittelfristige Klimaprognosen, Decadal Climate Prediction) in order to investigate the prediction potential of global and regional climate models (RCMs). In this paper we describe a regional predictive hindcast ensemble, its validation, and the added value of regional downscaling. Global predictions are obtained from an ensemble of simulations by the MPI-ESM-LR model (baseline 0 runs), which were downscaled for Europe using the COSMO-CLM regional model. Decadal hindcasts were produced for the 5 decades starting in 1961 until 2001. Observations were taken from the E-OBS data set. To identify decadal variability and predictability, we removed the long-term mean, as well as the long-term linear trend from the data. We split the resulting anomaly time series into two parts, the first including lead times of 1-5 years, reflecting the skill which originates mainly from the initialisation, and the second including lead times from 6-10 years, which are more related to the representation of low frequency climate variability and the effects of external forcing. We investigated temperature averages and precipitation sums for the summer and winter half-year. Skill assessment was based on correlation coefficient and reliability. We found that regional downscaling preserves, but mostly does not improve the skill and the reliability of the global predictions for summer half-year temperature anomalies. In contrast, regionalisation improves global decadal predictions of half-year precipitation sums in most parts of Europe. The added value results from an increased predictive skill on grid-point basis together with an improvement of the ensemble spread, i.e. the reliability.

  8. 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

  9. 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.

  10. 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.

  11. 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.

  12. 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

    2015-10-01

    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.

  13. 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.

  14. Assessing Hydrological and Energy Budgets in Amazonia through Regional Downscaling, and Comparisons with Global Reanalysis Products

    NASA Astrophysics Data System (ADS)

    Nunes, A.; Ivanov, V. Y.

    2014-12-01

    Although current global reanalyses provide reasonably accurate large-scale features of the atmosphere, systematic errors are still found in the hydrological and energy budgets of such products. In the tropics, precipitation is particularly challenging to model, which is also adversely affected by the scarcity of hydrometeorological datasets in the region. With the goal of producing downscaled analyses that are appropriate for a climate assessment at regional scales, a regional spectral model has used a combination of precipitation assimilation with scale-selective bias correction. The latter is similar to the spectral nudging technique, which prevents the departure of the regional model's internal states from the large-scale forcing. The target area in this study is the Amazon region, where large errors are detected in reanalysis precipitation. To generate the downscaled analysis, the regional climate model used NCEP/DOE R2 global reanalysis as the initial and lateral boundary conditions, and assimilated NOAA's Climate Prediction Center (CPC) MORPHed precipitation (CMORPH), available at 0.25-degree resolution, every 3 hours. The regional model's precipitation was successfully brought closer to the observations, in comparison to the NCEP global reanalysis products, as a result of the impact of a precipitation assimilation scheme on cumulus-convection parameterization, and improved boundary forcing achieved through a new version of scale-selective bias correction. Water and energy budget terms were also evaluated against global reanalyses and other datasets.

  15. Present and LGM permafrost from climate simulations: contribution of statistical downscaling

    NASA Astrophysics Data System (ADS)

    Levavasseur, G.; Vrac, M.; Roche, D. M.; Paillard, D.; Martin, A.; Vandenberghe, J.

    2011-11-01

    We quantify the agreement between permafrost distributions from PMIP2 (Paleoclimate Modeling Intercomparison Project) climate models and permafrost data. We evaluate the ability of several climate models to represent permafrost and assess the variability between their results. Studying a heterogeneous variable such as permafrost implies conducting analysis at a smaller spatial scale compared with climate models resolution. Our approach consists of applying statistical downscaling methods (SDMs) on large- or regional-scale atmospheric variables provided by climate models, leading to local-scale permafrost modelling. Among the SDMs, we first choose a transfer function approach based on Generalized Additive Models (GAMs) to produce high-resolution climatology of air temperature at the surface. Then we define permafrost distribution over Eurasia by air temperature conditions. In a first validation step on present climate (CTRL period), this method shows some limitations with non-systematic improvements in comparison with the large-scale fields. So, we develop an alternative method of statistical downscaling based on a Multinomial Logistic GAM (ML-GAM), which directly predicts the occurrence probabilities of local-scale permafrost. The obtained permafrost distributions appear in a better agreement with CTRL data. In average for the nine PMIP2 models, we measure a global agreement with CTRL permafrost data that is better when using ML-GAM than when applying the GAM method with air temperature conditions. In both cases, the provided local information reduces the variability between climate models results. This also confirms that a simple relationship between permafrost and the air temperature only is not always sufficient to represent local-scale permafrost. Finally, we apply each method on a very different climate, the Last Glacial Maximum (LGM) time period, in order to quantify the ability of climate models to represent LGM permafrost. The prediction of the SDMs (GAM

  16. 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

  17. 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. PMID:23836646

  18. 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.

  19. 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

  20. A comparison of downscaled and raw GCM output: implications for climate change scenarios in the San Juan River basin, Colorado

    NASA Astrophysics Data System (ADS)

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

    1999-11-01

    The fundamental rationale for statistical downscaling is that the raw outputs of climate change experiments from General Circulation Models (GCMs) are an inadequate basis for assessing the effects of climate change on land-surface processes at regional scales. This is because the spatial resolution of GCMs is too coarse to resolve important sub-grid scale processes (most notably those pertaining to the hydrological cycle) and because GCM output is often unreliable at individual and sub-grid box scales. By establishing empirical relationships between grid-box scale circulation indices (such as atmospheric vorticity and divergence) and sub-grid scale surface predictands (such as precipitation), statistical downscaling has been proposed as a practical means of bridging this spatial difference. This study compared three sets of current and future rainfall-runoff scenarios. The scenarios were constructed using: (1) statistically downscaled GCM output; (2) raw GCM output; and (3) raw GCM output corrected for elevational biases. Atmospheric circulation indices and humidity variables were extracted from the output of the UK Meteorological Office coupled ocean-atmosphere GCM (HadCM2) in order to downscale daily precipitation and temperature series for the Animas River in the San Juan River basin, Colorado. Significant differences arose between the modelled snowpack and flow regimes of the three future climate scenarios. Overall, the raw GCM output suggests larger reductions in winter/spring snowpack and summer runoff than the downscaling, relative to current conditions. Further research is required to determine the generality of the water resource implications for other regions, GCM outputs and downscaled scenarios.

  1. Wave climate projections along the French coastline: Dynamical versus statistical downscaling methods

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

    The estimation of possible impacts related to climate change on the wave climate is subject to several levels of uncertainty. In this work, we focus on the uncertainties inherent in the method applied to project the wave climate using atmospheric simulations. Two approaches are commonly used to obtain the regional wave climate: dynamical and statistical downscaling from atmospheric data. We apply both approaches based on the outputs of a global climate model (GCM), ARPEGE-CLIMAT, under three possible future scenarios (B1, A1B and A2) of the Fourth Assessment Report, AR4 (IPCC, 2007), along the French coast and evaluate their results for the wave climate with a high level of precision. The performance of the dynamical and the statistical methods is determined through a comparative analysis of the estimated means, standard deviations and monthly quantile distributions of significant wave heights, the joint probability distributions of wave parameters and seasonal and interannual variability. Analysis of the results shows that the statistical projections are able to reproduce the wave climatology as well as the dynamical projections, with some deficiencies being observed in the summer and for the upper tail of the significant wave height. In addition, with its low computational time requirements, the statistical downscaling method allows an ensemble of simulations to be calculated faster than the dynamical method. It then becomes possible to quantify the uncertainties associated with the choice of the GCM or the socio-economic scenarios, which will improve estimates of the impact of wave climate change along the French coast.

  2. 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

  3. 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

  4. 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

  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. Comparison of statistical and dynamical downscaling of extreme precipitations over France in present-day and future climate

    NASA Astrophysics Data System (ADS)

    Colin, Jeanne; Déqué, Michel; Sanchez Gomez, Emila; Somot, Samuel

    2010-05-01

    We present a comparison of two downscaling methods of extreme precipitations over France at a climatic time scale : a dynamical one performed with the Regional Climate Model ALADIN-Climate used at a resolution of 12 km, and a statistical one based on the weather regime approach and using the analog methodology to reconstruct daily fields of precipitations at a 8 km resolution. We focus on the most heavy precipitations of the area of interest, which occur in southeastern France in Autumn. Those involve small-scale processes than can be explicitly resolved only with 2-1 km resolution non-hydrostatic models. However, such models can not be used for climate simulations because of their computational cost is still too high. Yet these extreme events cause rather heavy damages, so that their possible evolution in the context of climate change is of great concern. Thus, there is strong need in assessing downscaling methods' ability to represent them. First, we downscale the low-resolution ERA40 re-analysis over the 1958-2000 time period with ALADIN-Climate, and from the year 1980 to the year 2000 with the statistical method. Then, we apply a quantile-quantile correction to the daily precipitations of the last twenty years of the ALADIN-Climate simulation. The correction rates are computed over the first part of the simulation (1958-1979) using a high-resolution gridded database : the SAFRAN analysis, which provides series of hourly fields for the 1958-2008 period over the french territory at a 8 km resolution. We assess the performances of each downscaling method in present-day climate by comparing the simulated precipitations to the SAFRAN database. The use of the ERA40 re-analysis allows to reproduce the real chronology in both downscalings, which enables to analyze the results not only from a statistical point of view but also through day-to-day diagnosis such as time correlations or spatial patterns of rain for given extreme events. Secondly, we apply these downscaling

  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. 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.

  11. Precipitation and temperature space-time variability and extremes in the Mediterranean region: evaluation of dynamical and statistical downscaling methods

    NASA Astrophysics Data System (ADS)

    Flaounas, Emmanouil; Drobinski, Philippe; Vrac, Mathieu; Bastin, Sophie; Lebeaupin-Brossier, Cindy; Stéfanon, Marc; Borga, Marco; Calvet, Jean-Christophe

    2013-06-01

    This study evaluates how statistical and dynamical downscaling models as well as combined approach perform in retrieving the space-time variability of near-surface temperature and rainfall, as well as their extremes, over the whole Mediterranean region. The dynamical downscaling model used in this study is the Weather Research and Forecasting (WRF) model with varying land-surface models and resolutions (20 and 50 km) and the statistical tool is the Cumulative Distribution Function-transform (CDF-t). To achieve a spatially resolved downscaling over the Mediterranean basin, the European Climate Assessment and Dataset (ECA&D) gridded dataset is used for calibration and evaluation of the downscaling models. In the frame of HyMeX and MED-CORDEX international programs, the downscaling is performed on ERA-I reanalysis over the 1989-2008 period. The results show that despite local calibration, CDF-t produces more accurate spatial variability of near-surface temperature and rainfall with respect to ECA&D than WRF which solves the three-dimensional equation of conservation. This first suggests that at 20-50 km resolutions, these three-dimensional processes only weakly contribute to the local value of temperature and precipitation with respect to local one-dimensional processes. Calibration of CDF-t at each individual grid point is thus sufficient to reproduce accurately the spatial pattern. A second explanation is the use of gridded data such as ECA&D which smoothes in part the horizontal variability after data interpolation and damps the added value of dynamical downscaling. This explains partly the absence of added-value of the 2-stage downscaling approach which combines statistical and dynamical downscaling models. The temporal variability of statistically downscaled temperature and rainfall is finally strongly driven by the temporal variability of its forcing (here ERA-Interim or WRF simulations). CDF-t is thus efficient as a bias correction tool but does not show any

  12. Assessment of climate change impacts on rainfall using large scale climate variables and downscaling models - A case study

    NASA Astrophysics Data System (ADS)

    Ahmadi, Azadeh; Moridi, Ali; Lafdani, Elham Kakaei; Kianpisheh, Ghasem

    2014-10-01

    Many of the applied techniques in water resources management can be directly or indirectly influenced by hydro-climatology predictions. In recent decades, utilizing the large scale climate variables as predictors of hydrological phenomena and downscaling numerical weather ensemble forecasts has revolutionized the long-lead predictions. In this study, two types of rainfall prediction models are developed to predict the rainfall of the Zayandehrood dam basin located in the central part of Iran. The first seasonal model is based on large scale climate signals data around the world. In order to determine the inputs of the seasonal rainfall prediction model, the correlation coefficient analysis and the new Gamma Test (GT) method are utilized. Comparison of modelling results shows that the Gamma test method improves the Nash-Sutcliffe efficiency coefficient of modelling performance as 8% and 10% for dry and wet seasons, respectively. In this study, Support Vector Machine (SVM) model for predicting rainfall in the region has been used and its results are compared with the benchmark models such as K-nearest neighbours (KNN) and Artificial Neural Network (ANN). The results show better performance of the SVM model at testing stage. In the second model, statistical downscaling model (SDSM) as a popular downscaling tool has been used. In this model, using the outputs from GCM, the rainfall of Zayandehrood dam is projected under two climate change scenarios. Most effective variables have been identified among 26 predictor variables. Comparison of the results of the two models shows that the developed SVM model has lesser errors in monthly rainfall estimation. The results show that the rainfall in the future wet periods are more than historical values and it is lower than historical values in the dry periods. The highest monthly uncertainty of future rainfall occurs in March and the lowest in July.

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

    NASA Astrophysics Data System (ADS)

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

    2007-12-01

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

  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. 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

  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. Wave climate projections using statistical downscaling for the Gold Coast (Australia)

    NASA Astrophysics Data System (ADS)

    Rueda, Ana; Camus, Paula; Méndez, Fernando; Sano, Marcello; Strauss, Darrel; Hemer, Mark

    2013-04-01

    Projections of future wave climate at the regional level are essential to develop climate change adaptation strategies for coastal areas. In our research we looked at wave climate projections along the Gold Coast, with a detailed assessment for Palm Beach, one of the most problematic coastal stretches. We adopted a statistical downscaling approach which is based on the statistical relationship between a local wave variable (predictand) and a global atmospheric variable (predictor). This is an efficient method to project regional wave climate based on the output of General Circulation Models (GCMs) forced by different emission scenarios, the main source of information of possible future climates. The methodology used relies on data availability for the area of study. In this case we used sea level pressure fields from 1 h x 0.5° resolution CFSR reanalysis to define the predictor. A CSIRO 1° spatial resolution wave hindcast was chosen to define the predictand; this was particularly reliable due to its long-term directional spectral information. A hybrid methodology was used before statistical downscaling to transfer wave climate to the study area as the CSIRO wave reanalysis was not available at high resolution in shallow water. In our method, the predictor is defined by the dynamical spatial patterns of atmospheric conditions considering the local area and the wave generation area in order to take into account the swell and sea wave components. A daily atmospheric field database is developed and classified in circulation patterns (weather types) using PCA and the k-means algorithm. The corresponding predictand are the sea states at the coastal area (Hs, Tm, ? and directional spectra). The total wave distribution at the target point can be reconstructed from the distribution of sea states and its corresponding probability of each weather type. This method allows estimating how local wave climate can be affected by changes on the atmospheric patterns, calculating

  19. Uncertainty analysis of statistical downscaling methods using Canadian Global Climate Model predictors

    NASA Astrophysics Data System (ADS)

    Khan, Mohammad Sajjad; Coulibaly, Paulin; Dibike, Yonas

    2006-09-01

    Three downscaling models, namely the Statistical Down-Scaling Model (SDSM), the Long Ashton Research Station Weather Generator (LARS-WG) model and an artificial neural network (ANN) model, have been compared in terms of various uncertainty attributes exhibited in their downscaled results of daily precipitation, daily maximum and minimum temperature. The uncertainty attributes are described by the model errors and the 95% confidence intervals in the estimates of means and variances of downscaled data. The significance of those errors has been examined by suitable statistical tests at the 95% confidence level. The 95% confidence intervals in the estimates of means and variances of downscaled data have been estimated using the bootstrapping method and compared with the observed data. The study has been carried out using 40 years of observed and downscaled daily precipitation data and daily maximum and minimum temperature data, starting from 1961 to 2000. In all the downscaling experiments, the simulated predictors of the Canadian Global Climate Model (CGCM1) have been used. The uncertainty assessment results indicate that, in daily precipitation downscaling, the LARS-WG model errors are significant at the 95% confidence level only in a very few months, the SDSM errors are significant in some months, and the ANN model errors are significant in almost all months of the year. In downscaling daily maximum and minimum temperature, the performance of all three models is similar in terms of model errors evaluation at the 95% confidence level. But, according to the evaluation of variability and uncertainty in the estimates of means and variances of downscaled precipitation and temperature, the performances of the LARS-WG model and the SDSM are almost similar, whereas the ANN model performance is found to be poor in that consideration. Further assessment of those models, in terms of skewness and average dry-spell length comparison between observed and downscaled daily

  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. A Dynamical Downscaling study over the Great Lakes Region Using WRF-Lake: Historical Simulation

    NASA Astrophysics Data System (ADS)

    Xiao, C.; Lofgren, B. M.

    2014-12-01

    As the largest group of fresh water bodies on Earth, the Laurentian Great Lakes have significant influence on local and regional weather and climate through their unique physical features compared with the surrounding land. Due to the limited spatial resolution and computational efficiency of general circulation models (GCMs), the Great Lakes are geometrically ignored or idealized into several grid cells in GCMs. Thus, the nested regional climate modeling (RCM) technique, known as dynamical downscaling, serves as a feasible solution to fill the gap. The latest Weather Research and Forecasting model (WRF) is employed to dynamically downscale the historical simulation produced by the Geophysical Fluid Dynamics Laboratory-Coupled Model (GFDL-CM3) from 1970-2005. An updated lake scheme originated from the Community Land Model is implemented in the latest WRF version 3.6. It is a one-dimensional mass and energy balance scheme with 20-25 model layers, including up to 5 snow layers on the lake ice, 10 water layers, and 10 soil layers on the lake bottom. The lake scheme is used with actual lake points and lake depth. The preliminary results show that WRF-Lake model, with a fine horizontal resolution and realistic lake representation, provides significantly improved hydroclimates, in terms of lake surface temperature, annual cycle of precipitation, ice content, and lake-effect snowfall. Those improvements suggest that better resolution of the lakes and the mesoscale process of lake-atmosphere interaction are crucial to understanding the climate and climate change in the Great Lakes region.

