Download Lecture-5d_climate_2009

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
WFM 6311: Climate Change Risk
Management
Lecture-5d: Climate Change Scenarios Network
Akm Saiful Islam
Institute of Water and Flood Management (IWFM)
Bangladesh University of Engineering and Technology (BUET)
December, 2009
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
Introduction to the Canadian
Climate Change Scenarios
Network (CCCSN)
www.cccsn.ca
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
Considerations:
Which
Models?
How do I get
information for
my location?
?
Which
Scenarios?
Uncertainty
in results?
What about
Downscaling?
Where do
I start?
CCCSN.CA
IPCC images
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
What Information does
CCCSN Provide?







New Climate Change Science from IPCC
25 GCMs from the recent 4th (AR4) assessment
Canadian Regional Model (North America)
New ‘Extreme’ Variables
New Scatterplots, Downscaling Tools, Bioclimate
Profiles for nearly 600 locations in Canada
Download GCM/RCM data for custom analysis
Download Downscaling software and input data
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
This Training Session:
• Use of GCM / RCM grid cell output from many
models and scenarios
• Best approach for the uncertainty
• More detailed investigation (of a single location)
would require statistical downscaling techniques
• Statistical Downscaling (using SDSM, LARS, ASD,
etc) is not the focus of this training
• CCCSN has downscaling tools and input data
required by them
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
The Typical Model Grid
• The models provide GRID
cell AVERAGED values not a single point location
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
Contents

Text
Menu
Driven
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
Contents

Text
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
Contents

Text
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
Contents

Text
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
CCCSN Visualization:




