Science.gov

Sample records for regional climate downscaling

  1. Regional climate downscaling: What's the point?

    NASA Astrophysics Data System (ADS)

    Pielke, Roger A., Sr.; Wilby, Robert L.

    2012-01-01

    Dynamical and statistical downscaling of multidecadal global climate models provides finer spatial resolution information for climate impact assessments [Wilby and Fowler, 2010]. Increasingly, some scientists are using the language of "prediction" with respect to future regional climate change and impacts [e.g., Hurrell et al., 2009; Shapiro et al., 2010], yet others note serious reservations about the capability of downscaling to provide detailed, accurate predictions [see Kerr, 2011]. Dynamic downscaling is based on regional climate models (usually just the atmospheric part) that have finer horizontal grid resolution of surface features such as terrain [Castro et al., 2005]. Statistical downscaling uses transfer functions (e.g., regression relationships) representing observed relationships between larger-scale atmospheric variables and local quantities such as daily precipitation and/or temperature [Wilby and Fowler, 2010]. These approaches have been successful in improving the skill of numerical weather prediction. Statistical downscaling can also be used as the benchmark (the control) against which dynamic downscaling skill is judged [Landsea and Knaff, 2000

  2. 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.

  3. Stochastic downscaling of climate model precipitation outputs in orographically complex regions: 2. Downscaling methodology

    NASA Astrophysics Data System (ADS)

    Bordoy, R.; Burlando, P.

    2014-01-01

    A new methodology of stochastic downscaling of climate model precipitation outputs to subdaily temporal resolution and in a multisite framework is presented. The methodology is based on the reparameterization for future climate of the Spatiotemporal Neyman-Scott Rectangular Pulses model. The reparameterization is carried out by estimating the model parameters as done for the calibration of the model for the historical climate and using future statistics that are obtained: (i) applying to the daily historical statistics a factor of change computed from the control and future climate model outputs and (ii) by rescaling the altered daily statistics according to the scaling properties exhibited by the historical raw moments, in order to generate the future statistics at the temporal resolutions required by the reparameterization procedure. The downscaled scenarios are obtained in a multisite framework accounting for cross correlations among the stations. The methodology represents a robust, efficient, and unique approach to generate multiple series of spatially distributed subdaily precipitation scenarios by Monte Carlo simulation. It presents thus a unique alternative for addressing the internal variability of the precipitation process at high temporal and spatial resolution, as compared to other downscaling techniques, which are affected by both computational and resolution problems. The application of the presented approach is demonstrated for a region of complex orography where the model has proved to provide good results, in order to analyze potential changes in such vulnerable areas.

  4. How Useful Are Regional Climate Models For Downscaling Seasonal Forecasts?

    NASA Astrophysics Data System (ADS)

    Robertson, A. W.; Qian, J.; Moron, V.; Tippett, M.; Lucero, A.

    2010-12-01

    A longstanding yet very important question concerns the additional value derived from labor intensive regional climate models (RCMs) nested within GCM seasonal forecast models, over and above simple statistical methods of downscaling. This paper compares the two types of downscaling of precipitation "hindcasts" over the data-rich region of the Philippines, using observed data from 77 raingauges for the April-June monsoon onset season. Spatial interpolation of RCM and GCM grid box values to station locations is compared with cross-validated regression-based techniques such as canonical correlation analysis. The GCM "hindcasts" are formed from an ensemble of simulations from the ECHAM4.5 model at T42 resolution made with observed SSTs prescribed, over the 1977-2004 period. The RegCM3 with 25km resolution is nested within each of a 10-member GCM ensemble over the Philippines. To first order, we find that anomaly correlation skill at the station scale for simulations of seasonal total rainfall and monsoon onset date is quite similar using all the techniques considered, including simple spatial interpolation of the GCM values. The RCM has significantly smaller RMS error than the "raw" interpolated GCM, although statistical correction can greatly improve the latter. We examine the role of the availability of sufficiently long records of observed data as a deciding factor, which enters as a means to validate both types of the hindcasts, while being needed in addition to train the more "data hungry" statistical downscaling methods.

  5. 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

  6. 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.

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

    NASA Astrophysics Data System (ADS)

    Termonia, P.

    2015-12-01

    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 8 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.

  8. Development of hi-resolution regional climate scenarios in Japan by statistical downscaling

    NASA Astrophysics Data System (ADS)

    Dairaku, K.

    2016-12-01

    Climate information and services for Impacts, Adaptation and Vulnerability (IAV) Assessments are of great concern. To meet with the needs of stakeholders such as local governments, a Japan national project, Social Implementation Program on Climate Change Adaptation Technology (SI-CAT), launched in December 2015. It develops reliable technologies for near-term climate change predictions. Multi-model ensemble regional climate scenarios with 1km horizontal grid-spacing over Japan are developed by using CMIP5 GCMs and a statistical downscaling method to support various municipal adaptation measures appropriate for possible regional climate changes. A statistical downscaling method, Bias Correction Spatial Disaggregation (BCSD), is employed to develop regional climate scenarios based on CMIP5 RCP8.5 five GCMs (MIROC5, MRI-CGCM3, GFDL-CM3, CSIRO-Mk3-6-0, HadGEM2-ES) for the periods of historical climate (1970-2005) and near future climate (2020-2055). Downscaled variables are monthly/daily precipitation and temperature. File format is NetCDF4 (conforming to CF1.6, HDF5 compression). Developed regional climate scenarios will be expanded to meet with needs of stakeholders and interface applications to access and download the data are under developing. Statistical downscaling method is not necessary to well represent locally forced nonlinear phenomena, extreme events such as heavy rain, heavy snow, etc. To complement the statistical method, dynamical downscaling approach is also combined and applied to some specific regions which have needs of stakeholders. The added values of statistical/dynamical downscaling methods compared with parent GCMs are investigated.

  9. Impacts of Lateral-Boundary-Condition Errors on Regional Climate Downscaling: Lessons Learned from the NASA Downscaling Project

    NASA Astrophysics Data System (ADS)

    Wang, W.; Iguchi, T.; Case, J.; Kemp, E. M.; Putman, W.; Wu, D.; Ferraro, R.; Peters-Lidard, C. D.; Nemani, R. R.

    2015-12-01

    The ongoing NASA dynamical climate downscaling project runs the NASA Unified WRF (NU-WRF) model to produce high-resolution climate information with prescribed lateral boundary conditions (LBCs) from the agency's latest global reanalysis product MERRA2. The domain of the project is set over a vast area including the continental US and the adjacent oceans, so that a variety of important meteorological phenomena (e.g., atmospheric river, mesoscale convective system, and the northeastern winter storms) can be simultaneously simulated. The regional modeling experiments include combinations of three spatial-resolution configurations (i.e., 24km, 12km, and 4km) and three spectral-nudging configurations (i.e., "no nudging", "nudging over 2000km scales", and "nudging over 600km scales"). In addition to the regional downscaling efforts, MERRA2 data are also being downscaled globally to 12km resolution with the NASA GEOS-5 general circulation model. Outputs from these modeling experiments provide a unique opportunity for the science community to study many important questions in the field of climate downscaling. This study reports initial results regarding the impacts of LBC errors on the model experiments. Because in the regional modeling experiments information flows only one-way from MERRA2 to NUWRF, over time integration of the latter inevitably diverges from the former at the lateral boundaries as well as the interior of the domain. The differences between the un-nudged NUWRF runs and the MERRA2 data become distinguishable within a month or two of integration. Spectral nudging helps constrain the model differences between the downscaled and the original coarse-resolution climate fields. Specifically, nudging with more spatial signals (e.g., "600km") seems to generate higher fidelity than between the NUWRF outputs and the MERRA2 forcing data. However, stronger nudging does not necessarily enhance the comparison results between the simulated climate fields and other

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

    NASA Astrophysics Data System (ADS)

    Mathis, Moritz; Elizalde, Alberto; Mikolajewicz, Uwe

    2017-06-01

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

  11. 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...

  12. 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...

  13. 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

  14. 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

  15. 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.

  16. 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.

  17. 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

  18. 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.

  19. Regional downscaling of Mediterranean droughts under past and future climatic conditions

    NASA Astrophysics Data System (ADS)

    Hertig, Elke; Tramblay, Yves

    2017-04-01

    The complexity of the Mediterranean climate with its high precipitation variability and its unequal seasonal distribution with a wet season from approximately October to April and a dry season in summer set general conditions for a high vulnerability of the Mediterranean area to droughts. In the last few decades the risk of drought episodes appears to be enhanced in the Mediterranean area due to temperature increases combined with precipitation decreases. This general change towards warmer and dryer conditions is expected to continue in the future. In the present study droughts are represented by the Standardized Precipitation Index (SPI), at 114 stations located across the Mediterranean area. The SPI is a normalized measure of drought severity relative to a specific location, obtained from rainfall totals aggregated over different time periods. This allows a comparison of different locations and the delineation of homogeneous regions with similar SPI variability. 13 regions have been identified. A downscaling approach using circulation types based on geopotential heights and relative humidity as predictors has been set up to downscale the SPI time series in the different regions. The downscaling approach has been validated using running 21 years validation periods, in order to assess the skill of the method during different climatic conditions and to detect possible non-stationarities in the predictors-predictand relationships. Results show that the downscaling method provided satisfactory results, except for the most arid regions. Future projections, provided from a three member ensemble of the MPI-ESM-LR model under scenario RCP 8.5, indicate an increase in the drought severity and occurrence for the whole Mediterranean region for the period 2070-2100.

  20. Test of a dynamical downscaling chain for assessing climate at regional scale.

    NASA Astrophysics Data System (ADS)

    Vargiu, A.; Peneva, E.; Marrocu, M.

    2009-04-01

    During last years reanalysis datasets (ECMWF ERA40 or NCEP Reanalysis Project) have been widely used to investigate climate and detect some signals of global climate changes. Heavy limitations of those datasets are found when investigating the variables with intrinsic small coherence: precipitation, local winds, fogs, etc. Our aim was to perform a dynamical downscaling of ERA40 dataset using a local model (BOLAM, developed at the ISAC-CNR, Bologna, Italy). We focused our study mainly on precipitation verification. More specifically we verified the downscaling chain with CRU daily precipitation over Europe at 0.25 degrees. A test period, covering about a year, was studied adding up runs of 36 hours forecast. Some common verification indexes for precipitation, (ETS, POD, FAR, HK, etc.) were computed at different thresholds. The verification results have shown the benefits of the downscaling chain particularly for events of deep convective precipitation and precipitation forced by orography. Comparison of the results obtained using the BOLAM model and a specific regional climate model (REGCM3, developed at the ICTP, Trieste, Italy) will be also discussed.

  1. 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.

  2. 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

  3. Regional dynamical downscaling for urban environment to estimate the potential impact of climate change

    NASA Astrophysics Data System (ADS)

    Göndöcs, Júlia; Breuer, Hajnalka; Pongrácz, Rita; Bartholy, Judit

    2017-04-01

    The RegCM regional climate model is designed to capture the regional meteorological processes with finer horizontal resolution than the global climate models, however, the scale of urban processes requires even finer scale, and definitely non-hydrostatic approach. Furthermore, in our target area, i.e. the Carpathian Basin, the built-up areas are not presented well enough (this is especially true for Budapest, the capital of Hungary). That is why to analyse the effects of climate change on urban environment dynamical downscaling should use finer scale and more complex terrain. In our model configuration, downscaling is carried out with the non-hydrostatic mesoscale Weather Research and Forecasting (WRF) Model, with updated surface databases, such as land use (with 5 urban surface categories), climatological albedo, topography, and spatial distribution of urban parameters. To execute the model, the initial fields needed by WRF are initialized using the RegCM (RegCM4.3) RCP4.5 and RCP8.5 output fields at every 6 hours on selected dates during three periods (past: 1971-2000; future: 2016-2045 and 2061-2090). Earlier studies showed that the frequency and the lifetime of heat waves are projected to last longer and be more intense, which causes further stress both for the human body and the environment. Based on these considerations, the WRF model coupled to multilayer urban canopy parameterisation was run only for the heat wave days in July from the aforementioned periods in the past and in the future, as well. In order to keep the stability of the simulations, the entire downscaling is carried out in several steps using gradually smaller domains embedded to each other. Thus, three embedded target areas have been determined for this modelling study, the largest external area covers the whole Pannonian region with 10 km horizontal resolution, whereas the innermost domain covers Budapest and its surroundings with 1 km grid resolution. Among the numerous derived fields

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2013-04-01

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

  11. 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

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

    NASA Astrophysics Data System (ADS)

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

    2010-12-01

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

  13. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2016-12-01

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

  15. 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.

  16. 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.

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

    SciTech Connect

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

    2016-09-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2017-04-01

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

  19. 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.

  20. Projection of wave conditions in response to climate change: A community approach to global and regional wave downscaling

    USGS Publications Warehouse

    Erikson, Li H.; Hemer, M.; Lionello, Piero; Mendez, Fernando J.; Mori, Nobuhito; Semedo, Alvaro; Wang, Xiaolan; Wolf, Judith

    2015-01-01

    Future changes in wind-wave climate have broad implications for coastal geomorphology and management. General circulation models (GCM) are now routinely used for assessing climatological parameters, but generally do not provide parameterizations of ocean wind-waves. To fill this information gap, a growing number of studies use GCM outputs to independently downscale wave conditions to global and regional levels. To consolidate these efforts and provide a robust picture of projected changes, we present strategies from the community-derived multi-model ensemble of wave climate projections (COWCLIP) and an overview of regional contributions. Results and strategies from one contributing regional study concerning changes along the eastern North Pacific coast are presented.

  1. 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...

  2. 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...

  3. 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.

  4. Dynamical downscaling of historical climate over CORDEX Central America domain with a regionally coupled ocean-atmosphere model

    NASA Astrophysics Data System (ADS)

    Quintanar, Arturo I.; Sein, Dmitry; Martinez-Lopez, Benjamin; Cabos, William; Ochoa-Moya, Carlos-Abraham

    2017-04-01

    ABSTRACT Recently, there has been a concerted effort by several research groups to model precipitation variability for North America and Central America in the context of the Coordinated Regional Climate Downscaling Experiment (CORDEX). One important objective of CORDEX is to dynamically downscale global output from coupled and non-coupled models and to objectively gauge a measure of added value from higher resolution boundary conditions forcing regional atmospheric and ocean models at their lateral walls. Up to now, a sufficiently large computational domain covering from the southern US to the north of South America including the eastern Pacific Ocean, the southern Atlantic and the Caribbean Seas has been lacking. Such a computational domain permits the analysis of an ample range of inter-annual and intra-seasonal time-scales phenomena and the possibility of exploring sensitivity to horizontal resolution. To date, most simulations performed for the region remain too coarse to be of any use at the regional scale but also, and most importantly, most modeling studies of the region rely on regional atmospheric models forced at their lower boundaries by prescribed sea surface temperature. In this work we explore both, climate sensitivity to coupling and, to the choice of horizontal resolution, using a regional atmospheric model (REMO) coupled to a global ocean model (MPI-OM). External atmospheric forcing is applied to REMO at its lateral walls and over the ocean surface that is not coupled to REMO. In order to gain insight into the sensitivity to the choice of the atmospheric forcing, two sources are used: 1) ERA-Interim and 2) a global free run of the MPI-ESM coupled system. Preliminary results suggest that the original biases between the ERA-Interim and the MPI-ESM forcing data tend to become similar when comparing the downscaled simulations at 50 km and 25 km atmospheric resolutions. Additionally, biases at 25 km tend to become smaller over most of the computational

  5. Influences of Regional Climate Change on Air Quality Across the Continental U.S. Projected from Downscaling IPCC AR5 Simulations. Chapter 2

    NASA Technical Reports Server (NTRS)

    Nolte, Christopher; Otte, Tanya; Pinder, Robert; Bowden, J.; Herwehe, J.; Faluvegi, Gregory; Shindell, Drew

    2013-01-01

    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 Change (IPCC) Fifth Assessment Report (AR5) do not fully resolve regional-scale processes and therefore cannot capture regional-scale changes in temperatures and precipitation. We use a regional climate model (RCM) to dynamically downscale the GCM's large-scale signal to investigate the changes in regional and local extremes of temperature and precipitation that may result from a changing climate. In this paper, we show preliminary results from downscaling the NASA/GISS ModelE IPCC AR5 Representative Concentration Pathway (RCP) 6.0 scenario. We use the Weather Research and Forecasting (WRF) model as the RCM to downscale decadal time slices (1995-2005 and 2025-2035) and illustrate potential changes in regional climate for the continental U.S. that are projected by ModelE and WRF under RCP6.0. The regional climate change scenario is further processed using the Community Multiscale Air Quality modeling system to explore influences of regional climate change on air quality.

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

    NASA Astrophysics Data System (ADS)

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

    2013-05-01

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

  7. 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.

  8. 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

    2017-08-01

    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.

  9. Application of physical scaling towards downscaling climate model precipitation data

    NASA Astrophysics Data System (ADS)

    Gaur, Abhishek; Simonovic, Slobodan P.

    2017-03-01

    Physical scaling (SP) method downscales climate model data to local or regional scales taking into consideration physical characteristics of the area under analysis. In this study, multiple SP method based models are tested for their effectiveness towards downscaling North American regional reanalysis (NARR) daily precipitation data. Model performance is compared with two state-of-the-art downscaling methods: statistical downscaling model (SDSM) and generalized linear modeling (GLM). The downscaled precipitation is evaluated with reference to recorded precipitation at 57 gauging stations located within the study region. The spatial and temporal robustness of the downscaling methods is evaluated using seven precipitation based indices. Results indicate that SP method-based models perform best in downscaling precipitation followed by GLM, followed by the SDSM model. Best performing models are thereafter used to downscale future precipitations made by three global circulation models (GCMs) following two emission scenarios: representative concentration pathway (RCP) 2.6 and RCP 8.5 over the twenty-first century. The downscaled future precipitation projections indicate an increase in mean and maximum precipitation intensity as well as a decrease in the total number of dry days. Further an increase in the frequency of short (1-day), moderately long (2-4 day), and long (more than 5-day) precipitation events is projected.

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

    NASA Astrophysics Data System (ADS)

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

    2012-08-01

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

  11. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2012-04-01

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

  13. CORDEX: A Coordinated Regional Downscaling Experiment (Invited)

    NASA Astrophysics Data System (ADS)

    Jones, C.

    2010-12-01

    CORDEX is an initiative of the Regional Climate Downscaling community, supported by the World Climate Research Program (WCRP). CORDEX has four major objectives: (i)Evaluating and improving dynamical and statistical regional climate downscaling (RCD) techniques, (ii) To produce a new generation of RCD-based climate projections for the majority of land-regions of the world,(iii) To enhance communication between climate scientists and impact, adaptation and vulnerability (IAV) community and (iv) to increase the engagement of climate scientists from developing countries in regional climate change science. CORDEX entails the completion and intercomparison of ensembles of different Regional Climate Model (RCM) simulations using the same domains, resolution and lateral boundary conditions, derived either from reanalysis data or coupled Global Climate Model (GCM) simulations. In the latter case, boundary data from a selected set of GCM historical runs and future projections made in the 5th Coupled Model Intercomparison Project (CMIP5) will form an ensemble of driving data. In this presentation I outline the main objectives of CORDEX, procedures so far followed in terms of regions under study, domain structure, boundary condition data and subsequent plans for accessibility of the CORDEX output to interested users around the world. Internationally, RCD groups have agreed to target Africa as an initial coordinated region in terms of generating and analysing regional climate projections in support of the IAV community. As a first step an intercomparison exercise is underway comparing RCM simulations over the African continent, driven by the ERA-interim reanalysis data for the period 1989-2008. To date 14 RCM groups have submitted results for this intercomparsion, on a common grid. A preliminary evaluation of these simulations will be given in the talk.

  14. Regional Climate Downscaling Of African Climate Using A High-Resolution Global Atmospheric Model: Validation And Future Projection

    NASA Astrophysics Data System (ADS)

    Raj, J.; Stenchikov, G. L.; Bangalath, H.

    2013-12-01

    Climate change impact assessment and adaptation planning require region specific information with high spatial resolution, since the climate and weather effects are directly felt at the local scale. While most of the state-of-the-art General Circulation Models lack adequate spatial resolution, regional climate models (RCM) used in a nested domain are generally incapable of incorporating the two-way exchanges between regional and global climate. In this study we use a very high resolution atmospheric general circulation model HiRAM, developed at NOAA GFDL, to investigate the regional climate changes over CORDEX African domain. The HiRAM simulations are performed with a horizontal grid spacing of 25 km, which is an ample resolution for regional climate simulation. HiRAM has the advantage of naturally describing interaction between regional and global climate. Historic (1975-2004) simulations and future (2007-2050) projections, with both RCP 4.5 and RCP 8.5 pathways, are conducted in line with the CORDEX protocol. A coarse resolution sea surface temperature (SST) is prescribed from the GFDL Earth System Model runs of IPPC AR5, as bottom boundary condition over ocean. The GFDL Land Surface Model (LM3) is employed to calculate physical processes at surface and in soil. The preliminary analysis of the performance of HiRAM, using historic runs, shows it reproduces the regional climate adequately well in comparison with observations. Significant improvement in the simulation of regional climate is evident in comparison with the coarse resolution driving model. Future projections predict an increase in atmospheric temperature over Africa with stronger warming in the subtropics than in tropics. A significant strengthening of West African Monsoon and a southward shift of the summer rainfall maxima over Africa is predicted in both RCP 4.5 and RCP8.5 scenarios.

  15. Downscaling with a Nested Regional Climate Model in Near-Surface Fields over the Contiguous United States

    SciTech Connect

    Wang, Jiali; Kotamarthi, Veerabhadra R.

    2014-07-27

    The Weather Research and Forecasting (WRF) model is used for dynamic downscaling of 2.5 degree National Centers for Environmental Prediction-U.S. Department of Energy Reanalysis II (NCEP-R2) data for 1980-2010 at 12 km resolution over most of North America. The model's performance for surface air temperature and precipitation is evaluated by comparison with high-resolution observational data sets. The model's ability to add value is investigated by comparison with NCEP-R2 data and a 50 km regional climate simulation. The causes for major model bias are studied through additional sensitivity experiments with various model setup/integration approaches and physics representations. The WRF captures the main features of the spatial patterns and annual cycles of air temperature and precipitation over most of the contiguous United States. However, simulated air temperatures over the south central region and precipitation over the Great Plains and the Southwest have significant biases. Allowing longer spin-up time, reducing the nudging strength, or replacing the WRF Single-Moment 6-class microphysics with Morrison microphysics reduces the bias over some subregions. However, replacing the Grell-Devenyi cumulus parameterization with Kain-Fritsch shows no improvement. The 12 km simulation does add value above the NCEP-R2 data and the 50 km simulation over mountainous and coastal zones.

