Changes in the characteristics of
precipitation and temperature in
india
S. K. Dash
Acknowledgements to Co-authors in papers:
J. C. R. Hunt, F. Giorgi, M. Kulkarni, A.P.Dimri, U. C. Mohanty, S.
R. Kalsi, K. Prasad, R. K. Jenamani, M. S. Shekhar, S. K. Panda,
Ashu Mamgain and Archana
Centre for Atmospheric Sciences
Indian Institute of Technology Delhi
Outline of the talk
 Spatial & Temporal Changes in
Temperature and Precipitation
 Agriculture & Health Applications
 Model Simulations
 Future Challenges
Spatial & Temporal Changes in Temperature
and Rainfall
Homogeneous rainfall zones of India. The numbers inside
the zones indicate mean monsoon rainfall (mm), standard
deviation (mm) and coefficient of variation (%) from top to
bottom respectively
Dash et al., 2002, Mausam, 53(2), 133-144
Western Himalaya
+ 0.9 0C
+0.5 0C
Northwest
+ 0.6 0C
- 0.2 0C
North Central
+ 0.8 0C
+ 0.2 0C
Northeast
+ 1.0 0C
+0.2 0C
Interior Peninsula
+ 0.5 0C
+ 0.5 0C
West Coast
+ 1.2 0C 0
+ 0.4 0C
East Coast
+ 0.6 0C
+ 0.2 0C
Changes in the maximum and minimum temperatures in different temperature
zones during the last century. The upper numbers indicate maximum and lower
ones represent minimum temperatures. Also + sign is for an increase and – is for
decrease.
The
map
of
seven
zones
has
been
obtained
from
http://www.tropmet.res.in
Surface air temperature (0C) changes during different seasons
averaged over the whole of India (+ sign indicates an increase
and – represents decrease)
Months
Mean (0C)
Maximum (0C)
Minimum (0C)
Jan & Feb
(Winter)
+1.0
+1.0 – +1.2
+0.2 – +0.7
March-May
(Pre-monsoon)
+0.3
+0.6 – +0.8
- 0.1 – +0.2
June-September
(Monsoon)
+0.4
+0.4 – +0.6
-0.2 – +0.4
OctoberDecember
(Post-monsoon)
+1.1
+1.1 – +1.3
+0.6 – +0.8
Temperature
Indices
TX90p, TX95p,
TX99p
TN90p, TN95p,
TN99p
TX10p, TX05p,
TX01p
TN10p, TN05p,
TN01p
WSDI
CSDI
Extreme temperature indices used
Names
Definitions
Warm days
Count
of
days
where
maximum
temperature TX > 90th, 95th and 99th
percentile respectively
Warm nights Count
of
days
where
minimum
temperature TN > 90th, 95th and 99th
percentile respectively
Cold days
Count
of
days
where
maximum
temperature TX < 10th, 5th and 1th
percentile respectively
Cold nights
Count
of
days
where
minimum
temperature TN < 10th, 5th and 1th
percentile respectively
Warm Spell
Count of events where maximum
Duration Index temperature TX >90th percentile for at
least six days continuously
Cold Spell
Count of events where minimum
Duration Index temperature TN <10th percentile for at
least six nights continuously
IETCCDI Klein Tank et al. (2009) http://www.clivar.org/organization/etccdi/etccdi.php
Dash et al., 2011, J Appl Met & Climat
Summary of trends in annual and seasonal means of maximum and minimum
temperatures with trends in different categories of warm days and nights in
summer
Dash et al., 2011, J Appl Met & Climat
Summary of trends in annual and seasonal mean of maximum and minimum
temperatures with trends in different categories of cold days and nights in
winter
Dash et al., 2011, J Appl Met & Climat
(b) Summer
nights
(c) Winter days
(d) Winter nights
Density
Density
(a) Summer days
Temperature (oC)
Temperature (oC)
Decadal variations in the temperature probability density functions (PDFs)
for entire India in the years from 1969 to 2005 during (a) summer days, (b)
summer nights, (c) winter days and (d) winter nights. The smoothed curves
represent the fit of the GEV distribution.
Dash et al., 2011, J Appl Met & Climat
Some inferences on surface temperature
 Indication of warming:
Differences in
the trends in the minimum and maximum
temperatures in the North and south. Different
impacts of Ocean and Himalayas to be examined.
