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Cooling effect of clouds ‘underestimated’ by climate models, says new study
Clouds could have a greater cooling effect on the planet than climate models currently suggest, according to new research.
The paper, published in Nature Climate Change, aims to correct a “long-standing” and “unaddressed” problem in climate modelling – namely, that existing models simulate too much rainfall from clouds and, therefore, underestimate their lifespan and cooling effect.
The authors have updated an existing climate model with a more realistic simulation of rainfall from “warm” clouds – those that contain water only, rather than a combination of water and ice. They find that this update makes the “cloud-lifetime feedback” – a process in which warmer temperatures increase the lifespan of clouds – almost three times bigger.
The authors note that the newest generation of global climate models – the sixth Coupled Model Intercomparison Project (CMIP6) – predicts faster future warming than its predecessors. This is largely because the new models simulate a smaller cooling effect from clouds.
However, the lead author of the study tells Carbon Brief that fixing the “problem” in rainfall simulations “reduces the amount of warming predicted by the model, by about the same amount as the warming increase between CMIP5 and CMIP6”.
Due to this, he says that the key takeaway from the study is to “take the extra warming in CMIP6 with a grain of salt until some of the other known cloud problems are also fixed in the models”.
Cooling clouds
The impact of clouds on global temperature is a complex area of research that scientists have been working on for decades.
In a Carbon Brief guest post published in 2018, Prof Ellie Highwood – professor of climate physics in the Department of Meteorology at the University of Reading – explains how clouds can affect global temperatures:
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The ten worst predictions in history: learning from past mistakes
The ten worst predictions in history: learning from past mistakes
Ugo Bardi experiments with new predictive methods.
This post was inspired mainly by the shock I had with the various failed attempts to predict the outcome of the Covid-19 epidemic. It was truly a sobering experience: bad predictions, clueless politicians, arrogant scientists, idiotic journalists, and more. It made me doubt of the usefulness of models in general. I think we are doing several (too many) things wrong with the way we use models and (sometimes) we trust them. I’ll be discussing more on this subject in future posts, for the time being, here is a list of failed predictions that I think can teach us something.
1. Coronavirus Deaths. In 2020, the model developed in large part by Neil Ferguson at the Imperial College in London was the main element that led the British government to engage in a strict “lockdown” policy to avoid the hundreds of thousands (perhaps millions) of deaths that the model predicted as a result of the COVID-19 disease. Most European States followed suit. It is still early to evaluate how the real world followed the model but, if we look at the result proposed in the “Report n. 9“, we see that the model was clearly overly pessimistic. The authors of the model defended their work saying that their prediction of doom was just one of several scenarios, which is correct, but weak as a defense. In the future, we’ll be able to say if Europeans truly wrecked their economies for nothing but, for the time being, the coronavirus experience can be seen as a sobering experience on the limits of the models as predictive tools.
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