How do anvil clouds affect global warming?
Anvil clouds, which are formed by thunderstorms, are ubiquitous in the tropics. It is expected that their collective area will shrink with warming owing to the "stability iris effect". What effect does this have on global warming? And does the reflectivity of these clouds change too? To answer these questions, we turned to observations of year-to-year variations in these clouds from 2006 - 2016 and we inferred their affects on global warming with simple equations we derived with pencil-and-paper. We found that anvil clouds would need to halve in size with every degree of warming to have a substantial feedback effect on global warming, owing to the fact that anvil clouds reflect as much sunlight as they insulate Earth's surface. However, observations and theory suggest anvil clouds change closer to -4% per degree of warming, which is about 12 times smaller. We can therefore conclude that the anvil cloud area feedback is small. Using the same techniques, we find that anvil cloud reflectivity increases with warming, and that this change could have a more substantial impact on warming, but we are not confident in this assessment because our results diverge from previous research and because there is little theory to support or refute how anvil cloud reflectivity should change. There may also be corresponding changes in the anvil cloud greenhouse effect that result in a smaller net effect on global warming. We conclude that changes in anvil cloud opacity are much more uncertain and therefore pose an obstacle for bounding Earth's climate sensitivity. See McKim et al. (2024) for more details. For additional context, see here and here.
Does the water vapor feedback change with temperature and humidity?
Feedbacks are processes that can either amplify or dampen the warming from increased carbon dioxide. Take the water vapor feedback for instance. As the surface warms it does what humans do—evaporate water to cool off. But that water enters the atmosphere where it then acts as a greenhouse gas that prevents cooling to space known as outgoing longwave radiation (OLR). It turns out that the dependence of OLR on surface temperature, which is mostly determined by water vapor, dominates the magnitude of Earth's climate sensitivity to carbon dioxide. Calculations of Earth's climate sensitivity often assume this feedback is constant, but I'm interested in if this feedback grows or shrinks depending on the baseline surface temperature and relative humidity because it might affect our estimates.
We use a simple column model of the atmosphere to compute this feedback over a large range of climates and test this idea. We find that the feedback is insensitive to humidity and temperature until the surface is about 280 K. Above 280 K, the feedback becomes sensitive to temperature and relative humidity. It's stronger in hot and moist regions like the deep tropics and weaker in dryer and colder regions. This happens because water vapor absorption strengthens increasingly fast at high temperatures, a well-known result in radiative physics. Our work gives a renewed appreciation for how the hottest and driest regions, the subtropics, have the weakest feedback and thus act like radiator fins for our planet. See McKim et al. (2021) for more details.
Idealized models of cloud mixing
Most of the uncertainty in estimates of Earth's warming from increased carbon dioxide comes from clouds. Predictions are particularly sensitive to how clouds mix with their surroundings, so we'd like to understand this better.
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We take a back to the basics approach and use high resolution simulations to study how a single buoyant bubble rises and mixes with its environment. The prevailing idea is that mixing occurs because of the turbulent interface between cloudy and clear air, but we find that mixing persists even in simulations with no turbulence. Following somewhat forgotten theories from the 1960s and 70s, we hypothesize that mixing depends instead on buoyancy. We derive a simple formula for cloud mixing based on this idea which we verify with simulations. Then we remove buoyancy from our simulations and find that our bubbles no longer mix, supporting our hypothesis. Our work gives a new appreciation of the central role of buoyancy in cloud mixing. See McKim et al. (2020) for more details.
