Understanding the role of sea surface temperature-forcing for variability in global temperature and precipitation extremes

• Published in: Weather and Climate Extremes
• Main Authors: Dittus, AJ, Karoly, DJ, Donat, MG, Lewis, SC, Alexander, LV
• Format: Article in Journal/Newspaper
• Language: English
• Published: ELSEVIER SCIENCE BV 2018
• Subjects: Sea ice
• Online Access: http://hdl.handle.net/11343/219957; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000444526600001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=d4d813f4571fa7d6246bdc0dfeca3a1c; https://doi.org/10.1016/j.wace.2018.06.002

The oceans are a well-known source of natural variability in the climate system, although their ability to account for inter-annual variations of temperature and precipitation extremes over land remains unclear. In this study, the role of sea-surface temperature (SST)-forcing is investigated for variability and trends in a range of commonly used temperature and precipitation extreme indices over the period 1959 to 2013. Using atmospheric simulations forced by observed SST and sea-ice concentrations (SIC) from three models participating in the Climate of the Twentieth Century Plus (C20C+) Project, results show that oceanic boundary conditions drive a substantial fraction of inter-annual variability in global average temperature extreme indices, as well as, to a lower extent, for precipitation extremes. The observed trends in temperature extremes are generally well captured by the SST-forced simulations although some regional features such as the lack of warming in daytime warm temperature extremes over South America are not reproduced in the model simulations. Furthermore, the models simulate too strong increases in warm day frequency compared to observations over North America. For extreme precipitation trends, the accuracy of the simulated trend pattern is regionally variable, and a thorough assessment is difficult due to the lack of locally significant trends in the observations. This study shows that prescribing SST and SIC holds potential predictability for extremes in some (mainly tropical) regions at the inter-annual time-scale.