CORDEX South Asia: A framework for addressing regional
monsoon issues in a changing climate
R. Krishnan, T.P. Sabin, J. Sanjay, M. Mujumdar, P. Priya, M.V. Rama Rao, Sandip Ingle, V. Ramesh, Madhura Kane
Centre for Climate Change Research
Indian Institute of Tropical Meteorology, Pune
Regional climate change projections
over South Asia
Reliable assessment of regional climate change
•High resolution is essential to represent processes properly on all ranges of possible statistical
outcomes, especially climate extremes and their impacts
•Multi-model ensemble simulations necessary for reliable regional climate projections, and to
Quantify and reduce uncertainties in regional climate projections
Scientific challenges in modeling extreme climate events
•Most occur relatively infrequently and are inherently unpredictable
•Require large ensembles to simulate the statistics
•Assess the impact of external drivers on their statistics
•Depend on dynamical processes that require relatively high resolution to represent them
WCRP CORDEX South Asia – led by CCCR, IITM
Co-ordinated Regional Downscaling Experiment – CORDEX South Asia
South Asia
CORDEX: Model Experiments

Evaulation / Baseline run with ERA – Interim boundary conditions (1989 – 2008)

Historical run (1950 – 2005)

Future projection : 2005 - 2100 (eg., RCP 4.5, 6.0, 8.5 Scenario)
Participating Modeling Groups

LMDZ model (~ 35 km ) CCCR (IITM), IPSL

RegCM model (~ 50 km)

HadRM3P model (~ 50 km) CCCR (IITM), Hadley Centre

WRF model (~ 50 km) - CCCR (IITM), BCCR and TERI

MRI model (~ 20 km) global model (MRI, Japan)

RCA model (~ 50 km) Rossby Centre, Sweden

COSMO-CLM (~ 50 km) University of Frankfurt, Germany

CCAM model ( ~ 50 km) CSIRO, Australia
CCCR (IITM)
CORDEX-South Asia Multi-Model Output
Evaluation Run (1989 – 2008); Historical (1950 – 2005); RCP4.5 scenario (2006- 2100)
(To be available by end of September 2013)
Variable name
(Monthly and Daily)
SMHI-RCA4
RegCM4GFDL
RegCM4-LMDZ
COSMO-CLM
LMDZ
Rainfall
Y
Y
Y
Y
Y
Surface Temperature
Y
Y
Y
Y
Y
Maximum Temperature
Y
Y
Y
Y
Y
Minimum Temperature
Y
Y
Y
Y
Y
Sea-level Pressure
Y
Y
Y
Y
Y
Surface Specific Humidity
Y
Y
Y
Y
Y
Surface Zonal Wind
Y
Y
Y
Y
Y
Surface Meridional Wind
Y
Y
Y
Y
Y
Downward Shortwave
Radiation
Y
Y
Y
Y
Y
LMDZ grid setup for CORDEX South Asia (shaded region has grid-size < 35 km)
Source: Sabin, CCCR, IITM
Topography (m) and model grids over the Asian region
Hindu Kush Western Ghats
Himalayas
PRITHVI (High Performance Computing System) , IITM, Pune
Configuration of PRITHVI, HPC at IITM:

IBM P6 575 nodes totaling 117 numbers including the 2 nodes for GPFS quorum
and one Login node. Each node is populated with 32 cores of IBM P 6 CPU
running at 4.7 G Hz. Total of 3744 cores with Peak Performance of 70 Tflops.

High end Servers P570’s, P550’s, 20 Visual Workstations.

