RT Journal Article
SR 00
ID 10.1007/s00382-024-07350-8
A1 Doury, Antoine
A1 Gadat, Sébastien
A1 Somot, Samuel
T1 On the suitability of a Convolutional Neural Network based RCM-Emulator for fine spatio-temporal precipitation
JF Climate Dynamics
YR 2024
FD 2024-07-26
VO vol.62
SP 8587
OP 8613
K1 Emulator
K1 Hybrid downscaling
K1 Regional climate modeling
K1 Statistical downscaling
K1 Precipitations
K1 Deep neural network
K1 Machine learning
K1 EURO-CORDEX
K1 CORDEX
AB High resolution regional climate models (RCM) are necessary to capture local precipitation but are  too expensive to fully explore the uncertainties associated with future projections. To resolve the  large cost of RCMs, Doury et al. (2023) proposed a neural network based RCM-emulator for the  near-surface temperature, at a daily and 12 km-resolution. It uses existing RCM simulations to  learn the relationship between low-resolution predictors and high resolution surface variables.   When trained the emulator can be applied to any low resolution simulation to produce ensembles of  high resolution emulated simulations. This study assesses the suitability of applying the  RCM-emulator for precipitation thanks to a novel asymmetric loss function to reproduce the entire  precipitation distribution over any grid point. Under a perfect conditions framework, the resulting  emulator shows striking ability to reproduce the RCM original series with an excellent  spatio-temporal cor- relation. In particular, a very good behaviour is obtained for the two tails  of the distribution, measured by the number of dry days and the 99th quantile. Moreover, it creates  consistent precipitation objects even if the highest frequency details are missed. The emulator  quality holds for all simulations of the same RCM, with any driving GCM, ensuring transferability   of the tool to GCMs never downscaled by the RCM. A first showcase of downscaling GCM simulations  showed that the RCM-emulator brings significant added-value with respect to the GCM as it produces  the correct high resolution spatial nd heavy precipitation intensity. Nevertheless, further work is  needed to establish a relevant evaluation framework GCM applications.
PB Springer
SN 0930-7575
LK https://publications.ut-capitole.fr/id/eprint/50335/
UL http://tse-fr.eu/pub/130256