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What is the net feedback from clouds?

What the science says...

Select a level... Basic Intermediate

Evidence is building that net cloud feedback is likely positive and unlikely to be strongly negative.

Climate Myth...

Clouds provide negative feedback

"Climate models used by the International Panel on Climate Change (IPCC) assume that clouds provide a large positive feedback, greatly amplifying the small warming effect of increasing CO2 content in air. Clouds have made fools of climate modelers. A detailed analysis of cloud behavior from satellite data by Dr. Roy Spencer of the University of Alabama in Huntsville shows that clouds actually provide a strong negative feedback, the opposite of that assumed by the climate modelers. The modelers confused cause and effect, thereby getting the feedback in the wrong direction." (Ken Gregory)

The effect of clouds in a warming world is complicated. One challenge is that clouds cause both warming and cooling. Low-level clouds tend to cool by reflecting sunlight. High-level clouds tend to warm by trapping heat.

clouds

As the planet warms, clouds have a cooling effect if there are more low-level clouds or less high-level clouds.  Clouds would cause more warming if the opposite is true.  To work out the overall effect, scientists need to know which types of clouds are increasing or decreasing. 

Some climate scientists, such as Richard Lindzen and Roy Spencer, are skeptical that greenhouse gas emissions will cause dangerous warming. Their skepticism is based mainly on uncertainty related to clouds.  They believe that when it warms, low-level cloud cover increases. This would mean the Earth's overall reflectiveness would increase. This causes cooling, which would cancel out some of the warming from an increased greenhouse effect. 

However, recent evidence indicates this is not the case. Two separate studies have looked at cloud changes in the tropics and subtropics using a combination of ship-based cloud observations, satellite observations and climate models. Both found that cloud feedback in this region appears to be positive, meaning more warming.

Another study used satellite measurements of cloud cover over the entire planet to measure cloud feedback.  Although a very small negative feedback (cooling) could not be ruled out, the overall short-term global cloud feedback was probably positive (warming).  It is very unlikely that the cloud feedback will cause enough cooling to offset much of human-caused global warming.

Other studies have found that the climate models that best simulate cloud changes are the ones that find it to be a positive feedback, and thus have higher climate sensitivities.  Steven Sherwood explains one such study:

While clouds remain an uncertainty, the evidence is building that clouds will probably cause the planet to warm even further, and are very unlikely to cancel out much of human-caused global warming.  It's also important to remember that there many other feedbacks besides clouds. There is a large amount of evidence that the net feedback is positive and will amplify global warming.

Basic rebuttal written by dana1981


Update July 2015:

Here is the relevant lecture-video from Denial101x - Making Sense of Climate Science Denial

Last updated on 25 July 2017 by skeptickev. View Archives

Printable Version  |  Offline PDF Version  |  Link to this page

Comments

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Comments 251 to 256 out of 256:

  1. Licorj - I would suggest following the links in the opening post; there is considerable evidence for a small positive cloud feedback based on observations, on constraints from other forcings and feedbacks, from paleo evidence, etc. 

    Not a "game", not a guess - a small positive feedback comes from the best estimates of the various evidence available. 

  2. Licorj @250, by "estimate", it is meant estimate based on empirical observations and predictions from models.  Both, separately, suggest that a small positive cloud feedback is more likely than not.  The uncertainty is large so that negative feedbacks are not excluded, but neither are large positive feedbacks excluded.  

    You say that to get the true cloud feedback, we need lots of measurements around the world.  Those measurements have been done.  Three examples of such measurements are linked to in the OP.  Unfortunately the measurements do not tightly constrain the result because the feedback is complicated and the observational data is noisy.

    Of course, it is always possible to avoid the whole issue by looking at empirical estimates of the net feedback from historical and paleo data.  These overwhelmingly suggest a large net positive feedback.  As these are estimates based on what has actually occured on the Earth, they of necessity include all feedbacks.  Therefore, if the cloud feedback does in fact turn out to be negative, that merely means that some other combination of feedbacks is more strongly positive than currenly estimated.

  3. On Safari, the final link to the Steven Sherwood video is broken. There appears to be a leading '/' that shouldn't be there.

  4. Speaking of clouds and manmade climate change, here’s a handy reference document recently published by the WMO…

    Humanity has a primordial fascination with clouds. The meteorological and hydrological communities have come to understand through decades of observation and research that cloud processes – from the microphysics of initial nucleation to superstorms viewed from satellites – provide vital information for weather prediction, and for precipitation in particular. Looking at clouds from a climate perspective introduces new and difficult questions that challenge our overall assumptions about how our moist, cloudy atmosphere actually works.

    Clouds are one of the main modulators of heating in the atmosphere, controlling many other aspects of the climate system. Thus, “Clouds, Circulation and Climate Sensitivity” is one of the World Climate Research Programmes (WCRP) seven Grand Challenges. These Grand Challenges represent areas of emphasis in scientific research, modelling, analysis and observations for WCRP and its affiliate projects in the coming decade.

    Understanding Clouds to Anticipate Future Climate by Sandrine Bony, Bjorn Stevens & David Carlson, Bulletin nº Vol 66 (1) – 2017

  5. Interesting paper finds "surprising" results from CERES with a negative trend of Earth Energy Imbalance as well as a negative trend of Ocean Heat Content Time Derivative :

    "Decadal Changes of the Reflected Solar Radiation and the Earth Energy Imbalance" by Dewitte , Clerbaux and Cornelis.

    Abstract: Decadal changes of the Reflected Solar Radiation (RSR) as measured by CERES from 2000 to 2018 are analysed. For both polar regions, changes of the clear-sky RSR correlate well with changes of the Sea Ice Extent. In the Arctic, sea ice is clearly melting, and as a result the earth is becoming darker under clear-sky conditions. However, the correlation between the global all-sky RSR and the polar clear-sky RSR changes is low. Moreover, the RSR and the Outgoing Longwave Radiation (OLR) changes are negatively correlated, so they partly cancel each other. The increase of the OLR is higher then the decrease of the RSR. Also the incoming solar radiation is decreasing. As a result, over the 2000–2018 period the Earth Energy Imbalance (EEI) appears to have a downward trend of −0.16 ± 0.11 W/m2dec. The EEI trend agrees with a trend of the Ocean Heat Content Time Derivative of −0.26 ± 0.06 (1 σ) W/m2dec.

    ...

    "The Earth Energy Imbalance (EEI) shows a trend of −0.16 ± 0.11 W/m2dec. The decreasing trend in EEI is in agreement with a decreasing trend of −0.26 ± 0.06 W/m2dec in the Ocean Heat Content Time Derivative (OHCTD) after 2000.
    The OHCTD over the period 1960–2015 shows three different regimes, with low OHCTD prior to 1982, rising OHCTD from 1982 to 2000, and decreasing OHCTD since 2000. These OHCTD periods correspond to periods of slow/rapid/slow surface temperature rise [16,17], to periods of strong La Ninas/El Ninos/La Ninas [14,18], and to periods of increasing/decreasing/increasing aerosol loading [19,20]. "

    https://www.mdpi.com/2072-4292/11/6/663/htm#Purple curve: running yearly mean EEI. Green line: linear fit to running yearly mean EEI. Blue curve: 10 year running mean OHCTD. Orange curve: piecewise linear fit to OHCTD.

  6. Hefaistos @255 ,

    please comment on the Dewitte et al., 2019  paper you cite.

    My first impression of the EEI graph is (ignoring error bars) that it's very noisy.

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