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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Cited articles

Beucler, T., Ebert-Uphoff, I., Rasp, S., Pritchard, M., and Gentine, P.: Machine Learning for Clouds and Climate (Invited Chapter for the AGU Geophysical Monograph Series “Clouds and Climate”), ESS Open Archive, in review, https://doi.org/10.1002/essoar.10506925.1, 2021. a, b
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Bodas-Salcedo, A., Williams, K. D., Ringer, M. A., Beau, I., Cole, J. N. S., Dufresne, J. L., Koshiro, T., Stevens, B., Wang, Z., and Yokohata, T.: Origins of the solar radiation biases over the Southern Ocean in CFMIP2 models, J. Climate, 27, 41–56, https://doi.org/10.1175/JCLI-D-13-00169.1, 2014. a
Bodas-Salcedo, A., Hill, P. G., Furtado, K., Williams, K. D., Field, P. R., Manners, J. C., Hyder, P., and Kato, S.: Large contribution of supercooled liquid clouds to the solar radiation budget of the Southern Ocean, J. Climate, 29, 4213–4228, https://doi.org/10.1175/JCLI-D-15-0564.1, 2016. a, b, c, d
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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.
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