This document summarizes research from the CORDEX-South Asia climate modeling experiments for the Himalayan region. It discusses: 1) 11 regional climate models that were forced by different global climate models to produce higher resolution climate projections for South Asia. 2) Analysis of the models' ability to simulate present day precipitation and temperature patterns compared to observations, including the representation of seasonal cycles and variability. 3) Projections of future climate changes from the models under different emissions scenarios, including increasing precipitation trends in the Himalayas and changes to the seasonal cycle of precipitation.

1. Himalayan Climate : Past and Future
A P Dimri
School of Environmental Sciences
Jawaharlal Nehru University
New Delhi, India
Email: apdimri@Hotmail.com
Website: https://crsl.jnu.ac.in

2. CORDEX-South Asia Experiments
• COordinated Regional Climate Downscaling EXperiment – South
Asia (CORDEX-SA) domain.
• Sponsored by World Climate Research Programme (WCRP) to
direct an international coordinated framework to produce an
improved generation of regional climate change projections (Giorgi
et al. 2009).
• Regional coordinating institute – IITM,Pune, India
• 12 different suites of dynamically downscaled GCMs.
• 6 Regional RCMs with 10 initial and boundary conditions from
different GCMs.
• ~ 0.44° resolution (approx. 50 km grid spacing).
• Database – Center for Climate Change Research (CCCR), Indian
Institute of Tropical Meteorology, Pune, India.
2

3. S. No. Experiment Name
Name used
RCM Description Driving GCM Contributing Institute
1 COSMO-CLM
COSMO COnsortium for Small-scale
MOdelling (COSMO) model in
CLimate Mode version 4.8 (CCLM;
Dobler and Ahrens, 2008)
Max Planck Institute for
Meteorology, Germany, Earth
System Model (MPI-ESM-LR;
Giorgetta et al 2013)
Institute for Atmospheric and Environmental
Sciences (IAES), Goethe University, Frankfurt
am Main (GUF), Germany
2 ICHEC-EC-EARTH-SMHI-RCA4
ICHEC
Rossby Centre regional atmospheric
model version 4 (RCA4; Samuelsson
et al., 2011)
Irish Centre for High-End
Computing (ICHEC), European
Consortium ESM (EC-EARTH;
Hazeleger et al. 2012)
Rosssy Centre, Swedish Meteorological and
Hydrological Institute (SMHI), Sweden
3 ACCESS-CSIRO-CCAM ACCESS
Commonwealth Scientific and
Industrial Research Organisation
(CSIRO), Conformal-Cubi
Atmospheric Model (CCAM;
McGregor and Dix, 2001)
ACCESS1.0
CSIRO Marine and
Atmospheric Research, Melbourne, Australia
4 CNRM-CM5-CSIRO-CCAM CNRM CNRM-CM5
5 CCSM4-CSIRO-CCAM CCSM4 CCSM4
6 GFDL-CM3-CSIRO-CCAM GFDL-CM3 GFDL-CM3
7 MPI-ESM-LR-CSIRO-CCAM MPI MPI-ESM-LR
8 NorESM1-M-CSIRO-CCAM NorESM NorESM-M
9 LMDZ-IITM-LMDZ
LMDZ
Institut Pierre-Simon Laplace (IPSL)
Laboratoire de Me´te´orologie
Dynamique Zoomed version 4
(LMDZ4) atmospheric general
circulation model ( Sabin et al., 2013)
IPSL Coupled Model version 5
(IPSL-CM5-LR; Dufresne et al.
2013)
Centre for Climate Change Research (CCCR),
Indian Institute of Tropical Meteorology (IITM),
India
10 LMDZ-IITM-RegCM4
LMDZ-RegCM4
The Abdus Salam International Centre
for Theoretical Physics (ICTP)
Regional Climatic Model version 4
(RegCM4; Giorgi et al., 2012)
IPSL LMDZ4 CCCR, IITM
11 GFDL-ESM2M-IITM-RegCM4
GFDL-ESM2M
ICTP RegCM4
Geophysical Fluid Dynamics
Laboratory, USA, Earth System
Model (GFDL-ESM2M-LR;
Dunne et al. 2012)
CCCR, IITM
Table 1: List of CORDEX-SA (RCM) Experiments
(Source: CORDEX South-Asia Database, CCCR, IITM http://cccr.tropmet.res.in/cordex/files/downloads.jsp)

