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Projected Changes in Climate Extremes over the Northeastern United States

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  • 1 Key Laboratory of Virtual Geographic Environment Ministry of Education, School of Geography Science, and Jiangsu Key Laboratory for Numerical Simulation of Large Scale Complex System, School of Mathematical Science, Nanjing Normal University, Nanjing, China, and Northeast Climate Science Center, and Department of Geosciences, University of Massachusetts Amherst, Amherst, Massachusetts, and Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China
  • | 2 Department of Geoscience, University of Massachusetts Amherst, Amherst, Massachusetts
  • | 3 Northeast Climate Science Center, and Department of Geosciences, University of Massachusetts Amherst, Amherst, Massachusetts
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Abstract

Projections of historical and future changes in climate extremes are examined by applying the bias-correction spatial disaggregation (BCSD) statistical downscaling method to five general circulation models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5). For this analysis, 11 extreme temperature and precipitation indices that are relevant across multiple disciplines (e.g., agriculture and conservation) are chosen. Over the historical period, the simulated means, variances, and cumulative distribution functions (CDFs) of each of the 11 indices are first compared with observations, and the performance of the downscaling method is quantitatively evaluated. For the future period, the ensemble average of the five GCM simulations points to more warm extremes, fewer cold extremes, and more precipitation extremes with greater intensities under all three scenarios. The changes are larger under higher emissions scenarios. The inter-GCM uncertainties and changes in probability distributions are also assessed. Changes in the probability distributions indicate an increase in both the number and interannual variability of future climate extreme events. The potential deficiencies of the method in projecting future extremes are also discussed.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-14-00150.s1.

Corresponding author address: Liang Ning, Department of Geosciences, University of Massachusetts Amherst, Morrill Science Center, 611 North Pleasant Street, Amherst, MA 01003. E-mail: lning@geo.umass.edu

Abstract

Projections of historical and future changes in climate extremes are examined by applying the bias-correction spatial disaggregation (BCSD) statistical downscaling method to five general circulation models (GCMs) from phase 5 of the Coupled Model Intercomparison Project (CMIP5). For this analysis, 11 extreme temperature and precipitation indices that are relevant across multiple disciplines (e.g., agriculture and conservation) are chosen. Over the historical period, the simulated means, variances, and cumulative distribution functions (CDFs) of each of the 11 indices are first compared with observations, and the performance of the downscaling method is quantitatively evaluated. For the future period, the ensemble average of the five GCM simulations points to more warm extremes, fewer cold extremes, and more precipitation extremes with greater intensities under all three scenarios. The changes are larger under higher emissions scenarios. The inter-GCM uncertainties and changes in probability distributions are also assessed. Changes in the probability distributions indicate an increase in both the number and interannual variability of future climate extreme events. The potential deficiencies of the method in projecting future extremes are also discussed.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-14-00150.s1.

Corresponding author address: Liang Ning, Department of Geosciences, University of Massachusetts Amherst, Morrill Science Center, 611 North Pleasant Street, Amherst, MA 01003. E-mail: lning@geo.umass.edu

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