Towards improving short-term sea ice predictability using deformation observations
Short-term sea ice predictability is challenging due to the lack of constraints on ice deformation features (open leads and ridges) at kilometre scale. Deformation observations capture these small-scale features and have the potential to improve the predictability. A new method for assimilation of s...
Main Authors: | , , , , |
---|---|
Other Authors: | , , |
Format: | Report |
Language: | English |
Published: |
HAL CCSD
2022
|
Subjects: | |
Online Access: | https://hal.science/hal-03796640 https://hal.science/hal-03796640/document https://hal.science/hal-03796640/file/tc-2022-46.pdf https://doi.org/10.5194/tc-2022-46 |
id |
ftunigrenoble:oai:HAL:hal-03796640v1 |
---|---|
record_format |
openpolar |
spelling |
ftunigrenoble:oai:HAL:hal-03796640v1 2023-06-11T04:16:30+02:00 Towards improving short-term sea ice predictability using deformation observations Korosov, Anton Rampal, Pierre Ying, Yue Ólason, Einar Williams, Timothy Institut des Géosciences de l’Environnement (IGE) Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ) Université Grenoble Alpes (UGA) 2022-10-10 https://hal.science/hal-03796640 https://hal.science/hal-03796640/document https://hal.science/hal-03796640/file/tc-2022-46.pdf https://doi.org/10.5194/tc-2022-46 en eng HAL CCSD info:eu-repo/semantics/altIdentifier/doi/10.5194/tc-2022-46 hal-03796640 https://hal.science/hal-03796640 https://hal.science/hal-03796640/document https://hal.science/hal-03796640/file/tc-2022-46.pdf doi:10.5194/tc-2022-46 info:eu-repo/semantics/OpenAccess https://hal.science/hal-03796640 2022 [SDU]Sciences of the Universe [physics] info:eu-repo/semantics/preprint Preprints, Working Papers, . 2022 ftunigrenoble https://doi.org/10.5194/tc-2022-46 2023-05-02T22:35:11Z Short-term sea ice predictability is challenging due to the lack of constraints on ice deformation features (open leads and ridges) at kilometre scale. Deformation observations capture these small-scale features and have the potential to improve the predictability. A new method for assimilation of satellite-derived sea ice deformation into the neXt generation Sea Ice Model (neXtSIM) is presented. Ice deformation provided by the Copernicus Marine Environmental Monitoring Service is computed from sea ice drift derived from Synthetic Aperture Radar at a spatio-temporal resolution of 10 km and 24 hours. We show that high values of ice deformation can be interpreted as reduced ice concentration and increased ice damage-scalar variables of neXtSIM. The proof-of-concept assimilation scheme uses a data nudging approach and deterministic forecasting with one member. Assimilation and forecasting experiments are run on example observations from January 2021 and show improvement of neXtSIM skills to predict sea ice deformation in 3-5 days horizon. It is demonstrated that neXtSIM is also capable of extrapolating the assimilated information in space-gaps in spatially discontinuous satellite observations of deformation are filled with a realistic pattern of ice cracks, confirmed by later satellite observations. The experiments also indicate that reduction in sea ice concentration plays a bigger role in improving ice deformation forecast on synoptic scales. Limitations and usefulness of the proposed assimilation approach are discussed in a context of ensemble forecasts. Pathways to estimate intrinsic predictability of sea ice deformation are proposed. Report Sea ice Université Grenoble Alpes: HAL |
institution |
Open Polar |
collection |
Université Grenoble Alpes: HAL |
op_collection_id |
ftunigrenoble |
language |
English |
topic |
[SDU]Sciences of the Universe [physics] |
spellingShingle |
[SDU]Sciences of the Universe [physics] Korosov, Anton Rampal, Pierre Ying, Yue Ólason, Einar Williams, Timothy Towards improving short-term sea ice predictability using deformation observations |
topic_facet |
[SDU]Sciences of the Universe [physics] |
description |
Short-term sea ice predictability is challenging due to the lack of constraints on ice deformation features (open leads and ridges) at kilometre scale. Deformation observations capture these small-scale features and have the potential to improve the predictability. A new method for assimilation of satellite-derived sea ice deformation into the neXt generation Sea Ice Model (neXtSIM) is presented. Ice deformation provided by the Copernicus Marine Environmental Monitoring Service is computed from sea ice drift derived from Synthetic Aperture Radar at a spatio-temporal resolution of 10 km and 24 hours. We show that high values of ice deformation can be interpreted as reduced ice concentration and increased ice damage-scalar variables of neXtSIM. The proof-of-concept assimilation scheme uses a data nudging approach and deterministic forecasting with one member. Assimilation and forecasting experiments are run on example observations from January 2021 and show improvement of neXtSIM skills to predict sea ice deformation in 3-5 days horizon. It is demonstrated that neXtSIM is also capable of extrapolating the assimilated information in space-gaps in spatially discontinuous satellite observations of deformation are filled with a realistic pattern of ice cracks, confirmed by later satellite observations. The experiments also indicate that reduction in sea ice concentration plays a bigger role in improving ice deformation forecast on synoptic scales. Limitations and usefulness of the proposed assimilation approach are discussed in a context of ensemble forecasts. Pathways to estimate intrinsic predictability of sea ice deformation are proposed. |
author2 |
Institut des Géosciences de l’Environnement (IGE) Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ) Université Grenoble Alpes (UGA) |
format |
Report |
author |
Korosov, Anton Rampal, Pierre Ying, Yue Ólason, Einar Williams, Timothy |
author_facet |
Korosov, Anton Rampal, Pierre Ying, Yue Ólason, Einar Williams, Timothy |
author_sort |
Korosov, Anton |
title |
Towards improving short-term sea ice predictability using deformation observations |
title_short |
Towards improving short-term sea ice predictability using deformation observations |
title_full |
Towards improving short-term sea ice predictability using deformation observations |
title_fullStr |
Towards improving short-term sea ice predictability using deformation observations |
title_full_unstemmed |
Towards improving short-term sea ice predictability using deformation observations |
title_sort |
towards improving short-term sea ice predictability using deformation observations |
publisher |
HAL CCSD |
publishDate |
2022 |
url |
https://hal.science/hal-03796640 https://hal.science/hal-03796640/document https://hal.science/hal-03796640/file/tc-2022-46.pdf https://doi.org/10.5194/tc-2022-46 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
https://hal.science/hal-03796640 2022 |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.5194/tc-2022-46 hal-03796640 https://hal.science/hal-03796640 https://hal.science/hal-03796640/document https://hal.science/hal-03796640/file/tc-2022-46.pdf doi:10.5194/tc-2022-46 |
op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.5194/tc-2022-46 |
_version_ |
1768374816496156672 |