MAGICC/SCENGEN Hands On Tutorial

By

Joel B. Smith

Stratus Consulting Inc.

Jsmith@stratusconsulting.com

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 human-induced 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

0.2

0.4

0.6

0.8

1

1.2

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 ResultsINTER-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 (continued)

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

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