Statistical Inference with Emergent Constraints

by Tapio Schneider

tags: Climate Modeling, Climate Sensitivity, Emergent Constraint

Various attempts have been made to narrow the likely range of the equilibrium climate sensitivity (ECS) through exploitation of “emergent constraints.” They generally use correlations between the response of climate models to increasing greenhouse gas (GHG) concentrations and a quantity in principle observable in the present climate (e.g., an amplitude of natural fluctuations) to constrain ECS given measurements of the present-day observable. However, recent studies have arrived at different conclusions about likely ECS ranges. The different conclusions arise at least in part because the studies have systematically underestimated statistical uncertainties.

How low clouds respond to warming remains the greatest source of uncertainty in climate projections. Climate models projecting that much less sunlight will be reflected by low clouds when the climate warms indicate that CO₂ concentrations can only reach 470 ppm before the 2℃ warming threshold of the Paris agreement is crossed—a CO₂ concentration that will probably be reached in the 2030s. By contrast, models projecting a weak decrease or increase in low-cloud reflection indicate that CO₂ concentrations may reach almost 600 ppm before the Paris threshold is crossed. In a new paper, we outline how new computational and observational tools enable us to reduce these vast uncertainties.