jcm.chooseclimate.org Stabilisation under uncertainty probabalistic & interactive exploration of scenarios using Java Climate Model Climneg User Group Meeting, Leuven 8 th Jan 2004 Ben Matthews Jean-Pascal van Ypersele Institut dastronomie et de géophysique G. Lemaître, Université catholique de Louvain, Louvain-la-Neuve, Belgium (UCL-ASTR) jcm.chooseclimate.org (interactive model)

UN Framework Convention on Climate Change Ultimate objective (Article 2): '...stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system. Such a level should be achieved within a time frame sufficient - to allow ecosystems to adapt naturally to climate change, - to ensure that food production is not threatened and - to enable economic development to proceed in a sustainable manner.' (technologies, lifestyles, policy instruments) Emissions pathways (biogeochemical cycles) Critical Levels (global temperature / radiative forcing) Critical Limits (regional climate changes) Key Vulnerabilities (socioeconomic factors) inverse calculation

Temperature and « reasons for concern » Source: IPCC WG2 (2001)

European Union 2 °C limit: EU Council Of Ministers 1996: "...the Council believes that global average temperatures should not exceed 2 degrees Celsius above pre-industrial level and that therefore concentration levels lower than 550 ppm CO 2 should guide global limitation and reduction efforts." "This means that the concentrations of all GHGs should also be stabilised. This is likely to require a reduction of emissions of GHGs other than CO 2, in particular CH 4 and N 2 O" However, widely varying interpretations of implications for emissions! Why? Java Climate Model may help to investigate...

Stabilisation scenarios in Java Climate Model (Article 2: critical limits => critical levels => emissions pathways) Inverse calculation to stabilise CO 2 concentration (as IPCC "S"/ WRE scenarios) Radiative Forcing (all-gases, "CO 2 equivalent") Global Temperature (e.g. to stay below 2C limit) (Sea-level -difficult due to inertia in ocean / ice) JCM core science very similar to IPCC-TAR models, but (unlike TAR SYR) JCM stabilisation scenarios include mitigation of all (21) greenhouse gases and aerosols, scaled w.r.t. SRES baseline.

Stabilisation scenarios in Java Climate Model CO2 concentration scenario is a Padé polynomial (similar to formula of Enting et al 1994 for IPCC S/WRE) defined by: 2000 concentration c 2000 gradient dc/dt, 2000 second derivative d 2 c/dt 2 (ensures smooth emissions trajectory), stabn year concentration stabn year gradient (zero if stabilising concentration) Also define quadratic curve to continue from stabn year until If stabilising radiative forcing or temperature (or...) iterate to find best concentration and gradient in stabilisation year. Iterates 1-10 times, depending on magnitude of change (reuse of correction factors so efficient for dragging control). Explore interactively by dragging target curve with mouse Or systematically calculate probabilistic analysis...

Systematically exploring uncertainty: 81 Carbon cycle variants 3* Land-use-change emissions (Houghton, scaled), 3* CO 2 fertilisation of photosynthesis ("beta"), 3* Temperature-soil respiration feedback ("q10"), 3* Ocean mixing rate (eddy diffusivity of Bern-Hilda model) 6 Ratios of emissions of different gases Emissions of all gases (including CH4, N2O, HFCs, sulphate/carbon aerosol and ozone precursors) reduced by same proportion as CO2 with respect to one of six SRES baselines note: atmospheric chemistry feedbacks included, but not varied 84 Forcing/Climate Model variants 3 * Solar variability radiative forcing 4* Sulphate aerosol radiative forcing 7* GCM parameterisations climate sensitivity, ocean mixing/upwelling, surface fluxes (W-R UDEB model tuned as IPCC TAR appx 9.1) note: for sea-level rise, should add more uncertainty in ice-melt

Demonstration of JCM

Carbon Cycle Other gases/Aerosols Climate Model

Shifting the Burden of Uncertainty On average, all sets of scenarios stabilise at the same temperature level of 2°C above preindustrial level. But their uncertainty ranges are very different! A Temperature limit rather than a Concentration limit reduces the uncertainty for Impacts/ Adaptation... (assuming we commit to adjust emissions to stay below the limit, as the science evolves)...however this increases the uncertainty regarding emissions Mitigation pathways. Which is better?

Relative probability of each set of parameters derived from inverse of "error" (model - data) Measured global temperatures (CRU + proxies) Measured CO 2 concentration (Mauna Loa + others) Reject low-probability variants (kept 468 / 6804) Ensures coherent combinations of parameters, e.g. : More sensitive climate models with higher sulphate forcing High historical landuse emissions with higher fertilisation factor Still 2808 curves per plot (including 6 SRES per set) So show 10% cumulative frequency bands (using probabilities) Probability from fit to historical data

Carbon Cycle Other gases/Aerosols Climate Model

What CO 2 level stabilises T<= 2°C ? note: 90% of cum freq means that 90% of variants weighted by probability fell below this level note: concentrations derived from IPCC-TAR science are lower than those from SAR, principally due to less sulphate cooling, and slightly higher sensitivity note: 550ppm "CO 2 equivalent" (all gases) would bring us close to 2C. However, to keep the temperature level, total radiative forcing (and hence CO2 equivalent) must decline gradually. This is possible while CO2 remains level, due to declining CH4 and O3 (which have short lifetimes).

