Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations
Abstract To increase our capability to predict Arctic sea ice and climate, we have developed a coupled atmosphere‐sea ice‐ocean model configured for the pan‐Arctic with sufficient flexibility. The Los Alamos Sea Ice Model is coupled with the Weather Research and Forecasting Model and the Regional Oc...
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ftdoajarticles:oai:doaj.org/article:2f65f2d1901e4a1ca10c5ccd39cf3a25 2023-05-15T14:46:09+02:00 Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations Chao‐Yuan Yang Jiping Liu Shiming Xu 2020-05-01T00:00:00Z https://doi.org/10.1029/2019MS001938 https://doaj.org/article/2f65f2d1901e4a1ca10c5ccd39cf3a25 EN eng American Geophysical Union (AGU) https://doi.org/10.1029/2019MS001938 https://doaj.org/toc/1942-2466 1942-2466 doi:10.1029/2019MS001938 https://doaj.org/article/2f65f2d1901e4a1ca10c5ccd39cf3a25 Journal of Advances in Modeling Earth Systems, Vol 12, Iss 5, Pp n/a-n/a (2020) fully coupled model sea ice data assimilation seasonal predictions Physical geography GB3-5030 Oceanography GC1-1581 article 2020 ftdoajarticles https://doi.org/10.1029/2019MS001938 2022-12-31T05:40:09Z Abstract To increase our capability to predict Arctic sea ice and climate, we have developed a coupled atmosphere‐sea ice‐ocean model configured for the pan‐Arctic with sufficient flexibility. The Los Alamos Sea Ice Model is coupled with the Weather Research and Forecasting Model and the Regional Ocean Modeling System in the Coupled Ocean‐Atmosphere‐Wave‐Sediment Transport modeling system. It is well known that dynamic models used to predict Arctic sea ice at short‐term periods strongly depend on model initial conditions. Parallel Data Assimilation Framework is implemented into the new modeling system to assimilate sea ice observations and generate skillful model initialization, which aid in the prediction procedures. The Special Sensor Microwave Imager/Sounder sea ice concentration, the CyroSat‐2, and Soil Moisture and Ocean Salinity sea ice thickness are assimilated with the localized error subspace transform ensemble Kalman filter. We conduct Arctic sea ice prediction for the melting seasons of 2017 and 2018. Predictions with improved initial sea ice conditions show reasonable sea ice evolution and small biases in the minimum sea ice extent, although the ice refreezing is delayed. Our prediction experiments suggest that the use of appropriate uncertainty for the observed sea ice thickness can lead to improved spatial distribution of the initial ice thickness and thus the predicted sea ice distribution. Our new modeling system initialized by the output of the National Centers for Environmental Prediction Climate Forecast System seasonal forecasts with data assimilation can significantly increase the sea ice prediction skills in sea ice extent for the entire Arctic as well as in the Northern Sea Route compared with the predictions by the National Centers for Environmental Prediction Climate Forecast System. Article in Journal/Newspaper Arctic Northern Sea Route Sea ice Directory of Open Access Journals: DOAJ Articles Arctic Journal of Advances in Modeling Earth Systems 12 5 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
fully coupled model sea ice data assimilation seasonal predictions Physical geography GB3-5030 Oceanography GC1-1581 |
spellingShingle |
fully coupled model sea ice data assimilation seasonal predictions Physical geography GB3-5030 Oceanography GC1-1581 Chao‐Yuan Yang Jiping Liu Shiming Xu Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations |
topic_facet |
fully coupled model sea ice data assimilation seasonal predictions Physical geography GB3-5030 Oceanography GC1-1581 |
description |
Abstract To increase our capability to predict Arctic sea ice and climate, we have developed a coupled atmosphere‐sea ice‐ocean model configured for the pan‐Arctic with sufficient flexibility. The Los Alamos Sea Ice Model is coupled with the Weather Research and Forecasting Model and the Regional Ocean Modeling System in the Coupled Ocean‐Atmosphere‐Wave‐Sediment Transport modeling system. It is well known that dynamic models used to predict Arctic sea ice at short‐term periods strongly depend on model initial conditions. Parallel Data Assimilation Framework is implemented into the new modeling system to assimilate sea ice observations and generate skillful model initialization, which aid in the prediction procedures. The Special Sensor Microwave Imager/Sounder sea ice concentration, the CyroSat‐2, and Soil Moisture and Ocean Salinity sea ice thickness are assimilated with the localized error subspace transform ensemble Kalman filter. We conduct Arctic sea ice prediction for the melting seasons of 2017 and 2018. Predictions with improved initial sea ice conditions show reasonable sea ice evolution and small biases in the minimum sea ice extent, although the ice refreezing is delayed. Our prediction experiments suggest that the use of appropriate uncertainty for the observed sea ice thickness can lead to improved spatial distribution of the initial ice thickness and thus the predicted sea ice distribution. Our new modeling system initialized by the output of the National Centers for Environmental Prediction Climate Forecast System seasonal forecasts with data assimilation can significantly increase the sea ice prediction skills in sea ice extent for the entire Arctic as well as in the Northern Sea Route compared with the predictions by the National Centers for Environmental Prediction Climate Forecast System. |
format |
Article in Journal/Newspaper |
author |
Chao‐Yuan Yang Jiping Liu Shiming Xu |
author_facet |
Chao‐Yuan Yang Jiping Liu Shiming Xu |
author_sort |
Chao‐Yuan Yang |
title |
Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations |
title_short |
Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations |
title_full |
Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations |
title_fullStr |
Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations |
title_full_unstemmed |
Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations |
title_sort |
seasonal arctic sea ice prediction using a newly developed fully coupled regional model with the assimilation of satellite sea ice observations |
publisher |
American Geophysical Union (AGU) |
publishDate |
2020 |
url |
https://doi.org/10.1029/2019MS001938 https://doaj.org/article/2f65f2d1901e4a1ca10c5ccd39cf3a25 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Northern Sea Route Sea ice |
genre_facet |
Arctic Northern Sea Route Sea ice |
op_source |
Journal of Advances in Modeling Earth Systems, Vol 12, Iss 5, Pp n/a-n/a (2020) |
op_relation |
https://doi.org/10.1029/2019MS001938 https://doaj.org/toc/1942-2466 1942-2466 doi:10.1029/2019MS001938 https://doaj.org/article/2f65f2d1901e4a1ca10c5ccd39cf3a25 |
op_doi |
https://doi.org/10.1029/2019MS001938 |
container_title |
Journal of Advances in Modeling Earth Systems |
container_volume |
12 |
container_issue |
5 |
_version_ |
1766317406424137728 |