CMIP X-MOS: Improving Climate Models with Extreme Model Output Statistics
This work addresses the challenge of predicting climate extremes for decision-makers assessing global climate-related risks, representing an incremental improvement over existing methods.
The paper tackled the problem of climate models inaccurately predicting climate extremes by introducing Extreme Model Output Statistics (X-MOS), which uses deep regression to map model outputs to real measurements, resulting in improved analysis of climate extremes.
Climate models are essential for assessing the impact of greenhouse gas emissions on our changing climate and the resulting increase in the frequency and severity of natural disasters. Despite the widespread acceptance of climate models produced by the Coupled Model Intercomparison Project (CMIP), they still face challenges in accurately predicting climate extremes, which pose most significant threats to both people and the environment. To address this limitation and improve predictions of natural disaster risks, we introduce Extreme Model Output Statistics (X-MOS). This approach utilizes deep regression techniques to precisely map CMIP model outputs to real measurements obtained from weather stations, which results in a more accurate analysis of the XXI climate extremes. In contrast to previous research, our study places a strong emphasis on enhancing the estimation of the tails of future climate parameter distributions. The latter supports decision-makers, enabling them to better assess climate-related risks across the globe.