North Atlantic climate far more predictable than models imply
Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change1,2,3. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain4. This le...
Published in: | Nature |
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Language: | English |
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Springer Nature
2020
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Online Access: | http://hdl.handle.net/2117/328258 https://doi.org/10.1038/s41586-020-2525-0 |
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ftupcatalunyair:oai:upcommons.upc.edu:2117/328258 |
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Open Polar |
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Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge |
op_collection_id |
ftupcatalunyair |
language |
English |
topic |
Àrees temàtiques de la UPC::Desenvolupament humà i sostenible Computer simulation North Atlantic oscillation Climatic changes Atmospheric circulation Climatology--Computer programs Climate models North Atlantic atmospheric circulation Projection of climate change Predictable signal Decadal predictions Simulació per ordinador Climatologia -- Models matemàtics |
spellingShingle |
Àrees temàtiques de la UPC::Desenvolupament humà i sostenible Computer simulation North Atlantic oscillation Climatic changes Atmospheric circulation Climatology--Computer programs Climate models North Atlantic atmospheric circulation Projection of climate change Predictable signal Decadal predictions Simulació per ordinador Climatologia -- Models matemàtics Smith, Doug Scaife, Adam A. Athanasiadis, P. Bellucci, A. Bethke, Ingo Bilbao, Roberto Borchert, Leonard F. Caron, Louis-Philippe Counillon, F. Danabasoglu, G. Delworth, Thomas Doblas-Reyes, Francisco Dunstone, Nick Estella-Perez, V. Flavoni, S. Hermanson, Leon Keenlyside, Noel Kharin, V. Kimoto, M. Merryfield, W.J. Mignot, Juliette Mochizuki, T. Modali, K. Monerie, P.-A. Müller, W.A. Nicolí, Dario Ortega Montilla, Pablo Pankatz, K. Pohlmann, H. Robson, Jon Ruggieri, P. Sospedra-Alfonso, Reinel Swingedouw, Didier Wang, Yiguo Wild, Simon Yeager, Stephen Yang, Xiaosong Liping, Zhang North Atlantic climate far more predictable than models imply |
topic_facet |
Àrees temàtiques de la UPC::Desenvolupament humà i sostenible Computer simulation North Atlantic oscillation Climatic changes Atmospheric circulation Climatology--Computer programs Climate models North Atlantic atmospheric circulation Projection of climate change Predictable signal Decadal predictions Simulació per ordinador Climatologia -- Models matemàtics |
description |
Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change1,2,3. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain4. This leads to low confidence in regional projections, especially for precipitation, over the coming decades5,6. The chaotic nature of the climate system7,8,9 may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models10, and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade. DMS, AAS, NJD, LH and RE were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra and by the European Commission Horizon 2020 EUCP project (GA 776613). FJDR, LPC, SW and RB also acknowledge the support from the EUCP project (GA 776613) and from the Ministerio de Econom´ıa y Competitividad (MINECO) as part of the CLINSA project (Grant No. CGL2017-85791-R). SW received funding from the innovation programme under the Marie Sk´lodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433 and PO from the Ramon y Cajal senior tenure programme of MINECO. The EC-Earth simulations were performed on Marenostrum 4 (hosted by the Barcelona Supercomputing Center, Spain) using Auto-Submit through computing hours provided by PRACE.WAM, HP, KMand KP were supported by the German FederalMinistry for Education and Research (BMBF) project MiKlip (grant 01LP1519A). NK, IB, FC and YW were supported by the Norwegian Research Council projects SFE (grant 270733) the Nordic Center of excellent ARCPATH (grant 76654) and the Trond Mohn Foundation, under the project number : BFS2018TMT01 and received grants for computer time from the