A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model
The evaluation and quantification of Southern Ocean cloud–radiation interactions simulated by climate models are essential in understanding the sources and magnitude of the radiative bias that persists in climate models for this region. To date, most evaluation methods focus on specific synoptic or...
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ftcopernicus:oai:publications.copernicus.org:gmd110296 2024-09-15T18:37:02+00:00 A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model Fiddes, Sonya L. Mallet, Marc D. Protat, Alain Woodhouse, Matthew T. Alexander, Simon P. Furtado, Kalli 2024-04-11 application/pdf https://doi.org/10.5194/gmd-17-2641-2024 https://gmd.copernicus.org/articles/17/2641/2024/ eng eng doi:10.5194/gmd-17-2641-2024 https://gmd.copernicus.org/articles/17/2641/2024/ eISSN: 1991-9603 Text 2024 ftcopernicus https://doi.org/10.5194/gmd-17-2641-2024 2024-08-28T05:24:15Z The evaluation and quantification of Southern Ocean cloud–radiation interactions simulated by climate models are essential in understanding the sources and magnitude of the radiative bias that persists in climate models for this region. To date, most evaluation methods focus on specific synoptic or cloud-type conditions that do not consider the entirety of the Southern Ocean's cloud regimes at once. Furthermore, it is difficult to directly quantify the complex and non-linear role that different cloud properties have on modulating cloud radiative effect. In this study, we present a new method of model evaluation, using machine learning that can at once identify complexities within a system and individual contributions. To do this, we use an XGBoost (eXtreme Gradient Boosting) model to predict the radiative bias within a nudged version of the Australian Community Climate and Earth System Simulator – Atmosphere-only model, using cloud property biases as predictive features. We find that the XGBoost model can explain up to 55 % of the radiative bias from these cloud properties alone. We then apply SHAP (SHapley Additive exPlanations) feature importance analysis to quantify the role each cloud property bias plays in predicting the radiative bias. We find that biases in the liquid water path are the largest contributor to the cloud radiative bias over the Southern Ocean, though important regional and cloud-type dependencies exist. We then test the usefulness of this method in evaluating model perturbations and find that it can clearly identify complex responses, including cloud property and cloud-type compensating errors. Text Southern Ocean Copernicus Publications: E-Journals Geoscientific Model Development 17 7 2641 2662 |
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English |
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The evaluation and quantification of Southern Ocean cloud–radiation interactions simulated by climate models are essential in understanding the sources and magnitude of the radiative bias that persists in climate models for this region. To date, most evaluation methods focus on specific synoptic or cloud-type conditions that do not consider the entirety of the Southern Ocean's cloud regimes at once. Furthermore, it is difficult to directly quantify the complex and non-linear role that different cloud properties have on modulating cloud radiative effect. In this study, we present a new method of model evaluation, using machine learning that can at once identify complexities within a system and individual contributions. To do this, we use an XGBoost (eXtreme Gradient Boosting) model to predict the radiative bias within a nudged version of the Australian Community Climate and Earth System Simulator – Atmosphere-only model, using cloud property biases as predictive features. We find that the XGBoost model can explain up to 55 % of the radiative bias from these cloud properties alone. We then apply SHAP (SHapley Additive exPlanations) feature importance analysis to quantify the role each cloud property bias plays in predicting the radiative bias. We find that biases in the liquid water path are the largest contributor to the cloud radiative bias over the Southern Ocean, though important regional and cloud-type dependencies exist. We then test the usefulness of this method in evaluating model perturbations and find that it can clearly identify complex responses, including cloud property and cloud-type compensating errors. |
format |
Text |
author |
Fiddes, Sonya L. Mallet, Marc D. Protat, Alain Woodhouse, Matthew T. Alexander, Simon P. Furtado, Kalli |
spellingShingle |
Fiddes, Sonya L. Mallet, Marc D. Protat, Alain Woodhouse, Matthew T. Alexander, Simon P. Furtado, Kalli A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model |
author_facet |
Fiddes, Sonya L. Mallet, Marc D. Protat, Alain Woodhouse, Matthew T. Alexander, Simon P. Furtado, Kalli |
author_sort |
Fiddes, Sonya L. |
title |
A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model |
title_short |
A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model |
title_full |
A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model |
title_fullStr |
A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model |
title_full_unstemmed |
A machine learning approach for evaluating Southern Ocean cloud radiative biases in a global atmosphere model |
title_sort |
machine learning approach for evaluating southern ocean cloud radiative biases in a global atmosphere model |
publishDate |
2024 |
url |
https://doi.org/10.5194/gmd-17-2641-2024 https://gmd.copernicus.org/articles/17/2641/2024/ |
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Southern Ocean |
genre_facet |
Southern Ocean |
op_source |
eISSN: 1991-9603 |
op_relation |
doi:10.5194/gmd-17-2641-2024 https://gmd.copernicus.org/articles/17/2641/2024/ |
op_doi |
https://doi.org/10.5194/gmd-17-2641-2024 |
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Geoscientific Model Development |
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17 |
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7 |
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2641 |
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2662 |
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1810481259495817216 |