NASA: We Can’t Model Clouds, So Climate Models Are 100 Times Less Accurate Than Needed For Projections

NASA has conceded that climate models lack the precision required to make climate projections due to the inability to accurately model clouds. 

Clouds have the capacity to dramatically influence climate changes in both radiative longwave (the “greenhouse effect”) and shortwave.

Cloud cover domination in longwave radiation

In the longwave, clouds thoroughly dwarf the CO2 climate influence. According to Wong and Minnett (2018):

• The signal in incoming longwave is 200 W/m² for clouds over the course of hours. The signal amounts to 3.7 W/m² for doubled CO2 (560 ppm) after hundreds of years.

• At the ocean surface, clouds generate a radiative signal 8 times greater than tripled CO2 (1120 ppm).

• The absorbed surface radiation for clouds is ~9 W/m². It’s only 0.5 W/m² for tripled CO2 (1120 ppm).

• CO2 can only have an effect on the first 0.01 mm of the ocean. Cloud longwave forcing penetrates 9 times deeper, about 0.09 mm.

Image Source: Wong and Minnett, 2018

Cloud cover domination in shortwave radiation

In its shortwave albedo capacity, cloud cover modulates the amount of solar radiation that warms the ocean. Changes in the Earth’s radiation budget “are caused by changes in tropical mean cloudiness.” (Wielicki et al., 2002).

When cloud cover increases, less shortwave radiation reaches the surface, leading to cooling. When cloud cover decreases – as it has since the 1980s – more solar radiation is absorbed.

The decrease in cloud cover in recent decades can therefore explain the 1979-2017 warming (Herman et al., 2013Poprovsky, 2019, Loeb et al., 2018).

Image Source: Herman et al., 2013

Image Source: Poprovsky, 2019

Image Source: Loeb et al., 2018

IPCC and NASA acknowledge that we can’t model clouds with requisite accuracy

The IPCC has admitted there is a great deal of “continuing uncertainty” in the sign and magnitude of the cloud influence. Most models indicate a positive feedback (more warming), but this “is not well understood” and the IPCC scientists “are not confident that it is realistic.”

Image Source: IPCC (2013)

NASA has been even more candid about the massive uncertainties associated with cloud climatology.

Some clouds “cool more than they heat” and other clouds “warm more than they cool.”

In some models “clouds decrease the net greenhouse effect, whereas in others they intensify it.”

Because the uncertainties are so pervasive, NASA concludes that “today’s models must be improved by about a hundredfold in accuracy” if we wish to make climate projections.

Image Source: NASA

Uncertainty in the effects of cloud forcing are 20-40 times larger than the projected greenhouse gas warming for the next century

Due to the enormous uncertainties associated with cloud cover changes, the IPCC’s CO2 emission scenarios used to calculate warming are reduced to the realm of nearly evidence-free presumption.

Using the IPCC’s emission scenarios, for example, the projected greenhouse gas-induced warming by 2100 is 3.7°C. Due to cloud forcing errors, the uncertainty in this projection is ±130°C!

When both the cloud and the forcing uncertainties are allowed to accumulate together, after 5 years the A2 [greenhouse gas-induced] scenario includes a 0.34°C warmer Earth but a ±8.8°C uncertainty. At 10 years this becomes 0.44±15° C, and 0.6±27.7°C in 20 years. By 2100, the projection is 3.7±130°C.”

Image Source: Frank, 2008

Unless we can model clouds, we cannot model climate with any precision

Due to the dominant influence of cloud cover in facilitating climate change, dramatically improving our woefully insufficient capacity to model clouds is both necessary and fundamental.

Climate science should rise to the challenge rather than continuing to gloss over or even dismiss the profound cloud modeling problem undermining climate projections.

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