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. 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.

  3. Statistical Downscaling Of Local Climate In The Alpine Region

    NASA Astrophysics Data System (ADS)

    Kaspar, Severin; Philipp, Andreas; Jacobeit, Jucundus

    2016-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  5. 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.

  6. Downscaled Regional Climate Information for the Southeastern US

    EPA Science Inventory

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

  7. Regional 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

  8. 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

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

    NASA Astrophysics Data System (ADS)

    Sigdel, M.

    2014-12-01

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

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

    PubMed

    Kara, Fatih; Yucel, Ismail

    2015-09-01

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

  11. 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.

  12. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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

  20. 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

  1. 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

  2. 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.

  3. 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.

  4. 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.

  5. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

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

    EPA Science Inventory

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

  8. 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.

  9. 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

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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.

  20. 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

  1. 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.

  2. 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

  3. Physically Based Global Downscaling: Regional Evaluation

    SciTech Connect

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

    2006-02-01

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

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. 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

  9. 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.

  10. 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.

  11. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2010-09-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  14. Downscaling GISS ModelE boreal summer climate over Africa

    NASA 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.

  15. 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.

  16. 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

  17. 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

  18. 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.

  19. 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.

  20. 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.

  1. 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 ...

  2. 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.

  3. 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.

  4. 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.

  5. 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

  6. 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

  7. 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.

  8. 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

  9. 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.

  10. 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

  11. 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.

  12. 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

  13. 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.

  14. 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.

  15. 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.

  16. 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

  17. 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

  18. 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.

  19. 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

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

    NASA Astrophysics Data System (ADS)

    Göncü, S.; Albek, E.

    2016-10-01

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

  3. 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.

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

    NASA Astrophysics Data System (ADS)

    Medvigy, David; Kim, Seung Hee; Kim, Jinwon; Kafatos, Menas C.

    2016-07-01

    Models that predict the timing of deciduous tree leaf emergence are typically very sensitive to temperature. However, many temperature data products, including those from climate models, have been developed at a very coarse spatial resolution. Such coarse-resolution temperature products can lead to highly biased predictions of leaf emergence. This study investigates how dynamical downscaling of climate models impacts simulations of deciduous tree leaf emergence in California. Models for leaf emergence are forced with temperatures simulated by a general circulation model (GCM) at ~200-km resolution for 1981-2000 and 2031-2050 conditions. GCM simulations are then dynamically downscaled to 32- and 8-km resolution, and leaf emergence is again simulated. For 1981-2000, the regional average leaf emergence date is 30.8 days earlier in 32-km simulations than in ~200-km simulations. Differences between the 32 and 8 km simulations are small and mostly local. The impact of downscaling from 200 to 8 km is ~15 % smaller in 2031-2050 than in 1981-2000, indicating that the impacts of downscaling are unlikely to be stationary.

  5. 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

  6. 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.

  7. 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.

  8. 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

  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. 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.

  11. 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

  12. The Impact of Spring Subsurface Soil Temperature Anomaly in the Western U.S. on North American Summer Precipitation: A Case Study Using Regional Climate Model Downscaling

    DTIC Science & Technology

    2012-06-02

    a regional climate model, found that a deep Rocky Mountain snowpack tended to hinder the poleward advance of the subtropical ridge and associated...monsoon rainfall in the southwest United States. A deep, extensive snowpack increases the surface albedo and provides an abundant surge of soil moisture...is the drastic decline in mountain snowpack since 1950 at about 75% of locations monitored in western North America [Mote, 2006], and since snow

  13. 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.

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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

  3. 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

  4. 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.

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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.

  11. 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

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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

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

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  2. 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. 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).

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

    NASA Astrophysics Data System (ADS)

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

    2016-09-01

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

  5. Downscaling future climate scenarios to fine scales for hydrologic and ecological modeling and analysis

    USGS Publications Warehouse

    Flint, Lorraine E.; Flint, Alan L.

    2012-01-01

    The methodology, which includes a sequence of rigorous analyses and calculations, is intended to reduce the addition of uncertainty to the climate data as a result of the downscaling while providing the fine-scale climate information necessary for ecological analyses. It results in new but consistent data sets for the US at 4 km, the southwest US at 270 m, and California at 90 m and illustrates the utility of fine-scale downscaling to analyses of ecological processes influenced by topographic complexity.

  6. Downscaling 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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  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. 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

  11. 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

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

    USGS Publications Warehouse

    Wilby, Robert L.; Dettinger, Michael D.

    2000-01-01

    Simulations of future climate using general circulation models (GCMs) suggest that rising concentrations of greenhouse gases may have significant consequences for the global climate. Of less certainty is the extent to which regional scale (i.e., sub-GCM grid) environmental processes will be affected. In this chapter, a range of downscaling techniques are critiqued. Then a relatively simple (yet robust) statistical downscaling technique and its use in the modelling of future runoff scenarios for three river basins in the Sierra Nevada, California, is described. This region was selected because GCM experiments driven by combined greenhouse-gas and sulphate-aerosol forcings consistently show major changes in the hydro-climate of the southwest United States by the end of the 21st century. The regression-based downscaling method was used to simulate daily rainfall and temperature series for streamflow modelling in three Californian river basins under current-and future-climate conditions. The downscaling involved just three predictor variables (specific humidity, zonal velocity component of airflow, and 500 hPa geopotential heights) supplied by the U.K. Meteorological Office couple ocean-atmosphere model (HadCM2) for the grid point nearest the target basins. When evaluated using independent data, the model showed reasonable skill at reproducing observed area-average precipitation, temperature, and concomitant streamflow variations. Overall, the downscaled data resulted in slight underestimates of mean annual streamflow due to underestimates of precipitation in spring and positive temperature biases in winter. Differences in the skill of simulated streamflows amongst the three basins were attributed to the smoothing effects of snowpack on streamflow responses to climate forcing. The Merced and American River basins drain the western, windward slope of the Sierra Nevada and are snowmelt dominated, whereas the Carson River drains the eastern, leeward slope and is a mix of

  13. 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

  14. 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

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

    PubMed

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

    2016-02-01

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

  16. 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.

  17. Some Advances in Downscaling Probabilistic Climate Forecasts for Agricultural Decision Support

    NASA Astrophysics Data System (ADS)

    Han, E.; Ines, A.

    2015-12-01

    Seasonal climate forecasts, commonly provided in tercile-probabilities format (below-, near- and above-normal), need to be translated into more meaningful information for decision support of practitioners in agriculture. In this paper, we will present two new novel approaches to temporally downscale probabilistic seasonal climate forecasts: one non-parametric and another parametric method. First, the non-parametric downscaling approach called FResampler1 uses the concept of 'conditional block sampling' of weather data to create daily weather realizations of a tercile-based seasonal climate forecasts. FResampler1 randomly draws time series of daily weather parameters (e.g., rainfall, maximum and minimum temperature and solar radiation) from historical records, for the season of interest from years that belong to a certain rainfall tercile category (e.g., being below-, near- and above-normal). In this way, FResampler1 preserves the covariance between rainfall and other weather parameters as if conditionally sampling maximum and minimum temperature and solar radiation if that day is wet or dry. The second approach called predictWTD is a parametric method based on a conditional stochastic weather generator. The tercile-based seasonal climate forecast is converted into a theoretical forecast cumulative probability curve. Then the deviates for each percentile is converted into rainfall amount or frequency or intensity to downscale the 'full' distribution of probabilistic seasonal climate forecasts. Those seasonal deviates are then disaggregated on a monthly basis and used to constrain the downscaling of forecast realizations at different percentile values of the theoretical forecast curve. As well as the theoretical basis of the approaches we will discuss sensitivity analysis (length of data and size of samples) of them. In addition their potential applications for managing climate-related risks in agriculture will be shown through a couple of case studies based on

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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

  3. 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.

  4. 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.

  5. 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...

  6. 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

  7. 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.

  8. 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...

  9. 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.

  10. 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.

  11. 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.

  12. 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

  13. 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.

  14. 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.

  15. 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

  16. 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.

  17. 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.

  18. 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

  19. 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.

  20. 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.

  1. 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

  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. 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

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  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. 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.

  7. 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

  8. 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.

  9. 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

  10. 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.

  11. 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

  12. 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

  13. 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.

  14. 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

  15. 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

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

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  17. FY08 LDRD Final Report Regional Climate

    SciTech Connect

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

    2009-05-19

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

  18. 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

  19. 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

  20. 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.

  1. 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.

  2. 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.

  3. 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…

  4. 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

  5. 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.

  6. 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.

  7. "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...

  8. 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.

  9. 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.

  10. 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

  11. 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

  12. 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

  13. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2010-09-01

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

  15. 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.

  16. 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:

  17. 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.

  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. 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.

  2. 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

  3. 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.

  4. 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

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. 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

  10. 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

  11. 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

  12. 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.

  13. 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

  14. 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...

  15. 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.

  16. 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

  17. 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.

  18. 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...

  19. 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

  20. Portuguese wine regions under a changing climate

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

  1. 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

  2. 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

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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

  8. 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.

  9. 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

  10. 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.

  11. 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

  12. 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

  13. 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.

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. 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.

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  20. 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

  1. 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.

  2. 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

  3. 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

  4. 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.

  5. 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.

