Optimal Precursors Identification for North Atlantic Oscillation using CESM and CNOP Method
The North Atlantic Oscillation (NAO) is the most prominent atmospheric seesaw phenomenon in the North Hemisphere. It has a profound influence on the westerly wind strength and storm tracks in North Atlantic, which further affect the winter climate in Northern Hemisphere. Therefore, the identificatio...
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ftcopernicus:oai:publications.copernicus.org:npgd86425 2023-05-15T17:29:13+02:00 Optimal Precursors Identification for North Atlantic Oscillation using CESM and CNOP Method Mu, Bin Li, Jing Yuan, Shijin Luo, Xiaodan Dai, Guokun 2020-07-01 application/pdf https://doi.org/10.5194/npg-2020-27 https://npg.copernicus.org/preprints/npg-2020-27/ eng eng doi:10.5194/npg-2020-27 https://npg.copernicus.org/preprints/npg-2020-27/ eISSN: 1607-7946 Text 2020 ftcopernicus https://doi.org/10.5194/npg-2020-27 2020-07-20T16:22:03Z The North Atlantic Oscillation (NAO) is the most prominent atmospheric seesaw phenomenon in the North Hemisphere. It has a profound influence on the westerly wind strength and storm tracks in North Atlantic, which further affect the winter climate in Northern Hemisphere. Therefore, the identification for optimal precursor (OPR) of the NAO event is of research value and practical significance. In this paper, the conditional nonlinear optimal perturbation (CNOP) method, which has been widely used in research on the OPR of climatic event, is adopted to explore which kind of initial perturbation is most likely to trigger the NAO anomaly pattern in the Community Earth System Model (CESM). Since the adjoint model of CESM has yet to be developed, this kind of problem cannot be solved using traditional strategies based on gradient information provided by the adjoint model. We utilize an adjoint-free algorithm to solve CNOP in such a high dimensional numerical model, and OPRs of the NAO can be successfully identified. The results reveal that OPRs obtained by CNOP can cause the basic state to develop into typical dipole mode, and the nonlinear process plays an important role in the last stage of the prediction period. The algorithm adopted in this work can avoid falling into a local optimum and is accelerated with multiple parallel frameworks to enhance performance. The solution scheme can also be generalized to the OPR research of other climate events or other complex numerical models. Text North Atlantic North Atlantic oscillation Copernicus Publications: E-Journals |
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The North Atlantic Oscillation (NAO) is the most prominent atmospheric seesaw phenomenon in the North Hemisphere. It has a profound influence on the westerly wind strength and storm tracks in North Atlantic, which further affect the winter climate in Northern Hemisphere. Therefore, the identification for optimal precursor (OPR) of the NAO event is of research value and practical significance. In this paper, the conditional nonlinear optimal perturbation (CNOP) method, which has been widely used in research on the OPR of climatic event, is adopted to explore which kind of initial perturbation is most likely to trigger the NAO anomaly pattern in the Community Earth System Model (CESM). Since the adjoint model of CESM has yet to be developed, this kind of problem cannot be solved using traditional strategies based on gradient information provided by the adjoint model. We utilize an adjoint-free algorithm to solve CNOP in such a high dimensional numerical model, and OPRs of the NAO can be successfully identified. The results reveal that OPRs obtained by CNOP can cause the basic state to develop into typical dipole mode, and the nonlinear process plays an important role in the last stage of the prediction period. The algorithm adopted in this work can avoid falling into a local optimum and is accelerated with multiple parallel frameworks to enhance performance. The solution scheme can also be generalized to the OPR research of other climate events or other complex numerical models. |
format |
Text |
author |
Mu, Bin Li, Jing Yuan, Shijin Luo, Xiaodan Dai, Guokun |
spellingShingle |
Mu, Bin Li, Jing Yuan, Shijin Luo, Xiaodan Dai, Guokun Optimal Precursors Identification for North Atlantic Oscillation using CESM and CNOP Method |
author_facet |
Mu, Bin Li, Jing Yuan, Shijin Luo, Xiaodan Dai, Guokun |
author_sort |
Mu, Bin |
title |
Optimal Precursors Identification for North Atlantic Oscillation using CESM and CNOP Method |
title_short |
Optimal Precursors Identification for North Atlantic Oscillation using CESM and CNOP Method |
title_full |
Optimal Precursors Identification for North Atlantic Oscillation using CESM and CNOP Method |
title_fullStr |
Optimal Precursors Identification for North Atlantic Oscillation using CESM and CNOP Method |
title_full_unstemmed |
Optimal Precursors Identification for North Atlantic Oscillation using CESM and CNOP Method |
title_sort |
optimal precursors identification for north atlantic oscillation using cesm and cnop method |
publishDate |
2020 |
url |
https://doi.org/10.5194/npg-2020-27 https://npg.copernicus.org/preprints/npg-2020-27/ |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
eISSN: 1607-7946 |
op_relation |
doi:10.5194/npg-2020-27 https://npg.copernicus.org/preprints/npg-2020-27/ |
op_doi |
https://doi.org/10.5194/npg-2020-27 |
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1766122867903168512 |