  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. 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.

  7. A Comprehensive Framework for Quantitative Evaluation of Downscaled Climate Predictions and Projections

    NASA Astrophysics Data System (ADS)

    Barsugli, J. J.; Guentchev, G.

    2012-12-01

    The variety of methods used for downscaling climate predictions and projections is large and growing larger. Comparative studies of downscaling techniques to date are often initiated in relation to specific projects, are focused on limited sets of downscaling techniques, and hence do not allow for easy comparison of outcomes. In addition, existing information about the quality of downscaled datasets is not available in digital form. There is a strong need for systematic evaluation of downscaling methods using standard protocols which will allow for a fair comparison of their advantages and disadvantages with respect to specific user needs. The National Climate Predictions and Projections platform, with the contributions of NCPP's Climate Science Advisory Team, is developing community-based standards and a prototype framework for the quantitative evaluation of downscaling techniques and datasets. Certain principles guide the development of this framework. We want the evaluation procedures to be reproducible and transparent, simple to understand, and straightforward to implement. To this end we propose a set of open standards that will include the use of specific data sets, time periods of analysis, evaluation protocols, evaluation tests and metrics. Secondly, we want the framework to be flexible and extensible to downscaling techniques which may be developed in the future, to high-resolution global models, and to evaluations that are meaningful for additional applications and sectors. Collaboration among practitioners who will be using the downscaled data and climate scientists who develop downscaling methods will therefore be essential to the development of this framework. The proposed framework consists of three analysis protocols, along with two tiers of specific metrics and indices that are to be calculated. The protocols describe the following types of evaluation that can be performed: 1) comparison to observations, 2) comparison to a "perfect model" simulation

  8. Downscaling large-scale circulation to local winter climate using neural network techniques

    NASA Astrophysics Data System (ADS)

    Cavazos Perez, Maria Tereza

    1998-12-01

    The severe impacts of climate variability on society reveal the increasing need for improving regional-scale climate diagnosis. A new downscaling approach for climate diagnosis is developed here. It is based on neural network techniques that derive transfer functions from the large-scale atmospheric controls to the local winter climate in northeastern Mexico and southeastern Texas during the 1985-93 period. A first neural network (NN) model employs time-lagged component scores from a rotated principal component analysis of SLP, 500-hPa heights, and 1000-500 hPa thickness as predictors of daily precipitation. The model is able to reproduce the phase and, to some decree, the amplitude of large rainfall events, reflecting the influence of the large-scale circulation. Large errors are found over the Sierra Madre, over the Gulf of Mexico, and during El Nino events, suggesting an increase in the importance of meso-scale rainfall processes. However, errors are also due to the lack of randomization of the input data and the absence of local atmospheric predictors such as moisture. Thus, a second NN model uses time-lagged specific humidity at the Earth's surface and at the 700 hPa level, SLP tendency, and 700-500 hPa thickness as input to a self-organizing map (SOM) that pre-classifies the atmospheric fields into different patterns. The results from the SOM classification document that negative (positive) anomalies of winter precipitation over the region are associated with: (1) weaker (stronger) Aleutian low; (2) stronger (weaker) North Pacific high; (3) negative (positive) phase of the Pacific North American pattern; and (4) La Nina (El Nino) events. The SOM atmospheric patterns are then used as input to a feed-forward NN that captures over 60% of the daily rainfall variance and 94% of the daily minimum temperature variance over the region. This demonstrates the ability of artificial neural network models to simulate realistic relationships on daily time scales. The

  9. 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...

  10. 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

  11. 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

  12. Stochastic Simulation of Rainfall Data Using a Markov Chain Model Calibrated to Dynamically Downscaled Climate Data

    NASA Astrophysics Data System (ADS)

    Chowdhury, A. F. M. K.; Lockart, N.; Willgoose, G. R.; Kuczera, G. A.; Nadeeka, P. M.

    2014-12-01

    This study used high resolution spatially distributed rainfall data produced by NSW/ACT Regional Climate Modelling (NARCliM) project. NARCliM dynamically downscaled four Global Climate Models using three Regional Climate Models within the Weather Research and Forecasting model to generate gridded climate data at 10 km spatial resolution for south eastern Australia. These dataset are being used in this project to evaluate the urban water security of reservoirs on the east coast of Australia. A stochastic model to simulate rainfall series was developed for runoff generation using parameters calibrated to NARCliM. This study has developed a Markov Chain model, which simulates the occurrence of daily rainfall using the transition probability of dry and wet days, while the precipitation for the wet days are generated using a two parameter gamma distribution. We have identified significant seasonal and intra- to inter-decadal variations of the model parameters at our field site. Incorporating the temporal variability (for instance, calibrating the model parameters to each decade independently), we have found that the model satisfactorily preserves the daily, monthly and annual characteristics of the NARCliM rainfall. In addition to the temporal variability, we have observed that the model parameters vary spatially at our site with potential orographic effects that vary both seasonally and decadally. However, the parameters of the model fitted to the NARCliM rainfall are significantly different from the parameters fitted to the ground-based climate station rainfall. Suitability of the model for the generation of long time series (e.g. 1000 years) required for reservoir simulation will be discussed.

  13. 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.

  14. Climate Change Projections Using Regional Regression Models

    NASA Astrophysics Data System (ADS)

    Griffis, V. W.; Gyawali, R.; Watkins, D. W.

    2012-12-01

    A typical approach to project climate change impacts on water resources systems is to downscale general circulation model (GCM) or regional climate model (RCM) outputs as forcing data for a watershed model. With downscaled climate model outputs becoming readily available, multi-model ensemble approaches incorporating mutliple GCMs, multiple emissions scenarios and multiple initializations are increasingly being used. While these multi-model climate ensembles represent a range of plausible futures, different hydrologic models and methods may complicate impact assessment. In particular, associated loss, flow routing, snowmelt and evapotranspiration computation methods can markedly increase hydrological modeling uncertainty. Other challenges include properly calibrating and verifying the watershed model and maintaining a consistent energy budget between climate and hydrologic models. An alternative approach, particularly appealing for ungauged basins or locations where record lengths are short, is to directly predict selected streamflow quantiles from regional regression equations that include physical basin characteristics as well as meteorological variables output by climate models (Fennessey 2011). Two sets of regional regression models are developed for the Great Lakes states using ordinary least squares and weighted least squares regression. The regional regression modeling approach is compared with physically based hydrologic modeling approaches for selected Great Lakes watersheds using downscaled outputs from the Coupled Model Intercomparison Project (CMIP3) as inputs to the Large Basin Runoff Model (LBRM) and the U.S. Army Corps Hydrologic Modeling System (HEC-HMS).

  15. High resolution downscaling with WRF: reproducing observed climate in high topography islands

    NASA Astrophysics Data System (ADS)

    Miranda, P. M.; Tome, R.; Azevedo, E. B.; Teixeira, M.

    2013-12-01

    Isolated islands are specially vulnerable to climate change. However, their climate is generally not explicitly reproduced in GCMs, or even in most Regional Climate Models, due to their size and complex topography. On the other hand, the isolated nature of their location may allow the use of high resolution in domains of limited size, with oceanic boundary conditions all around directly given by a GCM. It is important to know, though, how far do we need to go in horizontal resolution in order to reproduce the main features of observed climate and if the proposed method has significant advantages in relation to simpler procedures. This paper uses the WRF model to downscale global fields given by ERA-Interim and by three runs of the EC-Earth Climate Model (Hazeleger et al 2010): a control run representing the 1961-1990 climate, and two scenario runs corresponding to scenarios RCP4.5 and RCP8.5 up to the end of the 21st century. The WRF simulations builds on experience reproducing the climate in Iberia, at 9km horizontal resolution (Soares et al 2012, Cardoso et al 2013), which resulted in a good match with observations not only in what concerns the mean values of temperature and precipitation, but also the statistical distribution of high rank quantiles of daily precipitation (up to percentile 99.9). Here the WRF model is used on a nested grid configuration, with a larger domain simulated at 27km resolution and an inner domain at 6km. The cases of Madeira and Azores, 11 islands of different sizes in the subtropical North Atlantic, are simulated. Broadly speaking, results indicate significant improvements in the representation of observed precipitation in all islands in the ERA-Interim period, at the highest resolution. In the case of Madeira, the largest and bulkiest of the set, the improvement is the most remarkable, whereas in smaller islands there is a suggestion that the used resolution is still too coarse. The excellent results obtained by WRF in the Madeira ERA

  16. Climate downscaling: Local mean sea-level rise, surge and wave modelling

    NASA Astrophysics Data System (ADS)

    Wolf, J.; Lowe, J.; Howard, T.

    2012-04-01

    The investigation of future climate impacts at the coast requires sufficiently detailed projections for the nearshore waves and sea levels in both the present day and a future climate scenario, to provide an offshore boundary condition. Here we discuss the future changes in surge and wave climate forced by winds and pressures from a version of the Met Office Hadley Centre Climate model, for various greenhouse gas emission scenarios and for various climate model parameter choices. The local spatial variation in mean sea level is also taken into account, incorporating deviations from global mean sea level change caused by regional variations in ocean density and circulation. Some parts of the UK are still subject to glacial isostatic readjustment after the last ice age, counter-acting sea level rise, although this will be overwhelmed by the projected effects of sea level rise due to global warming in the 21st century, for most future emission scenarios. Model downscaling from the global coupled atmosphere-ocean model using a regional climate model is needed to provide more realistic and detailed wind simulations over the NW European continental shelf. There is large uncertainty in projected changes in storminess for the NE Atlantic region, with different climate models providing conflicting results for the future. Results from this study show that large increases in mean sea level (even up to 5 metres) have very little effect on the dynamics of extreme surge events, the primary effect being on the speed of propagation of tide and surge (Howard et al., 2010). Increasing storminess is expected to increase surge heights but more direct effects can be attributed directly to increased mean sea level. Based on the wave model results, seasonal mean and annual maximum wave heights are generally expected to increase to the SW of the UK, reduce to the north of the UK and experience little change in the southern North Sea or eastern Irish Sea. This pattern is consistent with a

  17. Toward Robust and Efficient Climate Downscaling for Wind Energy

    NASA Astrophysics Data System (ADS)

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

    2011-12-01

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

  18. 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

  19. Nonparametric statistical temporal downscaling of daily precipitation to hourly precipitation and implications for climate change scenarios

    NASA Astrophysics Data System (ADS)

    Lee, Taesam; Jeong, Changsam

    2014-03-01

    Hydro-meteorological time series on finer temporal scales, such as hourly, are essential for assessing the hydrological effects of land use or climate change on medium and small watersheds. However, these time series are, in general, available at no finer than daily time intervals. An alternative method of obtaining finer time series is temporal downscaling of daily time series to hourly time series. In the current study, a temporal downscaling model that combines a nonparametric stochastic simulation approach with a genetic algorithm is proposed. The proposed model was applied to Jinju station in South Korea for a historical time period to validate the model performance. The results revealed that the proposed model preserves the key statistics (i.e., the mean, standard deviation, skewness, lag-1 correlation, and maximum) of the historical hourly precipitation data. In addition, the occurrence and transition probabilities are well preserved in the downscaled hourly precipitation data. Furthermore, the RCP 4.5 and RCP 8.5 climate scenarios for the Jinju station were also analyzed, revealing that the mean and the wet-hour probability (P1) significantly increased and the standard deviation and maximum slightly increased in these scenarios. The magnitude of the increase was greater in RCP 8.5 than RCP 4.5. Extreme events of different durations were also tested. The downscaled hourly precipitation adequately reproduced the statistical behavior of the extremes of the historical hourly precipitation data for all durations considered. However, the inter-daily relation between the 1st hour of the present day and the last hour of the previous day was not preserved. Overall, the results demonstrated that the proposed temporal downscaling model is a good alternative method for downscaling simulated daily precipitation data from weather generators or RCM outputs.

  20. 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

  1. 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. PMID:26146163

  2. 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

  3. 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.

  4. 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

  5. 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.

  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. Dynamical downscaling of IPSL-CM5 CMIP5 historical simulations over the Mediterranean: benefits on the representation of regional surface winds and cyclogenesis

    NASA Astrophysics Data System (ADS)

    Flaounas, Emmanouil; Drobinski, Philippe; Bastin, Sophie

    2013-05-01

    The Mediterranean region is identified as one of the two main hot-spots of climate change and also known to have the highest concentration of cyclones in the world. These atmospheric features contribute significantly to the regional climate but they are not reproduced by the Atmosphere-Ocean General Circulation Models (AOGCM), due to their coarse horizontal resolution, which have recently been run in the frame of the 5th Climate Model Intercomparison Project. This article investigates the benefit of dynamically downscaling the Institut Pierre Simon Laplace (IPSL) AOGCM (IPSL-CM5) historical simulation by the weather and research forecasting (WRF) for the representation of the Mediterranean surface winds and cyclonic activity. Indeed, when considering IPSL-CM5 atmospheric fields, the dramatic underestimation of the cyclonic activity in the most cyclogenetic region of the world jeopardizes our ability to investigate in-depth the Mediterranean regional climate and trend in the context of global change. The WRF model shows remarkable skill to reproduce regional cyclogenesis. Indeed, cyclones occurrence is quasi-absent in IPSL-CM5 data but when applying dynamical downscaling their spatial-temporal variability is very close to the re-analysis. This is a clear benefit of dynamical downscaling in regions of strong topographic forcing. This "steady" source of forcing allows the production of lee cyclogenesis and the development of strong cyclones, whatever the quality of the large-scale circulation provided at the WRF's boundaries by IPSL-CM5. However, dynamical downscaling still presents disadvantages as for instance the fact that large-scale inaccurate features of the IPSL-CM5 regional circulation are replicated by WRF due to the boundary controlled (small domain) simulation. The advantages and disadvantages of dynamical downscaling are thoroughly discussed in this paper revealing its importance for climate research, especially in the context of future scenarios and wind

  8. 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...

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

    NASA Astrophysics Data System (ADS)

    Fiddes, J.; Gruber, S.

    2014-02-01

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

  10. 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

  11. 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.

  12. 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

    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.

  13. Daily precipitation-downscaling techniques in three Chinese regions

    NASA Astrophysics Data System (ADS)

    Wetterhall, Fredrik; BáRdossy, AndráS.; Chen, Deliang; Halldin, Sven; Xu, Chong-Yu

    2006-11-01

    Four methods of statistical downscaling of daily precipitation were evaluated on three catchments located in southern, eastern, and central China. The evaluation focused on seasonal variation of statistical properties of precipitation and indices describing the precipitation regime, e.g., maximum length of dry spell and maximum 5-day precipitation, as well as interannual and intra-annual variations of precipitation. The predictors used in this study were mean sea level pressure, geopotential heights at 1000, 850, 700, and 500 hPa, and specific humidity as well as horizontal winds at 850, 700, and 500 hPa levels from the NCEP/NCAR reanalysis with 2.5° × 2.5° resolution for 1961-2000. The predictand was daily precipitation from 13 stations. Two analogue methods, one using principal components analysis (PCA) and the other Teweles-Wobus scores (TWS), a multiregression technique with a weather generator producing precipitation (SDSM) and a fuzzy-rule-based weather-pattern-classification method (MOFRBC), were used. Temporal and spatial properties of the predictors were carefully evaluated to derive the optimum setting for each method, and MOFRBC and SDSM were implemented in two modes, with and without humidity as predictor. The results showed that (1) precipitation was most successfully downscaled in the southern and eastern catchments located close to the coast, (2) winter properties were generally better downscaled, (3) MOFRBC and SDSM performed overall better than the analogue methods, (4) the modeled interannual variation in precipitation was improved when humidity was added to the predictor set, and (5), the annual precipitation cycle was well captured with all methods.

  14. 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

    2016-01-01

    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

  15. 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

  16. 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

  17. Coastal Downscaling Experiments: Can CESM Fields Successfully Force Regional Coastal Ocean Simulations with Strong Freshwater Forcing?

    NASA Astrophysics Data System (ADS)

    MacCready, P.; Bryan, F.; Tseng, Y. H.; Whitney, M. M.

    2014-12-01

    The coastal ocean accounts for about half of the global fish harvest, but is poorly resolved in global climate models (a one-degree grid barely sees the continental shelf). Moreover, coastal ocean circulation is strongly modified by river freshwater sources, often coming from estuarine systems that are completely unresolved in the coarse grid. River freshwater input in CESM is added in a practical but ad hoc way, by imposing a surface salinity sink over a region of the ocean approximating the plume area of a given river. Here we present results from a series of model experiments using a high-resolution (1.5 km) ROMS model of the NE Pacific, including the Columbia River and the inland waters of Puget Sound. The base model does multi-year hindcasts using the best available sources of atmospheric (MM5/WRF), ocean (NCOM), river (USGS), and tidal forcing. It has been heavily validated against observations of all sorts, and performs well, so it is an ideal test bed for downscaling experiments. The model framework also does biogeochemistry, including oxygen, and carbon chemistry is being added to make forecasts of Ocean Acidification.This high-resolution ROMS model is systematically run in downscaling experiments for the year 2005 with combinations of CESM forcing (CAM, POP, and rivers) swapped in. Skill is calculated using observations. It is found that the runs with CESM forcing generally retain much of the skill of the base model. A compact metric of response to freshwater forcing is used, which is the mechanical energy required to destratify a shallow coastal volume. This, along with the average temperature and salinity of the volume, are used to characterize and compare runs, including the original CESM-POP fields. Finally the model is run with projected CESM simulation forcing at the end of 21st century based on a set of RCP scenarios, and the compact metrics are used to quantify differences from 2005.

