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

MATCH paper 1: contributions to climate change SB-23 17 May 2006. Niklas Höhne. MATCH paper #1. Analysing countries’ contribution to climate change: Scientific uncertainties and methodological choices Michel den Elzen (RIVM, Netherlands) Jan Fuglestvedt (CICERO, Norway)

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

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

  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) 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 Modelling and assessment of contributions to climate change

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

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

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

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

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

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

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

  13. 1. Indicators *: 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 Modelling and assessment of contributions to climate change

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

  15. 1. Indicators • 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. Modelling and assessment of contributions to climate change

  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. 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. Source: RIVM-ACCC Modelling and assessment of contributions to climate change

  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 Source: RIVM-ACCC Modelling and assessment of contributions to climate change

  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) Source: RIVM-ACCC Modelling and assessment of contributions to climate change

  20. 3.Attribution methods • 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. Modelling and assessment of contributions to climate change

  21. 3.Attribution methods • 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. Modelling and assessment of contributions to climate change

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

  23. 3.Attribution methods • 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 Modelling and assessment of contributions to climate change

  24. 4.Greenhouse gas mixture Which gases are attributed to the regions? • Fossil CO2 • All anthropogenic CO2 • CO2, CH4, N2O • Kyoto basket (CO2, CH4, N2O, HFCs, PFCs, SF6) • Kyoto basket + more O3 precursors (NOx, CO and VOC) Modelling and assessment of contributions to climate change

  25. 4.Greenhouse gas mixture • Two main effects i) Going from fossil fuel CO2 emissions only to total anthropogenic CO2 emissions, ii) Inclusion of CH4 and N2O. Source: CICERO-SCM Modelling and assessment of contributions to climate change

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

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

  28. 1.Historical datasets • Fossil CO2 emissions: small differences in relative attribution • CO2 emissions from land-use changes: differences in estimates leading to large differences. Data sets need to be compared and improved. • CH4 and N2O: Only one dataset is available (EDGAR) • IVIG Dataset estimate is outside IPCC range; almost zero for DCs in 1980s! Source: RIVM-ACCC Modelling and assessment of contributions to climate change

  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 Source: RIVM-ACCC Modelling and assessment of contributions to climate change

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

  31. Overall conclusions • Policy choices (values can not be based on objective ‘scientific’ arguments) : • Indicator important • Timeframes important • Mixture of GHG important • Attribution method less important • 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. Modelling and assessment of contributions to climate change

  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. Backup slides

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

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

  36. Table 3

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

  38. 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 Source: CICERO-SCM Modelling and assessment of contributions to climate change

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

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