MAGICC/SCENGEN runs on a laptop computer. To obtain the software, which includes all data sets and a user manual, contact Tom Wigley at [e-mail].

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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_at_ucar.edu.
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MAGICC projections in the IPCC TAR
MAGICC projections in the IPCC TAR
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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.
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THE MAGICC/SCENGEN SOFTWARE MAGICC
Library of Emissions Scenarios
Gas Cycle Models
User Choices of Model Parameters
Atmospheric Composition Changes
Global-mean Temperature And Sea Level Model
User Choices Of Model Parameters
Global-mean Temperature and Sea Level Output
TO SCENGEN
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THE MAGICC/SCENGEN SOFTWARE SCENGEN
Global-mean Temperature from MAGICC
Library of GCM Data Sets
User Choices GCMs to use, Future Date, Region,
etc.
Regionalization Algorithm
Library of Baseline Climatology Data (1961-90)
Regional Climate or Climate Change Output
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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?
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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
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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.
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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
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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
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Here the A1FI illustrative scenario has been
selected as a Reference, and the B1 illustrative
scenario as a Policy case
Example 1
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Returning to the main window, MAGICC is run by
clicking on Run and then Run Model
Then click on View
Example 1
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View brings up the window below, from which we
first select Emissions
Example 1
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These are the input CO2 emissions, with fossil
and land-use emissions shown separately
Example 1
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Clicking on CH4 in the previous window displays
methane emissions in the two scenarios
Example 1
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Return to the main window, then select View and
Concentrations to view CO2 concentration
projections and uncertainties
Example 1
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Return to the main window, then select View and
Temperature Sea-Level to view global-mean
temperature projections and uncertainties
Example 1
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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
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Return to the main window, then click on SCENGEN
and Run SCENGEN to bring up the SCENGEN title
window
Click on OK
Example 2
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This will bring up a base map and access to the
SCENGEN Control Windows
Example 2
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SCENGEN CONTROL WINDOWS CHOICES
Analysis gt Select Change Models gt First select
HadCM2, then PCM Region gt Default is whole
globe Variable gt Default is Annual
Temperature Warming gt Default emissions scenario
is the Reference case (A1FI here). gt Default
time is a 30-year period centered on 2050 (for
which DT 1.67oC). gt Default MAGICC model
parameters are the IPCC TAR best guess values.
Example 2
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Patterns of annual-mean temperature change for
HadCM2 and PCM (including aerosol effects)
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).
Example 2
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Patterns of annual-mean precipitation change for
HadCM2 and PCM (including aerosol effects)
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.
Example 2
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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
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Changes in variability (for annual
precipitation).
In the SCENGEN Control Windows panel, under
Analysis, select S.D. Change ..
.. then, to get a representative result, select
All under Models.
Example 3
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Changes in variability (for annual precipitation).
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.
Example 3
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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
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Probability of a precipitation increase
Method To determine the probability of a
precipitation increase (probh) 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 probh 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.
Example 4
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Probability of a precipitation increase.
In the SCENGEN Control Windows panel, under
Analysis, select P(increase) ..
.. then, to get a representative result, select
All under Models.
Example 4
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Probability of a precipitation increaseannual
precipitation, based on 17 AOGCMs
Note Low values of probh indicate a high
probability of a precipitation decrease.
Example 4
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A FIFTH EXAMPLE EMISSIONS STABILIZATION
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.
Example 5
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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
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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
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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
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For methane, which has an atmospheric lifetime of
1012 years, the concentration effectively
stabilizes after 3-4 lifetimes.
Example 5
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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
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The continuous increase in CO2 concentration
ensures that global-mean warming continues over
the full period to 2400 (and beyond). Over
20002100, the warming rate is about twice that
observed over 19002000. The uncertainty range
shown corresponds to a climate sensitivity range
of 1.54.5oC equilibrium warming for 2xCO2.
Example 5
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APPENDIX Detailed MAGICC/SCENGEN flowchart, and
mathematical details for the scaling algorithm
used to produce the SCENGEN maps.
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Algorithm for producing regional details
Pattern scaling
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SIMPLE PATTERN SCALINGDY(x,t) DT(t)
Y(x)where DY(x,t) is the pattern of change at
time t of some variable Y (winter precipitation,
July maximum temperature, etc.), DT(t) is the
global-mean temperature change at time t, Y(x)
is the normalized pattern of change for variable
Y (i.e., the change per 1C global-mean warming).

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GENERAL PATTERN SCALINGDY(x,t) SDTi(t)
Yi(x)where DY(x,t) is the pattern of change
at time t for variable Y, DTi(t) is the
global-mean temperature change at time t due to
factor i, Yi(x) is the normalized pattern of
change for variable Y due to factor i.
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