1 

## MAGICC/SCENGEN 5.3 : USER MANUAL (version 2)

Tom M.L. Wigley, NCAR, Boulder, CO. ( wigley@ucar.edu )

September, 2008



### CONTENTS

1. Installation ..... 1

2. Introduction

– background ..... 2

3. Modifications s ince version 4.1 ..... 4

3.1 MAGICC c hanges ..... 4 3.2 SCENGEN changes

..... 12

4. Running MAGICC

... ..21

5. Running SCENGEN .....36

6. Choosing AOGCMs .....63

Appendix 1: Halocarbons .....69 Appendix 2 : CO 2 concentration stabilization .....70 Acknowledgments .....74 Printing Tips .....75

References .....76 Directory Structure .....80



### TERMS OF USE

Users of the MAGICC/SCENGEN software are bound by the UCAR/NCAR/UOP ―Terms of

Use‖. For details see ...

http://www.ucar.edu/legal/terms_of_use.shtml



### 1. Installation:

MAGICC/SCENGEN comes complete as a zipped set of directories (folders) , SG53.zip

. In unzipping, when asked where the folders and files should be extracted to, select C: \ . Unzipping will create a new top level folder, C: \ SG53, and all fold ers and files will automatically go into this folder. It is important that the new SG53 folder should be created directly under C:

-- i.e., as

C: \ SG53. The full directory structure is shown in the flowchart at the end of this document.2 

### 2. Introduction

– b ackground

MAGICC/SCENGEN is a coupled gas

- cycle/climate model (MAGICC ; M odel for the A ssessment
of G reenhouse

- gas I nduced C limate C hange ) that driv es a spatial climate

- change SCEN ario

GEN erator (SCENGEN). MAGICC has been one of the primary model s used by IPCC since 1990 to produce projections of future global

- mean temperature and sea level rise. The climate model in MAGICC is an upwelling

- diffusion

, energy

- balance model that produces global

- and hemispheric

- mean temperature output together with results fo r oceanic thermal expansion

. The

4.1 version of the software uses the IPCC Third Assessment Report, Working Group 1 (TAR)

version of MAGICC. The 5.3 version of the software is consistent with the IPCC Fourth Assessment Report, Working Group 1 (AR4)

. The MA GICC climate model is coupled interactively with a range of gas

- cycle models that give projections for the concentrations of the key greenhouse gases. Climate feedbacks on the carbon cycle are therefore accounted for.

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 . The pattern scaling me thod is based on the separation of the global

- mean and spatial

- pattern components of future climate change, and the further separation of the latter into greenhouse

- gas and aerosol components. Spatial patterns in the data base are ―normalized‖ and expresse d as changes per 1 o C change in global

- mean temperature. These normalized greenhouse

- gas and aerosol components are appropriately weighted, added, and scaled up to the global

- mean temperature defined by MAGICC for a given year, emissions

scenario and set of climate model parameters. For the SCENGEN scaling component, the user can select from a number of different AOGCMs for the patterns of greenhouse

- gas

- induced

climate.

The method for using MAGICC/SCENGEN is essentially unchanged from the year

- 2000 version (Version 2.4; Hulme et al., 2000). What has changed is the MAGICC code (2.4 used the IPCC

SAR

– Second Assessment Report

– version of MAGICC) , the data base of AOGCMs used for pattern scaling, and the much greater number of SCENGEN output options open to the user.

As before, the first step is to run MAGICC. The user begins by selecting a pair of emissions scenarios, referred to as a Reference scenario and a Policy scenario. The emissions library

from which these selections are made is now based on the no

- climate

- policy SRES scenarios, and includes new versions of the WRE (Wigley et al., 1996) CO 2 stabilization scenarios. The SRES scenarios have a much wider range of gases for which emissions are prescribed than

was the case with the scenarios used in the SAR. Because of this, emissions scenarios can now only be edited or added to off

- line, using whatever editing software the user chooses. The labels ―Reference‖ and ―Policy‖ are arbitrary, and the user may compare any two emissions scenarios in the library

.

The user then selects a set of gas

- cycle and climate model para meters. The default (―best

estimate ‖) set may be cho sen, or a user set prescribed. B oth default and user results are carried through to SCENGEN. A flow chart describing how MAGICC/SCENGEN is configured is shown on the next page.3 

## Gas Cycle

Models

Atmospheric Composition Changes User Choices

of Model

Parameters Library of

Emissions

Scenarios

Global

- mean Temperature

and Sea Level

Model User Choices

of Model

Parameters

Global

- mean Temperature

and Sea Level Output

Regionalization Algorithm Library of

AOGCM data

sets

Library of

Observed

data sets User Choices : Variable, AOGCMs, Future Date, Region, etc.



## Regional Climate

or Climate Change Output 

# MAGICC

SCENGEN Feedback 

## STRUCTURE OF THE MAGICC/SCENGEN SOFTWARE4 

### 3

. M odifications since version 4.1 :

Version 5.3 has been modified extensively from the previous public

- access version (4.1). The main changes in MAGICC are described first followed by the changes in SCENGEN.

3.1 MAGICC CHANGES

Forcing changes

Changes have been made to MAGICC to ensure, as nearly as possible, consistency with the IPCC AR4. In version 4.1, various forcings were initialized in 1990 (or 2000 in the case of tropospheri c ozone), and subsequent forcings are dependent on these initializations. The

version 4.1 initialization values were consistent with best

- estimate forcings given in the TAR. In AR4, new best

- estimate forcings have been given for 2005. This has meant that t he 1990 initialization parameters had to be changed to give projected 2005 values consistent with these new AR4 results. As MAGICC includes historical values only to 1990 or (for CO 2 ) 2000, the 2005 values it produces depend on the chosen emissions scenari o. Thus, it has not been possible to precisely emulate the AR4 2005 values. The differences, however, are very small, as

will be shown at the end of this section. First, we give full details of the AR4 and MAGICC 4.1 forcings, followed by the forcing initi alization changes employed in MAGICC 5.3

.5

Table 1 : 2005 AR4 forcings (W/m 2 ) compared with forcings used for 1990 in MAGICC 4.1 or calculated for 2005 in MAGICC 5.3

. In column 3, headed “AR4, 2005”, t he outer numbers give the 90% confidence interval, whi le the central (or sole) number gives the best estimate. In column 5, headed “MAG53, 2005”, 2005 values are best estimate values and are scenario dependent. The range given is the best estimate range over the six SRES illustrative scenarios. Magenta is us ed to show forcings that are either the components of other forcings or component

sums. Component sum comparisons for AR4 forcings (column 3) are shown in bold blue type. For example, items 11 through 16 are the components of 10 (total direct aerosol forci ng). Summing the components (10a) gives a value slightly less than given in 10. Total forcing is given in row 21, which is the sum of 1, 2, 3, 4, 7, 8, 9, 10, 17, 18, 19 and 20. The sum of the individual components (21a) is slightly higher than the indepen dent best estimate for the total (1.72 compared with 1.6).



### COMPONENT AR4, 2005 1 

### MAG41 , 1990 MAG53 , 2005

1 CO2 1.49(1.66)1.83 1.645 to 1.661

2 CH4 0.43(0.48)0.53

2a CH4 + strat. H2O 0.55 0.524 to 0.528

3 N2O 0.14(0.16)0.18 0 .165 to 0.167

4 Halocarb. direct 0.31( 0.34 )0.37 0.375

4a 1 + 2a + 3 + 4 2.71 2.711 to 2.731

5 Montreal gases 0.29(0.32)0.35 0.353

6 HFCs,PFCs,SF6 0.017 0.0216

4a 5 + 6 0.337 0.374

7 Trop. O3 0.25(0.35)0.65 0.35 (year 2000) 0.342 to 0.358

8 Strat. O3

- 0.15(

- 0.05)0.05

- 0.203

9 Strat. H2O from CH4 0.02(0.07)0.12

10 Aerosol direct total

- 0.1 (

- 0.5 )

- 0.9

11 SO4 direct

- 0.2(

- 0.4)

- 0.6

- 0.3(

- 0.4)

- 0.5

- 0.377 to

- 0.440

12 Fossil fuel organic C

- 0.1(

- 0.05)0.0 See FOC (19a)

13 Fossil fuel black C 0.05(0.2)0.35 See FOC (19a)

14 Biomass burning

- 0.09(0.03)0.15 0.023 to 0.025

15 Nitrate

- 0.2(

- 0.1)0.1 Not included

16 Mineral dust

- 0.3(

- 0.1)0.1 Not included

- 0.2 (items 15 + 16)

1 0a Sum 11 through 16

- 0.42

1 7 Aerosol indirect

- 0.3(

- 0.7)

- 1.8

- 0.4(

- 0.8)

- 1.2

- 0.674 to

- 0. 743

1 8 Land use

- 0.2 Not included

- 0.2

19 Black C on snow 0.1 See FOC (19a)

19a 12 + 13 + 19 (=FOC) 0.25 0.1 0.230 to 0.269

20 Contrails 0.01 Not included

21 TOTAL 0.6( 1.6 )2.4

21a Component sum 1.72 1.596 to 1.673

1 Ranges give the 90% confidence interval s . Values assumed to be mid

- year values.6

We now describe the forcing initialization changes. (All numbers are W/m 2 .)

Tropospheric O3 : Previously 0.35 was hardwired at the start of 2000. This gives a mid 2005 value averaged over the illustrative scenarios of 0.373 (0.362 to 0.378). 0.35 has been changed

to 0.33. This leads to an error of less than 0.01 in 2005.

Biomass burning : Previously (in MAGICC 4.1) the value was

- 0.2 in 1990. The AR4 best

estimate is +0.03 in 2005. If the19 90 value is set to 0.03 in MAGICC , the 1990 to 2005 change ranges from +0.0035 to +0.0070 for the SRES illustrative scenarios (mean = +0.0053). We therefore change the 1990 initialization value to 0.025. For the 90% uncertainty range, we use the AR4 estima te of +/

- 0.12. (Previously used zero range.)

Fossil organic and black carbon : This is denoted by FOC in MAGICC. Previously, the 1990 value of FOC (FOC90) was set at 0.1. Now, if black C on snow is included, the value is 0.25 in

2005. If FOC90 is set to 0. 25, the change over 1990 to 2005 ranges from

- 0.0139 to +0.0255 (mean = 0.0036). The average of the highest and lowest changes is +0.006. The 1990

initialization value is therefore set at 0.244 . For the uncertainty range, the AR4 black carbon range of +/

- 0 .15 is used (previously +/

- 0.1).

Nitrate : This was not included in MAGICC 4.1 and has now be en added as a new aerosol forcing term (QNO3). The 1990 value is set at

- 0.1 (the AR4 best estimate) and QNO3 is kept

constant at

- 0.1 after 1990 (based on small c hanges given in Bauer et al., 2007, and the fact that changes in nitrate aerosol require inf ormation about NH3 changes that are not available in the SRES scenarios). QNO3 is ramped up linearly from zero in 176 5 to

- 0.1 in 1990.

Mineral dust : This was n ot included previously and is now added as a new aerosol forcing term (QMIN). The 1990 value is

- 0.1, and QMIN is kept constant at

- 0.1 after 1990 (based on the fact that changes are not available in the SRES scenarios

– although one would expect them to b e small). QMIN is r amp ed up linearly to

- 0.1 in 1990.

Stratospheric H2O : Previously this was 0.05*QCH4, which gives only 0.025 in 2005. The best AR4 value in 2005 is 0.07, with 90% confidence range of 0.02 to 0.12. We retain the TAR value,

which lies wit hin the AR4 uncertainty range.

SO4 direct and indirect : In MAGICC, aerosol forcing initialization values are specified for the year 1990 . Mode l ed changes in both direct and indirect forcings are very small over 1990 to

2005 , so we retain 1990 as the ini tialization year . Given the AR4 best estimate of

- 0.4 in 2005,

t he 1990 direct forcing can stay the same as in version 4.1 (

- 0.4) . In accord with the AR4, t he

1990 ind irect forcing become s

- 0.7 (previously

- 0.8) . For uncertainty ranges we use +/

- 0.2 for

di rect forcing, the same as AR4 (previously +/

- 0.1). (This includes uncertainties in nitrate and mineral dust forcings.) For indirect forcing, we u se +/

- 0.4 for the range, the same as previously. AR4 gives a range that is asymmetrical about the central estim ate,

- 1.8 to

- 0.3. The

- 1.8 forcing

value as a lowe r bound (1.1 W/m 2 below the best estimate) would lead to extremely low total

historical anthropogenic forcing unless compensat ed by a large underestimate in some positive forcing term , and we consider this highly unlikely

. We therefore retain +/

- 0.4 for the uncertainty range for indirect aerosol forcing.

In support of this decision we note that such a large negative indirect forcing for the lower bound

would be inconsistent with detection and attribution (D&A) studies . Such studies to date have rarely considered indirect forcing explicitly, but they do so implicitly because the response7

patterns of direct and indirect forcing are almost certainly similar. These studies give best

estimate values of total s ulfate ae rosol forcing ranging from

- 0.1 to

- 1.7 W/m 2 , with a mean of

about

- 0.8 W/m 2 (Hegerl and Zwiers, 2007, p. 672). The lower bound here is much smaller in magnitude than the lower a priori uncertainty bound suggested by AR4. In addition, t he central empirical estimate of

- 0.8 W/m 2 is noticeably smaller in magnitude than the combined direct plus indirect forcing of

- 1.1 W/m 2 (

- 0.7

- 0.4) given as the a priori best estimate in the AR4. We

nevertheless retain the

- 1.1 value for initialization.

Although i ndirect forcing is defined and calculated specifically for sulfate aerosols, it is assumed to be a proxy for the sum of all indirect aerosol forcings.

Land use : This was not included in version 4.1. Since there are no standard projections we add

this as a nother forcing (QLAND), constant from 1990 and ramping up linearly prior to this.

