MAGICC projections in the IPCC TAR

2. MAGICC/SCENGEN runs on a laptop computer. To obtain the software, which includes all data sets and a user manual, contact Tom Wigley at wigley@ucar.edu.

5. MAGICC projections in the IPCC TAR MAGICC projections in the IPCC TAR

9. MAGICC OUTPUTS:• Gas concentrations, • Radiative forcing breakdown, • Global-mean temperature and sea level.SCENGEN OUTPUTS:•Baseline climate data, • Model validation results, • Changes in mean climate,• Changes in variability, • Signal-to noise ratios,• Probabilities of increase.

10. THE MAGICC/SCENGEN SOFTWARE : MAGICC Library of Emissions Scenarios Gas Cycle Models User Choices of Model Parameters Atmospheric Composition Changes Global-mean Temperature And Sea Level Model User Choices Of Model Parameters Global-mean Temperature and Sea Level Output TO SCENGEN

11. THE MAGICC/SCENGEN SOFTWARE : SCENGEN Global-mean Temperature from MAGICC Library of GCM Data Sets User Choices: GCMs to use, Future Date, Region, etc. Regionalization Algorithm Library of Baseline Climatology Data (1961-90) Regional Climate or Climate Change Output

12. QUESTIONS MAGICC/SCENGEN CAN ANSWER• How will global-mean temperature change for a given emissions scenario?• What are the uncertainties in such projections?• What must we do to stabilize greenhouse-gas concentrations?• How will climate patterns/regional details change?• How will climate variability change?• How different are the results from different models?• What is the probability of an increase/decrease in precipitation at a given location?

13. EXAMPLES(1) Global-mean changes for B1 and A1FI(2) Differences in patterns of change, HadCM2 vs PCM(3) Changes in variability(4) Probability of a precipitation increase

14. The remainder of this presentation used the MAGICC/SCENGEN software interactively to consider the four specific examples. In the following, these details are summarized using screen shots from the interactive analysis.

15. EXAMPLES(1) Global-mean changes for B1 and A1FI(2) Differences in patterns of change, HadCM2 vs PCM(3) Changes in variability(4) Probability of a precipitation increase

16. This is the first (main) window that appears when opening MAGICC/SCENGEN. The first step is to click on Edit and choose the emissions input files Example 1

17. Here the A1FI illustrative scenario has been selected as a Reference, and the B1 illustrative scenario as a Policy case Example 1

18. Returning to the main window, MAGICC is run by clicking on Run and then Run Model Then click on View Example 1

19. View brings up the window below, from which we first select Emissions Example 1

20. These are the input CO2 emissions, with fossil and land-use emissions shown separately Example 1

21. Clicking on CH4 in the previous window displays methane emissions in the two scenarios Example 1

22. Return to the main window, then select View and Concentrations to view CO2 concentration projections and uncertainties Example 1

23. Return to the main window, then select View and Temperature & Sea-Level to view global-mean temperature projections and uncertainties Example 1

24. EXAMPLES(1) Global-mean changes for B1 and A1FI(2) Differences in patterns of change, HadCM2 vs PCM(3) Changes in variability(4) Probability of a precipitation increase

25. Return to the main window, then click on SCENGEN and Run SCENGEN to bring up the SCENGEN title window Click on OK Example 2

26. This will bring up a base map and access to the SCENGEN Control Windows Example 2

27. SCENGEN ‘CONTROL WINDOWS’ CHOICES Analysis> Select ‘Change’ Models> First select HadCM2, then PCM Region> Default is whole globe Variable> Default is Annual Temperature Warming> Default emissions scenario is the Reference case (A1FI here). > Default time is a 30-year period centered on 2050 (for which DT = 1.67oC). > Default MAGICC model parameters are the IPCC TAR ‘best guess’ values. Example 2

28. Patterns of annual-mean temperature change for HadCM2 and PCM (including aerosol effects) HadCM2 (top) and PCM (bottom). Note amplified warming over land and in NH high latitude areas (SH too for PCM), and reduced warming in the North Atlantic (cooling in HadCM2). Example 2

29. Patterns of annual-mean precipitation change for HadCM2 and PCM (including aerosol effects) HadCM2 (top) and PCM (bottom). Note large increases over high latitude areas in NH (SH too in PCM), and decreases in the subtropical highs and around the Mediterranean Basin. Example 2