 Asymmetry in the increasing
temperature trends between
different seasons. In last 2-3 decades the
increase in maximum and minimum temperatures
during October to February is about 0.30C more than
during rest of the months.
 Significant decrease in the frequency of occurrence of
cold nights in the winter months and increasing trend
in the number of warm days in summer.
92.0
11-years running means (Monsoon)
1999
1991
1983
78.0
1975
78.0
1967
80.0
1959
80.0
1951
82.0
1943
82.0
1935
84.0
1927
84.0
1919
86.0
1911
86.0
1903
88.0
1895
88.0
1887
90.0
1879
90.0
1871
Rainfall (cm)
92.0
Year
Time series of rainfall in India during monsoon
months of June, July, August and September.
Dash et al., 2007, Climatic Change, DOI 10.1007
Classification of rain events based on intensities
Gamma Cumulative Distribution Function was fitted to
the daily rainfall values to categorise the rain events in
different groups.
The classification is made as follows
 Heavy : Inverse of gamma CDF for probability
≥ 0.99
 Moderate : Inverse of gamma CDF for probabilities
lie between ≥ 0.4 & <0.99
 Low
: Inverse of gamma CDF for probability
< 0.4
Characteristics of rain events based on duration of spells
Long Spell: Consecutive rainfall for ≥ 4 days
Short Spell: Consecutive rainfall for less than 4 days
 Dry Spell: <2.5mm/day
 Prolonged Dry Spell : consecutively dry for ≥ 4 days
400
Winter
Monsoon
Pre-Monsoon
Winter
Monsoon
Pre-Monsoon
1600
100
1400
n
n
n
200
1200
0
1000
1950
1960
1970
1980
1990
0
1950
2000
Years
500
Post-Monsoon
1960
1970
2500
Post-Monsoon
1980
Years
1990
2000
n
n
2000
1500
0
1950
1960
1970
1980
1990
2000
1950
1960
1970
1980
1990
Years
Number of long spell rain events.
Continuous rainfall for ≥4 days over all India
Years
1960
1970
1980
1990
2000
2010
Years
Annual
Annual
1950
2000
The number of long
spell
rainfall
events
shows decreasing trend
in monsoon season in
last 54 years. This
suggests that planetary
scale motions, may be
southwest
monsoon
over the country is
weakening.
in different seasons.
The red line is linear trend line.
Dash et al. 2009 J. Geophys. Res. Vol. 114
1000
Winter
Winter
Pre-Monsoon
Pre-Monsoon
2250
800
2000
1750
n
n
600
Annual
1500
400
Annual
10000
1250
9000
200
1950
1960
1970
1980
1990
2000
1950
1970
1980
Years
1990
2000
n
Years
1960
3000
5000
Monsoon
Post Monsoon
Monsoon
Post-Monsoon
8000
2500
7000
4500
2000
n
n
1950
1960
1970
1980
1990
2000
Years
1500
4000
1000
3500
1950
1960
1970
1980
Years
1990
2000
500
1950
1960
1970
1980
1990
2000
Years
Number of short spell rain events (Continuous rainfall for < 4 days) over all
India in different seasons. The red line is linear trend line.
Short spell rainfall events over India show increasing trend. This is an
indication of increasing or intensifying of meso-scale conventions and
synoptic scale motions.
Dash et al. 2009 J. Geophys. Res. Vol. 114
Summary of trends in heavy and moderate rain events in different
Indian regions for the summer monsoon season. Asterisks denote
Dash et al. 2009 J. Geophys. Res. Vol.114
significant trend at 5% level.
Summary of trends in long, short, dry and prolonged dry spells of
rainfall in different Indian regions for the monsoon season. Asterisks
denote significant trend at 5% level.
Dash et al. 2009 J. Geophys. Res. Vol.114
Some inferences on rain events
 Heavy rain events increase and moderate &
low events decrease
 Short & dry spells increase and long spells
decrease
 Trends not statistically significant in all zones
 Weakening of monsoon circulation?
Difference between the 850hPa mean monsoonal wind speeds in the two decades
(1991-2000) and (1951-1960). The shaded region shows the significant change
calculated using t test at 5% level.