With these new forcing initializations, total forcing in the AR4 reference year, 2005, should be

similar to the best

- estimate of total forcing given in the AR4. As noted ab ove, precise agreement is not possible as MAGICC‘s 2005 data are projections rather than specifically defined values. MAGICC values depend on the assumed emissions scenario. Nevertheless, the MAGICC/AR4

differences are very small, as shown in Table 2 below

Table 2 : Best

- estimate total forcing in 2005 since pre

- industrial times as produced by MAGICC

5.3 . For comparison, the best estimate in the IPCC AR4 is 1.6 W/m 2

.

SCENARIO 2005 TOTAL FORCING

(  T2x = 3 o C)

– W/m 2

A1B 1.596

A1FI 1.610

A1T 1.673

A2 1.634

B1 1.615

B2 1.653

AR4 1.6

In the AR4, the best

- estimate total forcing in 2005 is 1.6 W/m 2 , with a 90% uncertainty range of

0.6 to 2.4 W/m 2 . (Uncertainties are due primarily to uncertain ties in indirect aerosol forcing.) Note that the component sum (Table 1) is slightly higher, 1.72 W/m 2 , and the MAGICC 5.3

values lie between this and the best estimate total. While the MAGICC values are slightly above the AR4 best estimate total, the dif ferences are miniscule relative to the overall forcing uncertainty and have virtually no effect on projections of temperature or sea level change.8

Carbon cycle model and CO 2 concentration stabilization scenarios

Parameters in the carbon cycle model hav e been changed to give concentration projections

consistent with the results from the C4MIP carbon

- cycle model intercomparison exercise (Friedlingstein et al., 2006). In this exercise, the SRES A2 scenario was used as a test case. MAGICC projections for A 2 agree with the average of the ten C4MIP model results, and the uncertainty range that MAGICC gives matches the 90 th percentile of the C4MIP range. Further details are given in the Appendix below.

Because of changes in the carbon cycle and climate model s , it has been necessary to modify the stabilization scenarios (WRExxx and xxxNFB) to ensure that the concentration profiles produced when these scenarios are run with default (best estimate) climate model parameters

are the same as in MAGICC 4.1. This ha s been done for stabilization levels of 450 ppm upwards. For the 350 ppm stabilization case, the profile has been modified to use a later date of departure from the no

- climate

- policy (baseline) emissions scenario

.

The baseline emissions scenario for thes e stabilization calculations has also been changed. In MAGICC 4.1 we used the P50 (SRES median) emissions scenario as the baseline and, for consistency, used the same scenario for non

- CO 2 gases in all CO 2 stabilization cases. This is unlikely to be correct . If we are to introduce policies to stabilize CO 2 concentrations, then it is

both cost

- effective and consistent with the Kyoto Protocol that we should employ a multi

- gas emissions reduction strategy. For any CO 2 stabilization scenario then, we should try to apply a

consistent scenario for non

- CO 2 gases. Attempts have been made to do this (Clarke et al, 2008), but, in the MAGICC context where the CO 2 scenarios are defined externally to follow WRE pathway s, it is not possible to use fully consistent scenario s

. Nevertheless, we do now use

a stabilization scenario for non

- CO 2 gases, but we use the same non

- CO 2 gas scenario for all stabilization cases; namely, an extension of the MiniCAM Level 2 scenario given in Clarke et al. (more details are given in Wigley e t al., 2008). This scenario includes emissions reduction s for

non

- CO 2 gases that are consistent with a CO 2 stabilization target of 5 50 ppm. The emissions of

non

- CO 2 gases in the Level 1 (450 ppm stabilization), Level 2 (550 ppm stabilization) and Level 3 ( 650 ppm stabilization) are very similar. Although not perfect, this is a considerable conceptual improvement over MAGICC 4.1‘s use of P50 for non

- CO 2 gases. Users can modify the

emissions of non

- CO 2 gases, of course, but (because this will change the magni tude of climate feedbacks on the carbon cycle) this will mean that the resulting CO 2 concentrations will stabilize at values slightly different from those that are produced by the original scenarios. Further details are given in the Appendix.

In addition, a new overshoot scenario has been added (450OVER) where CO 2 concentration

rise s to 540 ppm before falling to a 450 ppm stabilization level. This is the same overshoot scenario as used in Wigley (2006). 450OVER uses the same extended MiniCAM Level 2

scenar io for non

- CO 2 gases.

Sea level rise

In the IPCC Third Assessment Report (TAR ; Church and Gregory, 2001 ), a new method was

used for projecting sea level rise from GSICs (Glac iers and Small Ice Caps). This method was only meant to be used out to 2100

– if applied beyond 2100 (as, for example, in stabiliz ation scenarios) it behaved quad ratically, with sea level rise from GSIC melt rising to a maximum and then declining. Extended scenarios could therefore lead to large negative GSIC melt (i.e., a gain9

in G SIC ice mass relative to pre

- industrial times) even when temperatures were still rising. In MAGICC 4.1, this problem was avoided simply by keeping the GSIC melt term at its maximum

value once the maximum was reached. The TAR formulation constrained this ma ximum to a melt of 18.72 cm relative to pre

- industrial times

– effectively fixing the total amount of GSIC ice

mass at 18.72 cm sea

- level equivalent.

A more realistic , physically based formulation has been given by Wigley and Raper (2005). This gives resu lts that are consistent with the TAR out to 2100, but allows the total GSIC ice mass to be specified externally. This new formulation produces GSIC melt that rises asymptotically towards the total available amount of GSIC ice as warming continues

– i.e., e ventually, almost all of the GSIC ice melts if the world becomes warm enough. MAGICC 5.3 uses this new

formulation. The default total GSIC ice mass (V 0 ) i s set at 29 cm (it can be changed off line in the MAGICE.CFG configuration file). This is effectively the best

- estimate value given in the

IPCC Fourth Assessment Report ( Meehl and Stocker, 2007) . AR4 gives a best

- estimate of 24 cm and scales up GSIC melt projections by 20% to account for outlet glaciers in Greenland and Antarctica. With the present GSIC mo del, the same effect can be achieved by scaling up V 0 . For

V 0 uncertaintie s we use the scaled

- up AR4 uncertainty range, 18 to 44 cm. For timescales more than a few centuries, if warming were substantial, the Greenland/Antarctic ―GSIC‖ contribution

could be much higher than implied by the 20% V 0 scaling , as their total ice mass is well over 50

cm.

The other change made in MAGICC5.3 is to ignore the contribution s from : (1) Greenland and Antartica due to the ongoing adjustment to past climatic change, (2) ru noff from thawing of permafrost, and (3) deposition of sediment on the ocean floor. (Referred to as ―non

- melt‖ terms below.) These terms were assumed in the TAR to contribute to sea level rise at a constant rate, independent of the amount of future warming . It is now thought that these terms are small, smaller than was assumed in the TAR, so they were not considered in the AR4 (Jonathan Gregory, personal communication). For consistency they are ignored here.

N o other changes have been made to the sea level mode ling components. In the AR4 report (p. 845) it is stated that AR4 projections for the Antarctic sea level contribution ―are similar to those of the TAR‖, while ―Greenland ... projections are larger by 0.01

– 0.04 m. (i.e., by 2100, these projections are 1 to 4 cm larger than the TAR projections). We have not adjusted the Greenland

model to account for this.

MAGICC sea level projections are very similar to those in AR4, as the Table below shows.10

Table 3 : S ea level rise projections (cm) over 1990 to 2095 given by MAGICC

– top numbers in each row. In column 4, the lower numbers in square brackets give the results published in the

AR4. AR4 numbers (Meehl and Stocker, 2007, p. 820) are based on AOGCM results and are changes between 1980 to 1999 and 2090 to 2 099.

 T2x 1.5 3.0 3.0 3.0 6.0

Ice melt Low Low Mid High High

A1B 14 24 35 [35] 46 68

A1FI 19 32 45 [43] 5 9 8 6

A1T 13 21 33 [33] 4 4 65

A2 16 27 38 [37] 50 7 3

B1 10 17 26 [28] 35 52

B2 12 20 31 [31] 41 6 1

The MAGICC/AR4 similarity is partly for tuitous as MAGICC gives slightly higher expansion and slightly lower results for GSIC and Greenland contributions. The differences in these component

sea level terms are , however, within t heir uncertainty ranges. Nevertheless , the positive bias in

thermal expansion results from MAGICC compared with AOGCMs (noted in the AR4, p.844) is a concern that is currently under investigation. (AR4, p. 844, also claims that MAGICC has a slight warm bias in projections of global

- mean temperature, but this is unfounded. The apparent bias is due partly to forcing differences between the standard MAGICC forcings and those used in AR4 AOGCMs, and to other factors that make a true like

- with

- like comparison difficult

– see Meinshausen et al

. , 2008.)

The uncertainty bounds for sea level rise in Table 3 differ from those given in the AR4. This is because we concatenate uncertainty limits for all factors that contribute to sea level rise uncertainties. It is unlikely that all of these factors would act in the same direction (alth ough some would because they are determined by the same underlying and more fundamental uncertainties, such as those in the climate sensitivity). Thus, within the limitations of the models used, the uncertainties given by MAGICC represent extreme, low prob ability values. AR4 uncertainty ranges can be simulated approximately from MAGICC results by halving the

differences between the MAGICC extreme and best

- estimate values. AR4 uncertainties (AR4, p. 820) are stated to be ―5 to 95% intervals characterizing th e spread of model results‖. Given that the models used do not represent the full uncertainty range (they are often referred to as an ―ensemble of opportunity‖), it is likely that the 5 to 95% range given in the AR4 underestimates the ―true‖ 5 to 95% range.

It should be noted that neither the AR4 nor the TAR projections (nor MAGICC) include the possible effects of accelerated ice flow in Greenland and/or Antarctica. In the AR4 this is judged to increase the upper bound for AR4 projections to 2100 by 9 to 1 7 cm (AR4, p. 821) . The same

increase should be considered applicable to the MAGICC projections.11

Balancing the CH4 and N2O budgets

In the TAR (and in earlier IPCC reports), because of uncertainties in the present

- day CH 4 and

N 2 O budgets, and because emissions data produced in most scenarios give only anthropogenic emissions, it was necessary to balance the gas budgets. This was done using a simple box

model relationship: dC/dt = E/  + C/  , where C is concentration, E is emissions,  is a units
conversion factor, and  is lifetime. If dC/dt, C and  are known in some reference year, the n E

can be calculated. If the scenario value is E 0 , then a correction factor (E

– E 0 ) can be calcul ated and this is applied to all future emissions. If E 0 is solely the anthropogenic emissions value, then

the difference E

– E 0 represents the present contribution from natural emissions sources. Applying this correction to all future emissions is based on the assumption that natural

emissions will remain constant. For CH 4 at least, there is evidence that this has not be so in the past (Osborn and Wigley, 1994), and strong evid ence that it will not be so in the future. Version

5.3 of MAGICC does not account for future natural emissions changes, although it is relatively easy to do this if one has an idea of the possible effects of global warming on natural emissions.

In MAGICC 5.3, a minor change has been made to the rate of change of methane concentration

in the year 2000 that is used for balancing the initial methane budget. The small decrease, from 8.0 ppb/yr to 3.5 ppb/yr, is in bette r accord with observations. This reduce s the calculated natural methane emissions level in 1990 (and subsequently) from 27 9.0 to 266.5 TgCH4/yr

. Consequently, future CH 4 concentrations are reduced relative to th ose calculated by version

4.1. F or example, 2100 concentrations for the A1B scenario drop from 1965 to 1908 ppb. The

effect of this on future climate projections is ne gligible.

Changes to the climate sensitivity

The only other change s are to the estimates of climate sensitivity. In accord with AR4, t he best

estimate of the climate sensitivity (  T2x) is now 3.0 o C

– previously 2.6 o C. The AR4 uncertain ty range for sensitivity is 2.0

– 4.5 o C, designated as the ―likely‖ range (66% confidence interval). If the distribution is assumed to be log

- normal, this corresponds to a 90% confidence interval of

1.4 9

– 6.04 o C. In MAGICC 4.1, the 90% confidence interval and best estimate values were set at

1.5 o C (low), 2.6 o C (mid), and 4.5 o C (high). These have been re

- set to 1.5 o C (low), 3.0 o C (mid),

and 6.0 o C (high). The increase at the high end is substantial, and lea ds to noticeably higher ―upper bound‖ projections of temperature and sea level . This increased probability of a high sensitivity value is in accord with the latest empirical estimates of the climate sensitivity. The

AR4 reviews probabilistic sensitivity es timates from the recent literature in two places, in the Technical Summary (Solomon et al.,. 2007) and in the ―detection and attribution‖ chapter (Hegerl and Zwiers, 2007). In the Technical Summary (p. 65)

, 95 th percentile results from 12 studies range fro m 4.4 o C to 9.2 o C, while the probability of a sensitivity above 6.0 o C ranges from

near zero to 38%. In Hegerl and Zwiers (p. 672), 7 of these studies are summarized. The 95 th
percentile values here range from 4.4 o C to 9.2 o C. (The slightly different lower bo und probably

results from difficulties in extracting numerical values fro the graphical results that are shown.)12

3.2 SCENGEN CHANGES

(1) New AOGCMs

. The AOGCM data base used in version 4.1 (viz. CMIP2) has been replaced

to make use of model resul ts generated for the IPCC Fourth Assessment Report (AR4). The primary advantages here are that these are more up

- to

- date model results (state

- of

- the

- art as of June 2007), and that these newer models have, in general, higher spatial resolution than the

olde r model s. With higher native spatial resolution in the newer AOGCMs it has been possible to

re

- grid all model results to 2.5 by 2.5 degrees, latitude/longitude without loss of information. Model results in SCENGEN 4.1 were at 5 by 5 degrees. For the AR4 mo dels, most have resolution that is finer than 2.5 by 2.5.

These data sets are housed at the Program for Climate Model Development and Intercomparison (PCMDI) at the US DOE Lawrence Livermore National Laboratory (LLNL). This

data set is now referred to as the CMIP3 data base. Details are available at:

http://www

- pcmdi.llnl.gov/ipcc/model_documentation/ipcc_model_documentation.php

(See the folder C: \ SG53 \ SCEN

- 53 \ SG

- MANS \ ModelDoc for d ocumentation data .)

