Climate Models as Guidance for the Design of Observing Systems: the Case of Polar Climate and Sea Ice Prediction
Purpose of review. The Arctic and Antarctic are among the regions most exposed to climate change, but ironically, they are also the ones for which the least observations are available. Climate models have been instrumental in completing the big picture. It is generally accepted that observations fee...
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ftzenodo:oai:zenodo.org:3550961 2024-09-15T17:48:25+00:00 Climate Models as Guidance for the Design of Observing Systems: the Case of Polar Climate and Sea Ice Prediction Massonnet, François 2019-11-13 https://doi.org/10.1007/s40641-019-00151-w eng eng Zenodo https://zenodo.org/communities/applicate https://zenodo.org/communities/eu https://doi.org/10.1007/s40641-019-00151-w oai:zenodo.org:3550961 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode polar prediction observing system design climate models data assimilation info:eu-repo/semantics/article 2019 ftzenodo https://doi.org/10.1007/s40641-019-00151-w 2024-07-26T21:17:34Z Purpose of review. The Arctic and Antarctic are among the regions most exposed to climate change, but ironically, they are also the ones for which the least observations are available. Climate models have been instrumental in completing the big picture. It is generally accepted that observations feed the development of climate models: parameterizations are designed based on empirically observed relationships, climate model predictions are initialized using observational products, and numerical simulations are evaluated given matching observational datasets. Recent findings. Recent research suggests that the opposite also holds: climate models can feed the development of polar observational networks by indicating the type, location, frequency, and timing of measurements that would be most useful for answering a specific scientific question. Summary. Here, we review the foundations of this emerging notion with five cases borrowed from the field of polar prediction with a focus on sea ice (sub-seasonal to centennial time scales). We suggest that climate models, besides their usual purposes, can be used to objectively prioritize future observational needs – if, of course, the limitations of the realism of these models have been recognized. This idea, which has been already extensively exploited in the context of Numerical Weather Prediction, reinforces the notion that observations and models are two sides of the same coin rather than distinct conceptual entities. Article in Journal/Newspaper Antarc* Antarctic Climate change Sea ice Zenodo Current Climate Change Reports 5 4 334 344 |
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English |
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polar prediction observing system design climate models data assimilation |
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polar prediction observing system design climate models data assimilation Massonnet, François Climate Models as Guidance for the Design of Observing Systems: the Case of Polar Climate and Sea Ice Prediction |
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polar prediction observing system design climate models data assimilation |
description |
Purpose of review. The Arctic and Antarctic are among the regions most exposed to climate change, but ironically, they are also the ones for which the least observations are available. Climate models have been instrumental in completing the big picture. It is generally accepted that observations feed the development of climate models: parameterizations are designed based on empirically observed relationships, climate model predictions are initialized using observational products, and numerical simulations are evaluated given matching observational datasets. Recent findings. Recent research suggests that the opposite also holds: climate models can feed the development of polar observational networks by indicating the type, location, frequency, and timing of measurements that would be most useful for answering a specific scientific question. Summary. Here, we review the foundations of this emerging notion with five cases borrowed from the field of polar prediction with a focus on sea ice (sub-seasonal to centennial time scales). We suggest that climate models, besides their usual purposes, can be used to objectively prioritize future observational needs – if, of course, the limitations of the realism of these models have been recognized. This idea, which has been already extensively exploited in the context of Numerical Weather Prediction, reinforces the notion that observations and models are two sides of the same coin rather than distinct conceptual entities. |
format |
Article in Journal/Newspaper |
author |
Massonnet, François |
author_facet |
Massonnet, François |
author_sort |
Massonnet, François |
title |
Climate Models as Guidance for the Design of Observing Systems: the Case of Polar Climate and Sea Ice Prediction |
title_short |
Climate Models as Guidance for the Design of Observing Systems: the Case of Polar Climate and Sea Ice Prediction |
title_full |
Climate Models as Guidance for the Design of Observing Systems: the Case of Polar Climate and Sea Ice Prediction |
title_fullStr |
Climate Models as Guidance for the Design of Observing Systems: the Case of Polar Climate and Sea Ice Prediction |
title_full_unstemmed |
Climate Models as Guidance for the Design of Observing Systems: the Case of Polar Climate and Sea Ice Prediction |
title_sort |
climate models as guidance for the design of observing systems: the case of polar climate and sea ice prediction |
publisher |
Zenodo |
publishDate |
2019 |
url |
https://doi.org/10.1007/s40641-019-00151-w |
genre |
Antarc* Antarctic Climate change Sea ice |
genre_facet |
Antarc* Antarctic Climate change Sea ice |
op_relation |
https://zenodo.org/communities/applicate https://zenodo.org/communities/eu https://doi.org/10.1007/s40641-019-00151-w oai:zenodo.org:3550961 |
op_rights |
info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode |
op_doi |
https://doi.org/10.1007/s40641-019-00151-w |
container_title |
Current Climate Change Reports |
container_volume |
5 |
container_issue |
4 |
container_start_page |
334 |
op_container_end_page |
344 |
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1810289622513614848 |