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

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  17. 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

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

    NASA Astrophysics Data System (ADS)

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

    2016-10-01

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

  19. 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

  20. Dynamical Downscaling over the Great Lakes Basin of North America using the WRF Regional Climate Model: The impact of the Great Lakes system on regional greenhouse warming

    NASA Astrophysics Data System (ADS)

    Gula, J.; Peltier, W. R.

    2011-12-01

    In this study we investigate the regional climate changes to be expected over the Great Lakes Basin of North America during the next century. Large freshwater systems, such as the Great Lakes, play a key role in determining the climate of their basins and adjacent regions by air mass modification through the exchange of heat and moisture with the atmosphere. Even systems as extensive as the Great Lakes are unresolved in coarse resolution global climate simulations but may be accurately captured in finer-mesh regional simulations by dynamical downscaling. Historical (1979-2001) and future (2050-2060 and 2090-2100) conditions are simulated using the Weather Research and Forecasting model (WRF) forced by CCSM3 global simulations. Our analyses are based upon the IPCC SRES A2 and A1B emissions scenarios. A two-step nesting procedure is employed for the purpose of downscaling, in which the first nested WRF model is of North American continental scale at 30 km resolution, whereas the innermost domain at 10 km resolution covers the Great Lakes Basin and the Canadian Province of Ontario. The differences in extreme temperature and precipitation events delivered by the different scales of simulation are discussed. As the WRF model does not currently have an explicit lake component, lake ice and lake surface temperature need to be prescribed in the model. A first set of simulations is performed using climatological 1979-2001) data for lake ice and lake surface temperature. A second set is performed using outputs from the freshwater lake model "FLake" (Mironov, D. V., 2008, COSMO Technical Report, No. 11, Deutscher Wetterdienst, Offenbach am Main, Germany) forced by atmospheric fields from the global simulations. A third set is performed using an interactive coupling of the lake model FLake with the regional model WRF. Changes in surface temperatures and ice cover, and especially ice-out dates, for the Great Lakes under future atmospheric conditions are discussed. The trends in

  1. Dynamic downscaling of 22-year CFS winter seasonal hindcasts with the UCLA-ETA regional climate model over the United States

    NASA Astrophysics Data System (ADS)

    De Sales, Fernando; Xue, Yongkang

    2013-07-01

    This study evaluates the UCLA-ETA regional model's dynamic downscaling ability to improve the National Center for Environmental Prediction Climate Forecast System (NCEP CFS), winter season predictions over the contiguous United States (US). Spatial distributions and temporal variations of seasonal and monthly precipitation are the main focus. A multi-member ensemble means of 22 winters from 1982 through 2004 are included in the study. CFS over-predicts the precipitation in eastern and western US by as much as 45 and 90 % on average compared to observations, respectively. Dynamic downscaling improves the precipitation hindcasts across the domain, except in the southern States, by substantially reducing the excessive precipitation produced by the CFS. Average precipitation root-mean-square error for CFS and UCLA-ETA are 1.5 and 0.9 mm day-1, respectively. In addition, downscaling improves the simulation of spatial distribution of snow water equivalent and land surface heat fluxes. Despite these large improvements, the UCLA-ETA's ability to improve the inter-annual and intra-seasonal precipitation variability is not clear, probably because of the imposed CFS' lateral boundary conditions. Preliminary analysis of the cause for the large precipitation differences between the models reveals that the CFS appears to underestimate the moisture flux convergence despite producing excessive precipitation amounts. Additionally, the comparison of modeled monthly surface sensible and latent heat fluxes with Global Land Data Assimilation System land data set shows that the CFS incorrectly partitioned most of surface energy into evaporation, unlike the UCLA-ETA. These findings suggest that the downscaling improvements are mostly due to a better representation of land-surface processes by the UCLA-ETA. Sensitivity tests also reveal that higher-resolution topography only played a secondary role in the dynamic downscaling improvement.

  2. 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

  3. 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.

  4. The Coordinated Regional Downscaling Experiment (CORDEX): A Framework for Mitigation and Adaptation Information (Invited)

    NASA Astrophysics Data System (ADS)

    Gutowski, W. J.; Wcrp Task Force On Regional Climate Downscaling

    2010-12-01

    The Coordinated Regional Downscaling Experiment (CORDEX) is a program developed by the Task Force on Regional Climate Downscaling of World Climate Research Programme (WCRP). The Task Force’s mandate is to develop a framework to evaluate regional climate downscaling techniques; foster an international coordinated effort to develop improved downscaling techniques and to provide feedback to the global modeling community; and promote greater interactions between global climate modelers, downscalers and end-users. Within this mandate, the primary goal of CORDEX is to extend to a global framework the lessons learned from regional climate downscaling programs focused on one continent. The framework includes statistical downscaling as well as regional climate models (RCMs), with an aim of evaluating the strengths and weaknesses of downscaled climate information. CORDEX also provides coordination among existing and emerging downscaling programs around the world. This talk will emphasize the statistical downscaling component of CORDEX. CORDEX has defined a set of target regions covering most land areas of the planet. A primary region of emphasis is Africa, which has received less attention than most other continents in regional climate-change and climate-impacts research. Baseline downscaling efforts by statistical downscaling and RCMs have started, focusing on the period covered by the ERA-Interim Reanalysis: 1987-2007. Future work will include downscaling GCM output for extended periods in the twentieth and twenty-first centuries, where future projections will be based on Representative Concentration Pathway (RCP) greenhouse gas and aerosol scenarios, specifically RCP 4.5 and RCP 8.5. CORDEX has established a preliminary set of archival protocols and targeted variables for output that will be stored in a central, openly accessible repository. Although CORDEX intends to produce simulations and analyses for the IPCC Fifth Assessment Report, the WCRP Task Force views CORDEX

  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. Climate change indices for Greenland applied directly for other arctic regions - Enhanced and utilized climate information from one high resolution RCM downscaling for Greenland evaluated through pattern scaling and CMIP5

    NASA Astrophysics Data System (ADS)

    Olesen, M.; Christensen, J. H.; Boberg, F.

    2016-12-01

    Climate change indices for Greenland applied directly for other arctic regions - Enhanced and utilized climate information from one high resolution RCM downscaling for Greenland evaluated through pattern scaling and CMIP5Climate change affects the Greenlandic society both advantageously and disadvantageously. Changes in temperature and precipitation patterns may result in changes in a number of derived society related climate indices, such as the length of growing season or the number of annual dry days or a combination of the two - indices of substantial importance to society in a climate adaptation context.Detailed climate indices require high resolution downscaling. We have carried out a very high resolution (5 km) simulation with the regional climate model HIRHAM5, forced by the global model EC-Earth. Evaluation of RCM output is usually done with an ensemble of downscaled output with multiple RCM's and GCM's. Here we have introduced and tested a new technique; a translation of the robustness of an ensemble of GCM models from CMIP5 into the specific index from the HIRHAM5 downscaling through a correlation between absolute temperatures and its corresponding index values from the HIRHAM5 output.The procedure is basically conducted in two steps: First, the correlation between temperature and a given index for the HIRHAM5 simulation by a best fit to a second order polynomial is identified. Second, the standard deviation from the CMIP5 simulations is introduced to show the corresponding standard deviation of the index from the HIRHAM5 run. The change of specific climate indices due to global warming will then be possible to evaluate elsewhere corresponding to the change in absolute temperature.Results based on selected indices with focus on the future climate in Greenland calculated for the rcp4.5 and rcp8.5 scenarios will be presented.

  7. An evaluation of dynamical downscaling of Central Plains summer precipitation using a WRF-based regional climate model at a convection-permitting 4 km resolution

    NASA Astrophysics Data System (ADS)

    Sun, Xuguang; Xue, Ming; Brotzge, Jerald; McPherson, Renee A.; Hu, Xiao-Ming; Yang, Xiu-Qun

    2016-12-01

    A significant challenge with dynamical downscaling of climate simulations is the ability to accurately represent convection and precipitation. The use of convection-permitting resolutions avoids cumulus parameterization, which is known to be a large source of uncertainty. A regional climate model (RCM) based on the Weather Research and Forecasting model is configured with a 4 km grid spacing and applied to the U.S. Great Plains, a region characterized by many forms of weather and climate extremes. The 4 km RCM is evaluated by running it in a hindcast mode over the central U.S. region for a 10 year period, forced at the boundary by the 32 km North America Regional Reanalysis. The model is also run at a 25 km grid spacing, but with cumulus parameterization turned on for comparison. The 4 km run more successfully reproduces certain observed features of the Great Plains May-through-August precipitation. In particular, the magnitude of extreme precipitation and the diurnal cycle of precipitation over the Great Plains are better simulated. The 4 km run more realistically simulates the low-level jet and related atmospheric circulations that transport and redistribute moisture from Gulf of Mexico. The convection-permitting RCM may therefore produce better dynamical downscaling of future climate when nested within global model climate projections, especially for extreme precipitation magnitudes. The 4 km and 25 km simulations do share similar precipitation biases, including low biases over the central Great Plains and high biases over the Rockies. These biases appear linked to circulation biases in the simulations, but determining of the exact causes will require extensive, separate studies.

  8. A review on regional dynamical downscaling in intraseasonal to seasonal simulation/prediction and major factors that affect downscaling ability

    NASA Astrophysics Data System (ADS)

    Xue, Yongkang; Janjic, Zavisa; Dudhia, Jimy; Vasic, Ratko; De Sales, Fernando

    2014-10-01

    Regional climate models (RCMs) have been developed and extensively applied for dynamically downscaling coarse resolution information from different sources, such as general circulation models (GCMs) and reanalyses, for different purposes including past climate simulations and future climate projection. Thus far, the nature, the methods, and a number of crucial issues concerning the use of dynamic downscaling are still not well understood. The most important issue is whether, and if so, under what conditions dynamic downscaling is really capable of adding more information at different scales compared to the GCM or reanalysis that imposes lateral boundary conditions (LBCs) to the RCMs. There are controversies regarding the downscaling ability. In this review paper we present several factors that have consistently demonstrated strong impact on dynamic downscaling ability in intraseasonal and seasonal simulations/predictions and future projection. Those factors include setting of the RCM experiment (e.g. imposed LBC quality, domain size and position, LBC coupling, and horizontal resolution); as well as physical processes, mainly convective schemes and vegetation and soil processes that include initializations, vegetation specifications, and planetary boundary layer and surface coupling. These studies indicate that RCMs have downscaling ability in some aspects but only under certain conditions. Any significant weaknesses in one of these aspects would cause an RCM to lose its dynamic downscaling ability. This paper also briefly presents challenges faced in current RCM dynamic downscaling and future prospective, which cover the application of coupled ocean-atmosphere RCMs, ensemble applications, and future projections.

  9. Globally downscaled climate projections for assessing the conservation impacts of climate change.

    PubMed

    Tabor, Karyn; Williams, John W

    2010-03-01

    Assessing the potential impacts of 21st-century climate change on species distributions and ecological processes requires climate scenarios with sufficient spatial resolution to represent the varying effects of climate change across heterogeneous physical, biological, and cultural landscapes. Unfortunately, the native resolutions of global climate models (usually approximately 2 degrees x 2 degrees or coarser) are inadequate for modeling future changes in, e.g., biodiversity, species distributions, crop yields, and water resources. Also, 21st-century climate projections must be debiased prior to use, i.e., corrected for systematic offsets between modeled representations and observations of present climates. We have downscaled future temperature and precipitation projections from the World Climate Research Programme's (WCRP's) CMIP3 multi-model data set to 10-minute resolution and debiased these simulations using the change-factor approach and observational data from the Climatic Research Unit (CRU). These downscaled data sets are available online and include monthly mean temperatures and precipitation for 2041-2060 and 2081-2100, for 24 climate models and the A1B, A2, and B1 emission scenarios. This paper describes the downscaling method and compares the downscaled and native-resolution simulations. Sharp differences between the original and downscaled data sets are apparent at regional to continental scales, particularly for temperature in mountainous areas and in areas with substantial differences between observed and simulated 20th-century climatologies. Although these data sets in principle could be downscaled further, a key practical limitation is the density of observational networks, particularly for precipitation-related variables in tropical mountainous regions. These downscaled data sets can be used for a variety of climate-impact assessments, including assessments of 21st-century climate-change impacts on biodiversity and species distributions.

  10. Climate change at local level : let's look around downscaling

    NASA Astrophysics Data System (ADS)

    Ravenel, H.; Jan, J.; Moisselin, J. M.; Pagé, C.

    2009-09-01

    Weather services and climatologists in research centre are overwhelmed by requests from local authorities about climate change in their regions. Most of the times local authorities want initially a level of precision in terms of time and space scale which far beyond the scientific knowledge we have for the time being. The communication will build upon several experiences of such requests and show the importance of building common language and confidence between the different actors that are to be involved in downscaling exercise. The goal is to bridge the gap between initial requests by decision makers and existing scientific knowledge. UNDP (United Nations Development Program) set up recently a unit called ClimSAT to help regions (sub national authorities) to establish mitigation and adaptation action plans. ClimSAT already initiated such plans in Uruguay, Albania, Uganda, Senegal, Morocco, … Météo-France takes part to ClimSAT for instance by explaining the importance of data rescue, providing with latest information about climate change impacts and stressing the interests to involve national weather services in regional climate change action plans, … In Basse Normandie, Bretagne and Pays de Loire, Météo-France has been involved in several processes aiming ultimately at building local climate change action plans. For the time being, no real dynamical or statistical downscaling exercise have been launched : For impacts on precipitation pattern, IPCC models do not really agree on this zone, so downscaling is not really pertinent. For temperature, the climate change signal is clearer, but downscaling won't give much more information. Of course on other meteorogical parameters or on other variable that are linked to meteorological parameters, downscaling could be of interest and will probably be necessary. With or without downscaling, the stake is to build, at a local level, mechanisms which are similar to IPCC and UNFCCC. In that context, downscaling could

  11. 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.

  12. 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

  13. 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.

  14. 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.

  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. 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.

  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. Comparison of runoff modelled using rainfall from different downscaling methods for historical and future climates

    NASA Astrophysics Data System (ADS)

    Chiew, F. H. S.; Kirono, D. G. C.; Kent, D. M.; Frost, A. J.; Charles, S. P.; Timbal, B.; Nguyen, K. C.; Fu, G.

    2010-06-01

    SummaryThis paper: (i) assesses the rainfall downscaled from three global climate models (GCMs) using five downscaling models, (ii) assesses the runoff modelled by the SIMHYD rainfall-runoff model using the downscaled daily rainfall, and (iii) compares the modelled changes in future rainfall and runoff characteristics. The modelling study is carried out using rainfall and streamflow data from eight unimpaired catchments near the headwaters of the Murray River in south-east Australia. The downscaling models used, in increasing order of complexity, are a daily scaling model, an analogue statistical downscaling model, GLIMCLIM and NHMM parametric statistical downscaling models, and CCAM dynamic downscaling model. All the downscaling models can generally reproduce the observed historical rainfall characteristics. The rainfall-runoff modelling using downscaled rainfall also generally reproduces the observed historical runoff characteristics. The future simulations are most similar between the daily scaling, analogue and NHMM models, all of them simulating a drier future. The GLIMCLIM and CCAM models simulate a smaller decrease in future rainfall. The differences between the modelled future runoff using the different downscaled rainfall can be significant, and this needs to be further investigated in the context of projections from a large range of GCMs and different hydrological models and applications. The simpler to apply daily scaling and analogue models (they also directly provide gridded rainfall inputs) can be relatively easily used for impact assessments over very large regions. The parametric downscaling models offer potential improvements as they capture a fuller range of daily rainfall characteristics.

  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 Astrophysics Data System (ADS)

    Druyan, Leonard M.; Fulakeza, Matthew

    2016-12-01

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

  2. 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.

  3. 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.

  4. 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

  5. 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

  6. 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.

  7. 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.

  8. 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.; hide

    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.

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

    USGS Publications Warehouse

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

    2000-01-01

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

  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. 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 ...

  12. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2016-12-01

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

  14. WCRP COordinated Regional Downscaling EXperiment (CORDEX): a diagnostic MIP for CMIP6

    NASA Astrophysics Data System (ADS)

    Gutowski, William J., Jr.; Giorgi, Filippo; Timbal, Bertrand; Frigon, Anne; Jacob, Daniela; Kang, Hyun-Suk; Raghavan, Krishnan; Lee, Boram; Lennard, Christopher; Nikulin, Grigory; O'Rourke, Eleanor; Rixen, Michel; Solman, Silvina; Stephenson, Tannecia; Tangang, Fredolin

    2016-11-01

    The COordinated Regional Downscaling EXperiment (CORDEX) is a diagnostic model intercomparison project (MIP) in CMIP6. CORDEX builds on a foundation of previous downscaling intercomparison projects to provide a common framework for downscaling activities around the world. The CORDEX Regional Challenges provide a focus for downscaling research and a basis for making use of CMIP6 global climate model (GCM) output to produce downscaled projected changes in regional climates and assess sources of uncertainties in the projections, all of which can potentially be distilled into climate change information for vulnerability, impacts and adaptation studies. CORDEX Flagship Pilot Studies advance regional downscaling by targeting one or more of the CORDEX Regional Challenges. A CORDEX-CORE framework is planned that will produce a baseline set of homogeneous high-resolution, downscaled projections for regions worldwide. In CMIP6, CORDEX coordinates with ScenarioMIP and is structured to allow cross comparisons with HighResMIP and interaction with the CMIP6 VIACS Advisory Board.

  15. 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.

  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. Downscaling of climate parameters using Active Learning Method (ALM)

    NASA Astrophysics Data System (ADS)

    Sodoudi, S.; Reimer, E.

    2009-12-01

    This study is a part of main program RIMAX "risk management of extreme flood events“, which concerns itself of extremes floodwater and damage potential in the Bode river basin in Germany with the variable occurrence of flood events in this area for the past 1000 years. The objective of the project is to produce the local climate time series (climate downscaling) as the input for a runoff model in the Bode basin for the last 1000 years on a grid of 5x5 km as well as the estimation of the spatial distributions and temporal variability of the precipitation, the amount of precipitation and further meteorological parameter (temperature, radiation and relative humidity) for this area. A nonlinear downscaling based on Fuzzy rules has been used to produce 1000 year climate time series. The global model ECHO from Max Planck institute for Meteorology (MPI) with T30 resolution and 1000 years data has been used as the global model (GCM). The regional model REMO, with 10 km resolution and 20 years data has been used as the regional input. The observations, which include 30 years precipitation, radiation, temperature, wind and relative humidity, have been used as output (predictand). In this study, two set fuzzy rules have been trained to describe the relationship between ECHO/REMO and REMO/Observation. The Fuzzy method used in this work is Active Learning Method (ALM). The heart of calculation of ALM is a fuzzy interpolation and curve fitting which is entitled Ink Drop Spread (IDS). The IDS searches fuzzily for continuous possible paths of interpolated data points on data planes. The ability of ALM to simulate the high values as well as the fluctuation of time series is much better than Takagi-Sugeno models, which have been used for downscaling in the last decade. In the next steps, considering predictors from the ECHO time series As well as the predictands from the REMO grid points, some ALM models are developed, which describe the fuzzy rules and the relationship between

  18. Estimating climate change for Southeast Europe: a dynamical downscaling approach

    NASA Astrophysics Data System (ADS)

    Sotiropoulou, Rafaella-Eleni P.; Tagaris, Efthimios; Sotiropoulos, Andreas; Spanos, Ioannis; Milonas, Panagiotis; Michaelakis, Antonios

    2015-04-01

    Mediterranean region is considered to be the most prominent climate response Hot-Spot since it is located in a transition zone between the arid climate of northern Africa and the wet climate of central Europe. Even a minor change in large scale climatic factors might impose large impacts on the climatic conditions of different Mediterranean areas. Furthermore, the complex topography and the vast coastlines suggest a fine scale spatial variability of the climatic conditions. Because of these, there is an increasing interest for this area. The objective of this study is to estimate the changes in climatic parameters (such as temperature and precipitation) over southeast Europe in the near future at a very fine grid resolution. The NASA GISS GCM ModelE is used to simulate current and future climate at a horizontal resolution of 2° × 2.5° latitude by longitude. The model accounts for both the seasonal and the diurnal solar cycles in its temperature calculations. It simulates the emissions, transport, chemical transformation and deposition of several chemical tracers. Sea surface temperatures (SST) are calculated using model-derived surface energy fluxes and specified ocean heat transports. The simulations cover the period from 1880 to 2061. Greenhouse gas concentrations up to 2008 are prescribed using ice-core measurements, while for the period 2009-2061 the GHG levels are supplied from the IPCC A1B emissions scenario. Since the outputs from the GCM are relatively coarse for applications to regional and local scales, the Weather Research and Forecasting (WRF version 3.4.1) model is used to dynamically downscale GCM simulations. The domain covers the south - southeast Europe in 273 x 161 horizontal grids of 9 km x 9 km, with 28 vertical layers. Because of the time needed for the downscaling procedure meteorological conditions are presented, here, for five current (i.e., 2008 - 2012) and five future (i.e., 2058-2062) years. Annual temperature is estimated to be higher

  19. 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.

  20. On a Phase Space Reconstruction Approach to Improve the Statistical Downscaling of Regional Precipitation

    NASA Astrophysics Data System (ADS)

    C T, D.; Rajendran, V.

    2015-12-01

    Climate change impact assessment studies associated with water resources and its management, presently, rely on a great extent on statistical downscaling techniques, to undertake fine resolution hydrologic modelling over a region. Though, recent literature is flooded with many new downscaling methodologies with varying complexities, the inefficiency of these downscaling techniques in accurately capturing the regional extremes, limits the employment of these downscaling techniques in any impact studies in water resources. It merely increases the monstrosity of uncertainty by adding one more factor: downscaling uncertainty. While chaotic nature of climate systems and existence of attractors are irrefutable, the knowledge on system dynamics is not incorporated in the statistical downscaling methodologies so far. A few questions rise from this context: (1) How possibly can we incorporate the information about system dynamics in statistical downscaling techniques? (2) Will it help in accurately simulating the characteristics of actual system, in statistical terms? (3) If so, will such as approach be able to capture the extremes better?. The present study attempts to address the above questions by adopting a phase space reconstruction approach, which reproduces the attractor of the system and inherent dynamics. The dominant variables to replicate the system dynamics are selected as surface air temperature, specific humidity, precipitable water, long wave radiation, and vertical velocity, in addition to rainfall, which is the variable to be downscaled. A multi-variate phase space reconstruction approach, taking into account the relevance and information contained in one variable, is adopted, to estimate the dimension of system and reconstruct the attractor. The downscaling of future rainfall is done by applying a local prediction approach, enforcing the known information of future dominant variables and employing classification and regression trees (CART). The present

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

    PubMed

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

    2016-07-01

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

  2. Downscaling Global Emissions and Its Implications Derived from Climate Model Experiments

    PubMed Central

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

    2017-01-01

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

  3. Downscaling Global Emissions and Its Implications Derived from Climate Model Experiments.

    PubMed

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

    2017-01-01

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

  4. 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.