  6. 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

  7. 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.

  8. 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

  9. 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...

  10. 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 ...

  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. 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.

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

    USGS Publications Warehouse

    Pervez, Md Shahriar; Henebry, Geoffrey M.

    2014-01-01

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

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

    USGS Publications Warehouse

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

    2012-01-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1999-01-01

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

  20. Regional climate projections for the MENA-CORDEX domain: analysis of projected temperature and precipitation changes

    NASA Astrophysics Data System (ADS)

    Hänsler, Andreas; Weber, Torsten; Eggert, Bastian; Saeed, Fahad; Jacob, Daniela

    2014-05-01

    Within the CORDEX initiative a multi-model suite of regionalized climate change information will be made available for several regions of the world. The German Climate Service Center (CSC) is taking part in this initiative by applying the regional climate model REMO to downscale global climate projections of different coupled general circulation models (GCMs) for several CORDEX domains. Also for the MENA-CORDEX domain, a set of regional climate change projections has been established at the CSC by downscaling CMIP5 projections of the Max-Planck-Institute Earth System Model (MPI-ESM) for the scenarios RCP4.5 and RCP8.5 with the regional model REMO for the time period from 1950 to 2100 to a horizontal resolution of 0.44 degree. In this study we investigate projected changes in future climate conditions over the domain towards the end of the 21st century. Focus in the analysis is given to projected changes in the temperature and rainfall characteristics and their differences for the two scenarios will be highlighted.

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

    NASA Astrophysics Data System (ADS)

    Zhao, Zhan

    2009-12-01

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

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

    PubMed

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

    2014-06-17

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

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

    NASA Astrophysics Data System (ADS)

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

    2017-02-01

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

  4. Inter-comparison of precipitable water among reanalyses and its effect on downscaling in the tropics

    NASA Astrophysics Data System (ADS)

    Takahashi, H. G.; Fujita, M.; Hara, M.

    2012-12-01

    This paper compared precipitable water (PW) among four major reanalyses. In addition, we also investigated the effect of the boundary conditions on downscaling in the tropics, using a regional climate model. The spatial pattern of PW in the reanalyses agreed closely with observations. However, the absolute amounts of PW in some reanalyses were very small compared to observations. The discrepancies of the 12-year mean PW in July over the Southeast Asian monsoon region exceeded the inter-annual standard deviation of the PW. There was also a discrepancy in tropical PWs throughout the year, an indication that the problem is not regional, but global. The downscaling experiments were conducted, which were forced by the different four reanalyses. The atmospheric circulation, including monsoon westerlies and various disturbances, was very small among the reanalyses. However, simulated precipitation was only 60 % of observed precipitation, although the dry bias in the boundary conditions was only 6 %. This result indicates that dry bias has large effects on precipitation in downscaling over the tropics. This suggests that a simulated regional climate downscaled from ensemble-mean boundary conditions is quite different from an ensemble-mean regional climate averaged over the several regional ones downscaled from boundary conditions of the ensemble members in the tropics. Downscaled models can provide realistic simulations of regional tropical climates only if the boundary conditions include realistic absolute amounts of PW. Use of boundary conditions that include realistic absolute amounts of PW in downscaling in the tropics is imperative at the present time. This work was partly supported by the Global Environment Research Fund (RFa-1101) of the Ministry of the Environment, Japan.

  5. Production and use of regional climate model projections - A Swedish perspective on building climate services.

    PubMed

    Kjellström, Erik; Bärring, Lars; Nikulin, Grigory; Nilsson, Carin; Persson, Gunn; Strandberg, Gustav

    2016-09-01

    We describe the process of building a climate service centred on regional climate model results from the Rossby Centre regional climate model RCA4. The climate service has as its central facility a web service provided by the Swedish Meteorological and Hydrological Institute where users can get an idea of various aspects of climate change from a suite of maps, diagrams, explaining texts and user guides. Here we present the contents of the web service and how this has been designed and developed in collaboration with users of the service in a dialogue reaching over more than a decade. We also present the ensemble of climate projections with RCA4 that provides the fundamental climate information presented at the web service. In this context, RCA4 has been used to downscale nine different coupled atmosphere-ocean general circulation models (AOGCMs) from the 5th Coupled Model Intercomparison Project (CMIP5) to 0.44° (c. 50 km) horizontal resolution over Europe. Further, we investigate how this ensemble relates to the CMIP5 ensemble. We find that the iterative approach involving the users of the climate service has been successful as the service is widely used and is an important source of information for work on climate adaptation in Sweden. The RCA4 ensemble samples a large degree of the spread in the CMIP5 ensemble implying that it can be used to illustrate uncertainties and robustness in future climate change in Sweden. The results also show that RCA4 changes results compared to the underlying AOGCMs, sometimes in a systematic way.

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

    NASA Astrophysics Data System (ADS)

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

    2010-12-01

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

  7. Assessing climate change impacts on water resources in remote mountain regions

    NASA Astrophysics Data System (ADS)

    Buytaert, Wouter; De Bièvre, Bert

    2013-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

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

    PubMed Central

    Dobrowski, Solomon Z.; Parks, Sean A.

    2016-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Dobrowski, Solomon Z.; Parks, Sean A.

    2016-08-01

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

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

    EPA Science Inventory

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

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

    NASA Astrophysics Data System (ADS)

    Dosio, Alessandro; Panitz, Hans-Jürgen

    2016-03-01

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

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

    PubMed

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

    2013-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-11-01

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

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

    PubMed

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

    2015-09-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

  17. Soil moisture downscaling using a simple thermal based proxy

    NASA Astrophysics Data System (ADS)

    Peng, Jian; Loew, Alexander; Niesel, Jonathan

    2016-04-01

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

  18. Reliability of regional climate simulations

    NASA Astrophysics Data System (ADS)

    Ahrens, W.; Block, A.; Böhm, U.; Hauffe, D.; Keuler, K.; Kücken, M.; Nocke, Th.

    2003-04-01

    Quantification of uncertainty becomes more and more a key issue for assessing the trustability of future climate scenarios. In addition to the mean conditions, climate impact modelers focus in particular on extremes. Before generating such scenarios using e.g. dynamic regional climate models, a careful validation of present-day simulations should be performed to determine the range of errors for the quantities of interest under recent conditions as a raw estimate of their uncertainty in the future. Often, multiple aspects shall be covered together, and the required simulation accuracy depends on the user's demand. In our approach, a massive parallel regional climate model shall be used on the one hand to generate "long-term" high-resolution climate scenarios for several decades, and on the other hand to provide very high-resolution ensemble simulations of future dry spells or heavy rainfall events. To diagnosis the model's performance for present-day simulations, we have recently developed and tested a first version of a validation and visualization chain for this model. It is, however, applicable in a much more general sense and could be used as a common test bed for any regional climate model aiming at this type of simulations. Depending on the user's interest, integrated quality measures can be derived for near-surface parameters using multivariate techniques and multidimensional distance measures in a first step. At this point, advanced visualization techniques have been developed and included to allow for visual data mining and to qualitatively identify dominating aspects and regularities. Univariate techniques that are especially designed to assess climatic aspects in terms of statistical properties can then be used to quantitatively diagnose the error contributions of the individual used parameters. Finally, a comprehensive in-depth diagnosis tool allows to investigate, why the model produces the obtained near-surface results to answer the question if the

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

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

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

    NASA Astrophysics Data System (ADS)

    Stagge, James; Tallaksen, Lena; Rizzi, Jonathan

    2015-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-05-01

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

  3. Assessment of projected climate change signals over central Africa based on a multitude of global and regional climate projections

    NASA Astrophysics Data System (ADS)

    Hänsler, Andreas; Saeed, Fahad; Jacob, Daniela

    2013-04-01

    It is well accepted within the scientific community that only a large ensemble of different projections will allow achieving robust climate change information for a specific region. In the framework of the project "Climate changes scenarios for the Congo basin" (funded by the German Ministry for Environment, Nature Conservation and Nuclear Safety) a regional climate change assessment is conducted by the Climate Service Center (CSC) over the greater Congo basin region. The analysis is based on a state-of-the-art multi-model multi-scenario ensemble of global and regional climate change projections. In this ensemble the results of several GCM projections from the CMIP3 and the CMIP5 projects are combined with some of the recently downscaled regional CORDEX-Africa projections. Altogether data from 77 different climate change projections are analysed; separated into 31 projections for a "high" and 46 for a "low" emission scenario. In the study several parameters and indices related to temperature and precipitation are considered for the assessment of projected climate change. The large size of the analyzed ensemble is expected to be useful for not only quantifying the magnitude of projected changes, but also to analyze their robustness as well. Moreover, potential differences between projected changes from GCMs and RCMs can also be analysed.

  4. Assessment of projected climate change signals over central Africa based on a multitude of global and regional climate projections

    NASA Astrophysics Data System (ADS)

    Haensler, A.; Saeed, F.; Jacob, D.

