Probabilistic seasonal forecasts of North Atlantic atmospheric circulation using complex systems modelling and comparison with dynamical models
Abstract Dynamical seasonal forecast models are improving with time but tend to underestimate the amplitude of atmospheric circulation variability and to have lower skill in predicting summer variability than in winter. Here, we construct Nonlinear AutoRegressive Moving Average models with eXogenous...
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crwiley:10.1002/met.2178 2024-06-02T08:11:09+00:00 Probabilistic seasonal forecasts of North Atlantic atmospheric circulation using complex systems modelling and comparison with dynamical models Sun, Yiming Simpson, Ian Wei, Hua‐Liang Hanna, Edward 2024 http://dx.doi.org/10.1002/met.2178 https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/met.2178 en eng Wiley http://creativecommons.org/licenses/by/4.0/ Meteorological Applications volume 31, issue 1 ISSN 1350-4827 1469-8080 journal-article 2024 crwiley https://doi.org/10.1002/met.2178 2024-05-03T11:29:02Z Abstract Dynamical seasonal forecast models are improving with time but tend to underestimate the amplitude of atmospheric circulation variability and to have lower skill in predicting summer variability than in winter. Here, we construct Nonlinear AutoRegressive Moving Average models with eXogenous inputs (NARMAX) to develop the analysis of drivers of North Atlantic atmospheric circulation and jet‐stream variability, focusing on the East Atlantic (EA) and Scandinavian (SCA) patterns as well as the North Atlantic Oscillation (NAO) index. New time series of these indices are developed from empirical orthogonal function (EOF) analysis. Geopotential height data from the ERA5 reanalysis are used to generate the EOFs. Sets of predictors with known associations with these drivers are developed and used to formulate a sliding‐window NARMAX model. This model demonstrates a high degree of predictive accuracy, as indicated by its average correlation coefficients over the testing period (2006–2021): 0.78 for NAO, 0.83 for EA and 0.68 for SCA. In comparison, the SEAS5 and GloSea5 dynamical forecast models exhibit lower correlations with observed circulation changes: for NAO, the correlation coefficients are 0.51 for SEAS5 and 0.34 for GloSea5, for EA they are 0.15 and 0.09, respectively, and for SCA, they are 0.28 and 0.24, respectively. Comparison of NARMAX predictions with forecasts and hindcasts from the SEAS5 and GloSea5 models highlights areas where NARMAX can be used to help improve seasonal forecast skill and inform the development of dynamical models, especially in the case of summer. Article in Journal/Newspaper North Atlantic North Atlantic oscillation Wiley Online Library Meteorological Applications 31 1 |
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English |
description |
Abstract Dynamical seasonal forecast models are improving with time but tend to underestimate the amplitude of atmospheric circulation variability and to have lower skill in predicting summer variability than in winter. Here, we construct Nonlinear AutoRegressive Moving Average models with eXogenous inputs (NARMAX) to develop the analysis of drivers of North Atlantic atmospheric circulation and jet‐stream variability, focusing on the East Atlantic (EA) and Scandinavian (SCA) patterns as well as the North Atlantic Oscillation (NAO) index. New time series of these indices are developed from empirical orthogonal function (EOF) analysis. Geopotential height data from the ERA5 reanalysis are used to generate the EOFs. Sets of predictors with known associations with these drivers are developed and used to formulate a sliding‐window NARMAX model. This model demonstrates a high degree of predictive accuracy, as indicated by its average correlation coefficients over the testing period (2006–2021): 0.78 for NAO, 0.83 for EA and 0.68 for SCA. In comparison, the SEAS5 and GloSea5 dynamical forecast models exhibit lower correlations with observed circulation changes: for NAO, the correlation coefficients are 0.51 for SEAS5 and 0.34 for GloSea5, for EA they are 0.15 and 0.09, respectively, and for SCA, they are 0.28 and 0.24, respectively. Comparison of NARMAX predictions with forecasts and hindcasts from the SEAS5 and GloSea5 models highlights areas where NARMAX can be used to help improve seasonal forecast skill and inform the development of dynamical models, especially in the case of summer. |
format |
Article in Journal/Newspaper |
author |
Sun, Yiming Simpson, Ian Wei, Hua‐Liang Hanna, Edward |
spellingShingle |
Sun, Yiming Simpson, Ian Wei, Hua‐Liang Hanna, Edward Probabilistic seasonal forecasts of North Atlantic atmospheric circulation using complex systems modelling and comparison with dynamical models |
author_facet |
Sun, Yiming Simpson, Ian Wei, Hua‐Liang Hanna, Edward |
author_sort |
Sun, Yiming |
title |
Probabilistic seasonal forecasts of North Atlantic atmospheric circulation using complex systems modelling and comparison with dynamical models |
title_short |
Probabilistic seasonal forecasts of North Atlantic atmospheric circulation using complex systems modelling and comparison with dynamical models |
title_full |
Probabilistic seasonal forecasts of North Atlantic atmospheric circulation using complex systems modelling and comparison with dynamical models |
title_fullStr |
Probabilistic seasonal forecasts of North Atlantic atmospheric circulation using complex systems modelling and comparison with dynamical models |
title_full_unstemmed |
Probabilistic seasonal forecasts of North Atlantic atmospheric circulation using complex systems modelling and comparison with dynamical models |
title_sort |
probabilistic seasonal forecasts of north atlantic atmospheric circulation using complex systems modelling and comparison with dynamical models |
publisher |
Wiley |
publishDate |
2024 |
url |
http://dx.doi.org/10.1002/met.2178 https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/met.2178 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
Meteorological Applications volume 31, issue 1 ISSN 1350-4827 1469-8080 |
op_rights |
http://creativecommons.org/licenses/by/4.0/ |
op_doi |
https://doi.org/10.1002/met.2178 |
container_title |
Meteorological Applications |
container_volume |
31 |
container_issue |
1 |
_version_ |
1800757197620117504 |