Constraining extreme precipitation projections using past precipitation variability

Wenxia Zhang; Kalli Furtado; Tianjun Zhou; Peili Wu; Xiaolong Chen

doi:10.1038/s41467-022-34006-0

Nature Communications (Nov 2022)

Constraining extreme precipitation projections using past precipitation variability

Wenxia Zhang,
Kalli Furtado,
Tianjun Zhou,
Peili Wu,
Xiaolong Chen

Affiliations

Wenxia Zhang: State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences
Kalli Furtado: Met Office
Tianjun Zhou: State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences
Peili Wu: Met Office
Xiaolong Chen: State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences

DOI: https://doi.org/10.1038/s41467-022-34006-0
Journal volume & issue: Vol. 13, no. 1
pp. 1 – 11

Abstract

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This study finds that projections of future extreme precipitation can be made more reliable using a constraint from observed present-day precipitation variability, which reduces projection uncertainty by 20–40% over the extra-tropics.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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