Predictability of Arctic sea ice drift in coupled climate models
Skillful sea ice drift forecasts are crucial for scientific mission planning and marine safety. Wind is the dominant driver of ice motion variability, but more slowly varying components of the climate system, in particular ice thickness and ocean currents, bear the potential to render ice drift more...
Published in: | The Cryosphere |
---|---|
Main Authors: | , |
Format: | Text |
Language: | English |
Published: |
2022
|
Subjects: | |
Online Access: | https://doi.org/10.5194/tc-16-2927-2022 https://tc.copernicus.org/articles/16/2927/2022/ |
id |
ftcopernicus:oai:publications.copernicus.org:tc101427 |
---|---|
record_format |
openpolar |
spelling |
ftcopernicus:oai:publications.copernicus.org:tc101427 2023-05-15T14:54:51+02:00 Predictability of Arctic sea ice drift in coupled climate models Reifenberg, Simon Felix Goessling, Helge Friedrich 2022-07-20 application/pdf https://doi.org/10.5194/tc-16-2927-2022 https://tc.copernicus.org/articles/16/2927/2022/ eng eng doi:10.5194/tc-16-2927-2022 https://tc.copernicus.org/articles/16/2927/2022/ eISSN: 1994-0424 Text 2022 ftcopernicus https://doi.org/10.5194/tc-16-2927-2022 2022-07-25T16:22:41Z Skillful sea ice drift forecasts are crucial for scientific mission planning and marine safety. Wind is the dominant driver of ice motion variability, but more slowly varying components of the climate system, in particular ice thickness and ocean currents, bear the potential to render ice drift more predictable than the wind. In this study, we provide the first assessment of Arctic sea ice drift predictability in four coupled general circulation models (GCMs), using a suite of “perfect-model” ensemble simulations. We find the position vector from Lagrangian trajectories of virtual buoys to remain predictable for at least a 90 ( 45 ) d lead time for initializations in January (July), reaching about 80 % of the position uncertainty of a climatological reference forecast. In contrast, the uncertainty in Eulerian drift vector predictions reaches the level of the climatological uncertainty within 4 weeks. Spatial patterns of uncertainty, varying with season and across models, develop in all investigated GCMs. For two models providing near-surface wind data (AWI-CM1 and HadGEM1.2), we find spatial patterns and large fractions of the variance to be explained by wind vector uncertainty. The latter implies that sea ice drift is only marginally more predictable than wind. Nevertheless, particularly one of the four models (GFDL-CM3) shows a significant correlation of up to −0.85 between initial ice thickness and target position uncertainty in large parts of the Arctic. Our results provide a first assessment of the inherent predictability of ice motion in coupled climate models; they can be used to put current real-world forecast skill into perspective and highlight the model diversity of sea ice drift predictability. Text Arctic Sea ice Copernicus Publications: E-Journals Arctic The Cryosphere 16 7 2927 2946 |
institution |
Open Polar |
collection |
Copernicus Publications: E-Journals |
op_collection_id |
ftcopernicus |
language |
English |
description |
Skillful sea ice drift forecasts are crucial for scientific mission planning and marine safety. Wind is the dominant driver of ice motion variability, but more slowly varying components of the climate system, in particular ice thickness and ocean currents, bear the potential to render ice drift more predictable than the wind. In this study, we provide the first assessment of Arctic sea ice drift predictability in four coupled general circulation models (GCMs), using a suite of “perfect-model” ensemble simulations. We find the position vector from Lagrangian trajectories of virtual buoys to remain predictable for at least a 90 ( 45 ) d lead time for initializations in January (July), reaching about 80 % of the position uncertainty of a climatological reference forecast. In contrast, the uncertainty in Eulerian drift vector predictions reaches the level of the climatological uncertainty within 4 weeks. Spatial patterns of uncertainty, varying with season and across models, develop in all investigated GCMs. For two models providing near-surface wind data (AWI-CM1 and HadGEM1.2), we find spatial patterns and large fractions of the variance to be explained by wind vector uncertainty. The latter implies that sea ice drift is only marginally more predictable than wind. Nevertheless, particularly one of the four models (GFDL-CM3) shows a significant correlation of up to −0.85 between initial ice thickness and target position uncertainty in large parts of the Arctic. Our results provide a first assessment of the inherent predictability of ice motion in coupled climate models; they can be used to put current real-world forecast skill into perspective and highlight the model diversity of sea ice drift predictability. |
format |
Text |
author |
Reifenberg, Simon Felix Goessling, Helge Friedrich |
spellingShingle |
Reifenberg, Simon Felix Goessling, Helge Friedrich Predictability of Arctic sea ice drift in coupled climate models |
author_facet |
Reifenberg, Simon Felix Goessling, Helge Friedrich |
author_sort |
Reifenberg, Simon Felix |
title |
Predictability of Arctic sea ice drift in coupled climate models |
title_short |
Predictability of Arctic sea ice drift in coupled climate models |
title_full |
Predictability of Arctic sea ice drift in coupled climate models |
title_fullStr |
Predictability of Arctic sea ice drift in coupled climate models |
title_full_unstemmed |
Predictability of Arctic sea ice drift in coupled climate models |
title_sort |
predictability of arctic sea ice drift in coupled climate models |
publishDate |
2022 |
url |
https://doi.org/10.5194/tc-16-2927-2022 https://tc.copernicus.org/articles/16/2927/2022/ |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_source |
eISSN: 1994-0424 |
op_relation |
doi:10.5194/tc-16-2927-2022 https://tc.copernicus.org/articles/16/2927/2022/ |
op_doi |
https://doi.org/10.5194/tc-16-2927-2022 |
container_title |
The Cryosphere |
container_volume |
16 |
container_issue |
7 |
container_start_page |
2927 |
op_container_end_page |
2946 |
_version_ |
1766326606320631808 |