Transcript Tools for Assessing Regional Model Output (continued)

MAGICC/SCENGEN Hands On
Tutorial
By
Joel B. Smith
Stratus Consulting Inc.
[email protected]
NCAR Summer 2006 Colloquium on
Climate and Health
July 18, 2006
Outline
• Brief Introduction on Climate Change
Scenarios
• Then, we’ll spend most of the time on the
tutorial on MAGICC/SCENGEN
Why Use Climate
Change Scenarios?
• We are unsure exactly how regional climate will
change
• Scenarios are plausible combinations of variables
consistent with what we know about humaninduced climate change
• One can think of them as the prediction of a
model, contingent upon the greenhouse gas
emissions scenario
• Since estimates of regional change by models
differ substantially, an individual model estimate
should be treated more as a scenario
What Are
Reasonable Scenarios?
• Scenarios should be:
– Consistent with our understanding of the anthropogenic
effects on climate
– Internally consistent
• e.g., clouds, temperature, precipitation
• Scenarios are a communication tool about what is
known and not known about climate change
– Should reflect plausible range for key variables
Scenarios for
Impacts Analysis
• Need to be at a scale necessary for analysis
• Spatial
– e.g., to watershed or farm level
• Temporal
– Monthly
– Daily
– Sub-daily
Regional Climate Change
Scenarios
• Present range of possible regional changes
in climate
• Two roles
– Use ranges of climate changes to help
understand sensitivity of affected systems
– Use ranges to communicate what is known and
not known about regional climate change
• Temperature rise and range of precipitation changes
Tools for Assessing Regional
Model Output
• We’ll learn how to use a tool that enables us
to examine output from a number of climate
models
• Can see degree to which models agree and
disagree about regional changes
Sources of Uncertainty on
Regional Climate Change
• GHG Emissions
• Greenhouse Gas Concentrations
• Climate Sensitivity, e.g., 2xCO2
• Regional pattern of climate change
– Distribution of changes in temperature and precipitation
• Climate Variability
GHG Emissions and
Concentrations Projections
Source: Houghton et al., 2001.
Projections of Global Mean
Temperature Change
Source: Houghton et al., 2001.
Normalized Annual-Mean Temperature
Changes in CMIP2 Greenhouse Warming
Experiments
0 .4
0 .2
0 .8
0 .6
1 .2
1
1 .4
MAGICC/SCENGEN
• User can:
– Select GHG emission scenarios e.g., from IPCC
SRES
– Can select CO2 concentration
– Select climate sensitivity
– Select GCMs to examine
• Regional pattern is hard wired in
– Can examine change in seasonal variability
• Not interannual or daily
MAGICC/SCENGEN
• MAGICC is a simple model
of global T and SLR
• Used in IPCC TAR
• SCENGEN uses pattern
scaling for 17 GCMs
• Yield
–
–
–
–
Model by model changes
Mean change
Intermodel SD
Interannual variability
changes
– Current and future climate on
5 x 5°grid
Using MAGICC/SCENGEN
MAGICC: Selecting Scenarios
SO2 Scenarios
MAGICC: Selecting Scenarios
(continued)
MAGICC: Selecting Forcings
MAGICC: Displaying Results
MAGICC: Displaying Results
(continued)
SCENGEN
Normalizing GCM Output
• Expresses regional change relative to an increase
of 1°C in mean global temperature
– This is a way to avoid high sensitivity models
dominating results
– It allows us to compare GCM output based on relative
regional change
• Normalized temperature change =
ΔTRGCM/ΔTGMTGCM
• Normalized precipitation change =
ΔPRGCM/ΔTGMTGCM
Pattern Scaling
• Is a technique for estimating change in
regional climate using normalized
patterns of change and changes in GMT
• Pattern scaled temperature change:
– ΔTRΔGMT = (ΔTRGCM/ΔTGMTGCM) x ΔGMT
• Pattern scaled precipitation
– ΔPRΔGMT = (ΔPRGCM/ΔTGMTGCM) x ΔGMT
Running SCENGEN
(continued)
SCENGEN: Analysis
SCENGEN: Model Selection
SCENGEN: Area of Analysis
SCENGEN: Select Variable
SCENGEN: Scenario
SCENGEN: Global Results
SCENGEN: Map Results
SCENGEN: Quantitative Results
INTER-MOD S.D. : AREA AVERAGE = 5.186 % (FOR NORMALIZED GHG DATA)
INTER-MOD SNR : AREA AVERAGE = -.067 (FOR NORMALIZED GHG DATA)
PROB OF INCREASE : AREA AVERAGE = .473 (FOR NORMALIZED GHG DATA)
GHG ONLY
: AREA AVERAGE = -.411 % (FOR SCALED DATA)
AEROSOL ONLY : AREA AVERAGE = -.277 % (FOR SCALED DATA)
GHG AND AEROSOL : AREA AVERAGE = -.687 % (FOR SCALED DATA)
*** SCALED AREA AVERAGE RESULTS FOR INDIVIDUAL MODELS ***
(AEROSOLS INCLUDED)
MODEL = BMRCD2 : AREA AVE = 2.404 (%)
MODEL = CCC1D2 : AREA AVE = -5.384 (%)
MODEL = CCSRD2 : AREA AVE = 6.250 (%)
MODEL = CERFD2 : AREA AVE = -2.094 (%)
MODEL = CSI2D2 : AREA AVE = 6.058 (%)
MODEL = CSM_D2 : AREA AVE = 1.245 (%)
MODEL = ECH3D2 : AREA AVE = .151 (%)
MODEL = ECH4D2 : AREA AVE = -1.133 (%)
MODEL = GFDLD2 : AREA AVE = 1.298 (%)
MODEL = GISSD2 : AREA AVE = -3.874 (%)
MODEL = HAD2D2 : AREA AVE = -5.442 (%)
MODEL = HAD3D2 : AREA AVE = -.459 (%)
MODEL = IAP_D2 : AREA AVE = -.088 (%)
MODEL = LMD_D2 : AREA AVE = -6.548 (%)
MODEL = MRI_D2 : AREA AVE = .065 (%)
MODEL = PCM_D2 : AREA AVE = -3.451 (%)
MODEL = MODBAR : AREA AVE = -.687 (%)
SCENGEN: Global Analysis
SCENGEN: Error Analysis
SCENGEN Error Analysis
UNWEIGHTED STATISTICS
MODEL CORREL RMSE MEAN DIFF NUM PTS
mm/day mm/day
BMRCTR .632 1.312 1.026 20
CCC1TR .572 1.160 -.207 20
CCSRTR .587 .989
.322 20
CERFTR .634 1.421 -1.167 20
CSI2TR .553 1.112 -.306 20
CSM_TR .801 1.044 -.785 20
ECH3TR .174 1.501 -.649 20
ECH4TR .767 1.121 -.881 20
GFDLTR .719 .954 -.553 20
GISSTR .688 .799
.123 20
HAD2TR .920 .743 -.598 20
HAD3TR .923 .974 -.883 20
IAP_TR .599 1.408 -.734 20
LMD_TR .432 2.977 -2.103 20
MRI_TR .216 2.895 -2.026 20
PCM_TR .740 1.372 -1.041 20
MODBAR .813 .879 -.654 20
(continued)
What’s New (and Exciting)
• SCENGEN is being updated
– Have IPCC AR4 models
– 2.5o resolution
– May have other bells and whistles
• Another very useful tool are the NCAR
created PDFs
Thank You!
I’d be happy to take questions