4. CORDEX South Asia Domain (Source:
http://www.clm-community.eu/images/420_r06.png)
Fig. 1: Topography (m) over (a)
Himalayan and Tibetan region (m, grey
shaded) and over (b) study area (m,
color shaded).

5. Fig. 2: Observed JJAS precipitation (mm/day)
climatology during 1970-2005 over the study
area as shown in Fig.1b in (a) APHRODITE
(b) GPCC and (c) CRU.
Observations
Fig. Variation in the observed precipitation (mm/day) and trend (mm/day/year)
with elevation.
Altitudinal variation observed JJAS mean precipitation
(from APHRODITE 0.25)

6. Fig. precipitation bias (mm/day) for different
experiments.
Fig. (a) Mean annual cycle of precipitation (mm/day) over the
period of 1970-2005 and (b) monsoon months (JJAS) precipitation
cycle from the 11 CORDEX experiments, their ensemble and
corresponding observation. Nomenclature given in Fig.3b
corresponds to the respective CORDEX experiment as described in
detail in Table 1.
Seasonal Cycle

7. Fig. 7: Variation of precipitation bias (mm/day) with elevation for the experiments and ENS.
Altitudinal variation of model bias (from APHRODITE) of JJAS mean precipitation

8. Fig. (a) Standard deviation (mm/day) for observation and (b-m)
same as Fig. 4, but for standard deviation of precipitation.
Interannual variability
Fig. JJAS precipitation trend (mm/day/year).
Annual Trend

9. S.N. Experiments b(slope
parameter,Regression
coefficient)
Standard
error
t-ratio p-value
(significance)
1 ACCESS-CSIRO-CCAM -0.025 0.013 -1.864 0.071
2 GFDL-CCM3-CSIRO-CCAM -0.021 0.014 -1.573 0.125
3 CCSM4-CSIRO-CCAM -0.018 0.011 -1.643 0.11
4 CNRM-CM5-CSIRO-CCAM -0.013 0.011 -1.165 0.252
5 GFDL-ESM2M-IITM-RegCM4 0.003 0.008 0.405 0.688
6 LMDz - IITM - LMDz 0.027 0.017 1.554 0.129
7 LMDz - IITM - RegCM4 0.007 0.005 1.521 0.137
8 MPI-ESM-LR-CSIRO-CCAM -0.003 0.014 -0.189 0.851
9 ICHEC-EC-EARTH-SMHI-
RCA4
0.012 0.011 1.09 0.284
10 Nor-ESM1M-CSIRO-CCAM -0.015 0.012 -1.224 0.229
11 COSMO-CLM -0.013 0.012 -1.06 0.297
12 ENSEMBLE -0.005 0.003 -1.605 0.118
13 APHRODITE -0.006 0.008 -0.834 0.41
Table 2: Precipitation trends (mm/day/year) in the study during summer (JJAS) for the
experiments, ENS and APH

10. Fig. (a) Time series of seasonal JJAS precipitation (mm/day) of 11
CORDEX experiments, their ensemble and the corresponding
observation averaged over the study region (Fig. 1b), and
Fig. standard deviation of precipitation (mm/day) for
1970-2005 over the study area for 11 experiments
Uncertainty/Spread :
Spatial variation
Uncertainty/Spread : Temporal variation

11. Experiment selection
Spatial correlation of the JJAS precipitation (mm/day) between
the ICHEC and ensemble with observation (APHRODITE)

12. Future projections of climate
over Himalayan region from
CORDEX-SA experiments
A. Choudhary and A. P. Dimri. Assessment of CORDEX-South Asia experiments for monsoonal precipitation over
Himalayan region for future climate. Climate Dynamics, 2017, 1-22, DOI 10.1007/s00382-017-3789-4

13. Mean percentage change in JJAS precipitation averaged over
Himalayan region for different RCPs and future time periods
from different experiments and their ensemble.