Dash et al. 2009 J. Geophys. Res. Vol.114
16.0
(a)
Depressions
11-year Running Means
14.0
12.0
12.0
10.0
10.0
8.0
8.0
6.0
6.0
4.0
4.0
2.0
2.0
0.0
0.0
1889
1893
1897
1901
1905
1909
1913
1917
1921
1925
1929
1933
1937
1941
1945
1949
1953
1957
1961
1965
1969
1973
1977
1981
1985
1989
1993
1997
2001
Frequency
14.0
16.0
Year
16.0
14.0
(b)
Lows
16.0
11-Year Running Means
14.0
12.0
10.0
10.0
8.0
8.0
6.0
6.0
4.0
4.0
2.0
2.0
0.0
0.0
1889
1893
1897
1901
1905
1909
1913
1917
1921
1925
1929
1933
1937
1941
1945
1949
1953
1957
1961
1965
1969
1973
1977
1981
1985
1989
1993
1997
2001
Frequency
12.0
Year
Eleven-year running means of annual frequency of disturbances
with the minimum intensity of (a) monsoon depressions and (b) low pressure areas
over the Indian region (1889-2003)
Dash et al., 2004, Current Science,86(10), 1404-1411.
35
30
0
Latitude ( N)
25
20
(a)
15
10
5
0
0
2
4
6
8
10
12
14
16
18
Wind (m/s)
50
(b)
40
550E-950E
30
a)850 hPa
20
0
Latitude ( N)
Latitudinal
variation of zonal
wind
component
in July over Indian
region between
10
b)200 hPa
0
-10
-20
-30
-30
-20
-10
0
10
Wind (m/s)
20
30
40
50
Dash et al., 2004, Current Science,86(10),
1404-1411.
Shear(m/s) / Frequency
1.2
0.8
(a)
Shear
Frequency
0.4
0.0
-0.4
-0.8
-1.2
1953 1957 1961 1965 1969 1973 1977 1981 1985 1989 1993
Year
2.0
Shear(m/s) / Frequency
1.5
(b)
Shear
Frequency
1.0
0.5
0.0
-0.5
-1.0
-1.5
-2.0
1953 1957 1961 1965 1969 1973 1977 1981 1985 1989 1993
Year
(a) 11-year running
means of anomalies
of horizontal wind
shear at 850hPa in
August
between
latitudes 00 and 250
N
averaged
over
longitudes 550E to
950E.
(b) 11-year running
means of vertical wind
shear
anomalies
in
August between
850&200 hPa
levels
averaged
over
the
0
Indian region 0 to 250
N and 550E to 950E.
Dash et al., 2004, Current Science,86(10),
1404-1411.
Further Classification of Rain
Events






HL:
ML:
LL:
HS:
MS:
LS:
High intensity Long spell
Moderate intensity Long spell
Low intensity Long spell
High intensity Short spell
Moderate intensity Short spell
Low intensity Short spell
Trends in heavy-intensity long spell (HL), heavy-intensity short spell
(HS), moderate-intensity long spell (ML), moderate-intensity short spell
(MS), low-intensity long spell (LL), and low-intensity short spell (LS) in
different regions
Dash et al., 2011, Theor Appl Climato,
Climate models with affiliated country, their surface
resolution, incorporated convection schemes and key
references
1. CCSM3 (Community Climate System Model, version 3, USA, NCAR)
2. ECHAM5 (European Centre for Medium Range Weather Forecasts and Max Planck
Institute for Meteorology in Hamburg version 5, Germany)
3. GFDL-CM2.1 (Geophysical Fluid Dynamics Laboratory Coupled Model, version 2.1,
US Department of commerce, NOAA)
Convect
ion
scheme
Key
Reference
Scenario
1.4x1.4
AS
Collins et. al
(2006)
A2, B1
1.9x1.9
MF
Jungclaus et.
al (2006)
A2, B1
USA
2.5x2.0
RAS
GFDLDelworth et.
CM2.1
al (2006)
AS- Arakawa-Schubert, MF-Mass Flux, RAS- Relaxed ArakawaSchubert
A2, B1
S.
No.
