Near-surface air temperature data, measured 2 meters above ground level ($T_{2m}$) are first obtained from the ERA5 reanalysis. The dataset provides hourly values at a horizontal resolution of 0.5\u00b0 in both latitude ($\\phi$) and longitude ($\\lambda$). As an initial step, daily mean temperatures are computed from the hourly data.<\/p>\n\n\n\n
For each geographical location and calendar day of the year, the 10th<\/sup> and 90th<\/sup> percentiles of the $T_{2m}$ distribution from 1991 to 2020 are calculated and denoted as $T_{2m}^{10}(\\phi,\\lambda,day)$ and $T_{2m}^{90}(\\phi,\\lambda,day)$, respectively. While cold extremes (or cold spells) are defined as when temperatures fall below the 10th<\/sup> percentile ($C(\\phi,\\lambda,day)=T_{2m}(\\phi,\\lambda,day)<T_{2m}^{10}(\\phi,\\lambda,day)$), warm extremes (or heat waves) are defined as when temperatures rise above the 90th<\/sup> percentile ($W(\\phi,\\lambda,day)=T_{2m}(\\phi,\\lambda,day)>T_{2m}^{90}(\\phi,\\lambda,day)$). By definition, cold and warm extremes occur 10% of the time.<\/p>\n\n\n\n Persistent extremes are identified when cold ($C(\\phi,\\lambda,day)$) or warm ($W(\\phi,\\lambda,day)$) extremes are present for any period of 3 consecutive days or longer.<\/p>\n\n\n\n Persistent extremes that are neighbors in the space and time dimensions are grouped together using the connected component labeling method. This yields geographically-coherent areas that are affected by the same extremes.<\/p>\n\n\n\n Once spatiotemporal extreme events<\/strong> are identified, several properties can be assessed<\/p>\n\n\n\n Data source Near-surface air temperature data, measured 2 meters above ground level ($T_{2m}$) are first obtained from the ERA5 reanalysis. The dataset provides hourly values at a horizontal resolution of 0.5\u00b0 in both latitude ($\\phi$) and longitude ($\\lambda$). As an initial step, daily mean temperatures are computed from the hourly data. Identifying extremes For each … Continue reading “Method”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-696","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/wexa.jamstec.go.jp\/home\/wp-json\/wp\/v2\/pages\/696","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wexa.jamstec.go.jp\/home\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/wexa.jamstec.go.jp\/home\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/wexa.jamstec.go.jp\/home\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/wexa.jamstec.go.jp\/home\/wp-json\/wp\/v2\/comments?post=696"}],"version-history":[{"count":0,"href":"https:\/\/wexa.jamstec.go.jp\/home\/wp-json\/wp\/v2\/pages\/696\/revisions"}],"wp:attachment":[{"href":"https:\/\/wexa.jamstec.go.jp\/home\/wp-json\/wp\/v2\/media?parent=696"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Identifying persistent extremes<\/h3>\n\n\n\n
Grouping persistent extremes into spatiotemporal extreme events<\/h3>\n\n\n\n
Statistics<\/h3>\n\n\n\n
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