  5. 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

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

    PubMed

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

    2016-02-29

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

  7. 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.

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

    NASA Technical Reports Server (NTRS)

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

    2016-01-01

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

  9. 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

  10. Multi-year downscaling application of two-way coupled WRF v3.4 and CMAQ v5.0.2 over east Asia for regional climate and air quality modeling: model evaluation and aerosol direct effects

    NASA Astrophysics Data System (ADS)

    Hong, Chaopeng; Zhang, Qiang; Zhang, Yang; Tang, Youhua; Tong, Daniel; He, Kebin

    2017-06-01

    In this study, a regional coupled climate-chemistry modeling system using the dynamical downscaling technique was established by linking the global Community Earth System Model (CESM) and the regional two-way coupled Weather Research and Forecasting - Community Multi-scale Air Quality (WRF-CMAQ) model for the purpose of comprehensive assessments of regional climate change and air quality and their interactions within one modeling framework. The modeling system was applied over east Asia for a multi-year climatological application during 2006-2010, driven with CESM downscaling data under Representative Concentration Pathways 4.5 (RCP4.5), along with a short-term air quality application in representative months in 2013 that was driven with a reanalysis dataset. A comprehensive model evaluation was conducted against observations from surface networks and satellite observations to assess the model's performance. This study presents the first application and evaluation of the two-way coupled WRF-CMAQ model for climatological simulations using the dynamical downscaling technique. The model was able to satisfactorily predict major meteorological variables. The improved statistical performance for the 2 m temperature (T2) in this study (with a mean bias of -0.6 °C) compared with the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-models might be related to the use of the regional model WRF and the bias-correction technique applied for CESM downscaling. The model showed good ability to predict PM2. 5 in winter (with a normalized mean bias (NMB) of 6.4 % in 2013) and O3 in summer (with an NMB of 18.2 % in 2013) in terms of statistical performance and spatial distributions. Compared with global models that tend to underpredict PM2. 5 concentrations in China, WRF-CMAQ was able to capture the high PM2. 5 concentrations in urban areas. In general, the two-way coupled WRF-CMAQ model performed well for both climatological and air quality applications. The coupled

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

    NASA Astrophysics Data System (ADS)

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

    2016-12-01

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

  12. 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

  13. Downscaling CESM1 climate change projections for the MENA-CORDEX domain using WRF

    NASA Astrophysics Data System (ADS)

    Zittis, George; Hadjinicolaou, Panos; Lelieveld, Jos

    2017-04-01

    According to analysis of observations and global climate model projections, the broader Middle East, North Africa and Mediterranean region is found to be a climate change hotspot. Substantial changes in precipitation amounts and patterns and strong summer warming (including an intensification of heat extremes) is a likely future scenario for the region, but a recent uncertainty analysis indicated good model agreement for temperature but much less for precipitation. Although the horizontal resolution of global models has increased over the last years, it is still not adequate for impact and adaptation assessments of regional or national level and further downscaling of the climate information is required. The region is now studied within the CORDEX initiative (Coordinated Regional Climate Downscaling Experiment) with the establishment of a domain covering the Middle East - North Africa (MENA-CORDEX) region (http://mena-cordex.cyi.ac.cy/). In this study, we present the first climate change projections for the MENA produced by dynamically downscaling a bias-corrected output of the CESM1 global earth system model. For the downscaling, we use a climate configuration of the Weather, Research and Forecasting model (WRF). Our simulations use a standard CORDEX Phase I 50-km grid in three simulations, a historical (1950-2005) and two scenario runs (2006-2100) with the greenhouse gas forcing following the RCP 4.5 and 8.5. We evaluate precipitation, temperature and other surface meteorological variables from the historical using gridded and station observational datasets. Maps of projected changes are constructed for different periods in the future as differences of the two scenarios model output against the data from the historical run. The main spatial and temporal patterns of change are discussed, especially in the context of the United Nations Framework Convention on Climate Change agreement in Paris to limit the global average temperature increase to 1.5 degrees above pre

  14. 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.

  15. Do Downscaled General Circulation Models Reliably Simulate Current Climatic Conditions?

    NASA Astrophysics Data System (ADS)

    Hay, L.; Bock, A. R.; McCabe, G. J., Jr.; Markstrom, S. L.; Atkinson, D.

    2016-12-01

    The accuracy of statistically-downscaled (SD) General Circulation Model (GCM) simulations of monthly surface climate for historical conditions (1950-2000) used to drive a monthly water balance model (MWBM) were assessed for the conterminous United States (CONUS). SD monthly precipitation (PPT) and atmospheric temperature (TAVE) from 95 GCMs (38 from the coupled model intercomparison project (CMIP) 3 and 57 from CMIP5) were used as inputs to a MWBM. Input (PPT, TAVE) and output (snow water equivalent (SWE), and runoff (RUN)) MWBM variables were evaluated by comparing variables computed using historical climate forcings (developed from gridded station data (GSD)) with those computed using historical SD climate. Distributions of GSD- and SD-based MWBM variables were compared using the two-sample Kolmogorov-Smirnov test (KS Test). When all MWBM variables were considered, the KS Test results showed an overall improvement by the CMIP5- relative to CMIP3-based simulations, likely due to improvements in PPT simulations. Results from this study indicate that for the majority of the CONUS, there are downscaled GCMs that can reliably simulate current climatic conditions. But, in some locations (particularly in California), there are no downscaled GCMs tested that replicate historical conditions for all four MWBM variables. In these locations, improved GCM simulations of precipitation are needed to more reliably estimate components of the hydrologic cycle.

  16. Downscaling transient climate change scenarios for water resource management

    NASA Astrophysics Data System (ADS)

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

    2009-04-01

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

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

    USGS Publications Warehouse

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

    2015-01-01

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

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

    PubMed

    Behnke, R; Vavrus, S; Allstadt, A; Albright, T; Thogmartin, W E; Radeloff, V C

    2016-07-01

    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? (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. © 2016 by the Ecological Society of America.

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

    Treesearch

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

    2016-01-01

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

  20. 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

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  2. 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

    2017-04-01

    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.

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

    NASA Astrophysics Data System (ADS)

    Göncü, S.; Albek, E.

    2016-10-01

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

  4. 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.

  5. 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.

  6. 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

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

    USGS Publications Warehouse

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

    2017-01-01

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

  8. Downscaling Regional Wind Forecasts for Use in High Resolution, Operational Snow Models

    NASA Astrophysics Data System (ADS)

    Winstral, A. H.; Jonas, T.; Helbig, N.

    2015-12-01

    High resolution model forcings are required to adequately simulate snow accumulation, melt, and streamflow in mountain environments. Wind, especially the high winds that induce snow redistribution and drive turbulent heat fluxes during rain-on-snow events, have been shown to play a vital role in these processes. Yet wind observations are sparse and rarely capture the large variability present in alpine regions. High resolution (1-10km) climate data is becoming more readily available but even these data are too coarse to properly represent alpine snow processes. Much attention has been focused on downscaling precipitation and air temperature for fine resolution modeling. However there is very little in the literature that has addressed techniques for deterministically downscaling wind speeds. This work addresses means of downscaling large-scale wind products for high-resolution operational modeling purposes. Though both dynamical and statistical means are available for downscaling purposes, the time constraints imposed by operational modeling restricts this work to the latter. The statistical downscaling is done by means of terrain parameters that determine topographic position related to wind exposure and shelter. First, raw hourly wind data from ~2km and ~7km resolution weather forecasts were compared to observations at well over 100 sites located throughout the Swiss Alps. As might be expected, there was a large range of scatter between model-predicted and observed winds, and predictions at high wind sites were biased low. Terrain parameters derived from a 25m resolution DEM aptly identified high and low wind speed sites and climate model biases related to the higher resolution terrain structure. The statistical downscaling differentiated windward and leeward slopes not resolved in the climate models, reduced modeling errors, and substantially reduced biases at the all-important high wind sites.

  9. 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.

  10. 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

  11. Evaluating Downscaling Methods for Seasonal Climate Forecasts over East Africa

    NASA Astrophysics Data System (ADS)

    Roberts, J. B.; Robertson, F. R.; Bosilovich, M. G.; Lyon, B.

    2013-12-01

    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.

  12. 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

  13. Comparing empirical downscaling methods within different kinds of terrain applied on the edge to climate impact research

    NASA Astrophysics Data System (ADS)

    Zuvela-Aloise, Maja; Matulla, Christoph; Auer, Inge; Böhm, Reinhard; Lexer, Manfred J.; Scheifinger, Helfried

    2010-05-01

    We use some statistical downscaling techniques to derive local scale scenarios of future daily and monthly temperature and precipitation for the Alpine region. We utilize large scale NCEP/NCAR reanalysis data to establish empirical models and evaluate their performance against long term climate records from Austrian monitoring stations (forest sites, riverside fish population distributions, glaciers or phenological gardens across Europe etc.) for the second half of the 20th century. The performance of different downscaling methods (multiple linear regression, canonical correlation analysis, the analog method) is analyzed. These methods are applied to derive transient climate change scenarios from ECHAM4/5 runs. Downscaled data have been used in climate risk assessment studies to evaluate the sensitivity of the Austrian forests, fish stocks, phenological occurrence dates etc. to scenarios of climatic change.

  14. Multisite stochastic downscaling of climate model precipitation outputs to high resolution scenarios

    NASA Astrophysics Data System (ADS)

    Bordoy, R.; Burlando, P.

    2011-12-01

    The coarse spatial and temporal resolution of current climate model outputs, even in case of the dynamically downscaled Regional Circulation Models (RCMs), prevents from resolving regional and local scale processes relevant for understanding the hydro-climatic response at the basin scale. Therefore, further downscaling is necessary to make climate projections suitable for hydrologic applications and impact studies. In this study we introduce a novel methodology to downscale climate model simulated precipitation by means of the spatiotemporal Neyman-Scott Rectangular Pulses model (ST-NSRP). This models the spatial rainfall process as a temporal sequence of overlapping circular raincells with constant and homogeneous precipitation intensity that resemble the physical process. Their distribution across the region, temporal generation and duration are described through several stochastic processes. The simulated total precipitation intensity at a particular coordinate and time is given by the sum of the intensities of all the active cells over that point and throughout a given time and rescaled, to account for local particularities, according to locally observed statistics. The model has six parameters, which describe diverse characteristics of the precipitation process and are calibrated from observed statistics at different temporal aggregations. In order to use the model for the generation of future (local) climate scenarios, the observed statistics at each site are perturbed with the ratios between the statistics of the future and control scenarios computed from previously debiased climate RCM model outputs. The so perturbed set of statistics is then used to estimate the parameters of the model for future climates. However, because RCMs outputs can be used to compute in the best case precipitation statistics only at the daily scale, we introduce a technique to obtain future statistics at temporal resolutions not available or of doubtful accuracy. In particular we

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

    NASA Astrophysics Data System (ADS)

    Yang, Hao; Jiang, Zhihong; Li, Laurent

    2016-11-01

    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.

  16. Multivariate Downscaling of Decadal Climate Change Projections over the Sunbelt

    NASA Astrophysics Data System (ADS)

    DAS Bhowmik, R.; Arumugam, S.; Sinha, T.; Mahinthakumar, K.

    2014-12-01

    Bias Correction and Statistical downscaling (BCSD) of precipitation and temperature are commonly required to bring the large scale variables available from GCMs to a finer grid-scale for ingesting them into watershed models. Most of the currently employed procedures on BCSD primarily consider a univariate approach by developing a statistical relationship between large-scale precipitation/temperature with the local-scale precipitation/temperature ignoring the interdependency between the two variables. In this study, an asynchronous Canonical Correlation Analysis (CCA) approach is proposed for downscaling multiple climatic variables by preserving the temporal correlations among them. The method was first applied on historical runs of climate model inter-comparison project-5 (CMIP5) for the period 1951-1999 and compared with bias corrected dataset using univariate approach from Bureau of Reclamation. Further, the method was applied on decadal runs of CMIP5 models and compared with univariate asynchronous regression results. A metric, fraction bias was defined, and distribution of fraction bias from ensemble was considered for comparing with univariate approach. CCA relatively performs better in preserving the cross-correlation at grids where observed cross correlations are significant, while reducing fraction biases in mean and standard deviation.

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

    NASA Astrophysics Data System (ADS)

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

    2017-04-01

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

  18. 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.

  19. Surface Mass Balance Distributions: Downscaling of Coarse Climates to drive Ice Sheet Models realistically

    NASA Astrophysics Data System (ADS)

    Rodehacke, Christian; Mottram, Ruth; Langen, Peter; Madsen, Marianne; Yang, Shuting; Boberg, Fredrik; Christensen, Jens

    2017-04-01

    The surface mass balance (SMB) is the most import boundary conditions for the state of glaciers and ice sheets. Hence its representation in numerical model simulations is of highest interest for glacier, ice cap and ice sheet modeling efforts. While descent SMB distributions of the current climate could be interfered with the help of various observation techniques and platforms, its construction for older past and future climates relies on input from spatially coarse resolved global climate models or reconstructions. These coarse SMB estimates with a footprint in the order of 100 km could hardly resolve the marginal ablations zones where the Greenland ice sheets, for instance, loses snow and ice. We present a downscaling method that is based on the physical calculation of the surface mass and energy balance. By the consequent application of universal and computationally cheap parameterizations we get an astonishing good representation of the SMB distribution including its marginal ablation zone. However the method has its limitations; for example wrong accumulation rates due to an insufficient precipitation field leaves its imprint on the SMB distribution. Also the still not satisfactory description of the bare ice albedo, in particular, in parts of Greenland is a challenge. We inspect our Greenland SMB fields' for various forcings and compare them with some widely used reference fields in the community to highlight the weakness and strength of our approach. We use the ERA-Interim reanalyzes period starting in 1979 directly as well as dynamically downscaled by our regional climate model HIRHAM (5 km resolution). Also SMB distributions obtained from the climate model EC-Earth with a resolution of T159 (approx. 125 km resolution in Greenland) are used either directly or downscaled with our regional climate model HIRHAM. Model-based End-of-the-century SMB estimates give an outlook of the future.

  20. Evaluation of cool season precipitation event characteristics over the Northeast US in a suite of downscaled climate model hindcasts

    NASA Astrophysics Data System (ADS)

    Loikith, Paul C.; Waliser, Duane E.; Kim, Jinwon; Ferraro, Robert

    2017-08-01

    Cool season precipitation event characteristics are evaluated across a suite of downscaled climate models over the northeastern US. Downscaled hindcast simulations are produced by dynamically downscaling the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2) using the National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (WRF) regional climate model (RCM) and the Goddard Earth Observing System Model, Version 5 (GEOS-5) global climate model. NU-WRF RCM simulations are produced at 24, 12, and 4-km horizontal resolutions using a range of spectral nudging schemes while the MERRA2 global downscaled run is provided at 12.5-km. All model runs are evaluated using four metrics designed to capture key features of precipitation events: event frequency, event intensity, even total, and event duration. Overall, the downscaling approaches result in a reasonable representation of many of the key features of precipitation events over the region, however considerable biases exist in the magnitude of each metric. Based on this evaluation there is no clear indication that higher resolution simulations result in more realistic results in general, however many small-scale features such as orographic enhancement of precipitation are only captured at higher resolutions suggesting some added value over coarser resolution. While the differences between simulations produced using nudging and no nudging are small, there is some improvement in model fidelity when nudging is introduced, especially at a cutoff wavelength of 600 km compared to 2000 km. Based on the results of this evaluation, dynamical regional downscaling using NU-WRF results in a more realistic representation of precipitation event climatology than the global downscaling of MERRA2 using GEOS-5.

  1. Optimizing dynamic downscaling in one-way nesting using a regional ocean model

    NASA Astrophysics Data System (ADS)

    Pham, Van Sy; Hwang, Jin Hwan; Ku, Hyeyun

    2016-10-01

    Dynamical downscaling with nested regional oceanographic models has been demonstrated to be an effective approach for both operationally forecasted sea weather on regional scales and projections of future climate change and its impact on the ocean. However, when nesting procedures are carried out in dynamic downscaling from a larger-scale model or set of observations to a smaller scale, errors are unavoidable due to the differences in grid sizes and updating intervals. The present work assesses the impact of errors produced by nesting procedures on the downscaled results from Ocean Regional Circulation Models (ORCMs). Errors are identified and evaluated based on their sources and characteristics by employing the Big-Brother Experiment (BBE). The BBE uses the same model to produce both nesting and nested simulations; so it addresses those error sources separately (i.e., without combining the contributions of errors from different sources). Here, we focus on discussing errors resulting from the spatial grids' differences, the updating times and the domain sizes. After the BBE was separately run for diverse cases, a Taylor diagram was used to analyze the results and recommend an optimal combination of grid size, updating period and domain sizes. Finally, suggested setups for the downscaling were evaluated by examining the spatial correlations of variables and the relative magnitudes of variances between the nested model and the original data.

  2. Stochastic extreme downscaling model for an assessment of changes in rainfall intensity-duration-frequency curves over South Korea using multiple regional climate models

    NASA Astrophysics Data System (ADS)

    So, Byung-Jin; Kim, Jin-Young; Kwon, Hyun-Han; Lima, Carlos H. R.

    2017-10-01

    A conditional copula function based downscaling model in a fully Bayesian framework is developed in this study to evaluate future changes in intensity-duration frequency (IDF) curves in South Korea. The model incorporates a quantile mapping approach for bias correction while integrated Bayesian inference allows accounting for parameter uncertainties. The proposed approach is used to temporally downscale expected changes in daily rainfall, inferred from multiple CORDEX-RCMs based on Representative Concentration Pathways (RCPs) 4.5 and 8.5 scenarios, into sub-daily temporal scales. Among the CORDEX-RCMs, a noticeable increase in rainfall intensity is observed in the HadGem3-RA (9%), RegCM (28%), and SNU_WRF (13%) on average, whereas no noticeable changes are observed in the GRIMs (-2%) for the period 2020-2050. More specifically, a 5-30% increase in rainfall intensity is expected in all of the CORDEX-RCMs for 50-year return values under the RCP 8.5 scenario. Uncertainty in simulated rainfall intensity gradually decreases toward the longer durations, which is largely associated with the enhanced strength of the relationship with the 24-h annual maximum rainfalls (AMRs). A primary advantage of the proposed model is that projected changes in future rainfall intensities are well preserved.

  3. 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

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  5. Anticipating Future Extreme Climate Events for Alaska Using Dynamical Downscaling and Quantile Mapping

    NASA Astrophysics Data System (ADS)

    Lader, R.; Walsh, J. E.

    2016-12-01

    Alaska is projected to experience major changes in extreme climate during the 21st century, due to greenhouse warming and exacerbated by polar amplification, wherein the Arctic is warming at twice the rate compared to the Northern Hemisphere. Given its complex topography, Alaska displays extreme gradients of temperature and precipitation. However, global climate models (GCMs), which typically have a spatial resolution on the order of 100km, struggle to replicate these extremes. To help resolve this issue, this study employs dynamically downscaled regional climate simulations and quantile-mapping methodologies to provide a full suite of daily model variables at 20 km spatial resolution for Alaska, from 1970 to 2100. These data include downscaled products of the: ERA-Interim reanalysis from 1979 to 2015, GFDL-CM3 historical from 1970 to 2005, and GFDL-CM3 RCP 8.5 from 2006 to 2100. Due to the limited nature of long-term observations and high-resolution modeling in Alaska, these data enable a broad expansion of extremes analysis. This study uses these data to highlight a subset of the 27 climate extremes indices, previously defined by the Expert Team on Climate Change Detection and Indices, as they pertain to climate change in Alaska. These indices are based on the statistical distributions of daily surface temperature and precipitation and focus on threshold exceedance, and percentiles. For example, the annual number of days with a daily maximum temperature greater than 25°C is anticipated to triple in many locations in Alaska by the end of the century. Climate extremes can also refer to long duration events, such as the record-setting warmth that defined the 2015-16 cold season in Alaska. The downscaled climate model simulations indicate that this past winter will be considered normal by as early as the mid-2040s, if we continue to warm according to the business-as-usual RCP 8.5 emissions scenario. This represents an accelerated warming as compared to projections

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. Climate Change Impacts on Precipitation and Groundwater Recharge in Denmark: A Distributed Hydrological Modeling Study using Multiple Downscaling Methods on the Climate Inputs

    NASA Astrophysics Data System (ADS)

    Seaby, L. P.; Refsgaard, J.; Sonnenborg, T.; Jensen, K. H.

