Comparison of climate time series – Part 5: Multivariate annual cycles
This paper develops a method for determining whether two vector time series originate from a common stochastic process. The stochastic process considered incorporates both serial correlations and multivariate annual cycles. Specifically, the process is modeled as a vector autoregressive model with p...
Published in: | Advances in Statistical Climatology, Meteorology and Oceanography |
---|---|
Main Authors: | , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
Copernicus Publications
2024
|
Subjects: | |
Online Access: | https://doi.org/10.5194/ascmo-10-1-2024 https://doaj.org/article/0a607301673d471499a6bca79e6a7b8f |
id |
ftdoajarticles:oai:doaj.org/article:0a607301673d471499a6bca79e6a7b8f |
---|---|
record_format |
openpolar |
spelling |
ftdoajarticles:oai:doaj.org/article:0a607301673d471499a6bca79e6a7b8f 2024-09-15T18:23:53+00:00 Comparison of climate time series – Part 5: Multivariate annual cycles T. DelSole M. K. Tippett 2024-01-01T00:00:00Z https://doi.org/10.5194/ascmo-10-1-2024 https://doaj.org/article/0a607301673d471499a6bca79e6a7b8f EN eng Copernicus Publications https://ascmo.copernicus.org/articles/10/1/2024/ascmo-10-1-2024.pdf https://doaj.org/toc/2364-3579 https://doaj.org/toc/2364-3587 doi:10.5194/ascmo-10-1-2024 2364-3579 2364-3587 https://doaj.org/article/0a607301673d471499a6bca79e6a7b8f Advances in Statistical Climatology, Meteorology and Oceanography, Vol 10, Pp 1-27 (2024) Oceanography GC1-1581 Meteorology. Climatology QC851-999 Probabilities. Mathematical statistics QA273-280 article 2024 ftdoajarticles https://doi.org/10.5194/ascmo-10-1-2024 2024-08-05T17:49:59Z This paper develops a method for determining whether two vector time series originate from a common stochastic process. The stochastic process considered incorporates both serial correlations and multivariate annual cycles. Specifically, the process is modeled as a vector autoregressive model with periodic forcing, referred to as a VARX model (where X stands for exogenous variables). The hypothesis that two VARX models share the same parameters is tested using the likelihood ratio method. The resulting test can be further decomposed into a series of tests to assess whether disparities in the VARX models stem from differences in noise parameters, autoregressive parameters, or annual cycle parameters. A comprehensive procedure for compressing discrepancies between VARX models into a minimal number of components is developed based on discriminant analysis. Using this method, the realism of climate model simulations of monthly mean North Atlantic sea surface temperatures is assessed. As expected, different simulations from the same climate model cannot be distinguished stochastically. Similarly, observations from different periods cannot be distinguished. However, every climate model differs stochastically from observations. Furthermore, each climate model differs stochastically from every other model, except when they originate from the same center. In essence, each climate model possesses a distinct fingerprint that sets it apart stochastically from both observations and models developed by other research centers. The primary factor contributing to these differences is the difference in annual cycles. The difference in annual cycles is often dominated by a single component, which can be extracted and illustrated using discriminant analysis. Article in Journal/Newspaper North Atlantic Directory of Open Access Journals: DOAJ Articles Advances in Statistical Climatology, Meteorology and Oceanography 10 1 1 27 |
institution |
Open Polar |
collection |
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 Comparison of climate time series – Part 5: Multivariate annual cycles |
topic_facet |
Oceanography GC1-1581 Meteorology. Climatology QC851-999 Probabilities. Mathematical statistics QA273-280 |
description |
This paper develops a method for determining whether two vector time series originate from a common stochastic process. The stochastic process considered incorporates both serial correlations and multivariate annual cycles. Specifically, the process is modeled as a vector autoregressive model with periodic forcing, referred to as a VARX model (where X stands for exogenous variables). The hypothesis that two VARX models share the same parameters is tested using the likelihood ratio method. The resulting test can be further decomposed into a series of tests to assess whether disparities in the VARX models stem from differences in noise parameters, autoregressive parameters, or annual cycle parameters. A comprehensive procedure for compressing discrepancies between VARX models into a minimal number of components is developed based on discriminant analysis. Using this method, the realism of climate model simulations of monthly mean North Atlantic sea surface temperatures is assessed. As expected, different simulations from the same climate model cannot be distinguished stochastically. Similarly, observations from different periods cannot be distinguished. However, every climate model differs stochastically from observations. Furthermore, each climate model differs stochastically from every other model, except when they originate from the same center. In essence, each climate model possesses a distinct fingerprint that sets it apart stochastically from both observations and models developed by other research centers. The primary factor contributing to these differences is the difference in annual cycles. The difference in annual cycles is often dominated by a single component, which can be extracted and illustrated using discriminant analysis. |
format |
Article in Journal/Newspaper |
author |
T. DelSole M. K. Tippett |
author_facet |
T. DelSole M. K. Tippett |
author_sort |
T. DelSole |
title |
Comparison of climate time series – Part 5: Multivariate annual cycles |
title_short |
Comparison of climate time series – Part 5: Multivariate annual cycles |
title_full |
Comparison of climate time series – Part 5: Multivariate annual cycles |
title_fullStr |
Comparison of climate time series – Part 5: Multivariate annual cycles |
title_full_unstemmed |
Comparison of climate time series – Part 5: Multivariate annual cycles |
title_sort |
comparison of climate time series – part 5: multivariate annual cycles |
publisher |
Copernicus Publications |
publishDate |
2024 |
url |
https://doi.org/10.5194/ascmo-10-1-2024 https://doaj.org/article/0a607301673d471499a6bca79e6a7b8f |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Advances in Statistical Climatology, Meteorology and Oceanography, Vol 10, Pp 1-27 (2024) |
op_relation |
https://ascmo.copernicus.org/articles/10/1/2024/ascmo-10-1-2024.pdf https://doaj.org/toc/2364-3579 https://doaj.org/toc/2364-3587 doi:10.5194/ascmo-10-1-2024 2364-3579 2364-3587 https://doaj.org/article/0a607301673d471499a6bca79e6a7b8f |
op_doi |
https://doi.org/10.5194/ascmo-10-1-2024 |
container_title |
Advances in Statistical Climatology, Meteorology and Oceanography |
container_volume |
10 |
container_issue |
1 |
container_start_page |
1 |
op_container_end_page |
27 |
_version_ |
1810464170946068480 |