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MAGICC/SCENGEN 5.3: USER MANUAL (version 2)

1. ERVED DATA D 2 58 Ds 2 NUM PTS 10368 10368 10368 10368 10368 10368 10368 10368 10368 10368 10368 10368 10368 10368 10368 10368 10368 10368 10368 10368 10368 will de select as this is the default only for precipitation Annual precipitation has already been selected from the previous example Now click on RUN in the SCENGEN window and the map below will appear 48 Model Error 100 Mod Obs Obs fae Annual Tie Global range 81 2 to 1000 0 Def 2 with aerosols 90 00 70 00 50 00 30 00 10 00 10 00 30 00 50 00 70 00 90 00 Models BCCRBCM2 CSIRO 30 GFDLCM21 IPSL_CM4 MRI 232A CCCMA 31 ECHO G GISS EH MIROC HI NCARPCM1 CCSM 30 FGOALSIG GISS ER MIROCMED UKHADCM3 CNRM CM3 GFDLCM20 INMCM 30 MPIECH 5 UKHADGEM 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 the two chosen models see the two maps below Model Error 100 Mod Obs Obs for Annual Precipitation Global range 92 5 to 1000 0 See os ie r T kA ea oie Def 2 with aerosols 90 00 70 00 50 00 30 00 10 00 10 00 30 00 50 00 70 00 90 00 EE k i tS
2. E Pol user faao faaoo 1990 2400 1765 1990 1765 2400 ae V N AW W N A 7 Help oft PEE OK Z 200 20 zi 20 230 2400 2150 20 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 E Ref range E Ref best E Ref user E Pol range E Pol best E Pol user 1990 12400 1990 2400 1765 1990 1765 2400 Help Print OK Ae WX oe Se SSNs Policy Best Guess SOS Reference Range Reference Best Guess Reference User Model Ye Policy Range 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 an estimate of the overall uncertainty in projections of sea level rise Carbon cycle climate feedback effects are relatively 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 the same direction The upper bound shown by MAGICC is what would be expected if the climate sensitivity were 6 C and if all ice melt parameters are set to maximize the ice me
3. SOS Reference Range W Ref range Reference Best Guess E Ref best Reference User Model YZ Policy Range E Ref user E Pol range E Pol best E Pol user i fao a100 1990 2100 1765 1990 1765 2100 Policy Best Guess Policy User Model ASIII VRIR LLLLL LA ee Help Print OK 250 Effect of climate sensitivity on CO2 concentration due to larger climate feedbacks that occur with the larger warming that results from choosing a larger climate sensivity AT2x 4 5 C User Model vs 3 0 C Best Guess The display shows noticeably higher concentrations for the User Model AT2x 4 5 than for the Default Model labeled Best Guess in the display for both emissions scenarios Note that the uncertainty bands 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 is 2 48 C for the default climate sensitivity 3 0 C and 3 37 C for the user sensitivity 4 5 C The corresponding 2100 CO concentrations are 576 ppm and 595 ppm an increase of 19 ppm for a warming increase of 0 9 C To further investigate climate feedbacks 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
4. 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 occur 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 Observable Contours MeanTemp Default Precipitation v Min Max y Pressure Palette Scaling rainbow linear E Reverse exponential Season Ann 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 field 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 intervals see item 6 in Section 3 2 Smoothing 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 cli
5. Scenario A1TMES Year 2063 Def 2 with aerosols 18 00 15 00 12 00 9 00 6 00 3 00 0 00 3 00 6 00 Models BCCRBCM2 CSIRO 30 GISS EH MIROCMED UKHADCH3 9 00 CCCMA 31 ECHO G INMCH 30 MPIECH S UKHADGEM CCSM 30 GFDLCH20 IPSL_CH4 MRI 232A CNRM CM3 GFDLCH21 MIROC HI NCARPCH1 Change in Annual Precipitation Global range 100 0 to 178 6 Global mean dT 2 0 deg C Scenario A1TMES Year 2063 Def 2 with aerosols Models CCSH 30 9 00 It can be seen that there are clear similarities between the multi model mean pattern and the CCSM3 result although the latter pattern is understandably more noisy In both cases precipitation increases in high latitudes and decreases 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 46 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 folder 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 mod
6. 448 943 t93 ATS 126 SITO x837 047 lt 077 452 620 473 685 139 005 peo NUM PTS 10368 10368 10368 10368 10368 10368 10368 10364 10368 10368 10358 10368 10368 10361 10363 10368 10368 10368 model differences can be obtained using Inter SNR and P Increase see these buttons on the Analysis window We explore these further below 47 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 consider the model average and two individual models For the individual models to 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 xxx 20 MODELS VARIABLE CMAP PRECIP SEASON ANN MODEL VALIDATION COMPARING MODEL i BASELIN
7. variable and season analysis year i e global 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 tabulated 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 14 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 15 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 firkds 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 ABS MOD OUT ABS OBS OUT AEROSOL OUT AREAAVES OUT DRIFT OUT ERROR OUT GHANDAER OUT GHGDELTA OUT IMCORRS OUT IMFIELDS OUT IMFIL
8. 2006 In Wigley 2006 the assumed baseline was the A1B emissions scenario and concentrations were assumed to follow A1B concentrations to 2020 The A1B scenario was also used for the emissions of non COz gases Here for consistency we use P50 as the baseline for CO2 concentrations and the MiniCAM Level 2 scenario for non COz 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 in 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 cycle 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 models used in the TAR is given below 73 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 AT2x of 2 5 C The models are those used in the IPCC TAR Bern Joos
9. 2007 Global climate projections In Climatic Change 2007 The Physical Basis S Solomon D Qin M Manning Z Chen M Marquis M K B Averyt M Tignor and H L Miller eds Cambridge University Press Cambridge UK and New York NY USA pp 747 845 Meinshausen M Raper S C B and Wogley T M L 2008 Emulating IPCC AR4 atmosphere ocean and carbon cycle models for projecting global mean hemispheric and land ocean temperature MAGICC 6 0 Atmos Chem Phys 8 6153 6272 Naki enovi N and Swart R Eds 2000 Special Report on Emissions Scenarios Cambridge University Press Cambridge UK 570 pp Osborn T J and Wigley T M L 1994 A simple model for estimating methane concentration and lifetime variations Climate Dynamics 9 181 193 Randall D A and Wood R A Coordinating Lead Authors together with 11 Lead Authors and 73 Contributing Authors 2007 Climate Models and Their Evaluation In Climate Change 2007 The Physical Science Basis S Solomon D Qin M Manning Z Chen M Marquis K B Averyt M Tignor and H J Miller eds Cambridge University Press Cambridge UK and New York NY USA pp 589 662 Reichler T and Kim J 2008 How well do coupled models simulate today s climate Bull Amer Met Soc 89 303 11 Santer B D Wigley T M L Schlesinger M E and Mitchell J F B 1990 Developing Climate Scenarios from Equilibrium GCM Results Max Planck Institut fur Meteorologie Report No
10. Brazil W FSU NH Africa ROLA y SH w Europe w SEASia Aerosol Region 3 wv Equ Pac India v W Pac N3 China Alaska N34 Japan w Grnind N4 w AusNZ v Antarc USA w C Asia w Arcs Lat 90 to 90 User Lon 180 to 180 After clicking on RUN we obtain Model Error Mod Obs for Annual Pressure Global range 6 1to 8 5 Def 2 with aerosols Models BCCRBCM2 CSIRO 30 GISS EH MIROCMED UKHADCH3 CCCMA 31 ECHO G INMCM 30 MPIECH 5 UKHADGEM CCSM 30 GFDLCM20 IPSL_CM4 MRI 232A CNRM CM3 GFDLCM21 MIROC HI NCARPCM1 It can be seen that there are model MSLP biases even over ocean areas but they 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 52 x 18 MODELS VARIABLE MSLPRESSURE SEASON 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 DIFFERENCE IN SPATIAL MEANS MOD MINUS OBS CORR RMSE IS RMSE CORRECTED FOR BIAS RK INDEX BASED ON REICHLER amp KIM 2008 DIMENSIONLESS INDEX AREA AVERAGE O
11. G GFDLCH20 GFDLCH21 GISS EH INMCH 30 IPSL_CM4 MIROC HI UKHADCN3 UKHADGEM Global range 43 9 to 55 4 Global mean dT 2 0 deg C Scenario A1TMES Year 2063 Def 2 with aerosols 24 00 20 00 16 00 12 00 8 00 4 00 0 00 4 00 8 00 12 00 In the above and subsequent displays the top and bottom parts of the full panel have been deliberately suppressed Next we retain the Min Max contouring and select the red blue color palette below i i Rg i gt eS a ew Change in Annual al Precipitation a ee ee ot ee ia ales hh Ee Models BCCRBCM2 CCCMA 31 CCSM 30 CNRM CH3 CSIRO 30 ECHO G GFDLCH20 GFDLCH21 GISS EH INMCH 30 IPSL_CM4 MIROC HI TKEADCHS UKHADGEM aa Global range 43 9 to 55 4 Global mean dT 2 0 deg C Scenario A1TMES Year 2063 Def 2 with aerosols 24 00 20 00 16 00 12 00 8 00 4 00 0 00 4 00 8 00 12 00 44 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 immediately below this 45 Change in Annual itll Global range 43 9 to 55 4 Global mean dT 2 0 deg C
12. in the C cycle Climate Feedbacks panel see below Note that the user selected climate sensitivity is still set at 4 5 C For illustrating only the effects of climate feedbacks on the carbon cycle i e on future CO2 concentrations we do not need to change this This is because the no feedback concentrations are necessarily independent of the sensitivity 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 C as below 27 1O x 74MAGICC 5 3 mo Forcing Controls Carbon Cycle Model High Mid v Low v User C cycle Climate Feedbacks On Off Aerosol Forcing y High Mid v Low Climate Model Parameters Sensitivity AT a10 DE Thermohaline Circulation Variable v Constant Vert Diffus K2 2a cm2is Ice Melt High Mid v Low Model User E The User Model now is the same as the Default Model 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 28 oncentr 5 x co2 Carbon Dioxide Concentration ppmv v CH4 Reference SRES 41T MESSAGE Illustrative Scenario Policy 450 ppm stab with feedback P50 CO2 base LEY2 others N20 Reference Best Guess _ Ref range Reference User Model E Ref best Policy