    2013-05-01

    It is well accepted within the scientific community that only a large ensemble of different projections will allow achieving robust climate change information for a specific region. In the framework of the project "Climate changes scenarios for the Congo basin" (funded by the German Ministry for Environment, Nature Conservation and Nuclear Safety) a regional climate change assessment is conducted by the Climate Service Center (CSC) over the greater Congo basin region. The analysis is based on a state-of-the-art multi-model multi-scenario ensemble of global and regional climate change projections. In this ensemble the results of several GCM projections from the CMIP3 and the CMIP5 projects are combined with some of the recently downscaled regional CORDEX-Africa projections. Altogether data from 77 different climate change projections are analysed; separated into 31 projections for a "high" and 46 for a "low" emission scenario. In the study several parameters and indices related to temperature and precipitation are considered for the assessment of projected climate change. The large size of the analyzed ensemble is expected to be useful for not only quantifying the magnitude of projected changes, but also to analyze their robustness as well. Moreover, potential differences between projected changes from GCMs and RCMs can also be analysed.

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

    NASA Astrophysics Data System (ADS)

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

    2016-07-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  8. Satellite-enhanced dynamical downscaling for the analysis of extreme events

    NASA Astrophysics Data System (ADS)

    Nunes, Ana M. B.

    2016-09-01

    The use of regional models in the downscaling of general circulation models provides a strategy to generate more detailed climate information. In that case, boundary-forcing techniques can be useful to maintain the large-scale features from the coarse-resolution global models in agreement with the inner modes of the higher-resolution regional models. Although those procedures might improve dynamics, downscaling via regional modeling still aims for better representation of physical processes. With the purpose of improving dynamics and physical processes in regional downscaling of global reanalysis, the Regional Spectral Model—originally developed at the National Centers for Environmental Prediction—employs a newly reformulated scale-selective bias correction, together with the 3-hourly assimilation of the satellite-based precipitation estimates constructed from the Climate Prediction Center morphing technique. The two-scheme technique for the dynamical downscaling of global reanalysis can be applied in analyses of environmental disasters and risk assessment, with hourly outputs, and resolution of about 25 km. Here the satellite-enhanced dynamical downscaling added value is demonstrated in simulations of the first reported hurricane in the western South Atlantic Ocean basin through comparisons with global reanalyses and satellite products available in ocean areas.

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

    NASA Astrophysics Data System (ADS)

    Torma, Csaba; Giorgi, Filippo; Coppola, Erika

    2015-05-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2016-04-01

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

  11. Impact of climate change on surface wind regime over the Peru-Chile upwelling region

    NASA Astrophysics Data System (ADS)

    Goubanova, K.; Echevin, V.; Dewitte, B.; Garreaud, R.; Terray, P.; Vrac, M.

    2009-04-01

    The ocean region off the Chile-Peru coast is characterized by upwelling of cold, nutrient-rich waters, which drives an exceptionally high biological productivity. This upwelling is induced by the persistent southerly winds along the coast that exhibit a coastal jet structure at intraseasonal scales. Recent climate change studies based on the coupled atmosphere-ocean general circulation models (AOGCM) show a strengthening of the large-scale southerlies along the subtropical coast that could lead to an increase in coastal upwelling. However the coastal jet events which represent a considerable source of the synoptic variability of the alongshore winds are characterized by horizontal scale comparable to a AOGCM grid cell size, and cannot be therefore explicitly resolved by the AOGCMs. In order to provide a regional estimate of the winds as predicted by the coarse-resolution AOGCMs, a statistical downscaling method based on multiple linear regression is proposed. Large-scale wind at 10 m and sea level pressure are chosen as the predictor variables for regional 10 m wind. The validation is performed in two steps. First, QuikSCAT and ERS satellite products and NCEP reanalysis for the period 1992-2006 are used to build and validate the statistical model for the present climate. Second, the model is validated under a warmer climate: it is applied to large-scale predictors extracted from HadCM3 AOGCM simulations for the A2 and B2 SRES scenarios (2071-2100); the downscaled wind is then compared with outputs of the PRECIS regional climate model, forced at its boundaries by the same HadCM3 scenarios. To assess climate change impact on the along-shore wind, the statistical downscaling is applied to two contrasted SRES scenarios, namely the so-called preindustrial and CO2 quadrupling. The outputs of the IPSL-CM4 AOGCM are used as predictors. Evolution of the along-shore wind regime with a focus on the change of the coastal jet characteristics is discussed. For this particular

  12. Advancing climate dynamics toward reliable regional climate projections

    NASA Astrophysics Data System (ADS)

    Xie, Shang-Ping

    2013-06-01

    With a scientific consensus reached regarding the anthropogenic effect on global mean temperature, developing reliable regional climate projections has emerged as a new challenge for climate science. A national project was launched in China in 2012 to study ocean's role in regional climate change. This paper starts with a review of recent advances in the study of regional climate response to global warming, followed by a description of the Chinese project including the rationale, objectives, and plan for field observations. The 15 research articles that follow in the special issue are highlighted, representing some of the initial results from the project.

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

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

    NASA Astrophysics Data System (ADS)

    Hong, Song-You; Kanamitsu, Masao

    2014-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  16. Evaluation of the regional climate response in Australia to large-scale climate modes in the historical NARCliM simulations

    NASA Astrophysics Data System (ADS)

    Fita, L.; Evans, J. P.; Argüeso, D.; King, A.; Liu, Y.

    2016-12-01

    NARCliM (New South Wales (NSW)/Australian Capital Territory (ACT) Regional Climate Modelling project) is a regional climate modeling project for the Australian area. It is providing a comprehensive dynamically downscaled climate dataset for the CORDEX-AustralAsia region at 50-km resolution, and south-East Australia at a resolution of 10 km. The first phase of NARCliM produced 60-year long reanalysis driven regional simulations to allow evaluation of the regional model performance. This long control period (1950-2009) was used so that the model ability to capture the impact of large scale climate modes on Australian climate could be examined. Simulations are evaluated using a gridded observational dataset. Results show that using model independence as a criteria for choosing atmospheric model configuration from different possible sets of parameterizations may contribute to the regional climate models having different overall biases. The regional models generally capture the regional climate response to large-scale modes better than the driving reanalysis, though no regional model improves on all aspects of the simulated climate.

  17. A Hierarchical Evaluation of Regional Climate Simulations

    SciTech Connect

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

    2013-08-20

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

  18. Future changes in daily snowfall intensity projected by large ensemble regional climate experiments

    NASA Astrophysics Data System (ADS)

    Kawase, H.

    2015-12-01

    We investigate the future changes in daily snowfall intensity in Japan analyzing the large ensemble regional climate experiments. Dynamical downscalings are conducted by Non-Hydrostatic Regional Climate Model (NHRCM) with 20 km from the global climate projections using Meteorological Research Institute-Atmospheric General Circulation Model (MRI-AGCM). Fifty ensemble experiments are performed in the present climate. For the future climate projections, 90 ensemble experiments are performed based on the six patterns of SST changes in the periods when 4 K rise in global-mean surface air temperature is projected. The accumulated snowfall in winter decreases in Japan except for the northern parts of Japan. Especially, the inland areas in the Sea of Japan side, which is famous for the heaviest snowfall region in the world, shows the remarkable decrease in snowfall in the future climate. The experiments also show increased number of days without snowfall and decreased number of days with weak snowfall due to significant warming in the most parts of Japan. On the other hand, the extreme daily snowfall, which occurs once ten years, would increase at higher elevations in the Sea of Japan side. This means that extreme daily snowfall in the present climate would occur more frequently in the future climate. The warmer atmosphere can contain more water vapor and warmer ocean can supply more water vapor to the low atmosphere. The surface air temperature at higher elevations is still lower than 0 degree Celsius, which could result in the increased extreme daily snowfall.

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

    NASA Astrophysics Data System (ADS)

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

    2016-11-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  2. Assessing the link between Atlantic Niño 1 and drought over West Africa using CORDEX regional climate models

    NASA Astrophysics Data System (ADS)

    Adeniyi, Mojisola Oluwayemisi; Dilau, Kabiru Alabi

    2016-12-01

    The skill of Coordinated Regional Climate Downscaling Experiment (CORDEX) models (ARPEGE, CCLM, HIRHAM, RACMO, REMO, PRECIS, RegCM3, RCA, WRF and CRCM) in simulating the climate (precipitation, temperature and drought) of West Africa is determined using a process-based metric. This is done by comparing the CORDEX models' simulated and observed correlation coefficients between Atlantic Niño Index 1 (ATLN1) and the climate over West Africa. Strong positive correlation is observed between ATLN1 and the climate parameters at the Guinea Coast (GC). The Atlantic Ocean has Niño behaviours through the ATLN indices which influence the climate of the tropics. Drought has distinct dipole structure of correlation with ATLN1 (negative at the Sahel); precipitation does not have distinct dipole structure of correlation, while temperature has almost a monopole correlation structure with ATLN1 over West Africa. The magnitude of the correlation increases with closeness to the equatorial eastern Atlantic. Correlations between ATLN1 and temperature are mostly stronger than those between ATLN1 and precipitation over the region. Most models have good performance over the GC, but ARPEGE has the highest skill at GC. The PRECIS is the most skilful over Savannah and RCA over Sahel. These models can be used to downscale the projected climate at the region of their highest skill.

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

    PubMed

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

    2013-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

  5. Regional Changes in Extreme Climatic Events

    NASA Astrophysics Data System (ADS)

    Bell, J. L.; Sloan, L. C.; Snyder, M. A.

