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Probability in the Attribution and Prediction of Climate Change Probability in the attribution and prediction of climate change Myles Allen Departments of Physics, University of Oxford [email protected] Oxford University What was the most unrealistic aspect of the film “The Day After Tomorrow”? Oxford University South Oxford on January 5th, 2003 Photo courtesy of Dave Mitchell of Photo courtesy Oxford University The problem in October 2000 and January 2003: a consistently displaced Atlantic jet-stream The Atlantic Jet Stream (500hPa wind speed) Autumn climatology (colours) & Autumn 2000 (contours) Blackburn & Hoskins, 2003 Oxford University But the jet-stream varies with the weather: how can we pin down the role of climate change? “Climate is what you expect, weather is what you get” (Lorenz, 1982) and in the 21st century: “Climate is what you affect, weather is what gets you” Oxford University Autumn 2000 events “were extreme, but cannot in themselves be attributed to climate change.” 1947 2000 Oxford University It has happened before: Shillingford historic flood levels 2003 Oxford University The 2001 conclusions of the Intergovernmental Panel on Climate Change “Most of the warming over the past 50 years is is likely (meaning a better than 2 in 3 chance) to have been due to the increase in greenhouse gas concentrations.” But what does this tell us about flooding in Oxford? Oxford University Model-simulated changes in extreme rainfall in southern England 4-year 12-year event event 2090 30-year event 2000 1860 Oxford University And now for the next century: carbon dioxide trends Note different scale! Source: IPCC Third Assessment Report Oxford University Uncertainty in global warming under two scenarios of future emissions Oxford University Changing emission path buys time but does not eliminate risk Oxford University Ranges of uncertainty in regional predictions require multi-model “ensembles” Global temperature change under 1% per year increasing CO2 (CMIP-2 model inter-comparison) Global precipitation change under 1% per year increasing CO2 Oxford University A shortage of models with high climate sensitivities? Oxford University Ranges of opinion in climate sensitivity (Morgan and Keith, 1995) Oxford University Dealing with uncertainty in modelling climate change Climate is predictable, but cannot be directly observed. Weather is observable, but unpredictable. Any statement about climate change involves probabilities: looking at the spread of results from lots of climate simulations. On long time-scales, simulations must allow for uncertainty in modelling, not just chaotic variability in the atmosphere-ocean system. But full-scale climate models are expensive to run: the largest conventional ensembles to date are only 20-50 members… Oxford University The problem with dealing with uncertainty in climate change prediction Objective: find as many as possible alternative, equally realistic, model versions that respond differently to increasing carbon dioxide, to explore the full range of possibilities. Perturbed Physics Initial Condition Forcing Ensemble Ensemble Ensemble Overall Grand Ensemble Standard model set-up 10000s10s 10s Model Versions Simulations Oxford University Climateprediction.net: the world’s largest climate modelling facility . .. ~100,000 volunteers, 130 countries, ~6M model-years Upload servers Oxford University Members of the public download and run a full 3-D climate model on their personal computers Oxford University Visualization software for school and undergraduate projects Oxford University Active, and self-regulating, user forum Oxford University Initial climateprediction.net experiment Using simplified model ocean to keep runs short 15-year calibration, 15-year control, 15-year 2xCO2 Up to 10-member initial-condition ensembles to reduce noise and quantify sampling variability Double CO2 15 yr, 2 x CO 2 Diagnostics from final 8 yrs. Derived fluxes Calibration 15 yr spin-up 15 yr, base case CO2 Control Oxford University Time-evolving frequency distribution Remove models that are unstable in the control. Few remaining negatively drifting 2xCO2 model versions are an unrealistic consequence of using a slab ocean. Oxford University Not The Day After Tomorrow: why we got some negative sensitivities… Oxford University Estimating effective climate sensitivity from short 2xCO2 runs Oxford University Exploration of parameter space, focussing on identifying non-linear interactions Perturbations to 21 atmospheric/surface parameters Three values each, including combinations Initial exploration of 6 parameters (clouds and convection) P2 High Standard Low LowStandard High P1 Oxford University Sensitivities from climateprediction.net Stainforth et al, 2005 Oxford University Regional responses: temperature and