13. in the Variable window Note that Ann remains selected Then click on RUN to get the following map 50 Model Error Mod Obs for Annual Pressure Global range 6 1to 8 5 Def 2 with aerosols Models BCCRBCM2 CSIRO 30 GISS EH MIROCMED UKHADCH3 CCCMA 31 ECHO G INMCM 30 MPIECH S UKHADGEM CCSN 30 GFDLCH20 IPSL_CH4 MRI 2324 CNRM CM3 GFDLCM21 MIROC HI NCARPCM1 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 additional consideration Because pressure data are reduced to sea level model observed differences can arise because of this reduction in turn because model orography is considerably smoothed relative to real world orography There are also differences in the 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 reason validation of MSLP should consider only ocean areas To do this click on Region in the SCENGEN window 74 SCENGEN 10 x Control Windows Actions Analysis QUIT Models Help Region Print Variable RUN Warming This opens the window displayed below Then select Ocean from the list of hard wired regions I 51 Aerosol Region 1 Globe w Canada w MEast Vv Land Mexico w E FSU Ocean
14. models 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 uncert in the Analysis window This gives an SNR for model baseline s d defined 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 Model mean Baseline S D Inter model SNR for Annual Precipitation Global range 0 7 to 12 0 Global mean dT 2 0 deg C Scenario A1TMES Year 2063 Def 2 with aerosols 3 50 3 00 2 50 2 00 1 50 1 00 0 50 0 00 0 50 1 00 Models BCCRBCM2 CSIR0 30 GISS EH MIROCMED UKHADCH3 CCCHA 31 ECHO G INMCH 30 MPIECH 5 UKHADGEM CCSM 30 GFDLCM20 IPSL_CM4 MRI 232A CNRM CM3 GFDLCM21 MIROC HI NCARPCM1 59 One can see that large areas of the map have SNR values above 2 Using the Min Max contour option shows the lower SNR regions more clearly Model mean Baseline S D Inter model SNR for Annual Precipitation Global range 0 7 to 12 0 Global mean dT 2 0 deg C r Scenario A1TMES a ari Year 2063 a ea I sin ty Def 2 with aerosols Us al i 7 1 rey amp mso w e eee E RERE Models BCCRBCM2 CSIR
15. 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 exercised with MIROC3 2 hires because this model appears to have a very high climate sensitivity estimated at 5 6 C equilibrium warming for 2xCOsz However as 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 in the SCENGEN 5 3 data base see Section 6 12 Table 4 AOGCMs used in SCENGEN 5 3 CMIP3 designator Country SCENGEN name BCCR BCM2 0 Norway BCCRBCM2 CCSM3 USA CCSM 30 CGCMs8 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 CM2 1 USA GFDLCM21 GISS EH USA GISS EH GISS ER 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 HadCM3 UK UKHADCM3 UKMO HadGEM1 UK UKHADGEM BCC CM1 China CGCMs8 1 T63 Canada GISS AOM USA INGV SXG Italy 2 Improved spatial resolution All the new CMIP3 AOGCM data hav
16. 2007 Global climate projections In Climatic Change 2007 The Physical Basis S Solomon D Qin M Manning Z Chen M Marquis M K B Averyt M Tignor and H L Miller eds Cambridge University Press Cambridge UK and New York NY USA pp 663 745 Hulme M Wigley T M L Barrow E M Raper S C B Centella A Smith S J and Chipanshi A C 2000 Using a Climate Scenario Generator for Vulnerability and Adaptation Assessments MAGICC and SCENGEN Version 2 4 Workbook Climatic Research Unit Norwich UK 52 pp Joos F Prentice C Sitch S Meyer R Hooss G Plattner G K Gerber S and Hasselmann K 2001 Global warming feedbacks on terrestrial carbon uptake under the 77 Intergovernmental Panel on Climate Change IPCC emissions scenarios Global Biogeochemical Cycles 15 891 908 doi 10 1029 2000GB001375 Kheshgi H S and Jain A K 2003 Projecting future climate change implications of carbon cycle model intercomparisons Global Biogeochemical Cycles 17 1047 doi 10 1029 2001GB001 842 see also http frodo atmos uiuc edu isam Leggett J Pepper W J and Swart R J 1992 Emissions scenarios for the IPCC An update In Climate Change 1992 The Supplementary Report to the IPCC Scientific Assessment J T Houghton et al eds Cambridge University Press Cambridge UK pp 71 95 Meehl G A and Stocker T F Coordinating Lead Authors together with 12 Lead Authors and 78 Contributing Authors
17. 47 Hamburg Germany 29 pp Solomon S Dahe Qin and Manning M Coordinating Lead Authors together with 28 Lead Authors and 18 Contributing Authors 2007 Technical Summary In Climatic Change 2007 The Physical Basis S Solomon D Qin M Manning Z Chen M Marquis M K B Averyt M Tignor and H L Miller eds Cambridge University Press Cambridge UK and New York NY USA pp 19 91 Tebaldi C Smith R Nychka D and Mearns L O 2004 Quantifying uncertainty in projections of regional climate change A Bayesian approach J Clim 18 1524 1540 78 Wigley T M L 2000 Stabilization of CO concentration levels In The Carbon Cycle eds T M L Wigley and D S Schimel Cambridge University Press Cambridge U K 258 276 Wigley T M L 2006 A combined mitigation geoengineering approach to climate stabilization Science 314 452 454 Wigley T M L Clarke L E Edmonds J A Jacoby H D Paltsev S Pitcher H Reilly J M Richels R Sarofim M C and Smith S J 2008 Uncertainties in climate stabilization submitted to Climatic Change Wigley T M L and Raper S C B 2005 Extended scenarios for glacier melt due to anthropogenic forcing Geophys Res Letts 32 L05704 doi 10 1029 2004GL021238 Wigley T M L Richels R and Edmonds J A 1996 Economic and environmental choices in the stabilization of atmospheric CO concentrations Nature 379 240 243 Wigley T M L Richels R a
18. 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 5 550 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 defined we use the inverse version of the MAGICC code to determine the emissions required to follow the profile essentially embedding the 5 3 climate model code in a iterative shell that marches through time running the forward model over and over again with gradually changing emissions until each particular concentration level is 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 analysis 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 differences are small To account for the WRE350
19. Best Guess W Ref user Policy User Model Pol range E Pol best E Pol user faso 2100 1990 2100 1765 1990 1765 2100 Help Print OK 250 20 25 210 Magnitude of climate feedbacks on the carbon cycle for a climate sensitivity of AT2x 3 0 C User 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 otherwise be obtained The 2100 concentration with feedbacks is 576 ppm as above Without feedbacks the concentration is 525 ppm so the feedbacks add 51 ppm to the 2100 concentration A small part of this arises because the magnitude of the feedback depends on the temperature change which is greater in the with feedback case 2 48 C in 2100 compared with 2 24 C For the Policy scenario WRE450 concentrations are lower and the effect of climate feedbacks is to increase the 2100 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 29 74MAGICC 5 3 Gas Concentrations E m x Carbon Dioxide Concentration ppmv Reference SRES 41T MESSAGE Illustrative Scenario Policy 450 ppm stab with feedback P50 CO2 base LEV2 othe
20. ER W MIROCMED W UKHADCH3 W CNRM CM3 W GFDLCM20 W INMCM 30 W MPIECH S W UKHADGEM 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 use all models except FGOALS and GISS ER for reasons stated above To get the above selection the 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 Definition 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 Def 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 effects 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 above co
21. Models ECHO G 49 Model Error 100 Mod Obs Obs for Annual Precipitation Global range 97 8to 1000 0 Def 2 with aerosols j Mpun TEL L Models NCARPCM1 Error patterns for both of these models are similar to each other and both are similar to the model mean result It is clear that when 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 smoother than in the real world although it is interesting that the error fields show that the models tend to over estimate precipitation in these regions There are also considerable uncertainties in the observational data Further validation statistics are given in the ENGINE IMOUT 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 second Error example we will now consider errors in model baseline mean sea level pressure MSLP First clear the existing maps select all models except FGOALS and GISS ER again and then click on pressure
22. Reference Range E Ref range Reference Best Guess JE Ref best A Wi Policy Range Policy Best Guess _ Ref user D TT E Pol range 7 E Pol best _ Pol user faao ar00 1990 2100 1765 1990 1765 2100 Help Currently only results for CO2 CH4 and N2O can be displayed The default is CO2 The selected display shows COz 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 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 feedbacks on the carbon cycle are modeled nor for uncertainties 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 same 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 window to a Word file or to use specialist software like SnagIt see below A few of 25 the graphical results in this document were produced using Alt Prnt Scrn Most results were produced usi
23. a user set prescribed Both default and user results are carried through to SCENGEN A flow chart describing how MAGICC SCENGEN is configured is shown on the next page STRUCTURE OF THE MAGICC SCENGEN SOFTWARE Atmospheric Composition Changes Global mean Temperature and Sea Level Output Regional Climate or Climate Change Output 3 Modifications 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 tropospheric 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 the 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 CO2 2000 the 2005 values it produces depend on the chosen emissions scenario 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 th
24. 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 A1Fl 555 567 564 958 970 976 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 41 74 Acknowledgements Over the years many people have contributed to 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 Qian 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 Leggett formerly EPA and Joel Smith Stratus deserve special thanks for their enthusiastic support over many years The AOGCM modeling groups are gratefully acknowledged for providing their climate simulation data through the Program for Climate Model Diagnosis 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 t
