An initialized attribution method for extreme events on subseasonal to seasonal time scales

Guomin Wang, Pandora Hope, Eun Pa Lim, Harry H. Hendon, Julie M. Arblaster

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

When record-breaking climate and weather extremes occur, decision-makers and planners want to know whether they are random natural events with historical levels of reoccurrence or are reflective of an altered frequency or intensity as a result of climate change. This paper describes a method to attribute extreme weather and climate events to observed increases in atmospheric CO2 using an initialized subseasonal to seasonal coupled global climate prediction system. Application of this method provides quantitative estimates of the contribution arising from increases in the level of atmospheric CO2 to individual weather and climate extreme events. Using a coupled subseasonal to seasonal forecast system differs from other methods because it has the merit of being initialized with the observed conditions and subsequently reproducing the observed events and their mechanisms. This can aid understanding when the reforecasts with and without enhanced CO2 are compared and communicated to a general audience. Atmosphere–ocean interactions are accounted for. To illustrate the method, we attribute the record Australian heat event of October 2015. We find that about half of the October 2015 Australia-wide temperature anomaly is due to the increase in atmospheric CO2 since 1960. This method has the potential to provide attribution statements for forecast events within an outlook period (i.e., before they occur). This will allow for informed messaging to be available as required when an extreme event occurs, which is of particular use to weather and climate services.

Original languageEnglish
Pages (from-to)1453-1465
Number of pages13
JournalJournal of Climate
Volume34
Issue number4
DOIs
Publication statusPublished - 15 Feb 2021

Keywords

  • Anthropogenic effects
  • Climate models
  • Coupled models
  • Experimental design
  • General circulation models

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