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Earth System Dynamics An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/esd-2019-90
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-2019-90
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 17 Jan 2020

Submitted as: research article | 17 Jan 2020

Review status
A revised version of this preprint was accepted for the journal ESD and is expected to appear here in due course.

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

James Douglas Annan1, Julia Catherine Hargreaves1, Thorsten Mauritsen2, and Bjorn Stevens3 James Douglas Annan et al.
  • 1Blue Skies Research Ltd, The Old Chapel, Albert Hill, Settle, BD24 9HE, UK
  • 2Department of Meteorology, Stockholm University, Stockholm, Sweden
  • 3Max Planck Institute for Meteorology, Hamburg, Germany

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 the trend in the 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 observed in the detrended observational record, can provide a stronger constraint with an equivalent 5–95 % posterior range of around 4 °C (e.g. 1.7–5.6 °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.

James Douglas Annan et al.

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James Douglas Annan et al.

James Douglas Annan et al.

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Latest update: 11 Jul 2020
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Short summary
In this manuscript we explore the potential of variability for constraining the equilibrium response of the climate system to external forcing. We show that the constraint is inherently skewed, with a long tail to high sensitivity, and that while the variability may contain some useful information, it is unlikely to generate a tight constraint.
In this manuscript we explore the potential of variability for constraining the equilibrium...
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