Norwegian Program for supercomputing (NOTUR2, NN9039K) and storage grants (NORSTORE, NS9039K). JM, LFB and DS are supported by Blue-Action (European Union Horizon 2020 research and innovation program, Grant Number: 727852) and EUCP (European Union Horizon 2020 research and innovation programme under grant agreement no 776613) projects. The National Center for Atmospheric Research (NCAR) is a major facility sponsored by the US National Science Foundation (NSF) under Cooperative Agreement No. 1852977. NCAR contribution was partially supported by the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under Climate Variability and Predictability Program Grant NA13OAR4310138 and by the US NSF Collaborative Research EaSM2 Grant OCE-1243015. Peer Reviewed Postprint (author's final draft) |
author2 |
Barcelona Supercomputing Center |
format |
Article in Journal/Newspaper |
author |
Smith, Doug Scaife, Adam A. Athanasiadis, P. Bellucci, A. Bethke, Ingo Bilbao, Roberto Borchert, Leonard F. Caron, Louis-Philippe Counillon, F. Danabasoglu, G. Delworth, Thomas Doblas-Reyes, Francisco Dunstone, Nick Estella-Perez, V. Flavoni, S. Hermanson, Leon Keenlyside, Noel Kharin, V. Kimoto, M. Merryfield, W.J. Mignot, Juliette Mochizuki, T. Modali, K. Monerie, P.-A. Müller, W.A. Nicolí, Dario Ortega Montilla, Pablo Pankatz, K. Pohlmann, H. Robson, Jon Ruggieri, P. Sospedra-Alfonso, Reinel Swingedouw, Didier Wang, Yiguo Wild, Simon Yeager, Stephen Yang, Xiaosong Liping, Zhang |
author_facet |
Smith, Doug Scaife, Adam A. Athanasiadis, P. Bellucci, A. Bethke, Ingo Bilbao, Roberto Borchert, Leonard F. Caron, Louis-Philippe Counillon, F. Danabasoglu, G. Delworth, Thomas Doblas-Reyes, Francisco Dunstone, Nick Estella-Perez, V. Flavoni, S. Hermanson, Leon Keenlyside, Noel Kharin, V. Kimoto, M. Merryfield, W.J. Mignot, Juliette Mochizuki, T. Modali, K. Monerie, P.-A. Müller, W.A. Nicolí, Dario Ortega Montilla, Pablo Pankatz, K. Pohlmann, H. Robson, Jon Ruggieri, P. Sospedra-Alfonso, Reinel Swingedouw, Didier Wang, Yiguo Wild, Simon Yeager, Stephen Yang, Xiaosong Liping, Zhang |
author_sort |
Smith, Doug |
title |
North Atlantic climate far more predictable than models imply |
title_short |
North Atlantic climate far more predictable than models imply |
title_full |
North Atlantic climate far more predictable than models imply |
title_fullStr |
North Atlantic climate far more predictable than models imply |
title_full_unstemmed |
North Atlantic climate far more predictable than models imply |
title_sort |
north atlantic climate far more predictable than models imply |
publisher |
Springer Nature |
publishDate |
2020 |
url |
http://hdl.handle.net/2117/328258 https://doi.org/10.1038/s41586-020-2525-0 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_relation |
https://www.nature.com/articles/s41586-020-2525-0 info:eu-repo/grantAgreement/EC/H2020/776613/EU/European Climate Prediction system/EUCP info:eu-repo/grantAgreement/EC/H2020/754433/EU/SupercompuTing And Related applicationS Fellows Program/STARS http://www.cesm.ucar.edu/projects/community-projects/DPLE/ https://esgf-node.llnl.gov/projects/cmip5/ and https://esgf-node.llnl.gov/projects/cmip6/ Smith, D. [et al.]. North Atlantic climate far more predictable than models imply. "Nature", Juliol 2020, vol. 583, p. 796-800. 1476-4687 http://hdl.handle.net/2117/328258 doi:10.1038/s41586-020-2525-0 |
op_rights |
Open Access |
op_doi |
https://doi.org/10.1038/s41586-020-2525-0 |
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Nature |
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583 |
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7818 |
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796 |
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800 |
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1766120234052222976 |
spelling |
