Source: Earth and Space Science
Clouds play an important role in Earth’s climate and can warm or cool the atmosphere depending on their particular characteristics and on when and where they form. Computer models of Earth’s future climate suffer from uncertainties linked to the challenge of predicting cloud cover and how the effects of clouds might evolve as the planet warms.
To help address those challenges, Vignesh et al. delved into cloud cover predictions of climate models that are part of the Coupled Model Intercomparison Project (CMIP). CMIP is a periodically updated climate-modeling framework used by scientists around the world so that the various models they develop can be analyzed and compared systematically.
The researchers compared cloud cover predictions from models developed under the current phase of CMIP, CMIP6, with predictions from models from the previous phase, CMIP5. They found some differences in cloud cover simulation between CMIP5 and CMIP6 models over a historical period from 1861 to 2005.
More specifically, CMIP6 models showed a wider spread, or variation, among their predictions of historical changes in the fraction of the cloud-covered sky than the CMIP5 models did. In addition, CMIP6 models predicted a global mean cloud fraction about 4.5% higher than predicted by CMIP5 models.
The scientists also used real-world cloud observations from NASA’s CALIPSO and CloudSat satellites to evaluate CMIP6 predictions for recent years. Overall, the predictions matched the observations well, but the team found some inconsistencies at certain altitudes and latitudes. But by applying a simple statistical technique, the researchers could improve agreement between CMIP6 predictions and the satellite observations.
These findings highlight specific areas of progress in simulating cloud cover and could help inform future improvements to climate models. (Earth and Space Science, https://doi.org/10.1029/2019EA000975, 2020)
—Sarah Stanley, Science Writer
Citation:
Stanley, S. (2020), Evaluating cloud cover predictions in climate models, Eos, 101, https://doi.org/10.1029/2020EO141682. Published on 23 March 2020.
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