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Title: Comparing regional precipitation and temperature extremes in climate model and reanalysis products

Abstract

A growing field of research aims to characterise the contribution of anthropogenic emissions to the likelihood of extreme weather and climate events. These analyses can be sensitive to the shapes of the tails of simulated distributions. If tails are found to be unrealistically short or long, the anthropogenic signal emerges more or less clearly, respectively, from the noise of possible weather. Here we compare the chance of daily land-surface precipitation and near-surface temperature extremes generated by three Atmospheric Global Climate Models typically used for event attribution, with distributions from six reanalysis products. The likelihoods of extremes are compared for area-averages over grid cell and regional sized spatial domains. Results suggest a bias favouring overly strong attribution estimates for hot and cold events over many regions of Africa and Australia, and a bias favouring overly weak attribution estimates over regions of North America and Asia. For rainfall, results are more sensitive to geographic location. Although the three models show similar results over many regions, they do disagree over others. Equally, results highlight the discrepancy amongst reanalyses products. This emphasises the importance of using multiple reanalysis and/or observation products, as well as multiple models in event attribution studies.

Authors:
; ; ; ; ; ; ; ;
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1345437
Alternate Identifier(s):
OSTI ID: 1377441
Grant/Contract Number:  
AC02- 05CH11231; GA01101; AC02-05CH11231
Resource Type:
Published Article
Journal Name:
Weather and Climate Extremes
Additional Journal Information:
Journal Name: Weather and Climate Extremes Journal Volume: 13 Journal Issue: C; Journal ID: ISSN 2212-0947
Publisher:
Elsevier
Country of Publication:
Netherlands
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Extremes; Evaluation; Event attribution; CAM5.1; MIROC5; HadGEM3-A-N216

Citation Formats

Angélil, Oliver, Perkins-Kirkpatrick, Sarah, Alexander, Lisa V., Stone, Dáithí, Donat, Markus G., Wehner, Michael, Shiogama, Hideo, Ciavarella, Andrew, and Christidis, Nikolaos. Comparing regional precipitation and temperature extremes in climate model and reanalysis products. Netherlands: N. p., 2016. Web. doi:10.1016/j.wace.2016.07.001.
Angélil, Oliver, Perkins-Kirkpatrick, Sarah, Alexander, Lisa V., Stone, Dáithí, Donat, Markus G., Wehner, Michael, Shiogama, Hideo, Ciavarella, Andrew, & Christidis, Nikolaos. Comparing regional precipitation and temperature extremes in climate model and reanalysis products. Netherlands. doi:10.1016/j.wace.2016.07.001.
Angélil, Oliver, Perkins-Kirkpatrick, Sarah, Alexander, Lisa V., Stone, Dáithí, Donat, Markus G., Wehner, Michael, Shiogama, Hideo, Ciavarella, Andrew, and Christidis, Nikolaos. Thu . "Comparing regional precipitation and temperature extremes in climate model and reanalysis products". Netherlands. doi:10.1016/j.wace.2016.07.001.
@article{osti_1345437,
title = {Comparing regional precipitation and temperature extremes in climate model and reanalysis products},
author = {Angélil, Oliver and Perkins-Kirkpatrick, Sarah and Alexander, Lisa V. and Stone, Dáithí and Donat, Markus G. and Wehner, Michael and Shiogama, Hideo and Ciavarella, Andrew and Christidis, Nikolaos},
abstractNote = {A growing field of research aims to characterise the contribution of anthropogenic emissions to the likelihood of extreme weather and climate events. These analyses can be sensitive to the shapes of the tails of simulated distributions. If tails are found to be unrealistically short or long, the anthropogenic signal emerges more or less clearly, respectively, from the noise of possible weather. Here we compare the chance of daily land-surface precipitation and near-surface temperature extremes generated by three Atmospheric Global Climate Models typically used for event attribution, with distributions from six reanalysis products. The likelihoods of extremes are compared for area-averages over grid cell and regional sized spatial domains. Results suggest a bias favouring overly strong attribution estimates for hot and cold events over many regions of Africa and Australia, and a bias favouring overly weak attribution estimates over regions of North America and Asia. For rainfall, results are more sensitive to geographic location. Although the three models show similar results over many regions, they do disagree over others. Equally, results highlight the discrepancy amongst reanalyses products. This emphasises the importance of using multiple reanalysis and/or observation products, as well as multiple models in event attribution studies.},
doi = {10.1016/j.wace.2016.07.001},
journal = {Weather and Climate Extremes},
number = C,
volume = 13,
place = {Netherlands},
year = {2016},
month = {9}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: 10.1016/j.wace.2016.07.001

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Works referencing / citing this record:

Evaluation of the HadGEM3-A simulations in view of detection and attribution of human influence on extreme events in Europe
journal, April 2018

  • Vautard, Robert; Christidis, Nikolaos; Ciavarella, Andrew
  • Climate Dynamics, Vol. 52, Issue 1-2
  • DOI: 10.1007/s00382-018-4183-6

Spatially Dependent Multiple Testing Under Model Misspecification, With Application to Detection of Anthropogenic Influence on Extreme Climate Events
journal, June 2018

  • Risser, Mark D.; Paciorek, Christopher J.; Stone, Dáithí A.
  • Journal of the American Statistical Association, Vol. 114, Issue 525
  • DOI: 10.1080/01621459.2018.1451335

Evaluation of the HadGEM3-A simulations in view of detection and attribution of human influence on extreme events in Europe
journal, April 2018

  • Vautard, Robert; Christidis, Nikolaos; Ciavarella, Andrew
  • Climate Dynamics, Vol. 52, Issue 1-2
  • DOI: 10.1007/s00382-018-4183-6

Spatially Dependent Multiple Testing Under Model Misspecification, With Application to Detection of Anthropogenic Influence on Extreme Climate Events
journal, June 2018

  • Risser, Mark D.; Paciorek, Christopher J.; Stone, Dáithí A.
  • Journal of the American Statistical Association, Vol. 114, Issue 525
  • DOI: 10.1080/01621459.2018.1451335