Details:

Journal Title:
Journal of Advances in Modeling Earth Systems
Personal Author:
Yang, Chao‐Yuan ; Liu, Jiping ; Xu, Shiming
Yang, Chao‐Yuan ; Liu, Jiping ; Xu, Shiming Less -
NOAA Program & Office:
OAR (Oceanic and Atmospheric Research)
Description:
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.
Keywords:
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Environmental Chemistry General Earth And Planetary Sciences Global And Planetary Change
Source:
Journal of Advances in Modeling Earth Systems, 12(5)
DOI:
https://doi.org/10.1029/2019ms001938
ISSN:
1942-2466;1942-2466;
Format:
PDF
Publisher:
American Geophysical Union (AGU)
Document Type:
Journal Article
Funding:
Grant no. NA15OAR4310163
License:
https://creativecommons.org/licenses/by/4.0/
Rights Information:
CC BY
Compliance:
Library
Main Document Checksum:
[+]
urn:sha256:2b64762ad2db63cb22a12a141c1df850e856af7ca2de36c0b6c03af6528d296d
Download URL:
https://repository.library.noaa.gov/view/noaa/58435/noaa_58435_DS1.pdf
File Type:

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