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

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

3. 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

4. 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

5. Decision Making Under Deep Uncertainty
Robert Lempert
long time horizons • poorly understood systems • surprise

6. 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

7. 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

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

9. 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

10. 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.

11. 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?

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

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

14. 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

15. 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

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