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Uncertainties in absolute attribution of climate change SB-24 17 May 2006 Joyce Penner University of Michigan 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 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 Method Emissions Global inventories of GHG emissions based on activities Emissions derived from atmospheric measurements Concentrations Radiative forcing Global average temperature change All sources of historical radiative forcing Observed temperature increase Modelling and assessment of contributions to climate change Total uncertainty of OECD Annex I countries contribution 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 We estimate a pdf for OECD Annex I N2O emissions using UNFCCC uncertainties for the next step (RF to T) Modelling and assessment of contributions to climate change 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 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 CO2 The increase in CO2 concentration can be explained by the following factors: • Anthropogenic emissions from fossil fuels and industrial processes • Anthropogenic emissions/removals from land use change and forestry • Natural removals by the biosphere • Natural removals by the ocean Measured Well known Unknown Modelled Modelled Modelling and assessment of contributions to climate change Global LUCF emissions are highly uncertain due to land use change data 2.5 Land Use (PgC/yr) 2.0 1.5 1.0 HH-Low HH-Base HH-High HYDE-Low HYDE-Base HYDE-High RF-Low RF-Base RF-High 0.5 0.0 1900 1920 1940 1960 Year 1980 2000 Even so, OECD Annex I LUCF emissions from inverse method since 1990 are well known: 0.8 HH-Low HH-Base HH-High HYDE-Low HYDE-Base HYDE-High RF-Low RF-Base RF-High 0.7 Land Use (PgC/yr) 0.6 0.5 0.4 0.3 0.2 0.1 0.0 -0.1 -0.2 1900 1920 1940 1960 Year 1980 2000 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) 0.2 Land Use Emissions (PgC/yr) 0.1 0 1990 1992 1994 1996 -0.1 -0.2 1998 2000 2002 HH-Low HH-Base HH-High HYDE-Low HYDE-Base HYDE-High RF-Low RF-Base RF-High UNFCCC OECD Annex 1 UNFCCC LUCF emissions -0.3 -0.4 Year Method Emissions Global inventories of GHG emissions based on activities Emissions derived from atmospheric measurements Concentrations Radiative forcing Global average temperature change All sources of historical radiative forcing Observed temperature increase Modelling and assessment of contributions to climate change Total uncertainty of OECD Annex I countries contribution Radiative Forcing and uncertainty was estimated for all of the important climate factors * 3 3 Solar Forcing (W/m2) 2 2 Tropospheric ozone CFCs, HCFCs and other ODS SF6 1 1 PFCs HFCs 0 0 N2O CH4 -1 -1 CO2 Vulcanic -2 -2 Areosol forcing indirect Carbon aerosol Sulfate aerosol -3 1750 1760 1770 1780 1790 1800 1810 1820 1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 -3 Stratospheric ozone Total * Refers to preliminary assessment Modelling and assessment of contributions to climate change Comparison of D and A (inverse model) aerosol forcing with bottom-up aerosol forcing 0 1700 1750 1800 1850 1900 1950 2000 2050 -0.5 -1 5Stott Forcing -1.5 50Stott 95Stott -2 5Penner 50Penner -2.5 95Penner -3 Bottom up method yields wider uncertainty range, but encompasses inverse method -3.5 -4 Year Modelling and assessment of contributions to climate change Uncertainty in natural forcing deduced using different reconstructions Volcanic Forcing (w/m2) sato 0.5 0 -0.51850 -1 -1.5 -2 -2.5 -3 -3.5 Ammann03 Solar Ammann06 HOYT&SCHATTEN LEAN LEAN_II 0.4 1870 1890 1910 1930 1950 1970 1990 0.2 0 1850 1870 1890 1910 1930 1950 1970 -0.2 -0.4 year year Modelling and assessment of contributions to climate change 1990 Additional contributions from land use albedo change and dust – based on TAR estimates 0.6 0.4 forcing 0.2 0 1700 -0.2 1750 1800 1850 1900 1950 2000 2050 case 1 case 2 -0.4 case 3 -0.6 -0.8 -1 -1.2 year Modelling and assessment of contributions to climate change What will alter median and spread of bottom up forcing? Uncertainty range Median magnitude 3 3 2.5 2.5 2 2 1.5 1.5 1 1 0.5 0.5 0 0 GHG Trop O3 Bottom-up AP D&A AP Other Uncertainty range in bottom up forcing GHG Trop O3 Bottom-up AP D&A AP Other Median magnitude of bottom up forcing Forcing calculated to year 2000 Modelling and