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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ftdoajarticles:oai:doaj.org/article:1d5372329d9949b5a6a5f9099382a8f0 2023-05-15T17:29:21+02:00 Comparing climate time series – Part 2: A multivariate test T. DelSole M. K. Tippett 2021-12-01T00:00:00Z https://doi.org/10.5194/ascmo-7-73-2021 https://doaj.org/article/1d5372329d9949b5a6a5f9099382a8f0 EN eng Copernicus Publications https://ascmo.copernicus.org/articles/7/73/2021/ascmo-7-73-2021.pdf https://doaj.org/toc/2364-3579 https://doaj.org/toc/2364-3587 doi:10.5194/ascmo-7-73-2021 2364-3579 2364-3587 https://doaj.org/article/1d5372329d9949b5a6a5f9099382a8f0 Advances in Statistical Climatology, Meteorology and Oceanography, Vol 7, Pp 73-85 (2021) Oceanography GC1-1581 Meteorology. Climatology QC851-999 Probabilities. Mathematical statistics QA273-280 article 2021 ftdoajarticles https://doi.org/10.5194/ascmo-7-73-2021 2022-12-31T09:20:47Z 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 Directory of Open Access Journals: DOAJ Articles Advances in Statistical Climatology, Meteorology and Oceanography 7 2 73 85 |
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Open Polar |
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Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
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 |
topic_facet |
Oceanography GC1-1581 Meteorology. Climatology QC851-999 Probabilities. Mathematical statistics QA273-280 |
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 |
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://doi.org/10.5194/ascmo-7-73-2021 https://doaj.org/article/1d5372329d9949b5a6a5f9099382a8f0 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Advances in Statistical Climatology, Meteorology and Oceanography, Vol 7, Pp 73-85 (2021) |
op_relation |
https://ascmo.copernicus.org/articles/7/73/2021/ascmo-7-73-2021.pdf https://doaj.org/toc/2364-3579 https://doaj.org/toc/2364-3587 doi:10.5194/ascmo-7-73-2021 2364-3579 2364-3587 https://doaj.org/article/1d5372329d9949b5a6a5f9099382a8f0 |
op_doi |
https://doi.org/10.5194/ascmo-7-73-2021 |
container_title |
Advances in Statistical Climatology, Meteorology and Oceanography |
container_volume |
7 |
container_issue |
2 |
container_start_page |
73 |
op_container_end_page |
85 |
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