Analysis of observed temperature and Analysis of observed temperature and precipitation extremes over South Asiaprecipitation extremes over South Asia
Jayashree RevadekarJayashree RevadekarCentre for Climate Change ResearchCentre for Climate Change Research
Indian Institute of Tropical MeteorologyIndian Institute of Tropical MeteorologyPashan, PUNEPashan, PUNE
Indices of extremes are computed to determineIndices of extremes are computed to determine
IntensityIntensityFrequencyFrequency
Spell DurationSpell DurationSeasonal LengthSeasonal LengthExtreme RangeExtreme Range
Using daily timeseries tmax,tmin, precipUsing daily timeseries tmax,tmin, precipUsing climdexUsing climdex
Initial analysis is based on 121 Indian stations for Initial analysis is based on 121 Indian stations for temperature and 146 for precipitationtemperature and 146 for precipitation
Alexander et al., (2006) in JGRTank et al., (2006) in JGR
197 South Asian stations197 South Asian stations
As a part of APN project on extremesPakistan, 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 seeAttempt is also made to see
Role of Nino Index on Temperature Extremes Role of Nino Index on Temperature Extremes (Revadekar et al., 2009, Int. J. Climatology)(Revadekar et al., 2009, Int. J. Climatology)
Change in one extreme leading to change in Change in one extreme leading to change in other extreme other extreme
Change in extreme of season leading to Change in extreme of season leading to change in extreme of another seasonchange in extreme of another season
Change in extreme at one place leading to Change in extreme at one place leading to change in extreme to another place change in extreme to another place
Attempt is also made to seeAttempt is also made to see
Ability of Models in simulating extremesAbility of Models in simulating extremes
Projection of Extremes Projection of Extremes
Reconstruction of past Extremes using Reconstruction of past Extremes using proxy dataproxy data
IMD gridded data setIMD 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 DATAPRECIS DATA
Work in progress with Work in progress with CORDEX South AsiaCORDEX South Asia
CORDEX ARABCORDEX ARAB
Background :Background : The fourth assessment report of the Intergovernmental Panel on The fourth assessment report of the Intergovernmental Panel on
Climate Change (IPCC 2007) has concluded that the global Climate Change (IPCC 2007) has concluded that the global mean surface temperatures have risen by 0.74 ± 0.18°C when mean surface temperatures have risen by 0.74 ± 0.18°C when estimated by a linear trend over the last 100 years (1906–2005). estimated by a linear trend over the last 100 years (1906–2005). The rate of warming over the recent 50 years is almost double The rate of warming over the recent 50 years is almost double of that over the last 100 years (IPCC 2007), which is largely of that over the last 100 years (IPCC 2007), which is largely attributed to anthropogenic influencesattributed to anthropogenic influences
Over India, the mean maximum as well as minimum Over India, the mean maximum as well as minimum temperatures have increased by about 0.2°C per temperatures have increased by about 0.2°C per decade during the period 1971–2003, for the country decade during the period 1971–2003, for the country as a whole (Kothawale and Rupa Kumar 2005).as a whole (Kothawale and Rupa Kumar 2005).
Trend in maximum and Trend in maximum and minimum minimum temperature over temperature over North and South of North and South of 20N over India 20N over India
Recent study of Recent study of Kothawale et Kothawale et al.,2012), TACal.,2012), TAC
Need of Analysis on ExtremesNeed of Analysis on Extremes
Detection of change in climate against its Detection of change in climate against its variability is a key issue in climate research. variability is a key issue in climate research. Climate change is often expressed simply in Climate change is often expressed simply in terms of changes in mean climate. terms of changes in mean climate.
Average conditions may not show appreciable Average conditions may not show appreciable change but may be characterized by a variety of change but may be characterized by a variety of extreme situations. extreme situations.
Extremes could have more significant socio-Extremes could have more significant socio-economic consequences than the changes in economic consequences than the changes in meanmean
Extremes are an expression of the variability, therefore the nature of Extremes are an expression of the variability, therefore the nature of
variability at different spatial and temporal scales is vital to our variability at different spatial and temporal scales is vital to our
understanding of extremes.understanding of extremes.
Expert Team on Climate Change Detection and Expert Team on Climate Change Detection and Indices (ETCCDI) coordinated the development Indices (ETCCDI) coordinated the development of a suite of climate change indices which of a suite of climate change indices which primarily focus on extremes. In all, 27 indices primarily focus on extremes. In all, 27 indices were defined which have been widely used for were defined which have been widely used for global and regional analyses of climate extremes. global and regional analyses of climate extremes. Present study is mainly based on same indices Present study is mainly based on same indices which are described at the link which are described at the link http://cccma.seos.uvic.ca/ETCCDMI/ . .
