Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting
Predicting Arctic sea ice extent is a notoriously difficult forecasting problem, even for lead times as short as one month. Motivated by Arctic intraannual variability phenomena such as reemergence of sea surface temperature and sea ice anomalies, we use a prediction approach for sea ice anomalies b...
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ftosti:oai:osti.gov:1480002 2023-07-30T04:00:30+02:00 Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting Comeau, Darin Giannakis, Dimitrios Zhao, Zhizhen Majda, Andrew J. 2023-06-28 application/pdf http://www.osti.gov/servlets/purl/1480002 https://www.osti.gov/biblio/1480002 https://doi.org/10.1007/s00382-018-4459-x unknown http://www.osti.gov/servlets/purl/1480002 https://www.osti.gov/biblio/1480002 https://doi.org/10.1007/s00382-018-4459-x doi:10.1007/s00382-018-4459-x 54 ENVIRONMENTAL SCIENCES 2023 ftosti https://doi.org/10.1007/s00382-018-4459-x 2023-07-11T09:29:50Z Predicting Arctic sea ice extent is a notoriously difficult forecasting problem, even for lead times as short as one month. Motivated by Arctic intraannual variability phenomena such as reemergence of sea surface temperature and sea ice anomalies, we use a prediction approach for sea ice anomalies based on analog forecasting. Traditional analog forecasting relies on identifying a single analog in a historical record, usually by minimizing Euclidean distance, and forming a forecast from the analog’s historical trajectory. An ensemble of analogs is used to make forecasts, where the ensemble weights are determined by a dynamics-adapted similarity kernel, which takes into account the nonlinear geometry on the underlying data manifold. We apply this method for forecasting pan-Arctic and regional sea ice area and volume anomalies from multi-century climate model data, and in many cases find improvement over the benchmark damped persistence forecast. Examples of success include the 3–6 month lead time prediction of Arctic sea ice area, the winter sea ice area prediction of some marginal ice zone seas, and the 3–12 month lead time prediction of sea ice volume anomalies in many central Arctic basins. Finally, we discuss possible connections between KAF success and sea ice reemergence, and find KAF to be successful in regions and seasons exhibiting high interannual variability. Other/Unknown Material Arctic Sea ice SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) Arctic Climate Dynamics 52 9-10 5507 5525 |
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SciTec Connect (Office of Scientific and Technical Information - OSTI, U.S. Department of Energy) |
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54 ENVIRONMENTAL SCIENCES |
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54 ENVIRONMENTAL SCIENCES Comeau, Darin Giannakis, Dimitrios Zhao, Zhizhen Majda, Andrew J. Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting |
topic_facet |
54 ENVIRONMENTAL SCIENCES |
description |
Predicting Arctic sea ice extent is a notoriously difficult forecasting problem, even for lead times as short as one month. Motivated by Arctic intraannual variability phenomena such as reemergence of sea surface temperature and sea ice anomalies, we use a prediction approach for sea ice anomalies based on analog forecasting. Traditional analog forecasting relies on identifying a single analog in a historical record, usually by minimizing Euclidean distance, and forming a forecast from the analog’s historical trajectory. An ensemble of analogs is used to make forecasts, where the ensemble weights are determined by a dynamics-adapted similarity kernel, which takes into account the nonlinear geometry on the underlying data manifold. We apply this method for forecasting pan-Arctic and regional sea ice area and volume anomalies from multi-century climate model data, and in many cases find improvement over the benchmark damped persistence forecast. Examples of success include the 3–6 month lead time prediction of Arctic sea ice area, the winter sea ice area prediction of some marginal ice zone seas, and the 3–12 month lead time prediction of sea ice volume anomalies in many central Arctic basins. Finally, we discuss possible connections between KAF success and sea ice reemergence, and find KAF to be successful in regions and seasons exhibiting high interannual variability. |
author |
Comeau, Darin Giannakis, Dimitrios Zhao, Zhizhen Majda, Andrew J. |
author_facet |
Comeau, Darin Giannakis, Dimitrios Zhao, Zhizhen Majda, Andrew J. |
author_sort |
Comeau, Darin |
title |
Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting |
title_short |
Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting |
title_full |
Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting |
title_fullStr |
Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting |
title_full_unstemmed |
Predicting regional and pan-Arctic sea ice anomalies with kernel analog forecasting |
title_sort |
predicting regional and pan-arctic sea ice anomalies with kernel analog forecasting |
publishDate |
2023 |
url |
http://www.osti.gov/servlets/purl/1480002 https://www.osti.gov/biblio/1480002 https://doi.org/10.1007/s00382-018-4459-x |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic Sea ice |
genre_facet |
Arctic Sea ice |
op_relation |
http://www.osti.gov/servlets/purl/1480002 https://www.osti.gov/biblio/1480002 https://doi.org/10.1007/s00382-018-4459-x doi:10.1007/s00382-018-4459-x |
op_doi |
https://doi.org/10.1007/s00382-018-4459-x |
container_title |
Climate Dynamics |
container_volume |
52 |
container_issue |
9-10 |
container_start_page |
5507 |
op_container_end_page |
5525 |
_version_ |
1772810994930679808 |