Abstract

Marine heatwaves can have disastrous impacts on ecosystems and marine industries. Given their potential consequences, it is important to understand how broad-scale climate variability influences the probability of localised extreme events. Here, we employ an advanced data-mining methodology, archetype analysis, to identify large scale patterns and teleconnections that lead to marine extremes in certain regions. This methodology is applied to the Australasian region, where it identifies instances of anomalous sea-surface temperatures, frequently associated with marine heatwaves, as well as the broadscale oceanic and atmospheric conditions associated with those extreme events. Additionally, we use archetype analysis to assess the ability of a low-resolution climate model to accurately represent the teleconnection patterns associated with extreme climate variability, and discuss the implications for the predictability of these impactful events.

Here, the authors use an advanced data-mining method to show how “extreme modes” of large-scale climate variability, such as El Niño, can lead to devastating marine heatwaves.

Details

Title
A large-scale view of marine heatwaves revealed by archetype analysis
Author
Chapman, Christopher C. 1   VIAFID ORCID Logo  ; Monselesan, Didier P. 2   VIAFID ORCID Logo  ; Risbey, James S. 2   VIAFID ORCID Logo  ; Feng, Ming 3   VIAFID ORCID Logo  ; Sloyan, Bernadette M. 1   VIAFID ORCID Logo 

 CSIRO Oceans and Atmosphere, Hobart Marine Laboratories, Hobart, Australia (GRID:grid.492990.f) (ISNI:0000 0004 0402 7163); Center for Southern Hemisphere Ocean Research, Hobart Marine Laboratories, Hobart, Australia (GRID:grid.492990.f) 
 CSIRO Oceans and Atmosphere, Hobart Marine Laboratories, Hobart, Australia (GRID:grid.492990.f) (ISNI:0000 0004 0402 7163) 
 Center for Southern Hemisphere Ocean Research, Hobart Marine Laboratories, Hobart, Australia (GRID:grid.492990.f); CSIRO Oceans and Atmosphere, Indian Ocean Marine Research Center, Crawley, Australia (GRID:grid.492990.f) (ISNI:0000 0004 0402 7163) 
Pages
7843
Publication year
2022
Publication date
2022
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2756508371
Copyright
© Crown 2022. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.