Interconnectivity using Infiniband Switches and Ethernet
Management purposes

Total of 3 Peta Bytes of Storage including Online, Near-line and Archival
Storage

GPFS, Tivoli and other Management Softwares
switches for
Monsoon rainfall (JJAS)
Zoom
Zoom
Zoom
No Zoom
Mean annual cycles of rainfall (mm day -1) and surface temperature (oC) over
the Indian landmass from the zoom and no-zoom runs
No Zoom
No Zoom
Understanding regional climate change over South Asia
High resolution (~ 35 km) dynamical downscaling at CCCR, IITM
Historical (1886-2005):
Includes natural and anthropogenic (GHG,
aerosols, land cover etc) climate forcing during
the historical period (1886 – 2005) ~ 120 years
Historical Natural (1886 – 2005):
Includes only natural climate forcing during the
historical period (1886– 2005) ~ 120 years
RCP 4.5 scenario (2006-2100) ~ 95 years:
Future projection run which includes both
natural and anthropogenic forcing based on the
IPCC AR5 RCP4.5 climate scenario. The evolution
of GHG and anthropogenic aerosols in RCP 4.5
scenario produces a global radiative forcing of +
4.5 W m-2 by 2100
17
Surface Air Temperature
16
Temp
15
Temp-Natural
RCP4.5
14
13
12
Global Mean
11
10
3 461886
1906
1926
1946
1966
1986
2006
2026
2046
2066
2086
27
26
Temp
25
Temp-Natural
RCP4.5
RCP4.5
24
Global Tropics (30oS – 30oN)
23
22
1886
32
46
30
1906
1926
1946
1966
1986
2006
2026
2046
RCP4.5
2066
2086
Temp
Temp-Natural
28
RCP4.5
26
24
3 46
22
Source: Sabin, CCCR, IITM
South Asian Monsoon (70oE– 90oE; 10oN– 25oN)
3.2 14
13.8 Annual mean precipitation
3.1
13.6
RCP4.5
Global Tropics (30oS – 30oN)
13.4
3
13.2
2.9 13
12.8
2.8
Precip
12.6
2.7
12.4
1886
12.2
12 12
Precip-Natural
1906
14
1926
1946
1966
1986
2006
2026
2046
2066
2086
13.8
JJAS precipitation
South Asian Monsoon Region (70oE– 90oE; 10oN– 25oN)
11.8
10
13.6
RCP4.5
113.4
4 7 10 13 16 19 22 25 28 31 34 37 40 43 46
8
13.2
6
13
4
12.8
Precip
2
12.6
Precip-Natural
Source: Sabin, CCCR, IITM
12.4
0
1886 12.2
1906 1926 1946 1966 1986 2006 2026 2046 2066 2086
High resolution climate data for regional applications
•Regional data products, indices, extreme events
•Understanding regional climate processes: Links between regional and
large-scale variability - (eg., Heavy precipitation events and floods, heat
waves, etc).
•Applications: Climate, Hydrological, Agriculture, Health, Economy
•Evaluation using CORDEX multiple models:
variability
eg., Patterns of regional rainfall
•Impact assessment: eg., River runoff and discharge using macroscale
hydrological models
•Training workshops: Develop synergistic linkage between climate
downscaling and VIA user communities in Asia through direct user engagement
Regional Climate Products: Examples of Applications
Heat wave days Mar-Apr-May (MAM) season w.r.t 90th percentile
2010-2020
2050-2060
2080-2090
Source: Sabin, CCCR, IITM
Number of days with temperatures exceeding 45°C in MAM season
2050-2060
2010-2020
2080-2090
Source: Sabin, CCCR, IITM
Monitoring Meteorological Drought using Standardized Precipitation Index (SPI)
2002
2005
2026
2.00 or More
Wet
1.50 to 1.99
1.00 to 1.49
0.00 to 0.99
0.00 to -0.99
-1.00 to -1.49
-1.50 to -1.99
-2.00 or Less
Source: Sabin, CCCR, IITM
: Extremely
: Severely Wet
: Moderate Wet
: Mildly Wet
: Mildly Dry
: Moderate Dry
: Severely Dry
: Extremely Dry
Trend in consecutive dry day index (JJAS)
2010-2020
2040-2050
2070-2080
Source: Sabin, CCCR, IITM
Trend in number of extreme rainfall (> 100mm/day) events
2050-2060
2010-2020
2080-2090
Source: Sabin, CCCR, IITM
Regional climate processes: Links between regional and large-scale variability
Pakistan Floods 2010:
Extent of Pakistan floods detected by AIRS satellite. The Atmospheric Infrared Sounder,
AIRS, in conjunction with the Advanced Microwave Sounding Unit, AMSU, senses emitted infrared and microwave
radiation from Earth to provide a three-dimensional look at Earth's weather and climate
Source: Milind Mujumdar
•Westward shift of sub-tropical
High (Mujumdar et al. 2012)
•Westward displacement of storms
and departure of synoptic scale
circulation (Houze et al. 2011)
•Influence of midlatitude circulation,
European blocking and interaction
with tropical storms (Hong et al.
2011, Saeed et al.2011)
http: airs.jpl.nasa.gov
•Extended range prediction (~ 15
