Sensitive but unclassified: Part II

The discussion and analysis of the latest round of climate models continues – but not always sensibly.

In a previous post, I discussed the preliminary results from the ongoing CMIP6 exercise – an international, multi-institutional, coordinated and massive suite of climate model simulations – and noted that they exhibited a wider range of equilibrium climate sensitivities (ECS) than in previous phases (CMIP5 and earlier) and wider than the assessed range based on observational constraints (of many kinds).

Since then, more model results have been added to the archive, and thanks to Mark Zelinka, we can see some of the analysis as it updates in real time.

By eye, it looks like there are two (or three) groups of models, one within the range of the assessed values (roughly 2 to 4.5ºC), one group with significantly higher values, and one institution/two models with a notably lower ECS. The question everyone has is whether this extended range is credible.

Mark and colleagues recent paper (Zelinka et al., 2020) demonstrated that a big part of the reason for the high sensitivities was in the Southern Ocean cloud feedback:

This is the key result from the Zelinka et al paper that just appeared. It shows that the big difference in sensitivities in some of the CMIP6 models is tied to the short wave low cloud feedbacks in the Southern Oceans (orange line). pic.twitter.com/RN9ply5kT8

— Gavin Schmidt (@ClimateOfGavin) January 4, 2020

Since my first post, there have been a number of papers have looked at the skill of these models to see whether there are some key observational data that might help in constraining the sensitivity (and by extension, the projections into the future). One set of papers has focused on the global mean trends from 1990 or so onward which is a period of stable or declining aerosol trends and which might therefore be a closer test of the models’ transient sensitivity to CO2 than earlier periods. Notably Tokarska et al. (2020) and Njisse et al. (2020) suggest that many of the high ECS group warm substantially faster than observed over this period and therefore should be downweighted in the constrained projections of the future.

Recently however, writing in Guardian, Jonathan Watts uses results from the UK’s new model (Williams et al., 2020) and a commentary from Tim Palmer to argue that that we nonetheless need to take these high sensitivities more seriously, and indeed that they may indicate that the assessed ECS range has been underestimating potential changes in the future. This is however flawed.

The Williams et al paper demonstrates that updates to the HadGEM3?GC3.1 model developed by the UK’s Hadley Centre that affect the clouds and aerosols, increase the skill of that model in short-term initialized weather forecasts. This is fine, and indeed, consistent with increases in skill in the newer models across the board when they are compared to a very broad range of observations.

But it is a logical leap to go from an observation of increased skill in one metric to assuming that therefore the overall ECS in this particular model is more likely. To demonstrate that, one would need to show that this particular measure of skill was specifically related to ECS which has not been done (a point Palmer acknowledges). To put in another way, it may be that all models that do well on this task have a range of ECS values, and that the coincidence of this one model doing well and having a high ECS, was just that, a coincidence.

The Williams et al paper and Palmer commentary point to one particular feature of this model which is that the newer (higher ECS) versions have greater amounts of cloud liquid water at cold temperatures. For background, clouds can consist of either ice crystals, or liquid water droplets which have quite different radiative behaviours (liquid water clouds are generally more reflective), and knowing whether clouds are ice or water has been historically difficult to determine globally. In recent years however, satellite data from CloudSAT/CALIPSO has shown that more clouds have liquid water and at colder temperatures than was assumed before, and hence newer models have reflected that updated information.

This has an impact on ECS because in a warming world, one expects more cloud water to turn from ice to liquid, and since liquid clouds are more reflective, this is a damping feedback on overall climate warming. But if there is less cloud ice around, then there will be less of that ice to turn to water, and thus the magnitude of this damping effect will be smaller, and thus the overall sensitivity will be higher.

In discussions with colleagues over the last few months, this effect has been frequently brought up as a potential reason to think that the higher ECS values are therefore justified. But closer analysis does not necessarily support this. Some models for instance, have increased their cloud liquid water but have only had modest increases in climate sensitivity. Thus the relationship between higher CLW and ECS may be less strong than assumed above. It may be that other features in the clouds (such as the transition of different cloud types) might be playing a bigger role.

This assessment is obviously an important task for the authors of the IPCC AR6 report which is currently in it’s second-order draft. One (very modest) positive impact of the pandemic is that the deadline for papers to be accepted in order for them to be included in the final version of AR6 has been delayed to January 31st 2021, which will allow much of this new science to be published in time.

In the meantime, claims that climate sensitivity is much higher, or that worst cases scenarios need to be revised upwards, are premature.

References

1. 
   M.D. Zelinka, T.A. Myers, D.T. McCoy, S. Po?Chedley, P.M. Caldwell, P. Ceppi, S.A. Klein, and K.E. Taylor, “Causes of Higher Climate Sensitivity in CMIP6 Models”, Geophysical Research Letters, vol. 47, 2020.
2. 
   K.B. Tokarska, M.B. Stolpe, S. Sippel, E.M. Fischer, C.J. Smith, F. Lehner, and R. Knutti, “Past warming trend constrains future warming in CMIP6 models”, Science Advances, vol. 6, pp. eaaz9549, 2020. http://dx.doi.org/10.1126/sciadv.aaz9549
3. 
   F.J.M.M. Nijsse, P.M. Cox, and M.S. Williamson, “An emergent constraint on Transient Climate Response from simulated historical warming in CMIP6 models”, 2020. http://dx.doi.org/10.5194/esd-2019-86
4. 
   K.D. Williams, A.J. Hewitt, and A. Bodas?Salcedo, “Use of Short?Range Forecasts to Evaluate Fast Physics Processes Relevant for Climate Sensitivity”, Journal of Advances in Modeling Earth Systems, vol. 12, 2020.