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Impact of climate-carbon cycle feedbacks on emissions scenarios to achieve stabilisation Chris Jones (1) Peter Cox (2), Chris Huntingford (3) 1. Hadley Centre, Met Office, Exeter 2. Centre for Ecology and Hydrology, Dorset 3. Centre for Ecology and Hydrology, Wallingford © Crown copyright 2004 Page 1 Outline Climate-Carbon cycle feedbacks Uncertainties/intercomparisons Implications for stabilisation emissions Results GCM experiments Simple “reduced form” model results Discussion Uncertainties – between and within models Reducing uncertainty? Model validation Defining “optimal” pathways to stabilisation? © Crown copyright 2004 Page 2 Climate Carbon Cycle feedbacks Well known that climate-carbon cycle models predict a positive feedback Climate change will reduce the carbon cycle’s ability to sequester CO2 Models have consensus on sign (+ve), but not magnitude of feedback (i.e. C4MIP) Uncertainties in the feedback strength mean large uncertainty in: Future CO2 levels given an emissions scenario Permissible emissions to stabilise CO2 at a given level © Crown copyright 2004 Page 3 Climate Carbon Cycle feedbacks If climate change weakens natural carbon sinks then we must reduce emissions by more than previously thought to stabilise atmospheric CO2 Passing mention in TAR but needs to be brought out more TAR showed range of permissible emissions but didn’t stress impact of climate feedbacks in reducing these Huge political implications Plea to AR4 authors – Needs to be given more prominence. Instead of “managing the carbon cycle” this comes under “being managed by the carbon cycle” © Crown copyright 2004 Page 4 WRE scenarios “WRE” is a family of scenarios of CO2 level, stabilising at 450, 550, 650, 750 and 1000ppm Wigley, Richels and Edmonds. ‘Economic and environmental choices in the stabilisation of atmospheric CO2 concentrations’. Nature, 1996 We run the carbon cycle GCM with the prescribed 550 CO2 scenario and infer the emissions required to achieve it Results shown in detail for 550ppm Summary of results for all levels © Crown copyright 2004 Page 5 WRE550 CO2 emissions Climate feedbacks imply reduced permissible emissions Lower peak Earlier peak Reduced integral © Crown copyright 2004 Page 6 WRE550 cumulative emissions Similar to previous experiments Ocean continues to uptake carbon, but at reduced rate Terrestrial sink saturates and reverses © Crown copyright 2004 Page 7 Reduced Form “simple” model GCM prohibitively expensive! Simple model has: Global means climate in terms of T Responds instantly to CO2 Carbon cycle calibrated to follow GCM from transient run of Cox et al 2000. Does good job at matching WRE550 GCM run Aim is to give broad idea of response – don’t trust exact details… © Crown copyright 2004 Page 8 WRE550 CO2 emissions – simple model ( WRE550 ) No feedbacks With feedbacks © Crown copyright 2004 Page 9 Permissible Emissions Total emissions, WRE 2000-2300 without feedbacks with feedbacks Stabilisation at 550 ppm 1355 GtC 1010 GtC 1393 GtC Without feedbacks, we get close to the WRE result Climate-Carbon cycle feedbacks significantly reduce the permissible emissions for stabilisation This is true for stabilisation at any level © Crown copyright 2004 Page 10 Other stabilisation levels Greater reductions at higher stabilisation levels Not surprising given greater level of climate change © Crown copyright 2004 Page 11 Uncertainties Large uncertainties undermine political impact of results Do we understand them? Can we reduce them? © Crown copyright 2004 Page 12 Sources of uncertainty The impact of carbon-cycle feedbacks on permissible emissions will depend on: “Political” uncertainties: Chosen level of stabilisation (and hence climate change) Scientific uncertainties: Climate sensitivity: Greater sensitivity will mean stronger feedbacks for given CO2 level carbon-cycle parameters vegetation sensitivity to warming/CO2 Soil sensitivity Ocean response to climate/circulation changes All climate-carbon cycle studies to date show future weakening of the natural carbon sink in response to climate change But significant uncertainty in strength of feedback © Crown copyright 2004 Page 13 Other models Without feedbacks With feedbacks UVic model – courtesy of Damon Matthews (in press at GRL) Stabilisation at 1000ppm Significant reduction in allowed emissions © Crown copyright 2004 Page 14 C4MIP models Stabilise at 1000ppm by 2350 Cumulative Emissions Reductions (GtC) © Crown copyright 2004 UVic Page 15 C4MIP models Stabilise at 1000ppm by 2350 Cumulative Emissions Reductions (GtC) UVic Hadley © Crown copyright 2004 Page 16 C4MIP models Stabilise at 1000ppm by 2350 C4MIP-min (g=0.04) Cumulative Emissions Reductions (GtC) UVic (g=0.2) Hadley (g=0.31) © Crown copyright 2004 Page 17 Range over C4MIP models Stabilise at 1000ppm by 2350 Cumulative Emissions Reductions (GtC) C4MIP-mean* (g=0.14) UVic * = C4MIP results estimated from gain factors derived from C4MIP transient expts © Crown copyright 2004 Page 18 Implications of uncertainty 2 main implications of the C4MIP uncertainty Uncertainty does not span zero All models agree on positive feedback and hence some degree of reduction in permissible emissions Required emissions vary greatly Reductions due to climate feedbacks uncertain by almost an order of magnitude © Crown copyright 2004 Page 19 Reducing that uncertainty? To what extent does the historical record constrain future behaviour? Climate sensitivity? No – can’t be well constrained