days lead) (Webster et al. 2011)
Climatology
2010
Uttarakhand (India) floods 2013
200 hPa winds 14-18 June 2013
Courtesy: Sir Prof. Brian Hoskins
Evolution of Uttarakhand heavy rainfall event (June 2013)
Interactions between southward intruding mid-latitude troughs and monsoon lows
L
14 June 2013
16 June 2013
L
15 June 2013
17 June 2013
Courtesy: Ramesh Vellore
Flood Inundated Areas in part of
Assam State: 8 June 2012 - Analysis
of Radarsat SAR data
Flood Hazard Zonation Map
of Brahmaputra and Barak
Rivers in Assam State –
Based on analysis of
satellite data during 1998 –
2005 floods
Courtesy: National Remote
Sensing Centre, India
Rainfall over the southern slopes of the Himalayas & adjoining plains during monsoon breaks
(Dhar, Soman and Mulye, 1984)
Composite during breaks (Ramesh et al. 2013 Under review)
Rainfall
Rainfall anomaly
Anomalous
northward shift
of monsoon trough
Ramesh et al. 2013
Monsoon break simulation by WRF high-resolution (10 km) model - Courtesy: Ramesh Vellore
SLP and 850 hPa winds
Precipitable water
Day 0
Day 1
500 hPa Geopotential height and winds
Day 0
Rainfall
Day 1
High resolution improves rainfall simulation over central-eastern
Hiimalayan foothills during monsoon breaks – Ramesh et al. 2013
Simulated annual mean precipitation climatology (1990-2004) bias (mm/d) against the CRU
data for 10 CMIP5 AOGCMs and their ensemble mean.
Table 2: Subset of CMIP5 AOGCMs.
Model
Label
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
CMIP5
Model Name
CanEsm2
GFDL-CM3
GFDL-ESM2M
EC-EARTH
HadCM3
HadGEM2-ES
IPSL-CM5A-LR
MIROC5
MPI-ESM-LR
MRI-CGCM3
Horizontal
Resolution
2.8 X 2.7
2.5 X 2.0
2.5X2.0
1.125o x 1.125o
3.75 X2.5
1.875x1.25
1.875x3.75
1.4x1.38
1.875x1.865
1.125x1.121
Courtesy: J. Sanjay
Simulated surface air temperature (oC)
annual cycle for the 10 selected subregions (a-j) over South Asia. The
ensemble mean (thick lines) and range
(shading) are shown for CMIP5 (green),
RCMs driven with ERAI (blue) and RCMs
driven with CMIP5 (red)
Subregion
Label
Description
Location
R1
R2
R3
R4
R5
R6
R7
R8
R9
R10
Central India (CLI)
South West India (SWI)
South East India (SEI)
North Pakistan and India (NPI)
South Pakistan and India (SPI)
Nepal (NEP)
Bangladesh (BLH)
Bhutan (BTN)
Srilanka (SLA)
Myanmar (MNR)
79oE-85oE; 20oN-26oN
73oE-76oE; 11oN-21oN
77oE-80oE; 8oN-16oN
70oE-80oE; 30oN-35oN
65oE-75oE; 23oN-30oN
80oE-89oE; 26oN-31oN
88oE-93oE; 20oN-27oN
88oE-93oE; 26oN-29oN
79oE-82oE; 5oN-10oN
92oE-101oE; 9oN-28oN
Courtesy: J. Sanjay
The simulated precipitation (mm/day)
annual cycle in the 10 selected subregions (a-j) over South Asia. The
ensemble mean (thick lines) and range
(shading) are shown for CMIP5 (green),
RCMs driven with ERAI (blue) and RCMs
driven with CMIP5 (red)
Courtesy: J. Sanjay
Patterns of rainfall variability over Himalayas from multiple models
APHRODITE
LMDZ
SMHI
COSMO-CLM
Source: Priya, CCCR
Macroscale Hydrological Modeling
 Macro-scale hydrological models(Liang et al. 1994, 1996)
are powerful tool to
 Understand and assess hydro-climatic variability and flood
processes on a river basin scale (Arnell 1999b, Nohara et al. 2006).
 Predict the river discharge at un-gauged stations
 Variable Infiltration Capacity (VIC) at 0.125 x 0.125 degree resolution
The key characterestics of VIC are
• Subgrid variablity in land- vegetation classes.
• Subgrid variability in soil moisture storage capacity
• Subgrid variability in topography with use of elevation bands.
 Meteorological inputs (precipitation, temperature and windspeed) are given
to VIC model as daily time-series.
 Land-atmosphere fluxes and water and energy balances are simulated at
daily time steps.
 Daily runoff and baseflow from VIC model is routed using separate
routing model (Lohman.et al.1996)
Source: Deepashree Raje and Priya, CCCR
VIC network Routing Model
30
2010 Pakistan floods
Analysis of extreme
precipitation
eg., Gumbell Distribution
Probabilty Density Function
0.025
0.020
TRMM Climatology
TRMM 2010
lmdz climatology
Lmdz 2010
The distribution toward which the sampling
distribution of largest values converge is called
Generalized Extreme Value (GEV) Distribution.
0.015
f(X)
Gumbell distribution is a special type of GEV
Distribution, generally known as EV Type I
distribution and it has two parameters.
0.010

