Codes for Deep learning ENSO prediction dictated by nonlinear atmosphere convection
Year: 2025
Type: article
Abstract: This repository contains Python 3.11 code and NetCDF files for generating the key findings and figures in the article by Cai et al. *ERA5_Nino3_olr_SSTA.nc contains the time series of SSTA and OLR anomalies in the Nino 3 region from ERA5. *model_nonlinear_convection_para.nc contains the nonlinear convective coefficients for each CMIP6 model used. *model_prediction_Nino34_ensemble_mean.nc contains the Nino3.4 index forecast results for each CMIP6 model, generated using our AI system. *model_true_Nino34.nc contains the Nino3.4 index in each CMIP6 model during 1970-2014. *obs_prediction.nc contains the observed and hindcast (from our AI system) Nino3.4 indices. *nonlinear_convection.py This script contains the code for calculating the nonlinear convective coefficient β, as well as the code for plotting the nonlinear convection in observations. *predictability_barrier.py This script contains the verification of ENSO forecast results against observations. It can generate the complete Extended Figure 1. It also includes validation of the key finding: that nonlinear convection helps weaken the Spring Predictability Barrier (Fig1E).
Authors: Cai, Wenju, Wang, Haodong, Chen, Faquan
Language: English
Subfield: Global and Planetary Change
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: green
OpenAlex ID: W7092283515
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