How are emergent constraints quantifying uncertainty and what do they leave behind?
Williamson, D; Sansom, P
Date: 7 January 2020
Journal
Bulletin of the American Meteorological Society
Publisher
American Meteorological Society
Publisher DOI
Abstract
The use of emergent constraints to quantify uncertainty for key policy relevant quantities such as Equilibrium Climate Sensitivity (ECS) has become
increasingly widespread in recent years. Many researchers, however, claim
that emergent constraints are inappropriate or even under-report uncertainty.
In this paper we contribute to ...
The use of emergent constraints to quantify uncertainty for key policy relevant quantities such as Equilibrium Climate Sensitivity (ECS) has become
increasingly widespread in recent years. Many researchers, however, claim
that emergent constraints are inappropriate or even under-report uncertainty.
In this paper we contribute to this discussion by examining the emergent constraints methodology in terms of its underpinning statistical assumptions. We
argue that the existing frameworks are based on indefensible assumptions,
then show how weakening them leads to a more transparent Bayesian framework wherein hitherto ignored sources of uncertainty, such as how reality
might differ from models, can be quantified. We present a guided framework
for the quantification of additional uncertainties that is linked to the confidence we can have in the underpinning physical arguments for using linear
constraints. We provide a software tool for implementing our general framework for emergent constraints and use it to illustrate the framework on a number of recent emergent constraints for ECS. We find that the robustness of any
constraint to additional uncertainties depends strongly on the confidence we
can have in the underpinning physics, allowing a future framing of the debate
over the validity of a particular constraint around the underlying physical arguments, rather than statistical assumptions. (Capsule Summary) Emergent
constraints under-report uncertainty and are based on strong, unrealistic, statistical assumptions, but they need not be. We show how to weaken the assumptions and quantify important uncertainties whilst retaining the simplicity
of the framework.
Mathematics and Statistics
Faculty of Environment, Science and Economy
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