Updated CMIP6 Climate Models Clouded by Scientific Biases

Satellite Captured Cloudy Southern Ocean

The cloudy Southern Ocean shows an improved radiation budget in the latest IPCC climate models, but there are still significant biases in the simulated cloud physical properties over the Southern Ocean. Those biases are largely canceled out when they jointly influence the cloud radiative effect. The cloud image is captured by the FY-3D satellite. Credit: National Satellite Meteorological Center of China Meteorological Administration

Clouds can either cool or warm the planet’s surface, a radiative effect that contributes substantially to the global energy budget and can be altered by human-caused pollution. Aptly named the Southern Ocean, the world’s southernmost ocean,  is far from human pollution but subject to abundant marine gases and aerosols. It is about 80% covered by clouds. How does this body of water and its relationship with clouds contribute to the world’s changing climate?

Scientists are still investigating to figure it out, and they’re now one step closer, due to an international collaboration identifying compensation errors in widely used climate model protocols known as CMIP6. They published their findings today (September 20) in the journal Advances in Atmospheric Sciences.

Cloud Radiative Effect

Clouds can act as a greenhouse ingredient to warm the Earth by trapping outgoing longwave infrared radiative flux at the top of the atmosphere. Clouds can also enhance the planetary albedo by reflecting shortwave solar radiative flux back to space to cool the Earth. The net effect of the two competing processes depends on the height, type, and the optical properties of the clouds. The cloud radiative effect (CRE) on the Earth’s present-day radiation budget can be inferred from satellite data by comparing upwelling radiation in cloudy and non-cloudy regions.

“Cloud and radiation biases over the Southern Ocean have been a long-lasting problem in the past generations of global climate models,” said corresponding author Yuan Wang. He is now an associate professor in the Department of Earth, Atmospheric, and Planetary Sciences at Purdue University. “After the latest CMIP6 models were released, we were anxious to see how they performed and whether the old problems were still there.”

CMIP Phase 6 (CMIP6) is a project of the World Climate Research Program (WCRP). It allows for the systematic assessment of climate models to illuminate how they compare to each other and real-world data. In this study, Wang and the researchers analyzed five of the CMIP6 models that aim to serve as standard references.

Wang said the researchers were also motivated by other studies in the field that point to the Southern Ocean’s cloud coverage as a contributing factor to some CMIP6 models’ high sensitivity, when the simulations predict a surface temperature that rises too quickly for the rate of increased radiation. In other words, if improperly simulated, the Southern Ocean clouds may cast a shadow of doubt on the projection of future climate change.

“This paper emphasizes compensating errors in the cloud physical properties in spite of overall improvement of radiation simulation over the Southern Ocean,” Wang said. “With space satellite observations, we are able to quantify those errors in the simulated cloud microphysical properties, including cloud fraction, cloud water content, cloud droplet size, and more, and further reveal how each contributes to the total bias in the cloud radiative effect.”

The cloud radiative effect — how clouds interfere with radiation to warm or cool the surface — is largely determined by the physical properties of the cloud. “Cloud radiative effects in CMIP6 are comparable with satellite observations, but we found there are large compensating biases in cloud fraction liquid water path and droplet effective radius,” Wang said. “The major implication is that, even though the latest CMIP models improve the simulation of their mean states, such as radiation fluxes at the top of the atmosphere, the detailed cloud processes are still of large uncertainty.”

According to Wang, this discrepancy also partially explains why the model climate sensitivity assessments do not perform as well, since those assessments rely on model detailed physics — rather than the mean state performance — to evaluate the overall effect on the climate.

“Our future work will aim to pin down individual parameterizations that are responsible for these biases,” Wang said. “Hopefully, we can work closely with model developers to get them solved. After all, the ultimate goal of any model evaluation study is to help improve those models.”

Reference: “Compensating Errors in Cloud Radiative and Physical Properties over the Southern Ocean in the CMIP6 Climate Models” by Lijun Zhao, Yuan Wang, Chuanfeng Zhao, Xiquan Dong and Yuk L. Yung, 20 September 2022, Advances in Atmospheric Sciences.
DOI: 10.1007/s00376-022-2036-z

Other contributors include Lijun Zhao and Yuk L. Yung, Division of Geology and Planetary Science, California Institute of Technology; Chuanfeng Zhao, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University; and Xiquan Dong, Department of Hydrology and Atmospheric Sciences, University of Arizona.

1 Comment on "Updated CMIP6 Climate Models Clouded by Scientific Biases"

  1. “…, the detailed cloud processes are still of large uncertainty.”

    The parameterization of cloud behavior has always been the weak point of Global Circulation Models. While acolytes of anthropogenic global warming are quick to claim that the models are based on ‘science’ (or more properly, physics) it is only a half-truth. Much in the models is based on physics; however, for the foreseeable future, computers cannot handle the computational detail of cloud-energy exchanges. Therefore, assumptions have to be made about cloud behavior, and detailed computation replaced by assumed average behavior. That is not unlike replacing Einstein’s elegant and succinct formula — E = mc^2 — with E = mc^2 +/- e, where “e” is an uncertainty introduced to adjust the final answer to what is subjectively thought to be the ‘correct’ answer. In other words, the result is no longer strictly ‘physics.’

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