S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts
The discipline of seasonal climate prediction began as an exercise in simple statistical techniques. However, today the large government forecast centers almost exclusively rely on complex fully coupled dynamical forecast systems for their subseasonal to seasonal (S2S) predictions while statistical...
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ftleibnizopen:oai:oai.leibnizopen.de:QEmhqIgBdbrxVwz6nd9O 2023-07-02T03:31:31+02:00 S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts Cohen, Judah Coumou, Dim Hwang, Jessica Mackey, Lester Orenstein, Paulo Totz, Sonja Tziperman, Eli 2018 application/pdf https://oa.tib.eu/renate/handle/123456789/10248 https://doi.org/10.34657/9284 eng eng Malden, MA : Wiley-Blackwell CC BY 4.0 Unported https://creativecommons.org/licenses/by/4.0/ Wiley Interdisciplinary Reviews Climate Change 10 (2019), Nr. 2 climate prediction machine learning polar vortex unsupervised learning 550 article Text 2018 ftleibnizopen https://doi.org/10.34657/9284 2023-06-11T23:33:49Z The discipline of seasonal climate prediction began as an exercise in simple statistical techniques. However, today the large government forecast centers almost exclusively rely on complex fully coupled dynamical forecast systems for their subseasonal to seasonal (S2S) predictions while statistical techniques are mostly neglected and those techniques still in use have not been updated in decades. In this Opinion Article, we argue that new statistical techniques mostly developed outside the field of climate science, collectively referred to as machine learning, can be adopted by climate forecasters to increase the accuracy of S2S predictions. We present an example of where unsupervised learning demonstrates higher accuracy in a seasonal prediction than the state-of-the-art dynamical systems. We also summarize some relevant machine learning methods that are most applicable to climate prediction. Finally, we show by comparing real-time dynamical model forecasts with observations from winter 2017/2018 that dynamical model forecasts are almost entirely insensitive to polar vortex (PV) variability and the impact on sensible weather. Instead, statistical forecasts more accurately predicted the resultant sensible weather from a mid-winter PV disruption than the dynamical forecasts. The important implication from the poor dynamical forecasts is that if Arctic change influences mid-latitude weather through PV variability, then the ability of dynamical models to demonstrate the existence of such a pathway is compromised. We conclude by suggesting that S2S prediction will be most beneficial to the public by incorporating mixed or a hybrid of dynamical forecasts and updated statistical techniques such as machine learning. publishedVersion Article in Journal/Newspaper Arctic LeibnizOpen (The Leibniz Association) Arctic |
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Open Polar |
collection |
LeibnizOpen (The Leibniz Association) |
op_collection_id |
ftleibnizopen |
language |
English |
topic |
climate prediction machine learning polar vortex unsupervised learning 550 |
spellingShingle |
climate prediction machine learning polar vortex unsupervised learning 550 Cohen, Judah Coumou, Dim Hwang, Jessica Mackey, Lester Orenstein, Paulo Totz, Sonja Tziperman, Eli S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts |
topic_facet |
climate prediction machine learning polar vortex unsupervised learning 550 |
description |
The discipline of seasonal climate prediction began as an exercise in simple statistical techniques. However, today the large government forecast centers almost exclusively rely on complex fully coupled dynamical forecast systems for their subseasonal to seasonal (S2S) predictions while statistical techniques are mostly neglected and those techniques still in use have not been updated in decades. In this Opinion Article, we argue that new statistical techniques mostly developed outside the field of climate science, collectively referred to as machine learning, can be adopted by climate forecasters to increase the accuracy of S2S predictions. We present an example of where unsupervised learning demonstrates higher accuracy in a seasonal prediction than the state-of-the-art dynamical systems. We also summarize some relevant machine learning methods that are most applicable to climate prediction. Finally, we show by comparing real-time dynamical model forecasts with observations from winter 2017/2018 that dynamical model forecasts are almost entirely insensitive to polar vortex (PV) variability and the impact on sensible weather. Instead, statistical forecasts more accurately predicted the resultant sensible weather from a mid-winter PV disruption than the dynamical forecasts. The important implication from the poor dynamical forecasts is that if Arctic change influences mid-latitude weather through PV variability, then the ability of dynamical models to demonstrate the existence of such a pathway is compromised. We conclude by suggesting that S2S prediction will be most beneficial to the public by incorporating mixed or a hybrid of dynamical forecasts and updated statistical techniques such as machine learning. publishedVersion |
format |
Article in Journal/Newspaper |
author |
Cohen, Judah Coumou, Dim Hwang, Jessica Mackey, Lester Orenstein, Paulo Totz, Sonja Tziperman, Eli |
author_facet |
Cohen, Judah Coumou, Dim Hwang, Jessica Mackey, Lester Orenstein, Paulo Totz, Sonja Tziperman, Eli |
author_sort |
Cohen, Judah |
title |
S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts |
title_short |
S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts |
title_full |
S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts |
title_fullStr |
S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts |
title_full_unstemmed |
S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts |
title_sort |
s2s reboot: an argument for greater inclusion of machine learning in subseasonal to seasonal forecasts |
publisher |
Malden, MA : Wiley-Blackwell |
publishDate |
2018 |
url |
https://oa.tib.eu/renate/handle/123456789/10248 https://doi.org/10.34657/9284 |
geographic |
Arctic |
geographic_facet |
Arctic |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Wiley Interdisciplinary Reviews Climate Change 10 (2019), Nr. 2 |
op_rights |
CC BY 4.0 Unported https://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.34657/9284 |
_version_ |
1770270896044376064 |