There are 24 models currently in the CMIP3 data base, but only 20 have the full set of data required for use in SCENGEN. The 20 models are listed in Table 4 , which gives their CMIP3 designation and the 8 chara cter label used by the SCENGEN software. The four models not

used are listed at the bottom of the Table. Note that these four models have no SCENGEN

label.

Some words of caution apply to some of the models. For the FGOALSg

- 1.0 model, under

―known biases a nd improvements‖, the model developers state: ―The ... model shows much more sea ice extension than the observation‖, and ―... while our submitted model data are suitable for tropical and subtropical studies, we do not suggest to use these data in mid

latitude s‖. An improved version of this model has been developed, but it is not available in the

CMIP3 data base.

Although the GISS

- ER model is included in the SCENGEN data base , one should be cautious in using this model as its projections differ markedly from t hose of other models . Either the model is very strange, or there are some serious errors in the model data sets housed in the CMIP3

archive. A similar not e of caution applies to NCAR‘s PCM . As with GISS

- ER, PCM projections differ markedly from those of oth er models. Furthermore, PCM‘s validation performance (i.e., in simulations of present

- day climate) is generally poor relative to other models. More information on these two models is given in Section 6 below.

Some caution should also be exer cised with MI ROC3.2(hires) because this model appears to have a very high climate sensitivity (estimated at 5.6 o C equilibrium warming for 2xCO 2 ). However, a s SCENGEN uses normalized data files, thereby removing the direct influence of climate sensitivity, this may not be a serious issue. Apart from its high sensitivity, the model appears to be quite consistent with the other models that are i n the SCENGEN 5.3 data base (see Section 6)

.13

Table 4 : AOGCMs used in SCENGEN 5.3.



### CMIP3 designator Country SCENGEN name

BCC R

- BCM2.0 Norway BCCRBCM2

CCSM3 USA CCSM

— 30

CGCM3. 1 (T47) Canada CCCMA

- 31

CNRM

- CM3 France CNRM

- CM3

CSIRO

- Mk3. 0 Australia CSIRO

- 30

ECHAM5 /MPI

- OM Germany MPIECH

- 5

ECHO

- G Germany/Korea ECHO

--- G

FGOALS

- g1.0 China FGOALS1G

GFDL

- CM2. 0 USA GFDLCM20

GFDL

- CM 2. 1 USA GFDLCM2 1

GISS

- E H USA GISS

— EH

GISS

- E R USA GISS

— ER

INM

- CM3. 0 Russia INMCM

- 30

IPSL

- CM4 France IPSL_CM4

MIROC3. 2 (hires) Japan MIROC

- HI

MIROC3. 2 (medres) Japan MIROCMED

MRI

- CGCM2.3. 2 Japan MRI

- 232A

PCM USA NCARPCM1

UKMO

- Had CM3 UK UKHADCM3

UKMO

- Had GEM1 UK UKHADGEM

BCC

- CM1 China CGCM3.1(T63) Canada

GISS

- AOM USA

INGV

- SXG Italy

(2) Improved spatial resolution . All the new (CMIP3) AOGCM data have been re

- gridded to a

common 2.5 o by 2.5 o latitude/longitude grid (compared with 5 o by 5 o in v ersion 4.1). For the CMIP3 models, most have resolution that is finer than 2.5 by 2.5. The exceptions are ECHO

- G,

GISS

- EH, GISS

- ER and INM

- CM3.0.

(3) Mean sea level pressure (MSLP) has been added as an output variable. Note that there are

no data for MSLP for the aerosol response patterns, so projected MSLP changes are simply the greenhouse

- gas responses scaled up to the true global

- mean temperature.

(4) New observed data bases (at 2.5 by 2.5 resolution) have been added, replacing the previous 5 by 5 reso lution data sets. These data sets have a common 20

- year reference period,

1980

- 99. Temperature data now come from the European Centre for Medium

- range Weather Forecasting‘s (ECMWF) reanalysis data set, ERA40. ERA40 is a spatially complete data set.

For the 20

- year averaging period, ERA40 data are indistinguishable from other spatially complete temperature data sets. For precipitati on data, we still use the CMAP data set . An earlier CMAP data set (at 5 by 5 resolution) was used in version 4.1 . Version 5.3 us es the latest 2.5 by 2.5 degree resolution version of CMAP . For MSLP, ERA40 data are used.14

(5) Spatial smoothing

. An option is available now to use and display spatially smoothed data. The smoothing is done simply by area averaging of the nine 2.5 by 2.5 cells surrounding a given grid box. Visually, the effect of this smoothing on the displayed maps is minor. However,

smoothed results for individual grid boxes can be significant ly different from unsmoothed data

. The value of smoothing is that it allows the user to obtain a 9

- box average by selecting or clicking on a single grid box. For impacts work, use of 9

- box averages produces less spatially noisy results then using single (unsmoothed) grid boxes. If the smoothing option is selected, all display files a re smoothed. These are the files that are also given as latitude/longitude arrays in IMOUT or SDOUT (see below). For all other output files (such as AREAAVES.OUT), smoothing is ignored, and raw, unsmoothed data are always used for calculations.

(6) New co lor palettes and contouring choices are now available. For color palettes, the original rainbow version is available as default. In addition, one may now choose either a red

blue color palette, or a palette similar to one that has been employed by the IPCC AR4. For contouring, the default is as in version 4.1. In addition, one may now select a max

- min contouring system where the lowest and highest contour values correspond as nearly as possible (given the constraint of having ―sensible‖ contour values and i ntervals) to the 90% range of grid

- box values. In other words, approximately 5% of the grid

- box values will be represented by the top color in the palette and 5% of the grid

- box values will be represented by

the bottom c olor in the palette. As in version 4 .1 , each map display gives the highest and lowest grid

- bo x values as numerical values (―Range‖ in version 4.1, now ―Global range‖ )

.

(7) Two new output displays may be selected using an overwrite facility for ―Temporal SNR‖.

The first is ―S.D. change SNR ‖ (SDSNR) , which shows an inter

- model Signal

- to

- Noise Ratio for changes in variability (where ―variability‖ here is determined by the inter

- annual standard deviation (s.d.) calculated over a 20

- year period ). SDSNR is defined as the model average of

the nor malized s.d. changes divided by the inter

- model s.d. of these normali zed s.d. chang es. This is a time

- independent quantity that shows the uncertainty in projections of s.d. relative to

inter

- model differen ces in these projections. SD SNR values are invariab ly small, showing that projections of variability changes are highly uncertain.

The second new display is for ―S.D. base u ncert.‖ (SDUNCERT), which shows uncertainties in model baseline s.d. values as determined by inter

- model differences in grid

- box s. d. values. These are also expressed as a Signal

- to

- Noise Ratio, the model

- mean baseline s.d. value divided by the inter

- model standard deviation of the model baseline s.d.s.

(8) New output files . A number of new output data files are produced and given i n ...ENGINE/IMOUT and ...ENGINE/SDOUT. The f ull set of output files is listed below in Table 5

. These results in these output files are spe cific to the user selections of: scenari o; MAGICC model (user or default); variable and season; analysis year (i. e., glob al

- mean warming amount); and scaling method.

There are two types of output file, latitude/longitude arrays and tabulated results. The tabulated results files in folder IMOUT are AREAAVES.OUT, IMCORRS.OUT, IMFILES.OUT,

OUTLIERS.OUT and VALIDN.OUT. The tab ulated results files in folder SDOUT are FILES.OUT and SDCORRS.OUT. Note that spatial smoothing is never applied to these files

– smoothing is only applied to the latitude/longitude array files. For the array files, full global arrays are always given even if the user has selected a smaller region. For the tabulated results files, the results always apply to the user

- selected region.15

Although the user must select a specific type of analysis, the software calculates results for all possible analyses, so the results in folders IMOUT and SDOUT are always complete.16

Table 5 : SCENGEN output files

. These files comprise three types of output: latitude/longitude arrays that are the numerical values for fields that are or can be displayed in SCENGEN (―displayable f irkds‖); supplementary latitude/longitude output fields that cannot be displayed; and tabulated results that may be used for diagnostic studies.

SG50/SCEN

- 50/ENGINE/IMOUT (* displayable fields, also given in ...ENGINE/SCENGEN)

ABSDEL.OUT : Model mean of a bsolute 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 are a

– (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

. M odel minus Observed for temperature and MSLP. % error

(100(M

– O)/O) ) for precipitation

GHANDAER.OUT : Scaled changes, model mean (with a erosols): 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, GHAN DAER, GHGDELTA, AEROSOL, INTER

- SD, IM

- SNR,

PROBINCR, NUM

- INCR, 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.O UT : 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 d eviation for normalized GHG change fields

*MODBASE.OUT : Model

- mean baseline
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 analys is

– 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 b aseline, 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 mode l

- i and model

- mean baselines with observed

baseline data. Uses pattern correlation, RMS difference, bias (M

– O), bias

corrected RMS differ ence, and RK index averaged over specified region.17

SG50/SCEN

- 50/ENGINE/SDOUT (* displayable fields, also given in ...ENGINE/SCENGEN) (** displayable fields, that are not given in ...ENGINE/SCENGEN)

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 1 s 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 : Inte r

- model pattern correlation results for normalized s.d. change fields and

baseline s.d. fields.

SDFIELDS.OUT : Summary of fields, GHGDELTA, BASE

- SD, DELTA

- SD, BAROFSNR,

SNROFBAR, INTERSNR, SDSNR, SDUNCERT. Plus correlation matrix for pattern correlations b etween 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.

SG50/SCEN

- 50/ENGINE/SCENGEN The fields that can be displa yed are all in this folder, except for SDSNR.OUT and SDUNCERT.OUT

which are in the ...ENGINE/SDOUT folder

. Copies of t

hese fields are also output to the

...ENGINE/IMOUT and ...ENGINE/SDOUT folders (see above) where they are given latitude

- longitude

labels . Note that DEL2USE is given a different file name in ...ENGINE/IMOUT (viz. GHANDAER)

.

ABS

- MOD.OUT

ABS

- OBS.OUT

BAROFSNR.OUT

BASE

- SD.OUT

DEL2USE.OUT : Sa me as ...ENGINE/IMOUT/GHANDAER.OUT

. DELTA

- SD.OUT

DRIFT.OUT

ERROR.OUT

IM

- SNR.OUT

MODBASE.OUT

OBSBASE.OUT

PROBINCR. OUT1 8

(9) Model selection tools

. For impacts work it is often preferable to use average results for a selection of models. A standard method for selecting models is on the basis of their ability to accurately represent current climate, either for a partic ular region and/or for the globe. The output file VALIDN.OUT (model validation) can be used here. Two new validation statistics have been added. Another model selection criterion is to eliminate models whose projection s are inconsisten t with those of other models (i.e., one could decide to eliminate ―outlier‖ models). The new output file ―OUTLIER S .OUT‖ can be used here.

In version 4.1, VALIDN.OUT gave results for the pattern correlation between observed and modeled present

- day climate , the root mean square (RMS) model/observed difference, and the model/observed bias (i.e., model area average minus observed area average) . For ―present

day‖ climate, version 4.1 used data from model control runs. Control run data are still used as the default, but it is now po ssible to use 1980

- 99 data from a 20 th century climate simulation with

the chosen model. To do this, the EXTRA.CFG file in folder ENGINE must be edited: NBASE must be changed to 4 from its default value of 3.

In neither case would one expect, even for a perfect model, perfect model/observed agreement. This is partly because neither the control nor the 20 th century simulations uses forcings that are the same as those in the real world; and partly because, even with 20

- year averages, the model

and real worl ds will have different manifestations of internally generated variability. Validation statistics differ by only small amounts for validation using control or 20 th century data.

T he new validation statistics are a bias

- corrected RMS difference, and a valid ation statistic employed by Reichler and Kim (2008) (―RKERROR‖). If two data sets have very different spatial means, then this can lead to inflated RMS differences. The bias

- corrected RMS difference removes the spatial

- mean difference before calculating th e RMS difference. The RKERROR term is defined as the square root of a normalized mean

- square model/observed difference (M

– O), where the normalization is achieved by dividing each grid

- box value of (M

– O) 2 by the observed grid

- box inter

- annual variance. (There is an option to use the variance from the chosen model for normalization, accessible via an off

- line CFG file edit.)

One should not place too much weight on RKERROR, as this can be very dependent on the normalizing term. Small local variances can lead to large grid

- box RKERROR values that can have an unduly large influence on area averages. Insights into this problem can be gained by examining the RKERROR.OUT file in ...ENGINE/IMOUT.

OUTLIER S .OUT uses a number of comparison statistics to define ou tliers. The comparisons are made between results for a chosen model and those for the average of all other selected

models. The comparison statistics are as used in VALIDN.OUT (except that RKERROR is not used) , viz. the pattern correlation, RMS difference, bias, and bias

- corrected RMS difference.

(10) Analysis of variability . Variability in SCENGEN is characterized by the inter

- annual standard deviation (s.d.) c alculated over a 20

- year reference period. Observed and model s.d.

data come from the same sour ces as the mean state data. In version 4.1 it was possible only to examine model average fields for baseline s.d. and s.d. changes. The latter are derived only

from CO 2 - based patterns of s.d. change (as there are no s.d. data available for the aerosol

fiel ds). Scaling uses the full global wa rming projection, so the code effectively assumes that the patterns of s.d. change for CO2 forcing and aerosol forcing are similar.19

Although these are still the primary s.d. display fields, it is now possible to display two fields that give an idea of the uncertainties in these displayed fields based on inter

- model differences.

These are: the inter

- model Signal

- to

- Noise Ratio for s.d. changes (i.e., SDSNR = model average of normalized s.d. changes divided by the inter

- mo del s.d. of these s.d. changes ); and an uncertainty index for the model

- mean baseline s.d. field (SDUNCERT = model average of baseline s.d. fields divided by the inter

- model s.d. of these baseline s.d.s).

In addition, a number of new output files give in formation about: similarities between model s.d. change fields and similarities between model s.d. baseline fields; observed versus model s.d. differences (in percentage terms); an s.d. bias fi eld based on work by Gleckler et al. (2008) ; and

observed s.d. data (note that only model baseline s.d. data were available in version 4.1). These new output files are ...

