An improved deep learning procedure for statistical downscaling of climate data

cg.contacta.kheir@cgiar.orgen_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.centerAgricultural Research Center Egypt - ARC Egypten_US
cg.contributor.centerCairo University - CU Egypten_US
cg.contributor.centerAgricultural Research Center, Soil, Water and Environment Research Institute - ARC - SWERIen_US
cg.contributor.funderInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.projectCommunication and Documentation Information Services (CODIS)en_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.creator.idGovind, Ajit: 0000-0002-0656-0004en_US
cg.identifier.doihttps://dx.doi.org/10.1016/j.heliyon.2023.e18200en_US
cg.isijournalISI Journalen_US
cg.issn2405-8440en_US
cg.journalHeliyonen_US
cg.subject.agrovocstandardizationen_US
cg.volume9en_US
dc.contributorElnashar, Abdelrazeken_US
dc.contributorMosaad, Alaaen_US
dc.contributorGovind, Ajiten_US
dc.creatorKheir, Ahmed M.S.en_US
dc.date.accessioned2023-11-08T18:01:00Z
dc.date.available2023-11-08T18:01:00Z
dc.description.abstractRecent 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.en_US
dc.formatPDFen_US
dc.identifierhttps://mel.cgiar.org/reporting/downloadmelspace/hash/4cd870452693495f24513765593ca4f8/v/b3195535fc62c90feab60c73d2deeb6cen_US
dc.identifier.citationAhmed M. S. Kheir, Abdelrazek Elnashar, Alaa Mosaad, Ajit Govind. (19/7/2023). An improved deep learning procedure for statistical downscaling of climate data. Heliyon, 9.en_US
dc.identifier.statusOpen accessen_US
dc.identifier.urihttps://hdl.handle.net/20.500.11766/68770
dc.languageenen_US
dc.publisherElsevier (12 months)en_US
dc.rightsCC-BY-NC-ND-4.0en_US
dc.sourceHeliyon;9,(2023)en_US
dc.subjectconvolutional neural networken_US
dc.subjectclimate change scenariosen_US
dc.subjectgcm sspen_US
dc.subjectbias adjustmenten_US
dc.titleAn improved deep learning procedure for statistical downscaling of climate dataen_US
dc.typeJournal Articleen_US
dcterms.available2023-07-19en_US
mel.impact-factor4.0en_US

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