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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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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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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...
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....
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...
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...
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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...
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...
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, were also provided to enable potential users with the necessary information to select and apply the models.
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When climate models are developed, researchers test how well they replicate the climate system by using them to model past climate. Ideally, the model output will match the climate conditions that were actually recorded in the past, indicating that the model correctly characterizes how the climate system works and can be used to reliably project future conditions. However, this approach assumes that models that reliably project past climate conditions will accurately predict future climate conditions, even though the climate system might have changed. This research contributes to generating more reliable local-scale climate projections by testing the assumption that the climatological relationships which existed...
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.
This fact sheet provides highlights from a comprehensive U.S. Geological Survey report that evaluates six widely used downscaled climate projections covering the southeastern United States and recommends best practices for use of downscaled datasets for ecological modeling and decision-making.
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Natural resource managers face uncertainties of many kinds, with limited budgets and ever-evolving hierarchies of management priorities. Not least among those uncertainties are questions regarding future climate conditions. Technological advancements have enhanced our ability to understand and model climate, which has led to improved climate forecasting capabilities. However, climate projections are usually produced at a global scale, which makes them impractical for natural resource managers who are concerned with how climate will change in the specific location or region in which they operate. While there have been a growing number of techniques for increasing the resolution of these projections, resource managers...
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Regional assessments of the impacts of climate change on both human systems and the natural environment require high-resolution projections to see the effects of global-scale change on the local environment. This project will address a critical and generally overlooked assumption inherent to these projections of regional, multi-decadal climate change: that the statistical relationship between global climate model simulation outputs and real, observed climate data remain constant over time. Utilizing a “perfect--ÔÇÉmodel” experimental design and the output of two high-resolution global climate model simulations, this study will evaluate and report on the ability of three different methods to simulate current and future...
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ClimateWNA: A program to generate high-resolution climate data for climate change studies and applications in western North America Overview ClimateWNA extracts and downscales PRISM (Daly et al. 2002) monthly data (2.5 x 2.5 arcmin) for the reference period (1961-1990), and calculates seasonal and annual climate variables for specific locations based on latitude, longitude and elevation (optional) for western North America, shown in the map on the right. This program also downscales and integrates historical (1901-2011) (Mitchell and Jones 2005 for 1901-2009) and future climate data (2020s, 2050s and 2080s) generated by various global circulation models. The output includes both directly calculated and derived...


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 Comparing and Evaluating Different Models to Simulate Current and Future Temperature and Precipitation Pacific Climate Impacts Consortium ClimateWNA Testing Downscaled Climate Projections: Is Past Performance an Indicator of Future Accuracy? Providing Natural Resource Managers with Guidance on the Application of Climate Information for Decision-Making 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 Analysis of Downscaled Climate Simulations and Projections and Their Use in Decision Making for the Southwest Improving Projections of Hydrology in the Pacific Northwest Dynamical Downscaling for the Midwest and Great Lakes Basin Testing Downscaled Climate Projections: Is Past Performance an Indicator of Future Accuracy? Comparing and Evaluating Different Models to Simulate Current and Future Temperature and Precipitation Very High-Resolution Dynamic Downscaling of Regional Climate for Use in Long-term Hydrologic Planning along the Red River Valley System Pacific Climate Impacts Consortium ClimateWNA Providing Natural Resource Managers with Guidance on the Application of Climate Information for Decision-Making