    2002-12-01

    This study focuses on California as a climatically complex region that is vulnerable to changes in water supply and delivery. A regional climate model is employed to assess changes in the frequency and intensity of extreme temperatures and precipitation. Significant increases in daily minimum and maximum temperatures occur with a doubling of atmospheric carbon dioxide concentration. Increases in daily temperatures lead to increases in prolonged heat waves and length of the growing season. Changes in total and extreme precipitation vary by geographic region.

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

    SciTech Connect

    Chin, H S

    2008-09-24

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

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2009-09-01

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

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

    SciTech Connect

    Smyth, Padhraic

    2013-07-22

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

  10. Probabilistic estimates of future changes in California temperature and precipitation using statistical and dynamical downscaling

    NASA Astrophysics Data System (ADS)

    Pierce, David W.; Das, Tapash; Cayan, Daniel R.; Maurer, Edwin P.; Miller, Norman L.; Bao, Yan; Kanamitsu, M.; Yoshimura, Kei; Snyder, Mark A.; Sloan, Lisa C.; Franco, Guido; Tyree, Mary

    2013-02-01

    Sixteen global general circulation models were used to develop probabilistic projections of temperature (T) and precipitation (P) changes over California by the 2060s. The global models were downscaled with two statistical techniques and three nested dynamical regional climate models, although not all global models were downscaled with all techniques. Both monthly and daily timescale changes in T and P are addressed, the latter being important for a range of applications in energy use, water management, and agriculture. The T changes tend to agree more across downscaling techniques than the P changes. Year-to-year natural internal climate variability is roughly of similar magnitude to the projected T changes. In the monthly average, July temperatures shift enough that that the hottest July found in any simulation over the historical period becomes a modestly cool July in the future period. Januarys as cold as any found in the historical period are still found in the 2060s, but the median and maximum monthly average temperatures increase notably. Annual and seasonal P changes are small compared to interannual or intermodel variability. However, the annual change is composed of seasonally varying changes that are themselves much larger, but tend to cancel in the annual mean. Winters show modestly wetter conditions in the North of the state, while spring and autumn show less precipitation. The dynamical downscaling techniques project increasing precipitation in the Southeastern part of the state, which is influenced by the North American monsoon, a feature that is not captured by the statistical downscaling.

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

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

    NASA Astrophysics Data System (ADS)

    Cepeda, Javier; Vargas, Ximena

    2015-04-01

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

  13. Towards predictive understanding of regional climate change

    NASA Astrophysics Data System (ADS)

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

    2015-10-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-09-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2002-12-01

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

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

    NASA Technical Reports Server (NTRS)

    Fox-Rabinovitz, Michael S.

    1999-01-01

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

  18. Great plains regional climate assessment technical report

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-04-01

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

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

    NASA Technical Reports Server (NTRS)

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

    1999-01-01

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

  2. Regional Climate Change Hotspots over Africa

    NASA Astrophysics Data System (ADS)

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

    2009-04-01

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

  3. Regional Climate Change Hotspots over Africa

    NASA Astrophysics Data System (ADS)

    Anber, U.

    2009-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-08-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  7. Statistical Downscaling of Large-Scale Wind Signatures Using a Two-Step Approach

    NASA Astrophysics Data System (ADS)

    Haas, R.; Born, K.; Georgiadis, A.; Karremann, M. K.; Pinto, J. G.

    2012-04-01

    Downscaling global scale climate data is an important issue in order to obtain high-resolution data desired for most applications in meteorology and hydrology and to gain a better understanding of local climate variability. Statistical downscaling transforms data from large to local scale by relating punctual climate observations, climate model outputs and high-resolution surface data. In this study, a statistical downscaling approach is used in combination with dynamical downscaling in order to produce gust characteristics of wind storms on a small-scale grid over Europe. The idea is to relate large-scale data, regional climate model (RCM) data and observations by transfer functions, which are calibrated using physically consistent features of the RCM model simulations. In comparison to purely dynamical downscaling by a regional model, such a statistical downscaling approach has several advantages. The computing time is much shorter and, therefore, such an approach can be easily applied on very large numbers of windstorm cases provided e.g. by long-term GCM model simulations, like millennium runs. The first step of the approach constructs a relation between observations and COSMO-CLM signatures with the aim of calibrating the modelled signatures to the observations in terms of model output statistics. For this purpose, parameters of the theoretical Weibull distribution, estimated from the observations at each test site, are interpolated to a 7km RCM grid with Gaussian weights and are compared to Weibull parameters from the COSMO-CLM modelled gust distributions. This allows for an evaluation and correction of gust signatures by quantile mapping. The second step links the RCM wind signatures and large-scale data by a multiple linear regression (MLR) model. One model per grid point is trained using the COSMO-CLM simulated and MOS-corrected gusts for selected wind storm events as predictands, and the according NCEP reanalysis wind speeds of the surrounding NCEP grid

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

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

    SciTech Connect

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

    2011-04-08

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

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

    SciTech Connect

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

    2011-04-08

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

  11. One regional ARM guide for climatic evaluation

    SciTech Connect

    Brown, R.M.

    1990-04-01

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

  12. One regional ARM guide for climatic evaluation

    SciTech Connect

    Brown, R.M.

    1990-04-01

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

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

    NASA Astrophysics Data System (ADS)

    Keener, V. W.; Finucane, M.

    2012-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

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

    NASA Astrophysics Data System (ADS)

    Ramarohetra, Johanna; Pohl, Benjamin; Sultan, Benjamin

    2015-12-01

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

  16. CLIMATE IMPACTS ON REGIONAL WATER

    EPA Science Inventory

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

  17. On the added value of the regional climate model REMO in the assessment of climate change signal over Central Africa

    NASA Astrophysics Data System (ADS)

    Fotso-Nguemo, Thierry C.; Vondou, Derbetini A.; Pokam, Wilfried M.; Djomou, Zéphirin Yepdo; Diallo, Ismaïla; Haensler, Andreas; Tchotchou, Lucie A. Djiotang; Kamsu-Tamo, Pierre H.; Gaye, Amadou T.; Tchawoua, Clément

    2017-02-01

    In this paper, the regional climate model REMO is used to investigate the added value of downscaling low resolutions global climate models (GCMs) and the climate change projections over Central Africa. REMO was forced by two GCMs (EC-Earth and MPI-ESM), for the period from 1950 to 2100 under the Representative Concentration Pathway 8.5 scenario. The performance of the REMO simulations for current climate is compared first with REMO simulation driven by ERA-Interim reanalysis, then by the corresponding GCMs in order to determine whether REMO outputs are able to effectively lead to added value at local scale. We found that REMO is generally able to better represent some aspects of the rainfall inter-annual variability, the daily rainfall intensity distribution as well as the intra-seasonal variability of the Central African monsoon, though few biases are still evident. It is also found that the boundary conditions strongly influences the spatial distribution of seasonal 2-m temperature and rainfall. From the analysis of the climate change signal from the present period 1976-2005 to the future 2066-2095, we found that all models project a warming at the end of the twenty-first century although the details of the climate change differ between REMO and the driving GCMs, specifically in REMO where we observe a general decrease in rainfall. This rainfall decrease is associated with delayed onset and anticipated recession of the Central African monsoon and a shortening of the rainy season. Small-scales variability of the climate change signal for 2-m temperature are usually smaller than that of the large-scales climate change part. For rainfall however, small-scales induce change of about 70% compared to the present climate statistics.

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

    NASA Astrophysics Data System (ADS)

    Shin, Y.; Lee, E.

    2015-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2010-12-01

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

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

    NASA Astrophysics Data System (ADS)

    Buizer, James; Goddard, Lisa; Guido, Zackry

    2015-04-01

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

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

    NASA Technical Reports Server (NTRS)

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

    2000-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Lin, S. J.

    2015-12-01

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

  3. Evaluation of soil moisture downscaling using a simple thermal based proxy - the REMEDHUS network (Spain) example

    NASA Astrophysics Data System (ADS)

    Peng, J.; Niesel, J.; Loew, A.

    2015-08-01

    Soil moisture retrieved from satellite microwave remote sensing normally has spatial resolution in the order of tens of kilometers, which are too coarse for many regional hydrological applications such as agriculture monitoring and drought predication. Therefore, various downscaling methods have been proposed to enhance the spatial resolution of satellite soil moisture products. The aim of this study is to investigate the validity and robustness of the simple Vegetation Temperature Condition Index (VTCI) downscaling scheme over a dense soil moisture observational network (REMEDHUS) in Spain. Firstly, the optimized VTCI was determined through sensitivity analyses of VTCI to surface temperature, vegetation index, cloud, topography and land cover heterogeneity, using data from MODIS and MSG SEVIRI. Then the downscaling scheme was applied to improve the spatial resolution of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative (ESA CCI) soil moisture, which is a merged product based on both active and passive microwave observations. The results from direct validation against soil moisture observations, spatial pattern comparison, as well as seasonal and land use analyses show that the downscaling method can significantly improve the spatial details of CCI soil moisture while maintain the accuracy of CCI soil moisture. The accuracy level is comparable to other downscaling methods that were also validated against REMEDHUS network. Furthermore, slightly better performance of MSG SEVIRI over MODIS was observed, which suggests the high potential of applying geostationary satellite for downscaling soil moisture in the future. Overall, considering the simplicity, limited data requirements and comparable accuracy level to other complex methods, the VTCI downscaling method can facilitate relevant hydrological applications that require high spatial and temporal resolution soil moisture.