Models
Country
Horizontal
Surface
Resolution
1
CCSM3
USA
2
ECHAM5
Germany
3
(b) CCSM3
(c) ECHAM5
(d) GFDL 2.1
Future projected changes in
mean JJA wind (m/s) at
850hPa in JJA during 20112040 w. r. t. 1961-1990 under
A2 scenario
Vertical shear (U200-U850) of zonal wind (m/s) in the
box 5o-15oN, 40o-70oE
Emissions Scenarios
Model
Base line
A2
B1
ECMWF
-30.1
CCSM3
-27.9
-26.1
-26.9
ECHAM5
-31.3
-28.6
-29.4
GFDL2.1
-32.5
-30.9
-31.9
(b) CCSM3
(c) ECHAM5
(d) GFDL 2.1
Future projected changes
in mean JJA rainfall
(mm/day) during 2011-2040
w. r. t. 1961-1990 under A2
scenario
Schematic diagram showing the snow line and height
of tropopause in
(a)
(b)
Normal atmosphere and
Warmer atmosphere.
Dash and Hunt, 2007, Current Science, Vol 93
Seasonal mean anomalies (DJF) associated with maximum, minimum
and daily mean temperatures (with trend line) at Gulmarg
Seasonal mean anomalies (DJF) associated with precipitation along with
excess and deficit values based on ± 0.10 standard deviation at Gulmarg
Trends and P values for temperature and precipitation indices
__________________________________________________________________________________
A1
A2
A3
34°11′49″N,
34°59′32″N,
35°34′55″N,
77°12′28″E, 3570 m 76°57′14″E, 5215 m 76°47′32″E, 5995 m
_________________ ________________ ________________
Temperature
Precipitation Indices
Trend
P(trend) Trend
P(trend) Trend
P(trend)
__________________________________________________________________________________
Warm days
0.0122
0.0384* 0.0182
0.0001* 0.0045
0.0317*
Warm nights
0.0084
0.6394
0.0253
0.0006* – 0.01
0.0176*
Cold days
0.0065
0.5214
0.0069
0.002*
– 0.0045 0.0309*
Cold nights
0.0109
0.0017* 0.0084
0.0041* – 0.0028 0.1812
Mean climatological
0.0297
0.1578
– 0.0731 0.0009* – 0.0183 0.2968
Precipitation
Heavy precipitation
0.3893
0.2727
– 0.3626 0.0005* – 0.0383 0.3061
Days
Maximum number
– 0.0102 0.8364
0.7461
0.0006* 0.2641
0.5073
Of consecutive dry days
Maximum number
1.2246
0.465
– 5.3202 0.0007* – 0.0351 0.0345*
of consecutive wet days
__________________________________________________________________________________
Dimri and Dash (2010) Curr. Sc.
Applications in Agriculture & Health
ERFS Project
Agricultural Universities
Palampur
Pantnagar
Jodhpur
Anand
Jabalpur
Akola
Bhubanswar
Rajendranagar
Coimbatore
Decadal variations in the numbers of heavy, moderate, and low
rain days in agro-met divisions
Dash et al., 2011, Theor Appl Climato,
Trends in the contributions of heavy, moderate, and low-intensity
rainfall categories to total respective rainfall in All India,
homogeneous zones, and agro-met divisions
Dash et al., 2011, Theor Appl Climato,
Contributions of different spells of rain to total summer monsoon rainfall
Dash et al., 2011, Theor Appl Climato,
Percentage changes in various categories of long and short spells in
the decade 1991–2000 compared with the 1951–1960 decade
Dash et al., 2011, Theor Appl Climato,
Some rainfall facts for Agriculture
 High intensity Short spells increase
and Moderate and Low intensity Long
spells decrease.
 Contribution of Moderate Long spells
to total rain decreases and Moderate
Short spells increases.
 Contribution of Heavy categories to
total rain increases and that of
Moderate decreases.
Number of workable days
18.00
16.00
14.00
12.00
10.00
Series1
8.00
Series2
6.00
4.00
2.00
0.00
1960-79
1980s
1990s
2000s
Decade
Fully workable days out of 30 by decade at Delhi in the month of
June. (Series 1 is heavy labour; Series 2 is light factory work)
Climate change in cities
• Numerous cities in South and Southeast Asia
are highly vulnerable to climate change.
