By Nic Lewis
Plain language summary
- A new paper led by a UK Met Office scientist claims that accounting for the difference in the spatial pattern of surface temperature change between that in the historical period and that projected under long-term CO2-forcing substantially increases historical estimates of climate sensitivity. The claims are based on simulations by global climate models (GCMs) from the UK Met Office and three other institutions, driven by historical (last ~150 years) observations of sea-surface temperature (SST) and sea-ice.
- The simulations show that the models’ effective climate sensitivity is substantially lower when driven by an observationally-based estimate of the evolution of SST and sea-ice over the historical period than when responding to long-term CO2 forcing. This finding underlies the authors’ conclusion that climate sensitivity estimates based on observed historical warming are too low.
- The sensitivity of the results to the data used was tested by repeating the simulations by the two UK Met Office GCMs using a more recent SST and sea-ice dataset – an updated and improved version of the dataset that provided the sea-ice data used in the original simulations. The results, which appeared in the paper’s Supporting Information but were not reported in the paper itself, were completely different.
- The divergence between the simulation results presented in the paper itself and in its Supporting Information show that the authors’ key claim, that climate sensitivity estimates based on observed historical warming are too low, are highly sensitive to the SST and sea-ice dataset used. Results using the more recent dataset contradict their claims, largely due to differences between the two datasets in the evolution of sea-ice more than counteracting the effects of evolving patterns of SST change over the open ocean. I therefore think it is difficult to draw any strong conclusions from the simulation results presented in the paper.
- Moreover, the study conflates two different temperature-change pattern effects, both of which affect estimated climate sensitivity in GCMs:
- that arising from the difference between the simulated spatial pattern in response to long-term CO2-forcing and the spatial pattern simulated when GCMs respond autonomously to evolving forcing over the historical period; and
- that arising from the difference between the spatial pattern over the historical period simulated when GCMs respond autonomously to evolving forcing and the spatial pattern when they are driven instead by a specified, observationally-based, evolution of SST and sea-ice, with unchanging forcing.
- The first pattern effect concerns forced changes and has been shown to lead to modest (~10%) underestimation of estimated equilibrium climate sensitivity (ECS) for typical current generation GCMs (range -10% to + 50%), although two published studies incorrectly claimed that the effect was much larger.
- The second pattern effect is only relevant to observational estimation of climate sensitivity to the extent that it is caused by natural climate system internal variability. That extent cannot be major – contrary to what the new paper implies – if current GCMs realistically simulate climate system internal variability. All or part of the second pattern effect might instead be attributable to GCMs incorrectly representing historical forcing and/or the climate system’s response thereto, and/or to inaccuracies in the observational SST and sea-ice dataset used.
Soundly-derived recent estimates of effective climate sensitivity (EffCS) based on observed warming over the historical period, EffCShist, have generally been in the 1.6–2.0°C range. That is well below EffCShist estimates for general circulation models (GCMs, also called global climate models) driven by historical forcing, which for current generation (CMIP5) models average 3.0°C (Lewis and Curry 2018). Those estimates are for GCMs with their atmospheric model coupled to a 3D dynamic ocean model (AOGCMs).
A new paper (Andrewsetal18) compares “amipPiForcing” simulations by six AGCMs (the atmospheric model components of AOGCMs) with CO2-forced simulations by their corresponding AOGCMs. Two of the AGCMs used were developed at the UK Met Office – where the lead author works – and the remainder at three other modelling centres. Almost all the simulation results have been published previously; this paper brings them together and makes comparisons.
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