Self-organizing maps identify windows of opportunity for seasonal European summer predictions
We combine a machine learning method and ensemble climate predictions to investigate windows of opportunity for seasonal predictability of European summer climate associated with the North Atlantic jet stream. We particularly focus on the impact of North Atlantic spring sea surface temperatures (SST...
Published in: | Frontiers in Climate |
---|---|
Main Authors: | , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2022
|
Subjects: | |
Online Access: | http://hdl.handle.net/21.11116/0000-000A-6A61-2 http://hdl.handle.net/21.11116/0000-000A-6A63-0 http://hdl.handle.net/21.11116/0000-000A-6A64-F http://hdl.handle.net/21.11116/0000-000A-C00A-2 |
id |
ftpubman:oai:pure.mpg.de:item_3380714 |
---|---|
record_format |
openpolar |
spelling |
ftpubman:oai:pure.mpg.de:item_3380714 2023-08-27T04:10:47+02:00 Self-organizing maps identify windows of opportunity for seasonal European summer predictions Carvalho-Oliveira, J. Borchert, L. Zorita, E. Baehr, J. 2022-04 application/pdf http://hdl.handle.net/21.11116/0000-000A-6A61-2 http://hdl.handle.net/21.11116/0000-000A-6A63-0 http://hdl.handle.net/21.11116/0000-000A-6A64-F http://hdl.handle.net/21.11116/0000-000A-C00A-2 eng eng info:eu-repo/grantAgreement/EC/H2020/776613 info:eu-repo/semantics/altIdentifier/doi/10.3389/fclim.2022.844634 info:eu-repo/semantics/altIdentifier/doi/10.3389/fclim.2022.925164 http://hdl.handle.net/21.11116/0000-000A-6A61-2 http://hdl.handle.net/21.11116/0000-000A-6A63-0 http://hdl.handle.net/21.11116/0000-000A-6A64-F http://hdl.handle.net/21.11116/0000-000A-C00A-2 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ Frontiers in Climate info:eu-repo/semantics/article 2022 ftpubman https://doi.org/10.3389/fclim.2022.84463410.3389/fclim.2022.925164 2023-08-02T01:09:46Z We combine a machine learning method and ensemble climate predictions to investigate windows of opportunity for seasonal predictability of European summer climate associated with the North Atlantic jet stream. We particularly focus on the impact of North Atlantic spring sea surface temperatures (SST) on the four dominant atmospheric teleconnections associated with the jet stream: the summer North Atlantic Oscillation (NAO) in positive and negative phases, the Atlantic Ridge (At. Ridge), and Atlantic Low (At. Low). We go beyond standard forecast practices by not only identifying these atmospheric teleconnections and their SST precursors but by making use of these identified precursors in the analysis of a dynamical forecast ensemble. Specifically, we train the neural network-based classifier Self-Organizing Maps (SOM) with ERA-20C reanalysis and combine it with model simulations from the Max Planck Institute Earth System Model in mixed resolution (MPI-ESM-MR). We use two different sets of 30-member hindcast ensembles initialized every May, one for training and evaluation between 1902 and 2008, and one for verification between 1980–2016, respectively. Among the four summer atmospheric teleconnections analyzed here, we find that At. Ridge simulated by MPI-ESM-MR shows the best agreement with ERA-20C, thereby representing with its occurrence windows of opportunity for skillful summer predictions. Conversely, At. Low shows the lowest agreement, which might limit the model skill for early warning of warmer than average summers. In summary, we find that spring SST patterns identified with a SOM analysis can be used to guess the dominant summer atmospheric teleconnections at initialization and guide a sub-selection of potential skillful ensemble members. This holds especially true for At. Ridge and At. Low and is unclear for summer NAO. We show that predictive skill in the selected ensemble exceeds that of the full ensemble over regions in the Euro-Atlantic domain where spring SST significantly correlates with summer ... Article in Journal/Newspaper North Atlantic North Atlantic oscillation Max Planck Society: MPG.PuRe Frontiers in Climate 4 |
institution |
Open Polar |
collection |
