WFM 6311: Climate Change Risk Management

WFM 6311: Climate Risk Management © Dr. Akm Saiful Islam

WFM 6311: Climate Change Risk Management

Akm Saiful Islam

Lecture-5d: Climate Change Scenarios Network

December, 2012

Institute of Water and Flood Management (IWFM)Bangladesh University of Engineering and Technology (BUET)


Introduction to the Canadian Climate Change Scenarios

Network (CCCSN)www.cccsn.ca


Considerations:

Which Models?

Which Scenarios?

How do I get information for my location?

Uncertainty in results??

What about Downscaling?

IPCC images

CCCSN.CA

Where do I start?


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


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


The Typical Model Grid

• The models provide GRID cell AVERAGED values - not a single point location


Contents

TextMenu Drive

n


Contents

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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


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


Some Considerations:

• Bioclimate profiles are a ‘hybrid’ of observed and model projection data

Baseline = Observed data

at a climate station

+ Model Anomaly

value=

Projected Value for 2020s,

2050s, 2080s

One of 583

stations

Grid cell

value


Toronto Area Bioclimate Stations


Bioclimate profilesExample: 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


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)


Emission Scenarios

‘A2’ – aggressive growth

‘A1B’ – moderate growth

‘B’ – low growth

(image sources: TGICA GUIDANCE, IPCC, 2007)


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


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)


Effect of Emission Scenario(holding model constant)

Example: CGCM3- Grid Cell Value (Toronto)

0

2

4

6

8

10

12

14

16

1960

1970

1980

1990

2000

2010

2020

2030

2040

2050

2060

2070

2080

2090

2100

Year

Mea

n A

nn

ual

Tem

per

atu

re

A2

A1B

B1


Effect of Model (holding emission scenario constant)

All models which

produce A1B

output

Example: All Models -A1B Emission Scenario (Toronto Grid Cell)

024

68

1012

141618

1961-1990 2020s 2050s 2080s

Period

Mean

An

nu

al T

em

pera

ture

(C)


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


What are the International Modeling Centres?BCM2.0 Bjerknes Centre for Climate Norway

CGCM3T47CGCM3T63

Canadian Centre for Climate and Modelling Analysis

Canada

CNRMCM3 Centre National de Recherches Meteorologiques

France

CSIROMK3

CSIROMK35

Commonwealth Scientific and Industrial Research Organisation (CSIRO)

Australia

ECHAM5OM

Max Planck Institute für Meteorologie Germany

ECHO-G Meteorological Institute, University of Bonn

Germany

FGOALS-G10

Institute of Atmospheric Physics, Chinese Academy of Sciences

China

GFDLCM20

GFDLCM21

Geophysical Fluid Dynamics Laboratory

USA

GISSE-H

GISSE-R

Goddard Institute for Space Studies USA

HADCM3

HADGEM1

Hadley Centre – UK Meteorological Office

UK


Centres…CGCM232 Meteorological Research Institute Japan

INMCM30 Institute for Numerical Mathematics Russia

IPSLCM4 Institut Pierre Simon Laplace France

MIROC32HI

MIROC32MED

National Institute for Environmental Studies

Japan

NCARPCM

NCARCCSM3

National Center for Atmospheric Research

USA

Coming up…INGV-SGX National Institute of Geophysics and Volcanology Italy

Also: Canadian Regional Climate Model (CRCM3.7.1, 4.1.1 and 4.2.0) from OURANOS

Consortium (EC a member) (Montreal, QC)


Some comments on Downscaling…

More advanced analysis


Two Main Downscaling Methods:(1) Dynamical Downscaling Regional Climate Models (RCMs)

Benefit: physically based – but still use parameterizationLimitations: computation time, complexity, dependent on initialization data

(GCM)

(2) Statistical Downscaling Establish relationships between model scale information and local ‘point’

information

Benefit: relatively easy to implement – but not for the untrainedLimitations: -stationarity – are the statistical relationships developed valid in the

future? -need good observational data and model predictor data


What is Statistical Downscaling?


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)


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 decision-making. Due diligence requires the consideration of more than a single possible outcome.

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WFM 6311: Climate Change Risk Management

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