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New Bayesian Approach Addressing Shared Biases Across Models Suggests That Stabilizing CO2 Concentrations at Current Levels Leaves 10% Chance of Exceeding 2°C Target

According to an analysis based on a new hierarchical Bayes framework developed by Derek Lemoine at UC Berkeley that addresses shared biases across models, stabilizing atmospheric concentration of CO2 at current levels leaves a 10% chance of exceeding the 2°C target increase relative to pre-industrial level that most policymakers have now accepted as a threshold for preventing dangerous climate change.

Under benchmark risk management metrics, allowable emission paths should have less than a 10% chance of overshooting the target, Lemoine notes in a new paper published in the American Meteorological Society’s Journal of Climate, so policymakers “may therefore require significant near-term abatement and eventual net negative emissions.”

Bayesian statistics incorporate prior knowledge, along with a given set of current data, to make statistical inferences. Lemoine developed a hierarchical Bayesian framework that explicitly represents sources of uncertainty in climate models such as common biases and unknown and unmodeled feedbacks.

The framework uses models’ estimates of albedo, carbon cycle, cloud, and water vapor-lapse rate feedbacks to generate posterior probability distributions for feedback strength and equilibrium temperature change. The posterior distributions are especially sensitive to prior beliefs about models’ shared structural biases, Lemoine notes. Nonzero probability of shared bias moves some probability mass towards lower values for climate sensitivity even as it thickens the distribution’s positive tail.

Obtaining additional models of these feedbacks would not constrain the posterior distributions as much as would narrowing prior beliefs about shared biases or, potentially, obtaining feedback estimates having biases uncorrelated with those impacting climate models, he writes.

The CO2 concentration needed to meet a 2 °C target relative to pre-industrial levels depends strongly on risk tolerance and on prior beliefs about shared model biases and about model completeness. If models are unrealistically assumed to be complete and to lack shared biases (prior combination 5), then CO2 concentrations could stabilize at 410 ppm (slightly above present levels) and still have less than a 5% chance of exceeding the 2 °C target.

If, on the other hand, models are believed to possibly have shared biases and omissions (prior combination 3), then, before accounting for the effects of non-CO2 GHGs or of aerosols, even stabilizing CO2 concentrations at current levels leaves a 10% chance of exceeding the 2 °C target.

—Derek Lemoine

In contrast to many previous studies, Lemoine’ proposed hierarchical Bayes methods recognize the possibility of structural biases shared across models, which limits the information gain from an unbounded increase in the number of models. This statistical framework also includes uncertainty about climate models’ completeness and about the similarity of the present and future higher-GHG world to the worlds represented by past climate observations.

The method Lemoine elaborates is extensible. Further work could incorporate more complex representations of model dependencies, could explore alternate types of prior beliefs, and could refine prior beliefs about shared structural biases and about unknown and unmodeled feedbacks. Further work could also develop temperature change distributions for planned emission pathways by including uncertainty about the operation of CO2 sinks in response to changing CO2 concentrations and uncertainty in monitoring negotiated emission allocations, Lemoine says.

A robust Bayesian approach may help to address the difficulty in choosing the “right” prior distribution for feedbacks and shared biases, and this paper’s use of multiple priors could complement an ambiguity aversion framework for decision-making. Finally, to make them more useful for adaptation work and impacts assessments, these probability distributions should be extended to consider transient climate change by including uncertainty about heat uptake by oceans, uncertainty about emission paths, and uncertainty about the timescales over which feedbacks operate.

—Derek Lemoine

Resources

  • Derek M. Lemoine (2010) Climate sensitivity distributions depend on the possibility that models share biases. Journal of Climate doi: 10.1175/2010JCLI3503.1

Comments

HarveyD

Regardless of who does the climate change forecast, deniers will never believe it. Let's wait and see what happens when the methane (sea bottom + overland) evaporation is factored in.

shopa

Is this Green Car Congress or a Statistical Journal?
I think they are telling us to reduce CO2 emissions.

My invention can help.

www.safersmallcars.com

Please help me get some support. I can't afford to design and build a car by myself.

ai_vin

@shopa

Say "scram" to SPAM!

TXGeologist

so lets hear it how much do China and India have to cut? We already know that we are expected to cut to 80% of our total emissions which is not technologically possible at this time with even a remotely close standard of living we have now to say nothing of the economic apocalypse those kinds of reductions would cause. we use just over 100 quads of energy in this country per year when the economy is good and generate over 30% of the WORLDS wealth with 5% of the worlds population we are the most efficient producer of wealth comparative to BTU consumption in the world bar none. Put another way we use less BTU per dollar generated than anyone else period.Of those 100 quads 80% was coal, natural gas, oil. repeat after me there are no alternatives that can ECONOMICALY replace 80 quads of energy in high density easy transportable form, not solar not wind not biomass NOTHING can replace these levels of energy consumption and energy consumption is wealth study after study has proven that you need access to energy to generate wealth period.

ai_vin

Then you'll just have to find a way to not need those 80 quads of energy in high density easy transportable form because coal, natural gas, oil are non-renewable and will run out.

SJC

It is a race between altering the planet and running out of fossil fuels. If we are smart, we will reduce fossil fuels and save more for later. Use fossil fuels to develop renewable energy resources.

Coke Machine

TxGeologist, you're incorrect. Nuclear power can do this and more. The extra heat can be used by nearby industries that require heat, the water pumped to cool the plant could first be put through a reverse osmosis water plant and use the waste stream to cool the plant before being pumped 50 miles out in the Gulf, Atlantic, or Pacific. Problem with geographic faults? Great, put the reactors on boats, the US Navy has done it since the 1950's. I believe the Nimitz class carriers have 4 reactors each. This nuclear reaction goes on anyway when uranium decays in nature, we just concentrate it and use the heat. There, your energy, and water problem solved at once. I think 1000 reactors on ships could do it. Puts the shipbuilder, steel worker, and others back to work and not have us sending our money to terrorists. Let's not pull any "reduce the coolant stunts" like Chernybol and Three mile island.

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