ftupcatalunyair:oai:upcommons.upc.edu:2117/328258 2023-05-15T17:27:54+02:00 North Atlantic climate far more predictable than models imply En el postprint el titulo es: Climate models underpredict North Atlantic atmospheric circulation changes Smith, Doug Scaife, Adam A. Athanasiadis, P. Bellucci, A. Bethke, Ingo Bilbao, Roberto Borchert, Leonard F. Caron, Louis-Philippe Counillon, F. Danabasoglu, G. Delworth, Thomas Doblas-Reyes, Francisco Dunstone, Nick Estella-Perez, V. Flavoni, S. Hermanson, Leon Keenlyside, Noel Kharin, V. Kimoto, M. Merryfield, W.J. Mignot, Juliette Mochizuki, T. Modali, K. Monerie, P.-A. Müller, W.A. Nicolí, Dario Ortega Montilla, Pablo Pankatz, K. Pohlmann, H. Robson, Jon Ruggieri, P. Sospedra-Alfonso, Reinel Swingedouw, Didier Wang, Yiguo Wild, Simon Yeager, Stephen Yang, Xiaosong Liping, Zhang Barcelona Supercomputing Center 2020-07 5 p. application/pdf http://hdl.handle.net/2117/328258 https://doi.org/10.1038/s41586-020-2525-0 eng eng Springer Nature https://www.nature.com/articles/s41586-020-2525-0 info:eu-repo/grantAgreement/EC/H2020/776613/EU/European Climate Prediction system/EUCP info:eu-repo/grantAgreement/EC/H2020/754433/EU/SupercompuTing And Related applicationS Fellows Program/STARS http://www.cesm.ucar.edu/projects/community-projects/DPLE/ https://esgf-node.llnl.gov/projects/cmip5/ and https://esgf-node.llnl.gov/projects/cmip6/ Smith, D. [et al.]. North Atlantic climate far more predictable than models imply. "Nature", Juliol 2020, vol. 583, p. 796-800. 1476-4687 http://hdl.handle.net/2117/328258 doi:10.1038/s41586-020-2525-0 Open Access Àrees temàtiques de la UPC::Desenvolupament humà i sostenible Computer simulation North Atlantic oscillation Climatic changes Atmospheric circulation Climatology--Computer programs Climate models North Atlantic atmospheric circulation Projection of climate change Predictable signal Decadal predictions Simulació per ordinador Climatologia -- Models matemàtics Article 2020 ftupcatalunyair https://doi.org/10.1038/s41586-020-2525-0 2021-08-03T23:04:01Z Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change1,2,3. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain4. This leads to low confidence in regional projections, especially for precipitation, over the coming decades5,6. The chaotic nature of the climate system7,8,9 may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models10, and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade. DMS, AAS, NJD, LH and RE were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra and by the European Commission Horizon 2020 EUCP project (GA 776613). FJDR, LPC, SW and RB also acknowledge the support from the EUCP project (GA 776613) and from the Ministerio de Econom´ıa y Competitividad (MINECO) as part of the CLINSA project (Grant No. CGL2017-85791-R). SW received funding from the innovation programme under the Marie Sk´lodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433 and PO from the Ramon y Cajal senior tenure programme of MINECO. The EC-Earth simulations were performed on Marenostrum 4 (hosted by the Barcelona Supercomputing Center, Spain) using Auto-Submit through computing hours provided by PRACE.WAM, HP, KMand KP were supported by the German FederalMinistry for Education and Research (BMBF) project MiKlip (grant 01LP1519A). NK, IB, FC and YW were supported by the Norwegian Research Council projects SFE (grant 270733) the Nordic Center of excellent ARCPATH (grant 76654) and the Trond Mohn Foundation, under the project number : BFS2018TMT01 and received grants for computer time from the Norwegian Program for supercomputing (NOTUR2, NN9039K) and storage grants (NORSTORE, NS9039K). JM, LFB and DS are supported by Blue-Action (European Union Horizon 2020 research and innovation program, Grant Number: 727852) and EUCP (European Union Horizon 2020 research and innovation programme under grant agreement no 776613) projects. The National Center for Atmospheric Research (NCAR) is a major facility sponsored by the US National Science Foundation (NSF) under Cooperative Agreement No. 1852977. NCAR contribution was partially supported by the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under Climate Variability and Predictability Program Grant NA13OAR4310138 and by the US NSF Collaborative Research EaSM2 Grant OCE-1243015. Peer Reviewed Postprint (author's final draft) Article in Journal/Newspaper North Atlantic North Atlantic oscillation Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge Nature 583 7818 796 800 |