Analysis of observed temperature and
precipitation extremes over South Asia
Jayashree Revadekar
Centre for Climate Change Research
Indian Institute of Tropical Meteorology
Pashan, PUNE
Indices of extremes are computed to determine
Intensity
Frequency
Spell Duration
Seasonal Length
Extreme Range
Using daily timeseries tmax,tmin, precip
Using climdex
Initial analysis is based on 121 Indian stations for
temperature and 146 for precipitation
Alexander et al., (2006) in JGR
Tank et al., (2006) in JGR
197 South Asian stations
As a part of APN project on extremes
Pakistan, India, Bagladesh, Nepal, Srilanka
Role of altitude & latitude : Revadekar et al., Int J. Climatology
(2013)
Regional Trends Analysis : Munir Sheikh et al, Int. J. Climatology
(2013) under revision
Attempt is also made to see
Role of Nino Index on Temperature Extremes
(Revadekar et al., 2009, Int. J. Climatology)
Change in one extreme leading to change in
other extreme
Change in extreme of season leading to
change in extreme of another season
Change in extreme at one place leading to
change in extreme to another place
Attempt is also made to see
Ability of Models in simulating extremes
Projection of Extremes
Reconstruction of past Extremes using
proxy data
IMD gridded data set
Relationship between summer monsoon Precipitation extremes
and kharif foodgrain production over India
With preethi (2012 & 2013) Int. J. Climatology
Analysis is also done for rabi foodgrain
PRECIS DATA
Work in progress with
CORDEX South Asia
CORDEX ARAB
Background :
The fourth assessment report of the Intergovernmental Panel on
Climate Change (IPCC 2007) has concluded that the global
mean surface temperatures have risen by 0.74 ± 0.18°C when
estimated by a linear trend over the last 100 years (1906–2005).
The rate of warming over the recent 50 years is almost double
of that over the last 100 years (IPCC 2007), which is largely
attributed to anthropogenic influences
Over India, the mean maximum as well as minimum
temperatures have increased by about 0.2°C per
decade during the period 1971–2003, for the country
as a whole (Kothawale and Rupa Kumar 2005).
Trend in maximum
and
minimum
temperature
over
North and South of
20N over India
Recent
study
Kothawale
al.,2012), TAC
of
et
Need of Analysis on Extremes
Detection of change in climate against its
variability is a key issue in climate research.
Climate change is often expressed simply in
terms of changes in mean climate.
Average conditions may not show appreciable
change but may be characterized by a variety of
extreme situations.
Extremes could have more significant socioeconomic consequences than the changes in
mean
IPCC, 2001
Extremes are an expression of the variability, therefore the nature of
variability at different spatial and temporal scales is vital to our
understanding of extremes.
Expert Team on Climate Change Detection and
Indices (ETCCDI) coordinated the development
of a suite of climate change indices which
primarily focus on extremes. In all, 27 indices
were defined which have been widely used for
global and regional analyses of climate extremes.
Present study is mainly based on same indices
which
are
described
at
the
link
http://cccma.seos.uvic.ca/ETCCDMI/ .
Network of stations for Global Analysis of 2001
Network of stations for Global Analysis of 2006
INDICES OF TEMPERATURE EXTREMES
FREQUENCY and SPELL DURATION INDICES :
HOT EVENTS
Number of Hot days (Tx > user defined threshold)
Number of Hot nights (Tn > user defined threshold)
Number of Hot days (Tx > 90th Percentile of Tx)
Number of Hot nights (Tn > 90th percentile of Tn)
Warm spell duration based on 90th percentile
COLD EVENTS
Number of Cold days (Tx < user defined threshold)
Number of Cold nights (Tn < user defined threshold)
Number of Cold days (Tx < 10th Percentile of Tx)
Number of Cold nights (Tn < 10th percentile of Tn)
Cold spell duration based on 10th percentile
INDICES OF TEMPERATURE EXTREMES
INTENSITY INDICES :
Hottest day temperature
Hottest night temperature
Coldest day temperature
Coldest night temperature
Diurnal temperature range
Range of Extreme : Hottest day minus coldest night
Growing Season Length
The growing season is defined as starting when the temperature on five consecutive
days exceeds 5 °C, and ends after five consecutive days of temperatures below 5
°C.
INDICES OF PRECIPITATION EXTREMES
FREQUENCY INDICES :
Number of days with RF > 10mm
Number of days with RF > 20mm
Number of days with RF > 30mm
INTENSITY INDICES :
One-day Maximum Precipitation
Five-day Maximum Precipitation
Daily Intensity (rainfall per rainydays)
INDICES OF PRECIPITATION EXTREMES
Rainfall due to Heavy Rain events based on 95th percentile
Rainfall due to Very Heavy Rain events based on 99th percentile
Continuous Dry Days
Continuous Wet Days
Extreme of a one place can be normal event of another place
Basic analysis is done at station level/grid level.
