What could we learn about climate sensitivity from variability in the surface temperature record?

Abstract

Abstract. We examine what can be learnt about climate sensitivity from variability in the surface air temperature record over the instrumental period, from around 1880 to the present. While many previous studies have used trends in observational time series to constrain equilibrium climate sensitivity, it has also been argued that temporal variability may also be a powerful constraint. We explore this question in the context of a simple widely used energy balance model of the climate system. We consider two recently proposed summary measures of variability and also show how the full information content can be optimally used in this idealised scenario. We find that the constraint provided by variability is inherently skewed, and its power is inversely related to the sensitivity itself, discriminating most strongly between low sensitivity values and weakening substantially for higher values. It is only when the sensitivity is very low that the variability can provide a tight constraint. Our investigations take the form of “perfect model” experiments, in which we make the optimistic assumption that the model is structurally perfect and all uncertainties (including the true parameter values and nature of internal variability noise) are correctly characterised. Therefore the results might be interpreted as a best-case scenario for what we can learn from variability, rather than a realistic estimate of this. In these experiments, we find that for a moderate sensitivity of 2.5 ∘C, a 150-year time series of pure internal variability will typically support an estimate with a 5 %–95% range of around 5 ∘C (e.g. 1.9–6.8 ∘C). Total variability including that due to the forced response, as inferred from the detrended observational record, can provide a stronger constraint with an equivalent 5 %–95 % posterior range of around 4 ∘C (e.g. 1.8–6.0 ∘C) even when uncertainty in aerosol forcing is considered. Using a statistical summary of variability based on autocorrelation and the magnitude of residuals after detrending proves somewhat less powerful as a constraint than the full time series in both situations. Our results support the analysis of variability as a potentially useful tool in helping to constrain equilibrium climate sensitivity but suggest caution in the interpretation of precise results.

On structural errors in emergent constraints

“…The results may also be sensitive to the metric and the set of models used; an earlier study using a similar idea found no constraint (Masson and Knutti, 2013), and in some cases reversed signs of correlations between CMIP and PPEs, thus questioning the robustness of the approach. Other studies (Annan et al, 2020) have performed objective Bayesian constraint of ECS through climate variability in simple models, finding a wider constrained range wider than suggested by Cox 2018. As such, a confirmation of the strength of the Cox 2018 610 relationship under CMIP6 would provide valuable additional data on its robustness.…”

Revisiting a Constraint on Equilibrium Climate Sensitivity From a Last Millennium Perspective

“…From a last-millennium perspective, we can effectively address significant questions posed by the research community regarding prior endeavors to constrain ECS via temperature variability. Notably, it was previously suggested that the instrumental record is too brief to accurately measure global temperature variability for the purposes of constraining ECS and cannot be used alone to constrain climate sensitivity (Annan et al, 2020). We find that while the observed estimate of interannual temperature variability is weakly sensitive to the period of analysis (Table S3 in Supporting Information S1), individual GCMs can exhibit meaningful differences in estimates of ψ produced when calculated over the past millennium (Figure S2 in Supporting Information S1).…”

“…Our constraints, which imply an ECS between 2.5 ± 0.8 K (for ψ ) and 2.7 ± 0.8 K (for σ b ), are slightly lower than the IPCC's central estimate of 3K, but substantially overlap with the IPCC's likely range. The ability of temperature variability to constrain ECS on its own has been shown to be limited, especially in cases where climate sensitivity is greater than 2.5 K (Annan et al., 2020). Therefore, instead of viewing our study as fully capable of constraining ECS on its own, we contend that our estimates and their associated uncertainties should be regarded as an additional source of knowledge within the existing body of work to generate the most accurate ECS estimate (Sanderson et al., 2021).…”

“…For one, FDT relies on an assumption of thermal equilibrium, which is violated by external forcing in the climate system (Brown et al., 2018; Po‐Chedley et al., 2018; Rypdal et al., 2018). For another, the instrumental record of GMST is limited to approximately 100 years, arguably too short to properly characterize its variability (Annan et al., 2020). Finally, the empirical foundation of the constraints is placed into question by a distinct lack of support for their relationships in the CMIP6 ensemble (Figure 1; Schlund et al., 2020).…”

Reply on CC1

“…The results may also be sensitive to the metric and the set of models used; an earlier study using a similar idea found no constraint (Masson and Knutti, 2013), and in some cases reversed signs of correlations between CMIP and PPEs, thus questioning the robustness of the approach. Other studies (Annan et al, 2020)…”