Improving the seasonal forecast by utilizing the observed relationship between the Arctic oscillation and Northern Hemisphere surface air temperature
Abstract Although the seasonal prediction skill of climate models has improved significantly in recent decades, the prediction skill of the Arctic Oscillation (AO), the dominant climate mode over the Northern Hemisphere, remains poor. Additionally, the local representation of AO impacts has diverged...
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Online Access: | http://dx.doi.org/10.1088/1748-9326/ad545b https://iopscience.iop.org/article/10.1088/1748-9326/ad545b https://iopscience.iop.org/article/10.1088/1748-9326/ad545b/pdf |
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crioppubl:10.1088/1748-9326/ad545b 2024-06-23T07:50:02+00:00 Improving the seasonal forecast by utilizing the observed relationship between the Arctic oscillation and Northern Hemisphere surface air temperature Sim, Ji-Han Kwon, Minho Jang, Yeon-Soo Kim, Ha-Rim Kim, Ju Heon Yang, Gun-Hwan Jeong, Jee-Hoon Kim, Baek-Min Korea ministry of Environment Korea government 2024 http://dx.doi.org/10.1088/1748-9326/ad545b https://iopscience.iop.org/article/10.1088/1748-9326/ad545b https://iopscience.iop.org/article/10.1088/1748-9326/ad545b/pdf unknown IOP Publishing https://creativecommons.org/licenses/by/4.0/ https://iopscience.iop.org/info/page/text-and-data-mining Environmental Research Letters ISSN 1748-9326 journal-article 2024 crioppubl https://doi.org/10.1088/1748-9326/ad545b 2024-06-10T04:11:12Z Abstract Although the seasonal prediction skill of climate models has improved significantly in recent decades, the prediction skill of the Arctic Oscillation (AO), the dominant climate mode over the Northern Hemisphere, remains poor. Additionally, the local representation of AO impacts has diverged from observations, which limits seasonal prediction skill of climate models. In this study, we attempted to improve prediction skill of surface air temperature (SAT) with two post-processing on dynamical model’s seasonal forecast: 1) correction of the AO impact on SAT pattern, and 2) correction of AO index (AOI). The first correction involved replacing the inaccurately simulated impact of AO on SAT with that observed. For the second correction, we employed a empirical prediction model of AOI based on multiple linear regression model based on three precursors: summer sea surface temperature, autumn sea-ice concentration, and autumn snow cover extent. The application of the first correction led to a decrease in prediction skills. However, a significant improvement in SAT prediction skills is achieved when both corrections are applied. The average correlation coefficients for the North America and Eurasian regions increased from 0.23 and 0.06 to 0.28 and 0.30, respectively. Article in Journal/Newspaper Arctic Sea ice IOP Publishing Arctic Environmental Research Letters 19 7 074039 |
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Abstract Although the seasonal prediction skill of climate models has improved significantly in recent decades, the prediction skill of the Arctic Oscillation (AO), the dominant climate mode over the Northern Hemisphere, remains poor. Additionally, the local representation of AO impacts has diverged from observations, which limits seasonal prediction skill of climate models. In this study, we attempted to improve prediction skill of surface air temperature (SAT) with two post-processing on dynamical model’s seasonal forecast: 1) correction of the AO impact on SAT pattern, and 2) correction of AO index (AOI). The first correction involved replacing the inaccurately simulated impact of AO on SAT with that observed. For the second correction, we employed a empirical prediction model of AOI based on multiple linear regression model based on three precursors: summer sea surface temperature, autumn sea-ice concentration, and autumn snow cover extent. The application of the first correction led to a decrease in prediction skills. However, a significant improvement in SAT prediction skills is achieved when both corrections are applied. The average correlation coefficients for the North America and Eurasian regions increased from 0.23 and 0.06 to 0.28 and 0.30, respectively. |
author2 |
Korea ministry of Environment Korea government |
format |
Article in Journal/Newspaper |
author |
Sim, Ji-Han Kwon, Minho Jang, Yeon-Soo Kim, Ha-Rim Kim, Ju Heon Yang, Gun-Hwan Jeong, Jee-Hoon Kim, Baek-Min |
spellingShingle |
Sim, Ji-Han Kwon, Minho Jang, Yeon-Soo Kim, Ha-Rim Kim, Ju Heon Yang, Gun-Hwan Jeong, Jee-Hoon Kim, Baek-Min Improving the seasonal forecast by utilizing the observed relationship between the Arctic oscillation and Northern Hemisphere surface air temperature |
author_facet |
Sim, Ji-Han Kwon, Minho Jang, Yeon-Soo Kim, Ha-Rim Kim, Ju Heon Yang, Gun-Hwan Jeong, Jee-Hoon Kim, Baek-Min |
author_sort |
Sim, Ji-Han |
title |
Improving the seasonal forecast by utilizing the observed relationship between the Arctic oscillation and Northern Hemisphere surface air temperature |
title_short |
Improving the seasonal forecast by utilizing the observed relationship between the Arctic oscillation and Northern Hemisphere surface air temperature |
title_full |
Improving the seasonal forecast by utilizing the observed relationship between the Arctic oscillation and Northern Hemisphere surface air temperature |
title_fullStr |
Improving the seasonal forecast by utilizing the observed relationship between the Arctic oscillation and Northern Hemisphere surface air temperature |
title_full_unstemmed |
Improving the seasonal forecast by utilizing the observed relationship between the Arctic oscillation and Northern Hemisphere surface air temperature |
title_sort |
improving the seasonal forecast by utilizing the observed relationship between the arctic oscillation and northern hemisphere surface air temperature |
publisher |
IOP Publishing |
publishDate |
2024 |
url |
http://dx.doi.org/10.1088/1748-9326/ad545b https://iopscience.iop.org/article/10.1088/1748-9326/ad545b https://iopscience.iop.org/article/10.1088/1748-9326/ad545b/pdf |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
Environmental Research Letters ISSN 1748-9326 |
op_rights |
https://creativecommons.org/licenses/by/4.0/ https://iopscience.iop.org/info/page/text-and-data-mining |
op_doi |
https://doi.org/10.1088/1748-9326/ad545b |
container_title |
Environmental Research Letters |
container_volume |
19 |
container_issue |
7 |
container_start_page |
074039 |
_version_ |
1802640792066457600 |