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5 - Empirical-Statistical Downscaling

Published online by Cambridge University Press:  05 February 2021

Rao Kotamarthi ,
Katharine Hayhoe ,
Linda O. Mearns ,
Donald Wuebbles ,
Jennifer Jacobs  and
Jennifer Jurado
Rao Kotamarthi
Affiliation:
Argonne National Laboratory, Illinois
Katharine Hayhoe
Affiliation:
Texas Tech University
Linda O. Mearns
Affiliation:
National Center for Atmospheric Research, Boulder, Colorado
Donald Wuebbles
Affiliation:
University of Illinois, Urbana-Champaign
Jennifer Jacobs
Affiliation:
University of New Hampshire
Jennifer Jurado
Affiliation:
Environmental Planning and Community Resilience Division, Broward County, Florida
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Summary

Empirical-statistical downscaling combines observations with global climate model outputs to generate high-resolution spatial and temporal projections. This chapter describes some of the common methods and models used in spatial and temporal disaggregation and bias correction, focusing on aspects of their design and performance that are relevant to their application for quantifying local to regional-scale impacts.

Keywords

empirical-statistical downscalinghigh resolutionspatial disaggregationtemporal dissagregationbias correctionregional-scale impactsweather stationstraining data
Type
Chapter
Information
Downscaling Techniques for High-Resolution Climate Projections
From Global Change to Local Impacts
 , pp. 82 - 101
Publisher: Cambridge University Press
Print publication year: 2021

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