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-6: Approaches to Select GCM data

February, 2013

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


Approaches for selecting a Global Climate Model for an Impact Study


The IPCC has a guidance document of interest…

“General Guidelines on the use of Scenario Data for Climate Impact and Adaptation Assessment”

Version 2, June 2007

Prepared by T.R. Carter

with contributions from other authors

The Task Group on Data and Scenario Support for Impact and Climate Assessment (TGICA) of IPCC

This PDF is provided on the CCCSN Training DVD

IPCC-TGICA, 2007


From the Range of Projections…

• IPCC recommends * the use of more than simply ONE model or scenario projection (one should use an ‘ensemble’ approach) – we saw why earlier

• The use of a limited number of models or scenarios provides no information of the uncertainty involved in climate modelling

• Alternatives to an ‘ensemble approach’ might involve the selection of models/scenario combinations which ‘bound’ the max/min of reasonable model projections (used in IJC Lake Ontario-St. Lawrence Regulatory Study)

* (IPCC-TGICA, 2007)


Two Tests for the selection of a Model:

TEST 1:

How well does a model reproduce the historical climate?

TEST 2:

How does the model compare with all other models for future projections?

Commonly called ‘Model Validation’


First test: Baseline (historical) climate

A model should be able to accurately reproduce past climate (baseline) as a criterion for further consideration

We can test how well a model has reproduced the historical baseline climate (Model VALIDATION)

Require reliable, long-term observed climate data

from the location of interest OR we could use GRIDDED

global datasets at the same scale as the models

IMPORTANT:

Remember we are comparing site-specific to a grid cell average, so an exact match is not to be expected.


Second test: Future Projection

We can check how a model performs in comparison with many others in a future projection

5 criteria outlined by IPCC:

1.Consistency with other model projections

2.Physical plausibility (realistic?)

3.Applicability for use (correct variables? timescale?)

4.Representative

5.Accessibility of dataA model should not be an outlier in the community of

model results


Check maps - CGCM3 - Temperature?

OBS Stations NCEP GRIDDED CGCM3T47

1961-1990 Mean ANNUAL TEMPERATURE

Reasonable pattern, with models slightly cold


Example: CGCM3 – Timeseries in the Historical Period

CGCM3 Grid Cell and Toronto Pearson Mean Annual Temperature

4

5

6

7

8

9

10

11

1960 1970 1980 1990 2000 2010

Year

Tem

per

atu

re (

C)

Toronto Pearson A

CGCM3T47

The model is too cold, but the TREND is good


Check maps - CGCM3 - Precipitation?

OBS Stations NCEP GRIDDED CGCM3T47

1961-1990 Mean ANNUAL PRECIPITATION

Pattern not quite right –units here are mm/day


Example: CGCM3 – Timeseries in the Historical Period

The model is too wet,TREND is reasonable

CGCM3 Grid Cell and Toronto Pearson Mean Annual Precipitation

400500600700800900

100011001200

1960 1970 1980 1990 2000 2010

Year

An

nu

al P

rec

ipit

ita

tio

n (

mm

)

Toronto Pearson A

CGCM3


Test 1: Baseline Methodology:

• Comparison of Annual, Seasonal, Monthly means over the same historical period

• Use the variables of interest – most common – precipitation and temperature from the Archive

• Keep in mind that we are comparing a single site location (meteorological station) against a gridded

value

• An improved method would be to include other nearby stations in the analysis as well with long records

• We then obtain from CCCSN the model baseline values for the same location using the SCATTERPLOT


Test 1: (continued)

• Compare the annual values and the distribution of temperature over the year

• Models which best match the annual mean and the monthly distribution pattern can be identified