14. (upper panel) Altitudinal variations of near future (2020-2049) trend
(mm/day/year) in JJAS mean precipitation over Himalayan region for (a)
RCP2.6 (b) RCP4.5 and (c) RCP8.5. Also shown is the number of grid points
falling in different elevation ranges. (lower panel) Same as (a) but for far future
(2070-2099).
128
68
45
62
110
82
2
0
20
40
60
80
100
120
140
128
68
45
62
110
82
2
0
20
40
60
80
100
120
140
128
68
45
62
110
82
2
0
20
40
60
80
100
120
140
128
68
45
62
110
82
2
0
20
40
60
80
100
120
140
128
68
45
62
110
82
2
0
20
40
60
80
100
120
140
128
68
45
62
110
82
2
0
20
40
60
80
100
120
140
(a) RCP2.6 (b) RCP4.5 (c) RCP8.5
(a) RCP2.6 (b) RCP4.5 (c) RCP8.5
Trend (mm/day/year)
Elevation(m)
Number of grid points
Variation of trend in precipitation with elevation

15. Elevation(m)
Difference in trend (mm/day/year)
128
68
45
62
110
82
2
0
20
40
60
80
100
120
140
128
68
45
62
110
82
2
0
20
40
60
80
100
120
140
128
68
45
62
110
82
2
0
20
40
60
80
100
120
140
128
68
45
62
110
82
2
0
20
40
60
80
100
120
140
128
68
45
62
110
82
2
0
20
40
60
80
100
120
140
128
68
45
62
110
82
2
0
20
40
60
80
100
120
140
Number of grid points
(a) RCP2.6 (b) RCP4.5 (c) RCP8.5
(c) RCP8.5(b) RCP4.5(a) RCP2.6
Variation of difference in trend in precipitation with elevation

16. (a) Change in annual cycle averaged over Himalayan region between near future scenario (2020-2049)
and present climate (1970-2005) for (aa) RCP2.6 (ab) RCP4.5 (ac) RCP8.5. (b) Same as (a) but for far
future scenario (2070-2099).

17. Temperature climatology over
Himalayan region from CORDEX-SA
T. Nengker, A. Choudhary and A. P. Dimri. Assessment of the performance of CORDEX-SA experiments in simulating
seasonal mean temperature over the Himalayan region for the present climate: Part I. Climate Dynamics, 2017, 1-31,
DOI:10.1007/s00382-017-3597-x.

18. • For the preliminary comparison of climatology, 5 gridded observational
datasets have been used:
a)APHROTEMP V1204R1 (Asian temperature from APHRODITE):
-0.25˚ resolution.
- 0.5˚ resolution.
[Yasutomi et al. (2011).]
b) Climatic Research Unit (CRU): 0.5˚ resolution
[Mitchell and Jones (2005) ]
c) National Centers for Environmental Prediction (NCEP): 1.87˚ resolution.
[Kalnay et al. (1996)]
d) University of Delaware (UDEL) : 0.5˚ resolution.
[Matsuura and Willmott (2012)]
• CORDEX data:
- Refer the table.
CORDEX database: Centre for Climate Change Research, IITM,Pune,
India. (cccr.tropmet.res.in)

19. Observed seasonal
temperature
(˚C) climatology during
1970-2005 over the study
area.
DJF MAM JJAS ON
APHROTEMP 0.25
APHROTEMP 0.5
CRU 0.5
UDEL 0.5
NCEP 1.87

