Deep Ensembles to Improve Uncertainty Quantification of Statistical Downscaling Models under Climate Change Conditions

• URL: http://arxiv.org/abs/2305.00975v1
• Date: Thu, 27 Apr 2023 19:53:18 GMT
• Title: Deep Ensembles to Improve Uncertainty Quantification of Statistical Downscaling Models under Climate Change Conditions
• Authors: Jose Gonz\'alez-Abad, Jorge Ba\~no-Medina
• Abstract summary: We propose deep ensembles as a simple method to improve the uncertainty quantification of statistical downscaling models. Deep ensembles allow for a better risk assessment, highly demanded by sectoral applications to tackle climate change.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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