  18. 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).

  19. Investigating the Impact of Climate Change on Dust Storms Over Kuwait by the Middle of the Century Simulated by WRF Dynamical Downscaling

    NASA Astrophysics Data System (ADS)

    Alsarraf, Hussain

    The aim of this study is to examine the impact of climate change on future dust storms in Kuwait. Dust storms are more frequent in summertime in the Arabian Peninsula, and can be highly influential on the climate and the environment in the region. In this study, the influence of climate change in the Middle East and especially in Kuwait was investigated by high-resolution (48, 12, and 4 km grid spacing) dynamic downscaling using the WRF (Weather Research & Forecasting) model. The WRF dynamic downscaling was forced by reanalysis using the National Centers for Environment Prediction (NCEP) model for the years 1997, 2000, and 2008. The downscaling results were first validated by comparing NCEP model outputs with the observational data. The global climate change dynamic downscaling model was run using current WRF regional climate model (RCM) simulations (2006--2010) and WRF-RCM climate simulations of the future (2056--2060). They were used to compare results between the present and the middle of the century. In general, the dominant features from (NCEP) runs were consistent with each other, as well as with WRF-RCM results. The influence of climate change in the Middle East and Kuwait can be projected from the differences between the current and model future run. The average temperature showed a positive trend in the future, as in other studies. The temperature was predicted to increase by around 0.5-2.5 °C over the next 50 years. No significant change in mean sea level pressure patterns was projected. However, amongst other things, a change in the trend of the surface wind speeds was indicated during summertime. As a result, the increase in temperature and the decline in wind speed in the future indicate a reduction in dust storm days in Kuwait by the middle of the century.

  20. Downscaling to the Climate Near the Ground: Measurements and Modeling Along the Macro-, Meso-, Topo-, and Microclimate Hierarchy

    NASA Astrophysics Data System (ADS)

    van de Ven, C.; Weiss, S. B.

    2009-12-01

    Most climate models are expressed at regional scales, with resolutions on the scales of kilometers. When used for ecological modeling, these climate models help explain only broad-scale trends, such as latitudinal and upslope migration of plants. However, more refined ecological models require down-scaled climate data at ecologically relevant spatial scales, and the goal of this presentation is to demonstrate robust downscaling methods. For example, in the White Mountains, eastern California, tree species, including bristlecone pine (Pinus longaeva) are seen moving not just upslope, but also sideways across aspects, and downslope into areas characterized by cold air drainage. Macroclimate in the White Mountains is semi-arid, residing in the rain shadow of the Sierra Nevada. Macroclimate is modified by mesoscale effects of mountain ranges, where climate becomes wetter and colder with elevation, the temperature decreasing according to the regionally and temporally-specific lapse rate. Local topography further modifies climate, where slope angle, aspect, and topographic position further impact the temperature at a given site. Finally, plants experience extremely localized microclimate, where surrounding vegetation provide differing degrees of shade. We measured and modeled topoclimate across the White Mountains using iButton Thermochron temperature data loggers during late summer in 2006 and 2008, and have documented effects of microclimatic temperature differences between sites in the open and shaded by shrubs. Starting with PRISM 800m data, we derived mesoscale lapse rates. Then, we calculated temperature differentials between each Thermochron and a long-term weather station in the middle of the range at Crooked Creek Valley. We modeled month-specific minimum temperature differentials by regressing the Thermochron-weather station minimum temperature differentials with various topographic parameters. Topographic position, the absolute value of topographic position

  1. Influence of downscaling methods in projecting climate change impact on hydrological extremes of upper Blue Nile basin

    NASA Astrophysics Data System (ADS)

    Taye, M. T.; Willems, P.

    2013-06-01

    Methods from two statistical downscaling categories were used to investigate the impact of climate change on high rainfall and flow extremes of the upper Blue Nile basin. The main downscaling differences considered were on the rainfall variable while a generally similar method was applied for temperature. The applied downscaling methods are a stochastic weather generator, LARS-WG, and an advanced change factor method, the Quantile Perturbation Method (QPM). These were applied on 10 GCM runs and two emission scenarios (A1B and B1). The downscaled rainfall and evapotranspiration were input into a calibrated and validated lumped conceptual model. The future simulations were conducted for 2050s and 2090s horizon and were compared with 1980-2000 control period. From the results all downscaling methods agree in projecting increase in temperature for both periods. Nevertheless, the change signal on the rainfall was dependent on the climate model and the downscaling method applied. LARS weather generator was good for monthly statistics although caution has to be taken when it is applied for impact analysis dealing with extremes, as it showed a deviation from the extreme value distribution's tail shape. Contrary, the QPM method was good for extreme cases but only for good quality daily climate model data. The study showed the choice of downscaling method is an important factor to be considered and results based on one downscaling method may not give the full picture. Regardless, the projections on the extreme high flows and the mean main rainy season flow mostly showed a decreasing change signal for both periods. This is either by decreasing rainfall or increasing evapotranspiration depending on the downscaling method.

  2. Hamburg 2K: Climate modeling and downscaling for Hamburg, Germany under a 2 K global warming scenario

    NASA Astrophysics Data System (ADS)

    Flagg, D. D.; Grawe, D.; Daneke, C.; Hoffmann, P.; Jacob, D.; Kirschner, P.; Kriegsmann, A.; Linde, M.; Mayer, B.; O'Driscoll, K. T.; Pohlmann, T.; Schlünzen, K. H.; Schoetter, R.; Teichert, W.; Zorita, E.

    2011-12-01

    The European Union has established a 2 K warming of average annual global surface temperature above pre-industrial levels as a target to avoid disruptive climate change. The Hamburg 2K project seeks to model the climate of Hamburg, Germany subject to this target warming by the end of the 21st century. A general circulation model (ECHAM5) with a greenhouse gas scenario consistent with this target (E1) provides a source for dynamical and statistical-dynamical model downscaling at the regional scale, using the Regional Model (REMO), and at the mesoscale, using the Mesoscale Transport and fluid (Stream) Model (METRAS). Regional scale model estimates provide forcing for off-line modeling of the North Sea circulation with the Hamburg Shelf-Ocean Model (HAMSOM). This presentation concentrates on the urban climate component of the 2K scenario. The approach quantifies the projected change in both the meteorology and the urban development. The modeling strategy allows for a discrete diagnosis of each contribution. For the meteorology, the project identifies an urban climate change signal between the late 20th and late 21st centuries using a statistical-dynamical downscaling technique. Cluster analysis of multiple REMO realizations generates a series of archetypical synoptic conditions, a.k.a., weather types. The frequency change of these weather types between present and future climate yields a climate change signal. The potential for distinctively new weather types in the future climate is also investigated. Regional weather types provide the forcing for simulations with METRAS at 1 km resolution. These simulations provide further assessment of urban climate change at a scale more sensitive to the heterogeneous urban surface. Some initial METRAS modeling results will be presented here. For the urban development, the METRAS model simulations benefit from a detailed surface cover map including over 50 classes of natural and artificial surfaces tailored specifically for

  3. The added value of dynamical downscaling in a climate change scenario simulation:A case study for European Alps and East Asia

    NASA Astrophysics Data System (ADS)

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

    2010-05-01

    Since anthropogenic climate change is a rather important factor for the future human life all over the planet and its effects are not globally uniform, climate information at regional or local scales become more and more important for an accurate assessment of the potential impact of climate change on societies and ecosystems. High resolution information with suitably fine-scale for resolving complex geographical features could be a critical factor for successful linkage between climate models and impact assessment studies. However, scale mismatch between them still remains major problem. One method for overcoming the resolution limitations of global climate models and for adding regional details to coarse-grid global projections is to use dynamical downscaling by means of a regional climate model. In this study, the ECHAM5/MPI-OM (1.875 degree) A1B scenario simulation has been dynamically downscaled by using two different approaches within the framework of RegCM3 modeling system. First, a mosaic-type parameterization of subgrid-scale topography and land use (Sub-BATS) is applied over the European Alpine region. The Sub-BATS system is composed of 15 km coarse-grid cell and 3 km sub-grid cell. Second, we developed the RegCM3 one-way double-nested system, with the mother domain encompassing the eastern regions of Asia at 60 km grid spacing and the nested domain covering the Korean Peninsula at 20 km grid spacing. By comparing the regional climate model output and the driving global model ECHAM5/MPI-OM output, it is possible to estimate the added value of physically-based dynamical downscaling when for example impact studies at hydrological scale are performed.

  4. Recent Developments in Statistical Downscaling of Extremes

    NASA Astrophysics Data System (ADS)

    Hertig, E.

    2014-12-01

    Based on the output of general circulation models (GCMs) regionalization techniques are usually applied to obtain fine-scale climate change information. Different types of regionalization techniques have been developed which comprise regional climate models and statistical downscaling approaches such as conditional weather generators, artificial neural networks, synoptic studies, and transfer functions. In the scope of climate variability and climate change the variations and changes of extremes are of special importance. Extreme events are not only of scientific interest but also have a profound impact on society. For the statistical downscaling of extremes, promising approaches have been introduced and/or developed further in the last few years. Aspects of recent developments in the scope of statistical downscaling of extremes will be presented. In this context, various approaches to downscale extremes, particularly those associated with extreme precipitation events, will be discussed. Key problems related to statistical downscaling of extremes will be addressed. Furthermore, information on Working Group 4 "Extremes" of the EU COST action VALUE (www.value-cost.eu) will be provided. VALUE systematically validates and develops downscaling methods for climate change research in order to improve regional climate change scenarios for use in climate impact studies.

  5. 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

  6. 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.

  7. 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

  8. 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

  9. 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

  10. Evaluation of future precipitation scenario using statistical downscaling model over humid, subhumid, and arid region of Nepal—a case study

    NASA Astrophysics Data System (ADS)

    Sigdel, Madan; Ma, Yaoming

    2016-02-01

    Statistical downscaling model (SDSM) was applied in downscaling precipitation in the three climatic regions of Nepal. The study includes the calibration of the SDSM model by using large-scale atmospheric variables encompassing National Centers for Environmental Prediction (NCEP) reanalysis data, the validation of the model, and the outputs of downscaled scenarios A2 and B2 of the HadCM3 model for the future. The average R 2 value during validation period was 0.84, indicating the good applicability of SDSM for simulating precipitation. Under both scenarios A2 and B2, 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 A2 and increase near about 11.68 % under scenario B2 in the 2050s. The model showed better performance over humid region; moreover, simulated results for the peak monsoon months seem to be overestimated over subhumid and arid regions.

  11. 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

  12. 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...

  13. 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.

  14. 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

  15. Dynamical downscaling of CMIP5 global circulation models over CORDEX-Africa with COSMO-CLM: evaluation over the present climate and analysis of the added value

    NASA Astrophysics Data System (ADS)

    Dosio, Alessandro; Panitz, Hans-Jürgen; Schubert-Frisius, Martina; Lüthi, Daniel

    2015-05-01

    In this work we present the results of the application of the consortium for small-scale modeling (COSMO) regional climate model (COSMO-CLM, hereafter, CCLM) over Africa in the context of the coordinated regional climate downscaling experiment. An ensemble of climate change projections has been created by downscaling the simulations of four global climate models (GCM), namely: MPI-ESM-LR, HadGEM2-ES, CNRM-CM5, and EC-Earth. Here we compare the results of CCLM to those of the driving GCMs over the present climate, in order to investigate whether RCMs are effectively able to add value, at regional scale, to the performances of GCMs. It is found that, in general, the geographical distribution of mean sea level pressure, surface temperature and seasonal precipitation is strongly affected by the boundary conditions (i.e. driving GCMs), and seasonal statistics are not always improved by the downscaling. However, CCLM is generally able to better represent the annual cycle of precipitation, in particular over Southern Africa and the West Africa monsoon (WAM) area. By performing a singular spectrum analysis it is found that CCLM is able to reproduce satisfactorily the annual and sub-annual principal components of the precipitation time series over the Guinea Gulf, whereas the GCMs are in general not able to simulate the bimodal distribution due to the passage of the WAM and show a unimodal precipitation annual cycle. Furthermore, it is shown that CCLM is able to better reproduce the probability distribution function of precipitation and some impact-relevant indices such as the number of consecutive wet and dry days, and the frequency of heavy rain events.

  16. High-Resolution Dynamical Downscaling of ERA-Interim Using the WRF Regional Climate Model for the Area of Poland. Part 1: Model Configuration and Statistical Evaluation for the 1981-2010 Period

    NASA Astrophysics Data System (ADS)

    Kryza, Maciej; Wałaszek, Kinga; Ojrzyńska, Hanna; Szymanowski, Mariusz; Werner, Małgorzata; Dore, Anthony J.

    2016-03-01

    In this work, we present the results of high-resolution dynamical downscaling of air temperature, relative humidity, wind speed and direction, for the area of Poland, with the Weather Research and Forecasting (WRF) model. The model is configured using three nested domains, with spatial resolution of 45 km × 45 km, 15 km × 15 km and 5 km × 5 km. The ERA-Interim database is used for boundary conditions. The results are evaluated by comparison with station measurements for the period 1981-2010. The model is capable of reproducing the main climatological features of the study area. The results are in very close agreement with the measurements, especially for the air temperature. For all four meteorological variables, the model performance captures seasonal and daily cycles. For the air temperature and winter season, the model underestimates the measurements. For summer, the model shows higher values, compared with the measurements. The opposite is the case for relative humidity. There is a strong diurnal pattern in mean error, which changes seasonally. The agreement with the measurements is worse for the seashore and mountain areas, which suggests that the 5 km × 5 km grid might still have an insufficient spatial resolution. There is no statistically significant temporal trend in the model performance. The larger year-to-year changes in the model performance, e.g. for the years 1982 and 2010 for the air temperature should therefore be linked with the natural variability of meteorological conditions.

  17. Downscaling and predictability of historical monthly mean surface winds over a region of complex terrain and marine influence: Western Canada

    NASA Astrophysics Data System (ADS)

    Curry, C.; van der Kamp, D.; Monahan, A. H.

    2010-12-01

    Surface wind is a vector quantity exhibiting high spatial and temporal variability. Consequently, it presents a challenge for methods of statistical downscaling, which are used to establish a relationship between the large-scale atmospheric flow (predictors) and local climate variables (predictands). Simple regression-based techniques, for example, used with success for smoother predictands such as temperature, may not be as effective when applied to wind. In this work, the predictability of surface wind magnitude and direction at 28 stations in Western Canada over the period 1979-2006 was assessed using NCEP-2 reanalysis fields to derive large-scale predictors. Specifically, a combined principal components (PC) analysis was employed with the wind components at 500 hPa and mean sea level pressure as input fields, and the first 5 PCs used as predictors. The predictands were either wind speed or vector wind components oriented along directions ranging from 0 to 170 degrees at 10 degree intervals. Multiple linear regression was used for the downscaling, and its robustness assessed via cross-validation with an associated Pearson R2 value. This approach might be expected to display relatively high predictability, since it is comprised almost entirely of observations. However, our findings show that often this is not the case. Overall, wind speed was poorly predicted (R2<0.5), with the exception of a handful of stations in autumn and winter. By contrast, wind components were predicted with better skill than wind speeds at nearly all stations year-round, with the highest R2 values in autumn (SON) and lowest values in summer (JJA). The predictability of wind components was found to depend upon the topographic character of the region surrounding a given station. In mountainous regions, e.g., predictive skill was strongly related to the orientation of the components, with the best predicted components oriented along topographically significant directions such as constricted

  18. 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.

  19. 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

  20. 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

  1. 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.

  2. 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

  3. Assessment of regional climate change and development of climate adaptation decision aids in the Southwestern US

    NASA Astrophysics Data System (ADS)

    Darmenova, K.; Higgins, G.; Kiley, H.; Apling, D.

    2010-12-01

    Current General Circulation Models (GCMs) provide a valuable estimate of both natural and anthropogenic climate changes and variability on global scales. At the same time, future climate projections calculated with GCMs are not of sufficient spatial resolution to address regional needs. Many climate impact models require information at scales of 50 km or less, so dynamical downscaling is often used to estimate the smaller-scale information based on larger scale GCM output. To address current deficiencies in local planning and decision making with respect to regional climate change, our research is focused on performing a dynamical downscaling with the Weather Research and Forecasting (WRF) model and developing decision aids that translate the regional climate data into actionable information for users. Our methodology involves development of climatological indices of extreme weather and heating/cooling degree days based on WRF ensemble runs initialized with the NCEP-NCAR reanalysis and the European Center/Hamburg Model (ECHAM5). Results indicate that the downscale simulations provide the necessary detailed output required by state and local governments and the private sector to develop climate adaptation plans. In addition we evaluated the WRF performance in long-term climate simulations over the Southwestern US and validated against observational datasets.

  4. 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

  5. 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

  6. 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.

  7. 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

  8. Development and Evaluation of a Hybrid Dynamical-Statistical Downscaling Method

    NASA Astrophysics Data System (ADS)

    Walton, Daniel Burton

    Regional climate change studies usually rely on downscaling of global climate model (GCM) output in order to resolve important fine-scale features and processes that govern local climate. Previous efforts have used one of two techniques: (1) dynamical downscaling, in which a regional climate model is forced at the boundaries by GCM output, or (2) statistical downscaling, which employs historical empirical relationships to go from coarse to fine resolution. Studies using these methods have been criticized because they either dynamical downscaled only a few GCMs, or used statistical downscaling on an ensemble of GCMs, but missed important dynamical effects in the climate change signal. This study describes the development and evaluation of a hybrid dynamical-statstical downscaling method that utilizes aspects of both dynamical and statistical downscaling to address these concerns. The first step of the hybrid method is to use dynamical downscaling to understand the most important physical processes that contribute to the climate change signal in the region of interest. Then a statistical model is built based on the patterns and relationships identified from dynamical downscaling. This statistical model can be used to downscale an entire ensemble of GCMs quickly and efficiently. The hybrid method is first applied to a domain covering Los Angeles Region to generate projections of temperature change between the 2041-2060 and 1981-2000 periods for 32 CMIP5 GCMs. The hybrid method is also applied to a larger region covering all of California and the adjacent ocean. The hybrid method works well in both areas, primarily because a single feature, the land-sea contrast in the warming, controls the overwhelming majority of the spatial detail. Finally, the dynamically downscaled temperature change patterns are compared to those produced by two commonly-used statistical methods, BCSD and BCCA. Results show that dynamical downscaling recovers important spatial features that the

  9. 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…

  10. 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.

