This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: The Southern Ocean plays a pivotal role in regulating Earth's climate, yet the clouds that blanket this remote region remain among the least understood features in atmospheric science. By controlling both incoming sunlight and outgoing heat, these clouds strongly influence Earth's energy balance.
Even small errors in representing them can introduce significant uncertainties into weather forecasts, climate models and projections of future global warming, making them a long-standing challenge for climate scientists. To better understand why these clouds remain so difficult to simulate, researchers from the National Institute of Polar Research (Japan) and Nagoya University analyzed cloud observations collected during the 64th Japanese Antarctic Research Expedition (JARE64) aboard the research icebreaker R/V Shirase. Professor Jun Inoue explains, "Numerical models have been reported to exhibit limited skill in reproducing clouds.
In particular, over the Southern Ocean and Antarctica, where cloud representation remains especially challenging, cloud-related biases have been shown to increase errors in the surface energy budget through biases in the radiative budget." Their findings are published in Geophysical Research Letters. From December 2022 to March 2023, ship-based instruments continuously measured cloud properties, atmospheric temperature and humidity, surface radiation and aerosol concentrations, providing a comprehensive benchmark for evaluating model performance. The team evaluated two widely used atmospheric reanalysis data sets, ERA5 and MERRA-2, alongside the CAM-ATRAS climate model using observations throughout the expedition.
Although all three data sets broadly captured cloud patterns over the Southern Ocean, important differences emerged. ERA5 and MERRA-2 consistently overestimated the occurrence of low-level clouds, whereas CAM-ATRAS most closely matched the observations, particularly in reproducing cloud occurrence and cloud phase. Surprisingly, despite simulating abundant low-level clouds, all three data sets underestimated the amount of downward longwave radiation reaching the surface.
Comparison with observations showed that the reanalysis data sets contain higher aerosol concentrations than observed. Therefore, the researchers also conducted sensitivity experiments with CAM-ATRAS by increasing aerosol emissions over the Southern Hemisphere to examine how aerosols influence cloud formation and surface radiation. However, the aerosol sensitivity experiments further showed that increasing aerosol concentrations produced more low-level clouds but had only a limited effect on surface radiation.
The researchers traced this discrepancy to the physical properties of the simulated clouds rather than to cloud amount alone. In the models, clouds contained excessive ice, reducing the heat emitted toward the surface. However, these results demonstrate that biases in cloud representation alone cannot explain the underestimated DLW.
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