SDCORRS.OUT : Inter

- model pattern correlation results for normalized s.d. change fields

and baseline s.d. fields.

SDERROR.OUT : Standard de viation 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

. SDOBS.OUT : Observed baseline standard deviation fi eld (denoted OSD above).

SDINDEX is useful when considering area averages. With ―raw‖ s.d. error data, positive errors and negative errors could cancel out giving a false impression of model skill. SDINDEX avoids this problem, but is still imperfect as it gives very small values (<1.0005, which rounds to 1.000 in the output) even when the absolute error is as large as 5%.

(11) A final new output field (ABSDEL.OUT) gives absolute changes in the mean state. This is only new for precipitation where, previou sly, only percentage change data were given.

(12) Map displays in SCENGEN have been modified to make the information displayed more clear. Examples showing the old (version 4.1) display followed by the new display are given

below ...20

Version 4.1 display

Version 5.3 display21 

### 4. Running MAGICC:

In Windows, from drive ―C‖ (Local disk), click successively on ―SG53 ‖

, ―SCEN

- 53 ‖ , and

― MAGICC ‖ to enter the operating directory. Then click on the MAGICC application (EXE) file. This will bring up the pri mary MAGICC/SCENGEN window

– below.

The MAGICC directory contains all the emissions files (***.GAS), various configuration files that set model parameters (***.CFG), and a range of output files generated by MAGICC.

The SCEN

- 53 directory also contains su b

- directories ―RETO‖ (which contains all the AOGCM data), ―NEWOBS‖ (which contains the new observed data), ―SCENGEN‖ (which contains some of the gui code) and ―ENGINE‖. ENGINE in turn contains sub

- directories ―IMOUT‖ and ―SGOUT‖ which give a ll the output f iles (see Table 5 above).

The first step in using MAGICC/SCENGEN is to click on ― Edit ‖ . This will display a pull

- down

menu with the choices ― Emissions Scenarios ‖

, ― Model Parameters ‖ and ― Output Years ‖

.

Under ― Emissions Scenarios ‖ , the user ca n select a Reference and Policy scenario. In the example below we use A1T

- MES as the Reference sce nario, and WRE450 as the Policy

scenario. A1T

- MES is one of the six illustrative scenarios from the SRES (Special Report on

Emissions Scenarios ; Nakićenović a nd Swart , 2000 ) set. WRE450 uses CO 2 emissions that22

lead to CO 2 concentration stabilization at 450 ppm along the WRE (Wigley et al., 1996) pathway, with compatible non

- CO 2 gas emissions that follow the extended MiniCAM Level 2

stabilization scenario (Wigle y et al., 2008; Clarke et al., 2008

– see Appendix for further details )

. Emissions for WRE450 are defined out to 2400. Emissions for A1T

- MES are defined only to

2100. The default setting for MAGICC is to run to 2100. A later run date can be selected by

cli cking on ―Output Years‖ and re

- setting the end date

– see below.

Under ― Model Parameters ‖ , most of the selections are self

- explanatory. Examples will be given

below. New features (in 4.1 and subsequently) are climate feedbacks on the carbon cycle, and (a ccessed by clicking on the default ― User ‖ model) the option to emulate a range of AOGCMs, specifically those used in Chapter 9 of the IPCC Third Assessment Report ( TAR

– Working Group I)

. AR4 AOGCMs will be added at a later date. The range of options under ― Model

Parameters ‖ allows the user to carry out a variety of sensitivity studies. Examples will be given

below.

Clicking on ―Output Years‖ will bring up t he ―Output parameters‖ window (see below). Here , the user can control the years covered by the displ ays

, and the years covered and time

- step interval for output to the Reports files . Buttons on the right of t he Output p arameters window can be used to return to the default settings. The Output Years selection controls what data are

available to SCENGEN. M ost emissions scenarios in the library run only to 2100, so selecting a higher number for the last year in these cases will have no effect. The CO 2 stabilization

scenarios , however, run to 2400. To obtain output over the full period it is nec essary to sele ct 2400 as the last year . Once done, this will allow SCENGEN results for these scenarios to be produced out to 2400.

For a spe cific example, as noted above, we use A1T

- MES for the Reference case and WRE4 50 for the Policy case. To select these, click on t he Edit window and then Emissions Scen arios

, scroll down to and select the chosen scenario(s), and then click on the appropriate selection

arrow

– as shown below

.

C lick on ― OK ‖ to preserve the selected scenarios. This will close the ― Emissions Scenar ios ‖

window.23

An important thing to note with the emissions (*.GAS) files

, should users wish to add

their own files

, is that there must be values given for the year 2000. This is because budget balancing for CH 4 and N 2 O uses year

- 2000 data from the input e missions file. MAGICC will still run if there are no year

- 2000 data, but the CH 4 and N 2 O results will be incorrect.

In the examples below we also consider the effects of a relatively high climate sensitivity, an equilibrium CO 2 - doubling temperature chan ge (  T2x) of 4.5 o C. For now, however, we stick with

the default model

- parameter settings. The Model Parameters window opens up as below. Note that a sensitivity of 3.0 o C, the default value, is shown in the Sensitivity box. We make no changes. Click on OK to cl ose the window.24

The next editing option is Output Years. Clicking on this will bring up the following window ...

The default Last year is, as shown here, 2100. In this cas e the reference scenario (A1T

- MES ) is defined only out to 2100, while t he Policy scenario (WRE450) is defined out to 2400. One could edit ―Last year‖ to 2400 to show the full extent of the WRE450 results

– but for now we will keep the default Last year. So simply click on OK with no changes.

U nless further editing of the i nputs is required, click on Run at the top of the main window. After a short time, the climate model will be run. Input emissions for the major gases and results for concentration changes, radiative forcing (by gas and total), global

- mean temperature and

g lobal

- mean sea level change can now be viewed by clicking on ― View ‖

.

If View is selected, the following window appears .....

The user can select either to view graphical output, or, in the Reports files, to access much more detailed tabulated output . E ach Report file has results for sensitivities of  T2x = 1.5, 3.0,

6.0 o C and the user

- selected sensitivity . Sea level output combines low sensitivity with low ice melt, and high sensitivity with high ice melt. Examples of the graphical output are shown b elow.25

In some cases, numerical values will be given in the text . These have been extracted directly from the Reports files. We show results for concentration and g lobal

- mean temperature below. First, concentration .....

Currently, only results for CO 2 , CH 4 and N 2 O can be displayed. The default is CO 2 . The selected display shows CO 2 concentrations for the default carbon cycle model, for both scenarios, together with an uncertainty range that is controlled solely by uncertainties in ocean uptake and

CO 2 fertilization. The central, or ― best ‖ results include the effects of climate feedbacks on the carbon cycle, but the uncertainty ranges do not account for parameter uncertainties in the way

climate fee dbacks on the carbon cycle are modeled, nor for uncerta inties associated with the effects of climate sensitivity uncertainties on the magnitude of these climate feedbacks.

Note that uncertainty ranges displayed in MAGICC are always those for the User model. In this case, the User and Default models are the s ame.

To print out graphical results from MAGICC, use the Print button (this may not work with all printers ). An alternative is to use the Alt

- Prnt Scrn facility to save the active window, and then copy the win dow to a Word file , or to use specialist softw are like ― SnagIt ‖

– see below . A few of26

the grap hical results in this document were produced using Alt

- Prnt Scrn

. Most results were produced using the commerc ial software ―SnagIt‖ ( http://www.techsmith.com/ ) , which is highly

recommended.

A key component o f CO 2 projections is the feedback on the carbon cycle due to global warming. This is really a complex set of different feedbacks operating on a regional scale, some positive and some negative. On balance, however, these climate feedbacks are positive leadi ng to significantly higher concentrations than would be the case if they were absent. We can illustrate the importance of these feedbacks with some specific permutations of the present example.

First, we increase the amount of warming simply by increasing the climate sensitivity. We do this by going back to the Edit button and editing Model Parameters. On the Model Parameters window we change Sensitivity to 4.5 o C

– as below.

We select this with the OK button, and then click on Run. Then, through ―View‖ we examine the

CO 2 concentration s , as shown below .....27

Effect of climate sensitivity on CO 2 concentration due to larger climate feedbacks that

occur with the larger warming that results from choosing a larger climate sensivity :  T2x

= 4.5 o C (U ser Model) vs 3.0 o C (Best Guess)

.

The display shows noticeably higher concentrations for the User Model (  T2x = 4.5) than for the Default Model (labeled ―Best Guess‖ in the display), for both emissions scenarios. Note that the uncertainty band s are for the User Model.

The additional warming that occurs when a higher sensitivity is selected leads to a larger climate feedback on the carbon cycle, and, hence, larger concentrations. For the Reference (A1T) emissions scenario, warming in 2100 i s 2.48 o C for the default climate sensitivity (3.0 o C) and

3.37 o C for the user sensitivity (4.5 o C). The corresponding 2100 CO 2 concentrations are 576 ppm and 595 ppm

– an increase of 19 ppm for a warming increase of 0.9 o C.

To further investigate climate fe edbacks on the carbon cycle , we can choose to turn these feedbacks off. To do this we go back to the ―Edit‖ button on the main window, and select ―Off‖ in

the C

- cycle Climate Feedbacks panel

– see below. Note that the user

- selected climate sensitivity is s till set at 4.5 o C. For illustrating only the effects of climate feedbacks on the carbon cycle (i.e., on future CO 2 concentrations) we do not need to change this. This is because the no

feedback concentrations are, necessarily, independent of the sensitivit y. However, if we want to examine the effects of these carbon cycle feedbacks alone on temperature, for example, we

need to re

- set the user climate sensitivity back to the default value of 3.0 o C

– as below.28

The User M odel now is the same as the Defa ult M odel except that climate feedbacks on the carbon cycle have been turned off. Clicking on Run again, and then on View and Concentrations will bring up the display below.29

Magnitude of climate feedbacks on the carbon cycle for a cl imate sensitivity of  T2x =

3.0 o C. U ser Model shows the no

- feedback case while Best Guess shows the default case

which includes climate feedbacks.

In this case, climate feedbacks on the carbon cycle are more noticeable, leading to significantly greater concentrations than would otherwi se be obtained. T he 2100 con centration with

feedbacks is 576 ppm (as above). Without feedbacks the concentration is 525 ppm, so the

feedbacks add 5 1 ppm to the 2100 concentration. A small part of this arises because the

mag nitude of the feedback depends on t he temperature change, which is greater in the with

feedback case , 2.48 o C in 2100 compared with 2.24 o C

.

For the Policy scenario, WRE450, concentrations are lower and the effect of climate feedbacks

is to increase the 210 0 concentration from 423 ppm to 450 ppm (+27 ppm). If we had run the analysis out to 2400 (by selecting 2400 in ―Output Years‖ at the start), it could be seen that the

difference increases over time, reaching 38 ppm by 2400 (see Figure below).30

For g lobal

- mean temperature we show results for the same case , i.e., where the only use r choice is to turn off climate feedbacks on the carbon cycle

. From the concentration results above we expect the ― U ser ‖ cases to have slightly less warming than the default ( ― B est ‖ ) cases

because of the lower CO 2 concentrations in the carbon

- cycle

, no

- feedback case

– as already

noted

.

Th e results for global

- mean temperature change out to 2400 are shown below ...31

The difference between the ―Best‖ (with feedbacks) an d the ―User‖ (no feedbacks) results tells us the magnitude of the effect of climate

- related carbon cycle feedbacks on global

- mean temperature. As expected from the concentration results, the eff ect of climate feedbacks is relatively small but significant

. In 2100 the additional warming is a bout 0.25 o C for the Reference

emissions scenario and 0.17 o C for the Policy scenario. By 2400 in the Policy scenario the

difference rises to 0.33 o C. (These are results for the default climate sensitivity case

. ) Note that

t emperature stabilizes in the WRE450 case. This is in part because the WRE stabilization

scenarios are now multi

- gas stabilization scenarios in which all concentrations stabilize. Results

for CH 4 and N 2 O are shown below.3233

Interestingly, the no

- cli mate

- policy emissions and concentrations for N 2 O in the A1T scenario are actually less than in the policy

- driven WRE450 emissions scenario, where N 2 O emissions

come from the extended MiniCAM Level 2 multi

- gas stabilization scenario. This illustrates the

pr ofound uncertainties in projecting N 2 O emissions both in the absence of or in response to climate policies.

It should be noted that t he CO 2 concentration results shown here are s omewhat dece ptive . By giving results only for one parameterization of climate feedbacks on the carbon cycle t hey hide very large uncertainties that surround quant ification of these feedbacks

. Although MAGICC has feedbacks that are similar in magnitude to other carbon cycle models used by IPCC, the Bern model ( Joos et al. , 2001 ) and the ISAM model ( Kheshgi and Jain, 2003)

– see Appendix

– s ome other models ha ve substantially larger feedback effects (Friedlingstein et al., 2006)

.

Nevertheless, warming uncertainties associated with this particular factor are small compared

with uncer tainties that arise from our relatively poor knowledge of the magnitude of the climate sensitivity. These uncertainties can be displayed by clicking on the two range buttons on the temperature change output display. The results are shown below .....34

Sea level results based on MAGICC for thermal expansion and TAR models (see p. 8) for all other components may be viewed by clicking on the ― Sea level ‖ button. The plot below shows the full range of results out to 2400

.35

This plot shows both the effect of carbon

- cycle climate feedbacks on the central estimate for sea level rise (Best Guess versus User Model results), and a n estimate of the overall uncertainty in projections of sea level rise . Carbon

- cycle climate feedback effects are rel atively small, but overall uncertainties are very large. It should be noted, however, that uncertainties in sea level rise in MAGICC represent the extreme (and likely very low probability) limits where all uncertainties operate in t he same direction. T he u pper bound shown by MAGICC i s what would be expected if the climate sensitivity were 6 o C a nd if all ice

- melt parameters a re set to maximize the ice melt contribution for this sensitivity. The probability of this combination must be considerably less than t he probability of a sensitivity as high as 6 o C (viz. 5%), but it is impossible to quantify this probability without carrying out a far more sophisticated analysis. Even the central estimates are important, however, as they show the large inertia in the cli mate components that contribute to sea level rise. Recall that temperatures stabilize in this case, yet sea level continues to rise inexorably.36 

### 5. Running SCENGEN:

We now move on to explore SCENGEN. The next step then is to go back to the main MAGI CC control window, click on the SCENGEN button and then on the ― Run SCENGEN ‖ button. This will bring up the SCENGEN title window (see below).

