Spatiotemporal Variation of Temperature Extremes over the Arctic Lands Based on In Situ and Reanalysis Data

Shoudong Zhao aState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China

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Minghu Ding aState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China

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Wenqian Zhang aState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China

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Ting Wei aState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China

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Wei Cheng bBeijing Institute of Applied Meteorology, Beijing, China

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Junming Chen aState Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing, China

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Cunde Xiao cState Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China

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Abstract

Changes in extreme temperatures have more effects on ecosystems and human society than changes in climate averages. As a hotspot of global warming, the Arctic has experienced unprecedented heatwaves recently, which highlights the importance of identifying long-term variations of extreme temperatures. However, spatial unbalance of observations and artificially chosen investigation periods limit our knowledge of extreme temperatures over the Arctic lands. Here, we build a complete and quality-controlled observation network on surface temperature over the Arctic lands and combine in situ and reanalysis data to evaluate changes of extreme temperatures during the period 1979–2020. Our results indicate that 1) the increase in extreme temperatures has accelerated since the 2000s, especially on the coast of Eurasia; 2) the change magnitude for cold events is larger than for warm events, in terms of intensity, frequency, and duration; and 3) increases in warm events only occur locally, for example, Alaska and central Siberia, while decreases in cold events occur throughout the Arctic lands. The long-term trends of extreme temperatures are synchronous with sea ice loss, and patterns of interannual variations are mainly related to the North Atlantic Oscillation. We suggest further efforts toward improvement over North America, especially for Greenland, through sufficient observations and regional models.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Minghu Ding, dingminghu@foxmail.com

Abstract

Changes in extreme temperatures have more effects on ecosystems and human society than changes in climate averages. As a hotspot of global warming, the Arctic has experienced unprecedented heatwaves recently, which highlights the importance of identifying long-term variations of extreme temperatures. However, spatial unbalance of observations and artificially chosen investigation periods limit our knowledge of extreme temperatures over the Arctic lands. Here, we build a complete and quality-controlled observation network on surface temperature over the Arctic lands and combine in situ and reanalysis data to evaluate changes of extreme temperatures during the period 1979–2020. Our results indicate that 1) the increase in extreme temperatures has accelerated since the 2000s, especially on the coast of Eurasia; 2) the change magnitude for cold events is larger than for warm events, in terms of intensity, frequency, and duration; and 3) increases in warm events only occur locally, for example, Alaska and central Siberia, while decreases in cold events occur throughout the Arctic lands. The long-term trends of extreme temperatures are synchronous with sea ice loss, and patterns of interannual variations are mainly related to the North Atlantic Oscillation. We suggest further efforts toward improvement over North America, especially for Greenland, through sufficient observations and regional models.

© 2023 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Minghu Ding, dingminghu@foxmail.com
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