A Framework to Evaluate the Climate Models

Many studies in climate change research rely on error metrics to evaluate the performance of climate models. However, the majority of these studies use only one or two metrics, which can limit the insights obtained from the analysis. This is because each metric evaluates only a specific aspect of the model-data relationship, and important information may be missed if not considered. To gain a more comprehensive understanding of model performance, it is necessary to use multiple error metrics. Doing so can reveal model strengths and weaknesses and provide insights for improving the model. Nevertheless, the choice of metrics should be based on the study's specific objectives and research questions, as different metrics may be more relevant or meaningful in different contexts. This study presents the Bergen Metric, a composite error metric that evaluates climate models' overall performance based on the p-norm framework. This approach utilizes a non-parametric clustering technique to reduce the number of error metrics without losing any relevant information. The research emphasizes the importance of using multiple error metrics to gain a thorough understanding of the model behavior. This study has evaluated 89 regional climate simulations of precipitation and temperature over Europe using 38 different error metrics for eight sub-regions in Europe, providing useful information about the metrics' performance in different regions. Furthermore, the study highlights the possibility of observing conflicting behavior among error metrics while examining a single model, underscoring the need for using multiple error metrics tailored to specific use cases. Overall, the Bergen Metric framework provides a useful tool to assess climate model performance and simplify the interpretation of results from multiple error metrics.