30. EXAMPLES(1) Global-mean changes for B1 and A1FI(2) Differences in patterns of change, HadCM2 vs PCM(3) Changes in variability(4) Probability of a precipitation increase

31. Changes in variability (for annual precipitation). In the SCENGEN Control Windows panel, under Analysis, select S.D. Change ….. ….. then, to get a representative result, select All under Models. Example 3

32. Changes in variability (for annual precipitation). Note the high spatial variability, a result of large spatial variability in individual models and large difference between models. High latitudes show a general increase in variability. The pattern of S.D. change shows some similarities with the pattern of changes in the mean. Example 3

33. EXAMPLES(1) Global-mean changes for B1 and A1FI(2) Differences in patterns of change, HadCM2 vs PCM(3) Changes in variability(4) Probability of a precipitation increase

34. Probability of a precipitation increase Method: To determine the probability of a precipitation increase (probh) in a particular grid box we assume that the primary uncertainty in precipitation change is represented by the differences between climate models (AOGCMs)1, and that the distribution of precipitation change is Gaussian with mean equal to the average across models and s.d. equal to the inter-model standard deviation. A high value for probh may result from a large increase in the mean or low inter-model differences, or both. 1 Note that the above assumption is justified because the model results used are ‘normalized’ (per unit global-mean warming), which removes the effect of inter-model differences in the climate sensitivity. Example 4

35. Probability of a precipitation increase. In the SCENGEN Control Windows panel, under Analysis, select P(increase) ….. ….. then, to get a representative result, select All under Models. Example 4

36. Probability of a precipitation increase[annual precipitation, based on 17 AOGCMs] Note: Low values of probh indicate a high probability of a precipitation decrease. Example 4

37. A FIFTH EXAMPLE : EMISSIONS STABILIZATION In the question period, I was asked to use the software to determine the climate consequences of stabilization of all emissions (greenhouse gases and SO2) at present-day levels. This is a particularly interesting case since it shows what is currently ‘locked into’ the system, and because it shows that stabilizing emissions does not by any means stabilize atmospheric composition or the climate. Recall that Article 2 of the UNFCCC has stabilization of atmospheric composition as its ultimate objective. Example 5

38. The current emissions library does not include the ‘stabilize emissions’ case. A new emissions file was generated externally by copying and then editing an existing file (shown in part below). Example 5

39. The following results use default values for all gas cycle and climate model parameters. I show concentration projections for CO2, CH4, and N2O, and global-mean temperature projections. Example 5

40. Over 2000-2400, CO2 concentration rises by about 500ppm. The almost linear increase is a result of the multiple time scales on which the carbon cycle operates. Over periods of many centuries, the longer time scale processes (e.g., associated with the deep ocean and soil zone) become increasingly important. Example 5

41. For methane, which has an atmospheric lifetime of 10–12 years, the concentration effectively stabilizes after 3-4 lifetimes. Example 5

42. N2O has a lifetime of about 120 years, so its concentration almost stabilizes by 2400. This behavior can be contrasted with that for CO2, where much longer time scale processes become important over a multi-century period. Example 5

43. The continuous increase in CO2 concentration ensures that global-mean warming continues over the full period to 2400 (and beyond). Over 2000–2100, the warming rate is about twice that observed over 1900–2000. The uncertainty range shown corresponds to a climate sensitivity range of 1.5–4.5oC equilibrium warming for 2xCO2. Example 5

44. APPENDIX : Detailed MAGICC/SCENGEN flowchart, and mathematical details for the scaling algorithm used to produce the SCENGEN maps.

47. Algorithm for producing regional details : Pattern scaling

48. SIMPLE PATTERN SCALINGDY(x,t) = DT(t)Ŷ(x)whereDY(x,t) is the pattern of change at time t of some variable Y (winter precipitation, July maximum temperature, etc.), DT(t) is the global-mean temperature change at time t, Ŷ(x) is the normalized pattern of change for variable Y(i.e., the change per 1°C global-mean warming).

49. GENERAL PATTERN SCALINGDY(x,t) = SDTi(t)Ŷi(x)whereDY(x,t) is the pattern of change at time t for variable Y, DTi(t) is the global-mean temperature change at time t due to factor ‘i’, Ŷi(x) is the normalized pattern of change for variable Ydue to factor ‘i’.