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Natural resource managers and researchers often need long-term averages of historical and future climate scenarios for their study area yet may not have the resources to make these summaries. This project will provide high quality, detailed maps of historical and projected future climate and hydrologic conditions for California and a finer scale version for southern California. The project will also assess the feasibility of expanding these reference data to the southwestern US and identify the most suitable online data portals for the public to view and analyze the data in support of local initiatives. The map products can be used to assess the impacts of ongoing climate change and to develop climate adaptation...
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The U.S. Great Plains is known for frequent hazardous convective weather and climate extremes. Across this region, climate change is expected to cause more severe droughts, more intense heavy rainfall events, and subsequently more flooding episodes. These potential changes in climate will adversely affect habitats, ecosystems, and landscapes as well as the fish and wildlife they support. Better understanding and simulation of regional precipitation can help natural resource managers mitigate and adapt to these adverse impacts. In this project, we aim to achieve a better precipitation downscaling in the Great Plains with the Weather Research and Forecast (WRF) model and use the high quality dynamic downscaling results...
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Resource managers, policymakers, and scientists require tools to inform water resource management and planning. Information on hydrologic factors – such as streamflow, snowpack, and soil moisture – is important for understanding and predicting wildfire risk, flood activity, and agricultural and rangeland productivity, among others. Existing tools for modeling hydrologic conditions rely on information on temperature and precipitation. This project sought to evaluate different methods for downscaling global climate models – that is, taking information produced at a global scale and making it useable at a regional scale, in order to produce more accurate projections of temperature and precipitation for the Pacific...
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Project involves analyzing datasets using two measures: Spatial similarity of the distributed precipitation and temperature fields of the study datasets Implications on hydrologic modeling We will then provide guidance on the choice of datasets for statistical downscaling of GCM outputs used in different types of scale-dependent planning assessments. We will evaluate these differences from a hydrological standpoint at specific Reclamation basins: Animas at Durango, Colorado; Snake at Heise, Idaho; Sacramento at Redding, California; Salt at Chrysotile, Arizona; Yellowstone River at Billings, Montana; and Colorado River at Lees Ferry Utah and Arizona. The analysis will indicate whether the choice of forcing a...
Global land-use/land-cover (LULC) change projections and historical datasets are typically available at coarse grid resolutions and are often incompatible with modeling applications at local to regional scales. The difficulty of downscaling and reapportioning global gridded LULC change projections to regional boundaries is a barrier to the use of these datasets in a state-and-transition simulation model (STSM) framework. Here we compare three downscaling techniques to transform gridded LULC transitions into spatial scales and thematic LULC classes appropriate for use in a regional STSM. For each downscaling approach, Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) LULC...
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Global climate models (GCMs) are numerically complex, computationally intensive, physics-based research tools used to simulate our planet’s inter-connected climate system. In addition to improving the scientific understanding of how the large-scale climate system works, GCM simulations of past and future climate conditions can be useful in applied research contexts. When seeking to apply information from global-scale climate projections to address local- and regional-scale climate questions, GCM-generated datasets often undergo statistical post-processing generally known as statistical downscaling (hereafter, SD). There are many different SD techniques, with all using information from observations to address GCM...
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Six global climate models (GCMs) from the Coupled Model Intercomparison Project Phase Five (CMIP5) were dynamically downscaled to 25-km grid spacing according to the representative concentration pathway 8.5 (RCP8.5) scenario using the International Centre for Theoretical Physics (ICTP) Regional Climate Model Version Four (RegCM4), interactively coupled to a 1D lake model to represent the Great Lakes. These GCMs include the Centre National de Recherches Meteorologiques Coupled Global Climate Model Version Five (CNRM-CM5), the Model for Interdisciplinary Research on Climate Version Five (MIROC5), the Institut Pierre Simon Laplace Coupled Model Version Five-Medium Resolution (IPSL-CM5-MR), the Meteorological Research...
Data Sources, inputs, parameters, and code for the MACA-VIC project final report. Consists of 3 tasks: 1. Consists of comparing results of the indexing method MTCLIM to estimate incoming short and long wave radiation, to observations, to BSRN station data, and to three Ameriflux towers in the Pacific Northwest. 2. Reports on forcing VIC with downscaled GCM forcings, with variations in two forcing variables: downward shortwave radiation (rad) and specific humidity (qair). For this task we consider the MACA downscaling method. Three cases are reported: a) both variables are downscaled; b) rad is indexed and qair is downscaled; c) rad is downscaled and qair is indexed. 3. Reports on forcing VIC with downscaled...
