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Tracking Climate Change through the Spatiotemporal Dynamics of the Teletherms, the Statistically Hottest and Coldest Days of the Year.
Dodds, Peter Sheridan; Mitchell, Lewis; Reagan, Andrew J; Danforth, Christopher M.
Afiliação
  • Dodds PS; Department of Mathematics and Statistics, University of Vermont, Burlington, VT 05401, United States of America.
  • Mitchell L; Vermont Center for Complex Systems, University of Vermont, Burlington, VT 05401, United States of America.
  • Reagan AJ; Computational Story Lab, University of Vermont, Burlington, VT 05401, United States of America.
  • Danforth CM; Vermont Advanced Computing Core, University of Vermont, Burlington, VT 05401, United States of America.
PLoS One ; 11(5): e0154184, 2016.
Article em En | MEDLINE | ID: mdl-27167740
ABSTRACT
Instabilities and long term shifts in seasons, whether induced by natural drivers or human activities, pose great disruptive threats to ecological, agricultural, and social systems. Here, we propose, measure, and explore two fundamental markers of location-sensitive seasonal variations the Summer and Winter Teletherms-the on-average annual dates of the hottest and coldest days of the year. We analyse daily temperature extremes recorded at 1218 stations across the contiguous United States from 1853-2012, and observe large regional variation with the Summer Teletherm falling up to 90 days after the Summer Solstice, and 50 days for the Winter Teletherm after the Winter Solstice. We show that Teletherm temporal dynamics are substantive with clear and in some cases dramatic shifts reflective of system bifurcations. We also compare recorded daily temperature extremes with output from two regional climate models finding considerable though relatively unbiased error. Our work demonstrates that Teletherms are an intuitive, powerful, and statistically sound measure of local climate change, and that they pose detailed, stringent challenges for future theoretical and computational models.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Mudança Climática / Modelos Estatísticos / Temperatura Baixa / Temperatura Alta Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans País/Região como assunto: America do norte Idioma: En Revista: PLoS One Assunto da revista: CIENCIA / MEDICINA Ano de publicação: 2016 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Mudança Climática / Modelos Estatísticos / Temperatura Baixa / Temperatura Alta Tipo de estudo: Prognostic_studies / Risk_factors_studies Limite: Humans País/Região como assunto: America do norte Idioma: En Revista: PLoS One Assunto da revista: CIENCIA / MEDICINA Ano de publicação: 2016 Tipo de documento: Article País de afiliação: Estados Unidos
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