Climate Surprises, Catastrophes & Fat Tails

Climate Surprises, Catastrophes& Fat Tails

Judith Curry

How the decision-analyticframework is influencingthe interpretation andassessment of climate change uncertainty

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oC(Fig. 9.20 IPCC AR4 WG I)

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IPCC AR4 “likely” [>66%]

“best estimate”

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The Precautionary Principle

"Where there are threats of serious or irreversible damage, lack of full scientific certainty shall not be used as a reason for postponing cost-effective measures to prevent environmental degradation."

Based upon the precautionary principle, the UNFCCC established a goal of stabilization of atmospheric greenhouse gases to preventdangerous climate change

Stabilization targets are set at the lowest critical threshold value

Optimal decision making

more research --> less uncertainty --> political consensus --> meaningful action

When uncertainty is well characterized and the model structure is well known, classical decision analysis can suggest statistically optimal strategies for decision makers.

Stabilization targets are optimized by climate model simulations

Decision Making Under Deep Uncertainty

long time horizons • poorly understood systems • surprise

Robert Lempert

Robust decision making

Robustness is a strategy that seeks to reduce the range of possible scenarios over which the strategy performs poorly:

robustness is a property of both degree of uncertainty and richness of policy options

compares regrets over a range of future scenarios

considers unlikely but not impossible scenarios without letting them completely dominate the decision

Low-probability, high-consequence events provide particular challenges to developing robust policies can be associated with a fat-tailed probability distribution.

Weitzman (2009) argues that climate change policy stands or falls on the issue of how tail probabilities are treated.

lessuncertainty

moreuncertainty

Catastrophes and Surprises

fat tail

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oC(Fig. 9.20 IPCC AR4 WG I)

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IPCC AR4 “likely” [>66%]

“best estimate”

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Possibility distribution

Possibility theory is an imprecise probability theory driven by the principle of minimal specificity that states that any hypothesis not known to be impossible cannot be ruled out.

A possibility distribution distinguishes what is plausible versus the normal course of things versus surprising versus impossible.

Necessary

Likely

Plausible

Surprising

Impossible

Modal logic classifies propositions as contingently true or false, possible, impossible, or necessary.Frames possible vs not possible worlds.

Principles for constructing future climate scenarios:

Modal induction: a statement about the future is possibly true only if it is positively inferred from our relevant background knowledge (IPCC).

Modal falsification: permits creatively constructed scenarios as long as they can’t be falsified by being incompatible with background knowledge.

Modal falsification of scenarios Betz (2009)

Possible/plausible(?) worst case scenarios

• Collapse of the West Antarctic Ice Sheet• Shut down of the North Atlantic thermohaline circulation• Release of the methane stored in permafrost• others?

What scenarios would be genuinely catastrophic?

What are possible/plausible timescales for the scenarios?

Can we “falsify” any of these scenarios based upon our background knowledge of natural plus anthro CC?

Abrupt climate change occurs faster than the apparent underlying driving forces.

Abrupt Climate Change

Figure from NAP Abrupt Climate Change: Inevitable Surprises (2002)

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oC(Fig. 9.20 IPCC AR4 WG I)

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IPCC AR4 “likely” [>66%]

“best estimate”

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The drive to reduce scientific uncertainty in support of precautionary and optimal decision making strategies regarding CO2 mitigation has arguably resulted in:

• unwarranted high confidence in assessments of climate change attribution, sensitivity and projections

• relative neglect of defining/understanding the plausible/possible worst case scenarios

• relative neglect of decadal and longer scale modes of natural climate variability

• conflicting “certainties” and policy inaction

Conclusions

Robust decision making frameworks under deep uncertainty emphasizes:

• scenario discovery

• identifying a broad range of robust decision strategies

Implications for climate research are an increased emphasis on:

• exploring and understanding the full range of uncertainty

• scenario discovery using a broader range of approaches

• natural climate variability, abrupt climate change, and regional climate variability

Conclusions (cont)

http://judithcurry.com

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“uncertainty monster” at the science-policy interface