Applied preliminary quality checks on each station
Used Well distributed station data
Once indices of extremes are computed for each station/grid
further analysis is done to see changes
Trend Analysis
PDF Analysis mean
Epochal mean
Annual Cycle
Regional means
etc
For Extreme Analysis on South Asian
Region :
Role of altitude and latitude on
changes in extremes over South Asia
during 1971 – 2000
Revadekar et al., Int. J. Climatology,
33, 2013
Using 197 stations in Bangladesh,
India, Nepal, Pakistan and Srilanka
FREQUENCY OF
COLD EVENTS
FREQUENCY OF
HOT EVENTS
INTENSITY
SPELL DURATION
IMPACT OF LATITUDE
COLD EVENTS
HOT EVENTS
Sign of trends in
warm nights at
stations with
elevation > 500 m.
Only trends with
absolute value
greater than 1.5
(%days/year) are
shown. Circles
represent negative
trends and stars
represent positive
trends.
Mean Trends for categorized elevation
Average trends are computed for a categorized
elevation rank for four different categories:
(1) <500 m;
(2) 500–1000 m;
(3) 1000–1500 m; and
(4) >1500 m.
Higher magnitude trends over high altitude are seen
through TX10p, TX90p, WSDI, TXx
Precipitation Extremes using Aphrodite
Computed indices of precipitation Extremes at each grid
(0.5 x 0.5) using daily precipitation data for 1951
onwards for
JF
MAM
JJAS
OND
Annual
Annual Precipitation Extremes
Climatology R10mm
Annual Precipitation Extremes
Trends R10mm
Impact of System on R10mm
System
Difference
Normal
RCM : PRECIS (Providing REgional Climates for Impacts
Studies) developed by the Hadley Centre for Climate
Prediction and Research, is applied for India to develop
high-resolution climate change scenarios.
The model has ~50 km resolution
Simulations using PRECIS have been performed to generate
the climate for present (1961-1990) and a future period
(2071-2100) for two different socio-economic scenarios both
characterized by regionally focused development but with
priority to economic issues in one (A2 scenario) and to
environmental issues in the other (B2 scenario).
The model simulations are performed with and without
including sulfur cycle, to understand the role of regional
patterns of sulfate aerosols in climate change.
PRECIS Simulations of Future Climate
Mean Annual Cycles of All-India Rainfall and Temperature
Coldest Night Temperature
Both A2 and B2 scenarios show
similar patterns of projected
changes in the mean climate and
extremes towards the end of 21st
century. However, B2 scenario
shows slight lower magnitudes of
the projected changes than that
of A2 scenarios.
Similar features are seen other
intensity indices also
Highest Maximum Temperature
Seasonal changes in a2 scenarios : wide spread warming
Daily precipitation in a calendar year
for base line, a2 and change
One-day maximum precipitation
for base line, a2, b2 and change
Both a2 and b2 show
similarity in changes;
A2 is higher than b2
Changes during
summer monsoon are
higher
Scenarios for one-day and 5-day
maximum. precipitation
International Conference on "Celebrating
the Monsoon", 24-28 July 2007, Bangalore,
India
44
Temperature and Precipitation Extremes
using CORDEX
daily maximum and minimum
temperature
Validation of models for categorized
elevation
Similar to obsevational analysis Average trends are
also computed for a categorized elevation rank for
four different categories:
(1) <500 m;
(2) 500–1000 m;
(3) 1000–1500 m; and
(4) >1500 m.
It is seen that models are able to capture elevation
dependency in temperature extremes in addition to
their spatial distribution
Trend analysis for Freezing nights RCP 85
Seasonal Length (Number of days between tmean > 5C to tmean < 5C)
Mean Seasonal length in RCP26 and RCP85 (TOP Panel)
Incremental Changes in RCP26 and RCP85 w.r.t. Historical (bottom Panel)
Trend analysis for warm spell duration RCP 85
Trend analysis for Diurnal Temperature Range : RCP 85
Trends in Maximum and Minimum Temperature for South Asia
as a whole
Mean Trends for categorized
elevation
Average trends are computed for a categorized
elevation rank for four different categories:
(1) <500 m;
(2) 500–1000 m;
(3) 1000–1500 m; and
(4) >1500 m.
Higher magnitude trends over high altitude
i
SUMMARY
Good skill in depicting the surface climate over the Indian region,
particularly the orographic patterns of precipitation and
temperature extremes.
Annual cycles of both precipitation and temperature extremes are
well captured
Cold biases while simulating cold events.
Scenarios of extremes:Model indicate marked increase in both rainfall and temperature
towards the end of 21st century
Simulations under both RCP 4.5 and 8.5 scenarios indicate ...........>
Increase in hot events
Decrease in cold events
Enhancement in intensity.
The changes in temperature extreme in winter season are prominent than the rest
of year.
Both scenarios show similar patterns of projected changes in the mean climate
and extremes towards the end of 21st century.
4.5 scenario shows slight lower magnitudes of the projected changes than that of
8.5 scenarios.
Elevation Dependency in changes in extremes is captured by models
THANK YOU . . .