Presentation on theme: "jcm.chooseclimate.org Stabilisation under uncertainty probabalistic & interactive exploration using."— Presentation transcript:
firstname.lastname@example.org@climate.be email@example.com jcm.chooseclimate.org Stabilisation under uncertainty probabalistic & interactive exploration using Java Climate Model ICTP Trieste 15/12/2003 Ben Matthews firstname.lastname@example.org with Jean-Pascal van Ypersele email@example.com Institut dastronomie et de géophysique G. Lemaître, Université catholique de Louvain, Louvain-la-Neuve, Belgium www.climate.be (UCL-ASTR) jcm.chooseclimate.org (interactive model) JCM also developed with: DEA-CCAT Copenhagen, UNEP-GRID Arendal, KUP Bern
One tool for both research and training Interactive Java Climate Model Fast efficient science models are needed both for interactive tool and for integrated assessment. But complexity of presentation differs. Research applications: Article 2- Stabilisation under uncertainty Equity- Distribution of responsibility (BP) and impacts Training applications: Role-play negotiation with students in UCL other universities, unep.net,...
One tool for both research and training Interactive Java Climate Model try JCM at jcm.chooseclimate.org Works in web browser, very efficient/compact Instantly responding graphics, Cause-effect from emissions to impacts, Based on IPCC-TAR methods / data, New flexible stabilisation scenarios Regional distributions of responsibility and climate fields. Transparent, open-source code, modular, scriptable, Interface in 10 languages, 50000 words documentation
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 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, gradient, dc/dt, and second derivative d 2 c/dt 2 (ensures smooth emissions trajectory), and final concentration and gradient. If stabilising radiative forcing or temperature (or...) iterate to find best concentration and gradient in stabilisation year. Also to define quadratic curve from then until 2300. 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...
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, Aerosol and Ozone precursors) reduced by same proportion as CO2 with respect to one of six SRES baseline scenarios 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 uncertainty in Ice-melt parameters
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! (note picture in abstract book) 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 ? Range: 380 - 620ppm, Mean ~ 475ppm, Median ~ 450ppm. Over 90% of variants are below 550ppm so a 550ppm target has a high risk of exceeding 2°C If we want 90% of variants below 2C, the concentration should not exceed 400ppm ! 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 (short lifetime gases).
Inertia in the climate system Stabilising CO2 alone doesn't stabilise temperature (as below from TARSYR Q6) However stable CO2 may correspond to stable Temperature if other gases with shorter lifetimes are also mitigated to a similar extent.
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. 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!
Role-play on Article 2 with students Louvain la Neuve, Belgium, Dec 2002, as if COP11, 2005, Presented at COP9 Milano, Dec 2003 60 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 !
firstname.lastname@example.org@climate.be email@example.com 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 2100-1990) + Sea-level rise (46cm 2100-1990) 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
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.
firstname.lastname@example.org@climate.be email@example.com jcm.chooseclimate.org Relevance to developing countries Distribution / Equity issues - compare distribution of responsibility (Brazilian Proposal) with distribution of regional impacts. Apply polluter pays principle to adaptation funds? To interest people more, we should complete the circle from local mitigation actions to regional climate impacts (all under uncertainty). Future JCM development, link DDC, GIS etc. How to reflect the reality of complex climate change, in a fast interactive tool?
firstname.lastname@example.org@climate.be email@example.com jcm.chooseclimate.org Experiment with Java Climate Model Try JCM at jcm.chooseclimate.org Trying to combine research and outreach Works in web browser, Instantly responding graphics, Based on IPCC-TAR methods / data, Open-source, Scriptable, Labels in 10 languages, 50000 words documentation