Sea ice data assimilation in ORAS6
- 1European Centre for Medium-Range Weather Forecasts (Reading, United Kingdom. Bonn, Germany. Bologna, Italy)
Abstract
Accurate weather and climate forecasting relies heavily on the precise modeling of sea ice, a critical component of the Earth's climate system. Sea ice influences global weather patterns, ocean circulation, and the exchange of heat and moisture between the atmosphere and oceans. Initialisation of the sea ice component of global coupled models relies on data assimilation techniques to incorporate information from observations to constrain the system. This study focuses on the development of sea ice data assimilation for ECMWF’s latest Ocean Reanalysis System 6 (ORAS6) that includes a multicategory sea ice model. The research addresses the challenge of appropriately distributing sea ice concentration increments across various thickness categories in the model. Here, we show that using a simple proportional increment splitting method improves the accuracy of sea ice concentration analyses compared to previous approaches. Our findings indicate that adding an additional sea ice-sea water temperature balance brings further performance benefits. These results suggest that the choice of increment distribution strategy significantly impacts the accuracy of sea ice representation in reanalysis systems. The system presented here will form the basis of ECMWF's data assimilation system for numerical weather prediction, as well as the next generation coupled reanalyses.
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BibTeX
@article{browne2026data,
title = {{Sea ice data assimilation in ORAS6}},
author = {Philip A. Browne and Eric de Boisséson and Sarah P. E. Keeley and Charles Pelletier and Hao Zuo},
journal = {The Cryosphere},
year = {2026},
month = jun,
doi = {10.5194/tc-20-3299-2026},
url = {https://doi.org/10.5194/tc-20-3299-2026},
note = {CC BY},
}
RIS
TY - JOUR
TI - Sea ice data assimilation in ORAS6
AU - Philip A. Browne
AU - Eric de Boisséson
AU - Sarah P. E. Keeley
AU - Charles Pelletier
AU - Hao Zuo
PY - 2026
DA - 2026/06/03
JO - The Cryosphere
DO - 10.5194/tc-20-3299-2026
UR - https://doi.org/10.5194/tc-20-3299-2026
AB - Accurate weather and climate forecasting relies heavily on the precise modeling of sea ice, a critical component of the Earth's climate system. Sea ice influences global weather patterns, ocean circulation, and the exchange of heat and moisture between the atmosphere and oceans. Initialisation of the sea ice component of global coupled models relies on data assimilation techniques to incorporate information from observations to constrain the system. This study focuses on the development of sea ice data assimilation for ECMWF’s latest Ocean Reanalysis System 6 (ORAS6) that includes a multicategory sea ice model. The research addresses the challenge of appropriately distributing sea ice concentration increments across various thickness categories in the model. Here, we show that using a simple proportional increment splitting method improves the accuracy of sea ice concentration analyses compared to previous approaches. Our findings indicate that adding an additional sea ice-sea water temperature balance brings further performance benefits. These results suggest that the choice of increment distribution strategy significantly impacts the accuracy of sea ice representation in reanalysis systems. The system presented here will form the basis of ECMWF's data assimilation system for numerical weather prediction, as well as the next generation coupled reanalyses.
SN - 1994-0416
SN - 1994-0424
ER -