An improved deep learning procedure for statistical downscaling of climate data

Ahmed M S Kheir; Abdelrazek Elnashar; Alaa Mosad; Ajit Govind

doi:10.1016/j.heliyon.2023.e18200

An improved deep learning procedure for statistical downscaling of climate data

Heliyon. 2023 Jul 20;9(7):e18200. doi: 10.1016/j.heliyon.2023.e18200. eCollection 2023 Jul.

Authors

Ahmed M S Kheir^{1

2}, Abdelrazek Elnashar³, Alaa Mosad^{1

2}, Ajit Govind¹

Affiliations

¹ International Center for Agricultural Research in the Dry Areas (ICARDA), Maadi 11728, Egypt.
² Soils, Water and Environment Research Institute, Agricultural Research Center, 9 Cairo University Street, Giza 12112, Egypt.
³ Department of Natural Resources, Faculty of African Postgraduate Studies, Cairo University, Giza 12613, Egypt.

Abstract

Recent climate change (CC) scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6) have just been released in coarse resolution. Deep learning (DL) based on statistical downscaling has recently been used, but more research is needed, particularly in arid regions, because little is known about their suitability for extrapolating future CC scenarios. Here we analyzed this issue by downscaling maximum, and minimum temperature over the Egyptian domain based on one General Circulation Model (GCM) as CanESM5 and two shared socioeconomic pathways (SSPs) as SSP4.5 and SSP8.5 from CMIP6 using Convolutional Neural Network (CNN) herein after called CNNSD. The downscaled maximum and minimum temperatures based CNNSD was able to reproduce the observed climate over historical and future periods at a finer resolution (0.1°), reducing the biases exhibited by the original scenario. To the best of our knowledge, this is the first time CNN has been used to downscale CMIP6 scenarios, particularly in arid regions. The downscaled analysis showed that maximum and minimum temperatures are expected to rise by 4.8 °C and 4.0 °C, respectively, in the future (2015-2100), compared to the historical period, under the moderate scenario (SSP4.5). Meanwhile, under the Fossil-fueled Development scenario (SSP8.5), these values will rise by 6.3 °C and 4.2 °C, respectively as analyzed by the CNNSD. The developed approach could be used not only in Egypt but also in other developing countries, which are especially vulnerable to climate change and has a scarcity of related research. The established downscaled approach's supply can be used to provide climate services, as a driver for impact studies and adaptation decisions, and as information for policy development. More research is needed, however, to include multi-GCMs to quantify the uncertainties between GCMs and SSPs, improving the outputs for use in climate change impacts and adaptations for food and nutrition security.

Keywords: Bias adjustment; Climate change scenarios; Convolutional neural network; GCM; SSP; Standardization.