MAGICC/SCENGEN: User-friendly software for GCM inter-comparisons,

climate scenario development and uncertainty assessment.

Tom M.L. Wigley,

National Center for Atmospheric Research,

Boulder, CO 80307, and

University of East Anglia, Norwich, UK, NR4 7TJ

wigley@ucar.edu

[Based on SBSTA18 side event presentation: June 10, 2003]

MAGICC/SCENGEN runs on a laptop computer. To obtain the

software, which includes all data sets and a user manual, contact Tom

Wigley at wigley@ucar.edu.

THE MAGICC/SCENGEN SOFTWARE : PURPOSES

Climate scenario development for expert

and non-expert users ‘Hands on’ education for climate change

issues Access to climate model and observed

climate data bases

What is MAGICC?

Model for the Assessment of Greenhouse-gas Induced Climate Change MAGICC is the climate model that has been used in all IPCC assessments to produce projections of global-mean temperature.

MAGICC projections in the IPCC TARMAGICC projections in the IPCC TAR

What is SCENGEN? SCENGEN is a climate SCENario GENerator. SCENGEN uses the output from MAGICC to produce maps showing the regional details of future climate.

PRIMARY INPUT :

Two emissions scenarios (nominally a baseline ‘no-climate-policy’ scenario and a ‘policy’ scenario)

GASES CONSIDERED : CO2 CH4 N2O SO2 Reactive gases (CO, NOx, VOCs) Halocarbons (CFCs, HCFCs, HFCs, PFCs, SF6)

SECONDARY INPUTS :

Gas cycle and climate model parameters AOGCM models to use for regionalization Region (or whole globe) Future date Type of output

MAGICC OUTPUTS: • Gas concentrations, • Radiative forcing breakdown, • Global-mean temperature and sea level.SCENGEN OUTPUTS: • Baseline climate data, • Model validation results, • Changes in mean climate, • Changes in variability, • Signal-to noise ratios, • Probabilities of increase.

Library of Emissions Scenarios

THE MAGICC/SCENGEN SOFTWARE : MAGICC

Gas Cycle Models

User Choices of Model

Parameters

AtmosphericComposition

Changes

Global-meanTemperature

and Sea Level Output

Global-mean Temperature

And Sea LevelModel

User ChoicesOf Model

Parameters

TO SCENGEN

THE MAGICC/SCENGEN SOFTWARE : SCENGEN

Global-mean Temperature from MAGICC

Regionalization Algorithm

Library ofGCM Data Sets

Library ofBaseline

ClimatologyData (1961-90)

User Choices:GCMs to use,Future Date,Region, etc.

RegionalClimate or

Climate ChangeOutput

QUESTIONS MAGICC/SCENGEN CAN ANSWER• How will global-mean temperature change for a given emissions scenario?

• What are the uncertainties in such projections?

• What must we do to stabilize greenhouse-gas concentrations?

• How will climate patterns/regional details change?

• How will climate variability change?

• How different are the results from different models?

• What is the probability of an increase/decrease in precipitation at a given location?

EXAMPLES

(1) Global-mean changes for B1 and A1FI

(2) Differences in patterns of change, HadCM2 vs PCM

(3) Changes in variability

(4) Probability of a precipitation increase

The remainder of this presentation used the MAGICC/SCENGEN software

interactively to consider the four specific examples. In the following, these details are summarized using screen shots from

the interactive analysis.

EXAMPLES

(1) Global-mean changes for B1 and A1FI

(2) Differences in patterns of change, HadCM2 vs PCM

(3) Changes in variability

(4) Probability of a precipitation increase

This is the first (main) window that appears when opening MAGICC/SCENGEN. The first step is to click on Edit and choose the emissions input files

Example 1

Here the A1FI illustrative scenario has been selected as a Reference, and the B1 illustrative

scenario as a Policy case

Example 1

Returning to the main window, MAGICC is run by clicking on Run and then Run Model

Example 1Then click on View

View brings up the window below, from which we first select Emissions

Example 1

These are the input CO2 emissions, with fossil and land-use emissions shown separately

Example 1

Clicking on CH4 in the previous window displays methane emissions in the two scenarios

Example 1

Return to the main window, then select View and Concentrations to view CO2 concentration

projections and uncertainties

Example 1

Return to the main window, then select View and Temperature & Sea-Level to view global-

mean temperature projections and uncertainties

Example 1

EXAMPLES

(1) Global-mean changes for B1 and A1FI

(2) Differences in patterns of change, HadCM2 vs PCM

(3) Changes in variability

(4) Probability of a precipitation increase

Return to the main window, then click on SCENGEN and Run SCENGEN to bring up the

SCENGEN title window

Example 2Click on OK

This will bring up a base map and access to the SCENGEN Control Windows

Example 2

SCENGEN ‘CONTROL WINDOWS’ CHOICES

Example 2

Analysis > Select ‘Change’

Models > First select HadCM2, then PCM

Region > Default is whole globe

Variable > Default is Annual Temperature

Warming > Default emissions scenario is the Reference case (A1FI here).

> Default time is a 30-year period centered on 2050 (for which T = 1.67oC).

> Default MAGICC model parameters are the IPCC TAR ‘best guess’ values.

Patterns of annual-mean temperature change for HadCM2 and PCM (including aerosol effects)

Example 2

HadCM2 (top) and PCM (bottom). Note amplified warming over land and in NH high latitude areas (SH too for PCM), and reduced warming in the North Atlantic (cooling in HadCM2).

