Uncertainties in absolute attribution of climate change SB-24 17 May 2006

1. Uncertainties in absolute attribution of climate changeSB-24 17 May 2006 Joyce Penner University of Michigan

2. Overview of paper #2 • Paper #1 examined the uncertainties associated with methodological choices in attributing relative temperature change • Here we assess scientific uncertainties in attributing absolute climate change • We use a closure method to evaluate uncertainties: • Emissions to concentrations for long lived greenhouse gases • Radiative forcing to temperature change for attribution • Attribution of OECD Annex I countries are used as an example because these (and their uncertainties) are available from UNFCCC reporting Modelling and assessment of contributions to climate change

3. Overview of paper #2 Contributors: Prather, Lowe, Raper, Stott, Höhne, Fuglestvedt, Romstad, Penner, Andronova, Kurosawa, Wagner, Jain, Pires de Campos, Meinshausen, van Aardenne Modelling and assessment of contributions to climate change

4. Method Global inventories of GHG emissions based on activities Emissions Match? Emissions derived from atmospheric measurements Total uncertainty of OECD Annex I countries contribution Concentrations All sources of historical radiative forcing Radiative forcing Match? Observed temperature increase Global average temperature change Modelling and assessment of contributions to climate change

5. Example: N2O Emissions from inverse model are well within the stated uncertainties of the EDGAR data base Emissions of OECD Annex I countries from EDGAR are within stated uncertainties from UNFCCC inventories Modelling and assessment of contributions to climate change

6. We estimate a pdf for OECD Annex I N2O emissions using UNFCCC uncertaintiesfor the next step (RF to T) Modelling and assessment of contributions to climate change

7. Example: CH4 Global emissions from Edgar bottom-up inventory match well the emissions required to fit observations of CH4. But the Edgar OECD Annex I emissions are significantly higher than the UNFCCC emissions. The uncertainties for UNFCCC emissions must be increased in RF to T calculations Modelling and assessment of contributions to climate change

8. Uncertainties in OECD Annex I countries are widened for the next step (RF to T) to account for mis-match between EDGAR and UNFCCC reported emissions Modelling and assessment of contributions to climate change

9. CO2 The increase in CO2 concentration can be explained by the following factors: Measured • Anthropogenic emissions from fossil fuels and industrial processes Well known • Anthropogenic emissions/removals from land use change and forestry Unknown • Natural removals by the biosphere Modelled • Natural removals by the ocean Modelled Modelling and assessment of contributions to climate change

10. Global LUCF emissions are highly uncertain due to land use change data

11. Even so, OECD Annex I LUCF emissions from inverse method since 1990 are well known:

12. But UNFCCC LUCF emissions from OECD Annex I countries are outside the uncertainty range from inverse method: Need to increase range of uncertainty considered in RF to T calculation! (not yet included) UNFCCC LUCF emissions

13. Method Global inventories of GHG emissions based on activities Emissions Match? Emissions derived from atmospheric measurements Total uncertainty of OECD Annex I countries contribution Concentrations All sources of historical radiative forcing Radiative forcing Match? Observed temperature increase Global average temperature change Modelling and assessment of contributions to climate change

14. Radiative Forcing and uncertainty was estimated for all of the important climate factors * Forcing (W/m2) * Refers to preliminary assessment Modelling and assessment of contributions to climate change

15. Comparison of D and A (inverse model) aerosol forcing with bottom-up aerosol forcing Bottom up method yields wider uncertainty range, but encompasses inverse method Modelling and assessment of contributions to climate change

16. Uncertainty in natural forcingdeduced using different reconstructions Volcanic Solar Modelling and assessment of contributions to climate change

17. Additional contributions from land use albedo change and dust – based on TAR estimates Modelling and assessment of contributions to climate change

18. What will alter median and spread of bottom up forcing? Uncertainty range in bottom up forcing Median magnitude of bottom up forcing Forcing calculated to year 2000 Modelling and assessment of contributions to climate change

19. Use uncertainties in individual components to define uncertainty in total forcing Modelling and assessment of contributions to climate change

20. Results space of modeled temperatures compared to observed warming since 1880 Observations are shown in black Modelling and assessment of contributions to climate change

21. Compare forcing from bottom up and forcing from inverse: not all forcing scenarios are consistent with the observed temperature change* Forcing from bottom up estimates* Forcing from inverse calculation *Preliminary values Modelling and assessment of contributions to climate change

22. Method Global inventories of GHG emissions based on activities Emissions Match? Emissions derived from atmospheric measurements Total uncertainty of OECD Annex I countries contribution Concentrations All sources of historical radiative forcing Radiative forcing Match? Observed temperature increase Global average temperature change Modelling and assessment of contributions to climate change

