1. 1 MATCH paper 1: contributions to climate change SB-23 17 May 2006 Niklas Höhne

2. 2 MATCH paper #1 Analysing countries contribution to climate change: Scientific uncertainties and methodological choices –Michel den Elzen (RIVM, Netherlands) –Jan Fuglestvedt (CICERO, Norway) –Niklas Höhne (Ecofys, Germany) –Cathy Trudinger (CSIRO, Australia) –Jason Lowe (Hadley, UK) –Ben Matthews (UCL, Belgium) –Bård Romstad (CICERO, Norway) –Christiano Pires de Campos (Brazil) –Natalia Andronova (UIUC, USA) Modelling and assessment of contributions to climate change M. den Elzen, J. Fuglestvedt, N. Höhne, C. Trudinger, J. Lowe, B. Matthews, B. Romstad, C. Pires de Campos, N. Andronova, 2005: Analysing countries contribution to climate change: Scientific uncertainties and methodological choices, Environmental Science and Policy, 8 (2005) 614–636

3. 3 Cause-effect chain Emissions Region A Emissions Region B Emissions Region C Emissions Region D Concentrations Radiative forcing Global average temperature change Impact in Region A Impact in Region B Impact in Region C Impact in Region D Modelling and assessment of contributions to climate change

4. 4 Temperature increase Unattributed Attribution start date, e.g. 1900 Region A Region B Region C Region D Time Attribution period Total temperature increase Attributed temperature increase Attributed effects Today

5. 5 Choices Policy choices (values can not be based on objective scientific arguments) : –Indicator (e.g. temperature increase, radiative forcing, …) –Timeframes –Mixture of greenhouse gases –Attribution method Scientific choices –Choice of the dataset on historical emissions –Choice of the representation of the climate system (different models) Modelling and assessment of contributions to climate change

6. 6 Main objective of paper #1 Summarise the studies and results so far (i.e. the contributions to the UNFCCC initiated process) Present new attribution calculations with non-linear carbon cycle and climate models using non-linear attribution methodologies and updated historical emissions datasets Investigate the effect of a range of scientific, methodological and policy-related choices on the attribution, but not the full range by all uncertainties. Modelling and assessment of contributions to climate change

7. 7 Models used Modelling and assessment of contributions to climate change

8. 8 Model show similar outcomes Modelling and assessment of contributions to climate change

9. 9 Models used Modelling and assessment of contributions to climate change

10. 10 Model show similar outcomes Modelling and assessment of contributions to climate change

11. 11 Policy choices 1. Indicator 2. Timeframes 3. Attribution method 4. Mixture of Greenhouse gases Modelling and assessment of contributions to climate change

12. 12 1. Indicators Modelling and assessment of contributions to climate change Source: Ecofys-ACCC

13. 13 1. Indicators Modelling and assessment of contributions to climate change *: Also discounting most recent emissions + : Can be made forward looking, when evaluating at a date after attributed emissions end. In such case also a time horizon is required

14. 14 1. Indicators Modelling and assessment of contributions to climate change Relative contributions using different indicators Source: Ecofys-ACCC

15. 15 1. Indicators Modelling and assessment of contributions to climate change Conclusions Two main factors: Whether a source emitted early versus late The share of emissions of short-lived / long-lived gases. Choosing the right indicator is ultimately a policy choice that also depends on the purpose of use of the results. Temperate increase: use evaluation date after the attribution end date Backward discounting and forward looking: weighted concentrations or integrated temperatures Not backward discounting: GWP-weighted cumulative emissions could be an option, which is simple and approximately represents the integrated impact on temperature.

16. 16 2. Timeframe Start date emissions 1890, 1950 and 1990 End date emissions 1990, 2000, 2050 and 2100 Evaluation date of attribution 2000, 2050, 2100, 2500 Modelling and assessment of contributions to climate change

17. 17 Start-date Choosing a shorter time horizon (e.g. 1950 or 1990 instead of 1890) reduces the contributions of OECD90 countries ('early emitters') to temperature increase. Modelling and assessment of contributions to climate change Source: RIVM-ACCC

18. 18 End-date A late end-date increases non-Annex-I contributions, because it gives more weight to their larger future emissions. Impact of emissions scenarios (error bars) can be large Modelling and assessment of contributions to climate change Source: RIVM-ACCC

19. 19 Evaluation-date A later evaluation-date raises OECD contributions due to: (1) their large share in historical CO2 emissions (long residence time) (2) and their small share of methane emissions (short residence time) Modelling and assessment of contributions to climate change Source: RIVM-ACCC

20. 20 3. Attribution methods Modelling and assessment of contributions to climate change Normalised marginal method - Attributes responsibility using total sensitivities determined "at the margin". Residual (all-but-one) method - Attributes responsibility by leaving out the emissions of each region in turn. Time-sliced - determines the effect of emissions from each time as if there were no subsequent emissions.

