magicc projections in the ipcc tar
DESCRIPTION
MAGICC/SCENGEN runs on a laptop computer. To obtain the software, which includes all data sets and a user manual, contact Tom Wigley at [email protected]. MAGICC projections in the IPCC TAR. MAGICC projections in the IPCC TAR. - PowerPoint PPT PresentationTRANSCRIPT
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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
[Based on SBSTA18 side event presentation: June 10, 2003]
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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 [email protected].
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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
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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.
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MAGICC projections in the IPCC TARMAGICC projections in the IPCC TAR
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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.
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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)
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SECONDARY INPUTS :
Gas cycle and climate model parameters AOGCM models to use for regionalization Region (or whole globe) Future date Type of output
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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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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
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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
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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
Example 1Then click on View
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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
Example 2Click on OK
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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
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.
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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).
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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.
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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).
Example 3
In the SCENGEN Control Windows panel, under Analysis, select S.D. Change …..
….. then, to get a representative result, select All under Models.
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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.
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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
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.
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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.
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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.
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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.
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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 10–12 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 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
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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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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
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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
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Algorithm for producing regional details : Pattern
scaling
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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).
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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’.
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