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Study of Changes in Climate Parameters at Regional Level:
Indian Scenarios
S K DashCentre for Atmospheric Sciences
Indian Institute of Technology Delhi
Climate Change and Animal Populations - The golden toad, last seen in Costa Rica’s cloud forest in 1989, is believed to be extinct. The ecology of the cloud forest depends on the frequent formation of clouds and mist. Warming of the oceans and atmosphere has contributed to declines in mist formation. This has, in turn, affected species native to the area
Leafing Dates of Oak (1746–present) - This graph shows how the leafing dates of oaks in southeastern England have changed over the past 256 years.
What climate information is available and what can be done?
• Climate science has grown using historical observed data and model output.
• Past temperature and rainfall at several places are available from number of sources.
• Climate models also give the future values into the next century.
• The above information can be utilized to know the future state of climate.
Challenging Future!Connecting climate change and societal parameters:• Extreme rainfall events to the area and number
of people to be affected by flood.• Change in land use pattern to increase in
temperature.• Increase in temperature to enhanced use of
power.• Extreme temperature and rainfall events and air
pollution to health issues such as heat stress, lungs, heart and water diseases.
For a management of future risks
…you need to understand mechanisms…you need to identify intervention points…you need to know where are the
hot spots located
Vulnerability assessments need to involve a cascade of analytical steps
Climate Impact Science cannotprovide planning tools. Climate Impact Science canoutline potential options and their potential effectiveness.
Impact chains are useful for the analysisof effects: starting points for action and transformation
Source: Reckien et al. 2009/PIK
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
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
Northeast+ 1.0 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
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
October-December
(Post-monsoon)
+1.1 +1.1 – +1.3 +0.6 – +0.8
Surface air temperature (0C) changes during different seasons averaged over the whole of India (+ sign indicates an increase and – represents decrease)
46.7 46.7 46.7
47.8
46.7
48
47.2
48 4848.2
47
49
45.5
46
46.5
47
47.5
48
48.5
49
49.5
HNK HNK HNK MPT ONG GNV
STATION
TEM
P(C
)
CLIMATE RECORDTEMP IN 2003
Highest maximum temperature recorded at some stations during heat wave of Andhra Pradesh in May & June 2003 and the earlier recorded highest maximum temperature.
Dash et al., 2007, Climatic Change, 85
1950 1960 1970 1980 1990 2000
0
100
1950 1960 1970 1980 1990 20000
200
400
1950 1960 1970 1980 1990 2000 2010
1000
1200
1400
1600
1950 1960 1970 1980 1990 20000
500
n
Years
Winter
n
Years
Pre-Monsoon
n
Years
Monsoonn
Years
Post-Monsoon
1950 1960 1970 1980 1990 2000
1500
2000
2500n
Years
Annual
Number of long spell rain events.Continuous rainfall for ≥4 days over all India
in different seasons. The red line is linear trend line.
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.
Dash et al. 2009 J. Geophys. Res. Vol. 114
Winter Pre-Monsoon Monsoon
Post-Monsoon Annual
Number of short spell rain events (Continuous rainfall for < 4 days) over all India in different seasons. The red line is linear trend line.
1 9 5 0 1 9 6 0 1 9 7 0 1 9 8 0 1 9 9 0 2 0 0 02 0 0
4 0 0
6 0 0
8 0 0
1 0 0 0
1 9 5 0 1 9 6 0 1 9 7 0 1 9 8 0 1 9 9 0 2 0 0 0
1 2 5 0
1 5 0 0
1 7 5 0
2 0 0 0
2 2 5 0
1 9 5 0 1 9 6 0 1 9 7 0 1 9 8 0 1 9 9 0 2 0 0 03 5 0 0
4 0 0 0
4 5 0 0
5 0 0 0
1 9 5 0 1 9 6 0 1 9 7 0 1 9 8 0 1 9 9 0 2 0 0 05 0 0
1 0 0 0
1 5 0 0
2 0 0 0
2 5 0 0
3 0 0 0
n
Y e a r s
W in te r
n
Y e a r s
P r e -M o n s o o nn
Y e a r s
M o n so o n
n
Y e a r s
P o s t M o n s o o n
1 9 5 0 1 9 6 0 1 9 7 0 1 9 8 0 1 9 9 0 2 0 0 0
7 0 0 0
8 0 0 0
9 0 0 0
1 0 0 0 0
n
Y e a r s
A n n u a l
Short spell rainfall events over India show increasing trend. This is an indication of increasing or intensifying of meso-scale conventions.