20. Temperature Bias
(°C) between model
and the observation
DJF MAM JJAS ON
COSMO-CLM
REMO
ICHEC
GFDL
ENSEMBLE
LMDz

21. Temperature trend
(°C/year) for
Observations for
1970-2005
DJF MAM JJAS ON
APHROTEMP 0.5
APHROTEMP 0.25
CRU
UDW
NCEP

22. Temperature Trend(°C) for 5
CORDEX experiment
for 1970-2005
DJF MAM JJAS ON
COSMO-CLM
LMDz
ICHEC
GFDL
ENSEMBLE
REMO

23. DJF MAM ONJJAS
COSMO-CLM
LMDZ
REMO
ICHEC
ENSEMBLE
GFDL
Variation in seasonal mean
temperature bias (˚C) with
elevation(m) for the 5
experiments and their ensemble.

24. Spatial variation of ensemble spread among the 5 CORDEX experiments for respective seasons

25. 0
1000
2000
3000
4000
5000
6000
-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25
0
1000
2000
3000
4000
5000
6000
-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25
86
61
59
60
161
46
0 50 100 150 200
No. of grid points
86
61
59
60
161
46
0 50 100 150 200
No. of grid points
0
1000
2000
3000
4000
5000
6000
-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2
86
61
59
60
161
46
0 50 100 150 200
No. of grid points
0
1000
2000
3000
4000
5000
6000
-0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25
86
61
59
60
161
46
0 50 100 150 200
No. of grid points
Variation in seasonal
mean temperature
trend (˚C/year) with
elevation (m) for
APHROTEMP 0.5
Elevation(m)
Elevation(m)
Elevation(m)
Elevation(m)
DJF Temperature (˚C)
JJAS Temperature (˚C) ON Temperature (˚C)
MAM Temperature (˚C)

26. COSMO-CLM
REMO
LMDZ
ICHEC
GFDL
ENSEMBLE
DJF ONJJASMAM
Variation in
seasonal mean
temperature trend
(˚C/year) with elevation
(m) 5 CORDEX-SA
experiments and their
ensemble

27. DJF MAM JJAS ON
COSMO-CLM
REMO
LMDZ
ICHEC
GFDL
ENSEMBLE
Variation of difference in
trend of seasonal mean
temperature (˚C/year) with
elevation (m) between 5
CORDEX-SA experiments and
APHROTEMP 0.5

28. Future temperature projections
over Himalayan region
A. P. Dimri, D. Kumar, A. Choudhary and P. Maharana. Future changes over the Himalayas: Maximum and minimum
temperature. Global and Planetary Change, 162, 212-234, https://doi.org/10.1016/j.gloplacha.2018.01.015.
A. P. Dimri, D. Kumar, A. Choudhary and P. Maharana. Future changes over the Himalayas: Mean temperature. Global
and Planetary Change, 162, 235-251, https://doi.org/10.1016/j.gloplacha.2018.01.015.

29. • Studies have suggested a general Increase in temperature over various parts of the
Himalayan region:
– Increase in winter temperature (+0.6°C) and decrease in summer temperature (~1°C)
over some of the stations (Fowler and Archer 2005).
– For western Himalaya; Dimri and Dash (2006) stated that for the period 1975-2006
the increase in temperature was found to be +0.6 to +1.3°C.
– For eastern Himalaya; Shreshtha and Devkota (2010) reported an increasing trend of
the order of 0.01°C/yr or more.

30. Trends of (a) DJF [a-e] (b) MAM [f-j] (c) JJAS [k-o] and (b)
ON [p-t] mean surface temperature (°C/yr) over the period
1970-2099 under RCP4.5 as simulated from CORDEX-SA
experiments and their respective ensembles, except for LMDZ-
IITM_REGCM4 experiment , for which the period is 1970-
2060.
RCP2.6
RCP4.5

31. Trends of (a) DJF [a-c] (b) MAM [d-f] (c) JJAS [kg-i] and (b) ON [j-l] mean surface temperature (°C/yr) over the period 1970-2099 under
RCP8.5 as simulated from CORDEX-SA experiments and their respective ensembles.
RCP8.5