  4. CARICOF - The Caribbean Regional Climate Outlook Forum

    NASA Astrophysics Data System (ADS)

    Van Meerbeeck, Cedric

    2013-04-01

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

  5. Regional Impacts of Climate Change on Water Resources: the Jucar River Basin, Spain

    NASA Astrophysics Data System (ADS)

    Chirivella Osma, V.; Capilla, J. E.; Perez Martin, M.; Sanchez Fuster, I.

    2011-12-01

    precipitation scenarios for 2010-40. Thus, the impact on water resources shows a great degree of dispersion, ranging from -13.45 to 18.1% with a mean value of -2.13%. These results question the suitability of current GCM results and downscaling techniques to generate future climate scenarios in the JB area. Moreover, they raise the need to apply downscaling methodologies able to honor known spatial patterns of rainfall, and the clear necessity of analyzing how accurate are GCM results for regional analyses in the area.

  6. Climate impacts of regional SO2 emissions

    NASA Astrophysics Data System (ADS)

    Lamarque, J. F.; Fiore, A. M.; Shindell, D. T.

    2015-12-01

    Climate impacts of regional SO2 emissions J.-F. Lamarque, A. M. Fiore and D. Shindell In this talk, we present the analysis of constant -forcing present-day simulations pertaining to the perturbation of SO2 emissions over the United States and China. Using 3 chemistry-climate models (CESM, GFDL and GISS), we show that the removal of SO2 anthropogenic emissions over each region leads to significant (at the 95% or above; significance is also assessed relative to internal variability as determined from a 200-year control simulation with perpetual year 2000 conditions) perturbations in temperature over multiple regions of the Northern Hemisphere. While more limited, significant perturbations in regional precipitation are also found. While the overall (global and zonal means) forcing from Chinese emissions is similar to the US case, we found that the regional response to the emissions has different regional distributions.

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

    SciTech Connect

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

    1996-04-01

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

  8. Test of High-resolution Global and Regional Climate Model Projections

    NASA Astrophysics Data System (ADS)

    Stenchikov, Georgiy; Nikulin, Grigory; Hansson, Ulf; Kjellström, Erik; Raj, Jerry; Bangalath, Hamza; Osipov, Sergey

    2014-05-01

    In scope of CORDEX project we have simulated the past (1975-2005) and future (2006-2050) climates using the GFDL global high-resolution atmospheric model (HIRAM) and the Rossby Center nested regional model RCA4 for the Middle East and North Africa (MENA) region. Both global and nested runs were performed with roughly the same spatial resolution of 25 km in latitude and longitude, and were driven by the 2°x2.5°-resolution fields from GFDL ESM2M IPCC AR5 runs. The global HIRAM simulations could naturally account for interaction of regional processes with the larger-scale circulation features like Indian Summer Monsoon, which is lacking from regional model setup. Therefore in this study we specifically address the consistency of "global" and "regional" downscalings. The performance of RCA4, HIRAM, and ESM2M is tested based on mean, extreme, trends, seasonal and inter-annual variability of surface temperature, precipitation, and winds. The impact of climate change on dust storm activity, extreme precipitation and water resources is specifically addressed. We found that the global and regional climate projections appear to be quite consistent for the modeled period and differ more significantly from ESM2M than between each other.

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

    USGS Publications Warehouse

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

    2010-01-01

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

  10. Reinitialised versus continuous regional climate simulations using ALARO-0 coupled to the land surface model SURFEXv5

    NASA Astrophysics Data System (ADS)

    Berckmans, Julie; Giot, Olivier; De Troch, Rozemien; Hamdi, Rafiq; Ceulemans, Reinhart; Termonia, Piet

    2017-01-01

    Dynamical downscaling in a continuous approach using initial and boundary conditions from a reanalysis or a global climate model is a common method for simulating the regional climate. The simulation potential can be improved by applying an alternative approach of reinitialising the atmosphere, combined with either a daily reinitialised or a continuous land surface. We evaluated the dependence of the simulation potential on the running mode of the regional climate model ALARO coupled to the land surface model Météo-France SURFace EXternalisée (SURFEX), and driven by the ERA-Interim reanalysis. Three types of downscaling simulations were carried out for a 10-year period from 1991 to 2000, over a western European domain at 20 km horizontal resolution: (1) a continuous simulation of both the atmosphere and the land surface, (2) a simulation with daily reinitialisations for both the atmosphere and the land surface and (3) a simulation with daily reinitialisations of the atmosphere while the land surface is kept continuous. The results showed that the daily reinitialisation of the atmosphere improved the simulation of the 2 m temperature for all seasons. It revealed a neutral impact on the daily precipitation totals during winter, but the results were improved for the summer when the land surface was kept continuous. The behaviour of the three model configurations varied among different climatic regimes. Their seasonal cycle for the 2 m temperature and daily precipitation totals was very similar for a Mediterranean climate, but more variable for temperate and continental climate regimes. Commonly, the summer climate is characterised by strong interactions between the atmosphere and the land surface. The results for summer demonstrated that the use of a daily reinitialised atmosphere improved the representation of the partitioning of the surface energy fluxes. Therefore, we recommend using the alternative approach of the daily reinitialisation of the atmosphere for

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  12. Simulating river discharge in a snowy region of Japan using output from a regional climate model

    NASA Astrophysics Data System (ADS)

    Ma, X.; Kawase, H.; Adachi, S.; Fujita, M.; Takahashi, H. G.; Hara, M.; Ishizaki, N.; Yoshikane, T.; Hatsushika, H.; Wakazuki, Y.; Kimura, F.

    2013-07-01

    Snowfall amounts have fallen sharply along the eastern coast of the Sea of Japan since the mid-1980s. Toyama Prefecture, located approximately in the center of the Japan Sea region, includes high mountains of the northern Japanese Alps on three of its sides. The scarcity of meteorological observation points in mountainous areas limits the accuracy of hydrological analysis. With the development of computing technology, a dynamical downscaling method is widely applied into hydrological analysis. In this study, we numerically modeled river discharge using runoff data derived by a regional climate model (4.5-km spatial resolution) as input data to river networks (30-arcseconds resolution) for the Toyama Prefecture. The five main rivers in Toyama (the Oyabe, Sho, Jinzu, Joganji, and Kurobe rivers) were selected in this study. The river basins range in area from 368 to 2720 km2. A numerical experiment using climate comparable to that at present was conducted for the 1980s and 1990s. The results showed that seasonal river discharge could be represented and that discharge was generally overestimated compared with measurements, except for Oyabe River discharge, which was always underestimated. The average correlation coefficient for 10-year average monthly mean discharge was 0.8, with correlation coefficients ranging from 0.56 to 0.88 for all five rivers, whereas the Nash-Sutcliffe efficiency coefficient indicated that the simulation accuracy was insufficient. From the water budget analysis, it was possible to speculate that the lack of accuracy of river discharge may be caused by insufficient accuracy of precipitation simulation.

  13. Downscaling of rainfall in Peru using Generalised Linear Models

    NASA Astrophysics Data System (ADS)

    Bergin, E.; Buytaert, W.; Onof, C.; Wheater, H.

    2012-04-01

    The assessment of water resources in the Peruvian Andes is particularly important because the Peruvian economy relies heavily on agriculture. Much of the agricultural land is situated near to the coast and relies on large quantities of water for irrigation. The simulation of synthetic rainfall series is thus important to evaluate the reliability of water supplies for current and future scenarios of climate change. In addition to water resources concerns, there is also a need to understand extreme heavy rainfall events, as there was significant flooding in Machu Picchu in 2010. The region exhibits a reduction of rainfall in 1983, associated with El Nino Southern Oscillation (SOI). NCEP Reanalysis 1 data was used to provide weather variable data. Correlations were calculated for several weather variables using raingauge data in the Andes. These were used to evaluate teleconnections and provide suggested covariates for the downscaling model. External covariates used in the model include sea level pressure and sea surface temperature over the region of the Humboldt Current. Relative humidity and temperature data over the region are also included. The SOI teleconnection is also used. Covariates are standardised using observations for 1960-1990. The GlimClim downscaling model was used to fit a stochastic daily rainfall model to 13 sites in the Peruvian Andes. Results indicate that the model is able to reproduce rainfall statistics well, despite the large area used. Although the correlation between individual rain gauges is generally quite low, all sites are affected by similar weather patterns. This is an assumption of the GlimClim downscaling model. Climate change scenarios are considered using several GCM outputs for the A1B scenario. GCM data was corrected for bias using 1960-1990 outputs from the 20C3M scenario. Rainfall statistics for current and future scenarios are compared. The region shows an overall decrease in mean rainfall but with an increase in variance.

