Cox et al. reply

Cox, Peter M.; Williamson, Mark S.; Nijsse, Femke J. M. M.; Huntingford, Chris

doi:10.1038/s41586-018-0641-x

Cited by 14 publications

(27 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…When only one realisation per model is used for ordinary least square regression, regression dilution takes place in which the slope is underestimated (Cox et al, 2018b). This leads to a slight overestimation of TCR (Figure 3d), as the observed warming is on the lower end of model warming.…”

Section: Regression Methodsmentioning

confidence: 99%

“…There have been numerous attempts to constrain ECS using the record of historical warming or palaeoclimate data (Knutti et al, 2017), and more recently using emergent constraints which relate observed climate trends or variations to ECS using an ensemble of models (Caldwell et al, 2018;Cox et al, 2018a). However, debate still rages about the likely range of ECS (Brown et al, 2018;Po-Chedley et al, 2018;Rypdal et al, 2018;Cox et al, 2018b), in part because observed global warming is a rather indirect measure of global warming at equilibrium. On the other hand, TCR is more closely related to the rate of warming, and therefore ought to be more amenable to constraint by the record of global warming (Jiménez-de-la Cuesta and Mauritsen, 2019).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An emergent constraint on Transient Climate Response from simulated historical warming in CMIP6 models

Nijsse

Cox

Williamson

2020

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. The transient climate response (TCR) is the metric of temperature sensitivity that is most relevant to warming in the next few decades, and contributes the biggest uncertainty to estimates of the carbon budgets consistent with the Paris targets (Arora et al., 2019). In the IPCC 5th Assessment Report (AR5), the stated likely range of TCR was given as 1.0 to 2.5 K, with a central estimate which was broadly consistent with the ensemble mean of the CMIP5 Earth System Models (ESMs) available at the time (1.8 ± 0.4 K). Many of the latest CMIP6 ESMs have larger climate sensitivities, with 6 of 23 models having TCR values above 2.5 K, and an ensemble mean TCR of 2.1 ± 0.4 K. On the face of it, these latest ESM results suggest that the IPCC likely range of TCR may need revising upwards, which would cast further doubt on the feasibility of the Paris targets. Here we show that rather than increasing the uncertainty in climate sensitivity, the CMIP6 models help to further constrain the likely range of TCR to 1.5–2.2 K, with a central estimate of 1.82 K. We reach this conclusion through an emergent constraint approach which relates the value of TCR to the global warming from 1970 onwards. We confirm a consistent emergent constraint on TCR when we apply the same method to CMIP5 models (Jiménez-de-la Cuesta and Mauritsen, 2019). Our emergent constraint on TCR benefits from both the large range of TCR values across the CMIP6 models, and also from the extension of the historical simulations into a period when the uncertain changes in aerosol forcing have had a far less significant impact on the trend in global warming.

show abstract

Section: Regression Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An emergent constraint on Transient Climate Response from simulated historical warming in CMIP6 models

Nijsse

Cox

Williamson

2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Although the theory we derive here assumes external, random forcing, we have shown that the Ψ-ECS linear proportionality will theoretically become more tightly defined in the presence of common (non-random) forcing across a model ensemble 37 . The gradient of the relationship does however change, being roughly inversely proportional to the amplitude of the forcing (see section III).…”

Section: Conceptual Models Relating Global Temperature Variabilitmentioning

confidence: 99%

Theoretical foundations of emergent constraints: relationships between climate sensitivity and global temperature variability in conceptual models

Williamson

Cox

Nijsse

2018

Dynamics and Statistics of the Climate System

View full text Add to dashboard Cite

Background: The emergent constraint approach has received interest recently as a way of utilizing multimodel General Circulation Model (GCM) ensembles to identify relationships between observable variations of climate and future projections of climate change. These relationships, in combination with observations of the real climate system, can be used to infer an emergent constraint on the strength of that future projection in the real system. However, there is as yet no theoretical framework to guide the search for emergent constraints. As a result, there are significant risks that indiscriminate data-mining of the multidimensional outputs from GCMs could lead to spurious correlations and less than robust constraints on future changes. To mitigate against this risk, Cox et al 1 (hereafter CHW18) proposed a theory-motivated emergent constraint, using the one-box Hasselmann model to identify a linear relationship between equilibrium climate sensitivity (ECS) and a metric of global temperature variability involving both temperature standard deviation and autocorrelation (Ψ). A number of doubts have been raised about this approach, some concerning the application of the one-box model to understand relationships in complex GCMs which are known to have more than the single characteristic timescale.Objectives: To study whether the linear Ψ-ECS proportionality in CHW18 is an artefact of the one-box model. More precisely we ask 'Does the linear Ψ-ECS relationship feature in the more complex and realistic two-box and diffusion models?'. Methods:We solve the two-box and diffusion models to find relationships between ECS and Ψ. These models are forced continually with white noise parameterizing internal variability. The resulting analytical relations are essentially fluctuation-dissipation theorems.Results: We show that the linear Ψ-ECS proportionality in the one-box model is not generally true in the two-box and diffusion models. However, the linear proportionality is a very good approximation for parameter ranges applicable to the current state-of-the-art CMIP5 climate models. This is not obviousdue to structural differences between the conceptual models, their predictions also differ. For example, the two-box and diffusion, unlike the one-box model, can reproduce the long term transient behaviour of the CMIP5 abrupt4xCO2 and 1pcCO2 simulations. Each of the conceptual models also predict different power spectra with only the diffusion model's pink 1/f spectrum being compatible with observations and GCMs. We also show that the theoretically predicted Ψ-ECS relationship exists in the piControl as well as historical CMIP5 experiments and that the differing gradients of the proportionality are inversely related to the effective forcing in that experiment. Conclusions:We argue that emergent constraints should ideally be derived by such theory-driven hypothesis testing, in part to protect against spurious correlations from blind data-mining but mainly to aid understanding. In this approach, an underlying model is proposed, the ...

show abstract

“…It is not trivial to directly calculate the exact value of the likelihood P ( o | ) for a given observed value o , as is itself a random variable arising from the stochastic model and thus depends on the sequence of random perturbations that were generated during the numerical integration of the model. Therefore, we use here the technique known as approximate Bayesian computation (Diggle and Gratton, 1984;Beaumont et al, 2002). This is a rejection-based sampling technique in which samples are drawn from the prior distribution, used to generate a simulated temperature time series, and rejected if the value of calculated from this time series does not lie within a small tolerance of the observed value.…”

Section: Unforced (Internal) Variabilitymentioning

confidence: 99%

“…Using the value of obtained from observations of surface air temperature, together with the empirical relationship between and S they had derived from the climate models, they produced a best estimate of the equilibrium climate sensitivity of 2.8 • C with a likely (66 % probability) range of 2.2-3.4 • C, a substantially tighter range than most previous research. However, questions have also been raised about this result (Brown et al, 2018;Rypdal et al, 2018;Po-Chedley et al, 2018;Cox et al, 2018b).…”

Section: Introductionmentioning

confidence: 99%

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

Hargreaves¹,

Mauritsen

Stevens

2020

Earth Syst. Dynam.

View full text Add to dashboard Cite

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.

show abstract

Cox et al. reply

Cited by 14 publications

References 9 publications

An emergent constraint on Transient Climate Response from simulated historical warming in CMIP6 models

An emergent constraint on Transient Climate Response from simulated historical warming in CMIP6 models

Theoretical foundations of emergent constraints: relationships between climate sensitivity and global temperature variability in conceptual models

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

Contact Info

Product

Resources

About