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Title: Changes in the dependence between global precipitation and temperature from observations and model simulations

Abstract

Abstract Precipitation and temperature are physically related to each other with significant dependences that vary with seasons at regional and global scales. The dependence between precipitation and temperature (P–T dependence) plays a central role in characterizing the joint behaviour of the two variables. Along with extensive studies on the variation in the mean, variance, or extremes of precipitation and temperature under global warming, it is of particular interest to understand the climate‐change impacts on the covariability of precipitation and temperature. The aim of this study therefore is to assess climate change impacts on the P–T dependence over global land areas from observations, and the performance of the current‐generation Coupled Model Intercomparison Project (CMIP5) models in simulating these patterns. In general, the CMIP5 models correctly simulate the overall pattern of the P–T dependence (around 87–94% of global land areas). However, they capture both the sign and change of P–T dependence for relatively small regions (around 41–47% of global land areas). Results from this study may provide useful insights for the future development of climate models.

Authors:
ORCiD logo [1];  [2];  [1];  [1]
  1. Beijing Normal Univ., Beijing (China)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Publication Date:
Research Org.:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1560116
Alternate Identifier(s):
OSTI ID: 1511872
Report Number(s):
LLNL-JRNL-756410
Journal ID: ISSN 0899-8418; 943935
Grant/Contract Number:  
AC52-07NA27344
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
International Journal of Climatology
Additional Journal Information:
Journal Volume: 39; Journal Issue: 12; Journal ID: ISSN 0899-8418
Publisher:
Royal Meteorological Society
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; climate change; CMIP5 model; covariability; dependence

Citation Formats

Hao, Zengchao, Phillips, Thomas J., Hao, Fanghua, and Wu, Xinying. Changes in the dependence between global precipitation and temperature from observations and model simulations. United States: N. p., 2019. Web. doi:10.1002/joc.6111.
Hao, Zengchao, Phillips, Thomas J., Hao, Fanghua, & Wu, Xinying. Changes in the dependence between global precipitation and temperature from observations and model simulations. United States. https://doi.org/10.1002/joc.6111
Hao, Zengchao, Phillips, Thomas J., Hao, Fanghua, and Wu, Xinying. 2019. "Changes in the dependence between global precipitation and temperature from observations and model simulations". United States. https://doi.org/10.1002/joc.6111. https://www.osti.gov/servlets/purl/1560116.
@article{osti_1560116,
title = {Changes in the dependence between global precipitation and temperature from observations and model simulations},
author = {Hao, Zengchao and Phillips, Thomas J. and Hao, Fanghua and Wu, Xinying},
abstractNote = {Abstract Precipitation and temperature are physically related to each other with significant dependences that vary with seasons at regional and global scales. The dependence between precipitation and temperature (P–T dependence) plays a central role in characterizing the joint behaviour of the two variables. Along with extensive studies on the variation in the mean, variance, or extremes of precipitation and temperature under global warming, it is of particular interest to understand the climate‐change impacts on the covariability of precipitation and temperature. The aim of this study therefore is to assess climate change impacts on the P–T dependence over global land areas from observations, and the performance of the current‐generation Coupled Model Intercomparison Project (CMIP5) models in simulating these patterns. In general, the CMIP5 models correctly simulate the overall pattern of the P–T dependence (around 87–94% of global land areas). However, they capture both the sign and change of P–T dependence for relatively small regions (around 41–47% of global land areas). Results from this study may provide useful insights for the future development of climate models.},
doi = {10.1002/joc.6111},
url = {https://www.osti.gov/biblio/1560116}, journal = {International Journal of Climatology},
issn = {0899-8418},
number = 12,
volume = 39,
place = {United States},
year = {Wed Apr 24 00:00:00 EDT 2019},
month = {Wed Apr 24 00:00:00 EDT 2019}
}

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Cited by: 25 works
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Works referencing / citing this record: