Government of Nepal
Ministry of Energy, Water Resources and Irrigation
Water Resources Research and Development Centre
Pulchowk, Lalitpur

WRRDC RESEARCH LETTER
Issue: 04
January 2021

Selection of General Circulation Model (GCM) ensemble over Nepal
Background
There are several GCMs available for study of dynamics of climate systems and climate change (CC) impact
assessment, however, GCMs are associated with uncertainties. Further it is not practical to use all GCMs in CC studies
due to constraints of resources. A suitable selection of GCM is therefore needed from a pool of GCMs by considering
their better capability in projecting the historical climate. This study is conducted to select GCM ensembles for CC
studies over Nepal.

Materials and Methodology
47 GCMs along with 205 ensemble members model output data from Coupled Model Inter-comparison Project Phase
5 (CMIP5) were used. Data processing and analysis were conducted at Data Integration and Analysis System (DIAS),The University of Tokyo (https://diasjp.net/en/; http://apps.diasjp.net/modelvis/cmip5/). GCM output are correlatedwith global reference dataset [Global Precipitation Climatology Project (GPCP) for precipitation, outgoing longwave
radiation (OLR) from the National Oceanic and Atmospheric Administration (NOAA), and zonal wind and meridional
wind, geo potential height, air temperature at 850 hpa and 200 hpa from Japanese 55- year Reanalysis (JRA-55)]. The
study area covers local domain (26-31 N, 79-89 E) for precipitation and Regional domain (0-50 N, 60-110 E) for othervariables. Climatological mean (1979-2005) of reference data and GCM output of these 10 parameters are analysed
for four seasons: pre-monsoon (March-May; MAM), monsoon (June-September; JJAS), post monsoon (OctoberNovember; ON) and winter (December-February; DJF). A simple scoring method is implemented throughperformance assessment of Spatial Correlation (Scorr) and Root Mean Square Error (RMSE). A score of 1 was given to
those GCMs whose Scorr are higher and RMSE are lower than the average of all GCMs; and a score of 0 is assigned ifeither of the two conditions is satisfied, and, if neither are satisfied, a score of -1 is assigned (Nyunt et al., 2016).
These scores were assigned to every key parameter, and all scores of each GCM was summed.

Results
Top ranked 10 GCM-ensemble mean is shown in Table 1.
Table 1: List of top ranked 10 GCMs
No12345678910GCM NameInstituteNational Science Foundation, Department of Energy, National Center for
CESM1(CAM5) Atmospheric Research
MPI-ESM-P
Max Planck Institute for Meteorology, Germany
CSIRO (Commonwealth Scientific and Industrial Research Organization, Australia),
ACCESS1.0
and BOM (Bureau of Meteorology, Australia)
GFDL-ESM2M Geophysical Fluid Dynamics Laboratory, USA
Atmosphere and Ocean Research Institute (The University of Tokyo), National
Institute for Environmental Studies, and Japan Agency for Marine-Earth Science
MIROC5and Technology
HadCM3
Met Office Hadley Centre, UK
CanCM4
Canadian Center for Climate Modeling and Analysis, Canada
Atmosphere and Ocean Research Institute (The University of Tokyo), National
Institute for Environmental Studies, and Japan Agency for Marine-Earth Science
MIROC4hand Technology
EC-EARTH
European Community-Earth Consortium
CanESM2
Canadian Centre for Climate Modelling and Analysis, Canada

Score
8.25
85.75
5.25

5.25
4.75
44
3.25
3.25
1

Fig. 1: Average Precipitation (mm/day) in 1979-2005
GPCP

CESM1(CAM5)

MPI-ESM-P

ACCESS1.0

MAM

Fig. 2: Average Zonal Wind (m/s) in JJAS (1979-2005)
JRA-55

CESM1(CAM5)

MPI-ESM-P

ACCESS1.0

850 hpa

200 hpa

JJAS

Fig. 3: Average Geopotential height (gpm) in JJAS (1979-2005)
JRA-55

ON850 hpa

DJF

200 hpa

CESM1(CAM5)

MPI-ESM-P

ACCESS1.0

Long term mean of spatial distribution of precipitation (mm/day) from GPCP and top ranked 3 GCMs in 4 seasons areprovided in Figure 1. Summer monsoon in JJAS and westerly flow in DJF are well correlated with good prediction.
Average zonal wind (m/s) in JJAS at 850 hpa and 200 hpa are provided in Figure 2 and the same for geopotential
height are provided in Figure 3. Both have good correlation with JRA-55 reference dataset.

Conclusions
A suitable selection of GCM over Nepal is carried out using spatiotemporal correlation coefficient and root meansquare error between reference climate dataset and GCM ensemble output. CESMI(CAM5), MPI-ESM-P, ACCESS1.0,GFDL-ESM2M, MIROC5, HadCM3, CanCM4, MIROC4h, EC-Earth and CanESM2 are found the top ranked 10 GCMensemble. Statistical bias correction of meteorological parameters for selected GCM over Nepal and their application
for climate change impact assessment on water resources would be the future tasks to be carried out.

Acknowledgment
Data Integration and Analysis System (DIAS), The University of Tokyo (http://apps.diasjp.net/modelvis/cmip5/) is
acknowledged for providing data integration resources.

Reference
Nyunt, C.T., Koike, T. and Yamamoto, A., 2016. Statistical bias correction for climate change impact on the basin scaleprecipitation in Sri Lanka, Philippines, Japan and Tunisia. Hydrol. Earth Syst. Sci. Discuss. [preprint],
https://doi.org/10.5194/hess-2016-14, 2016.

FOR FURTHER INFORMATION:
Government of Nepal
Ministry of Energy, Water Resources and Irrigation

Water Resources Research and Development Centre
Dr. Maheswor Shrestha, Executive Director, maheswor.shrestha@nepal.gov.np
Mahesh Khanal, Information Officer, mahesh.khanal@nepal.gov.np
Website: www.wrrdc.gov.np
Citation: Shrestha, M., Acharya, G., 2021. Selection of General Circulation Model (GCM) Ensemble over Nepal, WRRDC
Research Letter, Issue no: 04, January 2021, pp. 1-2.
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