NOTE: it doesn’t matter which emission scenario we select since for the historical period, the models use

the same baseline


Test 1: Baseline Methodology…

0

2

4

6

8

10

12

14

BCM2

.0CN

RMCM

3CS

IROM

k3.0

ECHA

M5OM

ECHO

-GFG

OALS

-g1.0

GFDL

CM2.0

GFDL

CM2.1

GISS

AOM

GISS

E-H

GISS

E-R

INMC

M3.0

IPSL

CM4

MIRO

C3.2h

ires

MIRO

C3.2m

edre

sCS

IROM

k3.5

INGV

-SXG

MRIC

GCM2

.3.2a

NCAR

PCM

NCAR

CCSM

3Ha

dGEM

1CG

CM3T

63HA

DCM3

CGCM

3T47

- 0

200

400

600

800

1000

1200

1400

BCM

2.0

CNRM

CM3

CSIR

OM

k3.0

ECHA

M5O

MEC

HO-G

FGO

ALS-

g1.0

GFD

LCM

2.0

GFD

LCM

2.1

GIS

SAO

MG

ISSE

-HG

ISSE

-RIN

MCM

3.0

IPSL

CM4

MIR

OC3

.2hir

esM

IRO

C3.2

med

res

CSIR

OM

k3.5

ING

V-SX

GM

RICG

CM2.

3.2a

NCAR

PCM

NCAR

CCSM

3Ha

dGEM

1CG

CM3T

63HA

DCM

3CG

CM3T

47

Annual Temperature Annual Precipitation

too wet

too drytoo cold

too warm observed means


Test 1: Baseline Methodology…Looking at Temp and Precip together

• Again, SCATTERPLOT on CCCSN – simply select BOTH variables at the same time and all models or combine the 2 initial results in a single spreadsheet

1961-1990 Mean Annual

0

2

4

6

8

10

12

14

0 200 400 600 800 1000 1200 1400

Annual Precipitation (mm)

Tem

pera

ture

(C)

‘Perfect’ model

• Almost all models are too wet

• Most models are too cold

• Outliers can be identified


Test 1: Baseline Methodology…

Rank the models for the baseline period - ANNUAL

Temperature Precipitation

Model A rankModel B rankModel C rankModel D rankModel E rankModel F rank

…

Total Score

+Model A rankModel B rankModel C rankModel D rankModel E rankModel F rank

…

Sum of Model A ranks

Sum of Model B ranks

Sum of Model C ranks

Sum of Model D ranks

Sum of Model E ranks

Sum of Model F ranks

…Lowest Score Model is Closest to Baseline


Test 1: Baseline Methodology

• The same analysis can be done on a month and seasonal basis –this can be very important

• This method is best used to reject models (models with largest scores)

• We effectively remove from consideration those models with lowest agreement (largest scores)

• The moderating effect of lakes, local elevation effects, lake-induced precip are all complicating factors


Test 2: Future Projections

• No complications like observed data!

• We look at the range of model projections for the same location and see how they vary

• Models with outlier projections (excessive anomalies – which are too large or too small) are best rejected

• Finding the anomalies is a simple process using SCATTERPLOT on CCCSN


Test 2: Future Projections

Which projection period are we interested in?(2050s is a common period for planning purposes)

Is an annual, seasonal or monthly projection needed?- depends on the study

The 1961-1990 or 1971-2000 period as baseline?


Annual Temperature/Precipitation Change

Scatterplot for Toronto Grid Cell: 2050s (ONLY SRES)

Median T and P for all

models/scenarios

1 Std. Dev


What do all the models and emission scenarios tell us for this gridcell?

Median Annual Temperature Change in 2050s

Median Annual Precipitation Change in 2050s

+2.6

+5.0%

o +3.3o+1.8o

+9.7%+0.4%

o7.2 C

To

ron

to

Pea

rso

n A

O

bse

rved

19

61-1

990

No

rmal

780.8mm

LOWER

LOWER

UPPER

UPPER


TEST 2: Which Models are Closest to the Median Projection?

Rank the models for the 2050s Projections - ANNUAL

Temperature Precipitation

Model A rankModel B rankModel C rankModel D rankModel E rankModel F rank

…

Total Score

+Model A rankModel B rankModel C rankModel D rankModel E rankModel F rank

…

Sum of Model A ranks

Sum of Model B ranks

Sum of Model C ranks

Sum of Model D ranks

Sum of Model E ranks

Sum of Model F ranks

…Lowest Score Model is Closest to ALL MODEL MEDIAN


NCARCCSM3

HADCM3

INMCM3.0

GISSAOM

CGCM3T47-Mean

CGCM3T63

GISSE-R

CNRMCM3

HadGEM1

Is there a ‘best’ model for both tests?

