Transcript Climate Change and Variability over India : Observations, Modelling

Regional Climate
Scenarios
K. Rupa Kumar
Deputy Director & Head
Climatology & Hydrometeorology Division
Indian Institute of Tropical Meteorology, Pune
[email protected]
TERI Workshop on Climate Change : Policy Options for India, New Delhi, September 5-6, 2002
Indian Institute of Tropical Meteorology,
Pune
(An Autonomous Institute under Dept. of Science & Technology, Govt. of India)
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Established 1962
Initially part of IMD
Autonomous in
1971
100 Scientists
Focus on Monsoon
Research
Climate
Diagnostics and
Modelling
OUTLINE
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Background
Observational Data Issues
Climate Scenario Development
Experiments with Climate Models
GCM Simulations of Indian Climate
Future Climatic Scenarios
Regionalization Techniques
Instrumental Records
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First Observatory in Chennai in 1792
Rainfall/Temperature data network since
early 19th century
Century-long rainfall/temperature records
for Kathmandu
IITM Homogeneous Monthly
Rainfall/Temperature data sets
(http://www.tropmet.res.in)
Interannual to Decadal scale variability
All-India/Regional Means
Teleconnection Studies
Proxy Climatic Sources
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Historical Records
Tree Rings
Corals
Ice Cores
Lake Sediments
Marine Sediments
REANALYSES
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Assimilation of global observational (conventional,
satellite, etc.) data archives into atmospheric general
circulation models to produce homogeneous and
complete data sets describing the state of the
atmosphere
Data on daily/monthly scale over resolutions ~ 2° x 2°
NCEP/NCAR Reanalysis (1948 to date)
ECMWF Reanalysis (ERA-15, 1979-93)
ECMWF Reanalysis (ERA-40, 1958-2001)
Useful to validate climate model simulations and also
to understand observed climate change and variability
on decadal to smaller time scales
Climate Scenarios:
What are they ?
A climate scenario is a plausible
representation of future climate that
has been constructed for explicit use in
investigating the potential impacts of
anthropogenic climate change.
Climate Scenarios:
How do we get them ?
By using climate projections
(description of the modelled response
of the climate system to scenarios of
greenhouse gas and aerosol
concentration), by manipulating the
model outputs and combining them
with observed climate data.
Types of Climate Scenarios
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Climate Model Projections
Incremental Scenarios
Analogue Scenarios
Others
Climate Model Projections
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Starting point for most climate
scenarios
Simple climate models
General Circulation Models
Large-scale response to
anthropogenic forcing
Variety of spatio-temporal
resolutions
Special Report on Emission
Scenarios (SRES)
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A1: Rapid economic growth;Global population peaks in midcentury; Rapid introduction of new and more efficient
technologies; Convergence among regions; increased
cultural/social interaction; reduction in income disparities
A2: Heterogeneous world; Self-reliance; Preservation of local
identities; Continuously increasing population; Regionally oriented
economic development; Fragmented per capita economic growth
and technological change
B1: Convergent world; Global population peaks in mid-century
and declines thereafter; Rapid change in economic structures
toward service and information; Reduction in material intensity
and introduction of clean/resource-efficient technologies; Global
solutions
B2: Local solutions; Continuously increasing population;
Intermediate levels of economic development; Less rapid and
more diverse technological change; Environmental protection and
social equity with focus on local/regional levels.