    2011-12-01

    Future changes in climate are expected to result in more extreme hydrological conditions globally. For Denmark, most climate models predict increases in annual precipitation, with higher intensity rainfall events occurring in winter and reduced precipitation and higher evapotranspiration in summer. Changes in the quantity, timing, and delivery of precipitation is expected to result in higher rates of groundwater recharge in the winter months, as well as flooding and water logging in low lying areas, and decreased water tables, dry root zones, and reduced low flows in the summer months. There is, however, variability between climate models on the direction and strength of the climate change signal. Additionally, regional climate models (RCMs) are subject to systematic errors making their outputs, especially precipitation, require further downscaling and bias correction prior to use in hydrological simulations. Consequently, hydrological outputs simulated under climate change compound the uncertainties within individual climate model predictions, between various climate models, and in the choice of downscaling and bias correction method. This study compares 11 transient climate change scenarios from the EU project ENSMEBLES, which makes available a matrix of GCM-RCM pairings for all of Europe at a 25 km2 grid scale to the year 2100. Temperature, precipitation, and potential evapotranspiration (calculated from climate model outputs) are downscaled using two methods: a monthly delta change approach that transfers absolute (state variables) or relative (flux variables) climate change from the RCM scenarios to the observed data, and a seasonal histogram equalization method that fits gamma distributions based on the instensity of daily observed and scenario data (flux variables) and scales scenario data based on the difference in gamma functions. Downscaling is spatially distributed within Denmark according to the seven sub-model regions in the National Water Resources

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

    NASA Astrophysics Data System (ADS)

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

    2016-12-01

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

  12. Regional projection of climate impact indices over the Mediterranean region

    NASA Astrophysics Data System (ADS)

    Casanueva, Ana; Frías, M.; Dolores; Herrera, Sixto; Bedia, Joaquín; San Martín, Daniel; Gutiérrez, José Manuel; Zaninovic, Ksenija

    2014-05-01

    Climate Impact Indices (CIIs) are being increasingly used in different socioeconomic sectors to transfer information about climate change impacts and risks to stakeholders. CIIs are typically based on different weather variables such as temperature, wind speed, precipitation or humidity and comprise, in a single index, the relevant meteorological information for the particular impact sector (in this study wildfires and tourism). This dependence on several climate variables poses important limitations to the application of statistical downscaling techniques, since physical consistency among variables is required in most cases to obtain reliable local projections. The present study assesses the suitability of the "direct" downscaling approach, in which the downscaling method is directly applied to the CII. In particular, for illustrative purposes, we consider two popular indices used in the wildfire and tourism sectors, the Fire Weather Index (FWI) and the Physiological Equivalent Temperature (PET), respectively. As an example, two case studies are analysed over two representative Mediterranean regions of interest for the EU CLIM-RUN project: continental Spain for the FWI and Croatia for the PET. Results obtained with this "direct" downscaling approach are similar to those found from the application of the statistical downscaling to the individual meteorological drivers prior to the index calculation ("component" downscaling) thus, a wider range of statistical downscaling methods could be used. As an illustration, future changes in both indices are projected by applying two direct statistical downscaling methods, analogs and linear regression, to the ECHAM5 model. Larger differences were found between the two direct statistical downscaling approaches than between the direct and the component approaches with a single downscaling method. While these examples focus on particular indices and Mediterranean regions of interest for CLIM-RUN stakeholders, the same study

  13. 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.

  14. 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

  15. 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

  16. 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.

  17. Statistical downscaling of the French Mediterranean climate: assessment for present and projection in an anthropogenic scenario

    NASA Astrophysics Data System (ADS)

    Lavaysse, C.; Vrac, M.; Drobinski, P.; Lengaigne, M.; Vischel, T.

    2012-03-01

    The Mediterranean basin is a particularly vulnerable region to climate change, featuring a sharply contrasted climate between the North and South and governed by a semi-enclosed sea with pronounced surrounding topography covering parts of the Europe, Africa and Asia regions. The physiographic specificities contribute to produce mesoscale atmospheric features that can evolve to high-impact weather systems such as heavy precipitation, wind storms, heat waves and droughts. The evolution of these meteorological extremes in the context of global warming is still an open question, partly because of the large uncertainty associated with existing estimates produced by global climate models (GCM) with coarse horizontal resolution (~200 km). Downscaling climatic information at a local scale is, thus, needed to improve the climate extreme prediction and to provide relevant information for vulnerability and adaptation studies. In this study, we investigate wind, temperature and precipitation distributions for past recent climate and future scenarios at eight meteorological stations in the French Mediterranean region using one statistical downscaling model, referred as the "Cumulative Distribution Function transform" (CDF-t) approach. A thorough analysis of the uncertainty associated with statistical downscaling and bi-linear interpolation of large-scale wind speed, temperature and rainfall from reanalyses (ERA-40) and three GCM historical simulations, has been conducted and quantified in terms of Kolmogorov-Smirnov scores. CDF-t produces a more accurate and reliable local wind speed, temperature and rainfall. Generally, wind speed, temperature and rainfall CDF obtained with CDF-t are significantly similar with the observed CDF, even though CDF-t performance may vary from one station to another due to the sensitivity of the driving large-scale fields or local impact. CDF-t has then been applied to climate simulations of the 21st century under B1 and A2 scenarios for the three

  18. 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

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

    NASA Astrophysics Data System (ADS)

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

    2016-02-01

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

  20. 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

  1. 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

  2. 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

  3. 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

  4. A downscaling method for the assessment of local climate change

    NASA Astrophysics Data System (ADS)

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

    2009-04-01

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

  5. Comparison of statistically downscaled precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec, Canada

    NASA Astrophysics Data System (ADS)

    Gaitan, Carlos F.; Hsieh, William W.; Cannon, Alex J.

    2014-12-01

    Given the coarse resolution of global climate models, downscaling techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical downscaling experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically downscaled daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and ensembles, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971-2000) and A2 (2041-2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree ensembles outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of downscaling models deteriorated in future climate.

  6. CMIP5 based downscaled temperature over Western Himalayan region

    NASA Astrophysics Data System (ADS)

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

    2016-12-01

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

  7. 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

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

    PubMed

    Solecki, William D; Oliveri, Charles

    2004-08-01

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

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

    USGS Publications Warehouse

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

    2016-07-14

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  12. 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

  13. 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

  14. 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

  15. 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

  16. 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

  17. Projected changes in climate extremes by a high-resolution dynamically downscaled ensemble over the continental United States

    NASA Astrophysics Data System (ADS)

    Zobel, Z.; Wang, J.; Wuebbles, D. J.; Kotamarthi, V. R.

    2016-12-01

    This study uses weather research and forecast (WRF) model to evaluate the performance of five dynamical downscaled decadal historical and projected simulations with 12-km resolution for a large domain (7200 km × 6180 km) that covers most of North America. The initial and boundary conditions are from three global climate models (GCMs). The GCMs employed in this study are the Geophysical Fluid Dynamics Laboratory Earth System Model with Generalized Ocean Layer Dynamics component (GFDL-ESG2G), Community Climate System Model, version 4 (CCSM4), and the Hadley Centre Global Environment Model, version 2-Earth System (HadGEM2-ES). These five sets of experiments span three different time periods: 11 years over historical period (1994-2004), 11 years over mid-21st century (2044-2054), and 11 years over late-21st century (2084-2094). The forcing scenarios for these future simulations in both the global and regional climate models are from CMIP5. We use RCP 4.5 and RCP 8.5 to evaluate both moderate and high emission scenarios, respectively. This research aims to quantify the historical biases and internal variability using an ensemble of dynamically downscaled climate runs in order to better capture future uncertainties. Without a firm understanding of the historical accuracy in modeling climate extremes using dynamical downscaling, we cannot properly assess the future variability, especially when discussing the potential risks and impacts from changing regional extreme climate events. For example, we find that there are significant increases in warm-season heavy precipitation events over the central United States, which could lead to devastating economic impacts (National Climatic Data Center, 2014), but the model spread in the locations of the highest increases in these events remains high. Leveraging the understanding of each simulation's historical capabilities and using metrics discussed in Zobel et al. (2016), we can make more confident projections for future regional

  18. A Computationally Efficient Platform To Examine the Efficacy of Regional Downscaling Methods

    NASA Astrophysics Data System (ADS)

    Vigh, J. L.; Ammann, C. M.; Rood, R. B.; Barsugli, J. J.; Guentchev, G.

    2013-12-01

    A primary goal of the National Climate Predictions and Projections (NCPP) platform is to develop an extensive set of standardized and interoperable evaluation tools to examine the efficacy of various regional climate downscaling techniques. To this end, a highly efficient NCL-based evaluation and comparison engine has been developed to compute period statistics on the monthly, seasonal, and annual timescales from many gridded daily datasets. The initial implementation of the engine computes metrics such as the mean, median, interannual standard deviation, monthly lag-1autocorrelation, and the various percentiles. These are computed for the daily maximum and minimum temperature, the daily mean temperature, the diurnal temperature range, and daily accumulated precipitation. Additionally, the engine computes the mean, median, max, and min of a number of application-oriented indices and climate extreme indices on the monthly and annual timescales. Output includes not only plots of the above metrics and indices, but the underlying NetCDF dataset used to create each plot. Metadata and internal attributes provide full data provenance of the source datasets and a description of the computations. Through this standardized approach to statistical analysis of massive datasets, we aim to provide end-users with a comprehensive suite of evaluations and comparisons between downscaling methods. This will enable applications-oriented users greater access to these data by lowering the barriers to data usage and understanding.

  19. Downscaling of climate parameters in Bode river basin in Germany using Active Learning Method (ALM)

    NASA Astrophysics Data System (ADS)

    Sodoudi, S.; Reimer, E.

    2009-04-01

    This study is a part of main program RIMAX "risk management of extreme flood events", which concerns itself of "extremes floodwater and damage potential in the Bode river basin in Germany „with the variable occurrence of flood events in this area for the past 1000 years. The objective of the project is to produce the local climate time series (climate downscaling) as the input for a runoff model in the Bode basin for the last 1000 years on a grid of 5x5 km as well as the estimation of the spatial distributions and temporal variability of the precipitation, the amount of precipitation and further meteorological parameter (temperature, radiation and relative humidity) for this area. A nonlinear downscaling based on Fuzzy rules has been used to produce 1000 year climate time series. The global model ECHO from Max Planck institute for Meteorology (MPI) with T30 resolution and 1000 years data has been used as the global model (GCM). The regional model REMO, with 10 km resolution and 20 years data has been used as the regional input. The observations, which include 30 years precipitation, radiation, temperature, wind and relative humidity, have been used as output (predictand). In this study, two set fuzzy rules have been trained to describe the relationship between ECHO/REMO and REMO/Observation. The Fuzzy method used in this work is Active Learning Method (ALM). The heart of calculation of ALM is a fuzzy interpolation and curve fitting which is entitled Ink Drop Spread (IDS). The IDS searches fuzzily for continuous possible paths of interpolated data points on data planes. The ability of ALM to simulate the high values as well as the fluctuation of time series is much better than Takagi-Sugeno models, which have been used for downscaling in the last decade. In the next steps, considering predictors from the ECHO time series and predictands from the REMO grid points, some ALM models are developed, which describe the fuzzy rules and the relationship between global and

  20. 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

  1. Analysis for Spatiotemporal Characteristics of Downscaled Hourly Precipitation for Climate Scenarios and Hydrological Responses

    NASA Astrophysics Data System (ADS)

    PARK, T.; Lee, T. S.; Lee, H.; Kim, J.

    2015-12-01

    lobal Climate Models (GCMs) have been widely used for adapting and mitigating water-related disasters affected by climate change. However, GCM outputs are too coarse to apply at a small basin scale. GCM outputs provide only daily precipitation data that are inadequate to analyze a small or medium basin because only few or several hours are used to determine the peak flows after it rains. Therefore, in the current study, we downscale the outputs to hourly time scale over South Korea for climate change scenarios (RCP 4.5 and RCP 8.5) and illustrate the spatiotemporal distribution of downscaled hourly precipitation. Furthermore, the hydrological application of downscaled scenarios is conducted for its hydrological responses employing a distributed rainfall-runoff model, Vflo.AcknowledgementsThis work was supported by the National Research Foundation of Korea (NRF) grant that was funded by the Korean Government (MEST) (2015R1A1A1A05001007).

  2. An atmospheric-to-marine synoptic classification for statistical downscaling marine climate

    NASA Astrophysics Data System (ADS)

    Camus, Paula; Rueda, Ana; Méndez, Fernando J.; Losada, Iñigo J.

    2016-12-01

    A regression-guided classification is implemented in statistical downscaling models based on weather types for downscaling multivariate wave climate and modelling extreme events. The semi-supervised method classifies the atmospheric circulation conditions (predictor) and the estimations from a regression model linking the circulation with local marine climate (filtered predictand). A weighted factor controls the influence of the predictor and predictand in the weather patterns to improve the performance of the classification to reflect local marine climate characteristics. In addition to the analysis of the variance explained by the predictor and the predictand, the selection of the optimal value of the weighted factor is based on the predictand response in order to avoid subjectivity in the solution. The statistical models using the guided classification are applied in the North Atlantic. The new technique reduces the dispersion of the multivariate predictand within weather types and improves the model skill to downscale waves and to reproduce extremes.

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

    NASA Astrophysics Data System (ADS)

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

    2006-11-01

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

  4. 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.

  5. 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

  6. 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

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

    USDA-ARS?s Scientific Manuscript database

    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...

  8. 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

  9. 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.

  10. 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.

  11. 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

  12. 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

  13. Downscaled Projections of 21st Century Bioclimatic Variables for U.S. Climate Divisions

    NASA Astrophysics Data System (ADS)

    Nicholas, R.

    2016-12-01

    Bioclimatic variables, which are derived from monthly precipitation, average minimum temperature, and average maximum temperature, are frequently used in ecological modeling efforts. In this study, a suite of empirically-downscaled, bias-corrected projections of 21st century bioclimatic variables are presented for all 344 Climate Divisions in the coterminous United States. The projections are derived from multimate global climate model ensembles in an effort to sample uncertainties due to model structure, model initial conditions, and forcing scenario.

  14. 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

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

    NASA Astrophysics Data System (ADS)

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

    2017-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2017-05-01

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

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

    USGS Publications Warehouse

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

    2017-01-01

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

  18. Internal variability of a dynamically downscaled climate over North America

    NASA Astrophysics Data System (ADS)

    Wang, Jiali; Bessac, Julie; Kotamarthi, Rao; Constantinescu, Emil; Drewniak, Beth

    2017-09-01

    This study investigates the internal variability (IV) of a regional climate model, and considers the impacts of horizontal resolution and spectral nudging on the IV. A 16-member simulation ensemble was conducted using the Weather Research Forecasting model for three model configurations. Ensemble members included simulations at spatial resolutions of 50 and 12 km without spectral nudging and simulations at a spatial resolution of 12 km with spectral nudging. All the simulations were generated over the same domain, which covered much of North America. The degree of IV was measured as the spread between the individual members of the ensemble during the integration period. The IV of the 12 km simulation with spectral nudging was also compared with a future climate change simulation projected by the same model configuration. The variables investigated focus on precipitation and near-surface air temperature. While the IVs show a clear annual cycle with larger values in summer and smaller values in winter, the seasonal IV is smaller for a 50-km spatial resolution than for a 12-km resolution when nudging is not applied. Applying a nudging technique to the 12-km simulation reduces the IV by a factor of two, and produces smaller IV than the simulation at 50 km without nudging. Applying a nudging technique also changes the geographic distributions of IV in all examined variables. The IV is much smaller than the inter-annual variability at seasonal scales for regionally averaged temperature and precipitation. The IV is also smaller than the projected changes in air-temperature for the mid- and late twenty-first century. However, the IV is larger than the projected changes in precipitation for the mid- and late twenty-first century.

  19. 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

  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. Copyright © 2015 Elsevier B.V. All rights reserved.

  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. Site Level Climate Downscaling for Forecasting Water Balance Stress and Reslience of Acadian Boreal Trees

    NASA Astrophysics Data System (ADS)

    Brooks, B. G.; Serbin, S.

    2014-12-01

    A downscaling framework is presented and applied to physiological and climatic data for projecting future climate resilience of one key boreal tree species, black spruce, in Cape Breton Highlands, Nova Scotia. The technique is based on a combination of probabilistic downscaling methods and control system theory, which together are used to transform large-scale future climate input (air temperature, humidity) to local scale climate parameters important to plant biophysical processes (vapor pressure deficit). Large-scale forecast data from the Community Earth System Model were downscaled spatially then temporally based on the cumulative distributions and sub-daily patterns from corresponding observational data at North Mountain (Cape Breton). Validation over historical decades shows that this technique provides hourly temperature and vapor pressure deficit data accurate to within 0.7%. Further we applied these environmental factors to a species specific empirical model of stomatal conductance for black spruce to compare differences in predicted water regulation response when large-scale (ESM) data are used as drivers versus localized data transformed using this new site-level downscaling technique. We observe through this synthetic study that over historical to contemporary periods (1850-2006) differences between large-scale and localized forecasts of stomatal conductance were small but that future climate extremes (2006-2100) have a strong effect on derived water balance in black spruce. These results also suggest that black spruce in the Cape Breton Highlands may have biophysical responses to climate change that are not predicted by spatially coarse (1°) data, which does not include site level extremes that in this study are shown to strongly curb future growth rates in black spruce as present day climate extremes become common place.

  4. An efficient statistical approach to multi-site downscaling of daily precipitation series in the context of climate change

    NASA Astrophysics Data System (ADS)

    Khalili, Malika; Van Nguyen, Van Thanh

    2016-11-01

    Global Climate Models (GCMs) have been extensively used in many climate change impact studies. However, the coarser resolution of these GCM outputs is not adequate to assess the potential effects of climate change on local scale. Downscaling techniques have thus been proposed to resolve this problem either by dynamical or statistical approaches. The statistical downscaling (SD) methods are widely privileged because of their simplicity of implementation and use. However, many of them ignore the observed spatial dependence between different locations, which significantly affects the impact study results. An improved multi-site SD approach is thus presented in this paper to downscaling of daily precipitation at many sites concurrently. This approach is based on a combination of multiple regression models for rainfall occurrences and amounts and the Singular Value Decomposition technique, which models the stochastic components of these regression models to preserve accurately the space-time statistical properties of the daily precipitation. Furthermore, this method was able to describe adequately the intermittency property of the precipitation processes. The proposed approach has been assessed using 10 rain gauges located in the southwest region of Quebec and southeast region of Ontario in Canada, and climate predictors from the National Centers for Environmental Prediction/National Centre for Atmospheric Research re-analysis data set. The results have indicated the ability of the proposed approach to reproduce accurately multiple observed statistical properties of the precipitation occurrences and amounts, the at-site temporal persistence, the spatial dependence between sites and the temporal variability and spatial intermittency of the precipitation processes.

  5. Satellite-Enhanced Regional Downscaling for Applied Studies: Extreme Precipitation Events in Southeastern South America

    NASA Astrophysics Data System (ADS)

    Nunes, A.; Gomes, G.; Ivanov, V. Y.

    2016-12-01

    Frequently found in southeastern South America during the warm season from October through May, strong and localized precipitation maxima are usually associated with the presence of mesoscale convective complexes (MCCs) travelling across the region. Flashfloods and landslides can be caused by these extremes in precipitation, with damages to the local communities. Heavily populated, southeastern South America hosts many agricultural activities and hydroelectric production. It encompasses one of the most important river basins in South America, the La Plata River Basin. Therefore, insufficient precipitation is equally prejudicial to the region socio-economic activities. MCCs are originated in the warm season of many regions of the world, however South American MCCs are related to the most severe thunderstorms, and have significantly contributed to the precipitation regime. We used the hourly outputs of Satellite-enhanced Regional Downscaling for Applied Studies (SRDAS), developed at the Federal University of Rio de Janeiro in Brazil, in the analysis of the dynamics and physical characteristics of MCCs in South America. SRDAS is the 25-km resolution downscaling of a global reanalysis available from January 1998 through December 2010. The Regional Spectral Model is the SRDAS atmospheric component and assimilates satellite-based precipitation estimates from the NOAA/Climate Prediction Center MORPHing technique global precipitation analyses. In this study, the SRDAS atmospheric and land-surface variables, global reanalysis products, infrared satellite imagery, and the physical retrievals from the Atmospheric Infrared Sounder (AIRS), on board of the NASA's Aqua satellite, were used in the evaluation of the MCCs developed in southeastern South America from 2008 and 2010. Low-level circulations and vertical profiles were analyzed together to establish the relevance of the moisture transport in connection with the upper-troposphere dynamics to the development of those MCCs.

  6. 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.

  7. Trend analysis of watershed-scale precipitation over Northern California by means of dynamically-downscaled CMIP5 future climate projections.

    PubMed

    Ishida, K; Gorguner, M; Ercan, A; Trinh, T; Kavvas, M L

    2017-03-11

    The impacts of climate change on watershed-scale precipitation through the 21st century were investigated over eight study watersheds in Northern California based on dynamically downscaled CMIP5 future climate projections from three GCMs (CCSM4, HadGEM2-ES, and MIROC5) under the RCP4.5 and RCP8.5 future climate scenarios. After evaluating the modeling capability of the WRF model, the six future climate projections were dynamically downscaled by means of the WRF model over Northern California at 9km grid resolution and hourly temporal resolution during a 94-year period (2006-2100). The biases in the model simulations were corrected, and basin-average precipitation over the eight study watersheds was calculated from the dynamically downscaled precipitation data. Based on the dynamically downscaled basin-average precipitation, trends in annual depth and annual peaks of basin-average precipitation during the 21st century were analyzed over the eight study watersheds. The analyses in this study indicate that there may be differences between trends of annual depths and annual peaks of watershed-scale precipitation during the 21st century. Furthermore, trends in watershed-scale precipitation under future climate conditions may be different for different watersheds depending on their location and topography even if they are in the same region.

  8. 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

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

    USGS Publications Warehouse

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

    2011-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Peleg, Nadav; Fatichi, Simone; Burlando, Paolo

    2015-04-01

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

  11. Analogue Downscaling of Seasonal Rainfall Forecasts

    NASA Astrophysics Data System (ADS)

    Charles, A. N.; Timbal, B.; Hendon, H.

    2010-12-01

    We have taken an existing statistical downscaling model (SDM), based on meteorological analogues that was developed for downscaling climate change projections (Timbal et al 2009), and applied it in the seasonal forecasting context to produce downscaled rainfall hindcasts from a coupled model seasonal forecast system (POAMA). Downscaling of POAMA forecasts is required to provide seasonal climate information at local scales of interest. Analogue downscaling is a simple technique to generate rainfall forecasts appropriate to the local scale by conditioning on the large scale predicted GCM circulation and the local topography and climate. Analogue methods are flexible and have been shown to produce good results when downscaling 20th century South Eastern Australian rainfall output from climate models. A set of re-forecasts for three month rainfall at 170 observing stations in the South Murray Darling region of Australia were generated using predictors from the POAMA re-forecasts as input for the analogue SDM. The predictors were optimised over a number of different GCMS in previous climate change downscaling studies. Downscaling with the analogue SDM results in predicted rainfall with realistic variance while maintaining the modest predictive skill of the dynamical model. Evaluation of the consistency between the large scale mean of downscaled and direct GCM output precipitation is encouraging.

  12. CMIP5 vs. CORDEX over the Indian region: how much do we benefit from dynamical downscaling?

    NASA Astrophysics Data System (ADS)

    Mishra, Saroj Kanta; Sahany, Sandeep; Salunke, Popat

    2017-08-01

    Given the general notion that dynamical downscaling leads to added accuracy in both historical simulations as well as climate change projections, this paper investigates its validity over India using historical data (1975-2005) from the CORDEX models and their driving global climate models (GCMs) from Coupled Model Intercomparison Project Phase 5 (CMIP5), and comparing them against observed temperature and rainfall. We find that downscaling invariably leads to an improvement in the spatial pattern of surface air temperature, but compared to the driving GCMs, the errors in magnitude after downscaling are even worse in some cases. In regard to JJAS rainfall simulations, the CMIP5 driving GCMs are found to be superior to their dynamically downscaled counterparts both in terms of spatial patterns as well as magnitude of errors. Both CMIP5 driving GCMs as well as the CORDEX models underestimate rainfall during JJAS; however, negative bias in CORDEX models is worse. Unlike the driving CMIP5 GCMs, their dynamically downscaled counterparts simulate an early onset followed by a slow and late withdrawal of the Indian summer monsoon rainfall. The frequency of occurrence of rainfall intensities is simulated well by both sets of models in the lower intensity regime (0-20 mm/day); however, for higher intensities, the driving CMIP5 GCMs underestimate whereas the CORDEX models overestimate.

