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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....
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...
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.
A convective precipitation model for use in regions of complex terrain has been developed and applied to the Gunnison River Basin in southwestern Colorado. Spring snowfall in the Rocky Mountain region often has a significant convective component which orographic precipitation models are unable to simulate. Additionally, summertime precipitation is predominately convective in this area and is responsible for a large portion of summer streamflow variability. Streamflow typically increases by 50 to 100 percent of baseflow for moderate rainfall events for periods of up to one week. Larger precipitation episodes can produce peak discharges that exceed the spring snowmelt peaks. Convective precipitation also is important...
Abstract (from http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-16-0038.1): A 20-yr simulation with a fine-resolution regional atmospheric model for projected late twenty-first-century conditions in Hawaii is presented. The pseudo-global-warming method is employed, and the boundary conditions are based on a multimodel mean of projections made with global coupled models run with a moderate greenhouse gas emissions scenario. Results show that surface air temperature over land increases ~2°–4°C with the greatest warming at the highest topographic heights. A modest tendency for the warming to be larger on the leeward sides of the major islands is also apparent. Climatological rainfall is projected to change up to...