Incremental Scenarios
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Also called synthetic scenarios
Critical climate elements are changed
by arbitrary but plausible increments
(e.g., +1, +2, +3°C change in
temperature)
Testing system sensitivity
Identifying key climate thresholds
Analogue Scenarios
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Identifying recorded climate changes that
may resemble future climate in a given
region
Palaeoclimatic (characterizing warmer
periods in the past)
Instrumental (exploring vulnerabilities and
adaptive capacities)
Temporal analogues
Spatial analogues
Other Scenarios
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Extrapolating observed trends
Statistical downscaling
Expert judgement
Uncertainties in Climate Scenarios
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Specifying alternative emissions futures
Uncertainties in converting emissions to
concentrations
Uncertainties in converting concentrations
to radiative forcing
Uncertainties in modelling the climate
response to a given radiative forcing
Uncertainties in converting model
response into inputs for impact studies
Approaches for representing
uncertainties
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Scaling climate response patterns
across a range of forcing scenarios
Defining appropriate climate change
signals
Risk assessment approaches
Annotation of climate scenarios to
reflect more qualitative aspects of
uncertainty
Climate Scenarios:
Suitability Criteria for
Impact Assessment
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Consistency
Physical plausibility and Realism
Appropriateness
Representativeness
Accessibility
CLIMATE SCENARIO CONSTRUCTION FOR IMPACT ASSESSMENT
ANTHROPOGENIC
FORCING
(GHG emissions, land use)
NATURAL
FORCING
(orbital; solar;volcanic)
Palaeoclimatic
reconstructions
GCM validation
Analogue
scenarios
Simple
Models
GCMs
Historical
Observations
Baseline
Climate
GCM present climate
Pattern
Scaling
GCM future climate
Regionalization
Incremental
Scenarios
For sensitivity
studies
Dynamical
methods
Statistical
Methods
GCM based
scenarios
IMPACTS
Direct GCM or
interpolated
Global mean annual
temperature change
Climate System
Climate Models
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Simplified mathematical representation of the Earth’s
climate system
Skill depends on the level of our understanding of the
physical, geophysical, chemical and biological
processes that govern the climate system
Substantial improvements over the last two decades
Sub-models : atmosphere, ocean, land surface,
cryosphere, biosphere
Typical Resolution of global models (atmosphere) :
Horizontal - 250 km; Vertical – 1 km
Small-scale processes : Parameterization
Coupled models (e.g., atmosphere-ocean)
Sensitivity studies/Future projections
Internal variability/Ensemble runs
Evolution of Climate Models
Climate Model Projections
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Idealized forcing (e.g., 1% compound
increase of greenhouse gas concentration
extending up to a doubling at year 70)
Time-evolving future forcing, with the
simulation starting in the 19th century run
with estimates of observed forcing
through the 20th century and future
climate with estimated forcings according
to various scenarios such as IS92a
Using an initial state the end of the 20th
century integrations, and following the A2
and B2 SRES forcing scenarios up to 2100
The Coupled Models Used
Horizontal
Resolution
Vertical levels atmosphere
Vertical levels ocean
Radiation
Convection
ECHAM4/
OPYC3
T42
~ 2.8°X2.8°
HadCM2
HadCM3
3.75°X2.5°
19 layers
19 layers
3.75°X2.5°
1.25°X1.25°
for ocean
19 layers
11 layers
20 layers
20 layers
SW-6, LW-4,
SW-6, LW-4,
SW-6, LW-8,
(Roeckner et al.,
1991)
(Slingo, 1989; Slingo
and Wilderspin,
1986)
(Edwards and
Slingo, 1996)
Mass-flux
scheme
A penetrative
convective
scheme, downdraught
A penetrative
convective
scheme, downdraught
(Gregory and
Rowntree, 1990)
(Gregory and
Rowntree, 1990)
30 minutes 1day Coupling
30 minutes 1day Coupling
(Tiedtke, 1989)
Time-step
24 minutes
except for
radiation (2 hrs.)
The Transient
Climate Change
Simulations
Control (CTL)
Carbon Dioxide, Methane and Nitrous Oxide fixed at 1990 levels.
Concentrations of the industrial gases (CFCs and others) are set
to zero, while ozone and aerosols are prescribed as
climatological distributions.
ECHAM4/OPYC3: After a 100-year spin-up the model was run,
with constant flux adjustment, for another 300 years.
HadCM2 and HadCM3: After 510-year spin-up, the model was run
for 240 years.
Greenhouse Gas Increase (GHG)
The GHG simulation starts from the end of the spin-up and the
forcing is slowly increased to represent the observed changes in
the atmospheric concentration of greenhouse gases during 18601990 and during 1990-2099 the forcings are increased at rates
specified by the IPCC scenarios (IS92a, 1% per year compounded
increase).
