Abstract

Abstract. Studies of emergent constraints have frequently proposed that a single metric can constrain future responses of the Earth system to anthropogenic emissions. Here, we illustrate that strong relationships between observables and future climate across an ensemble can arise from common structural model assumptions with few degrees of freedom. Such cases have the potential to produce strong yet overconfident constraints when processes are represented in a common, oversimplified fashion throughout the ensemble. We consider these issues in the context of a collection of published constraints and argue that although emergent constraints are potentially powerful tools for understanding ensemble response variation and relevant observables, their naïve application to reduce uncertainties in unknown climate responses could lead to bias and overconfidence in constrained projections. The prevalence of this thinking has led to literature in which statements are made on the probability bounds of key climate variables that were confident yet inconsistent between studies. Together with statistical robustness and a mechanism, assessments of climate responses must include multiple lines of evidence to identify biases that can arise from shared, oversimplified modelling assumptions that impact both present and future climate simulations in order to mitigate against the influence of shared structural biases.

Highlights

  • Models of the climate system face a particular challenge: their primary purpose is to project the future response of the Earth system to forcings that have yet to be realised

  • We know that heat uptake by the deep ocean is an important mechanism for Earth’s warming in transient scenarios (Geoffroy et al, 2013), so we have introduced a common structural flaw in models that do not account for the role of the deep ocean

  • Emergent constraints (ECs) can play a powerful role in identifying the dominant ensemble feedback variation and mechanism, potentially illuminating the strengths and limitations of ensemble process representation and highlighting relevant observables

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Summary

Introduction

Models of the climate system face a particular challenge: their primary purpose is to project the future response of the Earth system to forcings that have yet to be realised. The first is that because of the relatively small sample size in CMIP ensembles (or small effective sample size due to model interdependencies; Knutti et al, 2013; Masson and Knutti, 2011; Sanderson et al, 2015) and the large number of outputs, it is inevitable that some variables will be correlated with climate response metrics by chance (Caldwell et al, 2014) This means that our confidence in a constraint cannot arise from correlation across the ensemble alone, but must include the plausibility of the proposed mechanism that relates the predictor to the future climate response (Caldwell et al, 2018). We consider a situation where we know that our ensemble explores only a single model structure that is oversimplified compared to the real world

A lesson from parameter perturbation experiments
The nature of multi-model emergent constraints
Constraints of the first kind: bias persistence or signal emergence
Constraints of the second kind: process isolation
Constraints of the third kind: frequency substitution
Single-layer model
Two-layer model
Idealised experiments
Assessing structural robustness in CMIP emergent constraints
Persistent bias of CO2 concentrations
Historical constraints on soil–carbon temperature relationships
Constraints on future ocean carbon uptake
Constraining transient climate response with observed warming
Process-based constraints on climate sensitivity
Constraining climate sensitivity with fluctuation–dissipation relationships
Findings
Conclusions
Full Text
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