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RAPID: Representation of Atlantic Multidecadal Variability and its Hydroclimate and Surface Temperature Links in Climate Simulations

$30,000FY2011GEONSF

University Of Maryland, College Park, College Park MD

Investigators

Abstract

This is one of 16 Rapid Response (RAPID) projects funded as the result of a Dear Colleague Letter (NSF 11-006) encouraging diagnostic analyses of climate model simulations prepared for the Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5). Research conducted in these projects is expected to lead to more detailed model intercomparisons, better understanding of robust model behaviors, and better understanding and quantification of uncertainty in future climate simulations. This project will analyze the Coupled Model Intercomparison Project (CMIP5) models with a focus on the representation of Atlantic Multidecadal Oscillation (AMO) variability and its hydroclimate impacts. The AMO varies in phase on a timescale of 5-8 decades, and exerts considerable influence on North American hydroclimate, e.g. precipitation and droughts, surface air-temperature, and Atlantic hurricanes. Depending on its phase, the AMO impact can either offset or exacerbate the greenhouse gas forced warming signal over the continents in the Northern Hemisphere over a multidecadal period. Thus climate models undertaking decadal predictions and multidecadal projections cannot afford to misrepresent AMO variability. The broader impact of the project lies in its support of the IPCC AR5, which is intended to provide information on climate change and its consequences to decision makers worldwide. This project seeks in particular to evaluate the representation of the AMO in coupled climate models. Thus the research will lead to improved understanding of the role of multidecadal natural variability and secular change in the evolving climate of the 20th and 21st centuries. By providing a baseline on latest versions of the climate models, this study will directly address issues of model deficiencies on model credibility.

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