25. forcing error in doing this is tiny a few thousandths of a W m in 2100 70 Appendix2 CO 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 MAGICC There are two sets of CO2 concentration stabilization scenarios labeled WRExxx and xxxNFB where xxx gives the stabilization level The CO 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 normal situation while the xxxNFB scenarios are to be used when these feedbacks are turned off e g for scientific sensitivity studies The 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 that 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 climate policy case the IS92a scenario from Leggett et al 1992 This slow departure assumption was a som
26. in the ENGINE SDOUT folder Copies of these 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 DELTA SD OUT DRIFT OUT ERROR OUT IM SNR OUT MODBASE OUT OBSBASE OUT PROBINCR OUT Same as ENGINE IMOUT GHANDAER OUT 17 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 particular 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 projections are inconsistent with those of other models i e one could decide to eliminate outlier models The new output file OUTLIERS 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 defaul
27. large negative bias although the overall pattern is relatively good r 0 935 53 EXAMPLE 3 For the third example we consider changes in variability expressed in SCENGEN in terms 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 minimize or close any existing maps and select Globe again as the study region Next on the Analysis window select S D Change Then on the Models window if necessary select All and then de select FGOALS and GISS ER Finally select precipitation again 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 MAGICC settings and the year 2063 when the amount of global mean warming is 2 C Now click on RUN and the map below will be displayed Change in Model S D 100 New Base Base for Annual Precipitation Global range 61 6 to 215 5 j Global mean dT 2 0 deg C Scenario A1TMES Year 2063 Def 2 with aerosols Models BCCRBCM2 CSIRO 30 GISS EH MIROCMED UKHADCM3 CCCMA 31 ECHO G INMCM 30 MPIECH 5 UKHADGEM CCSM 30 GFDLCM20 IPSL_CM4 MRI 232A CNRM CM3 GFDLCM21 MIROC HI NCARPCM1 This is an extremely noisy pattern of change suggesting that
28. scenario A1T MES is one of the six illustrative scenarios from the SRES Special Report on Emissions Scenarios Naki enovi and Swart 2000 set WRE450 uses CO emissions that 21 lead to COz concentration stabilization at 450 ppm along the WRE Wigley et al 1996 pathway with compatible non COz2 gas emissions that follow the extended MiniCAM Level 2 stabilization scenario Wigley 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 clicking 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 accessed 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 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 the Output parameters window see below Here the user can control the years covered by the displays and the years covered and time
29. step interval for output to the Reports files Buttons on the right of the Output parameters window can be used to return to the default settings The Output Years selection controls what data are available to SCENGEN Most 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 COs stabilization scenarios however run to 2400 To obtain output over the full period it is necessary to select 2400 as the last year Once done this will allow SCENGEN results for these scenarios to be produced out to 2400 For a specific example as noted above we use A1T MES for the Reference case and WRE450 for the Policy case To select these click on the Edit window and then Emissions Scenarios scroll down to and select the chosen scenario s and then click on the appropriate selection arrow as shown below ioj x Policy scenario gt WRE450 Reference scenario gt A1T MES Note SRES illust scenarios are hyphenated 1 Help o Click on OK to preserve the selected scenarios This will close the Emissions Scenarios window 22 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 CH4 and N20 uses year 2000 data from the input emissions file MAGICC will still run if there are n
30. the detection and attribution chapter Hegerl and Zwiers 2007 In the Technical Summary p 65 95 percentile results from 12 studies range from 4 4 C to 9 2 C while the probability of a sensitivity above 6 0 C ranges from near zero to 38 In Hegerl and Zwiers p 672 7 of these studies are summarized The 95 percentile values here range from 4 4 C to 9 2 C The slightly different lower bound probably results from difficulties in extracting numerical values fro the graphical results that are shown 11 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 results 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 older models 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 models 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
31. there is considerable uncertainty in projections of variability change for precipitation as we will show more clearly below On average changes in variability are small even for a global mean warming of 2 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 that 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 extremes tend to cancel out 54 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 Analysis window and then on SD change SNR in the TSNR panel as below ioixi Data Variability w Change w S D Base Error w S D Change Mod Base Tempor SNR w Mod Change TSNR overwrite w Obs Base No overwrite w Obs Change SD change SNR Inter model y SD base uncert Inter SNR w P Increase This gives S D Change Inter model SNR for Annu
32. to 3 5 ppb yr is in better accord with observations This reduces the calculated natural methane emissions level in 1990 and subsequently from 279 0 to 266 5 TgCH4 yr Consequently future CH concentrations are reduced relative to those calculated by version 4 1 For example 2100 concentrations for the A1B scenario drop from 1965 to 1908 ppb The effect of this on future climate projections is negligible Changes to the climate sensitivity The only other changes are to the estimates of climate sensitivity In accord with AR4 the best estimate of the climate sensitivity AT2x is now 3 0 C previously 2 6 C The AR4 uncertainty range for sensitivity is 2 0 4 5 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 49 6 04 C In MAGICC 4 1 the 90 confidence interval and best estimate values were set at 1 5 C low 2 6 C mid and 4 5 C high These have been re set to 1 5 C low 3 0 C mid and 6 0 C high The increase at the high end is substantial and leads 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 estimates from the recent literature in two places in the Technical Summary Solomon et al 2007 and in
33. to use fully consistent scenarios Nevertheless we do now use a stabilization scenario for non COz gases but we use the same non CO 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 et al 2008 This scenario includes emissions reductions for non COz gases that are consistent with a CO stabilization target of 550 ppm The emissions of non COsz 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 COz gases Users can modify the emissions of non COsz gases of course but because this will change the magnitude of climate feedbacks on the carbon cycle this will mean that the resulting CO2 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 concentration rises 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 scenario for non COz 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 f
34. 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 there 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 As 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 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
35. we note that such a large negative indirect forcing for the lower bound would be inconsistent with detection and attribution D amp A studies Such studies to date have rarely considered indirect forcing explicitly but they do so implicitly because the response patterns of direct and indirect forcing are almost certainly similar These studies give best estimate values of total sulfate aerosol forcing ranging from 0 1 to 1 7 W m with a mean of about 0 8 W m 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 the central empirical estimate of 0 8 W m is noticeably smaller in magnitude than the combined direct plus indirect forcing of 1 1 W m 0 7 0 4 given as the a priori best estimate in the AR4 We nevertheless retain the 1 1 value for initialization Although indirect 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 another 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 above precise agreement is not possible as MAGICC s 2005 data are pro
36. year So simply click on OK with no changes Unless further editing of the inputs 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 global mean sea level change can now be viewed by clicking on View If View is selected the following window appears Graphs Emissions Concentrations Radiative forcing Temperature amp Sea Level Reports User Policy Default Policy User Reference Default Reference The user can select either to view graphical output or in the Reports files to access much more detailed tabulated output Each Report file has results for sensitivities of AT2x 1 5 3 0 6 0 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 below 24 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 global mean temperature below First concentration 74 MAGICC 5 3 Gas Ce co2 Carbon Dioxide Concentration ppmv CH4 Reference SRES 41T MESSAGE Illustrative Scenario Policy 450 ppm stab with feedback P50 CO2 base LEV2 others 10 x N20 z SOS
37. 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 0 772 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 gives 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 qualit