assessment of contributions to climate change Use uncertainties in individual components to define uncertainty in total forcing Modelling and assessment of contributions to climate change Results space of modeled temperatures compared to observed warming since 1880 Observations are shown in black Modelling and assessment of contributions to climate change 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 Method Emissions Global inventories of GHG emissions based on activities Emissions derived from atmospheric measurements Concentrations Radiative forcing Global average temperature change All sources of historical radiative forcing Observed temperature increase Modelling and assessment of contributions to climate change Total uncertainty of OECD Annex I countries contribution Effect of group’s emissions CH4 Undisturbed forcing 2.5th percentile 5th percentile 25th percentile 50th percentile 75th percentile 95th percentile 97.5th percentile W/m2 1.7 1.65 1.6 1.55 1.5 1.45 1.4 1.35 1.3 1.25 1.2 1990 0.5 0.48 0.46 0.44 0.42 0.4 0.38 0.36 0.34 0.32 0.3 1990 Undisturbed forcing 2.5th percentile 5th percentile 25th percentile 50th percentile 75th percentile 95th percentile 97.5th percentile 1995 2000 N2O 0.17 0.16 0.15 0.14 W/m2 W/m2 CO2 1992 1994 1996 1998 2000 2002 0.13 0.12 Undist urbed f orcing 2.5t h percent ile 0.11 5t h percent ile 25t h percent ile 0.1 50t h percent ile 75t h percent ile 0.09 95t h percent ile 97.5t h percent ile 0.08 1990 1992 1994 1996 1998 Modelling and assessment of contributions to climate change 2000 2002 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. 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) 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 GWP weighted emissions Radiative forcing Temperature increase Contribution Low 14160 MtCO2eq. 0.32 W/m2 0.10 °C High -6% -28% -49% 16% 53% 139% Modelling and assessment of contributions to climate change Backup slides 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 Comparison of OECD Annex I emissions with global emissions Modelling and assessment of contributions to climate change OECD Annex 1 warming due to CO2, and effect of uncertainty in climate sensitivity Modelling and assessment of contributions to climate change OECD Annex 1 warming due to CO2, and effect of uncertainty in ocean diffusivity Modelling and assessment of contributions to climate change Uncertainties in global mean forcing Modelling and assessment of contributions to climate change OECD Annex 1 warming due to CO2, and effect of uncertainty in global mean forcing Modelling and assessment of contributions to climate change Uncertainty in OECD Annex 1 forcing from N2O N2O 0.17 0.16 0.15 W/m2 0.14 0.13 0.12 Undist urbed f orcing 2.5t h percent ile 0.11 5t h percent ile 25t h percent ile 0.1 50t h percent ile 75t h percent ile 0.09 95t h percent ile 97.5t h percent ile 0.08 1990 1992 1994 1996 1998 2000 2002 Modelling and assessment of contributions to climate change 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. Uncertainty in OECD Annex 1 forcing from CH4 W/m2 CH4 0.5 0.48 0.46 0.44 0.42 0.4 0.38 0.36 0.34 0.32 0.3 1990 Undisturbed forcing 2.5th percentile 5th percentile 25th percentile 50th percentile 75th percentile 95th percentile 97.5th percentile 1995 2000 Modelling and assessment of contributions to climate change 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. Comparison of aerosol forcing to year 2000 from bottom-up with D and A reconstruction D and A reconstruction 100 100 90 90 80 80 70 70 Cumulative Prob cumulative prob Bottom-up reconstruction – used in subsequent analysis 60 50 40 30 60 50 40 30 20 20 10 10 0 -3.5 -3 -2.5 -2 -1.5 Forcing -1 -0.5 0 0 -3 -2.5 -2 -1.5 -1 -0.5 Forcing Modelling and assessment of contributions to climate change 0 Inverse model used to estimate forcing for 2 different values of climate sensitivities Time filtered forcing values smooth1.7 Annual values smooth4.2 2.5 1.5 Forcing Forcing 2 1 0.5 0 -0.51800 1850 1900 1950 2000 2050 4 3 2 1 0 -11800 -2 1850 ann1.7 ann4.2 1900 1950 2000 Year Year Inverse calculation showing plume of forcing curves for different climate sensitivity based on TAR GCM models. Modelling and assessment of contributions to climate change 2050 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