32. Trends of the mean near surface air temperature (°C) for the period (1970-
2099)including present (1970-2005) and future climate (2006-2100) under RCP4.5
scenarios atevery grid point over the Himalayan region plotted against elevation. The
scatter plot of trends from individual model experiments with ensembles (thick red
line) under CORDEX-SA framework for (a) DJF, (b) MAM, (c) JJAS and (d) ON
seasons have been shown as background dots; the curves in same color as their
corresponding dots represent the mean in 100 m classes of altitude smoothed by
LOWESS method (Clevland, 1979). The error bar in red shows the spatial variability
within each 100m class while that in black shows the intermodal variability within the
same class. The rectangular bars with numbers indicate the number of grid points
falling within each 1000m altitude range.
RCP8.5
RCP4.5

33. Regional Scale Impacts
North-East India, Indus River Basin, Satluj river
Basin, Upper Ganga Basin

34. Trend of precipitation (mm/day/yr) from APHRODITE (a-d)
and REMO model (e-h).
Climatology (1970-2005) precipitation (mm/day) from
APHRODITE (a-d) and REMO model (e-h) and
corresponding bias (i-l).
• Precipitation climatology are well represented in
space in all the seasons.
• Overestimation over the orographic regions in the
simulated precipitation.
• Drier precipitation climatology during monsoon
(JJAS) season attributed to insufficient
characterization of monsoonal flow (Jacob et al.,
2012; Dash et al., 2006).
• Stronger pre-monsoon precipitation indicating the
role of local evaporation and convective activities
during MAM seasons.
• Observation data suggest an increasing trends of
precipitation during MAM season across the area
homogeneously.
• Model is not able to capture the evolution of
precipitation across time, as the trends are not well
captured in the simulation.
• During post-monsoon season, both observation and
model displays a decreasing trend however, model
overestimates the trend.
Spatial distribution of Climatology,
Bias and linear trend of Precipitation
Source: D. Kumar and
A. P. Dimri. Regional
Climate projections for
Northeast India: An
Appraisal from CORDEX
South Asia Experiment.
Theoretical and Applied
Climatology.
https://doi.org/10.100
7/s00704-017-2318-z.

35. Precipitation Bias (mm/d) with respect to
elevation over NEI.
(a) (b)
(c) (d)
(a) (b)
(c) (d)
Temperature bias (mm/d) with respect to
elevation over NEI.
• Orographic precipitation in the RCM is
overestimated as reported in different studies
[Chaudhary et al., 2017; Giorgi et al., 1993]
• Seasons with higher rainfall distribution have
higher bias magnitude as well as spatial
variability w.r.t. elevation.
• Improper characterization of monsoon in the
RCM simulation caused underestimation of
precipitation in the lower altitude regions as
seen in climatology [refer previous slide].
• DJF precipitation is well represented by the
model with comparatively lesser magnitude of
Bias.
• Strong cold bias in all seasons, with
higher magnitude at higher elevations
attributed to the interactions of
complex terrain and associated land
surface feedbacks.
• Higher spatial variability of bias at
higher altitudes might be due to
different terrain features like slope,
aspect and relief.
Bias with elevation

36. (a) (b)
(c) (d)
• Higher year to year variability in daily
mean precipitation in all the seasons.
• Negative precipitation anomalies in JJAS
especially in the recent decades.
• Possible role of Eurasian snowfall, Arctic
Oscillation and Atlantic SSTs (Prabhu et
al., 2017).
• Similar pattern in the DJF season due to
reduced daily mean rainfall.
Standardized anomaly of the seasonal
daily mean 2m air temperature (e-h)
during the period 1970-2005 over the
study area from APHROTEMP (0.25°)
dataset.
(e) (f)
(g) (h)
Standardized anomaly of the seasonal daily
mean precipitation during the period 1902-
2015 over the study area from IMD (0.25°)
precipitation dataset.
• Mixed pattern of anomaly for temperature
for all the seasons.
• Pre-monsoon (MAM) season indicates
higher frequency of negative anomalies
especially in the recent decades,
portraying possible cooling due to higher
rate of evaporation because of increase in
temperature subject to further verification.
Inter-annual variability of
Precipitation and Temperature