  13. 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...

  14. 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...

  15. 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

  16. Optimal selection of MULTI-model downscaled ensembles for interannual and seasonal climate prediction in the eastern seaboard of Thailand

    NASA Astrophysics Data System (ADS)

    Bejranonda, W.; Koch, M.

    2010-12-01

    Because of the imminent threat of the water resources of the eastern seaboard of Thailand, a climate impact study has been carried out there. To that avail, a hydrological watershed model is being used to simulate the future water availability in the wake of possible climate change in the region. The hydrological model is forced by predictions from global climate models (GCMs) that are to be downscaled in an appropriate manner. The challenge at that stage of the climate impact analysis lies then the in the choice of the best GCM and the (statistical) downscaling method. In this study the selection of coarse grid resolution output of the GCMs, transferring information to the fine grid of local climate-hydrology is achieved by cross-correlation and multiple linear regression using meteorological data in the eastern seaboard of Thailand observed between 1970-1999. The grids of 20 atmosphere/ocean global climate models (AOGCM), covering latitude 12.5-15.0 N and longitude 100.0-102.5 E were examined using the Climate-Change Scenario Generator (SCENGEN). With that tool the model efficiency of the prediction of daily precipitation and mean temperature was calculated by comparing the 1980-1999 ECMWF reanalysis predictions with the observed data during that time period. The root means square errors of the predictions were considered and ranked to select the top 5 models, namely, BCCR-BCM2.0, GISS-ER, ECHO-G, ECHAM5/MPI-OM and PCM. The daily time-series of 338 predictors in 9 runs of the 5 selected models were gathered from the CMIP3 multi-model database. Monthly time-serial cross-correlations between the climate predictors and the meteorological measurements from 25 rainfall, 4 minimum and maximum temperature, 4 humidity and 2 solar radiation stations in the study area were then computed and ranked. Using the ranked predictors, a multiple-linear regression model (downscaling transfer model) to forecast the local climate was set up. To improve the prediction power of this

  17. Decadal application of WRF/Chem for regional air quality and climate modeling over the U.S. under the representative concentration pathways scenarios. Part 1: Model evaluation and impact of downscaling

    NASA Astrophysics Data System (ADS)

    Yahya, Khairunnisa; Wang, Kai; Campbell, Patrick; Chen, Ying; Glotfelty, Timothy; He, Jian; Pirhalla, Michael; Zhang, Yang

    2017-03-01

    An advanced online-coupled meteorology-chemistry model, i.e., the Weather Research and Forecasting Model with Chemistry (WRF/Chem), is applied for current (2001-2010) and future (2046-2055) decades under the representative concentration pathways (RCP) 4.5 and 8.5 scenarios to examine changes in future climate, air quality, and their interactions. In this Part I paper, a comprehensive model evaluation is carried out for current decade to assess the performance of WRF/Chem and WRF under both scenarios and the benefits of downscaling the North Carolina State University's (NCSU) version of the Community Earth System Model (CESM_NCSU) using WRF/Chem. The evaluation of WRF/Chem shows an overall good performance for most meteorological and chemical variables on a decadal scale. Temperature at 2-m is overpredicted by WRF (by ∼0.2-0.3 °C) but underpredicted by WRF/Chem (by ∼0.3-0.4 °C), due to higher radiation from WRF. Both WRF and WRF/Chem show large overpredictions for precipitation, indicating limitations in their microphysics or convective parameterizations. WRF/Chem with prognostic chemical concentrations, however, performs much better than WRF with prescribed chemical concentrations for radiation variables, illustrating the benefit of predicting gases and aerosols and representing their feedbacks into meteorology in WRF/Chem. WRF/Chem performs much better than CESM_NCSU for most surface meteorological variables and O3 hourly mixing ratios. In addition, WRF/Chem better captures observed temporal and spatial variations than CESM_NCSU. CESM_NCSU performance for radiation variables is comparable to or better than WRF/Chem performance because of the model tuning in CESM_NCSU that is routinely made in global models.

  18. 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...

  19. 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...

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

    USGS Publications Warehouse

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

    2012-01-01

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

  1. Downscaling GRACE satellite data for sub-region groundwater storage estimates in California's Central Valley

    NASA Astrophysics Data System (ADS)

    Kuss, A. M.; Newcomer, M. E.; Hsu, W.; Bourai, A.; Puranam, A.; Landerer, F. W.; Schmidt, C.

    2012-12-01

    The Central Valley aquifer (CVA) is a vital economic and environmental resource for California and the United States, and supplies water for one of the most agriculturally productive regions in the world. Recent estimates of groundwater (GW) availability in California have indicated declines in GW levels that may pose a threat to sustainable groundwater use in this region. The Gravity Recovery and Climate Experiment (GRACE) can be used to estimate variations in total water storage (TWS) and are therefore used to estimate GW storage changes within the CVA. However, using GRACE data in the CVA is challenging due to the coarse spatial resolution and increased error. To compensate for this, we used a statistical downscaling approach applied to GRACE data at the sub-region level using GW storage estimates from the California Department of Water Resources' (DWR) C2VSim hydrological model. This method produced a spatially and temporally variable GW anomaly dataset for sub-region GW management and for analysis of GW changes influenced by spatial and temporal variability. An additional challenge for this region is the influence of natural climate variability, altering GW recharge and influencing pumping practices. Understanding the effects of climate variability on GW storage changes, may improve GRACE TWS and GW estimates during periods of increased rain or droughts. Thus, the GRACE TWS and GW storage estimates were compared to the El Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) using singular spectral analysis (SSA). Results from SSA indicate that variations in GRACE TWS are moderately correlated to PDO (10-25 year cycle), although low correlations were observed when compared to ENSO (2-7 year cycle). The incorporation of these new methods for estimating variations in groundwater storage in highly productive aquifers may improve water management techniques in California.

  2. Dynamical downscaling with COSMO and COSMO-CLM in the Sino-Mongolian Altai region

    NASA Astrophysics Data System (ADS)

    Kurzrock, Frederik; Buerkert, Andreas; Byambaa, Oyunmunkh; Goenster, Sven; Jin, Luxi; Ohlwein, Christian; Simmer, Clemens; Simon, Thorsten

    2017-04-01

    For the first time, regional atmospheric simulations with spatial resolution down to 6 km have been performed in the Sino-Mongolian Altai region using the COSMO weather forecast and regional climate model. Two 5-year periods (1979-1982 and 2008-2012) have been simulated for a first evaluation of the model in this special region. The added value of a dynamical downscaling with the COSMO regional climate model CCLM towards the driving ERA-Interim reanalysis is investigated by comparison with weather station observations. In the mountainous region, the CCLM simulation much better relates to the observed monthly mean 2 m temperature and maximum monthly precipitation sums in summer than ERA-Interim. In addition, the intensity distribution of sub-daily precipitation amounts becomes more realistic with increasing altitude. CCLM does, however, overestimate convection in the mountains and accordingly simulates too much precipitation. Moreover, wintertime near-surface temperature inversions are underrated in the southern near-Gobi area, which leads to too high 2 m temperatures in that region. To examine the ability of the COSMO model to reproduce the vertical thermodynamic structure of the troposphere, additional simulations with the weather forecast version of COSMO were performed for July 2013 and compared to radiosonde measurements of the WATERCOPE field experiment in this region. The results indicate that the COSMO model is quite capable of qualitatively simulating a range of features of the local tropospheric stratification. Mean differences between observed and simulated dew point and temperature profiles were in the range of only to 1-2 °C in the lower troposphere.

  3. Dynamical downscaling with COSMO and COSMO-CLM in the Sino-Mongolian Altai region

    NASA Astrophysics Data System (ADS)

    Kurzrock, Frederik; Buerkert, Andreas; Byambaa, Oyunmunkh; Goenster, Sven; Jin, Luxi; Ohlwein, Christian; Simmer, Clemens; Simon, Thorsten

    2016-11-01

    For the first time, regional atmospheric simulations with spatial resolution down to 6 km have been performed in the Sino-Mongolian Altai region using the COSMO weather forecast and regional climate model. Two 5-year periods (1979-1982 and 2008-2012) have been simulated for a first evaluation of the model in this special region. The added value of a dynamical downscaling with the COSMO regional climate model CCLM towards the driving ERA-Interim reanalysis is investigated by comparison with weather station observations. In the mountainous region, the CCLM simulation much better relates to the observed monthly mean 2 m temperature and maximum monthly precipitation sums in summer than ERA-Interim. In addition, the intensity distribution of sub-daily precipitation amounts becomes more realistic with increasing altitude. CCLM does, however, overestimate convection in the mountains and accordingly simulates too much precipitation. Moreover, wintertime near-surface temperature inversions are underrated in the southern near-Gobi area, which leads to too high 2 m temperatures in that region. To examine the ability of the COSMO model to reproduce the vertical thermodynamic structure of the troposphere, additional simulations with the weather forecast version of COSMO were performed for July 2013 and compared to radiosonde measurements of the WATERCOPE field experiment in this region. The results indicate that the COSMO model is quite capable of qualitatively simulating a range of features of the local tropospheric stratification. Mean differences between observed and simulated dew point and temperature profiles were in the range of only to 1-2 °C in the lower troposphere.

  4. Projection of China's near- and long-term climate in a new high-resolution daily downscaled dataset NEX-GDDP

    NASA Astrophysics Data System (ADS)

    Bao, Yun; Wen, Xinyu

    2017-02-01

    The projection of China's near- and long-term future climate is revisited with a new-generation statistically downscaled dataset, NEX-GDDP (NASA Earth Exchange Global Daily Downscaled Projections). This dataset presents a high-resolution seamless climate projection from 1950 to 2100 by combining observations and GCM results, and remarkably improves CMIP5 hindcasts and projections from large scale to regional-to-local scales with an unchanged long-term trend. Three aspects are significantly improved: (1) the climatology in the past as compared against the observations; (2) more reliable near- and long-term projections, with a modified range of absolute value and reduced inter-model spread as compared to CMIP5 GCMs; and (3) much added value at regional-to-local scales compared to GCM outputs. NEX-GDDP has great potential to become a widely-used high-resolution dataset and a benchmark of modern climate change for diverse earth science communities.

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

    PubMed

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

    2016-07-05

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-07-01

    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.

  7. 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

  8. 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

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

    NASA Astrophysics Data System (ADS)

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

    2010-05-01

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

  10. 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.

  11. 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.

  12. 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).

  13. Climate change and hazardous convective weather in the United States: Insights from high-resolution dynamical downscaling

    NASA Astrophysics Data System (ADS)

    Hoogewind, Kimberly A.

    Global climate model (GCM) projections increasingly suggest that large-scale environmental conditions favorable for hazardous convective weather (HCW) may increase in frequency in the future due to anthropogenic climate change. However, this storm environment-based approach is undoubtedly limited by the assumption that convective-scale phenomena will be realized within these environments. The spatial resolution of GCMs remains much too coarse to adequately represent the scales at which severe convective storms occur, including processes that may lead to storm initiation. With the advancement of computing resources, however, it has now become feasible to explicitly represent deep convective storms within a high-resolution regional climate model. This research utilized the Weather Research and Forecasting (WRF) model to produce high-resolution, dynamically downscaled simulations for the continental United States under historical (1971-2000) and future (2071-2100) climate periods using GCM data provided by the Geophysical Fluid Dynamic Laboratory Climate Model version 3 (GFDL-CM3). Model proxies were used to provide an objective estimate of the occurrence of simulated severe weather and how their spatiotemporal distribution may change in the future under an aggressive climate change scenario. Results demonstrated that severe storms may increase in both their frequency and intensity in the future. In comparison to the projected changes in HCW favorable environments from the GCM, the dynamically downscaled largely agree in terms of the seasonal timing and spatial patterns of greatest potential change in activity by the end of the 21st century. Likewise, each approach supports the notion that severe weather activity may begin earlier within the annual cycle and also later within the calendar year, such that the severe weather season is lengthened. However, by all indications, the environment-event frequency relationship has been altered in future climate, such that the

  14. A simple method to include complex orography in the RainFARM stochastic precipitation downscaling method for climate studies

    NASA Astrophysics Data System (ADS)

    von Hardenberg, Jost; Terzago, Silvia; Palazzi, Elisa

    2017-04-01

    Orographic precipitation mechanisms play an important role in determining patterns of small-scale precipitation in areas with complex orography, but several current stochastic precipitation downscaling procedures cannot take into account orographic effects at scales smaller than those resolved by the original precipitation field to downscale. As a result, the long-term climatology at individual grid points may differ significantly from observations. We introduce a simple method to take fine-scale orography into account when the stochastic method RainFARM is applied to downscale precipitation from climate simulations. The method is based on the availability of a reference fine-scale climatology, such as gridded observations from a dense network or high-resolution dynamical simulations. This reference climatology allows to derive corrective weights which are applied to the realisations of stochastic fields generated by RainFARM, allowing to reproduce a more realistic long-term precipitation climatology at fine scales. We first demonstrate the method in a perfect model example: large-scale (64km) precipitation fields, obtained by aggregating high-resolution precipitation fields (4km) from WRF simulations over the Swiss Alps, in the period 1980-2008, are downscaled back to the original fine resolution. We compare the resulting probability distribution of precipitation extremes with that represented by the original fine-scale data. In this case, a perfect knowledge of the desired precipitation climatology is assumed, and the climatology resulting from the fine-scale WRF data is used. Further, we demonstrate the method in a more realistic setup, in which we downscale E-OBS precipitation data (25km to 1km) over the Swiss Alps and compare the resulting fields with high-quality in-situ observations from MeteoSWISS, in the period 1981-2010. We compare the impact of using different data sources for the weights and we show that a high-resolution climatology derived from

  15. 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.

  16. 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.

  17. 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

  18. WRF Dynamical Downscaling of the Twentieth Century Reanalysis for China 1.Climatic Means during 1981-2010

    NASA Astrophysics Data System (ADS)

    Kong, Xianghui; Bi, Xunqiang

    2015-04-01

    This study presents a dynamically downscaled climatology over East Asia by using the non-hydrostatic Weather Research and Forecasting (WRF) model, forced by the Twentieth Century Reanalysis (20CR-v2). The whole experiment is a 111 year (1900-2010) continuous run at 50 km horizontal resolution. Climatic means among observations, the driving fields and WRF results during the last three decades (1981-2010) are examined in continental China, and our focus is on surface air (2-m) temperature and precipitation in both summer and winter. WRF dynamically downscaling is able to reproduce the main features of surface air temperature in two seasons in China, and outperforms the driving fields in regional details due to topographic forcing. Surface air temperature biases are reduced as much as 1~2°.For precipitation, the simulated results can reproduce the decreasing pattern from southeast to northwest China in winter. For summer rainfall, the WRF simulated results reproduce the right magnitude of heavy rainfall center around the southeastern coastal area, better than the driving field. One of the significant improvements is that an unrealistic center of summer precipitation in Southeast China in 20CR-v2 is eliminated. However, the simulated results underestimate winter surface air temperature in northern China and winter rainfall in some regions in southeast China.

  19. 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

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

    NASA Astrophysics Data System (ADS)

    Sheffield, Justin; Li, Haibin; Wood, Eric

    2010-05-01

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

  1. 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

  2. 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.

  3. Downscaling of RCM outputs for representative catchments in the Mediterranean region, for the 1951-2100 time-frame

    NASA Astrophysics Data System (ADS)

    Deidda, Roberto; Marrocu, Marino; Pusceddu, Gabriella; Langousis, Andreas; Mascaro, Giuseppe; Caroletti, Giulio

    2013-04-01

    Within the activities of the EU FP7 CLIMB project (www.climb-fp7.eu), we developed downscaling procedures to reliably assess climate forcing at hydrologically relevant scales, and applied them to six representative hydrological basins located in the Mediterranean region: Riu Mannu and Noce in Italy, Chiba in Tunisia, Kocaeli in Turkey, Thau in France, and Gaza in Palestine. As a first step towards this aim, we used daily precipitation and temperature data from the gridded E-OBS project (www.ecad.eu/dailydata), as reference fields, to rank 14 Regional Climate Model (RCM) outputs from the ENSEMBLES project (http://ensembles-eu.metoffice.com). The four best performing model outputs were selected, with the additional constraint of maintaining 2 outputs obtained from running different RCMs driven by the same GCM, and 2 runs from the same RCM driven by different GCMs. For these four RCM-GCM model combinations, a set of downscaling techniques were developed and applied, for the period 1951-2100, to variables used in hydrological modeling (i.e. precipitation; mean, maximum and minimum daily temperatures; direct solar radiation, relative humidity, magnitude and direction of surface winds). The quality of the final products is discussed, together with the results obtained after applying a bias reduction procedure to daily temperature and precipitation fields.

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

    USDA-ARS?s Scientific Manuscript database

    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...

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

    NASA Astrophysics Data System (ADS)

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

    2017-03-01

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

  6. NASA Downscaling Project

    NASA Technical Reports Server (NTRS)

    Ferraro, Robert; Waliser, Duane; Peters-Lidard, Christa

    2017-01-01

    A team of researchers from NASA Ames Research Center, Goddard Space Flight Center, the Jet Propulsion Laboratory, and Marshall Space Flight Center, along with university partners at UCLA, conducted an investigation to explore whether downscaling coarse resolution global climate model (GCM) predictions might provide valid insights into the regional impacts sought by decision makers. Since the computational cost of running global models at high spatial resolution for any useful climate scale period is prohibitive, the hope for downscaling is that a coarse resolution GCM provides sufficiently accurate synoptic scale information for a regional climate model (RCM) to accurately develop fine scale features that represent the regional impacts of a changing climate. As a proxy for a prognostic climate forecast model, and so that ground truth in the form of satellite and in-situ observations could be used for evaluation, the MERRA and MERRA-2 reanalyses were used to drive the NU-WRF regional climate model and a GEOS-5 replay. This was performed at various resolutions that were at factors of 2 to 10 higher than the reanalysis forcing. A number of experiments were conducted that varied resolution, model parameterizations, and intermediate scale nudging, for simulations over the continental US during the period from 2000-2010. The results of these experiments were compared to observational datasets to evaluate the output.

  7. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

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

    Treesearch

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

    2016-01-01

    climate change for tropical islands were studied using output from 12 statistically downscaled general circulation models (GCMs) taking Puerto Rico as a test case. Two model selection/model averaging strategies were used: the average of all available GCMs and the av-erage of the models that are able to...

  10. 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

  11. 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.

    2017-02-01

    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.

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

    Treesearch

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

    2015-01-01

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

  13. 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

  14. Benefits from using combined dynamical-statistical downscaling approaches in building crop water demand scenarios in a semi-arid Mediterranean region.

    NASA Astrophysics Data System (ADS)

    Guyennon, Nicolas; Portoghese, Ivan; Romano, Emanuele; Calmanti, Sandro

    2013-04-01

    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 permits to obtain more suitable scenarios of crop water demand. The case study presented here focuses on the north-western part of the Apulia region named Capitanata plain (South East of Italy, surface area about 4000 km2), dominated by agriculture (about 15% of the national production of cereals and olive trees) and mainly depending on surface water. 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. Finally the crop water demand is estimated through the water mass-balance model G-MAP, considering monthly precipitation, monthly temperature and the major landscape features that determine the soil water balance. The latter introduces a strong non linearity with respect to the meteorological input, due to the non-linear solution of soil infiltration and moisture-dependent evapotranspiration and the threshold-based runoff mechanism, which prevents from forecasting the crop water demand as simple linear combination of the precipitation and temperature scenarios. The crop water demand scenarios resulting from the different downscaling and their combination are then compared in terms of bias, long term non stationarity and spatial variability.

  15. Prediction of climate change in Brunei Darussalam using statistical downscaling model

    NASA Astrophysics Data System (ADS)

    Hasan, Dk. Siti Nurul Ain binti Pg. Ali; Ratnayake, Uditha; Shams, Shahriar; Nayan, Zuliana Binti Hj; Rahman, Ena Kartina Abdul

    2017-06-01

    Climate is changing and evidence suggests that the impact of climate change would influence our everyday lives, including agriculture, built environment, energy management, food security and water resources. Brunei Darussalam located within the heart of Borneo will be affected both in terms of precipitation and temperature. Therefore, it is crucial to comprehend and assess how important climate indicators like temperature and precipitation are expected to vary in the future in order to minimise its impact. This study assesses the application of a statistical downscaling model (SDSM) for downscaling General Circulation Model (GCM) results for maximum and minimum temperatures along with precipitation in Brunei Darussalam. It investigates future climate changes based on numerous scenarios using Hadley Centre Coupled Model, version 3 (HadCM3), Canadian Earth System Model (CanESM2) and third-generation Coupled Global Climate Model (CGCM3) outputs. The SDSM outputs were improved with the implementation of bias correction and also using a monthly sub-model instead of an annual sub-model. The outcomes of this assessment show that monthly sub-model performed better than the annual sub-model. This study indicates a satisfactory applicability for generation of maximum temperatures, minimum temperatures and precipitation for future periods of 2017-2046 and 2047-2076. All considered models and the scenarios were consistent in predicting increasing trend of maximum temperature, increasing trend of minimum temperature and decreasing trend of precipitations. Maximum overall trend of Tmax was also observed for CanESM2 with Representative Concentration Pathways (RCP) 8.5 scenario. The increasing trend is 0.014 °C per year. Accordingly, by 2076, the highest prediction of average maximum temperatures is that it will increase by 1.4 °C. The same model predicts an increasing trend of Tmin of 0.004 °C per year, while the highest trend is seen under CGCM3-A2 scenario which is 0.009

  16. 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

  17. Comparison of two analog-based downscaling methods for regional reference evapotranspiration forecasts

    NASA Astrophysics Data System (ADS)

    Tian, Di; Martinez, Christopher J.