38. 0 30 GISS EH MIROCMED UKHADCH3 CCCMA 31 ECHO G INMCM 30 MPIECH 5 UKHADGEM CCSM 30 GFDLCM20 IPSL_CM4 MRI 232A CNRM CM3 GFDLCM21 MIROC HI NCARPCM1 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 over 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 selections annual precipitation 18 models will be retained Note that for this type of analysis a number of models must be selected since the probability of 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 60 Probability of Increase in Annual Precipitation Global range 0 00 to 1 00 Global mean dT 2 0 deg C Scenario A1TMES Year 2063 Def 2 with aerosols Models BCCRBCM2 CSIRO 30 GISS EH MIROCMED UKHADCH3 CCCMA 31 ECHO G INMCM 30 MPIECH 5 UKHADGEM CCSM 30 GFDLCM2
39. 0 IPSL_CM4 MRI 232A CNRM CM3 GFDLCM21 MIROC HI NCARPCM1 0 x Probability of Increase in Annual Precipitation Global mean dT 1 18 deg C Year 2050 Scenario SRES 50t Models 0 95 BMRC98 CCC199 0 90 CCSR96 CERF98 0 85 CSI296 CSM_98 0 80 ECH395 ECH498 0 60 GFDL90 GISS95 0 40 HAD295 HAD300 0 20 7 AP_97 LMD_98 0 15 esi MRI_96 PCM_00 0 10 WM__95 0 05 Range 0 0 to 1 0 Latitude Longitude Value Regions with P gt 0 9 indicate a high probability of an increase in precipitation restricted to the mid to high latitudes of both hemispheres Regions with P lt 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 regions of likely precipitation decrease are the Mediterranean Basin and the southern particularly southwestern part of Australia Note that these same regions of likely precipitation increases or decreases were also identified in version 4 1 using the previous generation of AOGCMs 61 A statistician might claim that the only significant results were where P gt 0 95 or P lt 0 05 From a practical point of view however the probabilistic results generated by SCENGEN are much more valuable As an example consider the western coastal regions of the USA Over much of this region the probability of a precipitation increase is in the range 0 2 to 0 4 Specific values can be seen below the map b
40. 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 previously 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 19 Version 4 1 display 7 15 x Change in Annual Precipitation Global mean dT 1 18 deg C Year 2050 Scenario SEEL p Models 18 00 BMRC98 CCC199 15 00 CCSR96 CERF98 12 00 ca E CSI296 CSM_98 9 00 ECH395 ECH498 6 00 GFDL90 GISS95 3 00 HAD295 HAD300 _ a00 gt IAP_97 LMD_98 L 300 MRI_96 PCM_00 6 00 WM__95 9 00 Range 17 3 to 58 7 Latitude Longitude Value Version 5 3 display loj x Global range 32 3 to 27 7 Global mean dT 1 29 deg C E 3 Scenario A2ASF Year 2050 Def 2 with aerosols 18 00 15 00 12 00 9 00 6 00 3 00 0 00 3 00 6 00 9 00 Change in Annual Precipitation Models BCCRBCH2 CSIRO 30 GFDLCM21 IPSL_CM4 MRI 232A CCCMA 31 ECHO G GISS EH MIROC HI NCARPCM1 CCSM 30 FGOALSIG GISS ER MIROCMED UKHADCM3 CNRM CM3 GFDLCM20 INMCM 30 MPIECH 5 UKHADGEM Latitude Longitude Value 20 4 Running MAGICC In Windows from drive C Local disk click successively on SG53 SCEN 53 and MAGI
41. 10 Balancing the CH4 and N2O budgets In the TAR and in earlier IPCC reports because of uncertainties in the present day CH4 and N2O 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 B C t where C is concentration E is emissions is a units conversion factor and t is lifetime If dC dt C and t are known in some reference year then E can be calculated If the scenario value is Eo then a correction factor E Eo can be calculated and this is applied to all future emissions If Eo is solely the anthropogenic emissions value then the difference E Eo 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 at least there is evidence that this has not be so in the past Osborn and Wigley 1994 and strong evidence 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
42. 3 6 CMAP PRECIP SEASON ANN REGION GLOB ZED CHANGE WITH AVERAGE OF EMAINING MOD RMSE BIAS CORR RMSE NUM PTS 873 SoLo 6 854 10368 2737 074 5 736 10368 810 1 093 8 742 10368 243 271 8 239 10368 548 709 9 521 10368 709 Seog 8 676 10368 647 1 145 8 571 10368 307 604 10 289 10368 107 2 058 10 914 10364 7895 609 7 871 10368 100 245 24 099 10300 166 274 7 161 10368 000 933 9 952 10358 665 632 5 630 10368 700 AA 5 699 10368 456 960 15 426 10361 679 3353 10 673 10363 363 914 1963 3 0 10368 149 838 10 114 10368 z313 021 6 513 10368 Boxxx ELS We now consider a specific example that makes use of these results future changes in annual mean temperature precipitation and MSLP under the A1T MES scenario at a time when global mean warming for central MAGICC model parameters is 2 C viz for the 30 year interval centered on 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 C global mean warming averaged over the 9 best AOGCMs These results are based on the A1T MES emissions scenario and deg C 4 50 3 50 3 00 2 50 2 00 1 50 1 00 0 50 0 00 0 50 include aerosol effects 67 18 00 15 00 12 00 9 00 6 00 3 00 0 00 3 00 6 00 9 00 Change in annual mean precipitation for 2 C global mean warming averaged o
43. 3 in 2005 If the1990 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 estimate 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 been added as a new aerosol forcing term QNO3 The 1990 value is set at 0 1 the AR4 best estimate and QNOS is kept constant at 0 1 after 1990 based on small changes given in Bauer et al 2007 and the fact that changes in nitrate aerosol require information about NH3 changes that are not available in the SRES scenarios QNO3 is ramped up linearly from zero in 1765 to 0 1 in 1990 Mineral dust This was not 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 n
44. CC to enter the operating directory Then click on the MAGICC application EXE file This will bring up the primary 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 sub 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 all the output files see Table 5 above F MAGICC 5 3 iol xi File Edit Run View SCENGEN Help NI Model for the Assessment of Greenhouse gas Induced Climate Change NCAR As used in the IPCC Third Assessment Report Version 5 3 Concept and Scientific Programming T M L Wigley S C B Raper Design T M L Wigley M Salmon M Hulme S C B Raper User Interface M Salmon S McGinnis 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 Emissions Scenarios Model Parameters Output Years Under Emissions Scenarios the user can select a Reference and Policy scenario In the example below we use A1T MES as the Reference scenario and WRE450 as the Policy
45. ES OUT IM SNR OUT INTER SD OUT MODBASE OUT NORMDEL OUT NUM INCR OUT OBSBASE OUT OUTLIERS OUT PROBINCR OUT RKERROR OUT SDERROR OUT SDINDEX OUT SDMEAN OUT SDOBS OUT VALIDN OUT Model mean of absolute changes New mean state with aerosols using model mean baseline New mean state with aerosols using observed baseline Scaled change field aerosols only Area averages over specified area 1 model by model results for normalized GHG changes 2 model by model results for baseline 3 various model mean results and observed baseline 4 model by model scaled results including aerosols This file will normally be blank By putting IDRIFT 1 in EXTRA CFG drift Def 2 minus Def 1 results will appear here Error fields Model minus Observed for temperature and MSLP error 100 M O O for precipitation Scaled changes model mean with aerosols sum of AEROSOL OUT and GHGDELTA OUT Scaled changes model mean GHG only Inter model correlation results for normalized changes in mean state calculated over the specified area Summary of fields GHANDAER GHGDELTA AEROSOL INTER SD IM SNR PROBINCR NUM INCR MODBASE OBSBASE ERROR ABS OBS ABS MOD List of data files opened and read by INTERNN2 FOR Also displays the selected area as a latitude longitude array of 1s and Os Inter model Signal to Noise Ratio for changes in mean state SNR change in mean state divided by inter mod
46. ES WITH OBS MODEL BASELINE FROM CONTROL RUNS BIAS IS DIFFERENCE IN SPATIAL MEANS MOD MINUS OBS CORR RMSE IS RMSE CORRECTED FOR BIAS RK INDEX BASED ON REICHLER amp KIM 2008 DIMENSIONLESS INDEX AREA AVERAGE OF MOD i MINUS OBS 2 MO AREA SPECIFIED BY MASK MASKFILE MASK A MASKNAME GLOBE COSINE WEIGHTED STATISTICS MODEL CORREL RMSE BIAS CORR RMSE RK INDEX mm day mm day mm day BCCRBCM2 PLIS 1314 307 T275 43 960 CCCMA 31 888 949 0 10 949 21 286 CCSM 30 797 1 327 160 sS T 36 782 CNRM CM3 772 1 438 540 173 333 19 566 CSIRO 30 814 1 209 i61 1 198 94 574 ECHO G 910 864 128 854 13 766 FGOALS1G 816 1 226 307 1 187 15 120 GFDLCM20 868 1 099 091 1 095 22 909 GFDLCM21 t057 1 149 s215 1 128 25 030 GISS EH 733 1 512 340 1 473 31 909 GISS ER 774 1 430 e297 1 399 34 008 INMCM 30 700 1 606 116 1 601 17 914 IPSL CM4 808 1 269 090 1 266 55 101 MIROC HI 800 1 340 281 1 311 28 908 MIROCMED 833 1 162 035 1 162 28 548 MPIECH 5 808 T351 247 1 328 18 631 MRI 232A 886 967 084 963 19 226 NCARPCM1 665 T715 343 1 680 40 144 UKHADCM3 858 1 256 2230 1235 24 384 UKHADGEM 197 1 614 23 8 1 568 44 852 MODBAR 910 870 184 850 120 441 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
47. F SORT MOD i MINUS OBS 2 OBS S D 2 AREA SPECIFIED BY MASK MASKFILE MASK C MASKNAME 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 CSIRO 30 978 26 72 036 2 612 2 493 6560 GFDLCM20 949 2 825 471 2 786 1624 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 Zee AA9 240 22105 1 964 6560 IPSL CM4 869 4 325 503 4 295 2 314 6560 MIROC HI 967 2 960 ot 2 956 2 838 6560 MIROCMED 957 2 984 697 2 902 2 285 6560 ECHO G 969 2 306 ALTS 2 299 i Pe ee A 6560 MPIECH 5 984 1 538 086 1535 1 109 6560 MRI 232A 968 2 210 144 2 205 L532 6560 CCSM 30 980 3 418 768 3 331 2 265 6560 NCARPCM1 980 2 443 a ed Bo 2 440 22415 6560 UKHADCM3 2975 2 116 el O 2 084 1 723 6560 UKHADGEM 987 LETZ 2223 1 798 1 407 6560 MODBAR 982 1 704 421 12652 2 410 6560 These results show that almost all models are very good at simulating the spatial pattern of annual MSLP 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 slightly low The exception to this small bias result is GISS EH which has a
48. MAGICC SCENGEN 5 3 USER MANUAL version 2 Tom M L Wigley NCAR Boulder CO wigley ucar edu September 2008 CONTENTS 1 Installation O 1 2 Introduction background eee 2 3 Modifications since version 4 1000 4 3 1MAGICC changes z y EC aa 4 3 2SCENGEN changes ea 12 4 Running MAGICO a 21 5 Running SCENGEN 36 6 Choosing AOGCMs 63 Appendix 1 Halocarbons eee 69 Appendix 2 CO concentration stabilization am 70 Acknowledgments 74 Printing Tips 75 R eferences o ECO a 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 folders 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 Introduction background MAGICC SCENGEN is a coupled gas cycle climate model MAGICC Model for the Assessment of Greenhouse gas Induced Climate Change that drives a spatial climate change SCENario GENerator SCENGEN MAGICC has been one of the primary models used b