precipitation Standard model version Low sensitivity model High sensitivity model Oxford University Can observations rule out high sensitivities? CMIP-2 coupled models Original Single perturbations model Stainforth et al, 2005 Oxford University Regional responses: temperature and precipitation Standard model version Low sensitivity model High sensitivity model Oxford University Still they come: 47334 simulations passing initial quality control Traditional range Courtesy of Ben Sanderson Oxford University Are these high sensitivities ruled out by the observed response to Mount Pinatubo? Oxford University No: EBM responses to Pinatubo forcing blue = 0.5K sensitivity, deep red = 20K sensitivity Frame et al, 2005, also fitting ENSO, background climate and effective heat capacity Oxford University Are these high sensitivities ruled out by temperatures in the Last Glacial Maximum? ∆F=-6.6±1.5W/m2 ∆T=-5.5±0.5K Numbers courtesy of Stefan Rahmstorf and Gavin Schmidt, realclimate.org Oxford University No: symmetric uncertainty in past forcing → asymmetric, open-ended range for sensitivity Oxford University Naïve sampling strategies can give the illusion of a tight upper bound on sensitivity Oxford University Would these high sensitivities necessarily be ruled out if we uploaded more diagnostics? Oxford University No: Murphy et al, 2004, distribution without prior weighting towards low sensitivities Oxford University Objective constraints on feedback parameter inferred from the climateprediction.net ensemble Oxford University Objective constraints on climate sensitivity inferred from climateprediction.net Piani et al, 2005 Oxford University Not the first to report a risk of high sensitivity Oxford University Forcing uncertainty: the main obstacle to constraining climate sensitivity Oxford University High risk of substantial warming even with today’s greenhouse gas levels Traditional range Oxford University Lots of studies, same message: weak upper bound on climate sensitivity If S is “likely” < 4K (P>0.67) then S is “very likely < ~7K (P>0.9) and we can only say S “virtually certain” < 10-15K (P>0.99) Oxford University And here’s why: Observable properties of climate scale with strength of atmospheric feedbacks Most constraints end up fairly Gaussian (Central Limit Theorem) A Gaussian distribution of inverse sensitivity gives… no formal upper bound on climate sensitivity Oxford University High sensitivities and the challenge for the IPCC In 2001, all studies reported detectable greenhouse warming at >90% confidence, yet IPCC stuck to “unlikely to be entirely natural in origin” (>67%) In 2005, no studies rule out S>5K at >90% confidence except by prior assumption: but will IPCC want to suggest sensitivity is only “likely” <5K? Of course, there is the fact that… No one is likely to drag an IPCC author through the US courts for underestimating the chance of a high sensitivity. Oxford University Why they care Failure to place an upper bound on sensitivity appears to undermine the policy relevance of IPCC. Huge pressure to come up with the killer paper for AR4 (the prize: world-wide fame, and a chocolate from Susan Solomon). New evidence will be indirect: direct observations of GHG-induced warming or TOA fluxes don’t cut it. Oxford University We have been here before Use of model-simulated variability, and the recovery from the Little Ice Age, undermined the policy relevance of “discernible human influence” in 1995. In 1998, a new hemispheric millennial temperature reconstruction appeared to obviate model-simulated variability (and the Little Ice Age). Of course, that reconstruction may yet turn out right: maybe there was no global Little Ice Age. And maybe the climate sensitivity really is <4K. Premature declarations, even if vindicated, permanently undermine the credibility of the IPCC. Oxford University The irony is, it doesn’t really matter, because we already agree on so much else Michaels et al, 2000, 2004 Oxford University The irony is, it doesn’t really matter, because we already agree on so much else Michaels + 7 years Oxford University Given we won’t actually stabilize concentrations indefinitely, why care about sensitivity? Equilibrium warming under 550ppm stabilisation Maximum warming under 550ppm peak in 2100 Oxford University Why can’t the IPCC just say For well-understood physical reasons, we cannot place an objective upper bound on climate sensitivity. So, we cannot estimate the risks associated with a given stabilisation CO2 concentration. But we can estimate the range of transient changes expected over the coming decades, and … We can also estimate the effort required to hit a given temperature target. Sometimes, admission of ignorance is the most policy-relevant option of all. Oxford University Implications… Many
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