INDICES OF TEMPERATURE EXTREMESINDICES OF TEMPERATURE EXTREMES
FREQUENCY and SPELL DURATION INDICES :FREQUENCY and SPELL DURATION INDICES :
HOT EVENTSHOT EVENTS
Number of Hot days (Tx > user defined threshold)Number of Hot days (Tx > user defined threshold)
Number of Hot nights (Tn > user defined threshold)Number of Hot nights (Tn > user defined threshold)
Number of Hot days (Tx > 90Number of Hot days (Tx > 90thth Percentile of Tx) Percentile of Tx)
Number of Hot nights (Tn > 90Number of Hot nights (Tn > 90thth percentile of Tn) percentile of Tn)
Warm spell duration based on 90Warm spell duration based on 90thth percentile percentile
COLD EVENTSCOLD EVENTS
Number of Cold days (Tx < user defined threshold)Number of Cold days (Tx < user defined threshold)
Number of Cold nights (Tn < user defined threshold)Number of Cold nights (Tn < user defined threshold)
Number of Cold days (Tx < 10Number of Cold days (Tx < 10thth Percentile of Tx) Percentile of Tx)
Number of Cold nights (Tn < 10Number of Cold nights (Tn < 10thth percentile of Tn) percentile of Tn)
Cold spell duration based on 10Cold spell duration based on 10thth percentile percentile
INDICES OF TEMPERATURE EXTREMESINDICES OF TEMPERATURE EXTREMES
INTENSITY INDICES :INTENSITY INDICES :
Hottest day temperature Hottest day temperature
Hottest night temperatureHottest night temperature
Coldest day temperatureColdest day temperature
Coldest night temperatureColdest night temperature
Diurnal temperature rangeDiurnal temperature range
Range of Extreme : Hottest day minus coldest nightRange of Extreme : Hottest day minus coldest night
Growing Season LengthGrowing Season Length
The growing season is defined as starting when the temperature on five consecutive 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 days exceeds 5 °C, and ends after five consecutive days of temperatures below 5 °C.°C.
INDICES OF PRECIPITATION EXTREMESINDICES OF PRECIPITATION EXTREMES
FREQUENCY INDICES :FREQUENCY INDICES :
Number of days with RF > 10mmNumber of days with RF > 10mmNumber of days with RF > 20mmNumber of days with RF > 20mmNumber of days with RF > 30mmNumber of days with RF > 30mm
INTENSITY INDICES :INTENSITY INDICES :
One-day Maximum PrecipitationOne-day Maximum PrecipitationFive-day Maximum PrecipitationFive-day Maximum PrecipitationDaily Intensity (rainfall per rainydays)Daily Intensity (rainfall per rainydays)
INDICES OF PRECIPITATION EXTREMESINDICES OF PRECIPITATION EXTREMES
Rainfall due to Heavy Rain events based on 95Rainfall due to Heavy Rain events based on 95thth percentile percentile
Rainfall due to Very Heavy Rain events based on 99Rainfall due to Very Heavy Rain events based on 99thth percentile percentile
Continuous Dry DaysContinuous Dry Days
Continuous Wet DaysContinuous Wet Days
Extreme of a one place can be normal event of another placeExtreme of a one place can be normal event of another place
Basic analysis is done at station level/grid level.Basic analysis is done at station level/grid level.
Applied preliminary quality checks on each stationApplied preliminary quality checks on each station
Used Well distributed station dataUsed Well distributed station data
Once indices of extremes are computed for each station/grid further analysis is done to see changes
Trend AnalysisPDF Analysis mean
Epochal meanAnnual Cycle
Regional meansetc
For Extreme Analysis on South Asian Region :
Role of altitude and latitude on changes in extremes over South Asia during 1971 – 2000Revadekar et al., Int. J. Climatology, 33, 2013
Using 197 stations in Bangladesh, India, Nepal, Pakistan and Srilanka
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
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.
Coldest Night Temperature
Both A2 and B2 scenarios show Both A2 and B2 scenarios show similar patterns of projected similar patterns of projected changes in the mean climate and changes in the mean climate and extremes towards the end of 21extremes towards the end of 21stst century. However, B2 scenario century. However, B2 scenario shows slight lower magnitudes of shows slight lower magnitudes of the projected changes than that the projected changes than that of A2 scenarios. of A2 scenarios.
Similar features are seen other Similar features are seen other intensity indices alsointensity indices also
Highest Maximum Temperature Highest Maximum Temperature Seasonal changes in a2 scenarios : wide spread warmingSeasonal changes in a2 scenarios : wide spread warming
Daily precipitation in a calendar year Daily precipitation in a calendar year for base line, a2 and changefor base line, a2 and change
One-day maximum precipitation One-day maximum precipitation for base line, a2, b2 and changefor base line, a2, b2 and change
Both a2 and b2 show Both a2 and b2 show similarity in changes;similarity in changes;
A2 is higher than b2A2 is higher than b2
Changes during Changes during summer monsoon are summer monsoon are higherhigher
International Conference on "Celebrating the Monsoon", 24-28 July 2007, Bangalore,
India
44
Scenarios for one-day and 5-day maximum. precipitation
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
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)
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
SUMMARYSUMMARY
Good skill in depicting the surface climate over the Good skill in depicting the surface climate over the Indian region, particularly the orographic patterns of Indian region, particularly the orographic patterns of precipitation and temperature extremes. precipitation and temperature extremes.
Annual cycles of both precipitation and temperature Annual cycles of both precipitation and temperature extremes are well capturedextremes are well captured
Cold biases while simulating cold events. Cold biases while simulating cold events.
SScenarios of extremes:-cenarios of extremes:-
Model indicate marked increase in both rainfall and Model indicate marked increase in both rainfall and temperature towards the end of 21temperature towards the end of 21stst century century
Simulations under both RCP 4.5 and 8.5 scenarios indicate ...........>Simulations under both RCP 4.5 and 8.5 scenarios indicate ...........>
Increase in hot events Increase in hot events
Decrease in cold eventsDecrease in cold events
Enhancement in intensity.Enhancement in intensity.
The changes in temperature extreme in winter season are prominent than the rest The changes in temperature extreme in winter season are prominent than the rest of year.of year.
Both scenarios show similar patterns of projected changes in the mean climate Both scenarios show similar patterns of projected changes in the mean climate and extremes towards the end of 21and extremes towards the end of 21stst century. century.
4.5 scenario shows slight lower magnitudes of the projected changes than that of 4.5 scenario shows slight lower magnitudes of the projected changes than that of 8.5 scenarios.8.5 scenarios.
Elevation Dependency in changes in extremes is captured by modelsElevation Dependency in changes in extremes is captured by models
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