49. Model us the magnitude of the effect of climate related carbon cycle feedbacks on global mean temperature As expected from the concentration results the effect of climate feedbacks is relatively small but significant In 2100 the additional warming is about 0 25 C for the Reference emissions scenario and 0 17 C for the Policy scenario By 2400 in the Policy scenario the difference rises to 0 33 C These are results for the default climate sensitivity case Note that temperature 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 and N20 are shown below 31 UM 32 74MAGICC 5 3 Gas Concentratic O x C02 Nitrous Oxide Concentration ppbv Reference SRES 41T MESSAGE Illustrative Scenario a Policy 450 ppm stab with feedback P50 CO2 base LEY 2 others CH4 N20 Reference Best Guess E Ref best sm _ Ref user E Pol best 30 Pol user fi 990 2400 1990 2400 30 1765 1990 30 1765 2400 Policy Best Guess 30 310 Help Print OK Interestingly the no climate policy emissions and concentrations for N2O in the A1T scenario are actually less than in the policy driven WRE450 emissions scenario where N2O emissions come from the extended MiniCAM Level 2 multi gas stabilization scenario This illustrates the profound uncerta
50. NRM CM3 GFDLCM21 MIROC HI NCARPCM1 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 changes 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 examining baseline variability S D Base 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 56 Model mean Variability Std Dev of Dec Jan Feb Pressure Global range 0 3 to 5 1 Def 2 with aerosols ol hPa 4 50 4 00 3 50 3 00 2 50 2 00 1 50 1 00 0 50 Models BCCRBCM2 CSIRO 30 GISS EH MIROCMED UKHADCH3 0 00 CCCMA 31 ECHO G INMCM 30 MPIECH S UKHADGEM CCSM 30 GFDLCM20 IPSL_CM4 MRI 232A CNRM CM3 GFDLCM21 MIROC HI NCARPCH1 Model mean Variability Std Dev of Jun Jul Aug Pressure Global range 0 4 to 5 3 Def 2 with aerosols hPa 4 50 4 00 3 50 3 00 2 50 2 00 1 50 1 00 0 50 Models BCCRBCM2 CSIRO 30 GISS EH MIROCMED UKHADCM3 0 00 CCCMA 31 ECHO G INMCM 30 MPIECH 5 UKHADGEM CCSM 30 GFDLCM20 IPSL_CM4 MRI 232A CNRM CM3 GFDLCM21 MIROC HI NCARPCM1 Hig
51. OUT SDFIELDS OUT SDSNR OUT SDUNCERT OUT SNROFBAR OUT Model average of changes in mean state including aerosols Model average of temporal SNRs SNR mean state change divided by baseline model standard deviation Model average of baseline s d s Model average of percentage changes in s d List of data files opened and read by STANDNN2 FOR Also displays the selected area as a latitude longitude array of 1s and Os 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 Inter model pattern correlation results for normalized s d change fields and baseline s d fields Summary of fields GHGDELTA BASE SD DELTA SD BAROFSNR SNROFBAR INTERSNR SDSNR SDUNCERT Plus correlation matrix for pattern correlations between these fields 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 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 Temporal SNR of model mean changes model average of mean state changes divided by model average of baseline s d s G50 SCEN 50 ENGINE SCENGEN The fields that can be displayed are all in this folder except for SDSNR OUT and SDUNCERT OUT which are
52. R 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 Table 3 Sea 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 2099 AT2x 1 5 3 0 3 0 3 0 6 0 Ice melt Low Low Mid High High A1B 14 24 35 46 68 35 A1FI 19 32 45 59 86 43 A1T 13 21 33 44 65 33 A2 16 27 38 50 73 37 B1 10 17 26 35 52 28 B2 12 20 31 41 61 31 The MAGICC AR4 similarity is partly fortuitous 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 their 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 wit
53. al Precipitation Global range 1 0 to 1 6 Global mean dT 2 0 deg C Scenario A1TMES Year 2063 Def 2 with aerosols t a sing Models BCCRBCM2 CSIRO 30 GISS EH MIROCMED UKHADCH3 CCCMA 31 ECHO G INMCM 30 MPIECH 5 UKHADGEM CCSH 30 GFDLCM20 IPSL_CM4 MRI 232A CNRM CM3 GFDLCM21 MIROC HI NCARPCM1 55 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 the 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 another 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 Pressure in the Variable window then on RUN This will give Change in Model S D 100 New Base Base for Annual Pressure Global range 44 8 to 65 0 Global mean dT 2 0 deg C Scenario A1TMES Year 2063 Def 2 with aerosols Models BCCRBCM2 CSIRO 30 GISS EH MIROCMED UKHADCM3 CCCMA 31 ECHO G INMCM 30 MPIECH 5 UKHADGEM CCSM 30 GFDLCM20 IPSL_CM4 MRI 232A C
54. aps is minor However smoothed results for individual grid boxes can be significantly 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 are 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 color 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 intervals 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 color in the palette As in version 4 1 each map display gives the high
55. at http www pcmdi lln gov ipcc model_documentation ipcc_model_documentation php See the folder C SG53 SCEN 53 SG MANS ModelDoc for documentation 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 character 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 F OALSg 1 0 model under known biases and 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 latitudes 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 those 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 note of caution applies to NCAR s PCM As with GISS ER PCM projections differ markedly from those of other models Furthermore PCM s validation performance i e in
56. carb direct 0 31 0 34 0 37 0 375 4a 1 2a 3 4 ae 2 71110 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 10a Sum 11 through 16 0 42 17 Aerosol indirect 0 3 0 7 1 8 0 4 0 8 1 2 0 674 to 0 743 18 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 intervals Values assumed to be mid year values We now describe the forcing initialization changes All numbers are W m 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 0
57. ck on the Min Max button 41 Return again to the SCENGEN window and click on Warming The following window will appear 76 Warmine 0 x Global mean AT 1 64 degC Scenario Year 2050 pete 2000 2050 2100 Scenario MAGICC Setup A1TMES Ref Default WRE450 Pol User 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 headers 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 configuration for the MAGICC model Default i e best guess or User These factors determine the global mean temperature change 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 components for the SO 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 default emissions scenario A1T MES the selected Reference scenario and default parameters for MAGICC We also slide the temperature bar across to 2064 to give a warming of 2 degC se
58. d future emissions of non COz 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 concentrations 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 COz gases In MAGICC 4 1 we used best estimate i e TAR default model parameters and historical forcings and the P50 SRES median emissions scenario for non COz gases Most importantly the best estimate sensitivity used in MAGICC 4 1 was 2 6 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 C so the stabilization emissions scenarios must be re calculated Furthermore as noted above we no longer use the P50 baseline for non COz gases preferring a non COz scenario that is more consistent with COz stabilization the extended MiniCAM Level 2 scenario The WRE concentration profiles will only be produced exactly if the same model parameters historical 71 forcings and future non COz emissions are used In fact the concentration profiles 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 emissio
59. dels BCCRBCM2 CSIRO 30 GISS EH MIROCMED UKHADCH3 CCCMA 31 ECHO G INMCM 30 MPIECH S UKHADGEM CCSM 30 GFDLCM20 IPSL_CM4 MRI 232A CNRM CM3 GFDLCM21 MIROC HI NCARPCM1 58 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 magnitude 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 it 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 directory 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 These pattern correlations range between 0 082 and 0 168 This confirms the above statement for precipitation variability change fields namely that
60. dels a total of nine because two models are ranked equal eighth 64 Table 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 FLUX MODEL Pattern RMSE Bias RMSE corr score ADJ correlation mm day mm day mm day 1 8 Yes CCCMA3 1 T47 0 888 0 836 0 949 0 547 0 010 0 079 0 949 0 541 1 8 Yes MRI 2 3 2 0 886 0 909 0 967 0 438 0 084 0 033 0 963 0 437 1 8 Yes ECHO G 0 910 0 840 0 864 0 609 0 128 0 290 0 854 0 535 4 3 HadCM3 0 858 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
61. discrepancy we have devised new with feedback and no feedback 72 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 path 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 pathway 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 path which is increasingly likely for lower stabilization targets or because an initially chosen target is judged at some later 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 4500VER overshoot to 540 ppm before declining to stabilization at 450 ppm as used in Wigley
62. e been re gridded to a common 2 5 by 2 5 latitude longitude grid compared with 5 by 5 in version 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 resolution 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 precipitation 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 uses the latest 2 5 by 2 5 degree resolution version of CMAP For MSLP ERA40 data are used 13 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 m
