Articles | Volume 17, issue 7
https://doi.org/10.5194/gmd-17-2641-2024
https://doi.org/10.5194/gmd-17-2641-2024
Methods for assessment of models
 | 
11 Apr 2024
Methods for assessment of models |  | 11 Apr 2024

A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model

Sonya L. Fiddes, Marc D. Mallet, Alain Protat, Matthew T. Woodhouse, Simon P. Alexander, and Kalli Furtado

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Short summary
In this study we present an evaluation that considers complex, non-linear systems in a holistic manner. This study uses XGBoost, a machine learning algorithm, to predict the simulated Southern Ocean shortwave radiation bias in the ACCESS model using cloud property biases as predictors. We then used a novel feature importance analysis to quantify the role that each cloud bias plays in predicting the radiative bias, laying the foundation for advanced Earth system model evaluation and development.