Open AccessDownscaling of monthly precipitation using CMIP5 climate models operated under RCPs
Author(s) -
Okkan Umut,
Kirdemir Umut
Publication year - 2016
Publication title -
meteorological applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.672
H-Index - 59
eISSN - 1469-8080
pISSN - 1350-4827
DOI - 10.1002/met.1575
Subject(s) - downscaling , precipitation , gcm transcription factors , environmental science , climatology , representative concentration pathways , general circulation model , meteorology , climate change , geology , geography , oceanography
Downscaling of general circulation model ( GCM ) outputs extracted from CMIP5 datasets to monthly precipitation for the Gediz Basin, Turkey, under Representative Concentration Pathways ( RCPs ) was performed by statistical downscaling models, multi‐ GCM ensemble and bias correction. The output databases from 12 GCMs were used for the projections. To determine explanatory predictor variables, the correlation analysis was applied between precipitation observed at 39 meteorological stations located over the Basin and potential predictors of ERA‐I nterim reanalysis data. After setting both artificial neural networks and least‐squares support vector machine‐based statistical downscaling models calibrated with determined predictor variables, downscaling models producing the most suitable results were chosen for each meteorological station. The selected downscaling model structure for each station was then operated with historical and future scenarios RCP 4.5, RCP 6.0 and RCP 8.5. Afterwards, the monthly precipitation forecasts were obtained from a multi‐ GCM ensemble based on Bayesian model averaging and bias correction applications. The statistical significance of the foreseen changes for the future period 2015–2050 was investigated using Student's t test. The projected decrease trend in precipitation is significant for the RCP8 .5 scenario, whereas it is less significant for the RCP4 .5 and RCP6 .0 scenarios.