  11. Evaluation of global and regional climate simulations over Africa

    NASA Astrophysics Data System (ADS)

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

    2013-04-01

    Two ensembles of climate simulations, one global and one regional, are evaluated and inter-compared over the Africa-CORDEX domain. The global ensemble includes eight coupled atmosphere ocean general circulation models (AOGCMs) from the CMIP5 project with horizontal resolution varying from about 1° to 3°, namely CanESM2, CNRM-CM5, HadGEM2-ES, NorESM1-M, EC-EARTH, MIROC5, GFDL-ESM2M and MPI-ESM-LR. In the regional ensemble all 8 AOGCMs are downscaled over the Africa-CORDEX domain at the Rossby Centre (SMHI) by a regional climate model - RCA4 at 0.44° resolution. The main focus is on ability of both global and regional ensembles to simulate precipitation in different climate zones of Africa. Precipitation climatology is characterized by seasonal means, inter-annual variability and by various characteristics of the rainy season: onset, cessation, mean intensity and intra-seasonal variability. To see potential benefits of higher resolution in the regional downscaling all precipitation statistics are inter-compared between the individual AOGCM-RCA4(AOGCM) pairs and between the two multi-model ensemble averages. A special attention in the study is on how the AOGCMs simulate teleconnection patterns of large-scale internal variability and how these teleconnection pattern are reproduced in the downscaled regional simulations.

  12. 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.

  13. 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

  14. 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

  15. 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

  16. Sensitivity of Low Flow Simulations by the HBV-EC Hydrological Model to the Choice of Downscaling Algorithm, Climate Predictors, and Global Climate Model

    NASA Astrophysics Data System (ADS)

    Cannon, A. J.

    2006-12-01

    Hydrological models are one of the main tools used to investigate low flows under future climate change scenarios. Climate data requirements range from high-resolution spatially gridded datasets for distributed hydrological models to site measurements for conceptual hydrological models. In either case, climatological information from coarse resolution Global Climate Models (GCMs) must be used to infer climate series at higher resolutions required by the hydrological models. This is typically done using a procedure known as climate downscaling. The effect of the choice of downscaling algorithm, synoptic-scale predictor dataset, and GCM on the sensitivity of low flow simulations by the HBV-EC hydrological model is the main focus of this study. Different statistical downscaling algorithms (an analog model, a non-parametric weather generator, and a conditional density artificial neural network), predictor datasets (drawn from global atmospheric model reanalyses), and GCMs (the Meteorological Service of Canada's CGCM2, the UK Met Office's HadCM3, and the US Department of Energy sponsored PCM) are used to drive the HBV-EC hydrological model in mountainous watersheds of British Columbia, Canada. The ability of the modeling system to reproduce low flows is validated on historical data and simulated low flows are analyzed for future climate change scenarios.

  17. 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.

  18. "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...

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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.

  4. 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.

  5. 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

  6. 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

  7. An evaluation of single-site statistical downscaling techniques in terms of indices of climate extremes for the Midwest of Iran

    NASA Astrophysics Data System (ADS)

    Farajzadeh, M.; Oji, R.; Cannon, A. J.; Ghavidel, Y.; Massah Bavani, A.

    2015-04-01

    Seven single-site statistical downscaling methods for daily temperature and precipitation, including four deterministic algorithms [analog model (ANM), quantile mapping with delta method extrapolation (QMD), cumulative distribution function transform (CDFt), and model-based recursive partitioning (MOB)] and three stochastic algorithms [generalized linear model (GLM), Conditional Density Estimation Network Creation and Evaluation (CaDENCE), and Statistical Downscaling Model-Decision Centric (SDSM-DC] are evaluated at nine stations located in the mountainous region of Iran's Midwest. The methods are of widely varying complexity, with input requirements that range from single-point predictors of temperature and precipitation to multivariate synoptic-scale fields. The period 1981-2000 is used for model calibration and 2001-2010 for validation, with performance assessed in terms of 27 Climate Extremes Indices (CLIMDEX). The sensitivity of the methods to large-scale anomalies and their ability to replicate the observed data distribution in the validation period are separately tested for each index by Pearson correlation and Kolmogorov-Smirnov (KS) tests, respectively. Combined tests are used to assess overall model performances. MOB performed best, passing 14.5 % (49.6 %) of the combined (single) tests, respectively, followed by SDSM, CaDENCE, and GLM [14.5 % (46.5 %), 13.2 % (47.1 %), and 12.8 % (43.2 %), respectively], and then by QMD, CDFt, and ANM [7 % (45.7 %), 4.9 % (45.3 %), and 1.6 % (37.9 %), respectively]. Correlation tests were passed less frequently than KS tests. All methods downscaled temperature indices better than precipitation indices. Some indices, notably R20, R25, SDII, CWD, and TNx, were not successfully simulated by any of the methods. Model performance varied widely across the study region.

  8. 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.

  9. 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

  10. 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

  11. Decision- rather than scenario-centred downscaling: Towards smarter use of climate model outputs

    NASA Astrophysics Data System (ADS)

    Wilby, Robert L.

    2013-04-01

    Climate model output has been used for hydrological impact assessments for at least 25 years. Scenario-led methods raise awareness about risks posed by climate variability and change to the security of supplies, performance of water infrastructure, and health of freshwater ecosystems. However, it is less clear how these analyses translate into actionable information for adaptation. One reason is that scenario-led methods typically yield very large uncertainty bounds in projected impacts at regional and river catchment scales. Consequently, there is growing interest in vulnerability-based frameworks and strategies for employing climate model output in decision-making contexts. This talk begins by summarising contrasting perspectives on climate models and principles for testing their utility for water sector applications. Using selected examples it is then shown how water resource systems may be adapted with varying levels of reliance on climate model information. These approaches include the conventional scenario-led risk assessment, scenario-neutral strategies, safety margins and sensitivity testing, and adaptive management of water systems. The strengths and weaknesses of each approach are outlined and linked to selected water management activities. These cases show that much progress can be made in managing water systems without dependence on climate models. Low-regret measures such as improved forecasting, better inter-agency co-operation, and contingency planning, yield benefits regardless of the climate outlook. Nonetheless, climate model scenarios are useful for evaluating adaptation portfolios, identifying system thresholds and fixing weak links, exploring the timing of investments, improving operating rules, or developing smarter licensing regimes. The most problematic application remains the climate change safety margin because of the very low confidence in extreme precipitation and river flows generated by climate models. In such cases, it is necessary to

  12. 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.

  13. Regional Air Quality Under Climate Change Using a Nested Global-Regional Modeling System

    NASA Astrophysics Data System (ADS)

    Dawson, J.; Racherla, P.; Lynn, B.; Adams, P.; Pandis, S.

    2006-12-01

    Strong links between climate, particulate matter and ozone make it likely that climate change will have impacts on air quality. This study examines the effects that climate change will have on concentrations of PM2.5 and ozone in the Eastern US. The changes examined are between the present day and the 2050s. This is accomplished by developing the Global-Regional Climate Air Pollution Modeling System (GRE-CAPS). GRE-CAPS couples a general circulation model (GCM) / global chemical transport model (CTM), a regional meteorological model, and a regional chemical transport model. Present and future climates are simulated by the GISS-II' GCM with an embedded gas-phase and aerosol chemistry model. Meteorology generated by the GCM is downscaled to the regional modeling domain using the MM5 regional climate model. The downscaled meteorology is passed to the regional chemical transport model PMCAMx. In addition to the downscaled meteorology, chemical boundary conditions for the regional model are derived from the global model. The coupled model system is evaluated for the present day by comparing model-predicted concentrations of O3 and PM2.5 to measured concentrations during the last decade. This comparison between typical present- day measurements and model predictions is made for three modeled present-day Julys (both PM2.5 and O3) and three modeled Januaries (PM2.5). Future concentrations (using the IPCC A2 scenario) are compared to present-day concentrations. Concentrations in specific sites and statistical distributions of concentrations will be examined.

  14. New statistical downscaling for Canada

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

    This poster will document the production of a set of statistically downscaled future climate projections for Canada based on the latest available RCM and GCM simulations - the North American Regional Climate Change Assessment Program (NARCCAP; Mearns et al. 2007) and the Coupled Model Intercomparison Project Phase 5 (CMIP5). The main stages of the project included (1) downscaling method evaluation, (2) scenarios selection, (3) production of statistically downscaled results, and (4) applications of results. We build upon a previous downscaling evaluation project (Bürger et al. 2012, Bürger et al. 2013) in which a quantile-based method (Bias Correction/Spatial Disaggregation - BCSD; Werner 2011) provided high skill compared with four other methods representing the majority of types of downscaling used in Canada. Additional quantile-based methods (Bias-Correction/Constructed Analogues; Maurer et al. 2010 and Bias-Correction/Climate Imprint ; Hunter and Meentemeyer 2005) were evaluated. A subset of 12 CMIP5 simulations was chosen based on an objective set of selection criteria. This included hemispheric skill assessment based on the CLIMDEX indices (Sillmann et al. 2013), historical criteria used previously at the Pacific Climate Impacts Consortium (Werner 2011), and refinement based on a modified clustering algorithm (Houle et al. 2012; Katsavounidis et al. 1994). Statistical downscaling was carried out on the NARCCAP ensemble and a subset of the CMIP5 ensemble. We produced downscaled scenarios over Canada at a daily time resolution and 300 arc second (~10 km) spatial resolution from historical runs for 1951-2005 and from RCP 2.6, 4.5, and 8.5 projections for 2006-2100. The ANUSPLIN gridded daily dataset (McKenney et al. 2011) was used as a target. It has national coverage, spans the historical period of interest 1951-2005, and has daily time resolution. It uses interpolation of station data based on thin-plate splines. This type of method has been shown to have

  15. 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...

  16. The more extreme nature of U.S. warm season climate in the recent observational record and two "well-performing" dynamically downscaled CMIP3 models

    NASA Astrophysics Data System (ADS)

    Chang, Hsin-I.; Castro, Christopher L.; Carrillo, Carlos M.; Dominguez, Francina

    2015-08-01

    Arid and semiarid regions located in subtropical zones are projected to experience the most adverse impacts of climate change. During the warm season, observations and Intergovernmental Panel on Climate Change global climate models generally support a "wet gets wetter, dry gets drier" hypothesis in these regions, which acts to amplify the climatological transitions in the context of the annual cycle. In this study, we consider changes in U.S. early warm season precipitation in the observational record and regional climate model simulations driven by two "well-performing" dynamically downscaled Coupled Model Intercomparison Project phase 3 (CMIP3) models (Hadley Centre Coupled Model, version 3 and Max Planck Institute (MPI) European Centre/Hamburg Model 5) that have a robust climatological representation of the North American Monsoon System (NAMS). Both observations and model results show amplification in historical seasonal transitions of temperature and precipitation associated with NAMS development, with Weather Research and Forecasting (WRF)-MPI better representing the observed signal. Assuming the influence of remote Pacific sea surface temperature (SST) forcing associated with the El Niño-Southern Oscillation and Pacific Decadal Variability (ENSO-PDV) on U.S. regional climate remains the same in the 21st century, similar extreme trends are also projected by WRF-MPI for the next 30 years. A methodology is also developed to objectively analyze how climate change may be synergistically interacting with ENSO-PDV variability during the early warm season. Our analysis suggests that interannual variability of warm season temperature and precipitation associated with Pacific SST forcing is becoming more extreme, and the signal is stronger in the observed record.

  17. 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

  18. 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.

  19. 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.

  20. 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

  1. 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

  2. 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

  3. 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

    2015-10-01

    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.

  4. 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.

  5. 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/).

  6. 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

  7. 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.

  8. 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...

  9. 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).

  10. 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

  11. Future climate of Tehran Province using dynamical downscaling of COSMO-CLM

    NASA Astrophysics Data System (ADS)

    Sodoudi, S.; Langer, I.; Walter, A.; Fallah, B.

    2013-12-01

    Aim of this study is to investigate the future climate for the province Tehran till 2100. The climate model ECHAM5 and the regional climate model COSMO-CLM were used to simulate the regional climate change in this region. Using double nesting, the temperature and precipitation have been simulated with a resolution of 7km for this region. Results (2m temperature and precipitation) have been evaluated in 4 time slices (1981-2000, 2001-2020, 2031-2050, 2081-2100). For the first time slice, the observation data from the local stations have been compared with COSMO-CLM, CRU and GPCC reanalyze data. The COSMO-CLM model is warm and humid for this region. The maximum mean absolute error is for the station Abali in summer. This station is located in mountainous area. Results of comparison of first and fourth time slices show, that in winter the temperature will increase about 2 -3°C and in summer about 4°C in Tehran province. By calculation of extreme events (number of days with temperature greater than 35°C, days with precipitation greater than 50mm, Frost days with a minimum temperature less than 0°C and Ice days with a maximum temperature less than 0°C, it is shown, that the number of extreme events of temperature and precipitation will increase, while there is a decreasing in the number of Ice days and Frost days in the future. The results from the precipitation time series in the simulated time slices show, that during January till April the precipitation amount will decrease for the time period 2081-2100 comparing to period 1981-2000, while from May to December the precipitation will increase in the fourth time slice comparing to the period of 1981-2000. The results of SPI will be also shown for all time slices and will be compared to each other in order to detect the drought frequency in this region in future.

  12. 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.

  13. 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.

  14. A Comparison of Nudging Techniques for Regional Climate Modeling Using NCEP-NCAR Reanalysis-Driven Simulations

    NASA Astrophysics Data System (ADS)

    Bowden, J. H.; Otte, T.; Nolte, C. G.; Pleim, J. E.; Herwehe, J. A.

    2009-12-01

    The WRF model is being used at the U.S. EPA for dynamical downscaling of GCM fields with the goal of assessing regional impacts of climate change in the United States, including water quality and availability, agriculture, ecosystems, human health, air quality resulting from emission control strategies, and energy demand. To understand the regional impacts, it will be necessary to focus on extreme events (e.g., heat waves, droughts, flooding, stagnation events), in addition to changes in local mean temperatures and precipitation. Although regional downscaling methods have been investigated previously using WRF and reanalysis fields, a different downscaling methodology and/or model configuration may be required to capture extreme events. As a prelude to deriving future regional climate scenarios from IPCC AR5 GCM fields, simulations are performed using reanalysis fields at a comparable resolution (2.5° × 2.5°) to the GCM to establish a downscaling methodology. NCEP-NCAR reanalysis data are downscaled using the WRF model and evaluated against 32-km NARR fields. The impacts of analysis and spectral nudging on regional climate projections are investigated. Inter- and intra-annual variabilities are examined for subregions of the United States with a focus on near-surface temperature and precipitation. Additionally, we will address the choice of the land-surface model in WRF and its relative impact on simulated climate when used with nudging.

  15. Downscaling Aerosols and the Impact of Neglected Subgrid Processes on Direct Aerosol Radiative Forcing for a Representative Global Climate Model Grid Spacing

    SciTech Connect

    Gustafson, William I.; Qian, Yun; Fast, Jerome D.

    2011-07-13

    Recent improvements to many global climate models include detailed, prognostic aerosol calculations intended to better reproduce the observed climate. However, the trace gas and aerosol fields are treated at the grid-cell scale with no attempt to account for sub-grid impacts on the aerosol fields. This paper begins to quantify the error introduced by the neglected sub-grid variability for the shortwave aerosol radiative forcing for a representative climate model grid spacing of 75 km. An analysis of the value added in downscaling aerosol fields is also presented to give context to the WRF-Chem simulations used for the sub-grid analysis. We found that 1) the impact of neglected sub-grid variability on the aerosol radiative forcing is strongest in regions of complex topography and complicated flow patterns, and 2) scale-induced differences in emissions contribute strongly to the impact of neglected sub-grid processes on the aerosol radiative forcing. The two of these effects together, when simulated at 75 km vs. 3 km in WRF-Chem, result in an average daytime mean bias of over 30% error in top-of-atmosphere shortwave aerosol radiative forcing for a large percentage of central Mexico during the MILAGRO field campaign.

  16. Satellite-Enhanced Dynamical Downscaling of Extreme Events

    NASA Astrophysics Data System (ADS)

    Nunes, A.

    2015-12-01

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

  17. 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.

  18. 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.

  19. 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

  20. 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

  1. 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.

  2. 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

  3. 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.

  4. Statistical downscaling of precipitation: state-of-the-art and application of bayesian multi-model approach for uncertainty assessment

    NASA Astrophysics Data System (ADS)

    Hashmi, M. Z.; Shamseldin, A. Y.; Melville, B. W.