Max Planck Society: MPG.PuRe |
op_collection_id |
ftpubman |
language |
English |
description |
We combine a machine learning method and ensemble climate predictions to investigate windows of opportunity for seasonal predictability of European summer climate associated with the North Atlantic jet stream. We particularly focus on the impact of North Atlantic spring sea surface temperatures (SST) on the four dominant atmospheric teleconnections associated with the jet stream: the summer North Atlantic Oscillation (NAO) in positive and negative phases, the Atlantic Ridge (At. Ridge), and Atlantic Low (At. Low). We go beyond standard forecast practices by not only identifying these atmospheric teleconnections and their SST precursors but by making use of these identified precursors in the analysis of a dynamical forecast ensemble. Specifically, we train the neural network-based classifier Self-Organizing Maps (SOM) with ERA-20C reanalysis and combine it with model simulations from the Max Planck Institute Earth System Model in mixed resolution (MPI-ESM-MR). We use two different sets of 30-member hindcast ensembles initialized every May, one for training and evaluation between 1902 and 2008, and one for verification between 1980–2016, respectively. Among the four summer atmospheric teleconnections analyzed here, we find that At. Ridge simulated by MPI-ESM-MR shows the best agreement with ERA-20C, thereby representing with its occurrence windows of opportunity for skillful summer predictions. Conversely, At. Low shows the lowest agreement, which might limit the model skill for early warning of warmer than average summers. In summary, we find that spring SST patterns identified with a SOM analysis can be used to guess the dominant summer atmospheric teleconnections at initialization and guide a sub-selection of potential skillful ensemble members. This holds especially true for At. Ridge and At. Low and is unclear for summer NAO. We show that predictive skill in the selected ensemble exceeds that of the full ensemble over regions in the Euro-Atlantic domain where spring SST significantly correlates with summer ... |
format |
Article in Journal/Newspaper |
author |
Carvalho-Oliveira, J. Borchert, L. Zorita, E. Baehr, J. |
spellingShingle |
Carvalho-Oliveira, J. Borchert, L. Zorita, E. Baehr, J. Self-organizing maps identify windows of opportunity for seasonal European summer predictions |
author_facet |
Carvalho-Oliveira, J. Borchert, L. Zorita, E. Baehr, J. |
author_sort |
Carvalho-Oliveira, J. |
title |
Self-organizing maps identify windows of opportunity for seasonal European summer predictions |
title_short |
Self-organizing maps identify windows of opportunity for seasonal European summer predictions |
title_full |
Self-organizing maps identify windows of opportunity for seasonal European summer predictions |
title_fullStr |
Self-organizing maps identify windows of opportunity for seasonal European summer predictions |
title_full_unstemmed |
Self-organizing maps identify windows of opportunity for seasonal European summer predictions |
title_sort |
self-organizing maps identify windows of opportunity for seasonal european summer predictions |
publishDate |
2022 |
url |
http://hdl.handle.net/21.11116/0000-000A-6A61-2 http://hdl.handle.net/21.11116/0000-000A-6A63-0 http://hdl.handle.net/21.11116/0000-000A-6A64-F http://hdl.handle.net/21.11116/0000-000A-C00A-2 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
Frontiers in Climate |
op_relation |
info:eu-repo/grantAgreement/EC/H2020/776613 info:eu-repo/semantics/altIdentifier/doi/10.3389/fclim.2022.844634 info:eu-repo/semantics/altIdentifier/doi/10.3389/fclim.2022.925164 http://hdl.handle.net/21.11116/0000-000A-6A61-2 http://hdl.handle.net/21.11116/0000-000A-6A63-0 http://hdl.handle.net/21.11116/0000-000A-6A64-F http://hdl.handle.net/21.11116/0000-000A-C00A-2 |
op_rights |
info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.3389/fclim.2022.84463410.3389/fclim.2022.925164 |
container_title |
Frontiers in Climate |
container_volume |
4 |
_version_ |
1775353097902620672 |