37. Total water storage (calculated as total precipitation (pr) minus
evaporation (evspsbl) minus runoff (mrro)) (pr-evspsbl-mrro)
climatology (mm/year) during 1970–2005 over Indus River
Basin (IRB) from regional climate model (REMO) regional model
(a–d) driven by MPI-ESM-LR global model under CORDEX-SA for
DJF (December, January, February), MAM (March, April, May),
JJAS (June, July, August, September) and ON (October,
November) seasons and trend of total water storage (mm/year)
during 1970–2099 over IRB from REMO regional model driven
by MPI-ESM-LR global model under CORDEX-SA, under RCP2.6
(e–h) and RCP8.5 (i–l) for DJF, MAM, JJAS and ON seasons
Source : A. P. Dimri, D. Kumar, S. Chopra and A. Choudhary. Indus: Climate
and Water Budget. International Journal of Climatology, 2018;1–12.
https://doi.org/10.1002/joc.5816.

38. Satluj river basin
Upper Satluj
river basin
JJAS mean temperature in the recent past
and future projections from REMO2009
simulations
JJAS mean precipitation in the recent past and
future projections from REMO2009 simulations

39. Topography over the Upper Ganga Basin from a) Control b)
Subgrid-BATS and c) CLM4.5 coupled model experiments.

40. Dynamics Hydrostatics
Regional Climate Model
Model Domain
RegCM-4.4.5.5
CORDEX-SA, 10°E-130°E and -
22°S-50°N
Resolution 50 Km horizontal and 18 vertical
sigma levels
Initial and boundary
conditions
MIROC5 from CMIP5 programme
Prognostic Variables (u, v, t, q, ps)
SST MIROC5
Land surface treatment A) Control
B) SUB-BATS
C) CLM4.5
Radiation Parameterization Modified CCM3
PBL parameterization Modified Holtslag
Convective
Parameterization
Grell over land and ocean for
Control and SUB-BATS
Emanuel over land and ocean for
CLM4.5
Period 1972-2005 (2 year spinup)
Observation
• India Meteorological Department
gridded daily precipitation dataset
(~0.5°) (Pai et al., 2014).
• Climatic Research Unit monthly mean
temperature dataset (~0.5°) (Harris et al.
2013).
• Station data from IMD stations as shown
in previous slide.

41. IMD
CRU
Control
SUB-BATS
CLM4.5
A) Daily mean precipitation climatology (mm/day) and B) Daily mean near surface air temperature climatology
(°C) for 1972-2005 over upper Ganga river basin for different seasons and experiments.
A) B)
In the next step, bias correction of the simulated output is planned with respect to the available observed station
data as shown in previous slides.
An inter-comparison of the four different bias correction method and their performance in minimizing the mean
bias over the upper Ganga basin will also be explored.

42. Conclusions
• Consistent increase in temperature during recent past across the Himalayan region as suggested by
observation as well as model data.
• Season specific response to warming pattern with greater warming rates in Winter and Post monsoon
season with possible dependence over the elevation.
• With some inherent biases associated with temperature as well as precipitation in the model
environment; still model could capture the spatial features of precipitation well.
• Such systematic biases are associated with orographic process, emphasizing the need for further
improvement in the model physics aware of the scale so that further high resolution simulations to
assess the more local forcing can be captured.
• Besides, the errors in the model simulations, it provides better avenues for providing the information
under future climate.
• Changes in temperature and precipitation shows different signatures under different emission scenarios
with temperature changes up to 10°C under the strongest emission scenario while DJF and ON
seasons being the warmest.
• Precipitation changes may lead to a deficit of up to 60-80% depending upon the season (especially
DJF) and scenario.