by Judith Curry
Risk assessment requires grappling with probability and ambiguity (uncertainty in the Knightian sense) and assessing the ethical, logical, philosophical and economic underpinnings of whether a target of ‘50 per cent chance of remaining under +2◦C’ is either ‘right’ or ‘safe’. How do we better stimulate advances in the difficult analytical and philosophical questions while maintaining foundational scientific work advancing our understanding of the phenomena? And provide immediate help with decisions that must be made now?
The paper by Lenny Smith and Nicholas Stern published in the Proc. Roy. Soc. Special Issue on Handling Uncertainty in Science is Uncertainty in science and its role in climate policy. The complete manuscript is available here [link]. The whole paper is full of insights and well worth reading.
I reproduce here some of the summary text presented in the Concluding remarks.
Sound policy-making embraces the causal chain connecting actions by people to impacts on people. Many varieties of uncertainty are encountered along this chain, including: imprecision, ambiguity, intractability and indeterminacy. Science regularly handles the first with probability theory; ambiguity and intractability are more often used by scientists to guide the advancement of science rather than being handled within science explicitly. A better understanding by scientists of the roles of uncertainty within policy-making may improve the support science offers policy-making. In particular, an improved understanding of which scientific uncertainties pose the greatest challenges to policy-making when projected along the entire causal chain considered by policy, and informed scientific speculation on the likelihood of reducing those specific uncertainties in the near future, would be of immediate value. Some of these roles have been illustrated in the context of a particular example: selecting a stabilization target for greenhouse gas concentration.
Handling ambiguity in science, and the communication of insights from science, has been discussed. The value of scientific insight to policy-making, particularly in cases where state-of-the-art models are not empirically adequate, is stressed. Specifying the robustness of insights, and ideally quantifying how quickly model simulations are likely to become mis-informative as one moves further into the future, are each of significant value to sound policy-making. No scientific extrapolation is complete without a quantitative estimate of the chance of its own irrelevance. Communicating to policy-makers the level of confidence scientists have that their model-based probabilities are not mis-informative is at least as important as communicating the model-based probabilities themselves. Engagement of scientists in the policy-making process, not merely by presenting the outputs of models but by explaining the insights from science, can significantly improve the formation of policy. This is especially true in climate policy, where the scale of the risk is great even if we cannot provide precise probabilities of specific events, and where many plausible changes are effectively irreversible should they occur. Scientists who merely communicate results within the comfortable area of reliable theory abandon the decision stage to those who often have little engagement with the science. Sound policy-making is then hindered by the lack of sound scientific speculation on high-impact events, which we cannot currently model but may plausibly experience. Failing to engage with the question ‘What might a 6◦C warmer world look like, if it were to occur?’ leaves only naive answers on the table for policy-makers to work with.
Complementary to the need for scientific engagement with the policy process is the need for more transparent communication of the limits of current models when presenting model output. Policy-makers are often told that the models ‘have improved’ and that representations of more phenomena ‘have been introduced’. Clear statements of the spatial and temporal scales at which model output is ‘likely’ to be mis-informative, and how these change between 2020, 2050, 2090 and so on, would be of great value in interpreting when the model output is useful for a particular policy purpose. Honesty here enhances credibility and thus effectiveness. Even when technically coherent, failing to lay the limits of today’s insights in plain view, as with the presentation of ‘temperature anomalies’ in summaries for policy-makers, hinders communication of large systematic model errors in today’s models, and hence the relevant level of ambiguity. The eventual realization that such figures show weaker evidence than originally thought can be blown dangerously out of proportion by the anti-science lobby, making the use of science in support of policy-making more difficult than it need be. Again, greater engagement of scientists in the policy process, openly explaining the insights of today’s science and limitations of today’s models, is a significant benefit. This may prove especially true in situations where decisions are based upon feelings as much as upon numbers.
The expected utility approach is difficult to apply when one is unable to translate possible outcomes into impacts on people. There is both imprecision and significant ambiguity in predictions of the Earth’s global mean temperature, yet even a precise value of that temperature cannot be translated into precise impacts on people. And where we have impacts on people, there remain deep ethical challenges in attaching values to outcomes. This approach also struggles with low-probability events; the vanishingly small probabilities that mathematical modelling may suggest are not actually zero should not distract policy-makers from action either.
In this paper, it has been suggested that the communication of science to policy-makers could be aided by:
— scientific speculation on policy-relevant aspects of plausible, high-impact, scenarios even where we can neither model them realistically nor provide a precise estimate of their probability;
— specifying the spatial and temporal scales at which today’s climate models are likely to be mis-informative, and how those scales change as we look farther into the future;
— identifying weaknesses in the science that are likely to reduce the robustness of policy options;
— clarifying where adaptation to current climate is currently lacking; — identifying observations which, in a few decades, we will wish we had taken today;
— distinguishing the types of uncertainty relevant to a given question, and providing some indication of the extent to which uncertainty will be reduced in the next few years; and
— designing model experiments to meet the needs of policy-making.
Similarly, policy-makers could encourage the engagement of scientists by:
— accepting that the current state of the science may not be able to answer questions as originally posed;
— working with scientists to determine how current knowledge with its uncertainties can best aid policy-making; and
— discrediting the view among some scientists that policy-makers are only interested in ‘one number’ which must be easy to understand, unchangeable and easily explained in less than 15 min.
The advance of science itself may be delayed by the widespread occurrence of Whitehead’s ‘fallacy of misplaced concreteness’. In areas of science, far removed from climate science, an insistence on extracting probabilities relevant in the world from the diversity of our model simulations exemplifies misplaced concreteness. Computer simulation both advances and retards science, as did the astonishing successes of the Newtonian model, Whitehead’s original target. In any event, better communication of uncertainty in today’s science, improved science education in the use of simulation modelling that values scientific understanding of the entire system, and the communication of all (known) varieties of uncertainty will both improve how science handles uncertainty in the future and improve the use of science in support of sound policy-making today. How science handles uncertainty matters.
JC comment: this paper provides a wealth of provocative insights and ideas for dealing with uncertainty at the climate-policy interface. I will definitely adopt “Whitehead’s ‘fallacy of misplaced concreteness’”, instead of the vaguer ‘overconfidence’ that I have been using.