DOI: 10.5061/dryad.p2ngf1w26
title: Data from: Convolutional neural networks trained on internal variability predict forced response of TOA radiation by learning the pattern effect

description: Predicting forced, long-term radiative feedbacks from internal climate
 variability has been a decades-long quest in climate science. We train a
 convolutional neural network (CNN) to predict annual- and global-mean top
 of the atmosphere radiation anomalies from time-varying maps of
 near-surface temperature in climate models. Trained on internal
 variability alone, the nonlinear CNN can predict radiation under strong
 climate change, outperforms a regularized linear regression approach, and
 works within and across different climate models.  We show with
 explainable artificial intelligence methods that the CNN draws predictive
 skill from physically meaningful regions but at much smaller spatial
 scales than currently assumed. 
description: # Relationships between monthly SST and TOA patterns Convolutional neural
 networks to predict global-mean radiation from surface temperature maps.
 Code available
 from [https://github.com/SnnVL/CNN_PatternEffect](https://github.com/SnnVL/CNN_PatternEffect) --- ## Tensorflow Code This code was written in python 3.10.13, tensorflow 2.15.0, tensorflow-probability 0.15.0 and numpy 1.26.4. The CNNs are trained by running `_train_driver.py`. All experiment settings are contained in `experiment_settings.py`. For plotting and interpreting the results, jupyter notebooks were created. --- ## File description All files are provided in the netCDF format `.nc`, and contain the data needed to recreate the figures in Ref. 1 (Rugenstein et al., 2025). Specifically: * `predictions_*.nc` files contain the predictions and truth of $R$ made from CNNs with different SST inputs * `prediction_IV.nc` contains the data for Fig. 1a (CNN/linear network trained on MPI data, predicting internal variability in MPI) * `prediction_hist_rcp85.nc` contains the data for Fig. 1b (CNN/linear network trained on MPI, predicting historical + RCP8.5 in MPI) * `prediction_1pctCO2.nc` contains the data for Fig. 1c (CNN/linear network trained on MPI, predicting 1% CO_2 experiment in MPI) * `predictions_MultiModel_*.nc` contains data for Fig. 3a-b. It contains the predictions of the CNN that was trained on 4 different climate models. The final part of the file name quantifies which SST data is used to make predictions (`IV`: Internal Variability; `IPSL`: historical + SSP2-4.5 from IPSL-CM6A-LR; `CanESM5`: historical + SSP2-4.5 from CanESM5; `MIROC6`: historical + SSP2-4.5 from MIROC6; `MPI`: historical + RCP8.5 from MPI-ESM) * `GRAD_*.nc` files contain the Gradients of the CNN/linear networks * `GRAD_MPI_IV_hpt7_s0.nc` contains the gradient of the MPI-ESM CNN on the original grid (Fig. 2a) * `GRAD_SingleModel_*.nc` contains the gradients for the single model CNNs; the second part of the file name denotes the model used. If the `_linear` flag is added, it contains the linear network gradient instead of the CNN gradient. * `GRAD_MultiModelELU_s3.nc` contains the gradient of the multi-model CNN (Fig. 3c) * the `s*` naming denotes the seed used to train the CNN or linear network. --- ## Credits This work is a collaborative effort between [Dr. Maria Rugenstein](https://www.atmos.colostate.edu/people/faculty/rugenstein/), [Dr. Senne Van Loon](https://scholar.google.com/citations?user=6h7ft20AAAAJ&hl=en), and [Dr. Elizabeth A. Barnes](https://barnes.atmos.colostate.edu). ### References \[1] Maria Rugenstein, Senne Van Loon, & Elizabeth A. Barnes (2025), Convolutional neural networks trained on internal variability predict forced response of TOA radiation by learning the pattern effect, Geophysical Research Letters (accepted). \[2] Senne Van Loon, Maria Rugenstein, & Elizabeth A. Barnes (2025), Observation-based estimate of Earth’s effective radiative forcing, [EarthArXiv](https://doi.org/10.31223/X5FM8P)