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

    NASA Astrophysics Data System (ADS)

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

    2016-09-01

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

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

    PubMed

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

    2016-09-01

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

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

    NASA Technical Reports Server (NTRS)

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

    2001-01-01

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

  17. Precipitation Downscaling Products for Hydrologic Applications (Invited)

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

    Hydrologists and engineers require climate data on high-resolution grids (4-12km) for many water resources applications. To get such data from climate models, users have traditionally relied on statistical downscaling techniques, with only limited use of dynamic downscaling techniques. Statistical techniques utilize a variety of assumptions, data, and methodologies that result in statistical artifacts that may impact hydroclimate representations. These impacts are often pronounced when downscaling precipitation. We will discuss four major statistical downscaling techniques: Bias Corrected Constructed Analogue (BCCA), Asynchronous Regression (AR), and two forms of Bias Corrected Spatial Disaggregation (BCSD.) The hydroclimate representations within many statistical methods often have too much drizzle, too small extreme events, and an improper representation of spatial scaling characteristics. These scaling problems lead some statistical methods substantially over estimate extreme events at hydrologically important scales (e.g., basin totals.) This can lead to large errors in future hydrologic predictions. In contrast, high-resolution dynamic downscaling using the Weather Research and Forecasting model (WRF) provides a better representation of precipitation in many respects, but at a much higher computational cost. This computational constraint prevents the use of high-resolution WRF simulations when examining the range of possible future scenarios generated as part of the Coupled Model Intercomparison Project (CMIP.) Finally, we will present a next generation psuedo-dynamical model that provides dynamic downscaling information for a fraction of the computational requirements. This simple weather model uses large scale circulation patterns from a GCM, for example wind, temperature and humidity, but performs advection and microphysical calculations on a high-resolution grid, thus permitting topography to be adequately represented. This model is capable of generating

  18. Climate change impacts on hydrological processes in Norway based on two methods for transferring regional climate model results to meteorological station sites

    NASA Astrophysics Data System (ADS)

    Beldring, Stein; Engen-Skaugen, Torill; Førland, Eirik J.; Roald, Lars A.

    2008-05-01

    Climate change impacts on hydrological processes in Norway have been estimated through combination of results from the IPCC SRES A2 and B2 emission scenarios, global climate models from the Hadley Centre and the Max-Planck Institute, and dynamical downscaling using the RegClim HIRHAM regional climate model. Temperature and precipitation simulations from the regional climate model were transferred to meteorological station sites using two different approaches, the delta change or perturbation method and an empirical adjustment procedure that reproduces observed monthly means and standard deviations for the control period. These climate scenarios were used for driving a spatially distributed version of the HBV hydrological model, yielding a set of simulations for the baseline period 1961-1990 and projections of climate change impacts on hydrological processes for the period 2071-2100. A comparison between the two methods used for transferring regional climate model results to meteorological station sites is provided by comparing the results from the hydrological model for basins located in different parts of Norway. Projected changes in runoff are linked to changes in the snow regime. Snow cover will be more unstable and the snowmelt flood will occur earlier in the year. Increased rainfall leads to higher runoff in the autumn and winter.

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

    NASA Astrophysics Data System (ADS)

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

    2012-04-01

    The vertical thermal structure of the atmosphere is defined by a combination of dynamic and radiation transfer processes and plays an important role in describing the meteorological conditions at local scales. The scope of this work is to develop and quantify the predictive ability of a hybrid dynamic-statistical downscaling procedure to estimate the vertical profile of ambient temperature at finer spatial scales. The study focuses on the warm period of the year (June - August) and the method is applied to an urban coastal site (Hellinikon), located in eastern Mediterranean. The two-step methodology initially involves the dynamic downscaling of coarse resolution climate data via the RegCM4.0 regional climate model and subsequently the statistical downscaling of the modeled outputs by developing and training site-specific artificial neural networks (ANN). The 2.5ox2.5o gridded NCEP-DOE Reanalysis 2 dataset is used as initial and boundary conditions for the dynamic downscaling element of the methodology, which enhances the regional representivity of the dataset to 20km and provides modeled fields in 18 vertical levels. The regional climate modeling results are compared versus the upper-air Hellinikon radiosonde observations and the mean absolute error (MAE) is calculated between the four grid point values nearest to the station and the ambient temperature at the standard and significant pressure levels. The statistical downscaling element of the methodology consists of an ensemble of ANN models, one for each pressure level, which are trained separately and employ the regional scale RegCM4.0 output. The ANN models are theoretically capable of estimating any measurable input-output function to any desired degree of accuracy. In this study they are used as non-linear function approximators for identifying the relationship between a number of predictor variables and the ambient temperature at the various vertical levels. An insight of the statistically derived input

  20. Regional climate change and national responsibilities

    NASA Astrophysics Data System (ADS)

    Hansen, James; Sato, Makiko

    2016-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  2. Atmosphere Processes Dynamic and Mountain Region Climate

    NASA Astrophysics Data System (ADS)

    Davitashvili, T.; Khvedelidze, Z.; Javakhishvili, Kh.; Sharikadze, I.

    As is known, on the whole regional climate is depended on the Sun's lope relation to the horizon and the characteristics of the Earth relief. In the mountain regions (Caucasian region) compound relief conduce additional turbulence craetion and flow round stream increasing or decreasing. All that bring climate change special feature in the mountain regions. Climate formation and change internal factors are enough interconnected. We had study reverse connection between temperature, moisture, cloudness radiation balance, the Sun's activity and its components on the basis of the data over last 140 years. For the central months of the seasons, there was comparison day-night, monthly an annual motion of the radiation and temperature, temperature and Sun's activity, with account of cloud and moisture. Reverse connection between climate elements was valuated with help of correlation coefficient (r>0.8), but period of its reiteration analysis of the calculated fields the available natural data and the semiempirical calculation it was shown, that in the Western Georgia temperature was not increased unlike the Eastern Georgia.

  3. Modeling nutrient transports and exchanges of nutrients between shallow regions and the open Baltic sea in present and future climate.

    PubMed

    Eilola, Kari; Rosell, Elin Almroth; Dieterich, Christian; Fransner, Filippa; Höglund, Anders; Meier, H E Markus

    2012-09-01

    We quantified horizontal transport patterns and the net exchange of nutrients between shallow regions and the open sea in the Baltic proper. A coupled biogeochemical-physical circulation model was used for transient simulations 1961-2100. The model was driven by regional downscaling of the IPCC climate change scenario A1B from two global General Circulation Models in combination with two nutrient load scenarios. Modeled nutrient transports followed mainly the large-scale internal water circulation and showed only small circulation changes in the future projections. The internal nutrient cycling and exchanges between shallow and deeper waters became intensified, and the internal removal of phosphorus became weaker in the warmer future climate. These effects counteracted the impact from nutrient load reductions according to the Baltic Sea Action Plan. The net effect of climate change and nutrient reductions was an increased net import of dissolved inorganic phosphorus to shallow areas in the Baltic proper.

  4. Statistical downscaling of daily precipitation over Llobregat River Basin in Catalunya, Spain using analog method.

    NASA Astrophysics Data System (ADS)

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

    2009-04-01

    Since anthropogenic climate change has become an important issue, the need to provide regional climate change information has increased, both for impact assessment studies and policy making. A regional climate is determined by interactions at large, regional and local scales. The general circulation models (GCMs) are run at too coarse resolution to permit accurate description of these regional and local interactions. So far, they have been unable to provide consistent estimates of climate change on a local scale. Several regionalization techniques have been developed to bridge the gap between the large-scale information provided by GCMs and fine spatial scales required for regional and environmental impact studies. Statistical downscaling technique is based on the view that regional climate may be seen to be conditioned by two factors: large-scale climatic state and regional/local features. Local climate information is derived by first developing a statistical model which relates large-scale variables or ‘‘predictors'' for which GCMs are trustable to regional or local surface ‘‘predictands'' for which models are less skilful. The main advantage of these techniques is that they are computationally inexpensive, and can be applied to outputs from different GCM experiments. In dynamical downscaling methods, a regional climate model (RCM) uses GCM outputs as its initial and boundary conditions. A statistical downscaling procedure based on an analogue technique has been used to determine projections for future climate change in the Llobregat River Basin in Catalunya, Spain. Llobregat Basin is one of the most important of Catalonia because it provides a significant amount of water for numerous cities that make up including Barcelona. This work is part of the European project "Water Change" (included in the LIFE + Environment Policy and Governance program). It deals with Medium and long term water resources modelling as a tool for planning and global change

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

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

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

  6. Impact of climate change on Precipitation and temperature under the RCP 8.5 and A1B scenarios in an Alpine Cathment (Alto-Genil Basin,southeast Spain). A comparison of statistical downscaling methods

    NASA Astrophysics Data System (ADS)

    Pulido-Velazquez, David; Juan Collados-Lara, Antonio; Pardo-Iguzquiza, Eulogio; Jimeno-Saez, Patricia; Fernandez-Chacon, Francisca

    2016-04-01

    In order to design adaptive strategies to global change we need to assess the future impact of climate change on water resources, which depends on precipitation and temperature series in the systems. The objective of this work is to generate future climate series in the "Alto Genil" Basin (southeast Spain) for the period 2071-2100 by perturbing the historical series using different statistical methods. For this targeted we use information coming from regionals climate model simulations (RCMs) available in two European projects, CORDEX (2013), with a spatial resolution of 12.5 km, and ENSEMBLES (2009), with a spatial resolution of 25 km. The historical climate series used for the period 1971-2000 have been obtained from Spain02 project (2012) which has the same spatial resolution that CORDEX project (both use the EURO-CORDEX grid). Two emission scenarios have been considered: the Representative Concentration Pathways (RCP) 8.5 emissions scenario, which is the most unfavorable scenario considered in the fifth Assessment Report (AR5) by the Intergovernmental Panel on Climate Change (IPCC), and the A1B emission scenario of fourth Assessment Report (AR4). We use the RCM simulations to create an ensemble of predictions weighting their information according to their ability to reproduce the main statistic of the historical climatology. A multi-objective analysis has been performed to identify which models are better in terms of goodness of fit to the cited statistic of the historical series. The ensemble of the CORDEX and the ENSEMBLES projects has been finally created with nine and four models respectively. These ensemble series have been used to assess the anomalies in mean and standard deviation (differences between the control and future RCM series). A "delta-change" method (Pulido-Velazquez et al., 2011) has been applied to define future series by modifying the historical climate series in accordance with the cited anomalies in mean and standard deviation. A

  7. Regional Actions to Address Climate Change Impacts on Water

    EPA Pesticide Factsheets

    EPA's ten regions work to address climate change on a local level, implementing regionally important solutions and working with stakeholders on the ground. Many regional partners work closely with EPA to better implement climate solutions

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

    NASA Astrophysics Data System (ADS)

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

    2016-09-01

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

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

    DOE PAGES

    Ashfaq, Moetasim; Rastogi, Deeksha; Mei, Rui; ...