    2009-10-01

    Global Circulation Models (GCMs) are a major tool used for future projections of climate change using different emission scenarios. However, for assessing the hydrological impacts of climate change at the watershed and the regional scale, the GCM outputs cannot be used directly due to the mismatch in the spatial resolution between the GCMs and hydrological models. In order to use the output of a GCM for conducting hydrological impact studies, downscaling is used. However, the downscaling results may contain considerable uncertainty which needs to be quantified before making the results available. Among the variables usually downscaled, precipitation downscaling is quite challenging and is more prone to uncertainty issues than other climatological variables. This paper addresses the uncertainty analysis associated with statistical downscaling of a watershed precipitation (Clutha River above Balclutha, New Zealand) using results from three well reputed downscaling methods and Bayesian weighted multi-model ensemble approach. The downscaling methods used for this study belong to the following downscaling categories; (1) Multiple linear regression; (2) Multiple non-linear regression; and (3) Stochastic weather generator. The results obtained in this study have shown that this ensemble strategy is very efficient in combining the results from multiple downscaling methods on the basis of their performance and quantifying the uncertainty contained in this ensemble output. This will encourage any future attempts on quantifying downscaling uncertainties using the multi-model ensemble framework.

  5. 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.

  6. 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.

    2015-10-01

    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.

  7. 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.

  8. Linking Output from regional Climat Models with Cryosphere Models

    NASA Astrophysics Data System (ADS)

    Winter, S.

    2003-04-01

    This study has the objective of linking the results of a low-resolution regional climate model (RCM) with high-resolution cryosphere models in order to determine the manner in which Alpine snow, ice and permafrost is likely to respond to enhanced atmospheric warming resulting from an increase in anthropogenic greenhouse gases. There are several constraints that need to be overcome prior to applying solutions to this problem. Firstly, as a result of the long response time of glaciers and alpine permafrost to climate change, long-term simulations of at least 30 years are required. Secondly, the smallest possible spatial resolution of current RCM still remains quite coarse (~ 50 km) because of the complex mathematical equations to be resolved in the RCM, the limited computer performance and the above mentioned long simulation period. On the other hand, cryosphere models used in the present study require gridded input climate variables with a typical mesh width of 50 m. The proposed solution consists in combining climate change data based on RCM scenarios with meteorological data of high elevation Alpine stations measured during a reference period. A RCM control run matching this reference period is required in order to quantify the expected change for each climate parameter. This approach allows breaking down the initial downscaling problem into two separate steps. First, the quantified change derived from RCM-control and scenario simulations is used to predict change for meteorological stations. Second, data sets of predicted change and meteorological measures of these stations are summed and then regionalized for the study area based on advanced algorithms and GIS techniques. Selecting a case study area close to one or more meteorological stations should minimize the associated regionalization error. A pilot study for a small area at Piz Corvatsch in the Eastern Swiss Alps has been designed. The A2 scenario of the IPCC (Intergovernmental Panel on Climate Change

  9. Statistical Downscaling for the Northern Great Plains

    NASA Astrophysics Data System (ADS)

    Coburn, J.

    2014-12-01

    The need for detailed, local scale information about the warming climate has led to the use of ever more complex and geographically realistic computer models as well as the use of regional models capable of capturing much finer details. Another class of methods for ascertaining localized data is known as statistical downscaling, which offers some advantages over regional models, especially in the realm of computational efficiency. Statistical downscaling can be described as the process of linking coarse resolution climate model output to that of fine resolution or even station-level data via statistical relationships with the purpose of correcting model biases at the local scale. The development and application of downscaling has given rise to a plethora of techniques which have been applied to many spatial scales and multiple climate variables. In this study two downscaling processes, bias-corrected statistical downscaling (BCSD) and canonical correlation analysis (CCA), are applied to minimum and maximum temperatures and precipitation for the Northern Great Plains (NGP, 40 - 53°N and 95 - 120°W) region at both daily and monthly time steps. The abilities of the methods were tested by assessing their ability to recreate local variations in a set of both spatial and temporal climate metrics obtained through the analysis of 1/16 degree station data for the period 1950 to 2000. Model data for temperature, precipitation and a set of predictor variables were obtained from CMIP5 for 15 models. BCSD was applied using direct comparison and correction of the variable distributions via quadrant mapping. CCA was calibrated on the data for the period 1950 to 1980 using a series of model-based predictor variables screened for increasing skill, with the derived model being applied to the period 1980 to 2000 so as to verify that it could recreate the overall climate patterns and trends. As in previous studies done on other regions, it was found that the CCA method recreated

  10. 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.

  11. 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.

  12. 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

  13. 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

  14. 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

  15. 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...

  16. 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...

  17. 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.

  18. Can Regional Climate Models Improve Warm Season Forecasts in the North American Monsoon Region?

    NASA Astrophysics Data System (ADS)

    Dominguez, F.; Castro, C. L.

    2009-12-01

    The goal of this work is to improve warm season forecasts in the North American Monsoon Region. To do this, we are dynamically downscaling warm season CFS (Climate Forecast System) reforecasts from 1982-2005 for the contiguous U.S. using the Weather Research and Forecasting (WRF) regional climate model. CFS is the global coupled ocean-atmosphere model used by the Climate Prediction Center (CPC), a branch of the National Center for Environmental Prediction (NCEP), to provide official U.S. seasonal climate forecasts. Recently, NCEP has produced a comprehensive long-term retrospective ensemble CFS reforecasts for the years 1980-2005. These reforecasts show that CFS model 1) has an ability to forecast tropical Pacific SSTs and large-scale teleconnection patterns, at least as evaluated for the winter season; 2) has greater skill in forecasting winter than summer climate; and 3) demonstrates an increase in skill when a greater number of ensembles members are used. The decrease in CFS skill during the warm season is due to the fact that the physical mechanisms of rainfall at this time are more related to mesoscale processes, such as the diurnal cycle of convection, low-level moisture transport, propagation and organization of convection, and surface moisture recycling. In general, these are poorly represented in global atmospheric models. Preliminary simulations for years with extreme summer climate conditions in the western and central U.S. (specifically 1988 and 1993) show that CFS-WRF simulations can provide a more realistic representation of convective rainfall processes. Thus a RCM can potentially add significant value in climate forecasting of the warm season provided the downscaling methodology incorporates the following: 1) spectral nudging to preserve the variability in the large scale circulation while still permitting the development of smaller-scale variability in the RCM; and 2) use of realistic soil moisture initial condition, in this case provided by the

  19. Projected impact of climate change in the North and Baltic Sea. Results from dynamical downscaling of global CMIP climate scenarios

    NASA Astrophysics Data System (ADS)

    Gröger, Matthias; Maier-Reimer, Ernst; Mikolajewicz, Uwe; Sein, Dmitry

    2013-04-01

    Climate models have predicted strongest climate change impact for the mid/high lattiude areas. Despite their importance, shelves seas (which are supposed to account for more than 20% of global marine primary production and for up to 50% of total marine carbon uptake) are not adequately resolved in climate models. In this study, the global ocean general circulation and biogeochemistry model MPIOM/HAMOCC has been setup with an enhanced resolution over the NW European shelf (~10 km in the southern North Sea). For a realistic representation of atmosphere-ocean interactions the regional model REMO has been implemented. Thus, this model configuration allows a physically consistent simulation of climate signal propagation from the North Atlantic over the North Sea into the Baltic Sea since it interactively simulates mass and energy fluxes between the three basins. The results indicate substantial changes in hydrographic and biological conditions for the end of the 21st Century. A freshening by about 0.75 psu together with a surface warming of ~2.0 K and associated circulation changes in and outside the North Sea reduce biological production on the NW European shelf by ~35%. This reduction is twice as strong as the reduction in the open ocean. The underlying mechanism is a spatially well confined stratification feedback along the shelf break and the continental slope which reduces the winter mixed layer by locally more than 200 m compared to current conditions. As a consequence winter nutrient supply from the deep Atlantic declines between 40 and 50%. In addition to this, the volume transport of water and salt into the North Sea will slightly reduce (~10%) during summer. At the end of the 21st Century the North Sea appears nearly decoupled from the deep Atlantic. The projected decline in biological productivity and subsequent decrease of phytoplankton (by averaged 25%) will probably negatively affect the local fish stock in the North Sea. In the Baltic Sea the climate

  20. 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.

  1. 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 ...

  2. 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.

  3. Climate services within a regional climate adaptation project

    NASA Astrophysics Data System (ADS)

    Hänsel, Stephanie; Heidenreich, Majana; Franke, Johannes; Riedel, Kathrin; Matschullat, Jörg; Bernhofer, Christian

    2013-04-01

    In recent years the demand for adapting to climate variability and change became more and more obvious. Thus a multitude of projects dealing with climate adaptation strategies and concrete measures was launched. Commonly, developing adaptation options is based on downscaled climate model outputs. These outputs have to be provided within the projects, but just providing the data is far from being sufficient. Obstacles connected with using climate projections for climate adaptation include uncertainties and bandwidths of climate projections and the inability of models to describe parameters such as extreme weather events, which are particularly relevant for many climate adaptation decisions. Climate scientists know that model outputs are no climate data and cannot be treated as observational data were treated in the past. Still, many practitioners demand precise values for future climate to replace past CLINO-values and to run their applications. Thus, climate adaptation involves adapting the instruments and processes used in deriving climate-related decisions. Communicating the challenges arising from this need in rethinking common procedures is of outstanding significance for any successful adaptation practice. Dealing with uncertainties of climate projections is a constant necessity, since they are always based on several simplifications, parameterisations and assumptions, e.g., on the future socioeconomic development or on climate sensitivity. Future climate should thus be communicated in bandwidths. Working with just one scenario, one climate model, or even working with ensemble means is risky as it evokes a higher than appropriate perceived confidence in the results. It encourages using familiar tools in processing climate information, rather than caution. Consequences are suboptimal adaption and misallocation of finances. We encourage working with bandwidths and testing climate adaptation options against a broad range of possible future climates. Climate

  4. 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...

  5. 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

  6. 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

  7. Inter-comparison of statistical downscaling methods for projection of extreme precipitation in Europe

    NASA Astrophysics Data System (ADS)

    Sunyer, M. A.; Hundecha, Y.; Lawrence, D.; Madsen, H.; Willems, P.; Martinkova, M.; Vormoor, K.; Bürger, G.; Hanel, M.; Kriaučiūnienė, J.; Loukas, A.; Osuch, M.; Yücel, I.

    2014-06-01

    Information on extreme precipitation for future climate is needed to assess the changes in the frequency and intensity of flooding. The primary source of information in climate change impact studies is climate model projections. However, due to the coarse resolution and biases of these models, they cannot be directly used in hydrological models. Hence, statistical downscaling is necessary to address climate change impacts at the catchment scale. This study compares eight statistical downscaling methods often used in climate change impact studies. Four methods are based on change factors, three are bias correction methods, and one is a perfect prognosis method. The eight methods are used to downscale precipitation output from fifteen regional climate models (RCMs) from the ENSEMBLES project for eleven catchments in Europe. The overall results point to an increase in extreme precipitation in most catchments in both winter and summer. For individual catchments, the downscaled time series tend to agree on the direction of the change but differ in the magnitude. Differences between the statistical downscaling methods vary between the catchments and depend on the season analysed. Similarly, general conclusions cannot be drawn regarding the differences between change factor and bias correction methods. The performance of the bias correction methods during the control period also depends on the catchment, but in most cases they represent an improvement compared to RCM outputs. Analysis of the variance in the ensemble of RCMs and statistical downscaling methods indicates that up to half of the total variance is derived from the statistical downscaling methods. This study illustrates the large variability in the expected changes in extreme precipitation and highlights the need of considering an ensemble of both statistical downscaling methods and climate models.

  8. 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.

  9. Technical challenges and solutions in representing lakes when using WRF in downscaling applications

    NASA Astrophysics Data System (ADS)

    Mallard, M. S.; Nolte, C. G.; Spero, T. L.; Bullock, O. R.; Alapaty, K.; Herwehe, J. A.; Gula, J.; Bowden, J. H.

    2015-04-01

    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 downscaled fields, lakes are often poorly resolved in the driving global fields, if they are resolved at all. In such an application, using WRF's default interpolation methods can result in unrealistic lake temperatures and ice cover at inland water points. Prior studies have shown that lake temperatures and ice cover impact the simulation of other surface variables, such as air temperatures and precipitation, two fields that are often used in regional climate applications to understand the impacts of climate change on human health and the environment. Here, alternative methods for setting lake surface variables in WRF for downscaling simulations are presented and contrasted.

  10. Spatiotemporal Downscaling of Global Climate Model Output for Assessing Soil Erosion and Crop Production Under Climate Change.

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Spatial and temporal mismatches between coarse resolution output of General Circulation Models (GCMs) and fine resolution data requirements of ecosystems models are the major obstacles for assessing the site-specific climatic impacts of climate change on natural resources and ecosystems. The object...

  11. 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.

  12. 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., III

    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.

  13. 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.

  14. 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

  15. 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

  16. 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.

  17. 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

  18. 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.

  19. Regional coupled ocean-atmosphere downscaling in the Southeast Pacific: impacts on upwelling, mesoscale air-sea fluxes, and ocean eddies

    NASA Astrophysics Data System (ADS)

    Putrasahan, Dian A.; Miller, Arthur J.; Seo, Hyodae

    2013-05-01

    Ocean-atmosphere coupling in the Humboldt Current System (HCS) of the Southeast Pacific is studied using the Scripps Coupled Ocean-atmosphere Regional (SCOAR) model, which is used to downscale the National Center for Environmental Prediction (NCEP) Reanalysis-2 (RA2) product for the period 2000-2007 at 20-km resolution. An interactive 2-D spatial smoother within the sea-surface temperature (SST)-flux coupler is invoked in a separate run to isolate the impact of the mesoscale (˜50-200 km, in the oceanic sense) SST field felt by the atmosphere in the fully coupled run. For the HCS, SCOAR produces seasonal wind stress and wind stress curl patterns that agree better with QuikSCAT winds than those from RA2. The SCOAR downscaled wind stress distribution has substantially different impacts on the magnitude and structure of wind-driven upwelling processes along the coast compared to RA2. Along coastal locations such as Arica and Taltal, SCOAR and RA2 produce seasonally opposite signs in the total wind-driven upwelling transport. At San Juan, SCOAR shows that upwelling is mainly due to coastal Ekman upwelling transport, while in RA2 upwelling is mostly attributed to Ekman pumping. Fully coupled SCOAR shows significant SST-wind stress coupling during fall and winter, while smoothed SCOAR shows insignificant coupling throughout, indicating the important role of ocean mesoscale eddies on air-sea coupling in HCS. Coupling between SST, wind speed, and latent heat flux is incoherent in large-scale coupling and full coupling mode. In contrast, coupling between these three variables is clearly identified for oceanic mesoscales, which suggests that mesoscale SST affects latent heat directly through the bulk formulation, as well as indirectly through stability changes on the overlying atmosphere, which affects surface wind speeds. The SST-wind stress and SST-heat-flux couplings, however, fail to produce a strong change in the ocean eddy statistics. No rectified effects of ocean

  20. Mean climate and representation of jet streams in the CORDEX South Asia simulations by the regional climate model RCA4

    NASA Astrophysics Data System (ADS)

    Iqbal, W.; Syed, F. S.; Sajjad, H.; Nikulin, G.; Kjellström, E.; Hannachi, A.

    2016-02-01

    A number of simulations with the fourth release of the Rossby Center Regional Climate Model (RCA4) conducted within the COordinated Regional climate Downscaling EXperiment (CORDEX) framework for South Asia at 50 km horizontal resolution are evaluated for mean winter (December-March) and summer (June-September) climate during 1980-2005. The two driving data sets ERA-Interim reanalysis and the general circulation model EC-Earth have been analyzed besides the RCA4 simulations to address the added value. RCA4 successfully captures the mean climate in both the seasons. The biases in RCA4 appear to come from the driving data sets which are amplified after downscaling. The jet streams influencing the seasonal precipitation variability in both seasons are also analyzed. The spatial and quantitative analysis over CORDEX South Asia generally revealed the ability of RCA4 to capture the mean seasonal climate as well as the position and strength of the jet streams despite weak/strong jet representation in the driving data. The EC-Earth downscaled with RCA4 exhibited cold biases over the domain and a weak Somali jet over the Arabian Sea. Moreover, the moisture transport from the Arabian Sea during summer is pronounced in RCA4 simulations resulting in enhanced monsoon rainfall over northwestern parts of India. Both the Somali jet and the tropical easterly jet become stronger during strong summer monsoon years. However, there is robust impact of wet years in summer over the Somali jet. Wet-minus-dry composites in winter indicate strengthening (weakening) of the subtropical jet in RCA4 run by ERA-Interim (EC-Earth). The driving data have clear reflections on the RCA4 simulations.