observationally Causes large spread in future climate and hence in future feedback strength © Crown copyright 2004 Page 20 Climate sensitivity Uncertainty in historical forcing – especially from aerosols – means large uncertainty in climate sensitivity TAR shows GCM range from 1.5-4.5, but values up to 8-10K can’t be ruled out completely from observations. © Crown copyright 2004 Andreae et al, Nature, 2005 Page 21 Reducing that uncertainty? To what extent does the historical record constrain future behaviour? Climate sensitivity? No – can’t be well constrained observationally Causes large spread in future climate and hence in future feedback strength Carbon cycle parameters? Not directly from observations – CO2 record can’t distinguish strong fertilisation/strong respiration from weak fertilisation/weak respiration. But give different future behaviour © Crown copyright 2004 Page 22 Single parameter perturbations WRE550 WRE450 CO2 fert’n Soil resp NPP(T) ∆T2x, 1.5-4.5 ∆T2x, 1.5-10 Large ensemble of simple model runs with perturbed parameters In these runs, NPP sensitivity to climate is most important carbon-cycle parameter More sensitivity than CO2 fertilisation strength or soil respiration sensitivity to temperature Similar conclusion to Matthews et al., GRL, 2005. Climate sensitivity outweighs carbon cycle uncertainty © Crown copyright 2004 Page 23 Multiple parameter perturbations Low climate sensitivity High climate sensitivity Varying all these parameters, but still fitting historical emissions, gives only very weak constraint on future permissible emissions High climate sensitivities lead to requirement for significant NEGATIVE emissions © Crown copyright 2004 Page 24 Reducing that uncertainty? To what extent does the historical record constrain future behaviour? Climate sensitivity? No – can’t be well constrained observationally Causes large spread in future climate and hence in future feedback strength Carbon cycle parameters? Not directly from observations – CO2 record can’t distinguish strong fertilisation/strong respiration from weak fertilisation/weak respiration. But give different future behaviour Model validation? Maybe – recreating observed behaviour is necessary but not sufficient test of a model C4MIP phase 1 is essential step! © Crown copyright 2004 Page 25 C4MIP phase 1 - validation Atmosphere only model with observed 20th century SSTs Just simulate terrestrial carbon cycle Validate against range of obs: Site-specific from flux towers Regional estimates from inversion studies Interannual variability – e.g. ENSO Validation is important if we are to know which C4MIP models to trust But, ability to get these right doesn’t constrain future feedback size – merely gives us clues about how to interpret the models See Jones & Warnier report on HadCM3LC at: http://www.metoffice.com/research/hadleycentre/pubs/HCTN/index.html © Crown copyright 2004 Page 26 C4MIP phase 1 - validation Flux tower validation from CarboEurope data Assess model sensitivity of GPP, Resp against T, P GPP RE Temp Temp GPP © Crown copyright 2004 precip Page 27 C4MIP phase 1 - validation Comparison with TransCom inversions study (Gurney et al, Nature, 2002) Regional carbon flux estimates from 1992-96 black = transcom pink = Hadley C4MIP experiment Agrees pretty well in most places © Crown copyright 2004 Page 28 Other potential issues How important is time to stabilisation? Emit soon and reduce strongly? Or more gradual? Can we define an “optimal” pathway? Sensitivity studies for stabilisation at 550ppm at different rates: Idealised profiles with asymptotic approach to stabilisation: CO2 = a0 + a1 * tanh (a2 + a3.τ) Match CO2 level and rate of change at 2000 τ =time to (95%) stabilisation. Range from 20-150 years. Not attempted to quantify likelihood – more illustrative How do climate-carbon cycle feedbacks affect resulting emissions profiles? © Crown copyright 2004 Page 29 ‘Optimal’ pathways to stabilisation “fast” (τ=30) and “slow” (τ=80) emissions profiles to 550 ppm Carbon cycle feedbacks reduce emissions in all cases © Crown copyright 2004 Page 30 ‘Optimal’ pathways to stabilisation Total 21st century emissions (higher may be seen as “desirable”) © Crown copyright 2004 Page 31 ‘Optimal’ pathways to stabilisation Max rate of required emissions reductions (higher may be seen as “undesirable”) © Crown copyright 2004 Page 32 ‘Optimal’ pathways to stabilisation Open Questions: Can we convert this into “desirability” somehow? E.g. Linearly combine “total emissions” and “max rate of reduction” deliberately simplistic – clearly many more factors to consider “desirability” varies with timescale to stabilisation “worse” How do climatecarbon cycle feedbacks affect our choice of “optimal”? Shifted optimum? “better” © Crown copyright 2004 Page 33 Conclusions Climate feedbacks on the carbon cycle will reduce future natural carbon uptake Hence, to stabilise CO2, significantly greater emissions reductions may be required This is true regardless of: Stabilisation level But higher levels see greater reduction Model But large spread of feedback strength between models Timescale to stabilise Strength of feedback may alter “optimal” shape of trajectory as well as magnitude © Crown copyright 2004 Page 34 Conclusions Large uncertainties between/within models Observational record directly offers only weak constraint on future behaviour Validation of complex carbon cycle models against all available data is lacking Will prove vital to reducing uncertainty © Crown copyright 2004 Page 35