Deep Ensembles to Improve Uncertainty Quantification of Statistical Downscaling Models under Climate Change Conditions
This work addresses uncertainty quantification in climate projections for sectoral applications, but it is incremental as it applies an existing method (deep ensembles) to a specific domain.
The paper tackles the problem of statistical downscaling models for climate change conditions, which struggle with generalization due to stationarity assumptions, and finds that deep ensembles improve uncertainty quantification, enabling better risk assessment for extreme weather events.
Recently, deep learning has emerged as a promising tool for statistical downscaling, the set of methods for generating high-resolution climate fields from coarse low-resolution variables. Nevertheless, their ability to generalize to climate change conditions remains questionable, mainly due to the stationarity assumption. We propose deep ensembles as a simple method to improve the uncertainty quantification of statistical downscaling models. By better capturing uncertainty, statistical downscaling models allow for superior planning against extreme weather events, a source of various negative social and economic impacts. Since no observational future data exists, we rely on a pseudo reality experiment to assess the suitability of deep ensembles for quantifying the uncertainty of climate change projections. Deep ensembles allow for a better risk assessment, highly demanded by sectoral applications to tackle climate change.