    2016-09-01

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

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

    SciTech Connect

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

    2016-09-01

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

  11. Analysis of the Effect of Interior Nudging on Temperature and Precipitation Distributions of Multi-year Regional Climate Simulations

    NASA Astrophysics Data System (ADS)

    Nolte, C. G.; Otte, T. L.; Bowden, J. H.; Otte, M. J.

    2010-12-01

    There is disagreement in the regional climate modeling community as to the appropriateness of the use of internal nudging. Some investigators argue that the regional model should be minimally constrained and allowed to respond to regional-scale forcing, while others have noted that in the absence of interior nudging, significant large-scale discrepancies develop between the regional model solution and the driving coarse-scale fields. These discrepancies lead to reduced confidence in the ability of regional climate models to dynamically downscale global climate model simulations under climate change scenarios, and detract from the usability of the regional simulations for impact assessments. The advantages and limitations of interior nudging schemes for regional climate modeling are investigated in this study. Multi-year simulations using the WRF model driven by reanalysis data over the continental United States at 36km resolution are conducted using spectral nudging, grid point nudging, and for a base case without interior nudging. The means, distributions, and inter-annual variability of temperature and precipitation will be evaluated in comparison to regional analyses.

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

    NASA Astrophysics Data System (ADS)

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

    2002-05-01

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

  13. Satellite-based climate information within the WMO RA VI Regional Climate Centre on Climate Monitoring

    NASA Astrophysics Data System (ADS)

    Obregón, A.; Nitsche, H.; Körber, M.; Kreis, A.; Bissolli, P.; Friedrich, K.; Rösner, S.

    2014-05-01

    The World Meteorological Organization (WMO) established Regional Climate Centres (RCCs) around the world to create science-based climate information on a regional scale within the Global Framework for Climate Services (GFCS). The paper introduces the satellite component of the WMO Regional Climate Centre on Climate Monitoring (RCC-CM) for Europe and the Middle East. The RCC-CM product portfolio is based on essential climate variables (ECVs) as defined by the Global Climate Observing System (GCOS), spanning the atmospheric (radiation, clouds, water vapour) and terrestrial domains (snow cover, soil moisture). In the first part, the input data sets are briefly described, which are provided by the EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) Satellite Application Facilities (SAF), in particular CM SAF, and by the ESA (European Space Agency) Climate Change Initiative (CCI). In the second part, the derived RCC-CM products are presented, which are divided into two groups: (i) operational monitoring products (e.g. monthly means and anomalies) based on near-real-time environmental data records (EDRs) and (ii) climate information records (e.g. climatologies, time series, trend maps) based on long-term thematic climate data records (TCDRs) with adequate stability, accuracy and homogeneity. The products are provided as maps, statistical plots and gridded data, which are made available through the RCC-CM website (www.dwd.de/rcc-cm).

  14. Combined effects of global climate change and regional ecosystem drivers on an exploited marine food web.

    PubMed

    Niiranen, Susa; Yletyinen, Johanna; Tomczak, Maciej T; Blenckner, Thorsten; Hjerne, Olle; Mackenzie, Brian R; Müller-Karulis, Bärbel; Neumann, Thomas; Meier, H E Markus

    2013-11-01

    Changes in climate, in combination with intensive exploitation of marine resources, have caused large-scale reorganizations in many of the world's marine ecosystems during the past decades. The Baltic Sea in Northern Europe is one of the systems most affected. In addition to being exposed to persistent eutrophication, intensive fishing, and one of the world's fastest rates of warming in the last two decades of the 20th century, accelerated climate change including atmospheric warming and changes in precipitation is projected for this region during the 21st century. Here, we used a new multimodel approach to project how the interaction of climate, nutrient loads, and cod fishing may affect the future of the open Central Baltic Sea food web. Regionally downscaled global climate scenarios were, in combination with three nutrient load scenarios, used to drive an ensemble of three regional biogeochemical models (BGMs). An Ecopath with Ecosim food web model was then forced with the BGM results from different nutrient-climate scenarios in combination with two different cod fishing scenarios. The results showed that regional management is likely to play a major role in determining the future of the Baltic Sea ecosystem. By the end of the 21st century, for example, the combination of intensive cod fishing and high nutrient loads projected a strongly eutrophicated and sprat-dominated ecosystem, whereas low cod fishing in combination with low nutrient loads resulted in a cod-dominated ecosystem with eutrophication levels close to present. Also, nonlinearities were observed in the sensitivity of different trophic groups to nutrient loads or fishing depending on the combination of the two. Finally, many climate variables and species biomasses were projected to levels unseen in the past. Hence, the risk for ecological surprises needs to be addressed, particularly when the results are discussed in the ecosystem-based management context.

  15. Climate change and adaptive water management measures in Chtouka Aït Baha region (Morocco).

    PubMed

    Seif-Ennasr, Marieme; Zaaboul, Rashyd; Hirich, Abdelaziz; Caroletti, Giulio Nils; Bouchaou, Lhoussaine; El Morjani, Zine El Abidine; Beraaouz, El Hassane; McDonnell, Rachael A; Choukr-Allah, Redouane

    2016-12-15

    This study evaluates the effect on the availability of water resources for agriculture of expected future changes in precipitation and temperature distributions in north-western Africa. It also puts forward some locally derived adaptation strategies to climate change that can have a positive impact on water resources in the Chtouka Aït Baha region. Historical baselines of precipitation and temperature were derived using satellite data respectively from CHIRPS and CRU, while future projections of temperature and precipitation were extracted from the Coordinated Regional Climate Downscaling Experiment database (CORDEX). Projections were also generated for two future periods (2030-2049 and 2080-2099) under two Representative Concentration Pathways: RCP4.5 and RCP8.5. Regional climate models and satellite data outputs were evaluated by calculating their bias and RMSE against historical baseline and observed data. Under the RCP8.5 scenario, temperature in the region shows an increase by 2°C for the 2030-2049 time period, and by 4 to 5°C towards the end of the 21st century. According to the RCP4.5 scenario, precipitation shows a reduction of 10 to 30% for the period 2030-2049, up to 60% for 2080-2099. Outputs from the climate change projections were used to force the HEC-HMS hydrological model. Simulation results indicate that water deficit at basin level will likely triple towards 2050 due to increase in water demand and decrease in aquifer recharge and dam storage. This alarming situation, in a country that already suffers from water insecurity, emphasizes the need for more efforts to implement climate change adaptation measures. This paper presents an assessment of 38 climate change adaptation measures according to several criteria. The evaluation shows that measures affecting the management of water resources have the highest benefit-to-efforts ratio, which indicates that decision makers and stakeholders should increasingly focus their efforts on management

  16. Future climate change impact assessment of watershed scale hydrologic processes in Peninsular Malaysia by a regional climate model coupled with a physically-based hydrology modelo.

    PubMed

    Amin, M Z M; Shaaban, A J; Ercan, A; Ishida, K; Kavvas, M L; Chen, Z Q; Jang, S

    2017-01-01

    Impacts of climate change on the hydrologic processes under future climate change conditions were assessed over Muda and Dungun watersheds of Peninsular Malaysia by means of a coupled regional climate and physically-based hydrology model utilizing an ensemble of future climate change projections. An ensemble of 15 different future climate realizations from coarse resolution global climate models' (GCMs) projections for the 21st century was dynamically downscaled to 6km resolution over Peninsular Malaysia by a regional climate model, which was then coupled with the watershed hydrology model WEHY through the atmospheric boundary layer over Muda and Dungun watersheds. Hydrologic simulations were carried out at hourly increments and at hillslope-scale in order to assess the impacts of climate change on the water balances and flooding conditions in the 21st century. The coupled regional climate and hydrology model was simulated for a duration of 90years for each of the 15 realizations. It is demonstrated that the increase in mean monthly flows due to the impact of expected climate change during 2040-2100 is statistically significant from April to May and from July to October at Muda watershed. Also, the increase in mean monthly flows is shown to be significant in November during 2030-2070 and from November to December during 2070-2100 at Dungun watershed. In other words, the impact of the expected climate change will be significant during the northeast and southwest monsoon seasons at Muda watershed and during the northeast monsoon season at Dungun watershed. Furthermore, the flood frequency analyses for both watersheds indicated an overall increasing trend in the second half of the 21st century.