63. e 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 difference between the weighted and unweighted patterns of change is very small and well within the uncertainties in any regional scale projection of change There is little 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 66 xxx 20 MODELS VARIABLE MODEL OUTLIER ANALYSIS COMPARING MODEL i NORMALI COSINE WEIGHTED STATISTICS MODEL CORREL rank BCCRBCM2 480 6 6 CCCMA 31 608 1 5 CCEM 30 319 15 8 CNRM CM3 260 18 8 CSIRO 30 291 17 9 ECHO G 293 16 8 FGOALS1G 513 4 8 GFDLCM20 424 7 10 GFDLCM21 414 9 LL GISS EH 394 12 7 GISS ER 124 19 24 INMCM 30 408 10 7 IPSL_ CM4 422 8 LO MIROC HI 497 5 5 MIROCMED 588 2 5 MPIECH 5 350 14 15 MRI 232A 369 13 10 NCARPCM1 099 20 iS UKHADCM3 404 11 10 UKHADGEM 525
64. e window below Global mean AT 2 0 degC Scenario Year 2063 T 2000 2050 2100 Scenario MAGICC Setup A1TMES Ref Default WRE450 Pol User 42 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 30 year interval centered on 2064 for the A1T emissions scenario and best guess climate model parameters in MAGICC averaged over all 18 selected AOGCMs E 15 x Global range 43 9 to 55 4 Global mean dT 2 0 deg C Scenario A1TMES Year 2063 Def 2 with aerosols Change in Annual Precipitation Models BCCRBCH2 CSIR0 30 GISS EH MIROCHED UKHADCH3 CCCMA 31 ECHO G INMCM 30 MPIECH 5S UKHADGEM CCSM 30 GFDLCM20 IPSL_CM4 MRI 232A CNRM CM3 GFDLCM21 MIROC HI NCARPCM1 Latitude Longitude Value 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 option on the Variable window which will ensure that approximately 5 of the grid box values will lie above below the highest lowest contour level 43 Models BCCRBCH2 CCCMA 31 CCSM 30 CNRM CH3 Change in Annual Precipitation CSIRO 30 ECHO
65. ed 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 COs 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 parameters The default best estimate set may be chosen or
66. el standard deviation independent of time Same as INTERSNR OUT in SDOUT but 3 decimals instead of 2 Inter model standard deviation for normalized GHG change fields Model mean baseline Model mean of normalized GHG change fields Number of models with GHG changes above zero Observed baseline Outlier analysis comparing model i normalized GHG changes with average of remaining models Analysis performed over the specified area Probability of a change above zero RK error field RK error SQRT M O OSD M model mean baseline O observed baseline OSD observed baseline standard deviation Standard deviation error field 100 MSD OSD OSD MSD model mean baseline standard deviation S D bias field SDINDEX SQRT 0 5 RRR 1 RRR where RRR observed s d model mean s d Model mean baseline standard deviation field denoted MSD above Observed baseline standard deviation field denoted OSD above Validation statistics comparing model i and model mean baselines with observed baseline data Uses pattern correlation RMS difference bias M O bias corrected RMS difference and RK index averaged over specified region 16 G50 SCEN 50 ENGINE SDOUT displayable fields also given in ENGINE SCENGEN displayable fields that are not given in ENGINE SCENGEN ALLDELTA OUT BAROFSNR OUT BASE SD OUT DELTA SD OUT FILES OUT INTERSNR OUT SDCORRS
67. els BIAS in the Table below is model i minus the mean of the remaining models for 1 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 precipitation 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 WEIGHT MODEL BCCRBCM2 CCCMA 31 CCSM 30 CNRM CM3 CSIRO 30 ECHO G GFDLCM20 GFDLCM21 GISS EH INMCM 30 PSL CM4 TROC HI TROCMED PIECH 5 RI 232A CARPCM1 UKHADCM3 UKHADGEM 2S S88 8H The above results provide a strong indication that there are large inter model differences between AOGCM precipitation change projections A further indication of these large inter CORREL 442 562 z312 312 oO 327 456 402 396 424 s397 523 V999 s342 365 067 424 522 ee rreren ADUD OoON NROS N OUN ED STATISTICS RMSE ole 050 997 507 945 214 sol 139 190 854 0 49 s135 478 624 A9 688 157 049 514 BIAS 420 171 1 002 T75 616 861 510 166 2915 178 085 7239 L219 870 z257 2822 940 SELTS CORR RMSE RR PRP PR NDOMNCHVUUVOTNnNdCOMHONAUA 038 994
68. est and lowest grid box 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 normalized s d changes divided by the inter model s d of these normalized s d changes This is a time independent quantity that shows the uncertainty in projections of s d relative to inter model differences in these projections SDSNR values are invariably small showing that projections of variability changes are highly uncertain The second new display is for S D base uncert 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 in ENGINE IMOUT and ENGINE SDOUT The full set of output files is listed below in Table 5 These results in these output files are specific to the user selections of scenario MAGICC model user or default
69. ewhat ad hoc way to account for the economic and technological challenges that are presented by mitigation which make a rapid departure from a no policy case virtually impossible Although ad hoc subsequent more sophisticated economic analyses have shown that the WRE pathways are close to optimum ina cost effectiveness sense i e they minimize mitigation costs over time These early analyses began with smooth concentration profiles and used a simple inverse carbon cycle model to calculate the emissions required to follow the prescribed concentration pathways The inverse model used did not account for climate feedbacks on the carbon cycle back in 1996 this was state of the art These 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 therefore less than would otherwise occur The emissions requirements given in the original paper are therefore overestimates mitigation is tougher if climate feedbacks are accounted for Climate feedbacks make it more difficult to define an emissions scenario to match a specified concentration profile This is because 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 parameters historical forcing estimates and assume
70. h 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 although 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 probability 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 the 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 17 cm AR4 p 821 The same increase should be considered applicable to the MAGICC projections
71. he model data analysis activity The CMIP3 AR4 multi model data set is supported by the Office of Science U S Department of Energy 75 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 screen 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 want to edit the image to trim off borders or annotations 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 76 References Bauer S E Koch D Unger N Metzger S M Shindell D T and Streets D G 2007 Nitrate aerosols today and in 2030 a global simulation including aerosols and tropospheric ozone Atmos Chem Phys 7 5043 5059 Friedlingstein P Cox P Betts R Bopp L von Bloh W Brovkin V Cadule P Doney S Eby M Fung l Bala G John J Jones C Joos F Kato T Kawamiya M Knorr W Lindsay K Matthews H D Raddatz T Rayner P Reick Roeckner E Schnitzler K G Schnur R Strassmann K Weaver A J Yoshikawa C and Zenget N 2006 Climate carbon cycle feedback analy
72. hest 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 speculative 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 this by looking at the SD change SNR results shown below Note that you have to first click on Tempor SNR in the Analysis window 57 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 S D Change Inter model SNR for Jun Jul Aug Pressure Global range 0 9 to 1 0 Global mean dT 2 0 deg C Scenario A1TMES Year 2063 Def 2 with aerosols 0 36 0 27 0 18 0 09 0 00 0 09 0 18 0 27 0 36 Models BCCRBCM2 CSIRO 30 GISS EH MIROCMED UKHADCH3 0 45 CCCMA 31 ECHO G INMCM 30 MPIECH 5 UKHADGEM CCSM 30 GFDLCM20 IPSL_CM4 MRI 232A CNRM CM3 GFDLCM21 MIROC HI NCARPCM1 S D Change Inter model SNR for Jun Jul Aug Pressure Global range mr lz feat Sc io A1TTMES i E Pid Year 2063 p 3 all a ia Def 2 with aerosols at vs mz x Se 1 20 0 80 0 40 0 00 0 40 0 80 1 20 1 60 2 00 2 40 Mo
73. his 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 OUTLIERS OUT uses a number of comparison statistics to define outliers 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 calculated over a 20 year reference period Observed and model s d data come from the same sources 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 CO2 based patterns of s d change as there are no s d data available for the aerosol fields Scaling uses the full global warming projection so the code effectively assumes that the patterns of s d change for CO2 forcing and aerosol forcing are similar 18 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 a
74. ill 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 affect 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 remove any bias It is related to RMSE by RMSE corr RMSE B Table 6 shows these statistics for all models in the SCENGEN data base To rank models have used a semi quantitative skill score that rewards relatively good models and penalizes 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 would 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 have 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 example based on the results in Table 6 one might chose the eight highest scoring mo
75. inties in projecting N2O emissions both in the absence of or in response to climate policies It should be noted that the CO concentration results shown here are somewhat deceptive By giving results only for one parameterization of climate feedbacks on the carbon cycle they hide very large uncertainties that surround quantification 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 some other models have substantially larger feedback effects Friedlingstein et al 2006 Nevertheless warming uncertainties associated with this particular factor are small compared with uncertainties 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 33 76MAGICC 5 3 Temperature amp Sea Level i j 0 x Temp Temperature Change C w r t 1990 Reference SRES 41T MESSAGE Illustrative Scenario y Sea level s Policy 450 ppm stab with feedback P50 CO2 base LEY 2 others Mm Ref range SOS Reference Range E Ref best Reference Best Guess Reference User Model JE Ref user 3 VW Policy Range WE Pol range Policy Best Guess E Pol best Policy User Model
