Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model
Abstract This paper describes and evaluates the assimilation component of a seamless sea ice prediction system, which is developed based on the fully coupled Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research Climate Model (AWI‐CM, v1.1). Its ocean/ice component with unstructur...
Published in: | Journal of Advances in Modeling Earth Systems |
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ftdoajarticles:oai:doaj.org/article:503cd22044f04378b25a4c8fc6596b4c 2023-11-12T04:00:27+01:00 Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model Longjiang Mu Lars Nerger Qi Tang Svetlana N. Loza Dmitry Sidorenko Qiang Wang Tido Semmler Lorenzo Zampieri Martin Losch Helge F. Goessling 2020-04-01T00:00:00Z https://doi.org/10.1029/2019MS001937 https://doaj.org/article/503cd22044f04378b25a4c8fc6596b4c EN eng American Geophysical Union (AGU) https://doi.org/10.1029/2019MS001937 https://doaj.org/toc/1942-2466 1942-2466 doi:10.1029/2019MS001937 https://doaj.org/article/503cd22044f04378b25a4c8fc6596b4c Journal of Advances in Modeling Earth Systems, Vol 12, Iss 4, Pp n/a-n/a (2020) Physical geography GB3-5030 Oceanography GC1-1581 article 2020 ftdoajarticles https://doi.org/10.1029/2019MS001937 2023-10-15T00:38:01Z Abstract This paper describes and evaluates the assimilation component of a seamless sea ice prediction system, which is developed based on the fully coupled Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research Climate Model (AWI‐CM, v1.1). Its ocean/ice component with unstructured‐mesh discretization and smoothly varying spatial resolution enables seamless sea ice prediction across a wide range of space and time scales. The model is complemented with the Parallel Data Assimilation Framework to assimilate observations in the ocean/ice component with an Ensemble Kalman Filter. The focus here is on the data assimilation of the prediction system. First, the performance of the system is tested in a perfect‐model setting with synthetic observations. The system exhibits no drift for multivariate assimilation, which is a prerequisite for the robustness of the system. Second, real observational data for sea ice concentration, thickness, drift, and sea surface temperature are assimilated. The analysis results are evaluated against independent in situ observations and reanalysis data. Further experiments that assimilate different combinations of variables are conducted to understand their individual impacts on the model state. In particular, assimilating sea ice drift improves the sea ice thickness estimate, and assimilating sea surface temperature is able to avert a circulation bias of the free‐running model in the Arctic Ocean at middepth. Finally, we present preliminary results obtained with an extended system where the atmosphere is constrained by nudging toward reanalysis data, revealing challenges that still need to be overcome to adapt the ocean/ice assimilation. We consider this system a prototype on the way toward strongly coupled data assimilation across all model components. Article in Journal/Newspaper Alfred Wegener Institute Arctic Arctic Ocean Sea ice Directory of Open Access Journals: DOAJ Articles Arctic Arctic Ocean Journal of Advances in Modeling Earth Systems 12 4 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Physical geography GB3-5030 Oceanography GC1-1581 |
spellingShingle |
Physical geography GB3-5030 Oceanography GC1-1581 Longjiang Mu Lars Nerger Qi Tang Svetlana N. Loza Dmitry Sidorenko Qiang Wang Tido Semmler Lorenzo Zampieri Martin Losch Helge F. Goessling Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model |
topic_facet |
Physical geography GB3-5030 Oceanography GC1-1581 |
description |
Abstract This paper describes and evaluates the assimilation component of a seamless sea ice prediction system, which is developed based on the fully coupled Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research Climate Model (AWI‐CM, v1.1). Its ocean/ice component with unstructured‐mesh discretization and smoothly varying spatial resolution enables seamless sea ice prediction across a wide range of space and time scales. The model is complemented with the Parallel Data Assimilation Framework to assimilate observations in the ocean/ice component with an Ensemble Kalman Filter. The focus here is on the data assimilation of the prediction system. First, the performance of the system is tested in a perfect‐model setting with synthetic observations. The system exhibits no drift for multivariate assimilation, which is a prerequisite for the robustness of the system. Second, real observational data for sea ice concentration, thickness, drift, and sea surface temperature are assimilated. The analysis results are evaluated against independent in situ observations and reanalysis data. Further experiments that assimilate different combinations of variables are conducted to understand their individual impacts on the model state. In particular, assimilating sea ice drift improves the sea ice thickness estimate, and assimilating sea surface temperature is able to avert a circulation bias of the free‐running model in the Arctic Ocean at middepth. Finally, we present preliminary results obtained with an extended system where the atmosphere is constrained by nudging toward reanalysis data, revealing challenges that still need to be overcome to adapt the ocean/ice assimilation. We consider this system a prototype on the way toward strongly coupled data assimilation across all model components. |
format |
Article in Journal/Newspaper |
author |
Longjiang Mu Lars Nerger Qi Tang Svetlana N. Loza Dmitry Sidorenko Qiang Wang Tido Semmler Lorenzo Zampieri Martin Losch Helge F. Goessling |
author_facet |
Longjiang Mu Lars Nerger Qi Tang Svetlana N. Loza Dmitry Sidorenko Qiang Wang Tido Semmler Lorenzo Zampieri Martin Losch Helge F. Goessling |
author_sort |
Longjiang Mu |
title |
Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model |
title_short |
Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model |
title_full |
Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model |
title_fullStr |
Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model |
title_full_unstemmed |
Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model |
title_sort |
toward a data assimilation system for seamless sea ice prediction based on the awi climate model |
publisher |
American Geophysical Union (AGU) |
publishDate |
2020 |
url |
https://doi.org/10.1029/2019MS001937 https://doaj.org/article/503cd22044f04378b25a4c8fc6596b4c |
geographic |
Arctic Arctic Ocean |
geographic_facet |
Arctic Arctic Ocean |
genre |
Alfred Wegener Institute Arctic Arctic Ocean Sea ice |
genre_facet |
Alfred Wegener Institute Arctic Arctic Ocean Sea ice |
op_source |
Journal of Advances in Modeling Earth Systems, Vol 12, Iss 4, Pp n/a-n/a (2020) |
op_relation |
https://doi.org/10.1029/2019MS001937 https://doaj.org/toc/1942-2466 1942-2466 doi:10.1029/2019MS001937 https://doaj.org/article/503cd22044f04378b25a4c8fc6596b4c |
op_doi |
https://doi.org/10.1029/2019MS001937 |
container_title |
Journal of Advances in Modeling Earth Systems |
container_volume |
12 |
container_issue |
4 |
_version_ |
1782327905786789888 |