A Bit More Sensitive…

by Michael E. Mann and Gavin Schmidt

This time last year we gave an overview of what different methods of assessing climate sensitivity were giving in the most recent analyses. We discussed the three general methods that can be used:

The first is to focus on a time in the past when the climate was different and in quasi-equilibrium, and estimate the relationship between the relevant forcings and temperature response (paleo-constraints). The second is to find a metric in the present day climate that we think is coupled to the sensitivity and for which we have some empirical data (climatological constraints). Finally, there are constraints based on changes in forcing and response over the recent past (transient constraints).

All three constraints need to be reconciled to get a robust idea what the sensitivity really is.

A new paper using the second ‘climatological’ approach by Steve Sherwood and colleagues was just published in Nature and like Fasullo and Trenberth (2012) (discussed here) suggests that models with an equilibrium climate sensitivity (ECS) of less than 3ºC do much worse at fitting the observations than other models.

Sherwood et al focus on a particular process associated with cloud cover which is the degree to which the lower troposphere mixes with the air above. Mixing is associated with reductions in low cloud cover (which give a net cooling effect via their reflectivity), and increases in mid- and high cloud cover (which have net warming effects because of the longwave absorption – like greenhouse gases). Basically, models that have more mixing on average show greater sensitivity to that mixing in warmer conditions, and so are associated with higher cloud feedbacks and larger climate sensitivity.

The CMIP5 ensemble spread of ECS is quite large, ranging from 2.1ºC (GISS E2-R – though see note at the end) to 4.7ºC (MIROC-ESM), with a 90% spread of ±1.3ºC, and most of this spread is directly tied to variations in cloud feedbacks. These feedbacks are uncertain, in part, because it involves processes (cloud microphysics, boundary layer meteorology and convection) that occur on scales considerably smaller than the grid spacing of the climate models, and thus cannot be explicitly resolved. These must be parameterized and different parameterizations can lead to large differences in how clouds respond to forcings.

Whether clouds end up being an aggravating (positive feedback) or mitigating (negative feedback) factor depends not just on whether there will be more or less clouds in a warming world, but what types of clouds there will be. The net feedback potentially represents a relatively small difference of much larger positive and negative contributions that tend to cancel and getting that right is a real challenge for climate models.

By looking at two reanalyses datasets (MERRA and ERA-Interim), Sherwood et al then try and assess which models have more realistic representations of the lower tropospheric mixing process, as indicated in the figure:


Figure (derived from Sherwood et al, fig. 5c) showing the relationship between the models’ estimate of Lower Tropospheric Mixing (LTMI) and sensitivity, along with estimates of the same metric from radiosondes and the MERRA and ERA-Interim reanalyses.

From that figure one can conclude that this process is indeed correlated to sensitivity, and that the observationally derived constraints suggest a sensitivity at the higher end of the model spectrum.

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  1. S.C. Sherwood, S. Bony, and J. Dufresne, "Spread in model climate sensitivity traced to atmospheric convective mixing", Nature, vol. 505, pp. 37-42, 2014. http://dx.doi.org/10.1038/nature12829
  2. J.T. Fasullo, and K.E. Trenberth, "A Less Cloudy Future: The Role of Subtropical Subsidence in Climate Sensitivity", Science, vol. 338, pp. 792-794, 2012. http://dx.doi.org/10.1126/science.1227465