  17. Climatic Effects of Regional Nuclear War

    NASA Technical Reports Server (NTRS)

    Oman, Luke D.

    2011-01-01

    We use a modern climate model and new estimates of smoke generated by fires in contemporary cities to calculate the response of the climate system to a regional nuclear war between emerging third world nuclear powers using 100 Hiroshima-size bombs (less than 0.03% of the explosive yield of the current global nuclear arsenal) on cities in the subtropics. We find significant cooling and reductions of precipitation lasting years, which would impact the global food supply. The climate changes are large and longlasting because the fuel loadings in modern cities are quite high and the subtropical solar insolation heats the resulting smoke cloud and lofts it into the high stratosphere, where removal mechanisms are slow. While the climate changes are less dramatic than found in previous "nuclear winter" simulations of a massive nuclear exchange between the superpowers, because less smoke is emitted, the changes seem to be more persistent because of improvements in representing aerosol processes and microphysical/dynamical interactions, including radiative heating effects, in newer global climate system models. The assumptions and calculations that go into these conclusions will be described.

  18. Downscaling of Extreme Precipitation: Proposing a New Statistical Approach and Investigating a Taken-for-Granted Assumption

    NASA Astrophysics Data System (ADS)

    Elshorbagy, Amin; Alam, Shahabul

    2015-04-01

    In spite of the ability of General Circulation Models (GCMs) to predict and generate atmospheric variables under pre-identified climate change scenarios, their coarse horizontal scale is an obstacle for impact studies. Therefore, downscaling of variables (e.g., precipitation) from coarse spatial and temporal scales to finer ones is inevitable. Downscaling methods are classified into various types ranging from applications related to short term numerical weather prediction to multidecadal global climate prediction. For engineering applications of impact assessment of climate change on infrastructure, the multidecadal global climate projection, is the most widely used type. One of the important engineering applications of climate change impact assessment is the development and reconstruction of intensity-duration-frequency (IDF) curves under possible climate change. IDF curves are widely used for design and management of urban hydrosystems. Their construction requires accurate information about intense short duration rainfall, including sub-hourly, extremes. Previous attempts were made to construct IDF curves in various places under climate change using dynamical and statistical downscaling. The deficiency of GCMs, and even RCMs, in representing local surface conditions, especially extreme weather and convective precipitation in many areas, necessitates the use of statistical downscaling for IDF-related applications. In statistical downscaling methods, and in particular regression-based methods, the search is always for the optimum set of inputs at a coarser scale that act as predictors for the desired surface weather variable (predictand) at the local finer scale. The grid box nearest to the local site may not provide the optimum predictor-predictand relationship. In fact, even the set of predictors varies from one region to another. In this study, a novel approach using genetic programming (GP) for specific application of downscaling annual maximum precipitation

  19. Climate change projections over three metropolitan regions in Southeast Brazil using the non-hydrostatic Eta regional climate model at 5-km resolution

    NASA Astrophysics Data System (ADS)

    Lyra, Andre; Tavares, Priscila; Chou, Sin Chan; Sueiro, Gustavo; Dereczynski, Claudine; Sondermann, Marcely; Silva, Adan; Marengo, José; Giarolla, Angélica

    2017-02-01

    The objective of this work is to assess changes in three metropolitan regions of Southeast Brazil (Rio de Janeiro, São Paulo, and Santos) based on the projections produced by the Eta Regional Climate Model (RCM) at very high spatial resolution, 5 km. The region, which is densely populated and extremely active economically, is frequently affected by intense rainfall events that trigger floods and landslides during the austral summer. The analyses are carried out for the period between 1961 and 2100. The 5-km simulations are results from a second downscaling nesting in the HadGEM2-ES RCP4.5 and RCP8.5 simulations. Prior to the assessment of the projections, the higher resolution simulations were evaluated for the historical period (1961-1990). The comparison between the 5-km and the coarser driver model simulations shows that the spatial patterns of precipitation and temperature of the 5-km Eta simulations are in good agreement with the observations. The simulated frequency distribution of the precipitation and temperature extremes from the 5-km Eta RCM is consistent with the observed structure and extreme values. Projections of future climate change using the 5-km Eta runs show stronger warming in the region, primarily during the summer season, while precipitation is strongly reduced. Projected temperature extremes show widespread heating with maximum temperatures increasing by approximately 9 °C in the three metropolitan regions by the end of the century in the RCP8.5 scenario. A trend of drier climate is also projected using indices based on daily precipitation, which reaches annual rainfall reductions of more than 50 % in the state of Rio de Janeiro and between 40 and 45 % in São Paulo and Santos. The magnitude of these changes has negative implications to the population health conditions, energy security, and economy.

  20. Using the WRF Regional Model to Produce High Resolution AR4 Simulations of Climate Change for Mesoamerica

    NASA Astrophysics Data System (ADS)

    Oglesby, R. J.; Rowe, C. M.; Hays, C.

    2010-12-01

    Mesoamerica (the countries from Mexico to Colombia) has been identified by IPCC as a low-latitude, developing region at considerable risk to climate change. Furthermore, the complex topography of the region, and interactions with adjacent tropical oceans, makes understanding of potential climate change from global climate models alone very problematic. Statistical downscaling techniques for the region are not very robust, largely due to the lack of sufficient observations upon which to base the large-scale to small-scale relationships. Therefore, we have used the WRF regional climate model to dynamically downscale global results from a NCAR CCSM simulation employing the A2 emission scenario that was made for IPCC AR4. All of Mesoamerica is covered with a domain with a spatial resolution of 12 km, with selected high elevation regions of Mexico, Colombia, and Peru also covered by 4 km domains. A three-year simulation was made forced by NCEP reanalyses for the years 1991, 1992, and 1993. This run has been evaluated using actual station observations in addition to gridded datasets, in order to identify model strengths and weaknesses (biases) for this region. This comparison clearly demonstrated the need to properly resolve elevation, including both topographic heights and valleys. It also showed the difficulty that the model has in simulating extreme precipitation events, as it usually underestimates the actual amount. Additional five-year simulations were made forced with CCSM output for 2000-2004 (present-day control) and 2050-2054 (climate change scenario for the A2 emission scenario) to investigate potential climate change for the region. Summarizing key results, all land regions, except for a narrow strip along the Pacific coast of Mexico, showed a warming. This warming was largest (up to 3-4 deg C) in the highland regions and the Amazonian basin. The warming was less (generally 1-2 deg C) in the lowland and intermountain regions. Changes in precipitation strongly

  1. Incorporating cold-air pooling into downscaled climate models increases potential refugia for snow-dependent species within the Sierra Nevada Ecoregion, CA.

    PubMed

    Curtis, Jennifer A; Flint, Lorraine E; Flint, Alan L; Lundquist, Jessica D; Hudgens, Brian; Boydston, Erin E; Young, Julie K

    2014-01-01

    We present a unique water-balance approach for modeling snowpack under historic, current and future climates throughout the Sierra Nevada Ecoregion. Our methodology uses a finer scale (270 m) than previous regional studies and incorporates cold-air pooling, an atmospheric process that sustains cooler temperatures in topographic depressions thereby mitigating snowmelt. Our results are intended to support management and conservation of snow-dependent species, which requires characterization of suitable habitat under current and future climates. We use the wolverine (Gulo gulo) as an example species and investigate potential habitat based on the depth and extent of spring snowpack within four National Park units with proposed wolverine reintroduction programs. Our estimates of change in spring snowpack conditions under current and future climates are consistent with recent studies that generally predict declining snowpack. However, model development at a finer scale and incorporation of cold-air pooling increased the persistence of April 1st snowpack. More specifically, incorporation of cold-air pooling into future climate projections increased April 1st snowpack by 6.5% when spatially averaged over the study region and the trajectory of declining April 1st snowpack reverses at mid-elevations where snow pack losses are mitigated by topographic shading and cold-air pooling. Under future climates with sustained or increased precipitation, our results indicate a high likelihood for the persistence of late spring snowpack at elevations above approximately 2,800 m and identify potential climate refugia sites for snow-dependent species at mid-elevations, where significant topographic shading and cold-air pooling potential exist.

  2. Incorporating cold-air pooling into downscaled climate models increases potential refugia for snow-dependent species within the Sierra Nevada Ecoregion, CA

    USGS Publications Warehouse

    Curtis, Jennifer A.; Flint, Lorraine E.; Flint, Alan L.; Lundquist, Jessica D.; Hudgens, Brian; Boydston, Erin E.; Young, Julie K.

    2014-01-01

    We present a unique water-balance approach for modeling snowpack under historic, current and future climates throughout the Sierra Nevada Ecoregion. Our methodology uses a finer scale (270 m) than previous regional studies and incorporates cold-air pooling, an atmospheric process that sustains cooler temperatures in topographic depressions thereby mitigating snowmelt. Our results are intended to support management and conservation of snow-dependent species, which requires characterization of suitable habitat under curre