Guest Commentary by Karen M. Shell, Oregon State University

Link to Part I.

Clouds are very pesky for climate scientists. Due to their high spatial and temporal variability, as well as the many processes involved in cloud droplet formation, clouds are difficult to model. Furthermore, clouds have competing effects on solar and terrestrial radiation. Increases in clouds increase reflected sunlight (a cooling effect) but also increase the greenhouse effect (a warming effect). The net effect of clouds at a given location depends the kind of clouds (stratus, cumulus etc.), their distribution in the vertical and on which radiative effect dominates.

Not only is it difficult to correctly represent clouds in climate models, but estimating how clouds and their radiative effects will change with global warming (i.e., the cloud feedback) is very difficult. Other physical feedbacks have more obvious links between temperature and the climate variable. For example, we expect and have strong evidence for the increase in water vapor in a warmer climate due to the increased saturation specific humidity, or the reduced reflection of sunlight due to the melting of snow and ice at higher temperature. However, there isn’t a simple thermodynamic relationship between temperature and cloud amount, and the complexities in the radiative impacts of clouds mean that an increase in clouds in one location may result in net heating, but would correspond to a cooling elsewhere. Thus, most of the uncertainty in the response of climate models to increases in CO 2 is due to the uncertainty of the cloud feedback.

So how cool is it then that the recent paper by Fasullo and Trenberth estimates the net climate sensitivity without getting into the details of the cloud feedback then? Quite cool.





Figure 1 from Soden and Held (2006) showing ranges for each model for each of the key atmospheric feedbacks for the CMIP3 models.

The complexity of the cloud feedback means that it is difficult to observe and evaluate. Ideally, we would have accurate global observations of clouds for a number of decades to compare to the climate models. However, without that, we have to estimate cloud feedbacks using the shorter high-quality observational record, and then try to relate the short-term behavior to long-term (century-scale) cloud feedbacks. For some feedbacks, such as the water vapor and ice-albedo feedbacks, there has been some success in evaluating models directly using observations. For example, the modeled water vapor feedback in response to the eruption of Pinatubo agrees with the satellite data (Soden et al., 2002), while modeled Northern Hemisphere (NH) snow and sea-ice feedbacks underestimate the observed feedback (Flanner et al., 2008). Evaluation of cloud feedbacks has been less successful, due to the difficulty of obtaining homogenous global cloud properties from either satellite or surface-based observations. Many assumptions (such as for cloud droplet size or vertical distribution) must be made for these retrievals, and instrument and calibration errors may still be significant. Additionally, there isn’t an obvious correspondence of the short-term cloud behavior with the long-term behavior in climate models (Dessler, 2010; Masters, 2012).



Correlation of zonal mean relative humidity against sensitivity, and the definition of two key regions.



Correlation between the two mid-tropospheric regions and model sensitivities.

The Fasullo and Trenberth paper identified a relationship between the modeled seasonal change in relative humidity in the subtropical dry zones (the downwelling branch of the Hadley circulation, centered around 20-30°N and S) and the long-term feedback behavior of clouds in models. This is a very promising methodology because, if the relationship holds, we could evaluate climate models using observations of the seasonal cycle of relative humidity (which are much easier to obtain than cloud measurements). We don’t actually have to observe clouds at all! Fasullo and Trenberth use satellite data to estimate the present-day (1980-1990) May through August relative humidity and find that the CMIP3 models that best match the observations have strong moist zones in the tropical lower troposphere, strong dry zones in the subtropical upper troposphere, and high climate sensitivities. Thus, Fasullo and Trenberth conclude that the relative humidity observations are most consistent with higher climate sensitivities (around 4°C for a doubling of CO 2 ).

One piece missing in this work is the direct link of the RH observations to the cloud feedback. Since clouds form in saturated air, the lower the RH, the lower the cloud amount (broadly speaking), and the lower the planetary albedo (since less sunlight is reflected to space). While Fasullo and Trenberth don’t specifically calculate cloud feedbacks, their figure 3 relates the climate sensitivity to changes in solar fluxes at the top-of-the-atmosphere (TOA) in the subtropics. Outside of the polar regions, clouds are really the only things which could be changing the TOA fluxes this much, but it would be nice to confirm this by comparing the northern hemisphere summer RH in the dry zones to cloud feedbacks specifically.

Another issue is that it is not clear how exactly to improve modeled RH. Subtle model details influence the emergent dynamics of the system. There’s not a single “knob” that can be tuned to influence the RH seasonal cycle, and models might not correctly capture the dependence of cloud properties on RH. Alternately, RH and clouds could be responding to something else that is controlling both processes. Even if models are capturing the response of subtropical clouds to climate change, other feedbacks (water vapor, temperature, snow and sea ice, or high-latitude cloud feedbacks) may not be related to subtropical RH or may behave differently for the seasonal cycle compared with climate change. Finally, the seasonal cycle omits cloud changes in response to CO 2 directly (Gregory and Webb, 2008; Colman and Mcavaney, 2011), which will influence climate change. Nevertheless, this new simple diagnostic is an encouraging step linking observations to climate sensitivity.

References