Dash et al. 2009 J. Geophys. Res. Vol. 114
Winter Pre-Monsoon
Monsoon Post-Monsoon
Annual
Summary of trends in heavy and moderate rain events in differentIndian regions for the summer monsoon season. Asterisks denote significant trend at 5% level. Dash et al. 2009 J. Geophys. Res. Vol.114
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
Temperature Indices
Names Definitions
TX90p, TX95p, TX99p
Warm days Count of days where maximum temperature TX > 90th, 95th and 99th percentile respectively
TN90p, TN95p, TN99p
Warm nights Count of days where minimum temperature TN > 90th, 95th and 99th percentile respectively
TX10p, TX05p, TX01p
Cold days Count of days where maximum temperature TX < 10th, 5th and 1th percentile respectively
TN10p, TN05p, TN01p
Cold nights Count of days where minimum temperature TN < 10th, 5th and 1th percentile respectively
WSDI Warm Spell Duration Index
Count of events where maximum temperature TX >90th percentile for at least six days continuously
CSDI Cold Spell Duration Index
Count of events where minimum 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 & ClimatDash et al., 2011, J Appl Met & Climat
Extreme temperature indices used
Dash et al., 2011, J Appl Met & ClimatDash 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 & ClimatDash 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
Classification of Rain Events• HL: High intensity Long spell• ML: Moderate intensity Long spell• LL: Low intensity Long spell• HS: High intensity Short spell• MS: Moderate intensity Short spell• LS: Low intensity Short spell
Palampur
Coimbatore
Rajendranagar
Akola
Anand
Jodhpur
Bhubanswar
Jabalpur
Pantnagar
ERFS ProjectAgricultural Universities
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,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,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,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.
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
18.00
1960-79 1980s 1990s 2000s
Decade
Series1
Series2
Fully workable days out of 30 by decade at Delhi in the month ofJune. (Series 1 is heavy labour; Series 2 is light factory work)
Number of workable days
Climate change in cities
• 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.
• Numerous cities in South and Southeast Asia are highly vulnerable to climate change.
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.
Climate change in cities
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.
VISHKHAPATNAMWT
2015‐2024
MADURAIWT T90p T95p
CT T10p T05p
KOCHIWTCT
HOWRAHWTCT
WARM TEMPERATURE (WT) & COLD TEMPERATURE (CT) EVENTS
Increasing (Decreasing) trends at 10%,5% and 1% significant levels are marked by the symbol , and ( , and ) respectively and represent no statistical
significance.
VISHKHAPATNAMWT T90p
2015‐2034
MADURAIWT T90p T95p
CT T10p T05p
KOCHIWTCT
HOWRAHWTCT
Increasing (Decreasing) trends at 10%,5% and 1% significant levels are marked by the symbol , and ( , and ) respectively and represent no statistical
significance.
WARM TEMPERATURE (WT) & COLD TEMPERATURE (CT) EVENTS
VISHKHAPATNAMWT T90p T95pCT T10p
2015‐2044
MADURAIWT T90p T95p T99p
CT T10p T05p T01p
KOCHIWT T90p T95pCT T10p T05p
HOWRAHWTCT
Increasing (Decreasing) trends at 10%,5% and 1% significant levels are marked by the symbol , and ( , and ) respectively and represent no statistical
significance.
WARM TEMPERATURE (WT) & COLD TEMPERATURE (CT) EVENTS
VISHKHAPATNAMWT
2071‐2099
MADURAI
WT T90p T95p
KOCHIWTCT
HOWRAHWTCT
Increasing (Decreasing) trends at 10%,5% and 1% significant levels are marked by the symbol , and ( , and ) respectively and represent no statistical
significance.
WARM TEMPERATURE (WT) & COLD TEMPERATURE (CT) EVENTS
Hot and Cool days are the quantitative indices calculated from daily mean air temperature.
These indices are widely used to describe the energy consumption of heating (cooling) so as to defend against temperature changing.
Hot Day (in degree) is defined as the accumulated Celsius degrees difference between the threshold temperature and daily mean temperature when the daily mean temperature is higher than the threshold value.
Cool Day (in degree) is defined as the accumulated Celsius degrees difference between the daily mean temperature and the threshold temperature when the daily mean temperature is lower than the threshold value.
The threshold value taken at Howrah is 26.71°C.
Hot
Day
s in
deg
ree
City: VISAKHAPATNAM
Data: IMD Gridded Mean Temperature 0.5x0.5 resolution
0
20
40
60
80
100
120
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Months
(1969-2000)
0
20
40
60
80
100
120
140
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecMonths
Hot
Day
s in
deg
ree
(2001-2005)
Coo
l Day
s in
deg
ree
Coo
l Day
s in
deg
ree
0
20
40
60
80
100
120
140
160
180
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Months
(1969-2000)
0
20
40
60
80
100
120
140
160
180
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Months
(2001-2005)
Hot
Day
s in
deg
ree
y = 0.2447x + 247.05R2 = 0.0026
0
50
100
150
200
250
300
350
400
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
Years
Coo
l D
ays
in d
egre
e
y = -1.0918x + 551.39R2 = 0.0579
300
350
400
450
500
550
600
650
700
1969
1971
1973
1975
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
2003
2005
Years
Ener
gy (M
illio
n K
Wh)
Years
Hot
Day
s in
Deg
ree
0
100
200
300
400
500
600
700
800
900
2004-05 2005-060
50
100
150
200
250
300
350ResidentialIndustrialCommercialHot Days
0
100
200
300
400
500
600
700
800
900
2004-05 2005-06460
480
500
520
540
560
580
600ResidencialIndustrialCommercialCool Days
Years
Ener
gy (M
illio
n K
Wh)
Coo
l D
ays
in D
egre
e
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, Health and City Amenities.