Investigating Parametric Dependence of Climate Feedbacks in the Atmospheric Component of CNRM-CM6-1

We sample 30 calibration parameters of the CNRM-CM6-1 atmospheric component to produce a perturbed parameter ensemble. An error score aggregating four annual-averaged metrics is used here as an example to represent the model performance. We propose a method using statistical emulators of climatic fields and feedback parameters to produce model candidates spanning the maximal range of net feedback while minimizing the error score. Optimal candidates are found for a range of climate sensitivity chosen to span 3.8-10.1 K. The candidates show large errors in the extremes of the climate sensitivity range and none of them have better scores than the released version of CNRM-CM6. However, 65% of the optimal candidates have errors within the inter-model standard deviation of the seven models participating in the Cloud Feedback Model Intercomparison Project, spanning and ECS range of (4.14-7.22 K). These optimal calibrations will be used to assess feedback diversity in coupled experiments.

Automated summary

This study samples 30 calibration parameters of the CNRM-CM6-1 atmospheric component to create a perturbed parameter ensemble. By using statistical emulators of climatic fields and feedback parameters, the study proposes a method to generate model candidates that cover the maximal range of net feedback while minimizing the error score. The optimal candidates have large errors in the extremes of climate sensitivity but fall within the inter-model standard deviation of other models participating in the Cloud Feedback Model Intercomparison Project.