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Observation-constrained projections reveal longer-than-expected dry spells.
Petrova, Irina Y; Miralles, Diego G; Brient, Florent; Donat, Markus G; Min, Seung-Ki; Kim, Yeon-Hee; Bador, Margot.
Afiliação
  • Petrova IY; H-CEL, Ghent University, Ghent, Belgium. irina.petrova@ugent.be.
  • Miralles DG; H-CEL, Ghent University, Ghent, Belgium. diego.miralles@ugent.be.
  • Brient F; LMD/IPSL, Sorbonne Université, Paris, France.
  • Donat MG; Barcelona Supercomputing Centre, Barcelona, Spain.
  • Min SK; Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain.
  • Kim YH; Division of Environmental Science and Engineering, Pohang University of Science and Technology, Pohang, South Korea.
  • Bador M; Institute for Convergence Research and Education in Advanced Technology, Yonsei University, Incheon, South Korea.
Nature ; 633(8030): 594-600, 2024 Sep.
Article em En | MEDLINE | ID: mdl-39294349
ABSTRACT
Climate models indicate that dry extremes will be exacerbated in many regions of the world1,2. However, confidence in the magnitude and timing of these projected changes remains low3,4, leaving societies largely unprepared5,6. Here we show that constraining model projections with observations using a newly proposed emergent constraint (EC) reduces the uncertainty in predictions of a core drought indicator, the longest annual dry spell (LAD), by 10-26% globally. Our EC-corrected projections reveal that the increase in LAD will be 42-44% greater, on average, than 'mid-range' or 'high-end' future forcing scenarios currently indicate. These results imply that by the end of this century, the global mean land-only LAD could be 10 days longer than currently expected. Using two generations of climate models, we further uncover global regions for which historical LAD biases affect the magnitude of projected LAD increases, and we explore the role of land-atmosphere feedbacks therein. Our findings reveal regions with potentially higher- and earlier-than-expected drought risks for societies and ecosystems, and they point to possible mechanisms underlying the biases in the current generation of climate models.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Secas / Modelos Climáticos Idioma: En Revista: Nature Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Bélgica País de publicação: Reino Unido

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Secas / Modelos Climáticos Idioma: En Revista: Nature Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Bélgica País de publicação: Reino Unido