Statistical downscaling and projection of future temperature and precipitation change in middle catchment of Sutlej River Basin, India

Abstract

Ensembles of two Global Climate Models (GCMs), CGCM3 and HadCM3, are used to project future maximum temperature (T Max), minimum temperature (T Min) and precipitation in a part of Sutlej River Basin, northwestern Himalayan region, India. Large scale atmospheric variables of CGCM3 and HadCM3 under different emission scenarios and the National Centre for Environmental Prediction/National Centre for Atmospheric Research reanalysis datasets are downscaled using Statistical Downscaling Model (SDSM). Variability and changes in T Max , T Min and precipitation under scenarios A1B and A2 of CGCM3 model and A2 and B2 of HadCM3 model are presented for future periods: 2020s, 2050s and 2080s. The study reveals rise in annual average T Max , T Min and precipitation under scenarios A1B and A2 for CGCM3 model as well as under A2 and B2 scenarios for HadCM3 model in 2020s, 2050s and 2080s. Increase in mean monthly T Min is also observed for all months of the year under all scenarios of both the models. This is followed by decrease in T Max during June, July August and September. However, the model projects rise in precipitation in months of July, August and September under A1B and A2 scenarios of CGCM3 model and A2 and B2 of HadCM3 model for future periods.

Assessment of Climate Change for Precipitation and Temperature Using Statistical Downscaling Methods in Upper Godavari River Basin, India

“…2) GCM data The GCMs selected in this study are CGCM3 ( [20]. The gridded predictor variables of NCEP/NCAR, CGCM3 and HadCM3 for the nearest grid in study area have been directly downloaded from the websites of Data Access Integration (DAI) (http://loki.qc.ec.gc.ca/DAI/predictors-e.html) and Canadian Climate Impacts Scenarios (CCIS) (http://www.cics.uvic.ca/scenarios/index.cgi) respectively.…”

“…In this study, bias correction (BC), which is discussed in detail below, is also applied to the downscaled data obtained from the SDSMs using HadCM3 and CGCM predictors, in order to obtain a more realistic and unbiased data of future climate. The bias correction approach is used to eliminate the biases from the daily time series of downscaled data [18]- [20]. In this method, the biases are obtained by subtracting (in the case of temperature) the long-term monthly mean (1971-1990, 20 years) of observed data, from the mean monthly simulated control data (downscaled data by SDSM for the period of 1971-1990, 20 years), and dividing (in the case of precipitation) the long-term observed monthly mean data with simulated control data.…”

Assessment of impact of climate change on water resources in a hilly river basin

“…It is generated stochastically using a series of serially independent Gaussian numbers and is added to the deterministic components on daily basis. The major steps adopted for downscaling of T Max , T Min , and PCP involve (Singh et al 2015b) (1) quality check, transformation, and screening of probable predictors; (2) calibration of monthly sub-model using station scale T Max , T Min , and PCP data and selected predictors of NCEP/NCAR; (3) generation of present and future time series for T Max , T Min , and PCP from the gridded data sets of NCEP/NCAR and GCMs (CGCM3 and HadCM3); and (4) statistical analysis of downscaled projected T Max , T Min , and PCP at each individual station.…”

A Statistical Downscaling Technique for Assessment of Meteorological Parameters under Climate Change Condition Using SDSM-DC Model in Raipur District

“…Availability of data in SDSM compatible format and literature review are the main reason behind selection of CGCM3 model. Further, this model has been in a widespread way used in statistical downscaling of climate variables over Indian Sub-continent (Anandhi et al, 2008;Mahmood et al, 2012;Singh et al, 2015). The gridded predictor variables of NCEP/NCAR and CGCM3 for the nearest grid in the study area have been downloaded directly from the websites (PCIC, 2004) of Data Access Integration (DAI), (NCEP, 2001) and Canadian Climate Impacts Scenarios (CCIS) respectively.…”