    2012-12-01

    SummaryThe objective of this study was to compare the performance of natural analog (NA) and constructed analog (CA) methods to produce both probabilistic and deterministic downscaled daily reference evapotranspiration (ETo) forecasts in the southeastern United States. The 1-15 day, 15-member ETo forecasts were produced from 1979 to 2009 using the Penman-Monteith equation and a forecast analog approach with a combination of the Global Forecast System (GFS) reforecasts and NCEP-DOE Reanalysis 2 climatology, and were downscaled using the North American Regional Reanalysis (NARR). The Pearson correlation coefficient (R), mean squared error skill score (MSESS), and Bias were used to evaluate the skill of downscaled deterministic forecasts. The Linear Error in Probability Space (LEPS) skill score, Brier Skill Score (BSS), relative operating characteristic, and reliability diagrams were used to evaluate the skill of downscaled probabilistic forecasts. Overall, CA showed slightly higher skill than NA in terms of the metrics for deterministic forecasts, while for probabilistic forecasts NA showed higher skill than CA regarding the BSS in five categories (terciles, and 10th and 90th percentiles) and lower skill than CA regarding the LEPS skill score. Both CA and NA produced skillful deterministic results in the first 3 lead days, while the skill was higher for CA than for NA. Probabilistic NA forecasts exhibited higher resolution and reliability than CA, likely due to a larger ensemble size. Forecasts by both methods showed the lowest skill in the Florida peninsula and in mountainous areas, likely due to the fact that these features were not well-resolved in the model forecast.

  18. 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

  19. 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

  20. 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.

  1. 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

  2. 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

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

    PubMed

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

    2012-01-01

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

  4. 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.

  5. A hybrid downscaling approach for the analysis of coastline response to changing climate forcing in the period 1900-2010

    NASA Astrophysics Data System (ADS)

    Alvarez Antolinez, J. A.; Murray, A. B.; Mendez, F. J.; Medrano Gutiérrez, M. Y.; Cofino, A. S.

    2016-02-01

    Long-term coastal evolution models can be forced with long-term time series of sea state parameters or with representative angular distribution of wave influences on alongshore sediment transport. The use of observational data -buoys- is usually restricted to 15-20 years, requiring transformation of waves, and in most of the cases, there are gaps in the time series. The development of atmospheric reanalysis can dramatically extend the period to more than 100 years, depending on the resolution of the wind forcing (1979-2015 for the hourly 0.3º spatial resolution CFSR atmospheric reanalysis, 1948-2015 for the 6-hourly 2º spatial res. NCEP/NCAR reanalysis, 1871-2010 for the 6-hourly 20th century reanalysis, 20CRv2). The spatio-temporal wind fields associated with these reanalysis are the source terms of global or regional 3rd generation wave model forcing that leads to different available state-of-the-art wave hindcast models at a spatial scale varying between 0.5-1º. The need to model wave transformations from deep water to at the base of the shoreface ( 20 meters depth), at a spatial resolution of 1 Km, requiring many hours of CPU time, presents an additional challenge. To address these challenges, we propose a hybrid approach combining: (a) the use of long-term deep water buoy time series; (b) efficient hybrid wave transformation to the shoreface with SWAN model and data mining techniques (Camus et al, 2011); (c) a statistical downscaling model based on daily sea level pressure-based (SLP) weather types that allows us to downscale multivariate sea state parameters (significant wave height, SWH, peak period, Tp, mean wave direction, MWD) (Camus et al, 2014); (d) reconstruction of daily values of SWH, Tp and MWD in the period 1871-2010 using the 20CRv2 reanalysis; (e) use of the Coastline Evolution Model, CEM (Ashton and Murray, 2006) forced with the resulting wave climate. Comparing the average wave climate for the whole century with different durations and time

  6. 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

  7. 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

  8. Possible impacts of climate change on freezing rain in south-central Canada using downscaled future climate scenarios

    NASA Astrophysics Data System (ADS)

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

    2007-01-01

    Freezing rain is a major atmospheric hazard in mid-latitude nations of the globe. Among all Canadian hydrometeorological hazards, freezing rain is associated with the highest damage costs per event. Using synoptic weather typing to identify the occurrence of freezing rain events, this study estimates changes in future freezing rain events under future climate scenarios for south-central Canada. Synoptic weather typing consists of principal components analysis, an average linkage clustering procedure (i.e., a hierarchical agglomerative cluster method), and discriminant function analysis (a nonhierarchical method). Meteorological data used in the analysis included hourly surface observations from 15 selected weather stations and six atmospheric levels of six-hourly National Centers for Environmental Prediction (NCEP) upper-air reanalysis weather variables for the winter months (November-April) of 1958/59-2000/01. A statistical downscaling method was used to downscale four general circulation model (GCM) scenarios to the selected weather stations. Using downscaled scenarios, discriminant function analysis was used to project the occurrence of future weather types. The within-type frequency of future freezing rain events is assumed to be directly proportional to the change in frequency of future freezing rain-related weather types The results showed that with warming temperatures in a future climate, percentage increases in the occurrence of freezing rain events in the north of the study area are likely to be greater than those in the south. By the 2050s, freezing rain events for the three colder months (December-February) could increase by about 85% (95% confidence interval - CI: ±13%), 60% (95% CI: ±9%), and 40% (95% CI: ±6%) in northern Ontario, eastern Ontario (including Montreal, Quebec), and southern Ontario, respectively. The increase by the 2080s could be even greater: about 135% (95% CI: ±20%), 95% (95% CI: ±13%), and 45% (95% CI: ±9%). For the

  9. MODFLOW datasets for simulations of groundwater flow with downscaled global climate model data for the Suwannee River Basin, Florida

    USGS Publications Warehouse

    Swain, Eric D.; Davis, J. Hal

    2016-01-01

    A previously-developed groundwater model of the Suwannee River Basin was modified and calibrated to represent transient conditions. A simulation of recent conditions was developed for the 372-month period 1970-2000, and was compared with a simulation of future conditions for a similar-length period 2039-2069, which uses downscaled GCM (Global Climate Model) data. The MODFLOW groundwater-simulation code was used in both of these simulations, and two different MODFLOW boundary condition “packages” (River and Streamflow Routing Packages) were used to represent interactions between surface-water and groundwater features. The parameters for the simulation of future conditions were developed from dynamically downscaled precipitation and evapotranspiration data generated by the Community Climate System Model. The model was developed to examine the effect of downscaled climate model data on the predictions of future hydrology in the Suwannee River Basin. The development of the model input and output files included in this data release are documented in a journal article for the American Journal of Climate Change. Support is provided for correcting errors in the data release and clarification of the modeling conducted by the U.S. Geological Survey. Users are encouraged to review the model documentation report to understand the purpose, construction, and limitations of this model.

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

    NASA Astrophysics Data System (ADS)

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

    2017-04-01

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

  11. 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.

  12. A parametric approach for simultaneous bias correction and high-resolution downscaling of climate model rainfall

    NASA Astrophysics Data System (ADS)

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

    2017-03-01

    Distribution mapping has been identified as the most efficient approach to bias-correct climate model rainfall, while reproducing its statistics at spatial and temporal resolutions suitable to run hydrologic models. Yet its implementation based on empirical distributions derived from control samples (referred to as nonparametric distribution mapping) makes the method's performance sensitive to sample length variations, the presence of outliers, the spatial resolution of climate model results, and may lead to biases, especially in extreme rainfall estimation. To address these shortcomings, we propose a methodology for simultaneous bias correction and high-resolution downscaling of climate model rainfall products that uses: (a) a two-component theoretical distribution model (i.e., a generalized Pareto (GP) model for rainfall intensities above a specified threshold u*, and an exponential model for lower rainrates), and (b) proper interpolation of the corresponding distribution parameters on a user-defined high-resolution grid, using kriging for uncertain data. We assess the performance of the suggested parametric approach relative to the nonparametric one, using daily raingauge measurements from a dense network in the island of Sardinia (Italy), and rainfall data from four GCM/RCM model chains of the ENSEMBLES project. The obtained results shed light on the competitive advantages of the parametric approach, which is proved more accurate and considerably less sensitive to the characteristics of the calibration period, independent of the GCM/RCM combination used. This is especially the case for extreme rainfall estimation, where the GP assumption allows for more accurate and robust estimates, also beyond the range of the available data.

  13. Mapping past, present, and future climatic suitability for invasive Aedes aegypti and Aedes albopictus in the United States: a process-based modeling approach using CMIP5 downscaled climate scenarios

    NASA Astrophysics Data System (ADS)

    Donnelly, M. A. P.; Marcantonio, M.; Melton, F. S.; Barker, C. M.

    2016-12-01

    The ongoing spread of the mosquitoes, Aedes aegypti and Aedes albopictus, in the continental United States leaves new areas at risk for local transmission of dengue, chikungunya, and Zika viruses. All three viruses have caused major disease outbreaks in the Americas with infected travelers returning regularly to the U.S. The expanding range of these mosquitoes raises questions about whether recent spread has been enabled by climate change or other anthropogenic influences. In this analysis, we used downscaled climate scenarios from the NASA Earth Exchange Global Daily Downscaled Projections (NEX GDDP) dataset to model Ae. aegypti and Ae. albopictus population growth rates across the United States. We used a stage-structured matrix population model to understand past and present climatic suitability for these vectors, and to project future suitability under CMIP5 climate change scenarios. Our results indicate that much of the southern U.S. is suitable for both Ae. aegypti and Ae. albopictus year-round. In addition, a large proportion of the U.S. is seasonally suitable for mosquito population growth, creating the potential for periodic incursions into new areas. Changes in climatic suitability in recent decades for Ae. aegypti and Ae. albopictus have occurred already in many regions of the U.S., and model projections of future climate suggest that climate change will continue to reshape the range of Ae. aegypti and Ae. albopictus in the U.S., and potentially the risk of the viruses they transmit.

  14. Mapping Past, Present, and Future Climatic Suitability for Invasive Aedes Aegypti and Aedes Albopictus in the United States: A Process-Based Modeling Approach Using CMIP5 Downscaled Climate Scenarios

    NASA Technical Reports Server (NTRS)

    Donnelly, Marisa Anne Pella; Marcantonio, Matteo; Melton, Forrest S.; Barker, Christopher M.

    2016-01-01

    The ongoing spread of the mosquitoes, Aedes aegypti and Aedes albopictus, in the continental United States leaves new areas at risk for local transmission of dengue, chikungunya, and Zika viruses. All three viruses have caused major disease outbreaks in the Americas with infected travelers returning regularly to the U.S. The expanding range of these mosquitoes raises questions about whether recent spread has been enabled by climate change or other anthropogenic influences. In this analysis, we used downscaled climate scenarios from the NASA Earth Exchange Global Daily Downscaled Projections (NEX GDDP) dataset to model Ae. aegypti and Ae. albopictus population growth rates across the United States. We used a stage-structured matrix population model to understand past and present climatic suitability for these vectors, and to project future suitability under CMIP5 climate change scenarios. Our results indicate that much of the southern U.S. is suitable for both Ae. aegypti and Ae. albopictus year-round. In addition, a large proportion of the U.S. is seasonally suitable for mosquito population growth, creating the potential for periodic incursions into new areas. Changes in climatic suitability in recent decades for Ae. aegypti and Ae. albopictus have occurred already in many regions of the U.S., and model projections of future climate suggest that climate change will continue to reshape the range of Ae. aegypti and Ae. albopictus in the U.S., and potentially the risk of the viruses they transmit.

  15. 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…

  16. 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…

  17. Can Quantile Mapping be Used for Downscaling?

    NASA Astrophysics Data System (ADS)

    Maraun, Douglas

    2013-04-01

    Quantile mapping is routinely applied to correct biases of regional climate model simulations compared to observational data. If the observations are of similar resolution as the regional climate model, quantile mapping is a feasible approach. But if the observations are of much higher resolution, quantile mapping also attempts to bridge this scale mismatch. Here I show for daily precipitation, that such quantile mapping based downscaling is not feasible but introduces similar problems as inflation of perfect prog downscaling: the spatial and temporal structure of the corrected time series is misrepresented, the drizzle effect for area means is over-corrected, area mean extremes are over-estimated and trends are affected. To overcome these problems, stochastic bias correction is required. D Maraun, Bias Correction, Quantile Mapping and Downscaling. Revisiting the Inflation Issue. J Climate, in press, 2013

  18. 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

  19. 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.

  20. 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.

  1. 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.

  2. "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...

  3. "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...

  4. 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.

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

    PubMed

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

    2009-05-26

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

  6. 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

  7. 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

  8. 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

  9. Stochastic downscaling of precipitation to high-resolution scenarios in orographically complex regions: 1. Model evaluation

    NASA Astrophysics Data System (ADS)

    Bordoy, R.; Burlando, P.

    2014-01-01

    The simulation of space-time precipitation has been studied since the late 1980s. However, there are still many open issues concerning the most appropriate approach to simulate it, specially in highly heterogeneous areas, such as in mountain environments. For this reason, we present here a comprehensive investigation of the Space-Time Neyman-Scott Rectangular Pulses model, with the purpose of analyzing its performance in a challenging Alpine environment of Switzerland and identifying weaknesses that can drive future improvements. The results point at the suitability of the model in reproducing not only the basic statistics at different temporal aggregations, but also the more challenging distributional and scaling properties. The intrinsic stationarity of the model in space, induced by the parameter estimation procedure, poses occasional limitations with regard to the accurate simulation of the variability of the observed climate characteristics, which are strongly influenced by local microclimates. However, the model is able, even in the complex Alpine environment, to preserve the spatial patterns observed in the actual precipitation process. The study allowed (i) to conclude about the robustness of the model and its suitability for multisite downscaling of precipitation estimated from climate model simulations, as reported in the companion paper, and (ii) to put in evidence some limitations that require further consideration to improve space-time rainfall generation.

  10. Assimilating Non-linear Effects of Customized Large-Scale Climate Predictors on Downscaled Precipitation over the Tropical Andes

    NASA Astrophysics Data System (ADS)

    Molina, J. M.; Zaitchik, B. F.

    2016-12-01

    Recent findings considering high CO2 emission scenarios (RCP8.5) suggest that the tropical Andes may experience a massive warming and a significant precipitation increase (decrease) during the wet (dry) seasons by the end of the 21st century. Variations on rainfall-streamflow relationships and seasonal crop yields significantly affect human development in this region and make local communities highly vulnerable to climate change and variability. We developed an expert-informed empirical statistical downscaling (ESD) algorithm to explore and construct robust global climate predictors to perform skillful RCP8.5 projections of in-situ March-May (MAM) precipitation required for impact modeling and adaptation studies. We applied our framework to a topographically-complex region of the Colombian Andes where a number of previous studies have reported El Niño-Southern Oscillation (ENSO) as the main driver of climate variability. Supervised machine learning algorithms were trained with customized and bias-corrected predictors from NCEP reanalysis, and a cross-validation approach was implemented to assess both predictive skill and model selection. We found weak and not significant teleconnections between precipitation and lagged seasonal surface temperatures over El Niño3.4 domain, which suggests that ENSO fails to explain MAM rainfall variability in the study region. In contrast, series of Sea Level Pressure (SLP) over American Samoa -likely associated with the South Pacific Convergence Zone (SPCZ)- explains more than 65% of the precipitation variance. The best prediction skill was obtained with Selected Generalized Additive Models (SGAM) given their ability to capture linear/nonlinear relationships present in the data. While SPCZ-related series exhibited a positive linear effect in the rainfall response, SLP predictors in the north Atlantic and central equatorial Pacific showed nonlinear effects. A multimodel (MIROC, CanESM2 and CCSM) ensemble of ESD projections revealed

  11. Climate model parameter sensitivity and selection for incorporating uncertainty in regional climate modeling

    NASA Astrophysics Data System (ADS)

    Li, S.; Mote, P.; Rupp, D. E.; McNeall, D. J.; Sarah, S.; Hawkins, L.

    2016-12-01

    Many processes - especially those involving clouds - that control climate responses to external forcings are still poorly understood, poorly modeled, and/or difficult to observe in nature. As such, model parameterizations representing these processes have large uncertainties. Therefore, even a Global Climate Model (GCM)'s `standard' configuration, which has been tuned to reproduce observed climate well, is subject to large uncertainty. To explore the influence of different parameter selections on regional climate, a large global/regional atmospheric perturbed physics ensemble was run using the volunteer computing network weather@home with the goal of finding model variants that have small top-of-atmosphere flux imbalance. This configuration reasonably reproduces the observed climates across the western US, while retaining the possibility of a range regional climate sensitivities. After this screening step, a subset of these parameter perturbations are used when downscaling the global model simulations with an embedded regional climate model. This work aims to identify model parameters that influence the quality of regional simulations, improve global and regional model performance through improved model parameterizations, and quantify uncertainty in downscaled simulations stemming from error in model parameterizations.

  12. 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

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

    NASA Astrophysics Data System (ADS)

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

    2016-08-01

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

  14. Improving the Resolution of Climate Models to Address Regional Climate Change

    NASA Astrophysics Data System (ADS)

    Rauscher, Sara A.; Ringler, Todd D.

    2009-12-01

    Simulating the Spatial-Temporal Patterns of Anthropogenic Climate Change: A Workshop in the Bridging Disciplines, Bridging Scale Series; Santa Fe, New Mexico, 20-22 July 2009; For decades, general circulation models (GCMs) have been one of the primary tools used to understand anthropogenic climate change. Now GCMs are faced with a substantially more complicated task: to provide information of sufficient spatial and temporal resolution to support policy initiatives that will address regional climate change. Different approaches are being developed and used to provide this information, including dynamic downscaling with limited area models (LAMs); high-resolution GCMs with quasi-uniform and variable-resolution grids; and statistical downscaling of limited area and global simulations. At a workshop in New Mexico, participants discussed each method's strengths and limitations as well as associated uncertainty.

  15. Probabilistic Predictions of Regional Climate Change

    NASA Astrophysics Data System (ADS)

    Harris, G. R.; Sexton, D. M.; Booth, B. B.; Brown, K.; Collins, M.; Murphy, J. M.

    2009-12-01

    quantified by validation with GCM ensemble output, and included as additional variance in our projections. The final step in the probabilistic projections is to downscale to 25km resolution over Europe. To do this we use statistical relationships relating coarse and fine scales obtained using an ensemble of perturbed versions of the HadRM3 regional model driven by an equivalent ensemble of HadCM3 global simulations. Our methodology allows us to assess the relative contributions from the major sources of uncertainty, including: parameter uncertainty, internal variability, carbon cycle uncertainty, scaling uncertainty, structural uncertainty, and downscaling. Although parameter uncertainty is generally the largest contributor, no one source of uncertainty is found to dominate. This methodology has recently been used for climate projections for the UK for the 21st century, and is more fully documented at:

  16. 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.

  17. NASA Downscaling Project: Final Report

    NASA Technical Reports Server (NTRS)

    Ferraro, Robert; Waliser, Duane; Peters-Lidard, Christa

    2017-01-01

    A team of researchers from NASA Ames Research Center, Goddard Space Flight Center, the Jet Propulsion Laboratory, and Marshall Space Flight Center, along with university partners at UCLA, conducted an investigation to explore whether downscaling coarse resolution global climate model (GCM) predictions might provide valid insights into the regional impacts sought by decision makers. Since the computational cost of running global models at high spatial resolution for any useful climate scale period is prohibitive, the hope for downscaling is that a coarse resolution GCM provides sufficiently accurate synoptic scale information for a regional climate model (RCM) to accurately develop fine scale features that represent the regional impacts of a changing climate. As a proxy for a prognostic climate forecast model, and so that ground truth in the form of satellite and in-situ observations could be used for evaluation, the MERRA and MERRA - 2 reanalyses were used to drive the NU - WRF regional climate model and a GEOS - 5 replay. This was performed at various resolutions that were at factors of 2 to 10 higher than the reanalysis forcing. A number of experiments were conducted that varied resolution, model parameterizations, and intermediate scale nudging, for simulations over the continental US during the period from 2000 - 2010. The results of these experiments were compared to observational datasets to evaluate the output.

  18. NASA Downscaling Project: Final Report

    NASA Technical Reports Server (NTRS)

    Ferraro, Robert; Waliser, Duane; Peters-Lidard, Christa

    2017-01-01

    A team of researchers from NASA Ames Research Center, Goddard Space Flight Center, the Jet Propulsion Laboratory, and Marshall Space Flight Center, along with university partners at UCLA, conducted an investigation to explore whether downscaling coarse resolution global climate model (GCM) predictions might provide valid insights into the regional impacts sought by decision makers. Since the computational cost of running global models at high spatial resolution for any useful climate scale period is prohibitive, the hope for downscaling is that a coarse resolution GCM provides sufficiently accurate synoptic scale information for a regional climate model (RCM) to accurately develop fine scale features that represent the regional impacts of a changing climate. As a proxy for a prognostic climate forecast model, and so that ground truth in the form of satellite and in-situ observations could be used for evaluation, the MERRA and MERRA - 2 reanalyses were used to drive the NU - WRF regional climate model and a GEOS - 5 replay. This was performed at various resolutions that were at factors of 2 to 10 higher than the reanalysis forcing. A number of experiments were conducted that varied resolution, model parameterizations, and intermediate scale nudging, for simulations over the continental US during the period from 2000 - 2010. The results of these experiments were compared to observational datasets to evaluate the output.

  19. 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

  20. 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

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

    NASA Astrophysics Data System (ADS)

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

    2017-02-01

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

  2. Impacts of Potential Evapotranspiration and Precipitation Patterns on Downscaling Soil Moisture in Regions with Large Topographic Relief

    NASA Astrophysics Data System (ADS)

    Niemann, J. D.; Cowley, G. S.; Green, T. R.; Seyfried, M. S.; Jones, A. S.; Grazaitis, P. J.

    2016-12-01

    Mapping of soil moisture is important for many applications such as flood forecasting, soil protection, and crop management. Soil moisture can be estimated at coarse resolutions (e.g., 10-40 km grid cells) using active/passive microwave remote-sensing methods, but information at that resolution is poorly suited for many applications. The Equilibrium Moisture from Topography, Vegetation, and Soil (EMT+VS) model downscales coarse-resolution soil moisture using fine-resolution topographic, vegetation, and soil information to produce fine-resolution (10-30 m) estimates of soil moisture. The EMT+VS model has been shown to perform well at catchments with low topographic relief (25-124 m), but it has not been applied to larger regions with substantial topographic relief. Large ranges of elevation can produce wide variations in the potential evapotranspiration (PET) and precipitation, which might affect the fine-resolution patterns of soil moisture. In this research, PET and precipitation downscaling methods are developed and included in a generalized EMT+VS model, and the effects of spatial variations in these variables on the soil moisture estimates are investigated. The methods are applied to the 239 km2 Reynolds Creek Watershed in southern Idaho, which has 1200 m of relief, for dates in late spring and summer. The PET and precipitation downscaling methods are able to capture the main features in the average spatial patterns of both variables, and the fine-resolution soil moisture estimates improve when these downscaling methods are used in the EMT+VS model. The PET downscaling provides a larger improvement in the soil moisture estimates than precipitation downscaling, likely because the PET pattern is more persistent through time than the precipitation pattern.