Resulting Models

TEST 1 TEST 2 (baseline)

(projections)

FGOALS-g1.0.SR-A1B

CSIROMk3.0.SR-A2

MRI-CGCM2.3.2a.SR-A1B

GISSAOM.SR-A1B

CGCM3T63.SR-B1

GFDLCM2.0.SR-B1

GFDLCM2.1.SR-A2

HADCM3.SR-A2

BCM2.0.SR-A1B

BCM2.0.SR-B1

MRI-CGCM2.3.2a.SR-A2

HADCM3

GISSAOM

CGCM3T63

Resulting Models

Best Models from both TESTS


The Caveats:• We have only considered ANNUAL values, not

SEASONAL or MONTHLY baseline (TEST 1) or projections (TEST 2)

The seasonal and monthly options are available on the SCATTERPLOT selector)

• ‘Extreme variables’ have greater uncertainty than normals

Models can show good ANNUAL agreement with baseline and good agreement with all model projections, but they can still have incorrect seasonal or monthly distributions


Will Regional Climate Model (RCM)s help?

• They offer higher spatial resolution (~50 x 50 km) versus GCM at 200-300 km

• The models are driven by an overlying model or gridded data source – so biases in those gridded datasets will also be included in the RCM

• The time requirements and processing power available means there are fewer emission scenarios available = fewer future pathways for consideration

• Some investigations will always require further statistical downscaling


Will RCMs Help in TEST 1?

0

2

4

6

8

10

12

14

BC

M2

.0

CN

RM

CM

3

CS

IRO

Mk3

.0

EC

HA

M5

OM

EC

HO

-G

FG

OA

LS

-g1

.0

GF

DL

CM

2.0

GF

DL

CM

2.1

GIS

SA

OM

GIS

SE

-H

GIS

SE

-R

INM

CM

3.0

IPS

LC

M4

MIR

OC

3.2

hir

es

MIR

OC

3.2

me

dre

s

CS

IRO

Mk3

.5

ING

V-S

XG

MR

ICG

CM

2.3

.2a

NC

AR

PC

M

NC

AR

CC

SM

3

Ha

dG

EM

1

CG

CM

3T

63

HA

DC

M3

CG

CM

3T

47

-

0

200

400

600

800

1000

1200

1400

BC

M2.0

CN

RM

CM

3

CS

IRO

Mk3.0

EC

HA

M5O

M

EC

HO

-G

FG

OA

LS

-g1.0

GF

DLC

M2.0

GF

DLC

M2.1

GIS

SA

OM

GIS

SE

-H

GIS

SE

-R

INM

CM

3.0

IPS

LC

M4

MIR

OC

3.2

hires

MIR

OC

3.2

medre

s

CS

IRO

Mk3.5

ING

V-S

XG

MR

ICG

CM

2.3

.2a

NC

AR

PC

M

NC

AR

CC

SM

3

HadG

EM

1

CG

CM

3T

63

HA

DC

M3

CG

CM

3T

47

Annual Temperature Annual Precipitation

too wet

too drytoo cold

too warm

CRCM3.7.1: 6.1 C

CRCM4.1.1: 4.9 C

CRCM4.2.2: 6.1 C

all coldCRCM3.7.1: 758.5mm too dry

CRCM4.1.1: 542.8mm too dry

CRCM4.2.2: 860.7mm too wet


Median T and P

1 Std. Dev

Will RCMs Help in TEST 2?

crcm3.7.1

crcm4.1.1

crcm4.2.0


Running Scatterplots for all parameters


CCCSN.CA websiteSelect Scenarios - Visualization


Select Scatterplots


Get data Input lat long Select AR4 Select

Variable Tmean

Select Model(s) validated to Tmean

Click Get Data


Website Output

Plus output table under chart


Get data for all variables including climate extremes

You can select an ensemble of models by using Ctrl-Enter


Ensemble of CCCSN.CA Results for Ptotal at Windsor


Climate Extremes available for some models


Future Consecutive Dry Days at Windsor Using 3 GCM model output

Can average all model results for ensemble

Documents

WFM 6311: Climate Change Risk Management