Comparing climate time series – Part 2: A multivariate test

This paper proposes a criterion for deciding whether climate model simulations are consistent with observations. Importantly, the criterion accounts for correlations in both space and time. The basic idea is to fit each multivariate time series to a vector autoregressive (VAR) model and then test th...

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Published in:Advances in Statistical Climatology, Meteorology and Oceanography
Main Authors: DelSole, Timothy, Tippett, Michael K.
Format: Article in Journal/Newspaper
Language:English
Published: Copernicus Publications 2021
Subjects:
Online Access:https://doi.org/10.5194/ascmo-7-73-2021
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spelling ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00058997 2024-09-15T18:21:46+00:00 Comparing climate time series – Part 2: A multivariate test DelSole, Timothy Tippett, Michael K. 2021-12 electronic https://doi.org/10.5194/ascmo-7-73-2021 https://noa.gwlb.de/receive/cop_mods_00058997 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00058605/ascmo-7-73-2021.pdf https://ascmo.copernicus.org/articles/7/73/2021/ascmo-7-73-2021.pdf eng eng Copernicus Publications Advances in Statistical Climatology, Meteorology and Oceanography -- http://advances-statistical-climatology-meteorology-oceanography.net/ -- https://www.adv-stat-clim-meteorol-oceanogr.net/volumes_and_issues.html -- http://www.bibliothek.uni-regensburg.de/ezeit/?2840620 -- 2364-3587 https://doi.org/10.5194/ascmo-7-73-2021 https://noa.gwlb.de/receive/cop_mods_00058997 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00058605/ascmo-7-73-2021.pdf https://ascmo.copernicus.org/articles/7/73/2021/ascmo-7-73-2021.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess article Verlagsveröffentlichung article Text doc-type:article 2021 ftnonlinearchiv https://doi.org/10.5194/ascmo-7-73-2021 2024-06-26T04:36:34Z This paper proposes a criterion for deciding whether climate model simulations are consistent with observations. Importantly, the criterion accounts for correlations in both space and time. The basic idea is to fit each multivariate time series to a vector autoregressive (VAR) model and then test the hypothesis that the parameters of the two models are equal. In the special case of a first-order VAR model, the model is a linear inverse model (LIM) and the test constitutes a difference-in-LIM test. This test is applied to decide whether climate models generate realistic internal variability of annual mean North Atlantic sea surface temperature. Given the disputed origin of multidecadal variability in the North Atlantic (e.g., some studies argue it is forced by anthropogenic aerosols, while others argue it arises naturally from internal variability), the time series are filtered in two different ways appropriate to the two driving mechanisms. In either case, only a few climate models out of three dozen are found to generate internal variability consistent with observations. In fact, it is shown that climate models differ not only from observations, but also from each other, unless they come from the same modeling center. In addition to these discrepancies in internal variability, other studies show that models exhibit significant discrepancies with observations in terms of the response to external forcing. Taken together, these discrepancies imply that, at the present time, climate models do not provide a satisfactory explanation of observed variability in the North Atlantic. Article in Journal/Newspaper North Atlantic Niedersächsisches Online-Archiv NOA Advances in Statistical Climatology, Meteorology and Oceanography 7 2 73 85
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collection Niedersächsisches Online-Archiv NOA
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language English
topic article
Verlagsveröffentlichung
spellingShingle article
Verlagsveröffentlichung
DelSole, Timothy
Tippett, Michael K.
Comparing climate time series – Part 2: A multivariate test
topic_facet article
Verlagsveröffentlichung
description This paper proposes a criterion for deciding whether climate model simulations are consistent with observations. Importantly, the criterion accounts for correlations in both space and time. The basic idea is to fit each multivariate time series to a vector autoregressive (VAR) model and then test the hypothesis that the parameters of the two models are equal. In the special case of a first-order VAR model, the model is a linear inverse model (LIM) and the test constitutes a difference-in-LIM test. This test is applied to decide whether climate models generate realistic internal variability of annual mean North Atlantic sea surface temperature. Given the disputed origin of multidecadal variability in the North Atlantic (e.g., some studies argue it is forced by anthropogenic aerosols, while others argue it arises naturally from internal variability), the time series are filtered in two different ways appropriate to the two driving mechanisms. In either case, only a few climate models out of three dozen are found to generate internal variability consistent with observations. In fact, it is shown that climate models differ not only from observations, but also from each other, unless they come from the same modeling center. In addition to these discrepancies in internal variability, other studies show that models exhibit significant discrepancies with observations in terms of the response to external forcing. Taken together, these discrepancies imply that, at the present time, climate models do not provide a satisfactory explanation of observed variability in the North Atlantic.
format Article in Journal/Newspaper
author DelSole, Timothy
Tippett, Michael K.
author_facet DelSole, Timothy
Tippett, Michael K.
author_sort DelSole, Timothy
title Comparing climate time series – Part 2: A multivariate test
title_short Comparing climate time series – Part 2: A multivariate test
title_full Comparing climate time series – Part 2: A multivariate test
title_fullStr Comparing climate time series – Part 2: A multivariate test
title_full_unstemmed Comparing climate time series – Part 2: A multivariate test
title_sort comparing climate time series – part 2: a multivariate test
publisher Copernicus Publications
publishDate 2021
url https://doi.org/10.5194/ascmo-7-73-2021
https://noa.gwlb.de/receive/cop_mods_00058997
https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00058605/ascmo-7-73-2021.pdf
https://ascmo.copernicus.org/articles/7/73/2021/ascmo-7-73-2021.pdf
genre North Atlantic
genre_facet North Atlantic
op_relation Advances in Statistical Climatology, Meteorology and Oceanography -- http://advances-statistical-climatology-meteorology-oceanography.net/ -- https://www.adv-stat-clim-meteorol-oceanogr.net/volumes_and_issues.html -- http://www.bibliothek.uni-regensburg.de/ezeit/?2840620 -- 2364-3587
https://doi.org/10.5194/ascmo-7-73-2021
https://noa.gwlb.de/receive/cop_mods_00058997
https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00058605/ascmo-7-73-2021.pdf
https://ascmo.copernicus.org/articles/7/73/2021/ascmo-7-73-2021.pdf
op_rights https://creativecommons.org/licenses/by/4.0/
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container_title Advances in Statistical Climatology, Meteorology and Oceanography
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