Transcript MAGIC-SCENGEN

MAGICC-SCENGEN-V53
Rizaldi Boer
Centre for Climate Risk and Opportunity Management
in Southeast Asia and Pacific, Bogor Agriculture University, Indonesia
E-mail: [email protected] and
[email protected]
MAGICC-SCENGEN
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MAGICC/SCENGEN is a
coupled gas-cycle/climate
model (MAGICC; Model
for the Assessment of
Greenhouse-gas Induced
Climate Change) that
drives a spatial climatechange SCENario
GENerator (SCENGEN)
Global-mean
temperatures from
MAGICC are used to
drive SCENGEN.
SCENGEN uses a version
of the pattern scaling
method described in
Santer et al. (1990) to
produce spatial patterns
of change from a data
base of
atmosphere/ocean GCM
(AOGCM) data from the
CMIP3/AR4 archive.
DIRECTORY STRUCTURE OF THE MAGICCSCENGEN
Storing outputs
Storing outputs of the GCMs
Storing emission scenarios
MAGICC
GHG emission scenarios
• Select Reference and policy
scenarios
– There are about 50 scenarios.
WRExxx uses CO2 emission
scenarios lead to CO2
concentration stabilization at
350, 450, 550, 650 and 750,
with compatible non-CO2 gas
emissions.
– You can create you own
emission scenarios by making
file with extension *.GAS and
the file should be stored in
directory SG5/SCEN53/magicc. You must fill data
for year 2000 otherwise
MAGICC will result incorrect
CH4 and N2O emission
Selecting Policy Scenarios: Emission paths for stabilizing
CO2 concentrations to limit T increase
BAU (>6°C)
WRE750
WRE650
(~3°C)
(~2°C)
WRE550
WRE450
WRE350
The path to avoid ∆Tavg >2°C (gold) requires much earlier, more
drastic action than path to avoid >3°C (green).
• To avoid
harmful effect
of climate
change, the
GHG emission
level should be
reduced up to
50% below
1990’s
emission by
2050 (This is
far below the
IPCC
scenarios)
• Need long term
commitment
Sumber: Meinshausen 2007.
Editing Inputs
• According to AR4, the
best estimate of the
climate sensitivity
(T2x)=3.0oC with
confidence interval of
90%. The range is
between 1.5oC (low)
and 6.0oC (High)
SCENGEN
You should arrange
all SCENGEN’s
menus in your screen
to allow you
modifying data inputs
as required
We know why.
Current computer
model with sensitivity ~0.75ºC per
W/m2, using best
estimates of natural
& human influences
(A) as input,
reproduces almost
perfectly the last
125 years of
observed
temperatures (B).
Other “fingerprints” of
GHG influence on
climate also match
observations.
Source: Hansen et al.,
Science 308, 1431, 2005.
Suggested Steps in using MAGICC-SCENGEN
Select appropriate models for your region by running SCENGEN
with single GCM and get ERROR output in IMFIELDS.OUT
ERROR = (M-O)/O * 100%
No
Small Error?
Select Models with small error in your region and use them when
you run SCENGEN
Select reference and policy emission scenarios using MAGICC
View MAGICC outputs as required to give you overview of GHG
emission and concentration and SLR projection
Run SCENGEN to generate future climate data under the
reference and policy emission scenarios using the selected GCMS
Checking Model Performance
• Select model that you want to evaluate
• Run the SCENGEN
• Open file output ‘IMFIELDS.OUT’ in sub-directory
SG53/SCEN-53/engine/imout with EXCEL
– For temperature and MSLP, Error = Model (M)-Observed (O)
– For rainfall, error = (M-O)/O x100%
• Convert the text to column using facility under menu
DATA of the EXCEL
• Select data in region of your interest and use surfer to
display error spatially and assess the performance of the
model over your region. The smaller the ERROR, the
better the model. For cambodia:
– Latitude: 10 N – 15 N and
– Longitude: 102 E- 108 E
Converting text into column
Highlight the first
column and select
the Data menu
and go to text to
column, and then
follow the
command appear
on the screen
Data that have been converted to column
Still text format
Column format
Error Mapping
All 20 Models
CCMA-31
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-2
-4
-6
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-10
-12
-14
-16
-18
-20
-22
-24
-26
-28
-30
-32
-34
-36
-38
-40
-42
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12
11
10
101
102
103
104
105
106
107
108
-30
-32
-34
-36
-38
-40
-42
-44
-46
-48
-50
-52
-54
-56
-58
-60
-62
-64
-66
-68
-70
-72
-74
-76
-78
-80
-82
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10
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102
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Exercise for Running MAGICC
• Select emission scenarios:
– Reference: A1T
– Policy: WRE450
• Use default values for forcing control and run the
MAGICC to project GHG emission, and
concentration as well as SLR by clicking run
menu of the MAGICC and select run
• Click menu View to see the result of the
projections under the two scenarios
Exercise for Running SCENGEN
• Use SCENGEN to generate
climate change scenarios
– Open all windows of the
SCENGEN and arrange
them to facilitate quick
selection of scenarios
– Select variable, e.g.
rainfall
– Select type of data
whether change, error etc
– Define time period: e.g.
2000, 2010, 2020, and
2030
– Select CCMA31 model
– After defining the selection
then click RUN
Selecting Outputs
• Note: the SCENGEN only displays a number of outputs as seen in
the Variable and Analysis Menus other outputs
• Other outputs are stored in the directory of
– SG53/SCEN-53/engine/imout
– SG53/SCEN-53/engine/sdout
• Extract from file OBSBASE.OUT and For your region and
SDOBS.OUT and plot using Surfer. Do visual assessment on
variability of Cambodian climate from the maps. Give your
opinion.