Is it 'realistic'? check trends % change in CO2 emissions per capita per year x-axis from 1950 to 2050, y-axis from +10% to -10% Left: Stabilisation at 450ppmRight: SRES A1B

As natural scientist, am not advocating 2C level, only that derivations from it should be consistent with latest science... Interpretation of Article 2 needs a global dialogue (Article 6) Risk/Value Judgements (including equity implications) : Impacts: Key Vulnerabilities? Acceptable level of Change? Risk: Target Indicator? Acceptable Level of Certainty? (choice of target indicator shifts the burden of uncertainty) Such risk/value decisions cannot be made by scientific experts alone.

jcm.chooseclimate.org Regional Distributions: JCM can also be used to explore... Attribution of responsibility Regional climate change patterns Abatement and Impact Costs In combination with stabilisation scenarios, and scientific uncertainties

jcm.chooseclimate.org Flexible region sets at both ends of the chain, to connect a variety of data-sources and applications. JCM12, JCM50, RICE, CWS15, SRES4, TGCIA, IMAGE, EDGAR, CDIAC/Houghton, All Nations, subdivisions... Idea: analyse sub-regions of large diverse countries (eg US states, Russian oblasts, Chinese provinces), to consider potential new coalitions if central govts can't agree policy?

Regional Climate Impacts GCM climate patterns scaled instantly to JCM average Latest datasets from IPCC-DDC JCM module to be used in DDC website Climate is multi-dimensional: Temperature: average, min, max, dtr Precipitation, humidity, clouds, sunlight, pressure, wind Seasonal cycle, view monthly animation Compare GCMs to see uncertainty Combine change with baseline climatology Calculate averages for any country / region (now for various region-sets: SRES, JCM, RICE, CWS15, EDGAR, 50, all nations...) Next challenge is to derive socioeconomic / ecological impacts from such data, using regional socioeconomic models to assess vulnerability.

Attribution of responsibility for climate change (Brazilian Proposal) Many potential applications, comparing impacts due to emissions from: countries (pay for adaptation?) projects(CDM) timeslices (inter-generational equity) gases (replace GWP ?) Calculations considered several gases: CO2 fossil, CO2 landuse, CH4, N2O regions: (4 SRES, 12 JCM, 15CWS, EDGAR...) indicators: concn, forcing, temp, sealevel time-slices and future scenarios (now including stabilisation scenarios) methods for attributing non-linear processes and Feedbacks Sensitivity to uncertainties is much less for relative attribution, compared to absolute (maybe similar effect applies to other problems in Climneg? - should test) JCM contributed to UNFCCC intercomparison (+ workshops Hadley centre 2002, Berlin 2003). Next stage of intercomparison during 2004, report to SBSTA Process helps to engage developing countries, who often mention historical responsibility.

What about historical responsibility per capita? - DCs have right to more than convergence of emissions! (issue raised by Chinese in COP9 Milan side-events)

Integrated assessment We have to assess balance of mitigation and impacts /adaptation, etc. Economic costs module was added to JCM, applying Climneg formulae. (abatement costs, damage costs, time-integral with discount rate...) Abatement costs (RICE / MACGEM) based on comparison to SRES baselines. Need to incorporate MACs for each gas. Much more work needed on climate impact cost functions What about uncertainty in these functions? Depends on socioeconomic scenarios, regional climate patterns, aggregation and valuation judgements. Flexible scripting code also developed for JCM, for problem -solving algorithms, probabalistic analysis etc.

Demonstration of JCM

jcm.chooseclimate.org The ultimate integrated assessment model remains the global network of human heads. To reach effective global agreements, we need an iterative global dialogue including citizens / stakeholders. The corrective feedback process is more important than the initial guess. So let's start this global debate! But we still need models to provide a quantitative framework for the discussion. JCM was developed to make models more accessible and transparent.

jcm.chooseclimate.org (game -theory, game practice...) Role-play on Article 2 with students Louvain la Neuve, Belgium, Dec 2002, as if COP11, 2005, Presented at COP9 Milano, Dec university students grouped in 17 delegations (Belgium, Denmark, Russia, USA, Australia, Saudi-Arabia, Venezuela, Brazil, Burkina-Faso, Marroco, Tuvalu, India, Greenpeace, GCC, FAO, WB/IMF, Empêcheurs) had the task to agree by consensus in a UNFCCC-style process: * a quantitative interpretation of Article 2, * an equitable formula for funding adaptation. Delegates used Java Climate Model to explore options / uncertainties. Can "justify" diverse positions by selecting parameters / indicators !

jcm.chooseclimate.org Conclusions of role-play Equity implications were key aspect of discussion Final compromise between Russia and Tuvalu (after US quit) Quantitative interpretation of Article 2: + Temperature rise (<1.9°C ) + Sea-level rise (46cm ) Principles for Adaptation funds : + Tax on emissions trading + Percapita emissions & GDP formula + Principles sufficiency/capacity Such "games" also help us to identify scientific issues, e.g.: Reconciling multi-criteria climate targets (inconsistency maybe realistic in policy compromises), Meaning of CO 2 "equivalents" in stabilisation context

jcm.chooseclimate.org Future development for global dialogue Could we combine such tools and experiences to link groups from all corners of the world? JCM also used for teaching in several countries: Univ Cath de Louvain (BE) Open University (UK), Univ Bern (CH), Univ Waterloo (CA),... Such web models might provide a quantative framework for a global dialogue. Model can be shared by saving snapshots of model parameters to pass to others in asynchronous discussion forum.

jcm.chooseclimate.org Experiment with Java Climate Model Try JCM at jcm.chooseclimate.org Trying to combine research and outreach Works in web browser, very efficient/compact Instantly responding graphics show cause-effect from emissions to impacts, Based on IPCC-TAR methods / data, New flexible stabilisation scenarios, Regional emissions, abatement, costs, responsibility Regional patterns of climate change Transparent, open-source code, modular, scriptable, Interface in 10 languages, 50,000 words documentation JCM also developed with: DEA-CCAT Copenhagen, UNEP-GRID Arendal, KUP Bern

Demonstration of JCM