Maps –see an overview of a single model across
Canada (zoomable)
Scatterplot – see an overview of one or many models for
a single location
Bioclimate Profiles – see an overview of a single model
at a single location
Advanced Spatial Search – see where on a map specific
criteria you select are found
Don’t like our visualizations? Download the data and
generate your own custom maps/charts/tables
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
Some Considerations:
• The models generally use 1961-1990 as their
‘baseline period’ - most recent is 1971-2000
• ‘Anomalies’ are the DIFFERENCE between a future
period projection and a baseline
• Maps can output model values OR anomalies
• Scatterplots output anomalies (the change) from the
baseline value
• Future projections tend to be averaged over standard
periods as well (but they don’t have to be):
2020s = 2011-2040
2050s = 2041-2070
2080s = 2071-2100
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
Some Considerations:
• Bioclimate profiles are a ‘hybrid’ of observed and
model projection data
Baseline =
Observed data
at a climate
station
+
Model
Anomaly
value
One of 583
Grid cell
stations
value
=
Projected Value
for 2020s,
2050s, 2080s
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
Toronto Area Bioclimate Stations
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
Bioclimate profiles
Example: Water Balance Profile:
Profiles available for these locations:
-Temperature
-Heating DD and Cooling DD
-Daily and Monthly GDD
-CHU
-Frost Profile
-Water Balance
-Frequency of Precipitation
-Temperature Threshold
-Freeze/Thaw Cycles
-Accumulated Precipitation
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
So… for any selected location:
• The model selected affects the result
• The emission scenario selected affects the result
• There are about 25 GCMs with 2 or 3 emission
scenarios for each (about 50-75 outcomes)
• Within Canada we also have the CRCM (several
versions) using one emission scenario (A2)
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
Emission Scenarios
(image sources: TGICA GUIDANCE, IPCC, 2007)
‘A2’ –
aggressive
growth
‘A1B’ –
moderate
growth
‘B’ – low
growth
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
What Variables? Timescale?
CCCSN has a reduced number of GCM/RCM
variables including:
• 2 m Air Temperature (mean, max, min) (C)
• Precipitation (mm/d)
• Sea Level Pressure (mb)
• Specific Humidity/Relative Humidity (kg/kg or %)
• 10 m Windspeed (mean, U and V) (m/s)
• Incoming Shortwave Radiation (W/m2)
TIMESCALE: minimum is MONTHLY on CCCSN
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
Extreme Variables include (some models):
•
•
•
•
•
•
•
•
•
•
2 m Air Temperature Range (C)
Consecutive Dry Days (days)
Days with Rain > 10 mm/d (days)
Fraction of Annual Total Precip > 95th percentile (%)
Fraction of Time < 90th percentile min temp (%)
Number of Frost Days (days)
Maximum Heat Wave Duration (days)
Maximum 5 Day Precipitation (mm)
Simple Daily Intensity Index (mm/day)
Growing Season Length (days)
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
Effect of Emission Scenario
(holding model constant)
Example: CGCM3- Grid Cell Value (Toronto)
A2
14
A1B
12
10
B1
8
6
4
2
Year
2100
2090
2080
2070
2060
2050
2040
2030
2020
2010
2000
1990
1980
1970
0
1960
Mean Annual Temperature
16
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
Effect of Model (holding
emission scenario constant)
Mean Annual Temperature
(C)
Example: All Models -A1B Emission Scenario (Toronto Grid Cell)
18
16
14
12
10
8
6
4
2
0
1961-1990
2020s
2050s
Period
2080s
All
models
which
produce
A1B
output
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
Model considerations:
• Newer versions of models are better than older
• Increase in temporal and spatial resolution is
preferable
Uncertainty in:
1. Emission scenarios
2. Parameterization of sub-grid scale processes
3. Climate sensitivity? Will it be constant?
Models represent the best method available
to project future climate
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
What are the International
Modeling
Centres?
BCM2.0
Bjerknes Centre for Climate
Norway
CGCM3T47 Canadian Centre for Climate and
CGCM3T63 Modelling Analysis
Canada
CNRMCM3
France
Centre National de Recherches
Meteorologiques
CSIROMK3 Commonwealth Scientific and
CSIROMK3 Industrial Research Organisation
(CSIRO)
5
Australia
ECHAM5O
M
Max Planck Institute für Meteorologie
Germany
ECHO-G
Meteorological Institute, University of
Bonn
Germany
FGOALSG10
Institute of Atmospheric Physics,
Chinese Academy of Sciences
China
GFDLCM20 Geophysical Fluid Dynamics
USA
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
Centres…
CGCM232
Meteorological Research Institute
Japan
INMCM30
Institute for Numerical Mathematics
Russia
IPSLCM4
Institut Pierre Simon Laplace
France
MIROC32HI National Institute for Environmental
MIROC32M Studies
ED
Japan
NCARPCM
NCARCCS
Also:
M3
USA
National Center for Atmospheric
Research
Canadian Regional Climate Model (CRCM3.7.1, 4.1.1 and 4.2.0) from OURANOS
Consortium (EC a member) (Montreal, QC)
Coming up…
INGV-SGX
National Institute of Geophysics and Volcanology
Italy
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
More advanced analysis
Some comments on Downscaling…
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
Two Main Downscaling
Methods:
(1) Dynamical Downscaling
Regional Climate Models (RCMs)
Benefit: physically based – but still use
parameterization
Limitations: computation time, complexity,
dependent on initialization data (GCM)
(2) Statistical Downscaling
Establish relationships between model scale
information and local ‘point’ information
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
What is Statistical Downscaling?
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
Statistical Downscaling
CCCSN provides tools:
 1. Automated Statistical Downscaling (ASD)
 2. Statistical Downscaling Model (SDSM)
 3. Weather Generator (LARS-WG)
CCCSN provides the necessary input data:
 1. Access to observed data (weatheroffice / DAI)
 2. Access to required projection predictors from HadCM3
and CGCM2/CGCM3 via Data Access Interface (DAI)
WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam
Conclusions:





Many GCMs and more and more Regional Climate Models coming
on-line (NARRCAP project)
Results can vary widely between models and emission scenario
selected
Some models do better than others at reproducing the historical
climate as we shall see
In complex environments (coastal, mountainous, sea ice), extra
care is required (grid cell averaging and process parameterization)
Downscaling of even RCMs is likely required for some
investigations
It is critical to not rely on any single model/scenario for decisionmaking. Due diligence requires the consideration of more than a
single possible outcome.