23. Effect of group’s emissions Modelling and assessment of contributions to climate change

24. Combined effect of uncertainties on warming from OECD Annex 1 countries due to CO2 Combined effect of uncertainty in global mean forcing, climate sensitivity, ocean diffusivity and OECD Annex 1 forcing uncertainty on warming from OECD Annex 1 countries due to CO2 A likelihood was estimated for the unperturbed case using agreement with observed warming. The prior probability for the OECD Annex 1 perturbations was also included. The fraction of warming attributable to OECD Annex I countries is 0.23 with a 95% confidence interval of 0.08 to 0.38.

25. Combined effect of uncertainty on warming from OECD Annex 1 countries due to CO2, CH4 and N2O* Combined effect of uncertainty in global mean forcing, climate sensitivity, ocean diffusivity and OECD Annex 1 forcing uncertainty on warming from OECD Annex 1 countries due to CO2, CH4, and N2O A likelihood was estimated for the unperturbed case using agreement with observed warming. The prior probability for the annex 1 perturbations was also included. The fraction of warming attributable to OECD Annex I countries is 0.34 with a 95% confidence interval of 0.23 to 0.53. (*preliminary analysis)

26. Conclusions • We examined uncertainties in emissions inventories for both global mean values and OECD Annex I GHG emissions • We examined the consistency between the emissions and observed concentrations • We estimated forcing and forcing uncertainty from all other known climate factors • We examined the implications of this uncertainty for predicted global average temperature change and the change associated with 1990 - 2002 OECD Annex I emissions Modelling and assessment of contributions to climate change

27. Backup slides

28. Closure for long-lived greenhouse gases • Compare bottom-up inventories to those determined from inverse models to determine uncertainty in global emissions • Define OECD Annex I emissions using UNFCCC reported emissions and reported uncertainties • Compare OECD Annex I emissions from inverse model and adjust uncertainty in UNFCCC emissions if needed Modelling and assessment of contributions to climate change

29. Comparison of OECD Annex Iemissions with global emissions Modelling and assessment of contributions to climate change

30. OECD Annex 1 warming due to CO2, and effect of uncertainty in climate sensitivity Modelling and assessment of contributions to climate change

31. OECD Annex 1 warming due to CO2, and effect of uncertainty in ocean diffusivity Modelling and assessment of contributions to climate change

32. Uncertainties in global mean forcing Modelling and assessment of contributions to climate change

33. OECD Annex 1 warming due to CO2, and effect of uncertainty in global mean forcing Modelling and assessment of contributions to climate change

34. Uncertainty in OECD Annex 1 forcing from N2O Modelling and assessment of contributions to climate change

35. Combined effect of uncertainty on warming from OECD Annex 1 countries due to N2O Combined effect of uncertainty in global mean forcing, climate sensitivity, ocean diffusivity and OECD Annex 1 forcing uncertainty on warming from OECD Annex 1 countries due to N2O A likelihood was estimated for the unperturbed case using agreement with observed warming. The prior probability for the OECD Annex 1 perturbations was also included. The fraction of warming attributable to OECD Annex I countries is 0.015 with a 95% confidence interval of 0.0075 to 0.045.

36. Uncertainty in OECD Annex 1 forcing from CH4 Modelling and assessment of contributions to climate change

37. Combined effect of uncertainty on warming from OECD Annex 1 countries due to CH4 Combined effect of uncertainty in global mean forcing, climate sensitivity, ocean diffusivity and OECD Annex 1 forcing uncertainty on warming from annex 1 countries due to CH4 A likelihood was estimated for the unperturbed case using agreement with observed warming. The prior probability for the OECD Annex 1 perturbations was also included. The fraction of warming attributable to OECD Annex I countries is 0.085 with a 95% confidence interval of 0.06 to 0.12.

38. Comparison of aerosol forcing to year 2000 from bottom-up with D and A reconstruction Bottom-up reconstruction – used in subsequent analysis D and A reconstruction Modelling and assessment of contributions to climate change

39. Inverse model used to estimate forcing for 2 different values of climate sensitivities Time filtered forcing values Annual values Inverse calculation showing plume of forcing curves for different climate sensitivity based on TAR GCM models. Modelling and assessment of contributions to climate change

40. Would need to add other forcings to make all scenarios from bottom up estimates consistent with observed T pdf from inverse pdf from bottom up Minus pdf of extra forcing that needs to be added to bottom up to achieve consistency with temperature record Sample all combinations Modelling and assessment of contributions to climate change