Climate Surprises, Catastrophes & Fat Tails How the decision-analytic framework is influencing the interpretation and assessment of climate change uncertainty Judith Curry

| |---------------| oC IPCC AR4 “likely” [>66%] “best estimate” (Fig. 9.20 IPCC AR4 WG I)

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 prevent dangerous 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 Robert Lempert long time horizons • poorly understood systems • surprise

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

Catastrophes and Surprises 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. less uncertainty more uncertainty fat tail

| |---------------| oC IPCC AR4 “likely” [>66%] “best estimate” (Fig. 9.20 IPCC AR4 WG I)

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 falsification of scenarios Betz (2009) 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.

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 Abrupt climate change occurs faster than the apparent underlying driving forces. Figure from NAP Abrupt Climate Change: Inevitable Surprises (2002)

| |---------------| oC IPCC AR4 “likely” [>66%] “best estimate” (Fig. 9.20 IPCC AR4 WG I)

Conclusions 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 (cont) 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

http://judithcurry.com “uncertainty monster” at the science-policy interface