Click on ― OK ‖

.

Clicking on OK will bring up a blank map .....37

..... and the main SCENGEN selection w indow.

We now work through four examples illustrating some of the capabilities of SCENGEN 5.3

.38

EXAMPLE 1:

This first example is a comparison of different model results for changes in the spatial patterns of annual

- mean precipitation. The MAGIC C case used is as above, a Reference

emissions scenario of A1T

- MES and a Policy scenario where CO 2 concentrations follow the

WRE450 stabilization profile.

The first step is to click on ― Analysis ‖ i n the above SCENGEN window. This will bring up the

― Analys is ‖ window shown below. The other windows will remain in place and can be moved around to more convenient positions if required.

Note that this window has changed from that used in version 4.1. The bottom right panel is new

and now allows users to ex amine inter

- model uncertainties in variability: specifically, in the

model

- mean baseline inter

- annual standard deviation, s.d. (―SD

- b ase uncert‖ ), and th e model

mean s.d. change (―SD

- c han ge SNR‖ )

– see item (10) in Section 3 .2 above. Uncertainties in s.d. change are very large

– i.e., there are large inter

- model differences in projections of variability change , as will be shown below

.

Under ―Data‖, the default selection is ―Change‖ indicating that the analysis to be performed by

default will be of changes in the mean state for a particular selected variable. If this button is not lit up, c lick on ― Change ‖ to select an analysis of climate change. The following steps will select: (1) the AOGCMs to be used (displayed results are for the average across the sele cted models); (2) the analysis region (we will use the full globe); (3) the analysis variable and season (we use annual precipitation) ; and (4 ) the analysis year, emissions scenario, and MAGICC parameter set. These selections (including the type of analysi s

– ―Change‖, etc.) may be made in any

order.

We first select the models to be used to define the change . As noted above, the displayed results will give the average change over the selected models . A crucial and unique aspect of SCENGEN is that averages across models are based on normalized results (following the original implementation of this idea in Santer et al. (1990)). Using normalized results ensure s that each model pattern of change receives equal weight and the average is not biased towards39

model s with high climate sensitivity. To select the models to use, g o back to the SCENGEN

window and click on ― Models ‖ . This will bring up the window shown be low

.

Certain models (a selection of U.S. models ) will be lit up as default. The user can select any set of models, from a single model to all models, and SCENGEN will produce results averaged over

the selected models. For further information on these models, see the IPCC Fourth Assessment Report ( Randall and Wood, 2007)

For the present example we u se all models except FGOALS and GISS

- ER (for reasons stated above). To get the above selection

, t he user should click on ― All ‖ and then click on FGOALS and

GISS

- ER to de

- select these two models . Next

, the user has the option of using Definition 1 or

Defini tion 2 changes. Def. 1 uses the difference between the start and end of a perturbation experiment. Def. 2 uses the difference between the perturbed state and the control climate at the same time. If a model has any spatial drift (and most models do) then D ef. 2 is a way of removing this drift (under the justifiable assumption that the drift is approximately common to both the perturbed and control runs)

– normally one should use Def. 2.

Next, the user must decide whether or not to include the spatial effec ts of aerosols. Normally, these effects should be included (which is done by clicking on the ― Aerosol effects ‖ button ) . The option not to include aerosol effects is to allow the user to determine how important these

effects are. The ― Models ‖ window shown a bove corresponds to these selections.

Next, return to the SCENGEN window and click on ― Region ‖

. The map below will be displayed.40

The map shows the regions used for the breakdown of SO 2 emissions in the MAGICC emissions files, together with a set of a nalysis region selections. (Emissions from ocean and air transport are divided equally over the three regions.) The default region is the whole globe, and this is what will be used in the present examples. The user can select from a range of ― hard

wired ‖ r egions, or can mouse out a rectangular latitude/longitude region on the map. To do this,

click on ― User ‖ and use the mouse to define a region. The latitude/longitude domain will be shown numerically on the right. The selected region appears as a red rectan gle

– see the map

below

-- and the domain limits appear on the bottom right of the window . (Note that the hard

wired regions are generally not rectangular.) For user

- selected rectangular regions t he latitude and longitude ranges shown correspond to the fu ll domain. L atitude values are in degrees north from the equator, and l ongitude values are in degrees east.41

Selecting a grid

- box region means that most calculations will be carried out specifically for that region. This includes area averages for th e selected variable (see below), and a range of other statistics. These results are not displayed, but are given in tabulated form in various output files in the ENGINE/IMOUT or ENGINE/SDOUT directory (se e Table 5 above)

.

After experimenting with the use r region option, return to using the whole globe by clicking on

―Clear‖ and then ―Globe‖.

Now return to the SCENGEN window and click on ― Variable ‖ . The ― Variable ‖ window (below) will appear. The default is annual

- mean temperature. Click on ― Ann ‖ to see the other season options, and then return to ― Ann ‖ . Next click on ― Precipitation ‖ , since this is the variable we will

use for the examples. Note that the ― Reverse ‖ light will come on, since the standard rainbow color scheme for precipitation (red for dry t o blue for wet) is the opposite of that usually used for temperature (blue for cold to red for hot). This can be de

- selected by clicking on the ―Reverse‖

button.

This window gives the user the option to use linear or power law (exponential) scaling. The latter is a way of avoiding physically unrealistic results that can (albeit only rarely) occ ur with linear scaling if the global

- mean warming is large . For these examples we will stick with linear scaling. For precipitation changes, exponential scaling is preferred. Users should experiment with both scaling methods to see the differences.

There are two new options on the ―Variable‖ window. First there is a spatial smoothing option that replaces all output fields by an area

- weighted 9

- box smoothed fiel d (see item (5) in Section

3 .2 above). Second, there is now a range of color palette schemes and an improved method for choosing contour levels and inter vals (see item (6) in Section 3 .2 ).

Selecting the spatial smoothing option means that, if a single 2. 5 by 2.5 degree grid box is selected as the region, the results will be area averages over the nine grid boxes centered on the selected grid box. If spatial smoothing is selected, this will be applied to all output array files and displays.

To change the palette, click on the ―rainbow‖ button. To change the contour levels to span the range of grid

- box values better, click on the Min/Max button.42

Return again to the SCENGEN window and click on ― Warming ‖ . The following window will

appear .....

This is where the user selects the following: (1) the emissions scenario, either the Reference or the Policy case. The names displayed show only the first nine letters of the h eaders on the emissions files. (2) the scenario year (i.e., the central year for a climate averaging interval of 30 years, as indicated by the length of the slider bar . The default year is 2050, as shown. (3) a particular confi guration for the MAGICC model, D efault (i.e., ― best guess ‖ ) or U ser.

These factors determine the global

- mean temperature c hange from 1990 to 2050 (red 1.64 degC at top of window in this case ) that is used for scaling the normalized patterns of change. Within the code, this global

- mean temperature change is broken down into four components (a ghg component, and aerosol compone nts for the SO 2 emissions in the three emissions regions shown above) and these are used as weights for the pattern scaling algorithm.

For the present examples we will use the de fault emissions scenario (A1T

- MES , the selected Reference scenario), and defa ult parameters for MAGI CC

. We also slide the temperature bar

across to 2064 to give a warming of 2 degC

– see window below.43

At this stage, all necessary user selections for SCENGEN have been made.

Return now to the SCENGEN window and click on ― RUN ‖ to run the SCENGEN software. After a short time, a map will appear

– see below. This shows the change in annual

- mean precipitation for the 3 0

- year interval centered on 2064 (for the A1T emissions scenario, and

― best guess ‖ climate model parameters in MAG ICC) averaged over all 18 selected AOGCM s

.

The default display is as shown above. Mousing over the map will show specific grid

- box values in the lowest panel of the display. We now illustrate other possible displays. First

, we use the ―Min/Max‖ opt ion on the ―Variable‖ window, which will ensure that approximately 5% of the grid

box values will lie above (below) the highest (l owest) contour level

.44

(In the above and subsequent displays the top and bottom parts of the full panel have been

delibera tely suppressed.) Next, we retain the Min/Max contouring and select the red

- blue color palette

– below.45

Finally we select the AR4 color palette . This palette has the yellow /blue boundary as the zero

contour level.

We now compare the multi

- model average results with those for a single AOGCM. For the single model we choose NCAR‘s Community Climate System Model (CCSM3). We show below

the multi

- model result for default contouring and palette (repeated from above) with the CCSM3

result immediate ly below this

.46

It can be seen that there are clear similarities between the multi

- model me an pattern and the

CCSM3 result

– although the latter pattern is, understandably, more noisy. In both cases

, precipitation increases in high latitudes and d ecreases in subtropical regions and in places like

the Mediterranean Basin and southwest Australia. Overall, changes in CCSM3 are much larger

than in the multi

- model mean, implying that there are cancelling effects when a number of models are averaged.47

The visual similarity, however, is deceptive, and the overall pattern correlation between CCSM3 changes and the mean of the remaining 17 selected models is quite small (r = 0.372). Pattern correlation results such as this may be found in OUTLIERS.OUT (in f older ...ENGINE/IMOUT)

, for which an extract is given below. (To produce this Table, you will have to go back to the original 18

- model selection and re

- run SCENGEN.)

Note that CCSM3 precipitation changes are biased high relative to other models ( ― BIAS ‖ in t he

Table below is model

- i minus the mean of the remaining models for 1 o C global

- mean warming).

Note also that the results in the Table below do not correspond precisely to the maps above, since OUTLIERS results are based solely on the normalized precipitat ion changes (i.e., they do not account for scaling up to the MAGICC global

- mean temperature change, nor do they account for aerosol effects on precipitation change). Nevertheless, these OUTLIERS results provide a good indication of the more general pattern similarities.

COSINE WEIGHTED STATISTICS

MODEL CORREL RMSE BIAS CORR

- RMSE NUM PTS

% % %

BCCRBCM2 .442 7.050 .420 7.038 10368

CCCMA

- 31 .562 5.997

- .171 5.994 10368

CCSM

-- 30 .372 8.507 1.002 8.448 10368

CNRM

- CM3 .312 7.945 .175 7.943 10368

CSIR0

- 30 .351 9.214 .616 9.193 10368

ECHO

--- G .327 8.519

- .861 8.475 10368

GFDLCM20 .456 10.139 .510 10.126 10368

GFDLCM21 .402 11.190

- 2.166 10.979 10364

GISS

-- EH .396 7.854 .515 7.837 10368

INMCM

- 30 .424 7.049 .178 7.047 10368

IP SL_CM4 .397 10.135

- 1.085 10.077 10358

MIROC

- HI .523 5.478 .539 5.452 10368

MIROCMED .599 5.624

- .219 5.620 10368

MPIECH

- 5 .342 15.497 .870 15.473 10361

MRI

- 232A . 365 10.688 .257 10.685 10363

NCARPCM1

- .067 15.157 .822 15.135 10368

UKHADCM3 .424 10.049

- .940 10.005 10368

UKHADGEM .522 6.514

- .119 6.513 10368

The above results provid e a strong indication that there are large inter

- model differences between AOGCM precipitation change projections. A further indicatio n of these large inter

model differences can b e obtained using Inter

- SNR and P(I ncrease)

– see these buttons on the ―Analy sis‖ window. We explore these further below.48

EXAMPLE 2:

In this example we investigate model errors in simulating present

- day patterns of precipitation and mean sea level pressure (MSLP) relative to the observed climate. For precipitation, we will co nsider the model average, and two individual model s

. For the individual models, t o span the range of model skill in simulating present

- day annual precipitation we choose the best and worst models by making use of results in VALIDN.OUT. Part of this output ( that for cosine

- weighted statistics) is shown in the Table below. (To produce this we have run SCENGEN with all models selected, including FGOALS and GISS

- ER.) The best model here (in

terms of the global pattern correlation) is ECHO

- G, while the worst is NCAR‘s PCM. (Note that the ECHO results are somewhat deceptive, because this is a flux

- corrected model.)

*** 20 MODELS : VARIABLE = CMAP PRECIP : SEASON = ANN ***

MODEL VALIDATION: COMPARING MODEL

- i BASELINES WITH OBSERVED DATA

MODEL BASELINE FROM CON TROL RUNS

BIAS IS DIFFERENCE IN SPATIAL MEANS: MOD MINUS OBS

CORR

- RMSE IS RMSE CORRECTED FOR BIAS

RK INDEX, BASED ON REICHLER & KIM (2008), DIMENSIONLESS

INDEX = AREA AVERAGE OF [(MOD

- i MINUS OBS)**2]/[(MOD

- i S.D.)**2)]

AREA SPECIFIED BY MASK. MAS KFILE = MASK.A : MASKNAME = GLOBE

COSINE WEIGHTED STATISTICS

MODEL CORREL RMSE BIAS CORR

- RMSE RK INDEX NUM PTS mm/day mm/day mm/day

BCCRBCM2 .793 1.311 .307 1.275 43.960 103 68

CCCMA

- 31 .888 .949

- .010 .949 21.286 10368

CCSM

-- 30 .797 1.327 .160 1.317 36.782 10368

CNRM

- CM3 .772 1.438 .540 1.333 19.566 10368

CSIR0

- 30 .814 1.209

- .16 1 1.198 94.574 10368

ECHO

--- G .910 .864 .128 .854 13.766 10368

FGOALS1G .816 1.226 .307 1.187 15.120 10368

GFDLCM20 .868 1.099 .091 1.095 22.909 10368

GFDL CM21 .857 1.149 .215 1.128 25.030 10368

GISS

-- EH .733 1.512 .340 1.473 31.909 10368

GISS

-- ER .774 1.430 .297 1.399 34.008 10368

INMCM

- 30 .700 1.606 .116 1 .601 17.914 10368 IPSL_CM4 .808 1.269

- .090 1.266 55.101 10368

MIROC

- HI .800 1.340 .281 1.311 28.908 10368

MIROCMED .833 1.162 .035 1.162 28.548 10368

MPIECH

- 5 .808 1.351 .247 1.328 18.631 10368

MRI

- 232A .886 .967

- .084 .963 19.226 10368

NCARPCM1 .665 1.715 .343 1.680 40.144 10368

UKHADCM3 .858 1.256 .230 1.235 24.384 10368

UKHADGEM .797 1.614 .385 1.568 44.852 10368

MODBAR .910 .870 .184 .850 120.441 10368

First, to clear the screen, either minimize or delete any existing maps . Now return to the Analysis window and select ― Error ‖

. (Note that the ―Reverse‖ palette on the ―Variable‖ window

will de

- select, as this is the default only for precipitation.) Annual precipitation has already been selected from the previous example. Now c lick on RUN in the SCENGEN window, and the map

be low will appear.49

This map shows the percentage error in annual precipitation averaged over the 18 selected models. On average, models are biased wet in the South Pacific and South Atlantic subtropical highs, western North America , the interior parts of Australia, and a few other regions. Models tend to be biased dry in the tropical Pacific and Antarctica. We now select th e two chosen

models

– see the two maps below ...50

Error patterns for both of these models are si milar to each other and both are similar to the

model

- mean result.

