Bivariate sea-ice assimilation for global-ocean analysis–reanalysis

Cipollone, Andrea; Banerjee, Deep Sankar; Iovino, Doroteaciro; Aydogdu, Ali; Masina, Simona

doi:https://doi.org/10.5194/os-19-1375-2023

Articles | Volume 19, issue 5

https://doi.org/10.5194/os-19-1375-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Data assimilation techniques and applications in coastal and...

https://doi.org/10.5194/os-19-1375-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 5

Research article

|

15 Sep 2023

Research article |

| 15 Sep 2023

Bivariate sea-ice assimilation for global-ocean analysis–reanalysis

Andrea Cipollone, Deep Sankar Banerjee, Doroteaciro Iovino, Ali Aydogdu, and Simona Masina

Supplement

https://doi.org/10.5194/os-19-1375-2023-supplement

Data sets

EUMETSAT Product Navigator - Global Sea Ice Concentration Climate Data Record v2.0 - Multimission OSISAF https://navigator.eumetsat.int/product/EO:EUM:DAT:MULT:OSI-450

IceBridge L4 Sea Ice Freeboard, Snow Depth, and Thickness N. Kurtz, M. Studinger, J. Harbeck, V. DePaul Onana, and D. Yi https://doi.org/10.5067/G519SHCKWQV6

Short summary

Sea-ice volume is characterized by low predictability compared to the sea ice area or the extent. A joint initialization of the thickness and concentration using satellite data could improve the predictive power, although it is still absent in the present global analysis–reanalysis systems. This study shows a scheme to correct the two features together that can be easily extended to include ocean variables. The impact of such a joint initialization is shown and compared among different set-ups.