Key regions in the modulation of seasonal GMST variability by analyzing the two hottest years: 2016 vs. 2020
Following the end of the decadal-warming-hiatus in 2016, the global mean surface temperature (GMST) abruptly showed a 3 yr warming slowdown and peaked again in 2020, overturning the conventional concept that highest GMST occurs with strong El Niño. The high GMST in 2016 was controlled by secular tre...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2022-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/ac8dab |
| _version_ | 1797747289519816704 |
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| author | Ke-Xin Li Fei Zheng De-Yang Luo Cheng Sun Jiang Zhu |
| author_facet | Ke-Xin Li Fei Zheng De-Yang Luo Cheng Sun Jiang Zhu |
| author_sort | Ke-Xin Li |
| collection | DOAJ |
| description | Following the end of the decadal-warming-hiatus in 2016, the global mean surface temperature (GMST) abruptly showed a 3 yr warming slowdown and peaked again in 2020, overturning the conventional concept that highest GMST occurs with strong El Niño. The high GMST in 2016 was controlled by secular trend and annual variability (ANV). However, the dominator of the sharp GMST rise in 2020 was SCT alone because the ANVs in different seasons canceled each other out in 2020, contributing little to the annual mean GMST. By analyzing the two hottest years, 2016 and 2020, we identified that seasonally varying ANVs are mainly located in Eurasia, North America, the Arctic Ocean, and the tropical eastern Pacific Ocean. Dominance by surface temperatures over the four crucial regions on the subseasonal-to-seasonal (S2S) GMST variations was also observed in 73% of the years during 1982–2021, indicating a potential opportunity to improve the S2S GMST forecast. |
| first_indexed | 2024-03-12T15:49:35Z |
| format | Article |
| id | doaj.art-1bdd1ea6396c4e248ca6d1449814016d |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T15:49:35Z |
| publishDate | 2022-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-1bdd1ea6396c4e248ca6d1449814016d2023-08-09T15:16:34ZengIOP PublishingEnvironmental Research Letters1748-93262022-01-0117909403410.1088/1748-9326/ac8dabKey regions in the modulation of seasonal GMST variability by analyzing the two hottest years: 2016 vs. 2020Ke-Xin Li0https://orcid.org/0000-0002-9123-1394Fei Zheng1https://orcid.org/0000-0002-6897-1626De-Yang Luo2Cheng Sun3https://orcid.org/0000-0003-0474-7593Jiang Zhu4https://orcid.org/0000-0001-9846-8944International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences , Beijing 100029, People’s Republic of China; University of Chinese Academy of Sciences , Beijing 100049, People’s Republic of ChinaInternational Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences , Beijing 100029, People’s Republic of ChinaInternational Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences , Beijing 100029, People’s Republic of China; Chengdu University of Information Technology , Chengdu 610225, People’s Republic of ChinaCollege of Global Change and Earth System Science (GCESS), Beijing Normal University , Beijing 100875, People’s Republic of ChinaInternational Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences , Beijing 100029, People’s Republic of China; University of Chinese Academy of Sciences , Beijing 100049, People’s Republic of ChinaFollowing the end of the decadal-warming-hiatus in 2016, the global mean surface temperature (GMST) abruptly showed a 3 yr warming slowdown and peaked again in 2020, overturning the conventional concept that highest GMST occurs with strong El Niño. The high GMST in 2016 was controlled by secular trend and annual variability (ANV). However, the dominator of the sharp GMST rise in 2020 was SCT alone because the ANVs in different seasons canceled each other out in 2020, contributing little to the annual mean GMST. By analyzing the two hottest years, 2016 and 2020, we identified that seasonally varying ANVs are mainly located in Eurasia, North America, the Arctic Ocean, and the tropical eastern Pacific Ocean. Dominance by surface temperatures over the four crucial regions on the subseasonal-to-seasonal (S2S) GMST variations was also observed in 73% of the years during 1982–2021, indicating a potential opportunity to improve the S2S GMST forecast.https://doi.org/10.1088/1748-9326/ac8dabsurface temperaturesubseasonal-to-seasonal forecastGMST modulatorglobal warming |
| spellingShingle | Ke-Xin Li Fei Zheng De-Yang Luo Cheng Sun Jiang Zhu Key regions in the modulation of seasonal GMST variability by analyzing the two hottest years: 2016 vs. 2020 Environmental Research Letters surface temperature subseasonal-to-seasonal forecast GMST modulator global warming |
| title | Key regions in the modulation of seasonal GMST variability by analyzing the two hottest years: 2016 vs. 2020 |
| title_full | Key regions in the modulation of seasonal GMST variability by analyzing the two hottest years: 2016 vs. 2020 |
| title_fullStr | Key regions in the modulation of seasonal GMST variability by analyzing the two hottest years: 2016 vs. 2020 |
| title_full_unstemmed | Key regions in the modulation of seasonal GMST variability by analyzing the two hottest years: 2016 vs. 2020 |
| title_short | Key regions in the modulation of seasonal GMST variability by analyzing the two hottest years: 2016 vs. 2020 |
| title_sort | key regions in the modulation of seasonal gmst variability by analyzing the two hottest years 2016 vs 2020 |
| topic | surface temperature subseasonal-to-seasonal forecast GMST modulator global warming |
| url | https://doi.org/10.1088/1748-9326/ac8dab |
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