The cloudy Southern Ocean shows an improved radiation budget in the latest IPCC climate models, although there are still significant biases in the simulated cloud physical properties over the Southern Ocean, which are largely canceled out when they jointly influence the cloud radiative effect. The FY-3D satellite captures the cloud image.
Clouds can either cool or warm the planet's surface, a radiative effect that contributes greatly to the global energy budget and can be influenced by human-caused pollution. The southern ocean, the world's southernmost ocean, is far from human pollution but subject to abundant marine gases and aerosols, but it is only about 80% covered by clouds.
Due to an international collaboration, scientists are still looking for solutions to the issue, known as CMIP6. Their findings were published today (September 20) in the journal Advances in Atmospheric Sciences.
Yuan Wang, a corresponding author, said cloud and radiation biases over the Southern Ocean have been a long-standing problem in previous generations of global climate models. "We were interested in seeing how the latest CMIP6 models performed, and whether or not the old problems were still there."
CMIP Phase 6 is a part of the World Climate Research Program (WCRP). It allows for the systematic assessment of climate models to illuminate how they differ from each other and real-world data. In this study, Wang and the researchers examined five of the CMIP6 models that aim to serve as standard references.
Wang said the researchers were also motivated by other research in the field that suggest the Southern Ocean's cloud coverage as a contributing factor to some CMIP6 models' high sensitivity when they model 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 on the forecast of future climate change.
Despite the overall improvement in radiation simulation over the Southern Ocean, this paper emphasizes compensating errors in the cloud physical properties. "We are able to quantify those deficiencies 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 physical properties of the cloud determine the cloud's cloud radiative effect, which is largely dependent on the surface's temperature. "We found substantial compensating biases in cloud fraction liquid water path and droplet effective radius," Wang said. "The major implications are 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 remain complex."
According to Wang, this discrepancy partly explains why the model climate sensitivity assessments do not perform as well, since they rely on model detailed physics rather than mean state performance to evaluate the whole effect on the climate.
Wang said his future research will focus on identifying individual parameterizations that are causing these biases. "Hopefully, we can collaborate with model developers to resolve them. After all, the ultimate objective of any model evaluation study is to improve those models."
Lijun Zhao, Yuan Wang, Chuanfeng Zhao, and Yuk L. Yung, 20 September 2022, Advances in Atmospheric Sciences. DOI: 10.1007/s00376-022-2036-z
Lijun Zhao and Yuk L. Yung, Division of Geology and Planetary Science, California Institute of Technology, Chuanfeng Zhao, Department of Atmospheric and Oceanic Sciences, Peking University; and Xiquan Dong, Department of Hydrology and Atmospheric Sciences, University of Arizona.