76. is section First we give full details of the AR4 and MAGICC 4 1 forcings followed by the forcing initialization changes employed in MAGICC 5 3 Table 1 2005 AR4 forcings W m compared with forcings used for 1990 in MAGICC 4 1 or calculated for 2005 in MAGICC 5 3 In column 3 headed AR4 2005 the outer numbers give the 90 confidence interval while 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 used 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 forcing 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 independent best estimate for the total 1 72 compared with 1 6 COMPONENT AR4 2005 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 N20 0 14 0 16 0 18 0 165 to 0 167 4 Halo
77. ives 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 model the same effect can be achieved by scaling up Vo For Vo uncertainties 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 Vo scaling as their total ice mass is well over 50 cm The other change made in MAGICC5 3 is to ignore the contributions from 1 Greenland and Antartica due to the ongoing adjustment to past climatic change 2 runoff 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 No other changes have been made to the sea level modeling 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 TA
78. jections 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 SCENARIO 2005 TOTAL FORCING AT2x 3 C W m A1B 1 596 A1Fl 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 with a 90 uncertainty range of 0 6 to 2 4 W m Uncertainties are due primarily to uncertainties in indirect aerosol forcing Note that the component sum Table 1 is slightly higher 1 72 W m 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 differences are miniscule relative to the overall forcing uncertainty and have virtually no effect on projections of temperature or sea level change Carbon cycle model and CO concentration stabilization scenarios Parameters in the carbon cycle model have 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 A2 agree with the ave
79. lt contribution for this sensitivity The probability of this combination must be considerably less than the probability of a sensitivity as high as 6 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 climate components that contribute to sea level rise Recall that temperatures stabilize in this case yet sea level continues to rise inexorably 35 5 Running SCENGEN We now move on to explore SCENGEN The next step then is to go back to the main MAGICC 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 SSCENGENS 3 O x SCENGEN i A Global and Regional Climate Change Scenario Generator NCAR Concept and Scientific Programming T M L Wigley Design T M L Wigley M Hulme M Salmon S McGinnis User Interface S McGinnis M Salmon Data Set Development C Doutriaux R Knutti S Sherrer Other Contributers O Brown T Jiang l P D Jones M New B D Santer Development supported by The U S Environmental Protection Agency Stratus Consulting Inc Version 5 3 May 2008 OK Clicking on OK will bring up a blank map 36 and the main SCENGEN selection window We now work through four examples illust
80. nalysis 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 click 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 selected 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 analysis 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 ensures that each model pattern of change receives equal weight and the average is not biased towards 38 models with high climate sensitivity To select the models to use go back to the SCENGEN window and click on Models This will bring up the window shown below TeModels lolx None All Default E Aerosol effects Def 1 Def 2 Both W BCCRBCM2 W CSIRO 30 W GFDLCH21 W IPSL CMH4 W MRI 232A W cCCMA 31 W ECHO G W GISS EH W MIROC HI W NCARPCM1 W cCcSM 30 4 FGOALSIG WJ GISS
81. nd Edmonds J A 2007 Overshoot pathways to COs stabilization in a multi gas context In Human Induced Climate Change An Interdisciplinary Assessment eds Michael Schlesinger Haroon Kheshgi Joel Smith Francisco de la Chesnaye John M Reilly Tom Wilson and Charles Kolstad Cambridge University Press 84 92 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 79 80 MAGICC SCENGEN 5 3 DIRECTORY STRUCTURE RETO MOD AOGCM data files C SG53 SCEN 53 MAGICC SCENGEN SCENGEN CHARLES5 Driver files for SCENGEN OBS Old observed data files S04 Aerosol response patterns SDOUT Output SG MANS Manuals ModelDoc AOGCM documentation NEWOBS IMOUT Output Files SIMON New observed data 81
82. ng out uncertainties 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 see 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 model 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 we 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
83. ng the commercial software SnagIt http Awww techsmith com which is highly recommended A key component of COz 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 leading 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 C as below lolx Forcing Controls Carbon Cycle Model High Mid v Low v User C cycle Climate Feedbacks On y Off Aerosol Forcing High Mid v Low Climate Model Parameters Sensitivity AT 2 Thermohaline Circulation Variable v Constant Vert Diffus K 2 3 cm is Ice Melt High Mid v Low Model User ia We select this with the OK button and then click on Run Then through View we examine the CO concentrations as shown below 26 ni iol x Carbon Dioxide Concentration ppmv Reference SRES 41T MESSAGE Illustrative Scenario e Policy 450 ppm stab with feedback P50 CO2 base LEV2 others
84. ns scenario with default model parameters to determine the baseline no climate policy concentration profile For 250 ppm to 750 ppm stabilization targets this profile is followed for a period from 5 to 20 years depending on the stabilization target before concentrations depart as a consequence of mitigation We then construct smoothly varying concentration profiles using the Pad approximant method as explained in Wigley 2000 The parameters used for fitting are given in the Table below Table A1 Pad approximant fitting parameters Yo 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 emissions are negligible For 350 ppm stabilization the original departure year used also in MAGICC 4 1 was 2000 5 Y and C 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 Yena is the year at which concentration stabilizes Note that the MAGICC 5 3 emissions library does not give the 250 and 1000 ppm stabilization cases Target ppm Yo Co ppm dC dt o Y C ppm _ Yena 250 2005 5 378 323 1 935 2040 5 414 0 2200 5 350 2005
85. o year 2000 data but the CH and N20 results will be incorrect In the examples below we also consider the effects of a relatively high climate sensitivity an equilibrium CO2 doubling temperature change AT2x of 4 5 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 C the default value is shown in the Sensitivity box We make no changes Click on OK to close the window T MAGICS oxi Forcing Controls Carbon Cycle Model w High Mid v Low v User C cycle Climate Feedbacks On wv Off Aerosol Forcing High Mid v Low Climate Model Parameters Sensitivity AT 2 Thermohaline Circulation Variable v Constant Vert Diffus K 3 23 cm2is Ice Melt y High Mid v Low Model User 23 The next editing option is Output Years Clicking on this will bring up the following window E put pa Oj x Reference year for climate model output 1990 1990 First year for climate model output 1990 1990 Last year for climate model run 2100 2100 Printout interval for climate model 5 5 OK Help The default Last year is as shown here 2100 In this case the reference scenario A1T MES is defined only out to 2100 while the 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
86. ot available in the SRES scenarios although one would expect them to be small QMIN is ramped up linearly to 0 1 in 1990 Stratospheric H20 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 within the AR4 uncertainty range 04 direct and indirect In MAGICC aerosol forcing initialization values are specified for the year 1990 Modeled changes in both direct and indirect forcings are very small over 1990 to 2005 so we retain 1990 as the initialization year Given the AR4 best estimate of 0 4 in 2005 the 1990 direct forcing can stay the same as in version 4 1 0 4 In accord with the AR4 the 1990 indirect forcing becomes 0 7 previously 0 8 For uncertainty ranges we use 0 2 for direct forcing the same as AR4 previously 0 1 This includes uncertainties in nitrate and mineral dust forcings For indirect forcing we use 0 4 for the range the same as previously AR4 gives a range that is asymmetrical about the central estimate 1 8 to 0 3 The 1 8 forcing value as a lower bound 1 1 W m below the best estimate would lead to extremely low total historical anthropogenic forcing unless compensated 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
87. over the continental USA region As a validation 63 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 circulation 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 one might also consider model skill evaluated over a small study region surrounding the site This 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 statistics used are pattern correlation r root mean square error RMSE bias B anda 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 are those that employ cosine weighting to account for the changing area of grid boxes with latitude Bias is simply the difference model minus observed averaged over the chosen validation region Of these four statistics bias is probably the least important since it is generally thought that biased models can st
88. rage of the ten C4MIP model results and the uncertainty range that MAGICC gives matches the 90 percentile of the C4MIP range Further details are given in the Appendix below Because of changes in the carbon cycle and climate models 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 has 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 these 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 COz gases in all CO stabilization cases This is unlikely to be correct If we are to introduce policies to stabilize CO2 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 stabilization scenario then we should try to apply a consistent scenario for non COz gases Attempts have been made to do this Clarke et al 2008 but in the MAGICC context where the CO scenarios are defined externally to follow WRE pathways it is not possible
