Comparing climate time series – Part 2: A multivariate test

<p>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 th...

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Autores principales:	T. DelSole, M. K. Tippett
Formato:	article
Lenguaje:	EN
Publicado:	Copernicus Publications 2021
Materias:	Oceanography GC1-1581 Meteorology. Climatology QC851-999 Probabilities. Mathematical statistics QA273-280
Acceso en línea:	https://doaj.org/article/1d5372329d9949b5a6a5f9099382a8f0
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spelling	oai:doaj.org-article:1d5372329d9949b5a6a5f9099382a8f02021-12-02T07:35:09ZComparing climate time series – Part 2: A multivariate test10.5194/ascmo-7-73-20212364-35792364-3587https://doaj.org/article/1d5372329d9949b5a6a5f9099382a8f02021-12-01T00:00:00Zhttps://ascmo.copernicus.org/articles/7/73/2021/ascmo-7-73-2021.pdfhttps://doaj.org/toc/2364-3579https://doaj.org/toc/2364-3587<p>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.</p>T. DelSoleM. K. TippettCopernicus PublicationsarticleOceanographyGC1-1581Meteorology. ClimatologyQC851-999Probabilities. Mathematical statisticsQA273-280ENAdvances in Statistical Climatology, Meteorology and Oceanography, Vol 7, Pp 73-85 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Oceanography GC1-1581 Meteorology. Climatology QC851-999 Probabilities. Mathematical statistics QA273-280
spellingShingle	Oceanography GC1-1581 Meteorology. Climatology QC851-999 Probabilities. Mathematical statistics QA273-280 T. DelSole M. K. Tippett Comparing climate time series – Part 2: A multivariate test
description	<p>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.</p>
format	article
author	T. DelSole M. K. Tippett
author_facet	T. DelSole M. K. Tippett
author_sort	T. DelSole
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://doaj.org/article/1d5372329d9949b5a6a5f9099382a8f0
work_keys_str_mv	AT tdelsole comparingclimatetimeseriespart2amultivariatetest AT mktippett comparingclimatetimeseriespart2amultivariatetest
_version_	1718399323968897024

Comparing climate time series – Part 2: A multivariate test

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