Updated CMIP6 Climate Models Clouded by Scientific Biases
The planet's surface can be either warmed or cooled by clouds, a radiative effect that makes a significant contribution to the global energy budget and can be changed by pollution brought on by humans. The world's southernmost ocean, appropriately called the Southern Ocean, is free of human pollution but is nonetheless exposed to a lot of marine gases and aerosols. About 80% of it is obscured by clouds. What role does this body of water's interaction with the clouds have in the global climate change?
Due to an international partnership that discovered compensating faults in commonly used climate model protocols known as CMIP6, scientists are now one step closer to understanding it. Today, September 20, they published their findings in the journal Advances in Atmospheric Sciences.
Through the trapping of emitted longwave infrared radiative flux at the top of the atmosphere, clouds can function as a greenhouse gas component to warm the Earth. By reflecting shortwave solar radiative flux back to space to cool the Earth, clouds can also increase the planetary albedo. The height, kind, and visual characteristics of the clouds have an impact on the overall outcome of the two opposing processes. Satellite data can be used to infer the cloud radiative effect (CRE) on the Earth's current radiation budget by contrasting upwelling radiation in cloudy and non-cloudy regions.
According to the corresponding author Yuan Wang, "cloud and radiation biases over the Southern Ocean have been a long-standing concern in the prior generations of global climate models." He is currently an associate professor in Purdue University's Department of Earth, Atmospheric, and Planetary Sciences. "We were eager to observe how the most recent CMIP6 models performed and whether the previous issues were still present," says the author.
The World Climate Research Program is working on the CMIP Phase 6 (CMIP6) project (WCRP). It enables the systematic evaluation of climate models to show how they differ from one another and from empirical data. Five of the CMIP6 models that are intended to serve as standard references were examined by Wang and the researchers for this study.
The Southern Ocean's cloud cover, Wang added, has been implicated in previous research in the area as a contributing reason to some CMIP6 models' high sensitivity, when the simulations forecast a surface temperature rise that is too rapid for the pace of rising radiation. In other words, if the Southern Ocean clouds are inaccurately simulated, the prediction of future climate change could be called into question.
Despite a general improvement in radiation simulation over the Southern Ocean, Wang stated that the focus of this work is on mitigating faults in the cloud's physical characteristics. We are able to quantify the mistakes in the simulated cloud microphysical parameters, such as cloud fraction, cloud water content, cloud droplet size, and more, using measurements from space satellites. We can also see how each one affects the overall bias in the cloud radiative impact.
The physical characteristics of the cloud play a big role in the cloud radiative effect, which describes how clouds interact with radiation to warm or cool the surface. Although the cloud radiative effects in CMIP6 are comparable to satellite data, Wang discovered that there are significant offsetting biases in the cloud proportion liquid water path and droplet effective radius. The main result is that the detailed cloud dynamics are still quite unclear, despite the fact that the most recent CMIP models simulate their mean states, such as radiation fluxes at the top of the atmosphere, better.
Since model climate sensitivity evaluations rely on model detailed physics — as opposed to the mean state performance — to gauge the overall impact on the climate, Wang claims that this disparity also largely explains why those assessments don't perform as well.
The goal of Wang's upcoming research is to identify and isolate the specific parameterizations that cause these biases. "Perhaps we might collaborate closely with model developers to find solutions. Any model evaluation study's ultimate purpose is to assist in the improvement of those models.
Other authors include Chuanfeng Zhao, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University; Lijun Zhao and Yuk L. Yung, Division of Geology and Planetary Science, California Institute of Technology; and Xiquan Dong, Department of Hydrology and Atmospheric Sciences, University of Arizona.
By INSTITUTE OF ATMOSPHERIC PHYSICS, CHINESE ACADEMY OF SCIENCES
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