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Folders: ROOT > ScienceBase Catalog > National and Regional Climate Adaptation Science Centers > Southeast CASC > FY 2011 Projects > SERAP: Comprehensive Web-based Climate Change Projections: Downscaled Maps and Data > Approved Products ( Show direct descendants )

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_ScienceBase Catalog
__National and Regional Climate Adaptation Science Centers
___Southeast CASC
____FY 2011 Projects
_____SERAP: Comprehensive Web-based Climate Change Projections: Downscaled Maps and Data
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Characterizing the risks of anthropogenic climate change poses considerable statistical challenges. A key problem is how to combine the information contained in large-scale observational data sets with simulations of Earth system models in a statistically sound and computationally tractable manner. Here, we describe a statistical approach for improving projections of the North Atlantic meridional overturning circulation (AMOC). The AMOC is part of the global ocean conveyor belt circulation and transfers heat between low and high latitudes in the Atlantic basin. The AMOC might collapse in a “tipping point” response to anthropogenic climate forcings. Assessing the risk of an AMOC collapse is of considerable interest...
We evaluate the ability of global climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) to reproduce observed seasonality and interannual variability of temperature over the Caribbean, and compare these with simulations from atmosphere-only (AMIP5) and previous-generation CMIP3 models. Compared to station and gridded observations, nearly every CMIP5, CMIP3 and AMIP5 simulation tends to reproduce the primary inter-regional features of the Caribbean annual temperature cycle. In most coupled model simulations, however, boreal summer temperature lags observations by about 1 month, with a similar lag in the simulated annual cycle of sea surface temperature (SST), and a systematic...
The asynchronous regional regression model (ARRM) is a flexible and computationally efficient statistical model that can downscale station-based or gridded daily values of any variable that can be transformed into an approximately symmetric distribution and for which a large-scale predictor exists. This technique was developed to bridge the gap between large-scale outputs from atmosphere–ocean general circulation models (AOGCMs) and the fine-scale output required for local and regional climate impact assessments. ARRM uses piecewise regression to quantify the relationship between observed and modelled quantiles and then downscale future projections. Here, we evaluate the performance of three successive versions...
We assess the ability of Global Climate Models participating in phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) to simulate observed annual precipitation cycles over the Caribbean. Compared to weather station records and gridded observations, we find that both CMIP3 and CMIP5 models can be grouped into three categories: (1) models that correctly simulate a bimodal distribution with two rainfall maxima in May–June and September–October, punctuated by a mid-summer drought (MSD) in July–August; (2) models that reproduce the MSD and the second precipitation maxima only; and (3) models that simulate only one precipitation maxima, beginning in early summer. These categories appear related...
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.
Extreme events such as heat waves, cold spells, floods, droughts, tropical cyclones, and tornadoes have potentially devastating impacts on natural and engineered systems and human communities worldwide. Stakeholder decisions about critical infrastructures, natural resources, emergency preparedness and humanitarian aid typically need to be made at local to regional scales over seasonal to decadal planning horizons. However, credible climate change attribution and reliable projections at more localized and shorter time scales remain grand challenges. Long-standing gaps include inadequate understanding of processes such as cloud physics and ocean–land–atmosphere interactions, limitations of physics-based computer models,...