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 poor 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 were published in Geophysical Research Letters on 31 July 2026.
During December 2022 and 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 datasets, ERA5 and MERRA-2, alongside the CAM-ATRAS climate model using observations throughout the expedition.
Although all three datasets 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 datasets underestimated the amount of downward longwave radiation reaching the surface. Comparison with observations showed that the reanalysis datasets 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. Instead, the numerical models exhibit an inherent cold temperature bias, which also plays a role in the underestimation of DLW. These findings indicate that accurately representing both cloud phase and temperature is more important than simply reproducing cloud frequency when simulating the Southern Ocean's surface energy budget.
By identifying the processes responsible for persistent cloud biases, the study provides valuable guidance for improving weather and climate models. Better representation of cloud microphysics, aerosol–cloud interactions, and background environment will help reduce uncertainties in simulations of Earth's energy balance, leading to more reliable predictions of future warming, sea ice change, and climate variability.
The researchers emphasize that continued progress will require not only expanded observations of clouds across the Southern Ocean and Antarctica but also increased observations of fundamental atmospheric variables, particularly temperature, to reduce the cold bias in numerical models. As Assistant Professor Kazutoshi Sato notes, "Because observations over Antarctica remain sparse, numerical models still contain substantial uncertainties in their representation of the Antarctic atmosphere. Therefore, incorporating existing but currently underutilized observations into numerical models may provide an effective solution. For example, assimilating observations from the PANSY radar at Japan's Syowa Station, which are not yet routinely used in numerical weather prediction systems, could help reduce model biases and improve forecast accuracy."
This study represents one of the most comprehensive observational evaluations of cloud and radiation simulations over the Southern Ocean using data collected during the JARE64 expedition. By revealing why current models struggle to reproduce these clouds and identifying the processes responsible for long-standing biases, the findings provide an important step toward more accurate weather forecasts, improved climate models, and more confident projections of Earth's changing climate.
About National Institute of Polar Research, Japan
Founded in 1973, the National Institute of Polar Research (NIPR) is an inter-university research institute dedicated to advancing scientific research and observations in the Arctic and Antarctic regions. As one of the four institutes under the [Research Organization of Information and Systems (ROIS)](https://www.rois.ac.jp/), NIPR conducts comprehensive polar research through observation stations and international collaborations. The institute also promotes polar science by supporting collaborative research projects and providing access to scientific data, samples, and materials. NIPR remains Japan's only institution devoted to comprehensive research activities in both polar regions.
About the Research Organization of Information and Systems (ROIS)
ROIS is a parent organization of four national institutes (National Institute of Polar Research, National Institute of Informatics, the Institute of Statistical Mathematics and National Institute of Genetics) and the Joint Support-Center for Data Science Research. It is ROIS's mission to promote integrated, cutting-edge research that goes beyond the barriers of these institutions, in addition to facilitating their research activities, as members of inter-university research institutes.
