Climate Scientists Admit Clouds are Still a Big Unknown

Page 6, Propagation of Error and the Reliability of Global Air Temperature Projections by Pat Frank

Guest essay by Eric Worrall

Admitting some cloud error is as close as most climate modellers come to admitting their projections are not fit for purpose. Note the image above is from Pat Frank’s paper about cloud error, not Paulo Ceppi and Ric Williams’ paper. More below.

Why clouds are the missing piece in the climate change puzzle

September 11, 2020 8.01pm AEST

Paulo Ceppi Lecturer in Climate Science, Imperial College London
Ric Williams Professor of Ocean Sciences, University of Liverpool

How much our world will warm this century depends on the actions we take in coming decades. In order to keep global temperature rise below 1.5°C and avoid dangerous levels of warming, governments need to know how much carbon they can emit, and over what timeframe.

But current climate models don’t agree on where that threshold lies. In new research, we discovered one of the reasons why there is such a large range of estimates for how much carbon can be safely emitted: the uncertain behaviour of clouds. In some climate models, clouds strongly amplify warming. In others, they have a neutral effect or even dampen warming slightly. So why are clouds likely to play such a pivotal role in deciding our fate?

Clouds can act like a parasol, cooling the Earth by reflecting sunlight away from the planet’s surface and back into space. But they can also act like an insulating blanket, warming the Earth by preventing some of the heat in our atmosphere from escaping into space as infrared radiation. This “blanket” effect is particularly noticeable during the winter, when cloudy nights are typically much warmer than cloud-free ones.

While we do know that clouds will likely amplify global warming, there is still a great deal of uncertainty about how strong this effect will be. Here climate models are of little help, as they can only simulate the bulk properties of the atmosphere over scales of tens of kilometres and several hours. Tiny cloud droplets form and evaporate in minutes. Models miss these small-scale details, but they’re needed for accurate predictions.

Climate models have to resort to simplifications in order to represent clouds, which introduces error. As different models make different simplifications in their portrayal of cloud processes, they also make different predictions of the cloud feedback, which results in a range of global warming projections and differences in our remaining carbon budget. For a given future carbon emissions scenario, clouds are the single most important factor behind the differences in future warming predicted between models.

Read more:

The abstract of the study;

Controls of the transient climate response to emissions by physical feedbacks, heat uptake and carbon cycling

Richard G Williams1,4, Paulo Ceppi2 and Anna Katavouta1,3

Published 11 September 2020 • © 2020 The Author(s).
Published by IOP Publishing Ltd

The surface warming response to carbon emissions is diagnosed using a suite of Earth system models, 9 CMIP6 and 7 CMIP5, following an annual 1% rise in atmospheric CO2 over 140 years. This surface warming response defines a climate metric, the Transient Climate Response to cumulative carbon Emissions (TCRE), which is important in estimating how much carbon may be emitted to avoid dangerous climate. The processes controlling these intermodel differences in the TCRE are revealed by defining the TCRE in terms of a product of three dependences: the surface warming dependence on radiative forcing (including the effects of physical climate feedbacks and planetary heat uptake), the radiative forcing dependence on changes in atmospheric carbon and the airborne fraction. Intermodel differences in the TCRE are mainly controlled by the thermal response involving the surface warming dependence on radiative forcing, which arise through large differences in physical climate feedbacks that are only partly compensated by smaller differences in ocean heat uptake. The other contributions to the TCRE from the radiative forcing and carbon responses are of comparable importance to the contribution from the thermal response on timescales of 50 years and longer for our subset of CMIP5 models and 100 years and longer for our subset of CMIP6 models. Hence, providing tighter constraints on how much carbon may be emitted based on the TCRE requires providing tighter bounds for estimates of the physical climate feedbacks, particularly from clouds, as well as to a lesser extent for the other contributions from the rate of ocean heat uptake, and the terrestrial and ocean cycling of carbon.

Read more:

The authors assert that if we had a better understanding clouds, the spread of model predictions could be reduced. But there is some controversy about how badly cloud errors affect model predictions, and that controversy is not just limited to climate alarmists.

Pat Frank, who produced the diagram at the top of the page in his paper “Propagation of Error and the Reliability of Global Air Temperature Projections“, argues that climate models are unphysical and utterly unreliable, because they contain known model cloud physics errors so large the impact of the errors dwarfs the effect of rising CO2. My understanding is Pat believes large climate model physics errors have been hidden away via a dubious tuning process, which adds even more errors to coerce climate models into matching past temperature observations, without fixing the original errors.

Climate skeptic Dr. Roy Spencer disagrees with Pat Frank; Dr. Spencer suggests the cloud error biases hilighted by Pat Frank are cancelled out by other biases, resulting in a stable top of atmosphere radiative balance. Dr. Spencer makes it clear that he also does not trust climate model projections, though for different reasons to Pat Frank.

Other climate scientists like the authors of the study above, Paulo Ceppi and Ric Williams, pop up from time to time and suggest that clouds are a significant problem, though Paulo and Ric’s estimate of the scale of the problem appears to be well short of Pat Frank’s estimate.

Whoever is right, I think what is abundantly clear is the science is far from settled.

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via Watts Up With That?

September 12, 2020 at 12:07PM

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