Patterns of annual-mean precipitation change for HadCM2 and PCM (including aerosol effects)

Example 2

HadCM2 (top) and PCM (bottom). Note large increases over high latitude areas in NH (SH too in PCM), and decreases in the subtropical highs and around the Mediterranean Basin.

EXAMPLES

(1) Global-mean changes for B1 and A1FI

(2) Differences in patterns of change, HadCM2 vs PCM

(3) Changes in variability

(4) Probability of a precipitation increase

Changes in variability (for annual precipitation).

Example 3

In the SCENGEN Control Windows panel, under Analysis, select S.D. Change …..

….. then, to get a representative result, select All under Models.

Changes in variability (for annual precipitation).

Example 3

Note the high spatial variability, a result of large spatial variability in individual models and large difference between models. High latitudes show a general increase in variability. The pattern of S.D. change shows some similarities with the pattern of changes in the mean.

EXAMPLES

(1) Global-mean changes for B1 and A1FI

(2) Differences in patterns of change, HadCM2 vs PCM

(3) Changes in variability

(4) Probability of a precipitation increase

Probability of a precipitation increase

Example 4

Method: To determine the probability of a precipitation increase (prob) in a particular grid box we assume that the primary uncertainty in precipitation change is represented by the differences between climate models (AOGCMs)1, and that the distribution of precipitation change is Gaussian with mean equal to the average across models and s.d. equal to the inter-model standard deviation. A high value for prob may result from a large increase in the mean or low inter-model differences, or both.

1 Note that the above assumption is justified because the model results used are ‘normalized’ (per unit global-mean warming), which removes the effect of inter-model differences in the climate sensitivity.

Probability of a precipitation increase.

Example 4

In the SCENGEN Control Windows panel, under Analysis, select P(increase) …..

….. then, to get a representative result, select All under Models.

Probability of a precipitation increase[annual precipitation, based on 17 AOGCMs]

Example 4

Note: Low values of prob indicate a high probability of a precipitation decrease.

A FIFTH EXAMPLE : EMISSIONS STABILIZATION

Example 5

In the question period, I was asked to use the software to determine the climate consequences of stabilization of all emissions (greenhouse gases and SO2) at present-day levels.

This is a particularly interesting case since it shows what is currently ‘locked into’ the system, and because it shows that stabilizing emissions does not by any means stabilize atmospheric composition or the climate. Recall that Article 2 of the UNFCCC has stabilization of atmospheric composition as its ultimate objective.

The current emissions library does not include the ‘stabilize emissions’ case. A new emissions file was generated externally by copying and then editing an

existing file (shown in part below).

Example 5

The following results use default values for all gas cycle and climate

model parameters. I show concentration projections for CO2, CH4, and N2O, and global-mean

temperature projections.

Example 5

Over 2000-2400, CO2 concentration rises by about 500ppm. The

almost linear increase is a result of the

multiple time scales on which the carbon cycle operates. Over

periods of many centuries, the longer time scale processes (e.g., associated with the deep ocean and soil zone) become

increasingly important.

Example 5

For methane, which has an atmospheric

lifetime of 10–12 years, the

concentration effectively

stabilizes after 3-4 lifetimes.

Example 5

N2O has a lifetime of about 120 years, so

its concentration almost stabilizes by 2400. This behavior can be contrasted with that for CO2,

where much longer time scale processes

become important over a multi-century

period.

Example 5

The continuous increase in CO2

concentration ensures that global-mean

warming continues over the full period to 2400 (and beyond).

Over 2000–2100, the warming rate is about twice that observed

over 1900–2000. The uncertainty range

shown corresponds to a climate sensitivity range of 1.5–4.5oC equilibrium warming

for 2xCO2.

Example 5

APPENDIX : Detailed MAGICC/SCENGEN flowchart, and mathematical details for the

scaling algorithm used to produce the SCENGEN maps.

EMISSIONS INPUT (SCENARIO LIBRARY) ` CARBON

CYCLE MODEL

METHANE MODEL REACTIVE

GASES

AEROSOL ALGORITHMS

N2O MODEL

RADIATIVE FORCING

SO2

TROP. OZONE

HALOCARB MODELS

U.D. ENERGY-BALANCE CLIMATE MODEL

GLOBAL-MEAN TEMP.

GLOBAL-MEAN SEA LEVEL

ICE MELT MODELS

EXPANSION USER MODEL CHOICES

MAGICC

SCENGEN

INTER-MODEL COMPARISON STATISTICS GRIDPOINT

CHANGES IN MEAN STATE AND VARIABILITY FUTURE

CLIMATE STATE

AOGCM DATA BASE (MEANS AND VARIABILITY)

OBSERVED CLIMATE DATA BASES SCALING

ALGORITHM

VALIDATION RESULTS

PROBABILISTIC OUTPUTS

MAGICC

SCENGEN

Algorithm for producing regional details : Pattern

scaling

SIMPLE PATTERN SCALING

Y(x,t) = T(t) Ŷ(x)

where

Y(x,t) is the pattern of change at time t of some variable Y (winter precipitation, July maximum

temperature, etc.),

T(t) is the global-mean temperature change at time t,

Ŷ(x) is the normalized pattern of change for variable Y (i.e., the change per 1°C global-mean warming).

GENERAL PATTERN SCALING

Y(x,t) = Ti(t) Ŷi(x)

where

Y(x,t) is the pattern of change at time t for variable Y,

Ti(t) is the global-mean temperature change at time t due to factor ‘i’,

Ŷi(x) is the normalized pattern of change for variable Y due to factor ‘i’.