Quantitative attribution of 2016 extreme arctic warmth: comparison between late winter and early spring
A deep, large-scale warmth occurred in the Arctic from January to April 2016, but the roles of various physical processes in this period have not been quantified. Here, we utilize an updated version of the coupled atmosphere‒surface climate feedback response analysis method to quantitatively attribu...
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| Format: | Article |
| Language: | English |
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IOP Publishing
2025-01-01
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| Series: | Environmental Research Letters |
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| Online Access: | https://doi.org/10.1088/1748-9326/adaed4 |
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| author | Junjie Zhu Ke Fan Shengping He Tuantuan Zhang Yi Deng Song Yang Deliang Chen Kaiqiang Deng Wei Yu Baoqiang Tian Hoffman H N Cheung |
| author_facet | Junjie Zhu Ke Fan Shengping He Tuantuan Zhang Yi Deng Song Yang Deliang Chen Kaiqiang Deng Wei Yu Baoqiang Tian Hoffman H N Cheung |
| author_sort | Junjie Zhu |
| collection | DOAJ |
| description | A deep, large-scale warmth occurred in the Arctic from January to April 2016, but the roles of various physical processes in this period have not been quantified. Here, we utilize an updated version of the coupled atmosphere‒surface climate feedback response analysis method to quantitatively attribute the extreme warmth. Our results show distinct characteristics associated with the warm anomaly in January‒February and March‒April. This extreme Arctic warmth is largely explained by the positive contributions of atmospheric dynamics, which are dominated by horizontal advection in January‒February and by adiabatic heating and vertical terms in March‒April. Compared with January‒February, an increase in solar radiation leads to an enhanced positive contribution from surface albedo processes in March‒April. Water vapor processes provide considerable positive contribution during both periods. In contrast, surface dynamic processes provide positive contribution in January‒February but negative contribution in March‒April, while cloud processes provide nearly negative contribution during both periods, primarily through their longwave effects. |
| format | Article |
| id | doaj-art-ff55aba0bfff4bdd860c0c75e733a346 |
| institution | Kabale University |
| issn | 1748-9326 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj-art-ff55aba0bfff4bdd860c0c75e733a3462025-02-07T10:10:20ZengIOP PublishingEnvironmental Research Letters1748-93262025-01-0120202406410.1088/1748-9326/adaed4Quantitative attribution of 2016 extreme arctic warmth: comparison between late winter and early springJunjie Zhu0https://orcid.org/0009-0006-2590-6599Ke Fan1https://orcid.org/0000-0002-0776-4017Shengping He2https://orcid.org/0000-0003-4245-357XTuantuan Zhang3https://orcid.org/0000-0002-1635-0625Yi Deng4Song Yang5https://orcid.org/0000-0003-1840-8429Deliang Chen6https://orcid.org/0000-0003-0288-5618Kaiqiang Deng7Wei Yu8Baoqiang Tian9https://orcid.org/0000-0001-6975-0756Hoffman H N Cheung10School of Atmospheric Sciences, Sun Yat-sen University; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) , Zhuhai, People’s Republic of China; Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University , Zhuhai, Guangdong, People’s Republic of ChinaSchool of Atmospheric Sciences, Sun Yat-sen University; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) , Zhuhai, People’s Republic of China; Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University , Zhuhai, Guangdong, People’s Republic of ChinaGeophysical Institute, University of Bergen and Bjerknes Centre for Climate Research , 5007 Bergen, NorwaySchool of Atmospheric Sciences, Sun Yat-sen University; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) , Zhuhai, People’s Republic of China; Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University , Zhuhai, Guangdong, People’s Republic of ChinaSchool of Earth and Atmospheric Sciences, Georgia Institute of Technology , Atlanta, GA 30319, United States of AmericaSchool of Atmospheric Sciences, Sun Yat-sen University; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) , Zhuhai, People’s Republic of China; Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University , Zhuhai, Guangdong, People’s Republic of ChinaDepartment of Earth Sciences, University of Gothenburg , Gothenburg, Sweden; Department of Earth System Science, Tsinghua University , Beijing 100084, People’s Republic of ChinaSchool of Atmospheric Sciences, Sun Yat-sen University; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) , Zhuhai, People’s Republic of China; Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University , Zhuhai, Guangdong, People’s Republic of ChinaSchool of Atmospheric Sciences, Sun Yat-sen University; Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) , Zhuhai, People’s Republic of China; Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, Sun Yat-sen University , Zhuhai, Guangdong, People’s Republic of ChinaNansen-Zhu International Research Centre, Institute of Atmospheric Physics , Chinese Academy of Sciences, Beijing, People’s Republic of ChinaERM-Hong Kong , Limited, Hong Kong, People’s Republic of ChinaA deep, large-scale warmth occurred in the Arctic from January to April 2016, but the roles of various physical processes in this period have not been quantified. Here, we utilize an updated version of the coupled atmosphere‒surface climate feedback response analysis method to quantitatively attribute the extreme warmth. Our results show distinct characteristics associated with the warm anomaly in January‒February and March‒April. This extreme Arctic warmth is largely explained by the positive contributions of atmospheric dynamics, which are dominated by horizontal advection in January‒February and by adiabatic heating and vertical terms in March‒April. Compared with January‒February, an increase in solar radiation leads to an enhanced positive contribution from surface albedo processes in March‒April. Water vapor processes provide considerable positive contribution during both periods. In contrast, surface dynamic processes provide positive contribution in January‒February but negative contribution in March‒April, while cloud processes provide nearly negative contribution during both periods, primarily through their longwave effects.https://doi.org/10.1088/1748-9326/adaed4extreme warmtharcticquantitative attribution |
| spellingShingle | Junjie Zhu Ke Fan Shengping He Tuantuan Zhang Yi Deng Song Yang Deliang Chen Kaiqiang Deng Wei Yu Baoqiang Tian Hoffman H N Cheung Quantitative attribution of 2016 extreme arctic warmth: comparison between late winter and early spring Environmental Research Letters extreme warmth arctic quantitative attribution |
| title | Quantitative attribution of 2016 extreme arctic warmth: comparison between late winter and early spring |
| title_full | Quantitative attribution of 2016 extreme arctic warmth: comparison between late winter and early spring |
| title_fullStr | Quantitative attribution of 2016 extreme arctic warmth: comparison between late winter and early spring |
| title_full_unstemmed | Quantitative attribution of 2016 extreme arctic warmth: comparison between late winter and early spring |
| title_short | Quantitative attribution of 2016 extreme arctic warmth: comparison between late winter and early spring |
| title_sort | quantitative attribution of 2016 extreme arctic warmth comparison between late winter and early spring |
| topic | extreme warmth arctic quantitative attribution |
| url | https://doi.org/10.1088/1748-9326/adaed4 |
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