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Clouds often come in contact with vegetation (often named fogs) within a certain elevation range on Hawai‘i’s mountains. Propelled by strong winds, cloud droplets are driven onto the stems and leaves of plants where they are deposited. Some of the water that accumulates on the plants in this way drips to the ground, adding additional water over and above the water supplied by rainfall. Prior observations show that the amount of cloud water intercepted by vegetation is substantial, but also quite variable from place to place. It is, therefore, important to create a map for the complex spatial patterns of cloud water interception (CWI) in Hawai‘i. In this project, we propose to create the CWI map at 0.8-km resolution...
This project used species distribution modeling, population genetics, and geospatial analysis of historical vs. modern vertebrate populations to identify climate change refugia and population connectivity across the Sierra Nevada. It is hypothesized that climate change refugia will increase persistence and stability of populations and, as a result, maintain higher genetic diversity. This work helps managers assess the need to include connectivity and refugia in climate change adaptation strategies. Results help Sierra Nevada land managers allocate limited resources, aid future scenario assessment at landscape scales, and develop a performance measure for assessing resilience.
Categories: Data, Project; Tags: 2011, 2013, CA, California Landscape Conservation Cooperative, Conservation Design, All tags...
The south-central U.S. exists in a zone of dramatic transition in terms of eco-climate system diversity. Ecosystems across much of the region rely on warm-season convective precipitation. These convective precipitation is subject to large uncertainties under climate change scenario, possibly leading to gradual or sudden changes in habitats, and ecosystems. The convective precipitation in this region, occurring on a range of time and space scales, is extremely challenging to predict in future climate scenario. In this project, we established a unique, cutting-edge, dynamic downscaling capability to address the challenge of predicting precipitation in the south-central U.S. in current and future climate scenarios....
The fundamental rationale for statistical downscaling is that the raw outputs of climate change experiments from General Circulation Models (GCMs) are an inadequate basis for assessing the effects of climate change on land-surface processes at regional scales. This is because the spatial resolution of GCMs is too coarse to resolve important sub-grid scale processes (most notably those pertaining to the hydrological cycle) and because GCM output is often unreliable at individual and sub-grid box scales. By establishing empirical relationships between grid-box scale circulation indices (such as atmospheric vorticity and divergence) and sub-grid scale surface predictands (such as precipitation), statistical downscaling...
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The U.S. Great Plains is known for frequent hazardous convective weather and climate extremes. Across this region, climate change is expected to cause more severe droughts, more intense heavy rainfall events, and subsequently more flooding episodes. These potential changes in climate will adversely affect habitats, ecosystems, and landscapes as well as the fish and wildlife they support. Better understanding and simulation of regional precipitation can help natural resource managers mitigate and adapt to these adverse impacts. In this project, we aim to achieve a better precipitation downscaling in the Great Plains with the Weather Research and Forecast (WRF) model and use the high quality dynamic downscaling results...
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Six global climate models (GCMs) from the Coupled Model Intercomparison Project Phase Five (CMIP5) were dynamically downscaled to 25-km grid spacing according to the representative concentration pathway 8.5 (RCP8.5) scenario using the International Centre for Theoretical Physics (ICTP) Regional Climate Model Version Four (RegCM4), interactively coupled to a 1D lake model to represent the Great Lakes. These GCMs include the Centre National de Recherches Meteorologiques Coupled Global Climate Model Version Five (CNRM-CM5), the Model for Interdisciplinary Research on Climate Version Five (MIROC5), the Institut Pierre Simon Laplace Coupled Model Version Five-Medium Resolution (IPSL-CM5-MR), the Meteorological Research...
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Global downscaled projections are now some of the most widely used climate datasets in the world, however, they are rarely examined for representativeness of local climate or the plausibility of their projected changes. Here we show steps to improve the utility of two such global datasets (CHELSA and WorldClim2) to provide credible climate scenarios for regional climate change impact studies. Our approach is based on three steps: 1) Using a standardized baseline period, comparing available global downscaled projections with regional observation-based datasets and regional downscaled datasets (if available); 2) bias correcting projections using observation-based data; and 3) creating ensembles to make use of the...