  1. WRF dynamically downscaling PCM data for climate change impacts in California & application of a signal technique to the source-receptor relationship in WRF

    NASA Astrophysics Data System (ADS)

    Zhao, Zhan

    2009-12-01

    My dissertation consists of three parts. Parts I and II are focused on the climate change impacts on meteorology and air quality conditions in California (CA), while Part III is focused on the source-receptor relationship. The WRF model is applied to dynamically downscaled PCM data, with a horizontal resolution of approximately 2.8°x2.8°, to 4km resolution under the Business as Usual (BAU) scenario. The dynamical downscaling method could retain the large-scale features of the global simulations with more meso-scale details. A seven year simulation is conducted for both present (2000˜2006) and future (2047˜2053) in order to avoid the El Nino related inter-annual variation. In order to assess the PCM data quality and estimate the simulation error inherited from the PCM data bias, the present seven year simulations are driven by NCEP's Global Forecast System (GFS) data with the same model configuration. Part I is focused on the comparisons of the present time climatology from the two sets of simulations and the driving global datasets (i.e., PCM vs. GFS), which illustrate that the biases of the downscaling results are mostly inherited from the driving GCM. The imprecise prediction for the location and strength of the Pacific Subtropical High (PSH) is a main source of the PCM data bias. The analysis also implies that using the simulation results driven by PCM data as the input of the air quality model will underrate the air pollution problems in CA. The regional averaged statistics of the downscaling results compared to observational data show that both the surface temperature and wind speed were overestimate for most times of the year, and WRF preformed better during summer than winter. The low summer PBLH in the San Joaquin Valley (SJV) is addressed, and two reasons causing this are the dominance of a high pressure system over the valley and, to a lesser extent, the valley wind at daytime during summer. Part II is focused on the future change of meteorology and

  2. Analyzing projected changes and trends of temperature and precipitation in the southern USA from 16 downscaled global climate models

    NASA Astrophysics Data System (ADS)

    Liu, Lu; Hong, Yang; Hocker, James E.; Shafer, Mark A.; Carter, Lynne M.; Gourley, Jonathan J.; Bednarczyk, Christopher N.; Yong, Bin; Adhikari, Pradeep

    2012-08-01

    This study aims to examine how future climate, temperature and precipitation specifically, are expected to change under the A2, A1B, and B1 emission scenarios over the six states that make up the Southern Climate Impacts Planning Program (SCIPP): Oklahoma, Texas, Arkansas, Louisiana, Tennessee, and Mississippi. SCIPP is a member of the National Oceanic and Atmospheric Administration-funded Regional Integrated Sciences and Assessments network, a program which aims to better connect climate-related scientific research with in-the-field decision-making processes. The results of the study found that the average temperature over the study area is anticipated to increase by 1.7°C to 2.4°C in the twenty-first century based on the different emission scenarios with a rate of change that is more pronounced during the second half of the century. Summer and fall seasons are projected to have more significant temperature increases, while the northwestern portions of the region are projected to experience more significant increases than the Gulf coast region. Precipitation projections, conversely, do not exhibit a discernible upward or downward trend. Late twenty-first century exhibits slightly more precipitation than the early century, based on the A1B and B1 scenario, and fall and winter are projected to become wetter than the late twentieth century as a whole. Climate changes on the city level show that greater warming will happened in inland cities such as Oklahoma City and El Paso, and heavier precipitation in Nashville. These changes have profound implications for local water resources management as well as broader regional decision making. These results represent an initial phase of a broader study that is being undertaken to assist SCIPP regional and local water planning efforts in an effort to more closely link climate modeling to longer-term water resources management and to continue assessing climate change impacts on regional hazards management in the South.

  3. 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.

  4. Climate change projections for CORDEX-Africa with COSMO-CLM regional climate model and differences with the driving global climate models

    NASA Astrophysics Data System (ADS)

    Dosio, Alessandro; Panitz, Hans-Jürgen

    2016-03-01

    In the framework of the coordinated regional climate downscaling experiment (CORDEX), an ensemble of climate change projections for Africa has been created by downscaling the simulations of four global climate models (GCMs) by means of the consortium for small-scale modeling (COSMO) regional climate model (RCM) (COSMO-CLM, hereafter, CCLM). Differences between the projected temperature and precipitation simulated by CCLM and the driving GCMs are analyzed and discussed. The projected increase of seasonal temperature is found to be relatively similar between GCMs and RCM, although large differences (more than 1 °C) exist locally. Differences are also found for extreme-event related quantities, such as the spread of the upper end of the maximum temperature probability distribution function and, in turn, the duration of heat waves. Larger uncertainties are found in the future precipitation changes; this is partly a consequence of the inter-model (GCMs) variability over some areas (e.g. Sahel). However, over other regions (e.g. Central Africa) the rainfall trends simulated by CCLM and the GCMs show opposite signs, with CCLM showing a significant reduction in precipitation at the end of the century. This uncertain and sometimes contrasting behaviour is further investigated by analyzing the different models' response to the land-atmosphere interaction and feedback. Given the large uncertainty associated with inter-model variability across GCMs and the reduced spread in the results when a single RCM is used for downscaling, we strongly emphasize the importance of exploiting fully the CORDEX-Africa multi-GCM/multi-RCM ensemble in order to assess the robustness of the climate change signal and, possibly, to identify and quantify the many sources of uncertainty that still remain.

  5. 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

  6. 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

  7. Probabilistic downscaling approaches: Application to wind cumulative distribution functions

    NASA Astrophysics Data System (ADS)

    Michelangeli, P.-A.; Vrac, M.; Loukos, H.

    2009-06-01

    A statistical method is developed to generate local cumulative distribution functions (CDFs) of surface climate variables from large-scale fields. Contrary to most downscaling methods producing continuous time series, our “probabilistic downscaling methods” (PDMs), named “CDF-transform”, is designed to deal with and provide local-scale CDFs through a transformation applied to large-scale CDFs. First, our PDM is compared to a reference method (Quantile-matching), and validated on a historical time period by downscaling CDFs of wind intensity anomalies over France, for reanalyses and simulations from a general circulation model (GCM). Then, CDF-transform is applied to GCM output fields to project changes in wind intensity anomalies for the 21st century under A2 scenario. Results show a decrease in wind anomalies for most weather stations, ranging from less than 1% (in the South) to nearly 9% (in the North), with a maximum in the Brittany region.

  8. 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

  9. 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

  10. 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.

  11. 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...

  12. 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

  13. 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

  14. A space-time downscaling model for rainfall

    NASA Astrophysics Data System (ADS)

    Venugopal, V.; Foufoula-Georgiou, Efi; Sapozhnikov, Victor

    1999-08-01

    Interpretation of the impact of climate change or climate variability on water resources management requires information at scales much smaller than the current resolution of regional climate models. Subgrid-scale variability of precipitation is typically resolved by running nested or variable resolution models or by statistical downscaling, the latter being especially attractive in ensemble predictions due to its computational efficiency. Most existing precipitation downscaling schemes are based on spatial disaggregation of rainfall patterns, independently at different times, and do not properly account for the temporal persistence of rainfall at the subgrid spatial scales. Such a temporal persistence in rainfall directly relates to the spatial variability of accumulated local soil moisture and might be important if the downscaled values were to be used in a coupled atmospheric-hydrologic model. In this paper we propose a rainfall downscaling model which utilizes the presence of dynamic scaling in rainfall [Venugopal et al., 1999] and which in conjunction with a spatial disaggregation scheme preserves both the temporal and spatial correlation structure of rainfall at the subgrid scales.

  15. Design of a regional climate modelling projection ensemble experiment - NARCliM

    NASA Astrophysics Data System (ADS)

    Evans, J. P.; Ji, F.; Lee, C.; Smith, P.; Argüeso, D.; Fita, L.

    2014-04-01

    Including the impacts of climate change in decision making and planning processes is a challenge facing many regional governments including the New South Wales (NSW) and Australian Capital Territory (ACT) governments in Australia. NARCliM (NSW/ACT Regional Climate Modelling project) is a regional climate modelling project that aims to provide a comprehensive and consistent set of climate projections that can be used by all relevant government departments when considering climate change. To maximise end user engagement and ensure outputs are relevant to the planning process, a series of stakeholder workshops were run to define key aspects of the model experiment including spatial resolution, time slices, and output variables. As with all such experiments, practical considerations limit the number of ensemble members that can be simulated such that choices must be made concerning which global climate models (GCMs) to downscale from, and which regional climate models (RCMs) to downscale with. Here a methodology for making these choices is proposed that aims to sample the uncertainty in both GCM and RCM ensembles, as well as spanning the range of future climate projections present in the GCM ensemble. The RCM selection process uses performance evaluation metrics to eliminate poor performing models from consideration, followed by explicit consideration of model independence in order to retain as much information as possible in a small model subset. In addition to these two steps the GCM selection process also considers the future change in temperature and precipitation projected by each GCM. The final GCM selection is based on a subjective consideration of the GCM independence and future change. The created ensemble provides a more robust view of future regional climate changes. Future research is required to determine objective criteria that could replace the subjective aspects of the selection process.

  16. 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.

  17. Downscaling precipitation extremes in a complex Alpine catchment

    NASA Astrophysics Data System (ADS)

    Dobler, C.

    2012-04-01

    Climate change is expected to have significant effects on the frequency and intensity of heavy precipitation events. Assessing the impacts of climate change on precipitation extremes is a challenging task. On the one hand, the output of Regional Climate Models (RCMs) is subjected to systematic biases in the case of precipitation, especially in a complex mountain topography, and on the other hand, yet only a few statistical downscaling techniques are known to downscale precipitation extremes reliably. In this investigation two statistical downscaling approaches were applied to simulate precipitation extremes in the Alpine part of the Lech catchment. The first one, Expanded Downscaling (EDS), is a perfect prognosis approach that is based on regression. EDS has been calibrated and validated using large-scale predictor variables derived from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis dataset and local station data. The EDS model was then applied to downscale the output of two GCMs (ECHAM5, HadGEM2) for current (1971-2000) and future (2071-2100) time horizons, forced with the SRES A1B emission scenario. The second approach is the Long Ashton Research Station Weather Generator (LARS-WG) which can be characterized as a change factor conditioned weather generator. LARS-WG was calibrated on local station data only and then applied to downscale the output of five different GCM-RCM combinations to meteorological stations. The RCMs have a horizontal resolution of ~25 km and were obtained from the ENSEMBLES project of the European Union. In order to assess precipitation extremes with higher return values, a generalized extreme value distribution was applied to the data. Confidence intervals were calculated by using the non-parametric bootstrapping technique. The results show that both downscaling approaches reproduce observed precipitation extremes fairly well. Even for very extreme precipitation events such as the 20-year event a good agreement

  18. Statistical Downscaling of Gridded Rainfall and Their Impacts on Hydrological Response Analysis

    NASA Astrophysics Data System (ADS)

    Fu, G.; Charles, S. P.; Chiew, F. H.; Teng, J.; Frost, A. J.

    2011-12-01

    Water resource management and planning increasingly need to incorporate the effects of global climate change on regional climate variability in order to accurately assess future water supplies. Therefore future climate projections, particularly of rainfall, are of utmost interest to water resource management and water-users. General circulation models (GCMs) are the primary tool used to simulate present climate and project future climate. The outputs of GCMs are useful in understanding how future global climate responds to prescribed greenhouse gases emission scenarios. However GCMs do not provide realistic daily rainfall at scales below about 200 km, at which hydrological processes are typically assessed. Statistical downscaling techniques have been developed to resolve the scale discrepancy between GCM climate change scenarios and the resolution required for hydrological impact assessment, based on the assumption that large-scale atmospheric conditions have a strong influence on local-scale weather. Gridded rainfall is important for a variety of scientific and engineering applications, including climate change detection, the evaluation of climate models, the parameterization of stochastic weather generators, as well as assessment of climate change impacts on regional hydrological regimes and water availability, whereas statistical downscaling has predominantly provided daily rainfall series at the site (point) scale. The first part of the study explores the application of statistical downscaling to gridded rainfall datasets using three methods: 1) statistically downscaling to sites and then post-processing to interpolate to gridded rainfall; 2) treating each grid cell as an "observed" site for statistical downscaling directly; and 3) treating each sub-catchment as an "observed" site and statistically downscaling to sub-catchment averaged rainfall. The statistical downscaling Nonhomogeneous Hidden Markov Model (NHMM), which models multi-site patterns of daily

  19. 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.

  20. Weather Typing Statistical downscaling with dsclim: diagnostics, and uncertainties in data provision for the impact community

    NASA Astrophysics Data System (ADS)

    Page, C.; Sanchez, E.; Terray, L.

    2010-12-01

    Recently, an innovative statistical methodology has been developed to downscale climate simulations in France using a weather-typing approach (Boé et al., 2006), and further developed (Pagé et al., 2009). It has been used to downscale 15 CMIP3 models as well as 7 Météo-France ARPEGE climate numerical model (Salas et al., 2005) simulations. In the framework of the ANR-SCAMPEI project, dsclim has been carefully configured to be able to downscale climate simulations over France mountainous areas. Some new diagnostics have been developed to analyze the performance of the methodology and its configuration. In parallel, several projects to make these downscaled climate scenarios available to the impact community are going on, notably GICC-DRIAS and EU-IS-ENES. In the context of IS-ENES, several national Use Cases have been developed to formalize the steps needed to provide climate scenarios suitable for the impact community starting from the global climate scenarios data, and also taking into account the uncertainties. References Pagé, C., L. Terray et J. Boé, 2009: dsclim: A software package to downscale climate scenarios at regional scale using a weather-typing based statistical methodology. Technical Report TR/CMGC/09/21, CERFACS, Toulouse, France. Boé, J., L. Terray, F. Habets, et E. Martin, 2006: A simple statistical-dynamical downscaling scheme based on weather types and conditional resampling. J. Geophys. Res., 111, D21106. Salas y Mélia, D., F. Chauvin, M. Déqué, H. Douville, J.-F. Guérémy, P. Marquet, S. Planton, J.-F. Royer, and S. Tyteca, 2005: Description and validation of CNRM-CM3 global coupled climate model. Technical report, Centre national de recherches météorologiques, Groupe de Météorologie de Grande Echelle et Climat, Météo-France.

  1. 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

  2. Assessing the effect of domain size over the Caribbean region using the PRECIS regional climate model

    NASA Astrophysics Data System (ADS)

    Centella-Artola, Abel; Taylor, Michael A.; Bezanilla-Morlot, Arnoldo; Martinez-Castro, Daniel; Campbell, Jayaka D.; Stephenson, Tannecia S.; Vichot, Alejandro

    2015-04-01

    This study investigates the sensitivity of the one-way nested PRECIS regional climate model (RCM) to domain size for the Caribbean region. Simulated regional rainfall patterns from experiments using three domains with horizontal resolution of 50 km are compared with ERA reanalysis and observed datasets to determine if there is an optimal RCM configuration with respect to domain size and the ability to reproduce important observed climate features in the Caribbean. Results are presented for the early wet season (May-July) and late wet season (August-October). There is a relative insensitivity to domain size for simulating some important features of the regional circulation and key rainfall characteristics e.g. the Caribbean low level jet and the mid summer drought (MSD). The downscaled precipitation has a systematically negative precipitation bias, even when the domain was extended to the African coast to better represent circulation associated with easterly waves and tropical cyclones. The implications for optimizing modelling efforts within resource-limited regions like the Caribbean are discussed especially in the context of the region's participation in global initiatives such as CORDEX.

  3. 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.

  4. Downscaling GCM-simulated precipitation for the last millennium

    NASA Astrophysics Data System (ADS)

    Eden, Jonathan; Widmann, Martin; Smith, Richard

    2014-05-01

    Climate variability in the pre-instrumental period can be estimated either from climate proxy data or from numerical simulations. Both approaches still have considerable uncertainties and consistency tests are crucial for identifying robust features. One of the problems when comparing simulations with proxy-based reconstructions are potential scale mismatches. If the proxy-based reconstructions represent regional climate a direct comparison with simulated variables from global climate models, which in palaeoclimate applications are run with coarse resolutions, can lead to misleading results for two reasons: (i) the climate model might be biased even on large spatial scales, and (ii) small-scale spatial variability cannot be represented by the climate model. This problem can be expected to be particularly relevant for precipitation because of its high spatial variability. One way of addressing this problem is by applying downscaling techniques to the simulations. We have applied a statistical downscaling and correction method to precipitation from a simulation for the last millennium with the MPI for Meteorology Earth System Model, which uses ECHAM5-T31 as the atmosphere component. Our downscaling method, which is based on model output statistics (MOS), has been shown to outperform more standard (so-called perfect-prog) statistical downscaling methods when applied to simulated precipitation from the second half of the twentieth century, but it has not yet been applied to palaeoclimate simulations. Our aim is two-fold: to assess (a) whether downscaling using MOS yields additional information about long-term changes in regional climate and (b) to what extent the downscaled simulations may be in greater agreement with proxy-based reconstructions than raw model output. Two MOS downscaling methods, based on local scaling and principal component regression, are calibrated 'event-wise' (i.e. between contemporaneous sequences of simulated and observed events) using

  5. The West African Monsoon simulated by global and regional climate models

    NASA Astrophysics Data System (ADS)

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

    2013-04-01

    We present results from two ensembles of global and regional climate simulations with a focus on the West African Monsoon (WAM). The first ensemble includes eight coupled atmosphere ocean general circulation models (AOGCMs) from the CMIP5 project, namely: CanESM2, CNRM-CM5, HadGEM2-ES, NorESM1-M, EC-EARTH, MIROC5, GFDL-ESM2M and MPI-ESM-LR. The second ensemble consists of corresponding downscaling of all 8 AOGCMs by a regional climate model - RCA4 produced at the Rossby Centre (SMHI) in the Africa-CORDEX activities. Spatial resolution varies from about 1° to 3° in the AOGCM ensemble while all regional simulations are at the same 0.44° resolution. To see what added value higher resolution can provide ability of the eight AOGCMs and the downscaled RCA4(AOGCMs) to simulate the key characteristics of the WAM rainy season are evaluated and then inter-compared between the global and regional ensembles. The main focus in our analysis is on the WAM rainy season onset, cessation, length, total precipitation, its mean intensity and intraseasonal variability. Future climate projections under the RCP45 and RCP85 scenarios are analyzed and again inter-compared for both ensembles in order to assess uncertainties in the future projections of the WAM rainy season from the global and regional ensembles.

  6. 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

  7. Consistency of regional climate projections with the global conditions that stimulated them

    NASA Astrophysics Data System (ADS)

    Sienkiewicz, E. A.; Thompson, E.; Smith, L. A.