Greenhouse Gas + Sulfate Aerosol Increase (SUL)
The second perturbed experiment involves the forcing due to
increasing concentration of greenhouse gases as well as sulfate
aerosols. The forcings in this experiment also follow the
historical rates during 1860-1990 and the IS92a specifications for
the period 1990-2099.
Model Simulations (Monthly)
(Source: IPCC-DDC and DKRZ, MPI)
DATA
Data Period : 240 years (1860-2099; nominal time scale)
Surface Climate variables for three coupled AOGCMs:
ECHAM4/OPYC3,HadCM2 and HadCM3
Precipitation
Surface Air Temperature (Maximum and Minimum).
Upper air fields for the levels 850, 500 and 200 hPa
(ECHAM4/OPYC3 only)
Winds (u,v)
Temperature
Specific Humidity
Observed Climate (Monthly)
(Source: IMD and others)
Precipitation at 306 stations over India (1871-1990)
Max./Min. Temperatures at 121 stations over India (1901-1990)
Global SST anomalies and Niño-3 SSTA (1856-1997)
Global land surface air temperature anomalies (1856-1999)
Temporal scales of monsoon variability
Factors
Features
Intraseasonal
Interannual
Decadal/Century
Millennia &
longer
Onset/withdrawal;
Active and breakmonsoon phases;
30-50 day
oscillations;
severe rainstorms
Droughts and
floods
Changes in the
frequency of
droughts and floods
Changes in the
areal extents of
monsoons
Atmospheric
variability;
tropicalmidlatitude
interactions;
Soil moisture;
Sea surface
temperatures
Atmospheric
interactions;
El Niño/
Southern
Oscillation;
Top layers of
tropical oceans;
Snow cover;
Land surface
characteristics
Monsoon circulation
variations;
Deep ocean
involvement;
Greenhouse gases
increase;
Human activities;
Biospheric changes;
Volcanic dust
Global climate
excursions;
Ice ages;
Warm epochs;
Sun-earth
geometry
Models – Annual Cycles
Models – Temp Maps
Models - RF Maps
Models – RF Contr
Models – RF CV
Temp Series
MonRF Series
Projections
Models-MonRF var
MonRF Change - ECHAM
MonRF Change – HadCM3
ENSO-Monsoon Correlations – Observations & Modelling
Projected Changes in ENSO-Monsoon Relationships
due to Transient increase in Greenhouse Gas Concentrations (ECHAM4/OPYC3)
Regionalization Techniques
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High/variable resolution AOGCMs
Regional/nested regional (limited
area) climate models
Empirical/statistical downscaling
The Hadley Centre Regional Climate Models
(HadRM2/HadRM3)
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High-resolution limited area model driven at its lateral and
sea-surface boundaries by output from HadCM
Formulation identical to HadAM
Grid : 0.44° x 0.44°
One-way nesting
Joint Indo-UK Collaborative research programme on climate
change impacts in India
Climate change simulations performed by the Hadley
Centre using HadRM2 for the Indian region (the output is
being currently analysed by IITM)
HadRM3 installed at IITM; Climate change simulations and
scenario development will be performed at IITM
Model Orographies
in GCM and RCM
Observed and Simulated Indian Summer Monsoon
Rainfall (GCM vs. RCM)
Observed and Simulated (GCM and RCM) Surface Air
Temperature over India
Simulated Surface Temperature Change due to
Greenhouse Gas Increase
Simulated Monsoon Rainfall Change (mm/day) due to
Greenhouse Gas Increase
Indian
Summer
Monsoon
Simulations by
HadRM2
Indian Annual
Surface
Temperature
Simulations by
HadRM2
Global Summer (JJAS) Precipitation Patterns
simulated by 9 coupled AOGCMs
Indian Summer Monsoon Patterns
simulated by 9 coupled AOGCMs
Annual Surface Air Temperature Patterns over India
simulated by 9 coupled AOGCMs
Monsoon
Precipitation Change
due to Greenhouse
Gas Increase
(GHG-Ctl)
Annual Surface
Temperature Change
due to Greenhouse
Gas Increase
(GHG-Ctl)