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Role of weather noise in El Niño-southern oscillation variability and prediction.

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Title: Role of weather noise in El Niño-southern oscillation variability and prediction.
Authors: Schneider, Edwin K.1 (AUTHOR), Colfescu, Ioana2,3 (AUTHOR) ic98@st-andrews.ac.uk, Chen, Hua4 (AUTHOR), Carey-Prieto, Emma5 (AUTHOR)
Source: Climate Dynamics. Apr2026, Vol. 64 Issue 4, p1-16. 16p.
Subjects: El Niño, General circulation model, Atmospheric models, Ocean temperature, Forecasting
Abstract: The impact atmospheric weather noise can have on the variability, and thus the predictability of El Niño-Southern Oscillation (ENSO) is investigated. A perfect model framework is used in which ENSO variability in 100 years of a coupled GCM (CGCM) simulation serves as the observations. An Interactive Ensemble version of the CGCM forced by the atmospheric weather noise from the CGCM simulation demonstrates that the weather noise forcing produces a substantial response in the CGCM ENSO. A simple recharge oscillator model is used to investigate the role of atmospheric weather noise in the CGCM ENSO variability and predictability. The recharge oscillator model forced by the time-evolving atmospheric noise from the CGCM simulation is tuned for best agreement with the CGCM. In this tuning, the contributions of the zonal mean equatorial wind stress noise forcing and the heat flux noise forcing averaged over the NINO3.4 region are comparable in explaining the agreement between the CGCM and simple model ENSO time series. A set of retrospective predictions using the simple model tuned by various approaches and run with no weather noise forcing is conducted in order to estimate the impact of initial conditions, model error, and weather noise on the predictability of the CGCM NINO3.4 sea surface temperature anomalies. The simulation tuning is compared to other approaches including multiple regression and direct tuning of the prediction model. The linearly stable approach found to produce the best predictions is multiple regression tuning informed by the CGCM weather noise. This multiple regression approach is shown to produce a best approximation to the initial state of the free mode of the simple model. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:The impact atmospheric weather noise can have on the variability, and thus the predictability of El Niño-Southern Oscillation (ENSO) is investigated. A perfect model framework is used in which ENSO variability in 100 years of a coupled GCM (CGCM) simulation serves as the observations. An Interactive Ensemble version of the CGCM forced by the atmospheric weather noise from the CGCM simulation demonstrates that the weather noise forcing produces a substantial response in the CGCM ENSO. A simple recharge oscillator model is used to investigate the role of atmospheric weather noise in the CGCM ENSO variability and predictability. The recharge oscillator model forced by the time-evolving atmospheric noise from the CGCM simulation is tuned for best agreement with the CGCM. In this tuning, the contributions of the zonal mean equatorial wind stress noise forcing and the heat flux noise forcing averaged over the NINO3.4 region are comparable in explaining the agreement between the CGCM and simple model ENSO time series. A set of retrospective predictions using the simple model tuned by various approaches and run with no weather noise forcing is conducted in order to estimate the impact of initial conditions, model error, and weather noise on the predictability of the CGCM NINO3.4 sea surface temperature anomalies. The simulation tuning is compared to other approaches including multiple regression and direct tuning of the prediction model. The linearly stable approach found to produce the best predictions is multiple regression tuning informed by the CGCM weather noise. This multiple regression approach is shown to produce a best approximation to the initial state of the free mode of the simple model. [ABSTRACT FROM AUTHOR]
ISSN:09307575
DOI:10.1007/s00382-025-08002-1