    2013-12-01

    Policy decisions related to climate impacts would benefit from robust regional projections if such information was reliable. Regional climate models can be used to add local detail to projections of global climate models. The regional models are usually driven by a global model in a one way fashion: no information from the regional model feeds back into the evolution of the global model which drives it. This research contrasts regional climate variables from a regional climate model with projections for the same region made by the global model driving it. Simulations from the North American Regional Climate Change Assessment Program (NARCCAP) provide a valuable test bed for this type of study. A number of global/regional model pairs are considered with the aim of testing the space and time scales on which the regional model projections remain consistent with the corresponding global model projections. A range of climate variables are considered, to determine criteria for when regional and global models deviate to such an extent that the reliability of both is in question. Differences in quantities like the net surface radiation balance can be related to the size of the climate change drivers expected to generate the signal of interest. For example, comparing the size of the anthropogenic direct radiative forcing with the size of the divergence between net surface radiation balance in the two models provides a useful estimate of the lead time at which the divergence of the two models will have likely swamped any anthropogenic signal. At a lead time of decades, annual averages of important atmospheric variables sometimes reveal a significant divergence between a given regional model and its driving global model. This implies a dynamical noise term that will cloud any physical interpretation of either model. The wider aim of this research is to assess the quality and reliability of climate simulations and the effectiveness of various downscaling methods, in order to

  8. Added value of regional climate modeling over areas characterized by complex terrain—Precipitation over the Alps

    NASA Astrophysics Data System (ADS)

    Torma, Csaba; Giorgi, Filippo; Coppola, Erika

    2015-05-01

    We present an analysis of the added value (AV) of downscaling via regional climate model (RCM) nesting with respect to the driving global climate models (GCMs). We analyze ensembles of driving GCM and nested RCM (two resolutions, 0.44° and 0.11°) simulations for the late 20th and late 21st centuries from the CMIP5, EURO-CORDEX, and MED-CORDEX experiments, with a focus on the Alpine region. Different metrics of AV are investigated, measuring aspects of precipitation where substantial AV can be expected in mountainous terrains: spatial pattern of mean precipitation, daily precipitation intensity distribution, and daily precipitation extremes tails. Comparison with a high-quality, fine-scale (5 km) gridded observational data set shows substantial AV of RCM downscaling for all metrics selected, and results are mostly improved compared to the driving GCMs also when the RCM fields are upscaled at the scale of the GCM resolution. We also find consistent improvements in the high-resolution (0.11°) versus medium-resolution (0.44°) RCM simulations. Finally, we find that the RCM downscaling substantially modulates the GCM-produced precipitation change signal in future climate projections, particularly in terms of fine-scale spatial pattern associated with the complex topography of the region. Our results thus point to the important role that high-resolution nested RCMs can play in the study of climate change over areas characterized by complex topographical features.

  9. 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.

  10. Estimating monthly rainfall in rural river basins under climate change: an improved bias-correcting statistical downscaling approach

    NASA Astrophysics Data System (ADS)

    Jayasekera, D. L.; Kaluarachchi, J. J.

    2013-06-01

    This study extended the work of Kim et al. (2008) to generate future rainfall under climate change using a discrete-time/space Markov chain based on historical conditional probabilities. A bias-correction method is proposed by fitting suitable statistical distributions to transform rainfall from the general circulation model (GCM) scale to watershed scale. The demonstration example used the Nam Ngum River Basin (NNRB) in Laos which is a rural river basin with high potential for hydropower generation and significant rain-fed agriculture supporting rural livelihoods. This work generated weekly rainfall for a 100 yr period using historical rainfall data from 1961 to 2000 for ten selected weather stations. The bias-correction method showed the ability to reduce bias of the mean values of GCMs when compared to the observed mean amount at each station. The simulated rainfall series is perturbed using the delta change estimated at each station to project future rainfall for the Special Report on Emission Scenarios (SRES) A2. GCMs consisting of third generation coupled general circulation model (CGCM3.1 T63) and European center Hamburg model (ECHAM5) projected an increasing trend of mean annual rainfall in the NNRB. Seasonal rainfall percent changes showed an increase in the wet and dry seasons with the highest increase in the dry season mean rainfall of about 31% from 2051 to 2090. While the GCM projections showed good results with appropriate bias corrections, the Providing REgional Climates for Impacts Studies (PRECIS) regional climate model significantly underestimated historical behavior and produced higher mean absolute errors compared to the corresponding GCM predictions.

  11. 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.

  12. 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.

  13. Comparison among different downscaling approaches in building water scarcity scenarios in an Alpine basin.

    NASA Astrophysics Data System (ADS)

    Guyennon, Nicolas; Romano, Emanuele; Mariani, Davide; Bruna Petrangeli, Anna; Portoghese, Ivan

    2014-05-01

    Various downscaling techniques have been developed to bridge the scale gap between global climate models (GCMs) and finer scales required to assess hydrological impacts of climate change. Although statistical downscaling (SD) has been traditionally seen as an alternative to dynamical downscaling (DD), recent works on statistical downscaling have aimed to combine the benefits of these two approaches. The overall objective of this study is to assess whether a DD processing performed before the SD is able to provide more reliable climate forcing for crop water demand models. The case study presented here focuses on the Maggiore Lake (Alpine region), with a watershed of approximately 4750 km2 and whose waters are mainly used for irrigation purposes in the Lombardia and Piemonte regions. The fifth-generation ECHAM model from the Max-Planck-Institute for Meteorology was adopted as GCM. The DD was carried out with the Protheus system (ENEA), while the SD was performed through a monthly quantile-quantile correction of the precipitation data collected in the period 1950-2012 by the 19 rainfall gauges located in the watershed area (some of them operating not continuously during the study period). The relationship between the precipitation regime and the inflow to the reservoir is obtained through a simple multilinear regression model, validated using both precipitation data and inflow measurements to the lake in the period 1996-2012 then, the same relation has been applied to the control (20c) and scenario (a1b) simulations downscaled by means of the different downscaling approaches (DD, SD and combined DD-SD). The resulting forcing has been used as input to a daily water balance model taking into account the inflow to the lake, the demand for irrigation and the reservoir management policies. The impact of the different downscaling approaches on the water budget scenarios has been evaluated in terms of occurrence, duration and intensity of water scarcity periods.

  14. Downscaling large-scale NCEP CFS to resolve fine-scale seasonal precipitation and extremes for the crop growing seasons over the southeastern United States

    NASA Astrophysics Data System (ADS)

    Lim, Young-Kwon; Cocke, Steven; Shin, D. W.; Schoof, Justin T.; Larow, Timothy E.; O'Brien, James J.

    2010-08-01

    Seasonally predicted precipitation at a resolution of 2.5° was statistically downscaled to a fine spatial scale of ~20 km over the southeastern United States. The downscaling was conducted for spring and summer, when the fine-scale prediction of precipitation is typically very challenging in this region. We obtained the global model precipitation for downscaling from the National Center for Environmental Prediction/Climate Forecast System (NCEP/CFS) retrospective forecasts. Ten member integration data with time-lagged initial conditions centered on mid- or late February each year were used for downscaling, covering the period from 1987 to 2005. The primary techniques involved in downscaling are Cyclostationary Empirical Orthogonal Function (CSEOF) analysis, multiple regression, and stochastic time series generation. Trained with observations and CFS data, CSEOF and multiple regression facilitated the identification of the statistical relationship between coarse-scale and fine-scale climate variability, leading to improved prediction of climate at a fine resolution. Downscaled precipitation produced seasonal and annual patterns that closely resemble the fine resolution observations. Prediction of long-term variation within two decades was improved by the downscaling in terms of variance, root mean square error, and correlation. Relative to the coarsely resolved unskillful CFS forecasts, the proposed downscaling drove a significant reduction in wet biases, and correlation increased by 0.1-0.5. Categorical predictability of seasonal precipitation and extremes (frequency of heavy rainfall days), measured with the Heidke skill score (HSS), was also improved by the downscaling. For instance, domain averaged HSS for two category predictability by the downscaling are at least 0.20, while the scores by the CFS are near zero and never exceed 0.1. On the other hand, prediction of the frequency of subseasonal dry spells showed limited improvement over half of the Georgia and

  15. 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

  16. 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.

  17. Prediction of future climate change for the Blue Nile, using a nested Regional Climate Model

    NASA Astrophysics Data System (ADS)

    Soliman, E.; Jeuland, M.

    2009-04-01

    Although the Nile River Basin is rich in natural resources, it faces many challenges. Rainfall is highly variable across the region, on both seasonal and inter-annual scales. This variability makes the region vulnerable to droughts and floods. Many development projects involving Nile waters are currently underway, or being studied. These projects will lead to land-use patterns changes and water distribution and availability. It is thus important to assess the effects of a) these projects and b) evolving water resource management and policies, on regional hydrological processes. This paper seeks to establish a basis for evaluation of such impacts within the Blue Nile River sub-basin, using the RegCM3 Regional Climate Model to simulate interactions between the land surface and climatic processes. We first present results from application of this RCM model nested with downscaled outputs obtained from the ECHAM5/MPI-OM1 transient simulations for the 20th Century. We then investigate changes associated with mid-21st century emissions forcing of the SRES A1B scenario. The results obtained from the climate model are then fed as inputs to the Nile Forecast System (NFS), a hydrologic distributed rainfall runoff model of the Nile Basin, The interaction between climatic and hydrological processes on the land surface has been fully coupled. Rainfall patterns and evaporation rates have been generated using RegCM3, and the resulting runoff and Blue Nile streamflow patterns have been simulated using the NFS. This paper compares the results obtained from the RegCM3 climate model with observational datasets for precipitation and temperature from the Climate Research Unit (UK) and the NASA Goddard Space Flight Center GPCP (USA) for 1985-2000. The validity of the streamflow predictions from the NFS is assessed using historical gauge records. Finally, we present results from modeling of the A1B emissions scenario of the IPCC for the years 2034-2055. Our results indicate that future

  18. 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

  19. 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.

  20. 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

  1. Greenland at 5km resolution during the 21st century as seen by the HIRHAM5 regional climate model

    NASA Astrophysics Data System (ADS)

    Boberg, Fredrik

    2015-04-01

    The demand for information on climate projections for the Arctic regions is rapidly growing. Is is also a challenge to convert the data produced by climate models into information suited for climate change impact studies. For this reason, we have extracted a large number of climate indices from five time slice downscaling experiments performed for the Greenland region. These experiments include a control period 1991-2010 run and two scenario period (2031-2050 and 2081-2100) runs for the emission scenarios RCP 4.5 and RCP 8.5 respectively. The downscaling is done, using the DMI HIRHAM5 regional climate model, for the EC-EARTH global climate model at a 5km horizontal resolution. The results show clear differences for a number of indices for the two emission scenarios but also for the two scenario periods within the two emission scenarios. The indices presented include a permafrost index, fishing index, amount of snowfall, days with daily maximum temperature > 10oC, daynumber of last spring frost, length of growing season and number of days with snow cover.

  2. 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.

  3. 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

  4. 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

  5. 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.

  6. Representative meteorological ensembles of change climate change in the Araucanía Region, Chile.

    NASA Astrophysics Data System (ADS)

    Cepeda, Javier; Vargas, Ximena

    2015-04-01

    One of the main uncertainties in hydrologic modeling is attributed to meteorological inputs. When climate change impact analysis is performed, uncertainty increases due to that meteorological time series are obtained through Global Circulation Models (GCM) for a specific climate change scenario. The Intergovernmental Panel on Climate Change (IPCC) in their last report (AR5, 2013 ) recommend the Representative Concentration Pathway. RCP scenarios, developed under the Coupled Model Intercomparison Project Phase 5 (CMIP5). Pathways for stabilization of radiative forcing by 2100 characterize these scenarios being a radiative forcing of 8.5 w/m2, the highest future condition considered. In order to reduce the meteorological uncertainties, we study the behavior of the daily precipitation series I three meteorological stations in the valley of the Araucanía region, in southern Chile, using ten ensembles from CGM MK-3.6 model for RCP 8.5. The main hypothesis is that good transformer functions between the observations and data obtained from the model is essential to have suitable future projections. To obtain these functions, statistical downscaling is performed; first spatial downscaling is carried out, and then a temporal downscaling of the daily precipitation data for each month is made. Ensembles whit transfer functions without discontinuities or those with the least were preferred. From this analysis we selected four ensembles. For the three gage stations we apply the transfer's functions during the observed period and compared the average seasonal variation curve, the duration curve of daily, monthly and annually precipitation and average number of rainy days. Finally, based on qualitative analysis and quantitative criteria we suggest which ensemble are the most representative historical conditions.

  7. The role of regional climate model setup in simulating two extreme precipitation events in the European Alpine region

    NASA Astrophysics Data System (ADS)

    Awan, Nauman Khurshid; Gobiet, Andreas; Suklitsch, Martin

    2015-01-01

    In this study we have investigated the role of domain settings and model's physics in simulating two extreme precipitation events. Four regional climate models, all driven with a re-analysis dataset were used to create an ensemble of 61 high-resolution simulations by varying physical parameterization schemes, domain sizes, nudging and nesting techniques. The two discussed events are three-day time slices taken from approximately 15-months long climate simulations. The results show that dynamical downscaling significantly improves the spatial characteristics such as correlation, variability as well as location and intensity of maximum precipitation. Spatial variability, which is underestimated by most of the simulations can be improved by choosing suitable vertical resolution, convective and microphysics scheme. The results further suggest that for studies focusing on extreme precipitation events relatively small domains or nudging could be advantageous. However, a final conclusion on this issue would be premature, since only two extreme precipitation events are considered.

  8. The role of regional climate model setup in simulating two extreme precipitation events in the European Alpine region

    NASA Astrophysics Data System (ADS)

    Awan, Nauman Khurshid; Gobiet, Andreas; Suklitsch, Martin

    2014-09-01

    In this study we have investigated the role of domain settings and model's physics in simulating two extreme precipitation events. Four regional climate models, all driven with a re-analysis dataset were used to create an ensemble of 61 high-resolution simulations by varying physical parameterization schemes, domain sizes, nudging and nesting techniques. The two discussed events are three-day time slices taken from approximately 15-months long climate simulations. The results show that dynamical downscaling significantly improves the spatial characteristics such as correlation, variability as well as location and intensity of maximum precipitation. Spatial variability, which is underestimated by most of the simulations can be improved by choosing suitable vertical resolution, convective and microphysics scheme. The results further suggest that for studies focusing on extreme precipitation events relatively small domains or nudging could be advantageous. However, a final conclusion on this issue would be premature, since only two extreme precipitation events are considered.

  9. 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.

  10. 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.

  11. 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.

  12. 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

  13. 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.

  14. Sampling downscaling in summertime precipitation over Hokkaido

    NASA Astrophysics Data System (ADS)

    Tamaki, Yuta; Inatsu, Masaru; Kuno, Ryusuke; Nakano, Naoto

    2016-04-01

    1. Introduction Recently, the mixture method of dynamical and statistical downscaling have been developed (cf. Kuno and Inatsu 2014, Pinto et al. 2014). Kuno and Inatsu (2014) developed the sampling downscaling (SmDS) method in which a regional atmospheric model is integrated for sampled years. However, in order to know how these mixture methods are able to effectively reduce the computational costs for dynamical downscaling, we need to apply them to other cases. The purpose of this study is to apply SmDS to summertime precipitation over Hokkaido as another case study. 2. Method Singular value decomposition (SVD) analysis is performed from 1981 to 2010 in June-July-August (JJA) months using the moisture flux convergence (JRA25/JCDAS) around Japan and precipitation (APHRO_JP/V1207) over Hokkaido. Next, we selected the top and bottom two years of the moisture flux convergence of the general circulation model projection onto the first SVD mode. This study conducts the dynamical downscaling for 30 years (full DDS) under the current climate experiment in advance to investigate the reproducibility of SmDS. 3. Result The spatial correlation coefficient between SmDS and full DDS shows 0.96 in daily-mean precipitation and 0.85 in 99 percentile value of daily precipitation. This indicates that SmDS can be applied to the place where the synoptic field strongly controls the local precipitation. In addition, we also statistically considered the error in SmDS and it turned out that the mean in SmDS depended on the correlation coefficient between local and synoptic variables, the number of samples, and the standard deviation of seasonal mean precipitation. It was also demonstrated the SmDS selected the group of years where extreme events likely occurred and another group where they rarely occurred. References Kuno, R., and M. Inatsu, 2014, Clim. Dyn., 43, 375-387. Pinto, J. O., A. J. Monaghan, L. D. Monache, E. Vanvyve, and D. L. Rife, 2014, J. Climate, 27, 1524-1538.

  15. 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

  16. 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.

  17. 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.

  18. The urban impact on the regional climate of Dresden

    NASA Astrophysics Data System (ADS)

    Sändig, B.; Renner, E.

    2010-09-01

    The principal objective of this research is to clarify the impact of urban elements such as buildings and streets on the regional climate and air quality in the framework of the BMBF-project "Regionales Klimaanpassungsprogramm f¨ur die Modellregion Dresden" (REGKLAM). Drawing on the example of Dresden this work explores how the presence of cities influences the atmospheric flow and the characteristics of the boundary layer. Persuing this target, an urban surface exchange parameterisation module (Martilli et al., 2002) was implemented in a high resolution version of the COSMO model, the forecast model of the German Weather Service (DWD). Using a mesoscale model for this regional climate study implies the advantage of embedding the focused area in a realistic large scale situation via downscaling by means of one way nesting and allows to simulate the urban impact for different IPCC-szenarios. The urban module is based on the assumption that a city could be represented by a bunch of "urban classes". Each urban class is characterised by specific properties such as typical street directions or probability of finding a building in a special height. Based on urban structure data of Dresden (vector shape-files containing the outlines of all buildings and the respective heights) an automated method of extracting the relevant geometrical input parameters for the urban module was developed. By means of this model setup we performed case studies, in which we investigate the interactions between the city structure and the meteorological variables with regard to special synoptical situations such as the Bohemian wind, a typical flow pattern of cold air, sourced from the Bohemian Basin, in the Elbe Valley, which acts then like a wind channel. Another focal point is formed by the investigation of different types of artificial cities ranging from densely builtup areas to suburban areas in order to illuminating the impact of the city type on the dynamical and thermal properties of

  19. 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...

  20. 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

  1. 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

  2. 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.

  3. Challenges in Modeling Regional Climate Change (Invited)

    NASA Astrophysics Data System (ADS)

    Leung, L.