It is clear that, w hen expressed as a percentage, there are appreciable errors in most if not all

AOGCMs . Some of these results are deceptive, however. Many of the largest errors occur in regions of low precipitation, such as over the oceanic sub

- tropical highs: in absolute terms these errors are quite small. Other areas of large percentage error (e.g. the western sides of North and South America) occur where model orography is much smoothe r than in the real world

– although it is interesting that the error fields show that the model s tend to over

- estimate precipitation in these regions. There are also considerable uncertainties in the observational

data.

Further validation statistics are g iven in the ENGINE/I MOUT directory (VALIDN.OUT)

. VALIDN.OUT gives results only for the selected models and the selected region. This is the whole globe here, but it is often of interest to see how well the model(s) perform over a smaller region.

As a seco nd ―Error‖ example, we will now consider errors in model baseline mean sea level pressure (MSLP). First clear the existing maps, select all model s except FGOALS and GISS

- ER again, and then click on ―pressure‖ in the ―Variable‖ window. (Note that Ann remain s selected.) Then click on RUN to get the following map ...51

The error for MSLP (and for temperature) is expressed in absolute units rather than relative units as used for precipitation . For assessment of MSLP skill, however, there is an important

addit ional consideration. Because pressure data a re reduced to sea level, model/observed

difference s can arise because of this reduction, in turn because model orography is considerably smoothed relative to real

- world orography. There are also differences in th e way different models reduce surface data to sea level, and these methods may differ from the reduction method employed by the ERA40 observed data base we employ. For this r eason, validation of MSLP should consider only ocean areas. To do this, click on R egion in the

SCENGEN window ...

This opens the window displayed below. Then select ―Ocean‖ from the list of hard

- wired regions/52

After clicking on RUN we obtain ...

It can be seen that there are model MSLP biases even over ocean areas, but t hey exceed 3 hPa only in high latitudes and around the Antarctic circumpolar trough. Further model

- specific insights into these errors can be obtained from the VALIDN.OUT file. Part of which is shown

below.53

*** 18 MODELS : VARIABLE = MSLPRESSURE : SEAS ON = ANN ***

MODEL VALIDATION: COMPARING MODEL

- i BASELINES WITH OBSERVED DATA

NOTE: BECAUSE OF DIFFERENCES IN OROGRAPHY AND REDUCTION TO SEA LEVEL,

VALIDATION OF MSLP SHOULD USE THE OCEAN

- ONLY MASK.

MODEL BASELINE FROM CONTROL RUNS.

BIAS IS DIFFE RENCE IN SPATIAL MEANS: MOD MINUS OBS

CORR

- RMSE IS RMSE CORRECTED FOR BIAS

RK INDEX, BASED ON REICHLER & KIM (2008), DIMENSIONLESS.

INDEX = AREA AVERAGE OF SQRT[((MOD

- i MINUS OBS)**2)/(OBS

- S.D.**2)]

AREA SPECIFIED BY MASK. MASKFILE = MASK.C : MASKN AME = OCEAN

COSINE WEIGHTED STATISTICS

MODEL CORREL RMSE BIAS CORR

- RMSE RK INDEX NUM PTS

hPa hPa hPa

BCCRBCM2 .930 3.635 1.046 3.482 1.864 6560

CCCMA

- 31 .961 2.465

- .061 2.464 2.187 6560

CNRM

- CM3 .908 4.155 .417 4.134 2.512 6560

CSIR0

- 30 .978 2.672

- .036 2.672 2.193 6560

GFDLCM20 .949 2.825

- .471 2.786 1.6 21 6560

GFDLCM21 .984 1.647

- .472 1.578 1.638 6560

GISS

-- EH .935 6.557

- 5.455 3.638 10.047 6560

INMCM

- 30 .972 2.119 .240 2.105 1.964 6560 IPSL_CM4 .869 4.32 5

- .503 4.295 2.314 6560

MIROC

- HI .967 2.960

- .151 2.956 2.838 6560

MIROCMED .957 2.984

- .697 2.902 2.285 6560

ECHO

--- G .969 2.306

- .175 2.299 1.770 6560

MPIECH

- 5 .984 1.538

- .086 1.535 1.109 6560

MRI

- 232A .968 2.210

- .144 2.205 1.532 6560

CCSM

-- 30 .980 3.418

- .768 3.331 2.265 6560

NCARPCM1 .980 2.443

- .115 2.440 2.115 6560

UKHADCM3 .975 2.116

- .370 2.084 1.723 6560

UKHADGEM .987 1.772 .223 1.758 1.407 6560

MODBAR .982 1.704

- .421 1.652 2.410 6560

These results show that almost all models are very good a t simulating the spatial pattern of

annual M SLP

– pattern correlations (except for the IPSL model) range from 0.908 to 0.987. There are, however, small biases in MSLP with most models biased sligh tly low. The exception

to this ―small bias‖ result is GISS

- EH which has a large negative bias (although the overall pattern is relatively good, r = 0.935).54

EXAMPLE 3:

For the third example we consider changes in variability (expressed in SCENGEN in te rms of percentage changes in inter

- annual standard deviation , s.d. ). We will do this using the average

of all models except FGOALS and GISS

- ER

. We will also examine uncertainties in both the model baseline s.d. values and in changes in s.d..

First , minim ize or close any existing maps and select ―Globe‖ again as the study region . Next, on the Analysis window, select ― S

. D . C hange ‖ . Then, on the Models window (if necessary)

select ― All ‖ and then de

- select FGOALS and GISS

- ER

. Finally, select precipitation aga in on the

―Variable‖ window. Note that the season (annual) is not changed. Also, the ―Warming‖ window has not been changed, so we are still considering the A1T emissions scenario with default

MA GICC settings, and the year 2063 when the amount of global

- mea n warming is 2 o C. Now click on RUN, and the map below will be displayed

.

This is an extremely noisy pattern of change, suggesting that there is considerable uncertainty in projections of variability change for precipitation

– as we will show more c learly below. On average, changes in variability are small even for a global

- mean warming of 2 o C

– most of the map has changes of magnitude less than 20%. This does not mean, however, that individual models all show small changes in variability (a fact tha t the user can easily verify by selecting individual models). Rather, the low variability changes arise from the fact that different models give quite different results for the patterns of change in annual precipitation variability, and the

individual extr emes tend to cancel out.55

To obtain a better idea of the uncertainty in variability projections we can look at the inter

- model signal

- to

- noise ratio for s.d. changes (SD

- change SNR). SD

- change SNR is the above model

mean pattern of change in s.d. divided by the pattern of inter

- model standard deviations of s.d. change , a dimensionless quantity . To display SD

- change SNR, one must first click on Temporal

SNR on the ―Ana lysis‖ window, and then on ―SD

- change SNR‖ in the TSNR panel, as below ..

.

This gi ves ...56

Note that almost all the map is either pink or orange, showing that, virtually everywhere, the

inter

- model SNR for s.d. changes is less than 0.5 in magnitude. In other words, the model

- mean signal for s.d. change is generally less than half th e inter

- model variability in these projected changes. This implies that, for annual precipitation, one can have little confidence in model

projected changes in s.d.

Not all variables have such noisy and uncertain patterns of change as precipitation. As an other example we consider changes in pressure (MSLP) variability

. To do this, first click on ― No

overwrite ‖ in the TSNR panel of the Analysis window to de

- select SD

- change SNR. Then click on the S.D. change button in the Variability window. Then click on P ressure in the Variable window; then on RUN. This will give ...

How does one interpret this result? First, it would be more appropriate to look at seasonal variability changes , as annual changes may reflect either compensating or additive seasonal

ch anges. In this case, seasonal changes show similar results to those for the annual case. One might then speculate that mid to high latitude changes in MSLP variability are associated with changes in storm tracks, while low latitude changes reflect changes in ENSO variability. Some support for this comes from examin ing baseline variability (S.D. B ase ). Results for Northern Hemisphere and Southern Hemisphere winter (DJF and JJA) are shown below (where the Min/Max contour option has been chosen for clarity)

...57

Highest variability areas are along the model winter storm track paths. In spite of the existence of ENSO variability, inter

- annual variability in MSLP is very low in tropical regions.

This example, however, is given as a warning against speculati ve interpretations of results in the analysis of climate change. Prior to speculation, one should first ask whether the changes found are statistically meaningful. In this case we can do th is by looking at the SD

- c hange SNR results, shown below. Note that you have to first click on ―Tempor. SNR‖ in the Analysis window58

before ―SD

- change SNR‖ can be selected. Note also that the Min/Max contour interval option is probably still selected. We show this result, together (below it) with the Default contour option result (which is less noisy)

.59

From this it can be seen that the projected model

- mean s.d. changes are relatively small compared with inter

- model differences in these changes

– over most of the globe the SNR

results are less than 0.4 in magnitud e , indicating that the model

- mean changes in annual MSLP variability are substantially less than the inter

- model variability of these changes. This does not mean that there will not be any changes associated, for example, with movements in storm

tracks

– i t simply means that any such model

- predicted changes must be highly uncertain.

We now return to the precipitation results by re

- selecting annual precipitation in the ―Variable‖ window and ―S.D. Change‖ in the ―Analysis‖ window

. The map for these changes is given above, where we noted that it was spatially very noisy. It is of interest to look at some of the other diagnostics for variability, which are given in the ENGINE/SDOUT directo ry. In SDCORRS.OUT, the inter

- model pattern correlations for normalized variability change fields are given for the selected models, variable and season: in this case, 18 models, and annual precipitation variability changes . T hese pattern c orrelations range between

– 0.082 and +0.168.

This confirms the above statement: for pre cipitation

- variability

- cha nge fields, namely that

m odels show very little agreement. By implication, one should be very circumspect in accepting any model results for changes in precipitation variability.

Although variability changes differ markedly from model to model, models are more consistent in their simulations of baseline variability. This can be seen by clicking on ―SD

- base u ncert.‖ in the ―Analysis‖ window. This gives an SNR for model baseline s.d., define d as the baseline grid point

s.d. divided by the inter

- model standard deviation of baseline s.d. values. The map below , which uses the default contour option, shows these SNRs ...60

One can see that large areas of the map have SNR values above 2. Using the M in/Max contour option shows the lower SNR regions more clearly.

Low SNR values occur primarily in low latitudes, reflecting inter

- model differences in baseline variability

– in turn probably associated with inter

- model differences in simulations of ENSO.

There are also low SNR values ov er Antarctica. This must reflect inter

- model differences in baseline s.d. values in this region.

EXAMPLE 4:

For our final example we consider the probability of an increase in annual precipitation

.

First, minimize or close existing maps . Next, select ― P(increase) ‖ in the ― Analysis ‖ window

– and then click on RUN. The previous variable and model selec tions (annual precipitation, 18 models) will be retained. Note that for this type of analysis a number of models must be selected, since the probability o f an increase is determined by comparing the model

- mean change with the

inter

- model standard deviation.

The output map is displayed below (note the nonlinear contour interval) using the default contour interval option , together with the corresponding map from MAGICC/SCENGEN 4.1

.....61

Regions with P>0.9 indicate a high probability of an increase in precipitation, restricted to the mid to high latitudes of both hemispheres. Regions with P<0.1 indicate a high probability of a precipitation decrease. These regions are restricted mainly to the subtropical highs, where precipitation is already low. Two other notable region s of likely precipitation decrease are the

Mediterranean Basin and the southern (particularly southwestern) part of Australia

. Note th at these same regions of likely precipitation increases or decreases were also identified in version 4.1 using the previous generation of AOGCMs62

A statistician might claim that the only significant results were where P>0.95 or P<0.05. From a practic al poi nt of view, however, the probabilistic results generated by SCENGEN are much

more valuable. As a n example, consider the western coastal regions of the USA. Over much of this region the probability of a precipitation inc rease is in the range 0.2 to 0.4 . (Sp ecific values can be seen below the map by moving the cursor over the gridbox of interest.) What this means is that a prec ipitation decrease is up to four times more likely than a precipi tation increase,

based on all 18 selected models . Policy makers are o ften perplexed by the large differences

between individual model climate

- change results at the regional level (and, hence, large uncertainties in any projections). How does one respond to this degree of uncertainty? Even with these uncertainties, as the ab ove results show, there can be clear differences between the probability of a wetter (or drier) future climate compared with the probability of a change in the

other direction. I nformation like this can help to decide which way the slant adaptation measure s and define adaptation strategies that are more robust to uncertainties.63 

### 6. Choosing AOGCMs:

For many applications of MAGICC/SCENGEN it is useful to consider, not just a single model, or a set of single models, but the average over a number of models . This is an idea first introduced by Santer et al. (1990). Other researchers have used multi

- model averages subsequently, but they have almost invariably failed to realize the power of averaging normalized changes (i.e., changes per unit global

- mean warmi ng) rather than raw changes. Use of raw changes has the serious disadvantage of weighting models with high climate sensitivity more than models with lower sensitivity. Use of normalized changes on the other hand has the advantage of factoring

out uncertain ties in the climate sensitivity, allowing these to be considered separately.