  (x   )  (x   ) 
f ( x)  exp exp





 



1
0.005
0.000
25 50 75 100125150175200225250275300325350375
Rainfall (X)
Source: Priya, CCCR, IITM
  x  

s 6

location Parameter
scale parameter
Macroscale Hydrological Modeling: Indus Basin
Attock
03/15/12
Source: Priya, CCCR, IITM
0.7
0.64
Nash-Sutcliffe Efficiency
N
E f  1
2
(
Q

Q
)
 mod,i obs ,i
i 1
N
2
(
Q

Q
)
 obs ,i obs
i 1
(Nash and Sutcliffe(1970)
Without 1974, NSE = 0.723
Full Period , NSE = 0.709
Global River Discharge
(GRD) database
Source: Priya, CCCR, IITM
WCRP CORDEX South Asia Training Workshop
In partnership with CCCR-IITM, START, ICTP, CSAG, SMHI and ICSU-ROAP
17 – 20 October 2012, Pune, India
http://cccr.tropmet.res.in/cccr/home/CORDEX/oct2012/index.html
Summary
•Generation of CORDEX South Asia multi-model simulations at IITM and Partner Institutions
- Evaluation run, Historical runs and future scenarios eg. RCP4.5.
•Multi-model approach to quantify uncertainties in regional climate projections
•Development of regional capacity - CORDEX training workshops proposed to be held in
South Asia, East Asia and South East Asia in 2013, 2014 and 2015
•Standardize the format of CORDEX South Asia model outputs from multi-model ensemble
simulations
•Archival, management and sharing of CORDEX South Asia model outputs - Mechanism to
consolidate model outputs from all partner institutions
•Framework for developing an ESG node at CCCR, IITM for CORDEX South Asia
•Framework for Evaluation of CORDEX South Asia model simulations
•Develop synergistic linkage between climate downscaling and VIA user communities in Asia
through direct user engagement