  3. AUTH Regional Climate Model Contributions to EURO-CORDEX. Part II

    NASA Technical Reports Server (NTRS)

    Katragkou, E.; Gkotovou, I.; Kartsios, S.; Pavlidis, V.; Tsigaridis, K.; Trail, M.; Nazarenko, L.; Karacostas, Theodore S.

    2017-01-01

    Regional climate downscaling techniques are being increasingly used to provide higher-resolution climate information than is available directly from contemporary global climate models. The Coordinated Regional Climate Downscaling Experiment (CORDEX) initiative was build to foster communication and knowledge exchange between regional climate modelers. The Department of Meteorology and Climatology of the Aristotle University of Thessaloniki has been contributing to the CORDEX initiative since 2010, with regional climate model simulations over the European domain (EURO-CORDEX). Results of this work are presented here, including two hindcasts and a historical simulation with the Weather Research Forecasting model (WRF), driven by ERA-interim reanalysis and the NASA Earth System Goddard Institute for Space Studies (GISS) ModelE2, respectively. Model simulations are evaluated with the EOBS climatology and the model performance is assessed.

  4. 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

  5. A method to treat climate changes of year-to-year variations in the pseudo-global-warming method as a dynamical downscaling

    NASA Astrophysics Data System (ADS)

    Wakazuki, Y.; Hara, M.; Kimura, F.; Regional Climate Modeling Research Team

    2010-12-01

    The pseudo-global-warming (PGW) method is a time-slice dynamical downscaling one developed by Kimura and Kitoh (2007) to obtain regional climate change information with finer resolutions. In the present climate experiment, regional climate model (RCM) experiment is carried out with objective analysis data (ANAL) as the lateral boundary conditions. On the other hand, in the future climate experiment, the lateral boundary conditions are the sum of the ANAL and the difference between the present and future climates by an atmosphere-ocean general circulation model (AOGCM). The advantage of this method is that the influences of biases of the AOGCMs are reduced. In addition, the number of downscaling experiments could be reduced in multi-model problems, namely, a RCM experiment result with the boundary conditions created by a multi-model ensemble mean of AOGCMs seems to be similar to the average of the results of RCMs with their AOGCMs. However, PGW method involves some problems. One of them is that climate changes in year-to-year variations are ignored. To overcome this problems, a new method is introduced. In the new method, a mean climatological difference of a AOGCM is added to ANAL in future climate experiment, which is the same as PGW method. Next, the year-to-year variation term of ANAL, AOGCM in the present climate (GCM-P), and that in the future climate (GCM-F) are normalized in each level and element of ANAL to X’a, X’p, and X’f, respectively. The eigenvector of X’a (Va) is extracted by Principal Component Analysis (PCA). Only Va is trusted among the variations of ANAL, GCM-P, and GCP-F. Thus, the coefficients (Ta, Tp, and Tf) of the Multiple Regression Analysis (MRA) with Va are examined, and a coefficient (Tw) for Va are newly estimated as the variation term of boundary conditions of RCM in the future climate (X’w). To create Tw, three-step calculations are included in the estimation. First, a matrix operator, that the covariance of coefficients

  6. Regional climate simulations over Vietnam using the WRF model

    NASA Astrophysics Data System (ADS)

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

    2016-10-01

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

  7. North American Regional Climate Change Assessment Program (NARCCAP): Producing Regional Climate Change Projections for Climate Impacts Studies

    NASA Astrophysics Data System (ADS)

    Arritt, R. W.; Mearns, L.; Anderson, C.; Bader, D.; Buonomo, E.; Caya, D.; Duffy, P.; Elguindi, N.; Giorgi, F.; Gutowski, W.; Held, I.; Nunes, A.; Jones, R.; Laprise, R.; Leung, L. R.; Middleton, D.; Moufouma-Okia, W.; Nychka, D.; Qian, Y.; Roads, J.; Sain, S.; Snyder, M.; Sloan, L.; Takle, E.

    2006-12-01

    The North American Regional Climate Change Assessment Program (NARCCAP) is constructing projections of regional climate change over the coterminous United States and Canada in order to provide climate change information at decision relevant scales. A major goal of NARCCAP is to estimate uncertainties in regional scale projections of future climate by using multiple regional climate models (RCMs) nested within multiple atmosphere-ocean general circulation models (AOGCMs). NARCCAP is using six nested regional climate models at 50 km resolution to dynamically downscale realizations of current climate (1971-2000) and future climate (2041-2070, following the A2 SRES emission scenario) from four AOGCMs. Global time slice simulations, also at 50 km resolution, will be performed using the GFDL AM2.1 and NCAR CAM3 atmospheric models forced by the AOGCM sea surface temperatures and will be compared with results of the regional models. Results from this multiple-RCM, multiple-AOGCM suite will be statistically analyzed to investigate the cascade of uncertainty as one type of model draws information from another. All output will be made available to the climate analysis and climate impacts assessment communities through an archiving and data distribution plan. The climate impacts community will have these data at unprecedented spatial and temporal (hourly to six-hourly) resolution to support decision-relevant evaluations for public policy. As part of our evaluation of uncertainties, simulations are presently being concluded that nest the participating RCMs within reanalyses of observations. These simulations can be viewed as nesting the RCMs within a GCM that is nearly perfect (constrained by available observations), allowing us to separate errors attributable to the RCMs from those attributable to the driving AOGCMs. Results to date indicate that skill is greater in winter than in summer, and greater for temperature than for precipitation. Temperature and precipitation errors

  8. 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

  9. 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.

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

    PubMed

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

    2012-09-01

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

  11. 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

  12. 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

  13. Validation of regional precipitation-related indices dynamically downscaled from ERA-Interim Reanalysis Data by a Mesoscale Atmospheric Model

    NASA Astrophysics Data System (ADS)

    Gan, T. Y.; Hanrahan, J.; Kuo, C. C.

    2012-04-01

    Extreme precipitation events in central Alberta have overwhelmed hydraulic structures several times in recent years, and it is expected that rainfall intensity in this region will continue to increase over the next several decades. Accurate rainfall projections, which are communicated in the form of Intensity-Duration-Frequency (IDF) curves, are thus needed to design sufficient municipal structures. Such data may be obtained through the use of Regional Climate Models (RCMs), and one in particular, the fifth-generation NCAR/Penn State mesoscale atmospheric model (MM5), is investigated here. MM5 is used to dynamically downscale ECMWF ERA-Interim reanalysis data to evaluate its ability to accurately simulate rainfall characteristics in central Alberta over two consecutive summers that represent contrasting precipitation regimes. Precipitation simulated at the local scale is verified with Edmonton's local rain gauge network, while larger-scale precipitation is compared with the High Resolution Precipitation Product (HRPP), CMORPH. This particular HRPP was compared with rain gauge data and radar images which revealed that it can be reliably used to validate MM5 output in this region. MM5 output is also compared to data from a local sounding station and other reanalysis variables. Precipitation data generated by MM5 revealed that this RCM can indeed distinguish between wet (2010) and dry (2009) years, but that simulated rainfall totals are too high during both precipitation regimes. This bias is attributed to enhanced moisture advection associated with large-scale flow anomalies, and should be taken into consideration when making projections regarding possible changes to future precipitation conditions in central Alberta.

  14. Statistical Downscaling of Seasonal Forecasts and Climate Change Scenarios using Generalized Linear Modeling Approach for Stochastic Weather Generators

    NASA Astrophysics Data System (ADS)

    Kim, Y.; Katz, R. W.; Rajagopalan, B.; Podesta, G. P.

    2009-12-01

    Climate forecasts and climate change scenarios are typically provided in the form of monthly or seasonally aggregated totals or means. But time series of daily weather (e.g., precipitation amount, minimum and maximum temperature) are commonly required for use in agricultural decision-making. Stochastic weather generators constitute one technique to temporally downscale such climate information. The recently introduced approach for stochastic weather generators, based generalized linear modeling (GLM), is convenient for this purpose, especially with covariates to account for seasonality and teleconnections (e.g., with the El Niño phenomenon). Yet one important limitation of stochastic weather generators is a marked tendency to underestimate the observed interannual variance of seasonally aggregated variables. To reduce this “overdispersion” phenomenon, we incorporate time series of seasonal total precipitation and seasonal mean minimum and maximum temperature in the GLM weather generator as covariates. These seasonal time series are smoothed using locally weighted scatterplot smoothing (LOESS) to avoid introducing underdispersion. Because the aggregate variables appear explicitly in the weather generator, downscaling to daily sequences can be readily implemented. The proposed method is applied to time series of daily weather at Pergamino and Pilar in the Argentine Pampas. Seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society (IRI) are used as prototypes. In conjunction with the GLM weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts (the IRI format only specifies probabilities for three categories: below normal, near normal, and above normal) into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and

  15. 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.

  16. 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

  17. 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...

  18. 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...

  19. Implications of the methodological choices for hydrologic portrayals of climate change over the contiguous United States: Statistically downscaled forcing data and hydrologic models

    USGS Publications Warehouse

    Mizukami, Naoki; Clark, Martyn P.; Gutmann, Ethan D.; Mendoza, Pablo A.; Newman, Andrew J.; Nijssen, Bart; Livneh, Ben; Hay, Lauren E.; Arnold, Jeffrey R.; Brekke, Levi D.

    2016-01-01

    Continental-domain assessments of climate change impacts on water resources typically rely on statistically downscaled climate model outputs to force hydrologic models at a finer spatial resolution. This study examines the effects of four statistical downscaling methods [bias-corrected constructed analog (BCCA), bias-corrected spatial disaggregation applied at daily (BCSDd) and monthly scales (BCSDm), and asynchronous regression (AR)] on retrospective hydrologic simulations using three hydrologic models with their default parameters (the Community Land Model, version 4.0; the Variable Infiltration Capacity model, version 4.1.2; and the Precipitation–Runoff Modeling System, version 3.0.4) over the contiguous United States (CONUS). Biases of hydrologic simulations forced by statistically downscaled climate data relative to the simulation with observation-based gridded data are presented. Each statistical downscaling method produces different meteorological portrayals including precipitation amount, wet-day frequency, and the energy input (i.e., shortwave radiation), and their interplay affects estimations of precipitation partitioning between evapotranspiration and runoff, extreme runoff, and hydrologic states (i.e., snow and soil moisture). The analyses show that BCCA underestimates annual precipitation by as much as −250 mm, leading to unreasonable hydrologic portrayals over the CONUS for all models. Although the other three statistical downscaling methods produce a comparable precipitation bias ranging from −10 to 8 mm across the CONUS, BCSDd severely overestimates the wet-day fraction by up to 0.25, leading to different precipitation partitioning compared to the simulations with other downscaled data. Overall, the choice of downscaling method contributes to less spread in runoff estimates (by a factor of 1.5–3) than the choice of hydrologic model with use of the default parameters if BCCA is excluded.

  20. Climate change effects on wildland fire risk in the Northeastern and Great Lakes states predicted by a downscaled multi-model ensemble

    NASA Astrophysics Data System (ADS)

    Kerr, Gaige Hunter; DeGaetano, Arthur T.; Stoof, Cathelijne R.; Ward, Daniel

    2016-11-01

    This study is among the first to investigate wildland fire risk in the Northeastern and the Great Lakes states under a changing climate. We use a multi-model ensemble (MME) of regional climate models from the Coordinated Regional Downscaling Experiment (CORDEX) together with the Canadian Forest Fire Weather Index System (CFFWIS) to understand changes in wildland fire risk through differences between historical simulations and future projections. Our results are relatively homogeneous across the focus region and indicate modest increases in the magnitude of fire weather indices (FWIs) during northern hemisphere summer. The most pronounced changes occur in the date of the initialization of CFFWIS and peak of the wildland fire season, which in the future are trending earlier in the year, and in the significant increases in the length of high-risk episodes, defined by the number of consecutive days with FWIs above the current 95th percentile. Further analyses show that these changes are most closely linked to expected changes in the focus region's temperature and precipitation. These findings relate to the current understanding of particulate matter vis-à-vis wildfires and have implications for human health and local and regional changes in radiative forcings. When considering current fire management strategies which could be challenged by increasing wildland fire risk, fire management agencies could adapt new strategies to improve awareness, prevention, and resilience to mitigate potential impacts to critical infrastructure and population.

  1. 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

  2. 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.

  3. 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...

  4. 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...

  5. Application of a hybrid method for downscaling of the global climate model fields for evaluation of future surface mass balance of mountain glaciers

    NASA Astrophysics Data System (ADS)

    Morozova, Polina; Rybak, Oleg; Kaminskaia, Mariia

    2017-04-01

    Mountain glaciers in the Caucasus have been degrading during the last century. During this time period they lost approximately one-third in area and half of their volume. Prediction of their evolution in changing climate is crucial for the local economy because hydrological regime in the territory north to the Main Caucasus Chain is mainly driven by glacier run-off. For future projections of glaciers' surface mass balance (SMB) we apply a hybrid method of downscaling of GCM-generated meteorological fields from the global scale to the characteristic spatial resolution normally used for modeling of a single mountain glacier SMB. A method consists of two stages. On the first, dynamical stage, we use the results of calculations of regional climate model (RCM) HadRM3P for the Black Sea-Caspian region with a spatial resolution of approximately 25 km. Initial and boundary conditions for HadRM3P are provided by an AO GCM INMCM developed in the Institute of Numerical Mathematics (Moscow, Russia). Calculations were carried out for two time slices: the present (reference) climate (1971-2000 years) and climate in the late 21st century (2071-2100 years) according to scenario of greenhouse gas emissions RCP 8.5. On the second stage of downscaling, further regionalization is achieved by projecting of RCM-generated data to the high-resolution (25 m) digital elevation models in a domain enclosing target glaciers (Marukh in the Western Caucasus and Djankuat in the Central Caucasus, both being typical valley glaciers). Elevation gradient of surface air temperature and precipitation were derived from the model data. Further, results were corrected using data of observations. The incoming shortwave radiation is calculated separately, taking into account slopes, aspects and shade effect. In the end of the current century expected air temperature growth in the Central and Western Caucasus is about 5-6 °C (summer), and 2-3 °C (winter). Reduction in annual precipitation is not

  6. The range of regional climate change projections in central Europe: How to deal with the spread of climate model results?

    NASA Astrophysics Data System (ADS)

    Rechid, D.; Jacob, D.; Podzun, R.

    2010-09-01

    The regional climate change projections for central Europe in the 21st century show a large spread of simulated temperature and precipitation trends due to natural variability and modelling uncertainties. The questions are how to extract robust climate change signals and how to transfer the range of possible temperature and precipitation trends to climate change impact studies and adaptation strategies? Within the BMBF funded research priority "KLIMZUG - Managing Climate Change in the Regions of the Future", innovative strategies for adaptation to climate change are developed. The funding activity particularly stresses the regional aspect since the global problem climate change must be tackled by measures at regional and local level. The focus of the joint project "KLIMZUG-NORD - Strategic Approaches to Climate Change Adaptation in the Hamburg Metropolitan Region" is to establish an interdisciplinary network between the research, administrative and economic sectors in this region. The regional climate change information is provided by the Max-Planck-Institute for Meteorology as input for climate change impact assessments. The cross-sectional task "climate change" is to prepare consistent regional climate data and to advise on its reasonable use. The project benefits from the results of the ENSEMBLES EU project, in which an extensive set of regional climate change simulations at 50 km horizontal resolution were performed for 1950 to 2100. For impact studies, higher horizontal resolutions are required. With the regional climate model REMO, three global climate change scenarios from ECHAM5-MPIOM were downscaled to 50 km with three ensemble members each. In a second step, some members were further downscaled to 10 km for central Europe. For the global and regional simulations, the trends were analysed and indicate a strong internal climate variability, which further increases the range of climate change simulation results. This all recommends the application of 1

  7. 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).

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

    NASA Astrophysics Data System (ADS)

    Alemseged, Tamiru Haile; Tom, Rientjes

    2015-07-01

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

  9. 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

  10. Downscaled Climate Change Projections for the Southern Colorado Plateau: Variability and Implications for Vegetation Changes

    NASA Astrophysics Data System (ADS)

    Garfin, G. M.; Eischeid, J. K.; Cole, K. L.; Ironside, K.; Cobb, N. S.

    2008-12-01

    Recent and rapid forest mortality in western North America and associated changes in fire frequency and area burned are among the chief concerns of ecosystem managers. These examples of climate change surprises demonstrate nonlinear and threshold ecosystem responses to increased temperatures and severe drought. A consistent management request from climate change adaptation workshops held during the last four years in the southwest U.S. is for region-specific estimates of climate and vegetation change, in order to provide guidance for management of federal and state forest, range, and riparian preserves and land holdings. Partly in response to these concerns, and partly in the interest of improving knowledge of potential ecosystem changes and their relationships with observed changes and changes demonstrated in the paleoecological record, we developed a set of integrated climate and ecosystem analyses. We selected five of twenty-two GCMs from the PCMDI archive of IPCC AR4 model runs, based on their approximations of observed critical seasonality for vegetation in the Southern Colorado Plateau (domain: 35°- 38°N, 114°-107°W), centered on the Four Corners states. We used three key seasons in our analysis, winter (November-March), pre-monsoon (May-June), and monsoon (July- September). Projections of monthly and seasonal temperature and precipitation from our five-model ensemble indicate steadily increasing temperatures in our region of interest during the twenty-first century. By 2050, the ensemble projects increases of 3.0°C during May and June, months critical for drought stress and tree mortality, and 4.5-5.0°C by 2090. Projected temperature changes for months during the heart of winter (December and January) are on the order of 2.5°C by 2050 and 3.0°C by 2090; such changes are likely to affect snow hydrology in middle to low elevations in the Southern Colorado Plateau. Summer temperature increases are on the order of 2.5°C (2050) and 4.0°C (2090). The

  11. Evaluation of a regional model climatology in Europe using dynamical downscaling from a seamless Earth prediction approach (EC-Earth)

    NASA Astrophysics Data System (ADS)

    Jimenez-Guerrero, Pedro; Montavez, Juan P.; Baldasano, Jose M.

    2010-05-01

    Climate and weather forecasting applications share a common ancestry and build on the same physical principles. Nevertheless, climate research and numerical weather prediction are commonly seen as different disciplines. The emerging concept of "seamless prediction" forges weather forecasting and climate change studies into a single framework (Palmer et al., 2008). In principle, as models develop towards higher resolution and more feedbacks are included, some aspects of model uncertainty should reduce. However, global models can only resolve processes down to 50-100 km at present. Moreover, users of climate information often require much higher detail and downscaling methods are needed to provide regional climate information consistent with global climate trajectories. Therefore, this work presents an evaluation of the ability of a regional climate model (RCM) to reproduce the present climatology over Europe using a high resolution (25 km). The RCM used in this study is a climate version of the MM5 model (Fernández et al., 2007). The analysis here focuses on the annual and seasonal biases and variability for temperature (mean, maximum and minimum) and precipitation. The statistical parameters are obtained by interpolating the simulated values on the E-OBS gridded dataset from the European Climate Assessment & Dataset (ECA&D) at a resolution of 0.5° for the period 1990-2000. The novel approach of this contribution is that the driving model is EC-Earth version 2 (Hazeleger et al., 2010), which follows the seamless prediction approach to provide climate forcings to the regional model. The atmospheric model of EC-Earth is based on ECMWF's Integrated Forecast System, cycle 31r1, corresponding to the current seasonal forecast system of ECMWF. The standard configuration runs at T159 horizontal spectral resolution with 62 vertical levels. The ocean component is based on version 2 of the NEMO model with a horizontal resolution of nominally 1 degree and 42 vertical levels

  12. 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.

  13. 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.

  14. 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.

  15. Regional features of global climate change in the Carpathian Basin

    NASA Astrophysics Data System (ADS)

    Pongrácz, R.; Bartholy, J.; Matyasovszky, I.; Schlanger, V.

    2003-04-01

    IPCC TAR suggests that eastern and central European countries could become highly vulnerable to global warming. Our investigations support these findings, especially, in case of two subregions: (1) Hungarian Great Plain, (2) watershed of the Lake Balaton. Severe shortage of precipitation occurred in the last few decades in both areas, thus, ecosystems must face to high risk of environmental change. The Great Plain is the largest agricultural area in Hungary where high variability of floods and droughts causes severe damages in crop yields and human settlements. Frequent extreme events may result in unstable climate conditions and increased vulnerability of agricultural activity in this region. One of the largest lake in Europe is the Lake Balaton with its unique 3.3 meter depth on average. In the last few years, the mean water level has decreased by 0.6-0.8 m several times for a few months period. The only outflow of the lake, a small creek (called Sio) has been regulated in 1863 in order to control the water runoff from the lake to the river Danube (120 km distance). The aim of our investigations is to compare climate change scenarios for these two sensitive regions. Two downscaling techniques have been compared, namely, (1) stochastical downscaling method nested in coupled ocean-atmosphere GCMs, (2) an upwelling diffusion energy balance model combined with GCM outputs and IPCC emission scenarios. The stochastical downscaling method includes large-scale circulation of the atmosphere, and also, it is able to represent the linkage between the local surface variables and large-scale circulation. Seasonal and annual changes in temperature and precipitation have been determined in case of the 2xCO2 climate and compared to historical data. Furthermore, several IPCC emission scenarios have been compared and GCM outputs have been analysed in order to project climate conditions for the 21st century in the Carpathian Basin.

  16. 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

  17. 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.

  18. 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.

  19. 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

  20. On the suitability of regional climate models for reconstructing climatologies

    NASA Astrophysics Data System (ADS)

    Tapiador, Francisco J.; Angelis, Carlos F.; Viltard, Nicolas; Cuartero, Fernando; de Castro, Manuel

    2011-08-01

    This paper discusses the potential of Regional Climate Models (RCMs) as reanalysis tools by presenting a reconstruction of the European climate using several RCMs with diverse physical parameterizations. The use of RCMs is intended to increase the spatial resolution of the analysis provided by Global Models through dynamic downscaling. At the same time, the use of several models allows us to characterize the uncertainties, as these can be estimated from the spread of the ensemble. When the RCMs are nested in reanalyses instead of in a Global Model it is possible to create climatologies of unprecedented robustness for variables such as temperature, precipitation, wind speed, and humidity, among others. While these climatologies are subject to further improvement as methods and computing power evolve, they point the way forward to the development of atmospheric information products suitable for a variety of studies including education, agriculture, renewable energies and climate change research, biogeography, insurance, risk assessment, hydrology, and regional planning.