89. rating some of the capabilities of SCENGEN 5 3 37 EXAMPLE 1 This first example is a comparison of different model results for changes in the spatial patterns of annual mean precipitation The MAGICC case used is as above a Reference emissions scenario of A1T MES and a Policy scenario where COz concentrations follow the WRE450 stabilization profile The first step is to click on Analysis in the above SCENGEN window This will bring up the Analysis window shown below The other windows will remain in place and can be moved around to more convenient positions if required Data Variability Change S D Base Error S D Change Mod Base Tempor SNR Ww Ww M Mod Change TSNR overwrite Obs Base No overwrite Obs Change SD change SNR Inter model w SD base uncert w Inter SNR P iIncrease Note that this window has changed from that used in version 4 1 The bottom right panel is new and now allows users to examine inter model uncertainties in variability specifically in the model mean baseline inter annual standard deviation s d SD base uncert and the model mean s d change SD change 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 a
90. re 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 model s d of these s d changes and an uncertainty index for the model mean baseline s d field GDUNCERT 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 information 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 field 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 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 SDOBS OUT Observed baseline standard deviation field 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 lt 1 0005 which rounds to
91. rom GSICs Glaciers and Small Ice Caps This method was only meant to be used out to 2100 if applied beyond 2100 as for example in stabilization scenarios it behaved quadratically 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 gain 8 in GSIC 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 maximum 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 results 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 eventually 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 Vo is 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 g
92. rresponds to these selections Next return to the SCENGEN window and click on Region The map below will be displayed 39 74 Region m iol x Aerosol Region 1 Globe wv Canada M East Land w Mexico w E FSU Ocean Brazil W FSU NH Africa v ROLA SH w Europe w SEASia Aerosol Region 3 wv Equ Pac v India v W Pac N3 China Alaska N34 w Japan Grnind w N4 w AusNZ w Antarc USA w C Asia Arc ls Lat 90 to 90 User Lon 180 to 180 The map shows the regions used for the breakdown of SO emissions in the MAGICC emissions files together with a set of analysis 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 regions 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 rectangle 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 the latitude and longitude ranges shown correspond to the full domain Latitude values are in degrees north from the equator and longitude
93. rs Reference Best Guess Reference User Model _ Ref range IE Ref best IE Ref user Pol range E Pol best E Pol user freso J2a00 1990 2400 1765 1990 1765 2400 Policy Best Guess Policy User Model Help Print OK 250 m0 For global mean temperature we show results for the same case i e where the only user choice is to turn off climate feedbacks on the carbon cycle From the concentration results above we expect the User cases to have slightly less warming than the default Best cases because of the lower COz concentrations in the carbon cycle no feedback case as already noted The results for global mean temperature change out to 2400 are shown below 30 Temp Sea level Ref range E Ref best E Ref user Pol range E Pol best E Pol user faao a00 1990 2400 1765 1990 1765 2400 Help Print The difference between the Best with feedbacks and the User no feedbacks results tells 74MAGICC 5 3 Temperature amp Sea Level Temperature Change C w r t 1990 Reference SRES 41T MESSAGE Illustrative Scenario Policy 450 ppm stab with feedback P50 CO2 base LEY 2 others OK 1 m0 50 O x Reference Best Guess Reference User Model Policy Best Guess Policy User
94. sis results from the C4MIP model intercomparison J Clim 19 3337 3353 Church J A and Gregory J M Coordinating Lead Authors together with 6 Lead Authors and 28 Contributing Authors 2001 Changes in sea level In Climate Change 2001 The Scientific Basis eds J T Houghton Y Ding D J Griggs M Noguer P J van der Linden X Dai K Maskell and C A Johnson Cambridge University Press Cambridge U K pp 639 693 Clarke L E Edmonds J A Jacoby H D Pitcher H Reilly J M and Richels R 2007 Scenarios of Greenhouse Gas Emissions and Atmospheric Concentrations Sub report 2 1a of Synthesis and Assessment Product 2 1 A Report by the Climate Change Science Program and the Subcommittee on Global Change Research Washington DC 154 pp Giorgi F and Mearns L O 2002 Calculation of average uncertainty range and reliability of regional climate change from AOGCM simulations via the Reliability Ensemble Averaging REA method J Clim 15 1141 1158 Gleckler P J Taylor K E and Doutriaux C 2008 Performance metrics for climate models J Geophys Res 113 D06104 doi 10 1029 2007JD008972 Gregory J M and Mitchell J F B 1997 The climate response to CO2 of the Hadley Centre coupled AOGCM with and without flux adjustment Geophys Res Letts 24 15 1943 1946 doi 10 1029 97GL01930 Hegerl G C and Zwiers F W Coordinating Lead Authors together with 7 Lead Authors and 44 Contributing Authors
95. t but it is now possible to use 1980 99 data from a 20 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 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 worlds will have different manifestations of internally generated variability Validation statistics differ by only small amounts for validation using control or 20 century data The new validation statistics are a bias corrected RMS difference and a validation 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 the 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 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 t
96. values are in degrees east 74 Region EE loj x Aerosol Region 1 y Globe w Canada v M East Land w Mexico E FSU Ocean Brazil W FSU y NH Africa ROLA SH w Europe SEASia Aerosol Region 3 w Equ Pac w India W Pac N3 China Alaska N34 w Japan Grnind w N4 AusNzZ w Antarc USA w C Asia Arc ls Lat 22 5 to 50 0 User Lon 130 0 to 57 5 40 Selecting a grid box region means that most calculations will be carried out specifically for that region This includes area averages for the 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 see Table 5 above After experimenting with the user 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 to 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
97. ver the 9 best AOGCMs These results are based on the A1T MES emissions scenario and include aerosol effects 4 00 3 00 2 00 1 00 0 00 1 00 2 00 3 00 4 00 5 00 Change in annual mean MSLP for 2 C global mean warming averaged over the 9 best AOGCMs These results are based on the A1T MES emissions scenario and include aerosol effects 69 Appendix 1 Halocarbons MAGICC includes the following 30 halocarbons CFC11 CFC12 CFC13 CF4 CFC113 CFC114 CFC115 CaFe CCl4 CHCls CH2Clo MCF Hai211 Ha1301 HCFC22 HCFC123 CH3Br HFC141b HFC142b HFC125 HFC134a Ha2402 HFC23 HFC32 HFC43 10 HFC143a HFC227ea HFC245ca C4F10 SFe In the input emissions files only the 8 most important can be specified These are CF4 CoFe HFC125 HFC134a HFC143a HFC227ea HFC245ca SFe The other 22 gases are divided into two groups 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 the emissions vary according to the SRES scenario but the differences 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 for the other gases again hard wired into the code The
98. y 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 for 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 MAGICC 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 method 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 expressed as changes per 1 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 defin
99. y moving the cursor over the gridbox of interest What this means is that a precipitation decrease is up to four times more likely than a precipitation increase based on all 18 selected models Policy makers are often 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 above 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 Information like this can help to decide which way the slant adaptation measures and define adaptation strategies that are more robust to uncertainties 62 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 warming 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 factori
100. y not be used In terms of validation statistics for annual precipitation these are clearly not the worst models We reject FGOALS primarily 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 other models as can be seen on the OUTLIERS 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 change PCM would also be rejected on the basis of its precipitation 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 factors 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 th
101. y should also be considered In SCENGEN we give a model outlier 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 examples 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 unrelated 65 errors in the different models that cancel out to some extent For example for global pattern correlations the 5 and 9 model averages are better than all individual models For the USA region however there are three models HadCM3 MRI and ECHAMS that are better than the 5 model and 9 model averages and four models these three plus HadGEM 1 that are better than the 20 model average Although the results for the 5 model average are better than the 9 model average the latter is likely to be more robust and allows a better assessment of inter model variability It also 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 should note that the above analysis uses all 20 models yet it has already been noted that FGOALS and GISS ER should probabl

MAGICC/SCENGEN 5.3: USER MANUAL (version 2)

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