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Global downscaled projections are now some of the most widely used climate datasets in the world, however, they are rarely examined for representativeness of local climate or the plausibility of their projected changes. Here we show steps to improve the utility of two such global datasets (CHELSA and WorldClim2) to provide credible climate scenarios for regional climate change impact studies. Our approach is based on three steps: 1) Using a standardized baseline period, comparing available global downscaled projections with regional observation-based datasets and regional downscaled datasets (if available); 2) bias correcting projections using observation-based data; and 3) creating ensembles to make use of the...
Abstract (from http://link.springer.com/article/10.1007/s10584-016-1598-0): Empirical statistical downscaling (ESD) methods seek to refine global climate model (GCM) outputs via processes that glean information from a combination of observations and GCM simulations. They aim to create value-added climate projections by reducing biases and adding finer spatial detail. Analysis techniques, such as cross-validation, allow assessments of how well ESD methods meet these goals during observational periods. However, the extent to which an ESD method’s skill might differ when applied to future climate projections cannot be assessed readily in the same manner. Here we present a “perfect model” experimental design that quantifies...
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To understand potential climate change impacts on ecosystems, water resources, and numerous other natural and managed resources, climate change data and projections must be downscaled from coarse global climate models to much finer resolutions and more applicable formats. This project conducted comparative analyses to better understand the accuracy and properties of these downscaled climate simulations and climate-change projections. Interpretation, guidance and evaluation, including measures of uncertainties, strengths and weaknesses of the different methodologies for each simulation, can enable potential users with the necessary information to select and apply the models.
The Geo Data Portal Blog provides news and updates about the Geo Data Portal (GDG), a portal that provides access to numerous climate datasets for particular areas of interest. Blog updates include information on new datasets, developments to the GDP and other such topics.
map background search result map search result map Understanding How Different Versions of Distributed Historical Weather Data Affect Hydrologic Model Calibration and Climate Projections Downscaling - BOR Project, FY2011 Improving Projections of Hydrology in the Pacific Northwest Analysis of Downscaled Climate Simulations and Projections and Their Use in Decision Making for the Southwest Dynamical Downscaling for the Midwest and Great Lakes Basin Very High-Resolution Dynamic Downscaling of Regional Climate for Use in Long-term Hydrologic Planning along the Red River Valley System Very High-Resolution Dynamic Downscaling of Regional Climate for Use in Long-term Hydrologic Planning along the Red River Valley System South Central Climate Projections Evaluation Project (C-PrEP) Very fine resolution dynamically downscaled climate data for Hawaii Dynamical Downscaling for the Midwest and Great Lakes Basin Downscaled CHELSA projections for the Hawaiian Islands under four representative concentration pathways (RCPs; 2.6, 4.5, 6.0, and 8.5) for mid- (2040-2059), and late-century (2060-2079) scenarios Downscaled WorldClim2 projections for the Hawaiian Islands under four representative concentration pathways (RCPs; 2.6, 4.5, 6.0, and 8.5) for mid- (2040-2059), and late-century (2060-2079) scenarios Rendering High-Resolution Hydro-Climatic Data for Southern California Understanding How Different Versions of Distributed Historical Weather Data Affect Hydrologic Model Calibration and Climate Projections Downscaling - BOR Project, FY2011 Very fine resolution dynamically downscaled climate data for Hawaii Downscaled CHELSA projections for the Hawaiian Islands under four representative concentration pathways (RCPs; 2.6, 4.5, 6.0, and 8.5) for mid- (2040-2059), and late-century (2060-2079) scenarios Downscaled WorldClim2 projections for the Hawaiian Islands under four representative concentration pathways (RCPs; 2.6, 4.5, 6.0, and 8.5) for mid- (2040-2059), and late-century (2060-2079) scenarios Rendering High-Resolution Hydro-Climatic Data for Southern California Analysis of Downscaled Climate Simulations and Projections and Their Use in Decision Making for the Southwest Improving Projections of Hydrology in the Pacific Northwest South Central Climate Projections Evaluation Project (C-PrEP) Dynamical Downscaling for the Midwest and Great Lakes Basin Dynamical Downscaling for the Midwest and Great Lakes Basin Very High-Resolution Dynamic Downscaling of Regional Climate for Use in Long-term Hydrologic Planning along the Red River Valley System Very High-Resolution Dynamic Downscaling of Regional Climate for Use in Long-term Hydrologic Planning along the Red River Valley System