    2013-12-01

    Precipitation, soil moisture, and runoff are vital to ecosystems and human activities. Predicting changes in the space-time characteristics of these water cycle processes has been a longstanding challenge in climate modeling. Different modeling approaches have been developed to allow high resolution to be achieved using available computing resources. Although high resolution is necessary to better resolve regional forcing and processes, improvements in simulating water cycle response are difficult to demonstrate and climate models have so far shown irreducible sensitivity to model resolution, dynamical framework, and physics parameterizations that confounds reliable predictions of regional climate change. Additionally, regional climate responds to both regional and global forcing but predicting changes in regional and global forcing such as related to land use/land cover and aerosol requires improved understanding and modeling of the dynamics of human-earth system interactions. Furthermore, regional response and regional forcing may be related through complex interactions that are dependent on the regional climate regimes, making decisions on regional mitigation and adaptation more challenging. Examples of the aforementioned challenges from on-going research and possible future directions will be discussed.

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

    NASA Astrophysics Data System (ADS)

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

    2009-09-01

    environmental impact studies. Downscaling methods to assess the effect of large-scale circulations on local parameters have. Statistical downscaling methods are based on the view that regional climate can be conditioned by two factors: large-scale climatic state and regional/local features. Local climate information is derived by first developing a statistical model which relates large-scale variables or "predictors" for which GCMs are trustable to regional or local surface "predictands" for which models are less skilful. The main advantage of these methods is that they are computationally inexpensive, and can be applied to outputs from different GCM experiments. Three statistical downscaling methods are applied: Analogue method, Delta Change and Direct Forcing. These methods have been used to determine daily precipitation projections at rain gauge location to study the intensity, frequency and variability of storms in a context of climate change in the Llobregat River Basin in Catalonia, Spain. This work is part of the European project "Water Change" (included in the LIFE + Environment Policy and Governance program). It deals with Medium and long term water resources modelling as a tool for planning and global change adaptation. Two stakeholders involved in the project provided the historical time series: Catalan Water Agency (ACA) and the State Meteorological Agency (AEMET).

  5. 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.

  6. 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

  7. Regional Climate Change Hotspots over Africa

    NASA Astrophysics Data System (ADS)

    Anber, U.; Zakey, A.; Abd El Wahab, M.

    2009-04-01

    Regional Climate Change Index (RCCI), is developed based on regional mean precipitation change, mean surface air temperature change, and change in precipitation and temperature interannual variability. The RCCI is a comparative index designed to identify the most responsive regions to climate change, or Hot- Spots. The RCCI is calculated for Seven land regions over North Africa and Arabian region from the latest set of climate change projections by 14 global climates for the A1B, A2 and B1 IPCC emission scenarios. The concept of climate change can be approaches from the viewpoint of vulnerability or from that of climate response. In the former case a Hot-Spot can be defined as a region for which potential climate change impacts on the environment or different activity sectors can be particularly pronounced. In the other case, a Hot-Spot can be defined as a region whose climate is especially responsive to global change. In particular, the characterization of climate change response-based Hot-Spot can provide key information to identify and investigate climate change Hot-Spots based on results from multi-model ensemble of climate change simulations performed by modeling groups from around the world as contributions to the Fourth Assessment Report of Intergovernmental Panel on Climate Change (IPCC). A Regional Climate Change Index (RCCI) is defined based on four variables: change in regional mean surface air temperature relative to the global average temperature change ( or Regional Warming Amplification Factor, RWAF ), change in mean regional precipitation (P % , of present day value ), change in regional surface air temperature interannual variability (T % ,of present day value), change in regional precipitation interannual variability (P % ,of present day value ). In the definition of the RCCI it is important to include quantities other than mean change because often mean changes are not the only important factors for specific impacts. We thus also include inter

  8. Regional Climate Change Hotspots over Africa

    NASA Astrophysics Data System (ADS)

    Anber, U.

    2009-04-01

    Regional Climate Change Index (RCCI), is developed based on regional mean precipitation change, mean surface air temperature change, and change in precipitation and temperature interannual variability. The RCCI is a comparative index designed to identify the most responsive regions to climate change, or Hot- Spots. The RCCI is calculated for Seven land regions over North Africa and Arabian region from the latest set of climate change projections by 14 global climates for the A1B, A2 and B1 IPCC emission scenarios. The concept of climate change can be approaches from the viewpoint of vulnerability or from that of climate response. In the former case a Hot-Spot can be defined as a region for which potential climate change impacts on the environment or different activity sectors can be particularly pronounced. In the other case, a Hot-Spot can be defined as a region whose climate is especially responsive to global change. In particular, the characterization of climate change response-based Hot-Spot can provide key information to identify and investigate climate change Hot-Spots based on results from multi-model ensemble of climate change simulations performed by modeling groups from around the world as contributions to the Assessment Report of Intergovernmental Panel on Climate Change (IPCC). A Regional Climate Change Index (RCCI) is defined based on four variables: change in regional mean surface air temperature relative to the global average temperature change ( or Regional Warming Amplification Factor, RWAF ), change in mean regional precipitation ( , of present day value ), change in regional surface air temperature interannual variability ( ,of present day value), change in regional precipitation interannual variability ( , of present day value ). In the definition of the RCCI it is important to include quantities other than mean change because often mean changes are not the only important factors for specific impacts. We thus also include inter annual

  9. Sensitivity of lake ice regimes to climate change in the Nordic region

    NASA Astrophysics Data System (ADS)

    Gebre, S.; Boissy, T.; Alfredsen, K.

    2014-08-01

    A one-dimensional process-based multi-year lake ice model, MyLake, was used to simulate lake ice phenology and annual maximum lake ice thickness for the Nordic region comprising Fennoscandia and the Baltic countries. The model was first tested and validated using observational meteorological forcing on a candidate lake (Lake Atnsjøen) and using downscaled ERA-40 reanalysis data set. To simulate ice conditions for the contemporary period of 1961-2000, the model was driven by gridded meteorological forcings from ERA-40 global reanalysis data downscaled to a 25 km resolution using the Rossby Centre Regional Climate Model (RCA). The model was then forced with two future climate scenarios from the RCA driven by two different general circulation models (GCMs) based on the Special Report on Emissions Scenarios (SRES) A1B. The two climate scenarios correspond to two future time periods namely the 2050s (2041-2070) and the 2080s (2071-2100). To take into account the influence of lake morphometry, simulations were carried out for four different hypothetical lake depths (5 m, 10 m, 20 m, 40 m) placed at each of the 3708 grid cells. Based on a comparison of the mean predictions in the future 30-year periods with the control (1961-1990) period, ice cover durations in the region will be shortened by 1 to 11 weeks in 2041-2070, and 3 to 14 weeks in 2071-2100. Annual maximum lake ice thickness, on the other hand, will be reduced by a margin of up to 60 cm by 2041-2070 and up to 70 cm by 2071-2100. The simulated changes in lake ice characteristics revealed that the changes are less dependent on lake depths though there are slight differences. The results of this study provide a regional perspective of anticipated changes in lake ice regimes due to climate warming across the study area by the middle and end of this century.

  10. Sensitivity of lake ice regimes to climate change in the nordic region

    NASA Astrophysics Data System (ADS)

    Gebre, S.; Boissy, T.; Alfredsen, K.

    2013-03-01

    A one-dimensional process-based multi-year lake ice model, MyLake, was used to simulate lake ice phenology and annual maximum lake ice thickness for the Nordic region comprising Fennoscandia and the Baltic countries. The model was first tested and validated using observational meteorological forcing on a candidate lake (Lake Atnsjøen) and using downscaled ERA-40 reanalysis data set. To simulate ice conditions for the contemporary period of 1961-2000, the model was driven by gridded meteorological forcings from ERA-40 global reanalysis data downscaled to a 25 km resolution using the Rossby Center Regional Climate Model (RCA). The model was then forced with two future climate scenarios from the RCA driven by two different GCMs based on the SRES A1B emissions scenario. The two climate scenarios correspond to two future time periods namely the 2050s (2041-2070) and the 2080s (2071-2100). To take into account the influence of lake morphometry, simulations were carried out for four different hypothetical lake depths (5 m, 10 m, 20 m, 40 m) placed at each of the 3708 grid cells. Based on a comparison of the mean predictions in the future 30 yr periods with the control (1961-1990) period, ice cover durations in the region will be shortened by 1 to 11 weeks in 2041-2070, and 3 to 14 weeks in 2071-2100. Annual maximum lake ice thickness, on the other hand, will be reduced by a margin of up to 60 cm by 2041-2070 and up to 70 cm by 2071-2100. The simulated changes in lake ice characteristics revealed that the changes are less dependent on lake depths though there are slight differences. The results of this study provide a~regional perspective of anticipated changes in lake ice regimes due to climate warming across the study area by the middle and end of this century.

  11. 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.

  12. 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/

  13. 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

  14. Field Significance of Performance Measures in the Context of Regional Climate Model Verification

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

    The purpose of this study is to rigorously evaluate the skill of dynamically downscaled global climate simulations. We investigate a dynamical downscaling of the ERA-Interim reanalysis using the Weather Research and Forecasting (WRF) model, coupled with the NOAH land surface model within the scope of EURO-CORDEX. WRF has a horizontal resolution of 11° and contains the following physics: the Yonsei university atmospheric boundary layer parameterization, the Morrison two-moment microphysics, the Kain-Fritsch-Eta convection and the Community Atmosphere Model radiation schemes. Daily precipitation is verified over Germany for summer and winter against high-resolution observation data from the German weather service for the first time. The ability of WRF to reproduce the statistical distribution of daily precipitation is evaluated using metrics based on distribution characteristics. Skill against the large-scale ERA-Interim data gives insight into the potential, additional skill of dynamical downscaling. To quantify it, we transform the absolute performance measures to relative skill measures against ERA-Interim. Their field significance is rigorously estimated and locally significant regions are highlighted. Statistical distributions are better reproduced in summer than in winter. In both seasons WRF is too dry over mountain tops due to underestimated and too rare high and underestimated and too frequent small precipitations. In winter WRF is too wet at windward sides and land-sea transition regions due to too frequent weak and moderate precipitation events. In summer it is too dry over land-sea transition regions due to underestimated small and too rare moderate precipitations, and too wet in some river valleys due to too frequent high precipitations. Additional skill relative to ERA-Interim is documented for overall measures as well as measures regarding the spread and tails of the statistical distribution, but not regarding mean seasonal precipitation. The added

  15. Precipitation Prediction in North Africa Based on Statistical Downscaling

    NASA Astrophysics Data System (ADS)

    Molina, J. M.; Zaitchik, B.

    2013-12-01

    Although Global Climate Models (GCM) outputs should not be used directly to predict precipitation variability and change at the local scale, GCM projections of large-scale features in ocean and atmosphere can be applied to infer future statistical properties of climate at finer resolutions through empirical statistical downscaling techniques. A number of such downscaling methods have been proposed in the literature, and although all of them have advantages and limitations depending on the specific downscaling problem, most of them have been developed and tested in developed countries. In this research, we explore the use of statistical downscaling to generate future local precipitation scenarios in different locations in Northern Africa, where available data is sparse and missing values are frequently observed in the historical records. The presence of arid and semiarid regions in North African countries and the persistence of long periods with no rain pose challenges to the downscaling exercise since normality assumptions may be a serious limitation in the application of traditional linear regression methods. In our work, the development of monthly statistical relationships between the local precipitation and the large-scale predictors considers common Empirical Orthogonal Functions (EOFs) from different NCAR/Reanalysis climate fields (e.g., Sea Level Pressure (SLP) and Global Precipitation). GCM/CMIP5 data is considered in the predictor data set to analyze the future local precipitation. Both parametric (e.g., Generalized Linear Models (GLM)) and nonparametric (e,g,, Bootstrapping) approaches are considered in the regression analysis, and different spatial windows in the predictor fields are tested in the prediction experiments. In the latter, seasonal spatial cross-covariance between predictant and predictors is estimated by means of a teleconnections algorithm which was implemented to define the regions in the predictor domain that better captures the

  16. Use of regional climate models in climate based ecosystem studies

    SciTech Connect

    Hostetler, S.

    1995-09-01

    Regional climate models (RCMs) use horizontal grid spacings on the order of tens of kilometers and thus are able to simulate the climate of a limited area at resolutions much higher than can be attained by general circulation models (horizontal scales of several degrees of hundreds of kilometers). The fine mesh of RCMs allows regional scale features that exert forcings on climate (e.g., lakes, mountains, coastlines) to be resolved. As a result, RCM simulations begin to reflect the heterogeneity of climate that supports the spatially diverse distribution of ecosystems in western North American. Examples of model simulations and comparisons with reconstructions of vegetation during the last glacial maximum (21 K CAL) will be presented.

  17. 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

  18. 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

  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. 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.

  1. "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

  2. Regional climate change projections over South America based on the CLARIS-LPB RCM ensemble

    NASA Astrophysics Data System (ADS)

    Samuelsson, Patrick; Solman, Silvina; Sanchez, Enrique; Rocha, Rosmeri; Li, Laurent; Marengo, José; Remedio, Armelle; Berbery, Hugo

    2013-04-01

    CLARIS-LPB was an EU FP7 financed Europe-South America Network for Climate Change Assessment and Impact Studies in La Plata Basin. CLARIS-LPB has created the first ensemble ever of RCM downscalings over South America. Here we present the climate change scenarios for a near future period (2011-2040) and for a far future period (2071-2100). The ensemble is based on seven RCMs driven by three CMIP3 GCMs for emission scenario SRES A1B. The RCM model domains cover all of South America, with a horizontal resolution of approximately 50 km, but project focus has been on results over the La Plata Basin. The ensemble mean for temperature change shows more warming over tropical South America than over the southern part of the continent. During summer (DJF) the Low-Parana and Uruguay regions show less warming than the surrounding regions. For the ensemble mean of precipitation changes the patterns are almost the same for near and far future but with larger values for far future. Thus overall trends do not change with time. The near future shows in general small changes over large areas (less than ±10%). For JJA a dry tendency is seen over eastern Brazil that becomes stronger and extends geographically with time. In near future most models show a drying trend over this area. In far future almost all models agree on the drying. For DJF a wet tendency is seen over the La Plata basin area which becomes stronger with time. In near future almost all downscalings agree on this wet tendency and in far future all downscalings agree on the sign. The RCM ensemble is unbalanced with respect to forcing GCMs. 6 out of 11(10) simulations use ECHAM5 for the near(far) future period while 4(3) use HadCM3 and only one IPSL. Thus, all ensemble mean values will be tilted towards ECHAM5. It is of course possible to compensate for this imbalance among GCMs by some weighting but no such weighting has been applied for the current analysis. The north-south gradient in warming is in general stronger in

  3. 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

  4. 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.

  5. 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...

  6. Integrating a 1D Thermal Lake Model into a Global and Regional Climate Model: Model Evaluation and Regional Climate Simulation

    NASA Astrophysics Data System (ADS)

    Subin, Z. M.; Riley, W. J.

    2009-12-01

    Compared to solid ground, lakes tend to have decreased albedo, increased ground heat conductance, and increased effective ground heat capacity. These features alter local surface fluxes compared to nearby vegetation, which in turn alter the climate of the nearby atmosphere and surrounding land areas. Interest in feedbacks between lake behavior and climate change provides motivation for including lakes in global climate models, as does the desire to do effective regional downscaling of climate model predictions over regions with large lake area fraction, like the Great Lakes region. Finally, the initiation, warming, and expansion of Arctic thermokarst lakes could provide an important geophysical and biogeochemical feedback to climate warming. The Community Land Model (CLM) 3.5 currently uses a 1D Hostetler lake scheme. We have updated this model to improve the characterization of surface fluxes, eddy diffusivity, and convective mixing. We also link the lake model with the full snow physics found over other land surface types (including 5 snow layers, aerosol deposition, partial transparency of snow layers, and snow aging), add phase change & ice physics to the lake model, and include soil layers beneath lakes. These soil layers will be an important component of future thermokarst lake modeling, as thermokarst lakes tend to form regions of unfrozen soil (talik) beneath them that become active sites for anaerobic decomposition of pre-modern peat. We have also integrated the updated lake model into a modified version of the Weather Research and Forecasting (WRF) Model 3.0. We will present comparisons between predicted and observed thermal conditions, snow and ice depths, and surface energy fluxes at several lake sites, using local meteorological forcing or integrated regional atmospheric coupling. The thermal predictions are generally reasonable and show a marked improvement from runs performed with the baseline CLM 3.5 version of the lake model. Over Sparkling Lake

  7. 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.

  8. 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

  9. The roles of bias-correction and resolution in regional climate simulations of summer extremes

    NASA Astrophysics Data System (ADS)

    PaiMazumder, Debasish; Done, James M.

    2015-09-01

    The suitability of dynamical downscaling in producing high-resolution climate scenarios for impact assessments is limited by the quality of the driving data and regional climate model (RCM) error. Multiple RCMs driven by a single global climate model simulation of current climate show a reduction in bias compared to the driving data, and the remaining bias motivates exploration of bias correction and higher RCM resolution. The merits of bias correcting the mean climate of the driving data (boundary bias correction) versus bias correcting the mean of the RCM output data are explored and compared to model resolution sensitivity. This analysis focuses on the simulation of summer temperature and precipitation extremes using a single RCM, the Nested Regional Climate Model (NRCM). The NRCM has a general cool bias for hot and cold extremes, a wet bias for wet extremes and a dry bias for dry extremes. Both bias corrections generally reduced the bias and overall error with some indication that boundary bias correction provided greater benefits than bias correcting the mean of the RCM output data, particularly for precipitation. High resolution tended not to lead to further improvements, though further work is needed using multiple resolution evaluation datasets and convection permitting resolution simulations to comprehensively assess the value of high resolution.

  10. 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.

  11. 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.

  12. 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., t