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The University of Massachusetts Amherst

Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the U.S. northeast

Authors:

Kazi Ahmed

Guiling Wang

John Silander

Adam Wilson

Jenica Allen

Radley Horton

Richard Anyah

Publication Type:
Journal Article
Year of Publication:
2013
Secondary Title:
Global and Planetary Change
ISSN:
09218181
DOI:
10.1016/j.gloplacha.2012.11.003
Pages:
320-332
Volume:
100
Year:
2013
Date:
1/2013

Abstract

Statistical downscaling can be used to efficiently downscale a large number of General Circulation Model (GCM) outputs to a fine temporal and spatial scale. To facilitate regional impact assessments, this study statistically downscales (to 1-8\textdegree spatial resolution) and corrects the bias of daily maximum and minimum temperature and daily precipitation data from six GCMs and four Regional Climate Models (RCMs) for the northeast United States (US) using the Statistical Downscaling and Bias Correction (SDBC) approach. Based on these downscaled data from multiple models, five extreme indices were analyzed for the future climate to quantify future changes of climate extremes. For a subset of models and indices, results based on raw and bias corrected model outputs for the present-day climate were compared with observations, which demonstrated that bias correction is important not only for GCM outputs, but also for RCM outputs. For future climate, bias correction led to a higher level of agreements among the models in predicting the magnitude and capturing the spatial pattern of the extreme climate indices. We found that the incorporation of dynamical downscaling as an intermediate step does not lead to considerable differences in the results of statistical downscaling for the study domain.