If a model average is to be used, then the question arises as to whether this should be a weighted or unweighted average

– and, if weighted , how to choose the weights (se e, e.g., Giorgi and Mearns, 2002 ; Tebaldi et al., 2004)

. Giorgi and Mearns (2002) have proposed that weights should reflect both model skill in simulating present

- day climate and convergence of a model‘s projections to the multi

- model average. There are, however, different ways to quantify these

criteria.

For skill, there are considerable uncertainties in quantifying skill (see, e.g., Gleckler et al., 2008)

. We give a specific method below. For the convergence criterion, all published work on this has

used raw mo del data, so that inter

- model differences must reflect both differences in the climate sensitivity and differences in the underlying (normalized) patterns of change. The method that MAGICC/SCENGEN uses separates out these two factors. Given these problems, w e are skeptical of the value of using weighted averages, but agree that the skill and convergence criteria can be useful in selecting a subset of models to average. We also consider that the use of convergence based on raw rather than normalized data is conceptually flawed. The approach recommended here is to use unweighted averages of normalized data from a subset of models (achieved using SCENGEN) , and then to scale up the average using an independent estimate of global

- mean temperature change (based on MAGICC)

. To average raw model data is clearly flawed since this will weight models by their sensitivity

– and t here is no reason to expect model

skill to be related to climate sensitivity

.

The justifications for use of a multi

- model average are two

- fold. First, as has already been demonstrated, multi

- model averages are less spatially noisy. Second, by many measures of skill, multi

- model averages are often better than any individual model at simulating present

- day

climate (as will be demonstrated below)

. A s implied above, however, whether skill at simulating
present

- day climate translates to prediction skill is still an unresolved issue.

As an alternative to weighting models by some skill and/or convergence factor , we can use just

a subset of models based on an assessment of skill

– effectively restricting the weights to 1.0 and 0.0. In VALIDN.OUT, SCENGEN gives five statistics for model evaluation, calculated by comparing observed and present

- day model control

- run or 20 th

- century run data for
temperature, precipitation and pressure. The statistics may be calculated by month, season or annually, over the whole globe or over any user

- selected region.

In the present example we will consider a case where we are using model results for impact

studies over the continental USA (i.e., excluding Hawaii and Alaska) . For this case we use both global statistics and statistics calculated over the continental USA region. As a validation64

variable we use annual precipitation. Precipitation is more difficult to model than temperature and models do less well in simulating precipitation than temperature, so using precipitation is a stringent test of model skill. There is some value in looking at skill in simulating pressure (which is a direct indicator of atmospheric circulat ion), but one must be careful to restrict the validation region(s) to ocean areas

– because of issues related to reduction to sea level already noted. For estimates of future change at a specific site, o ne might also consider model skill evaluated over

a s mall study region surrounding the site. T his is inherently less useful than assessing skill over a larger region because it is possible that a particular model may perform well over a relatively small region partly or even largely by chance.

The statisti cs used are: pattern correlation (r), root

- mean

- square error (RMSE), bias (B), and a

bias

- corrected RMSE (RMSE

- corr). (VALIDN.OUT also gives results for the RK (Reichler and Kim, 2008 ) index, but we will not consider these here.) All statistics used here a re those that employ cosine weighting to account for the changing area of grid boxes with latitude.

B ias is simply the difference, model minus observed, averaged over the chosen validation region. Of these four statistics, bias is probably the least impor tant, since it is generally thought that biased models can still produce good information regarding future change, provided the bias is not too large. Bias may reflect incorrect baseline forcing (i.e., atmospheric composition and/or loadings of radiatively important species), rather than a problem with model physics. Bias, however, can aff ect RMSE, which is why RMSE

- corr results are given as a text statistic.

RMSE

- corr is the root

- mean

- square error after a correction is applied to the model

- mean field to

re move any bias. It is related to RMSE by

(RMSE

- corr) 2 = (RMSE) 2

– B 2

Tab le 6 shows these statistics for all models in the SCENGEN data base. To rank models I have

used a semi

- quantitative skill score that rewards relatively good models and penaliz es relatively bad models. Each model gets a score of +1 if it is in the top seven (top third approximately) for any statistic over the globe or over the USA, and a score of

– 1 if it is in the bottom seven. The maximum skill score is therefore +8, which wou ld mean that the model was in the top seven for all four statistics over both regions. The worst possible score is

– 8. In Table 6 , models are listed

in order of their skill scores. Other skill scores could be devised

– but the results for others that I

hav e considered are similar.

Once the models have been ranked, a subjective choice must be made as to which models to

retain for multi

- model averaging. In the present case, for e xample, based on the results in Table

6

, one might chose the eight highest scori ng models (a total of nine because two models are

ra nked equal eighth ).65

T able 6 : Validation statistics used for ranking models. The variable used for ranking is annual precipitation. The first numbers in each column are for the globe, while the second numbers are for the continental USA. The top three models for each case are shown in bold red type, while the worst three models in each case are shown in bold blue type.

RANK (score) FLUX

ADJ? MODEL Pattern

correlation RMSE

(mm/day) Bias

(mm/day) RMSE

- corr

(mm/day)
1 (+8) Yes CC CMA3.1(T47) 0.8 88 /0.836 0.9 49 / 0.5 47

- 0.0 10 / +0.079 0.94 9 /0.541 1 (+8) Yes MRI

- 2.3.2 0.88 6 / 0.9 09 0.9 67 / 0. 438

- 0.084 / +0.0 33 0.9 63 / 0. 437 1 (+8 ) Yes ECHO

- G 0.91 0 /0.840 0.8 64 / 0.609 +0.128 /+0.290 0.8 54 /0.535 4 (+3) HadCM3 0.8 58/ 0.916 1.256/0.711 +0.230/ +0.590 1.235/ 0.397 4 (+3) MIROC3.2med 0.833/0.687 1.162/0.802 +0.035 /+0.279 1.162/0.752 6 (+2) GFDL2.0 0.868/0.773 1.099/0.938 +0.091/ +0.693 1.095/0.632 6 (+2) GFDL2.1 0.857/0.789 1.149/0.784 +0.215/+0.497 1.128/0.606 8 ( +1) CCSM3 0.797/0.777 1.327/0.627 +0.160/ +0.079 1.317/0.622 8 (+1) IPSL4 0.808/0.752 1.269/0.783

- 0.090/+0.384 1.266/0.682 10 (

- 1) ECHAM5 0.808/ 0.887 1.351/0.742 +0.247/+0.569 1.328/ 0.476 10 (

- 1) HadGEM1 0.797/0.851 1.614 /0.681 +0.385 /+0.312 1.568 / 0.605 10 (

- 1) CSIRO3.0 0.814/0.588 1.209/0.875

- 0.161/+0.288 1.198/0.826 10 (

- 1) GISS

- ER 0.774/0.795 1.430/0.723 +0.297/+0.406 1.399/0.598 14 (

- 3) BCCR 0.793/0.684 1.311/0.741 +0.307/+0.108 1.275/0.733 15 (

- 4) FGOALS

- g1.0 0.816/ 0.441 1.226/ 1.096 + 0.307/+0.512 1.187/ 0.969 15 (

- 4) MIROC3.2hi 0.800/0.650 1.340/ 1.110 +0.281/ +0.740 1.311/0.827 15 (

- 4) GISS

- EH 0.733 /0.726 1.512/0.766 +0.340/+0.338 1.473/0.688 18 (

- 5) Yes INM3.0 0.700 / 0.456 1.606 / 0.982 +0.116/+0.381 1.590 / 0.905 19 (

- 6) CNRM3 0.77 2/0.761 1.438/0.843 +0.540 /+0.532 1.333/0.654 20 (

- 7) PCM 0.665 / 0.474 1.715 /0.935 +0.343 /+0.328 1.680 / 0.875

Mean 5 best models 0.938/0.885 0.713 /0.531 +0.060 /+0.254 0.710 /0.467

Mean 9 best models 0.924 /0.860 0.787 /0.602 +0.075 /+0.325 0.783 /0. 507

Mean All 20 models 0.910/0.843 0.870/0.655 +0.184/+0.372 0.850/0.539

Note the clear superiority of the first three models

– but note also that these three models are all flux adjusted (see Randall and Wood, 2007) . This give s them an advantage in a model

validation exercise . Flux adjustment is not thought to be an issue for future climate change projections (see, e.g., Gregory and Mitchell, 1997)

. In other words, projections for a given model do not depend significantly on whether the model is flux adjusted or not. However, if a flux adjusted model validates well against present climate, this may not be a good indicator of model quality. In these cases, some other indicator of model quality should also be considered. In SCENGEN we give a model outli er analysis to help here

– see below.

Note also that models that perform well in terms of global statistics generally perform well over

the much smaller USA region. Models with high regional bias , however, need not perform poorly

with the other statistics

– HadCM3 and GFDL2.0 are example s

.

As noted above

, one reason for employing multi

- model means is because model

- average results are generally superior to almost all individual models implying the existence of unrelated66

errors in the different models that cancel out to some extent. For example, for global pattern

co rrelations, the 5

- and 9

- model averages are better than all individual models. For the USA region, however, there are three models (HadCM3, MRI and ECHAM5 ) that are bet ter than the

5

- model and 9

- model average s , and four models (these three plus HadGEM1) that are better

than the 20

- model average . Although the results for the 5

- mode l average are better than the 9

- model average, the latter is likely to be more robust and allows a better assessment o f inter

model variability. It al so puts less weight on the flux

- adjusted models.

In selecting models it is also useful to look at results in OUTLIERS.OUT. This is a way of factoring in the convergence criterion proposed by Giorgi and Mearns (2002). You s hould note that the above analysis uses all 20 models, yet it has already been noted that FGOALS and

GISS

- ER should probably not be used. In terms of validation statistics for annual precipitation, these are clearly not the worst models. We reject FGOALS p rimarily because this is the recommendation of the developers of this model. The model itself has known flaws. For GISS

ER, part of the reason for its rejection is because its projections differ radically (in terms of spatial patterns of change) from all o ther models

– as can be seen on the OUTLIER S Table

below (where models selected on the basis of skill are highlighted in red) . The OUTLIERS Table also shows PCM as an outlier for annual precipitation ch ange. PCM would also be rejected on

the basis of its p recipitation validation performance (although it should be noted that PCM performs better for other variables)

.

Based on convergence, the four ―worst‖ models have already been rejected for their poor validation performance. It is interesting that the next worst model based on convergence (ECHO) is equal best in terms of skill.

We recommend using model average results here, but do not recommend any firm rules for selecting which models to average. The example here is meant to give users an idea of what

fa ctors should be considered. Some practitioners have suggested that all available models should be used and a weighted average employed. (In our case, selecting a subset of models is equivalent to giving weights of 1 or 0.) Giorgi and Mearns (2002) propose a weighting scheme

based on skill and convergence criteria (the factors used here for model selection). With such a weighting scheme, ECHO would get a high weight based on skill, but a low weight based on convergence. Here, in this example, we would simply reject not using ECHO results.

If a skill

- convergence weighting scheme were used for the nine models selected above on the basis of skill alone, the diff erence between the weighted and unweighted patterns of change is very small

– and well within the un certainties in any regional

- scale projection o f change. There

is l ittle to be gained in using a sophisticated weighting scheme.

In the OUTLIERS Table below, the analysis uses normalized percentage changes in precipitation rather than absolute changes. If  ̳n‘ models are being considered, the normalized percentage changes for model  ̳i‘ are compared with the average changes over all n

- 1 remaining

models.67

*** 20 MODELS : VARIABLE = CMAP PRECIP : SEASON = ANN : REGION GLOBE ***

MODEL OUTLIER ANALYSIS:

C OMPARING MODEL

- i NORMALIZED CHANGE WITH AVERAGE OF REMAINING MODELS

COSINE WEIGHTED STATISTICS

MODEL CORREL (rank) RMSE BIAS CORR

- RMSE NUM PTS

% % %

BCCRBCM2 .480 ( 6) 6.873 .515 6.854 10368

CCCMA

- 31 .608 ( 1) 5.737

- .074 5.736 10368

CCSM

-- 30 .319 (15) 8.810 1.093 8.742 10368

CNRM

- CM3 .260 (18) 8.243 .271 8.239 10368

CSIR0

- 30 .291 (17) 9.548 .7 09 9.521 10368

ECHO

--- G .293 (16) 8.709

- .759 8.676 10368

FGOALS1G .513 ( 4) 8.647

- 1.145 8.571 10368

GFDLCM20 .424 ( 7) 10.307 .604 10.289 10368

GFDLCM21 .414 ( 9) 11.107

- 2.058 10.914 10364

GISS

-- EH .394 (12) 7.895 .609 7.871 10368

GISS

-- ER .124 (19) 24.100 .245 24.099 10300

INMCM

- 30 .408 (10) 7.166 .274 7.161 10368 IPSL_CM4 .422 ( 8) 10.000

- .983 9.952 10358

MIROC

- HI .497 ( 5) 5.665 .632 5.630 10368

MIROCMED .588 ( 2) 5.700

- .121 5.699 10368

MPIECH

- 5 .350 (14) 15.456 .960 15.426 10361

MRI

- 232A .369 (13) 10. 679 .353 10.673 10363

NCARPCM1

- .099 (20) 15.363 .914 15.336 10368

UKHADCM3 .404 (11) 10.149

- .838 10.114 10368

UKHADGEM .525 ( 3) 6.513

- .021 6.513 10368

We now consider a spec ific example that makes use of these results, f uture changes in annual

mean temperature, precipi tation and MSLP under the A1T

- MES scenario at a time when global

mean warming for central MAGICC model parameters is 2 o C (viz. f or the 3 0

- year interval

centered o n 2063 )

. Results using the 9

- model average are shown below. We have selected ―USA‖ as a specific region.