• future s.d. divided by initial (present-day) s.d. minus
1, expressed as a percentage. A zero value therefore
represents no change, while positive or negative values
represent increases or decreases in variability
respectively. This method of representation facilitates
significance testing using a standard F test.
SG50/SCEN-50/ENGINE/IMOUT
• (* displayable fields, also given in …ENGINE/SCENGEN)
• ABSDEL.OUT
: Model mean of absolute changes
• *ABS-MOD.OUT : New mean state (with aerosols) using
model-mean baseline
• *ABS-OBS.OUT : New mean state (with aerosols) using
observed baseline
• AEROSOL.OUT : Scaled change field (aerosols only)
• AREAAVES.OUT : Area averages over specified area – (1)
model-by-model results for normalized GHG changes; (2)
model-by-model results for baseline; (3) various model-mean
results and observed baseline; (4) model-by-model scaled
results (including aerosols)
• *DRIFT.OUT
: This file will normally be blank. By putting
IDRIFT=1 in EXTRA.CFG, drift (Def. 2 minus Def. 1) results
will appear here.
• ERROR.OUT
: Error fields. Model minus Observed for
temperature and MSLP. % error (100(M – O)/O)) for
precipitation
SG50/SCEN-50/ENGINE/IMOUT
• GHANDAER.OUT : Scaled changes, model mean (with
aerosols): sum of AEROSOL.OUT and GHGDELTA.OUT
• GHGDELTA.OUT : Scaled changes, model mean (GHG only)
• IMCORRS.OUT
: Inter-model correlation results for normalized
changes in mean state calculated over the specified area.
• IMFIELDS.OUT
: Summary of fields, GHANDAER,
GHGDELTA, AEROSOL, INTER-SD, IM-SNR, PROBINCR, NUMINCR, MODBASE, OBSBASE, ERROR, ABS-OBS, ABS-MOD
• IMFILES.OUT
: List of data files opened and read by
INTERNN2.FOR. Also displays the selected area as a
latitude/longitude array of 1s and 0s.
• *IM-SNR.OUT
: Inter-model Signal-to-Noise Ratio for
changes in mean state – SNR = change in mean state divided by
inter-model standard deviation (independent of time). Same as
INTERSNR.OUT in SDOUT, but 3 decimals instead of 2.
• INTER-SD.OUT
: Inter-model standard deviation for normalized
GHG change fields
• *MODBASE.OUT : Model-mean baseline
SG50/SCEN-50/ENGINE/IMOUT
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NORMDEL.OUT
: Model-mean of normalized GHG change fields
NUM-INCR.OUT
: Number of models with GHG changes above zero
*OBSBASE.OUT
: Observed baseline
OUTLIERS.OUT
: Outlier analysis – comparing model-i normalized
GHG changes with average of remaining models. Analysis performed
over the specified area.
*PROBINCR.OUT
: Probability of a change above zero
RKERROR.OUT
: RK error field – RK error = SQRT((M –
O)2/(OSD)2), M = model mean baseline, O = observed baseline, OSD
= observed baseline standard deviation.
SDERROR.OUT
: Standard deviation error field – 100((MSD –
OSD)/OSD), MSD = model-mean baseline standard deviation.
SDINDEX.OUT
: S.D. bias field – SDINDEX = SQRT(0.5(RRR +
1/RRR)) where RRR = ((observed s.d.)/(model-mean s.d.))2.
SDMEAN.OUT
: Model-mean baseline standard deviation field
(denoted MSD above).
SDOBS.OUT
: Observed baseline standard deviation field
(denoted OSD above).
VALIDN.OUT : Validation statistics, comparing model-i and model-mean
baselines with observed baseline data. Uses pattern correlation, RMS
difference, bias (M – O), bias-corrected RMS difference, and RK index
averaged over specified region.
SG53/SCEN-53/engine/sdout
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ALLDELTA.OUT
: Model average of changes in mean state (including
aerosols).
*BAROFSNR.OUT
: Model average of temporal SNRs – SNR = mean
state change divided by baseline model standard deviation
*BASE-SD.OUT
: Model average of baseline s.d.s
*DELTA-SD.OUT
: Model average of percentage changes in s.d.
FILES.OUT
: List of data files opened and read by STANDNN2.FOR. Also
displays the selected area as a latitude/longitude array of 1s and 0s.
INTERSNR.OUT
: Inter-model Signal-to-Noise Ratio for changes in
mean state – SNR = change in mean state divided by inter-model standard
deviation (independent of time). Same as IM-SNR.OUT in IMOUT, but 2
decimals instead of 3.
SDCORRS.OUT
: Inter-model pattern correlation results for normalized
s.d. change fields and baseline s.d. fields.
SDFIELDS.OUT
: Summary of fields, GHGDELTA, BASE-SD, DELTASD, BAROFSNR, SNROFBAR, INTERSNR, SDSNR, SDUNCERT. Plus
correlation matrix for pattern correlations between these fields.
**SDSNR.OUT : Inter-model SNRs for s.d. changes – SNR = model average of
normalized s.d. changes divided by inter-model s.d. of normalized s.d. changes.
**SDUNCERT.OUT
: Uncertainty index for model-mean baseline s.d. –
model average of baseline s.d.s divided by inter-model s.d. of baseline s.d.s.
SNROFBAR.OUT
: Temporal SNR of model-mean changes – model
average of mean state changes divided by model average of baseline s.d.s.