Development of the signal‐to‐noise paradox in subseasonal forecasting models: When? Where? Why?

Abstract Subseasonal forecast models are shown to suffer from the same inconsistency in the signal‐to‐noise ratio evident in climate models. Namely, predictable signals in these models are too weak, yet there is a relatively high level of agreement with observed variability of the atmospheric circulation. The net effect is subseasonal forecast models show higher correlation with observed variability than with their own simulations; that is, the signal‐to‐noise paradox. Also, similar to climate models, this paradox is particularly evident in the North Atlantic sector. The paradox is not evident in week 1 or week 2 forecasts, and hence is limited to subseasonal time‐scales. The paradox appears to be related to an overly fast decay of northern annular mode regimes. Three possible causes of this overly fast decay and for the paradox in the Northern Hemisphere are identified: a too‐fast decay of polar stratospheric signals, overly weak downward coupling from the stratosphere to the surface in some models, and overly weak transient synoptic eddy feedbacks. Though the paradox is clearly evident in the North Atlantic, it is relatively muted in the Southern Hemisphere: southern annular mode regimes persist realistically, the stratospheric signal is well maintained, and eddy feedback is, if anything, too strong and zonal.

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Publication Details

Journal
Quarterly Journal of the Royal Meteorological Society
Published
2024-09-04
DOI
https://doi.org/10.1002/qj.4822
Citations
4
Primary Topic
Climate variability and models
Type
article
Field-Weighted Citation Impact
1.13

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article

Development of the signal‐to‐noise paradox in subseasonal forecasting models: When? Where? Why?

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4 citations
Quarterly Journal of the Royal Meteorological Society
Climate variability and models
1.13
article

Development of the signal‐to‐noise paradox in subseasonal forecasting models: When? Where? Why?

Chen Schwartz, Amy Hawes Butler, Jeff R Knight, Chaim I. Garfinkel, Masakazu Taguchi, Daniela I. V. Domeisen, Wen Chen, Judah Cohen
article en
4 citations

Abstract

Abstract Subseasonal forecast models are shown to suffer from the same inconsistency in the signal‐to‐noise ratio evident in climate models. Namely, predictable signals in these models are too weak, yet there is a relatively high level of agreement with observed variability of the atmospheric circulation. The net effect is subseasonal forecast models show higher correlation with observed variability than with their own simulations; that is, the signal‐to‐noise paradox. Also, similar to climate models, this paradox is particularly evident in the North Atlantic sector. The paradox is not evident in week 1 or week 2 forecasts, and hence is limited to subseasonal time‐scales. The paradox appears to be related to an overly fast decay of northern annular mode regimes. Three possible causes of this overly fast decay and for the paradox in the Northern Hemisphere are identified: a too‐fast decay of polar stratospheric signals, overly weak downward coupling from the stratosphere to the surface in some models, and overly weak transient synoptic eddy feedbacks. Though the paradox is clearly evident in the North Atlantic, it is relatively muted in the Southern Hemisphere: southern annular mode regimes persist realistically, the stratospheric signal is well maintained, and eddy feedback is, if anything, too strong and zonal.

Quarterly Journal of the Royal Meteorological SocietyVol. 150(764)
Met Office (GB), Cooperative Institute for Research in Environmental Sciences (US), Yunnan University (CN), Hebrew University of Jerusalem (IL), ETH Zurich (CH), Aichi University of Education (JP), Exeter Hospital (GB), Atmospheric and Environmental Research (US), Massachusetts Institute of Technology (US), University of Lausanne (CH)
United States-Israel Binational Science Foundation, National Natural Science Foundation of China, Israel Science Foundation, Directorate for Geosciences
Openalex Percentile: Top 23%
Climate variability and models
1.13
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