 In India, the expected
increase
in
extreme
rainfall
events
and
changes
to
seasonal
monsoon patterns will
increase the risk of major
floods and the likelihood
of drought, with severe
consequences
for
the
health and livelihoods of
millions of people.
Climate change in cities
objectives
(1) to identify generic climate change parameters in four selected
cities in India and
(2) to scientifically contribute in the local climate adaptation plans.
 The four selected cities in
India are Howrah, Kochi,
Madurai and Visakhapatnam.
 For each of the above cities its
local indicators for climate
change adaptation are being
developed.
Model Simulations
Model domain used in RegCM3 simulations
o
o
•Central Lat and Long 20 N, 80 E
•99 x 118 points along x-y direction
•Domain covers 51o E to 109o E and
3oS to 43oN with 55 km grid
distance
Percentage difference in JJAS precipitation between
RegCM3 and Observations
RegCM3-IMD
RegCM3-GPCP
RegCM3-CRU
RegCM3-APHRODITE
RegCM3-CMAP
Dash et al. 2011. Spatial and temporal variations of Indian summer monsoon: An analysis of
June
August
July
September
JJAS
Correlation
Coefficients
between RegCM3 and IMD
observed ensemble mean
monsoon rainfall for the
period 1982-2009 spanning
28 years. The contours are
obtained
with
9
point
smoothing to the gridded
CC 0.53*
RMSE 3.40
JUNE
CC 0.67*
RMSE 3.90
Rainfall (cm)
JULY
CC 0.61*
RMSE 3.18
AUGUSTT
CC 0.15
RMSE 5.44
SEPTEMBER
CC 0.50*
RMSE 10.02
JJAS
Inter-annual variations in
precipitation simulated by RegCM3
*Significant at 0.05 level
Years
2003 good monsoon
RegCM3
IMD
Standardized Anomaly
2002 weak monsoon
Monsoon active spells (blue circle) and break spells (red circles)
in the contrasting monsoon years are shown over central India
(15-25oN, 75-85oE), the monsoon core zone.
Frequency
(a) North West India (70-80oE and 25-30oN)
RegCM3
IMD
(b) Central India (75-85oE and 15-25oN)
IMD
RegCM3
(c) Peninsular India (75-85oE and 09-15oN)
IMD
RegCM3
Rainfall (mm)
Frequency
distribution of area
weighted
average
daily rainfall from
June to September
in domain of (a)
North West 70-80oE
and 25-30oN, (b)
Central India 7585oE and 15-25oN
and (c) Peninsular
India 75-85oE and
09-15oN.
The
smooth curves are
obtained using 5point
binomial
filter.
RegCM3
IMD
(b) R99pTOT
Frequency
(a) R95pTOT
Years
Years
Frequency of occurrence of (a) very wet days and (b)
extremely wet days in JJAS per year over the Central India
domain (70-86oE and 19-25oN) are shown in bars. The
smooth curves are obtained using 5-point binomial filter.
Summary of RegCM simulations
•RegCM3 has well simulated rainfall over
the Central India with small dry bias.
•Monsoon breaks in the model are of
longer life span that those actually
observed.
•The model simulates less number of
active spells than those observed.
•These characteristics of model simulated
active and break phases of monsoon
contribute to less summer monsoon
Future Challenges
Coordinated Regional Downscaling Experiment
(CORDEX) is a program developed by the Task Force
on Regional Climate Downscaling of World Climate
Research Programme (WCRP).
The framework includes regional climate models
(RCMs) and statistical downscaling, with an aim of
evaluating the strengths and weaknesses of
downscaled climate information.
Schematic depiction of the primary uncertainties in regional climate
change projection
Schematic depiction of the first phase CORDEX experiment set-up
3
11
4
8
12
2
7
6
1
5
9
10
Regional domains planned for the CORDEX experiments (some still under
discussion); also indicated are existing projects that make use of the
corresponding domain
Immediate Goals
 Validating Regional Climate Models at all the
homogeneous regions in India, especially over
the Himalayas.
 Downscaling surface temperature and rainfall to
the resolutions of impact assessments.
 Determining the climate uncertainties in
temperature and rainfall for applications in
Agriculture and Health.