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Interdisciplinary Workshop on 'Arctic Climate And Weather Extremes: Detection, Attribution, And Future Projection'

General

Project start
01.01.2019
Project end
31.12.2020
Type of project
ARMAP/NSF
Project theme
Education & Outreach
Project topic
Education & Outreach

Project details

24.10.2019
Science / project summary

The Aspen Global Change Institute (AGCI) will host a week-long interdisciplinary scientific workshop in Spring 2020 entitled "Arctic Climate and Weather Extremes: Detection, Attribution, and Future Projection". Participants will be tasked with the scientific objectives of a) providing improved insight into the fundamental processes of each climate system component involved in extreme events, b) synthesizing results to improve understanding of feedback processes across the climate system components, c) identifying existing key questions and knowledge gaps, and d) suggesting a way forward to improve our ability to predict and model extremes in the Arctic. To achieve these objectives, The PIs will employ AGCI's established workshop model to convene thirty data analysts, dynamicists, and modelers from the atmosphere, ocean, and sea ice communities. Participants will include early career scientists, and individuals from diverse and underrepresented backgrounds. The trajectory of Arctic climate system change has exhibited highly nonlinear behavior, as manifested by the increased frequency of occurrence of extreme events superimposed on a long-term trend towards a warmer mean state. The most recent, striking extreme events include occurrences of record minima of sea ice extent in the summers of 2007, 2012, and 2016, and record maxima of surface air temperatures in the winters of 2015/2016 and 2017/2018. However, Arctic climate change studies have predominantly focused on the long-term changes or trends using monthly, seasonal, or annual mean data. But extreme events generally occur intermittently for periods from days to several months as outliers of the long-term trends. Even for the extreme events that occur across longer time periods, monthly or seasonal mean data may not be able to resolve the underlying physics supporting their rapid development. It therefore still remains unclear why these extreme events occur, what their multi-scale driving mechanisms are, and where the source of their predictability exists. This workshop seeks to address these critical knowledge gaps.

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