  1. Bias correction of temperature and precipitation data for regional climate model application to the Rhine basin

    NASA Astrophysics Data System (ADS)

    Terink, W.; Hurkmans, R. T. W. L.; Uijlenhoet, R.; Torfs, P. J. J. F.; Warmerdam, P. M. M.

    2009-04-01

    The Hydrology and Quantitative Water Management group of Wageningen University is involved in the EU research project NeWater. The objective of this project is to develop tools which provide medium range hydrological predictions by coupling catchment-scale water balance models and ensembles from mesoscale climate models. The catchment-scale distributed hydrological model used in this study is the Variable Infiltration Capacity (VIC) model. This hydrological model in combination with an ensemble from the climate model ECHAM5 (developed by Max Plank Institute für Meteorologie (MPI-M), Hamburg) is being used to evaluate the effects of climate change on the hydrological regime of the Rhine basin and to assess the uncertainties involved in the ensembles from the climate model used in this study. Three future scenarios (2001-2100) are used in this study, which are downscaled ECHAM5 runs which were forced by the IPCC carbon emission scenarios B1, A1B and A2. A downscaled ECHAM5 "Climate of the 20th Century" run (1951-2000) is used as the reference climate. Downscaled ERA15 data is used to calibrate the VIC model. Downscaling of both the ECHAM5 and ERA15 model was carried out with the regional climate model REMO at MPI-M to a resolution of 0.088 degrees. The assessment of uncertainties involved in the climate model ensembles is performed by comparing the model (ECHAM5-REMO and ERA15-REMO) ensemble precipitation and temperature data with observations. This resulted in the detection of a bias in both the downscaled reference climate data and downscaled ERA15 data. A bias-correction has been applied to both the downscaled ERA15 data and the reference climate data. This bias-correction corrects for the mean and coefficient of variation for precipitation and the mean and standard deviation for temperature. The results of the applied bias-correction are analyzed spatially and temporally. Despite the fact that the bias-correction only uses two parameters, the coefficient of

  2. 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

  3. Regional Scale Analyses of Climate Change Impacts on Agriculture

    NASA Astrophysics Data System (ADS)

    Wolfe, D. W.; Hayhoe, K.

    2006-12-01

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

  4. Impact of the bias correction and downscaling aspects of quantile mapping on simulated climate change signal: a case study over Central Italy

    NASA Astrophysics Data System (ADS)

    Sangelantoni, Lorenzo; Coluccelli, Alessandro; Russo, Aniello; Gennaretti, Fabio; Grenier, Patrick

    2017-04-01

    Quantile mapping (QM) is a widely used post-processing technique employed to connect climate model simulations to impact studies. This technique relies on transfer functions established on observed and simulated climate variables over a common period (calibration period) and then used to adjust the whole simulation. Depending on the simulation-observation spatial scale mismatch, QM can be used in two different configurations. The first one is to employ only the bias correction (BC) aspect of QM, establishing transfer functions with observations aggregated at the simulation scale. The second configuration includes a statistical downscaling (SD) aspect as well, when the reference (observed) product remains point-scale. Another important issue with QM, and other post-processing techniques, is its impact on the climate change signal (CCS) of a simulation (e.g. statistics difference between 2071-2100 and 1971-2000 periods). In this work, we compare the alteration of the simulated CCS by QM between the BC and the BC+SD configurations over a study area covering Central Italy. In the BC configuration QM is applied on each member of a five-RCMs ensemble from the ENSEMBLES project (25 km horizontal resolution). Grid-cell-wise correction function is derived from E-OBS observed dataset, having the same resolution of RCMs grid. In the BC+SD configuration, QM is used to adjust and downscale RCMs results (from the former five simulations plus additional three EURO-CORDEX simulations with 12.5 km resolution) towards representative point-wise observational sites belonging to the Marche Region Civil Protection network. In both the configurations, different statistical moments of annual and seasonal temperature and precipitation CCS were assessed before and after the application of a daily-based empirical QM. Further, we studied CCS alteration as a function of the calibration period observed to simulated variance ratio regarded as factor controlling QM tendency of altering original

  5. 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

  6. 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.

  7. 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.

  8. Projection of future changes in climate in the Mediterranean Region

    NASA Astrophysics Data System (ADS)

    -Eleni Sotiropoulou, Rafaella; Stergiou, Ioannis; Tagaris, Efthimios

    2017-04-01

    Mediterranean since it is one of the regions that will be affected most by climate change. The objective of this study is to estimate changes in the future climatic parameters in the Mediterranean Region at a very fine grid resolution. WRF model is used to dynamically downscale NASA GISS GCM ModelE simulations in the Mediterranean at a 9 km by 9 km resolution for five current (2008-2012) and five future (2048-2052) years using the Representative Concentration Pathway 8.5 (RCP8.5). Results for temperature and precipitation are assessed annually and seasonally providing information for the estimated changes in a very fine scale across the Mediterranean Region. The estimated changes suggest that precipitation change is much more location dependent compared to temperature. Acknowledgments LIFE CLIMATREE project "A novel approach for accounting & monitoring carbon sequestration of tree crops and their potential as carbon sink areas" (LIFE14 CCM/GR/000635).

  9. Evaluation of Statistical Downscaling Skill at Reproducing Extreme Events

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  10. Regional Climate Tutorial: Assessing Regional Climate Change and Its Impacts

    NASA Astrophysics Data System (ADS)

    Barron, E.; Fisher, A.

    2002-05-01

    Recent scientific progress now enables credible projections of global changes in climate over long time periods. But people will experience global climate change where they live and work, and have difficulty thinking of a future beyond their grandchildren's lifetime. Although the task of projecting climate change and its impacts is far more challenging for regional and relatively near-term time scales, these are the scales at which actions most easily can be taken to moderate negative impacts. This tutorial will summarize what is known about projecting changes in regional climate, and about assessing the impacts for sectors such as forests, agriculture, fresh water quantity and quality, coastal zones, human health, and ecosystems. The Mid-Atlantic Regional Assessment (MARA) is used to provide context and illustrate how adaptation within the region and feedback from other regions influence the impacts that might be experienced.

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

    USDA-ARS?s Scientific Manuscript database

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

  12. 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

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

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2017-02-01

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

  15. 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

  16. 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

  17. Impact of Dynamical Downscaling on Model Representation of Land-Atmosphere Coupling Strength

    NASA Astrophysics Data System (ADS)

    Santanello, J. A., Jr.; Roundy, J. K.; Ferguson, C. R.

    2015-12-01

    Extremes in the water cycle, such as drought and flood, threaten the sustainability of water resources and cause significant impacts on society that will likely increase due to growing populations and a changing climate. Reducing the impact of extreme events requires preparations enabled by reliable and relevant predictions of the climate. Climate predictions are made using Global Climate Models (GCMs) that typically have spatial resolutions that are too course for application at the local level where the prediction is needed to ensure a society resilient to extremes. Therefore, a common practice is to dynamically downscale results from GCM's using a regional model that can provide predictions at scales consistent with the application. Downscaled predictions are dependent on model physics and model setup (e.g. boundary conditions, nudging) and as a result the overall validity of dynamically downscaling has not been fully demonstrated to date. NASA has recently sponsored an intra-agency downscaling project to better understand the validity of dynamical downscaling. As part of this project, several 10-year simulations of the NASA Unified Weather Research and Forecast (NU-WRF) model that vary in resolution and large scale nudging were used to downscale MERRA-2 reanalyses over the continental U.S. This work leverages these model runs in order to understand the impact of model resolution and nudging on the representation of land-atmosphere coupling strength and its impact on downscaled predictions of the water cycle. The representation of land-atmosphere coupling strength is analyzed through a suite of local land-atmosphere coupling (LoCo) metrics that are compared across downscaling runs as well as coarse scale predictions from GEOS-5 and MERRA-2. The impact of downscaling approaches and resolution on the representation of land-atmosphere coupling is presented and the implications for future downscaling applications are discussed.

  18. Application of downscaled precipitation for hydrological climate-change impact assessment in the upper Ping River Basin of Thailand

    NASA Astrophysics Data System (ADS)

    Sharma, Devesh; Babel, Mukand S.

    2013-11-01

    This paper assesses the impacts of climate change on water resources in the upper Ping River Basin of Thailand. A rainfall-runoff model is used to estimate future runoff based on the bias corrected and downscaled ECHAM4/OPYC general circulation model (GCM) precipitation scenarios for three future 5-year periods; the 2023-2027 (2025s), the 2048-2052 (2050s) and 2093-2097 (2095s). Bias-correction and spatial disaggregation techniques are applied to improve the characteristics of raw ECHAM4/OPYC precipitation. Results of future simulations suggest a decrease of 13-19 % in annual streamflow compared to the base period (1998-2002). Results also indicate that there will be a shift in seasonal streamflow pattern. Peak flows in future periods will occur in October-November rather than September as observed in the base period. There will be a significant increase in the streamflow in April with overall decrease in streamflow during the rainy season (May-October) and an increase during the dry season (November-April) for all future time periods considered in the study.

  19. On regional dynamical downscaling for the assessment and projection of temperature and precipitation extremes across Tasmania, Australia

    NASA Astrophysics Data System (ADS)

    White, Christopher J.; McInnes, Kathleen L.; Cechet, Robert P.; Corney, Stuart P.; Grose, Michael R.; Holz, Gregory K.; Katzfey, Jack J.; Bindoff, Nathaniel L.

    2013-12-01

    The ability of an ensemble of six GCMs, downscaled to a 0.1° lat/lon grid using the Conformal Cubic Atmospheric Model over Tasmania, Australia, to simulate observed extreme temperature and precipitation climatologies and statewide trends is assessed for 1961-2009 using a suite of extreme indices. The downscaled simulations have high skill in reproducing extreme temperatures, with the majority of models reproducing the statewide averaged sign and magnitude of recent observed trends of increasing warm days and warm nights and decreasing frost days. The warm spell duration index is however underestimated, while variance is generally overrepresented in the extreme temperature range across most regions. The simulations show a lower level of skill in modelling the amplitude of the extreme precipitation indices such as very wet days, but simulate the observed spatial patterns and variability. In general, simulations of dry extreme precipitation indices are underestimated in dryer areas and wet extremes indices are underestimated in wetter areas. Using two SRES emissions scenarios, the simulations indicate a significant increase in warm nights compared to a slightly more moderate increase in warm days, and an increase in maximum 1- and 5- day precipitation intensities interspersed with longer consecutive dry spells across Tasmania during the twenty-first century.

  20. 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

  1. Application Of New Spatial Statistical Stream Models For Precise Downscaling Of Climate Change Effects On Temperatures In River Networks

    NASA Astrophysics Data System (ADS)

    Isaak, D.; Luce, C.; Peterson, E.

    2009-12-01

    effect local variability in stream warming rates is needed to optimize future downscaling efforts. However, the application of spatial models for streams provides a significant advance in our ability to translate climate change impacts to aquatic ecosystems. Moreover, the approach is widely applicable given the advent of GIS capabilities, increasing availability of stream temperature sensor networks, and flexibility to accommodate climatic forcing data from a variety of sources.

  2. The effect of climate change on urban drainage: an evaluation based on regional climate model simulation.

    PubMed

    Grum, M; Jørgensen, A T; Johansen, R M; Linde, J J

    2006-01-01

    That we are in a period of extraordinary rates of climate change is today evident. These climate changes are likely to impact local weather conditions with direct impacts on precipitation patterns and urban drainage. In recent years several studies have focused on revealing the nature, extent and consequences of climate change on urban drainage and urban runoff pollution issues. This study uses predictions from a regional climate model to look at the effects of climate change on extreme precipitation events. Results are presented in terms of point rainfall extremes. The analysis involves three steps: Firstly, hourly rainfall intensities from 16 point rain gauges are averaged to create a rain gauge equivalent intensity for a 25 x 25 km square corresponding to one grid cell in the climate model. Secondly, the differences between present and future in the climate model is used to project the hourly extreme statistics of the rain gauge surface into the future. Thirdly, the future extremes of the square surface area are downscaled to give point rainfall extremes of the future. The results and conclusions rely heavily on the regional model's suitability in describing extremes at timescales relevant to urban drainage. However, in spite of these uncertainties, and others raised in the discussion, the tendency is clear: extreme precipitation events effecting urban drainage and causing flooding will become more frequent as a result of climate change.

  3. A Comparison of Statistical and Dynamical Downscaling of Winter Precipitation Over Complex Terrain

    NASA Astrophysics Data System (ADS)

    Gutmann, E. D.; Rasmussen, R.; Liu, C.; Ikeda, K.; Gochis, D. J.; Clark, M. P.; Dudhia, J.; Thompson, G.

    2011-12-01

    Statistical downscaling is widely used to improve spatial and or temporal distributions of meteorological variables from regional and global climate models. This downscaling is important because climate models are spatially coarse (50-200km), and often misrepresent extremes in meteorological variables such as temperature and precipitation that are important to hydrologic models. However, these downscaling methods rely on current estimates of the spatial distributions of these variables, and largely assume that the small-scale spatial distribution will not change significantly in a modified climate. Here we compare data typically used to derive spatial distributions of precipitation (PRISM) to a high-resolution (2km) weather model (WRF) under current climate in the mountains of Colorado. We show that there are regions of significant difference in November-May precipitation totals (~100%) between the two, and discuss possible causes for these differences, including a new observation which shows WRF to be substantially more accurate in at least one location. We then present a simple statistical downscaling based on the 2km WRF data applied to a series of regional climate models from the North American Regional Climate Change Assessment Program (NARCCAP), and validate the downscaled precipitation data with observations at 65 SNOw TELemetry (SNOTEL) sites throughout Colorado for the winter seasons from 1988 to 2000. Finally, we use this statistical downscaling method to compare precipitation from a 36km model under an imposed warming scenario to dynamically downscaled data from a 2km model using the same boundary conditions. While the statistical downscaling improved the domain average precipitation and spatial distribution compared to the original 36km model, the changes in the spatial pattern of precipitation did not match the changes in the dynamically downscaled 2km model (r^2=0.05). This points to a serious deficiency in current statistical downscaling techniques

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

    NASA Astrophysics Data System (ADS)

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

    2009-05-01

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

  5. Regional sun climate interaction

    NASA Astrophysics Data System (ADS)

    Kilcik, A.

    2005-11-01

    It is a clear fact that the Earth's climate has been changing since the pre-industrial era, especially during the last three decades. This change is generally attributed to three main factors: greenhouse gases (GHGs), aerosols, and solar activity changes. However, these factors are not all-independent. Furthermore, contributions of the above-mentioned factors are still disputed. We sought whether a parallelism between the solar activity variations and the changes in the Earth's climate can be established. For this, we compared the solar irradiance model data reconstructed by J. Lean to surface air temperature variations of two countries: USA and Japan. Comparison was carried out in two categories: correlations and periodicities. We utilized data from a total of 60 stations, 18 in USA and 42 in Japan. USA data range from 1900 to 1995, while Japan data range from 1900 to 1990. Our analyses yielded a 42 per cent correlation for USA and a 79 per cent for Japan between the temperature and solar irradiance. Moreover, both data sets showed similar periodicities. Hence, our results indicate marked influence of solar activity variations on the Earth's climate.

  6. 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...

  7. 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...

  8. 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 ...

  9. 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 ...

  10. Meeting the Regional Climate Information Needs of Decision Makers: The CORDEX Framework

    NASA Astrophysics Data System (ADS)

    Asrar, G. R.; Jones, C.; Giorgi, F.

    2011-12-01

    Regional Climate Downscaling (RCD), both dynamical (e.g. regional climate modeling) and statistical, is an important approach to produce fine scale climate information for use in impact assessment and adaptation/mitigation studies and practices. RCD techniques have evolved significantly over the last decade, however a coherent and wide picture of regional climate change based on RCD products is still not available and the potentials, limitations and uncertainties of RCD methods need to be better understood by the user community. In order to address these issues a new initiative has been launched under the WCRP auspices, referred to as Coordinated Regional climate Downscaling EXperiment, or CORDEX. The aim of CORDEX is to bring together the international RCD community to assess different RCD techniques, recommend best practices and produce a next generation set of RCD-based projections of climate change for regions world-wide. This will involve close interactions between the RCD, global climate modeling, and end users communities. This paper will describe the motivations and design of the first phase of the CORDEX framework, which has a priority focus on Africa, along with the steps that are envisioned to achieve the CORDEX goals within the time framework of the Fifth IPCC assessment report. Some early results for Africa will be presented, together with a short summary of the CORDEX activities in Asia, Americas and other regions of the world.

  11. 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

  12. Downscaling biogeochemistry in the Benguela eastern boundary current

    NASA Astrophysics Data System (ADS)

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

    2015-06-01

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

  13. 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

  14. 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.

  15. 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

  16. Examining Extreme Events Using Dynamically Downscaled 12-km WRF Simulations

    EPA Science Inventory

    Continued improvements in the speed and availability of computational resources have allowed dynamical downscaling of global climate model (GCM) projections to be conducted at increasingly finer grid scales and over extended time periods. The implementation of dynamical downscal...

  17. High-resolution downscaling for hydrological management

    NASA Astrophysics Data System (ADS)

    Ulbrich, Uwe; Rust, Henning; Meredith, Edmund; Kpogo-Nuwoklo, Komlan; Vagenas, Christos

    2017-04-01

    Hydrological modellers and water managers require high-resolution climate data to model regional hydrologies and how these may respond to future changes in the large-scale climate. The ability to successfully model such changes and, by extension, critical infrastructure planning is often impeded by a lack of suitable climate data. This typically takes the form of too-coarse data from climate models, which are not sufficiently detailed in either space or time to be able to support water management decisions and hydrological research. BINGO (Bringing INnovation in onGOing water management; ) aims to bridge the gap between the needs of hydrological modellers and planners, and the currently available range of climate data, with the overarching aim of providing adaptation strategies for climate change-related challenges. Producing the kilometre- and sub-daily-scale climate data needed by hydrologists through continuous simulations is generally computationally infeasible. To circumvent this hurdle, we adopt a two-pronged approach involving (1) selective dynamical downscaling and (2) conditional stochastic weather generators, with the former presented here. We take an event-based approach to downscaling in order to achieve the kilometre-scale input needed by hydrological modellers. Computational expenses are minimized by identifying extremal weather patterns for each BINGO research site in lower-resolution simulations and then only downscaling to the kilometre-scale (convection permitting) those events during which such patterns occur. Here we (1) outline the methodology behind the selection of the events, and (2) compare the modelled precipitation distribution and variability (preconditioned on the extremal weather patterns) with that found in observations.

  18. Inter-comparison of statistical downscaling methods for projection of extreme flow indices across Europe

    NASA Astrophysics Data System (ADS)

    Hundecha, Yeshewatesfa; Sunyer, Maria A.; Lawrence, Deborah; Madsen, Henrik; Willems, Patrick; Bürger, Gerd; Kriaučiūnienė, Jurate; Loukas, Athanasios; Martinkova, Marta; Osuch, Marzena; Vasiliades, Lampros; von Christierson, Birgitte; Vormoor, Klaus; Yücel, Ismail

    2016-10-01

    The effect of methods of statistical downscaling of daily precipitation on changes in extreme flow indices under a plausible future climate change scenario was investigated in 11 catchments selected from 9 countries in different parts of Europe. The catchments vary from 67 to 6171 km2 in size and cover different climate zones. 15 regional climate model outputs and 8 different statistical downscaling methods, which are broadly categorized as change factor and bias correction based methods, were used for the comparative analyses. Different hydrological models were implemented in different catchments to simulate daily runoff. A set of flood indices were derived from daily flows and their changes have been evaluated by comparing their values derived from simulations corresponding to the current and future climate. Most of the implemented downscaling methods project an increase in the extreme flow indices in most of the catchments. The catchments where the extremes are expected to increase have a rainfall-dominated flood regime. In these catchments, the downscaling methods also project an increase in the extreme precipitation in the seasons when the extreme flows occur. In catchments where the flooding is mainly caused by spring/summer snowmelt, the downscaling methods project a decrease in the extreme flows in three of the four catchments considered. A major portion of the variability in the projected changes in the extreme flow indices is attributable to the variability of the climate model ensemble, although the statistical downscaling methods contribute 35-60% of the total variance.

  19. 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.

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

    USGS Publications Warehouse

    Pervez, Md Shahriar; Henebry, Geoffrey M.

    2014-01-01

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

  1. Statistical downscaling of climate change impacts on ozone concentrations in California

    NASA Astrophysics Data System (ADS)

    Mahmud, Abdullah; Tyree, Mary; Cayan, Dan; Motallebi, Nehzat; Kleeman, Michael J.

    2008-11-01

    The statistical relationship between the daily 1-hour maximum ozone (O3) concentrations and the daily maximum upper air temperature was explored for California's two most heavily polluted air basins: the South Coast Air Basin (SoCAB) and the San Joaquin Valley Air Basin (SJVAB). A coarse-scale analysis of the temperature at an elevation of 850-mbar pressure (T850) for the period 1980-2004 was obtained from the National Center for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) Reanalysis data set for grid points near Upland (SoCAB) and Parlier (SJVAB). Daily 1-hour maximum ozone concentrations were obtained from the California Air Resources Board (CARB) for these locations over the same time period. The ozone concentrations measured at any given value of the Reanalysis T850 were approximately normally distributed. The 25%, 50%, and 75% quartile ozone concentrations increased linearly with T850, reflecting the effect of temperature on emissions and chemical reaction rates. A 2D Lagrangian (trajectory) form of the UCD/CIT photochemical air quality model was used in a perturbation study to explain the variability of the ozone concentrations at each value of T850. Wind speed, wind direction, temperature, relative humidity, mixing height, initial concentrations for VOC concentrations, background ozone concentrations, time of year, and overall emissions were perturbed in a realistic fashion during this study. A total of 62 model simulations were performed, and the results were analyzed to show that long-term changes to emissions inventories were the largest sources of ozone variability at a fixed value of T850. Projections of future T850 values in California were obtained from the Geophysical Fluid Dynamics Laboratory (GFDL) model under the Intergovernmental Panel on Climate Change (IPCC) A2 and B1 emissions scenarios for the years 2001 to 2100. The future temperature trends combined with the historical statistical relationships suggest

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

    USGS Publications Warehouse

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

    2012-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-04-01

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

  4. Great Lakes' regional climate regimes

    NASA Astrophysics Data System (ADS)

    Kravtsov, Sergey; Sugiyama, Noriyuki; Roebber, Paul

    2016-04-01

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

  5. Regional Climate Simulation Experiments with a V