Change in annual

- mean temperature for 2 o C global

- me an warming, averaged over the 9

“ best ” AOGCMs. These results are based on the A1T

- MES emissions scenario and

include aerosol effects.68

Change in annual

- mean precipitation for 2 o C global

- me an warming, averaged over the 9

“ best ” AOGCMs. These results are based on the A1T

- MES emissions scenario and

include aerosol effects.69

Change in annual

- mean MSLP for 2 o C global

- me an warming, averaged over the 9 “ best ”

AOGCMs. Th es e results are based on the A1T

- MES emissions scenario and include

aerosol effects.70

Appendix 1: Halocarbons

MAGICC includes the following 30 halocarbons ...

CFC11, CFC12, CFC13, CF 4 , CFC113, CFC114, CFC115, C 2 F 6 , CCl 4 ,CHCl 3 , CH 2 Cl 2 , MCF, Ha1211, Ha1301, HCFC2 2

, HCFC123, CH 3 Br, HFC141b, HFC142b, HFC125, HFC134a, Ha2402, HFC23, HFC32, HFC43

- 10, HFC143a, HFC227ea, HFC245ca, C 4 F 10 , SF 6

In the input emissions files, on ly the 8 most important can be specified. These are ...

CF 4 , C 2 F 6 , HFC125, HFC13 4a, HFC143a, HFC227ea, HFC245ca, SF 6

The other 22 gases are divided into two grou ps, gases controlled under the Montreal Protocol and all other gases.

Montreal gases (CFC11, CFC12, HCFC22, etc.) have fixed future emissions, controlled by the

Protocol. The concentrations and forcings for these are hard wired into the code. For the other gases t he emissions vary according to the SRES scenario, but the differe nces between the

scenarios are small. Most inter

- scenario differences in halocarbon forcing arise through differences in the emissions of the above 8 gases. MAGICC therefore uses an average total radiative forcing fo r the other gases , again hard wired into the code. The forcing error in doing

thi s is tiny

-- a few thousandths of a W/m 2 in 2100.71

App endix 2 : CO 2 concentration stabilization :

The emissions scenarios in the MAGICC emissions scenario library that lead to concentration stabilization have been constructed specifically for the current (5.3) version of the code, using

an inverse version of MA GICC. There are two sets of CO 2 concentration stabilization scenarios, labeled WRExxx and xxxNFB where xxx gives the stabilization level. The CO 2 concentration

stabilization profiles used to define these emissions scenarios are based on and very similar to the set of WRE profiles originally published by Wigley et al. (1996 ). The WRExxx scenarios are to be used when climate feedbacks on the carbon cycle are operating (which is the nor mal situation), while the xxxNFB scenarios are to be used when these feedba cks are turned off ( e.g. for scientific sensitivity st udies). T he concentration pathways in MAGICC 5.3 are almost exactly the same as in MAGICC 4.1. However, the emissions scenarios that produce these concentration profiles differ slightly, for reasons tha t are explained below.

In Wigley et al. (1996), concentration profiles stabilizing at 350, 450, 550, 650 and 750 ppm were given. These profiles were devised in a way that ensured that the implied emissions changes departed only slowly from a baseline no

- c limate

- policy case (the IS92a scenari o from Leggett et al, 1992). This ―slow departure‖ assumption was a somewhat ad hoc way to account for the economic and technological challenges that are presented by mitigation , which make a rapid departure from a no

- p olicy case virtually impossible. Although ad hoc , subsequent more sophisticated economic analyses have shown that the WRE pathways are close to optimum in a

cost

- effectiveness sense (i.e., they minimize mitigation costs over time)

.

These early analyses be gan with smooth concentration profiles and used a simple inverse

carbon cycle model to calculate th e emissions required to follow the p rescribed concentration pathway s

. The inverse model used did not account for climate feedb acks on the carbon cycle

– back in 1996 this was ―state of the art‖ . T hese climate feedbacks are, on balance, positive

, leading, for any given emission scenario, to larger concentrations than would occur otherwise. The emissions required to follow a given concentration profile are there fore less than would

otherwise occur. The emissions requirements given in the original paper are therefore

over estimates

– mitigation is tougher if climate feedbacks are accounted for.

Climate feedbacks make it more difficult to define an emissions scena rio to match a sp ecified concentration profile. This is b ecause the emissions

- concentration relationship depends on temperature and thus on the many factors that determine future temperature changes

– the climate sensitivity and other climate model paramet ers, historical forcing estimates, and

assumed future emissions of non

- CO 2 gases.

MAGICC uses emissions as its primary input. So, to study concentration stabilization issues we need to determine specific emissions scenarios that will lead to concentratio ns that follow the WRE profiles. Climate feedbacks mean that the calculated emissions will be specific to a single set of climate model parameters and a single scenario for non

- CO 2 gases. In MAGICC 4.1 w e

use d best

- estimate (i.e., TAR default) model parame ters and historical forcings , and the P50 (SRES median) emissions scenario for non

- CO 2 gases. Most importantly, the best

- estimate sensitivity used in MAGICC 4.1 was 2.6 o C. With the new IPCC AR4 report, best

- estimate model parameters and historical forcings have changed (with a new best

- estimate sensitivity of 3.0 o C)

, so the stabilization emissions scenarios must be re

- calculate d. Furthermore, as noted above, we no longer use the P50 baseline for non

- CO 2 gases, preferring a non

- CO 2 scenario that is

more cons istent with CO 2 stabilization (the extended MiniCAM Level 2 scenario). The WRE concentration profiles will only be produced exactly if the same model parameters , historical72

forcings, and future non

- CO 2 emissions are used. (In fact, the concentration p rofil es are not produced precisely because of numerical rounding errors, but the differences are always less than 0.05 ppm.)

To determine the stabilization emissions scenarios that are in the MAGICC 5.3 data base we

first use the P50 emissions scenario with default model parameters to determine the baseline (no

- climate

- policy) concentration profile . For 250 ppm to 750 ppm stabilization targets, t his profile is followed for a period from 5 to 20 years (depending on the stabilization target) before

concentratio ns depart as a consequence of mitigation . We then construct smoothly varying concentration profiles using the Padé approximant method as explained in Wigley (2000). The parameters used for fitting are given in the Table below.

Table A1 : Padé approximant fitting parameters

. Y 0 is the year of departure from the baseline. Using 2005.5 as in the three lower concentration targets , which has already passed, is an idealization that retains closer similarity to the original WRE profiles. The effects on implied

em issions are negligible. For 350 ppm stabilization

, the original departure year (used also in MAGICC 4.1) was 2000.5. Y 1 and C 1 define the anchor points that the profiles are constrained to pass through. For 250 and 350 ppm stabilization, where the profiles necessarily overshoot the stabilization target, this is the point and value at which concentration maximizes. Y end is the year at which concentration stabilizes. Note that the MAGICC 5.3 emissions library does not give the 250 and 1000 ppm stabilization c ases.

Target (ppm) Y 0 C 0 (ppm) [dC/dt] 0 Y 1 C 1 (ppm) Y end 250 2005.5 378.323 1.935 2040.5 414.0 2200.5

350 2005.5 378.323 1.935 2040.5 414.0 2150.5

450 2005.5 378.323 1.935 2050.5 440.0 2100.5

450 overshoot 2020.5 412.584 2.639 2090.5 540.0 2300.5

55 0 2010.5 388.546 2.154 2070.5 514.6 2150.5

650 2015.5 399.954 2.408 2090.5 589.4 2200.5

750 2020.5 412.584 2.639 2110.5 667.9 2250.5

1000 2050.5 514.098 4.092 2200.5 885.0 2375.5

Once the concentration profile is define d , we use the inverse version o f the MAGICC code to determine the emissions required to follow the profile

– essentially embedding the 5.3 climate

model code i n a iterative shell that marches through time, running the forward model over and over again with gradually changing emissions u ntil each p articular concentration level i s reached at a specified accuracy level. When these emissions scenarios are run in forward mode with MAGICC, they reproduce the WRE concentration profiles with an error of less than 0.05 ppm

.

In the original WRE a nalysis, and in MAGICC 4.1, the dates of departure from the baseline were

mid

- years of 2000 (for WRE350), 2005 (for WRE450), 2010 (for WRE550), 2015 (for WRE650) and 2020 (for WRE750). It is now 2008, so the departure date assumptions for WRE350 and

WRE450 are already wrong. The difference for WRE450 is negligible, but it is significant for WRE350 where the concentrations in the stabilization profile out to 2008 are noticeably below those observed. (Concentrations are also below those in P50, but the differ ences are small.) To account for the WRE350 discrepancy we have devised new with

- feedback and no

- feedback73

profiles that use 2005 as the departure date. In future it will probably become necessary to revise all of the departure dates.

Even with the initial concentrations and stabilization date and level specified, there is still a range of possible stabilization pathways. The WRE profiles were chosen to follow monotonic trajectories that approach the stabilization point from below along a smoothly varying p ath that leads also to smoothly varying emissions changes (which, as noted above, is impossible for the 250 and 350 ppm stabilization cases as we have already passed these targets) . It is possible, however, that , even for higher concentration targets, a pa thway may, for one reason or another, overshoot the target and then have to decline towards the chosen target. This might occur if it turns out to be impossible to develop and deploy carbon

- neutral technologies sufficiently rapidly to follow a monotonic pa th (which is increasingly likely for lower stabilization targets), or because an initially chosen target is judged, at some late r date, to be too high to avoid serious climate consequences. Overshoot profiles are discussed in more detail in Wigley et al. ( 2007).

To provide an example of the overshoot possibility, a single overshoot case has been added to the MAGICC emissions scenario library (450OVER)

– overshoot to 540 ppm before declining to stabilization at 450 ppm, as used in Wigley (2006). In Wigle y (2006), the assumed baseline wa s the A1B emission s scenario, and concentrations we re assumed to follow A1B concentrations to

2020 . The A1B scenario was also used for the emissions of non

- CO 2 gases . H ere, for consistency, we use P50 as the baseline for CO 2 concentrations, and the MiniCAM Level 2

scenario for non

- CO 2 gases . The peak concentration of 540 ppm is assumed to occur in 2090, and stabilization at 450 ppm occurs in 2300.

A final important point is that some key parameters in the carbon cycle model i n MAGICC 5.3 have been changed from those used in MAGICC 4.1. These changes make very little difference to the concentration projections for the six IPCC illustrative scenarios. They do, however, affect the magnitude of climate feedbacks on the carbon cycl e

. Both with

- feedback and no

- feedback results are consistent with the average results for the models used in the C4MIP intercomparison exercise (Friedlingstein et al., 2006). A comparison of MAGICC 5.3 results with

those of the two other carbon cycle model s used in the TAR i s given below.74

Table A2 : Comparison of TAR carbon cycle model concentration projections (ppm) with MAGICC 5.3 projections. This is an update of results shown in Tables 7.1 and 7.2 of Wigley et al. (2007). For consistency with the TAR results

, all concentrations are beginning

- of

- year values,

and all simulations assume a climate sensitivity (  T2x) of 2.5 o C. The models are those used in
the IPCC TAR : Bern (Joos et al., 2001), and ISAM (Kheshgi and Jain, 2003)

2050 2100

SCENARIO Bern ISAM MAGICC 5.3 Bern ISAM MAGICC 5.3

A1B 522 532 529 703 717 707

A1T 496 501 497 575 582 569

A1FI 555 5 67 564 958 970 976

A2 522 532 529 836 856 852

B1 482 488 485 540 549 533

B2 473 478 473 611 621 612

IS92a 499 508 505 703 723 714 IS92a (NFB) 494 651 682 673

Feedback 11 52 41 4175

Acknowledgements:

Over the years, many people have contributed t o the development of MAGICC and SCENGEN and the science that these software packages encapsulate. These include: Olga Brown, Charles Doutriaux, Mike Hulme, Tao Jiang, Phil Jones, Reto Knutti, Seth McGinnis, Malte Meinshausen, Mark New, Tim Osborn, Taotao Q ian, Sarah Raper, Mike Salmon, Ben Santer, Simon Scherrer and Michael Schlesinger.

Versions 4.1 and 5.3 (and intermediate versions) were funded largely by the U.S. Environmental Protection Agency through Stratus Consulting Company. In this regard, Jane L eggett (formerly EPA) and Joel Smith (Stratus) deserve special thanks for their enthusiastic support over many

years.

T he AOGCM modeling groups are gratefully acknowledged for providing their climate simulation data through the Program for Climate Model D iagnosis and Intercomparison ( PCMDI ). We also acknowledge PCMDI for collecting and archiving these data, and the World Climate Research Programme‘s Working Group on Coupled Modelling for organizing the model data analysis activity. The CMIP3/AR4 multi

- mode l data set is supported by the Office of Science, U.S. Department of Energy.76

PRINTING TIPS

There is currently no built

- in printing capability for SCENGEN, but it is easy to import the maps into other programs and print them from there.

To perform a scre en

- capture of a SCENGEN map window, simply click on the window and

press Alt+Prnt Scrn . This copies an image of the window to the clipboard. You can then paste the image into a document in another program like Microsoft Word by typing CTRL+V. If you

wan t to edit the image (to trim off borders or annotat ions, for example), one can paste it into a simple image editor like Microsoft Paint, which is typically found in the ―Accessories‖ menu. An alternative is to use commercial software like ―SnagIt‖.77 

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Tom Wigley, National Center for Atmospheric Research, Boulder, CO 80307.

Version 1, June 2008 Version 2, September 2008

The primary modification in Version 2 is to the section on sea level rise. Additional information

about the carbon cycle model has been added, the Section on model selected has been modified with more information added on the OUTLIERS Table, and a new Appendix inserted giving information about how MAGICC handles halocarbons.8081 

# MAGICC/SCENGEN 5.3 D IRECTORY STRUCTURE

C: \ SG53

SCEN

- 53 SG

- MANS (Manuals)

ENGINE MAGICC SCENGEN ModelDoc (AOGCM documentation)

RETO SCENGEN

(Driver files

for

SCENGEN ) CHARLES5 SDOUT (Ou tput files) NEWOBS IMOUT (Output Files)

SIMON (New

observed data) MOD (AOGCM

d ata files) OBS

(Old

o bserved

d ata) SO4

(Aerosol

res ponse

p atterns)