21. 21 3. Attribution methods Modelling and assessment of contributions to climate change The Residual method, although simple to implement and explain, can be rejected on scientific grounds (not additive). The Normalised marginal and Time- sliced methods are harder to implement and explain. These methods differ in how they treat early vs. late emissions.

22. 22 3. Attribution methods The differences between methods are fairly small compared to the effects of many of the other choices already considered. Modelling and assessment of contributions to climate change Source: CSIRO-SCM

23. 23 3. Attribution methods Modelling and assessment of contributions to climate change Differences between methods are greater for later evaluation date (2100) In general, the results of the different methods vary most for regions with emissions that differ most from the average in terms of early versus late emissions, i.e. India and EU. Source: CSIRO-SCM

24. 24 4. Greenhouse gas mixture Which gases are attributed to the regions? 1.Fossil CO 2 2.All anthropogenic CO 2 3.CO 2, CH 4, N 2 O 4.Kyoto basket (CO 2, CH 4, N 2 O, HFCs, PFCs, SF 6 ) 5.Kyoto basket + more O 3 precursors (NOx, CO and VOC) Modelling and assessment of contributions to climate change

25. 25 4. Greenhouse gas mixture Two main effects i) Going from fossil fuel CO 2 emissions only to total anthropogenic CO 2 emissions, ii) Inclusion of CH 4 and N 2 O. Modelling and assessment of contributions to climate change Source: CICERO-SCM

26. 26 4. Greenhouse gas mixture Modelling and assessment of contributions to climate change Source: CICERO-SCM The effect is less pronounced on longer time scales (except for the shift from fossil CO2 to total CO2).

27. 27 Scientific uncertainties 1.Choice of the dataset on historical emissions 2.Choice of the representation of the climate system: carbon cycle and climate model and feedbacks Modelling and assessment of contributions to climate change

28. 28 1. Historical datasets Fossil CO 2 emissions: small differences in relative attribution CO 2 emissions from land-use changes: differences in estimates leading to large differences. Data sets need to be compared and improved. CH 4 and N 2 O: Only one dataset is available (EDGAR) IVIG Dataset estimate is outside IPCC range; almost zero for DCs in 1980s! Modelling and assessment of contributions to climate change Source: RIVM-ACCC

29. 29 2. Other scientific uncertainties The influence of other climate model parameters (e.g. IRFs), based on simulation experiments with nine GCMs and climate models is limited Modelling and assessment of contributions to climate change Source: RIVM-ACCC

30. 30 2. Other scientific uncertainties Modelling and assessment of contributions to climate change Source: UCL-SCM

31. 31 Overall conclusions Policy choices (values can not be based on objective scientific arguments) : –Indicatorimportant –Timeframesimportant –Mixture of GHGimportant –Attribution methodless important Modelling and assessment of contributions to climate change Scientific choices –Choice of the dataset on historical emissions important –Choice of the representation of the climate system (different models)less important for relative contr.

32. 32 Overall conclusions First summary of the work undertaken so to date Not a full assessment of the uncertainty range, but an evaluation of the influence of different policy-related and scientific choices The influence of scientific choices is notable. Therefore research is ongoing (see paper #2) However, the current work suggests, that the impact of policy choices, such as time horizon of emissions, climate change indicator and greenhouse-gas mix is larger than the impact of scientific uncertainties Impact of uncertainties on the relative contributions is smaller than impact of uncertainties on the absolute changes in temperature. Research needs: Historical emission datasets Modelling and assessment of contributions to climate change

33. 33 Backup slides

34. 34 Policy choices Modelling and assessment of contributions to climate change

35. 35 Models are calibrated Modelling and assessment of contributions to climate change

36. 36

37. 37 Table 3

38. 38 Contribution to radiative forcing Modelling and assessment of contributions to climate change

39. 39 Aerosol forcing Inclusion of SO2 emissions reduces the contributions from ASIA and REF, but the effect disappear when there is a gap between attribution end date and evaluation date. Again effect is less less pronounced on longer time scales Modelling and assessment of contributions to climate change Source: CICERO-SCM

40. 40 Overall conclusions Modelling and assessment of contributions to climate change