The logic rests on a piece of atmospheric physics that meteorologists have relied on for more than a century. Over most of the tropical and subtropical oceans, the lowest layer of the atmosphere is convectively unstable: the sun-warmed sea keeps the air above it slightly cooler than itself, so the layer is well mixed by turbulence. In such a well-mixed layer, the temperature falls with height at roughly the dry-adiabatic rate while the water vapor content stays nearly constant. As a result, relative humidity increases steadily with altitude, and clouds form at the lifting condensation level, the height at which the rising air finally reaches saturation. That height depends almost entirely on two things: the temperature and the humidity of the air near the surface. Warmer or drier air pushes cloud base higher; moister air pulls it lower. The cloud base, in other words, is a natural gauge of near-surface humidity, one that is written across the sky and readable by instruments that already exist.

What the team did was to invert the classical relationship. Instead of computing cloud base from known surface conditions, they treat the cloud base as the known quantity, measured by a lidar or ceilometer, and recover the humidity from it. The theoretical framework is built on the Clausius–Clapeyron relation, which governs how saturation vapor pressure changes with temperature, and on the assumption that relative humidity increases with height at a rate of roughly four percent per hundred meters in a well-mixed subcloud layer. Combining this lapse rate with the measured cloud base height yields the relative humidity at a reference height of about forty meters above the sea. A final conversion, using the sea surface temperature and the small temperature difference between the water and the overlying air, translates that relative humidity into specific humidity, the quantity that enters directly into the bulk aerodynamic formula for evaporation.

The beauty of the approach is that it attacks the weakest link in satellite estimates of ocean evaporation. In the bulk formula, the evaporative flux depends on the humidity deficit between the surface and the near-surface air, multiplied by the wind speed and an exchange coefficient. Sea surface temperature, and hence the saturation humidity at the surface, is well observed from orbit. Wind speed comes from scatterometers. But the near-surface air humidity has no true satellite proxy, and studies have estimated that errors in this single quantity contribute roughly sixty percent of the total uncertainty in satellite-derived latent heat fluxes. Existing climatologies such as HOAPS, SeaFlux, IFREMER, and J-OFURO rely on statistical correlations with other remotely sensed variables, and even in global annual averages their humidity errors reach about one gram per kilogram, with instantaneous retrieval errors over the subtropical oceans corresponding to tens of watts per square meter in the flux.

To test whether cloud base could do better, the researchers turned to one of the most ambitious field campaigns in modern atmospheric science. EUREC4A, short for ElUcidating the RolE of Cloud-Circulation Coupling in Climate, took place in January and February 2020 in the trade-wind region east of Barbados. The campaign deployed an extraordinary array of instruments: the German research aircraft HALO flew circles of roughly 220 kilometers in diameter at about nine and a half kilometers altitude, releasing 810 dropsondes that measured pressure, temperature, and humidity all the way down to the sea. The research vessel R/V Meteor and the Barbados Cloud Observatory launched radiosondes and operated ceilometers, ground-based laser instruments that fire pulses of light into the sky and time the returns from cloud droplets to determine cloud base height. HALO also carried WALES, an airborne differential absorption lidar capable of profiling aerosol and water vapor from above the clouds.

The ground-based validation used 171 coincident pairings of ceilometer cloud base measurements and radiosonde humidity profiles from the research vessel, collected between 18 January and 14 February 2020, along with 118 pairings from the Barbados Cloud Observatory. Because ceilometer cloud detections tend to be skewed toward elevated values, as clouds dissipate from their bases upward and wind shear stretches cloud layers, the team associated cloud base with the main peak of the distribution of detections rather than with the highest returns. The results were striking: predictions of near-surface specific humidity from cloud base height showed a mean bias of only 0.43 grams per kilogram compared with the radiosonde observations, a median absolute error of 0.52 grams per kilogram, and a correlation of 0.76 with the observed day-to-day variability. For a quantity that has resisted direct satellite measurement for decades, that is respectable skill from a simple laser ranging instrument.

The crucial question, of course, was whether the trick works from above, as it would need to for a satellite application. The team therefore applied the same method to WALES lidar measurements from HALO, identifying cloud base as the lowest altitude where the backscatter signal exceeded background aerosol levels, using a threshold chosen to be well above values produced even by heavy Saharan dust. Two case studies put the method through its paces. On 28 January 2020, the aircraft sampled the small, shallow trade cumulus known to the campaign community as sugar clouds; on 2 February, it flew through a regime of deeper flower clouds with stratiform layers near cloud top and a strong Saharan dust layer reaching up to 2.5 kilometers. The airborne lidar estimates of humidity showed remarkably low mean biases of 0.04 and minus 0.06 grams per kilogram, with correlations of 0.61 and 0.57 and median absolute errors of 0.31 grams per kilogram in both cases.

Just as instructive were the cases where the method failed. The researchers traced a handful of outliers in the lidar comparison back to their physical origins using backscatter profiles, nearby dropsondes, and visible satellite imagery from GOES-16. Some anomalies arose from cold pools, the pockets of rain-cooled air that spread outward from precipitating clouds and produce shallow, moist layers decoupled from the overlying cloud base. Others came from cloud fragments left behind by dissipating stratiform layers, or from multilayered cloud systems in which the lidar detects the base of an upper layer rather than the lowest one. These failure modes are not fatal, the authors argue, but they define the operating envelope: the method requires a convective, well-mixed subcloud layer and a detected cloud base that is genuinely the lowest layer coupled to the surface. Screening for optical depth, multilayer cloud structure, and cold pool contamination would be essential for any operational product.

The error analysis also identifies the two quantities that would need careful calibration in a satellite implementation. The first is the relative humidity lapse rate below cloud base, which the observations confirmed sits close to the theoretical value of four percent per hundred meters for cloudy profiles, with a median of 3.8 percent per hundred meters, but which broadens for non-cloudy soundings. The second is the sea-air temperature difference, which the ship measurements showed had a median of about one kelvin, with the ocean warmer than the air in 97 percent of nearly 48,000 measurements, consistent with the convective conditions the method assumes. Sensitivity calculations show that the humidity error is dominated by the product of cloud base height and the humidity lapse rate, with the temperature difference playing a secondary but non-negligible role.

The outlook is tantalizing because the required ingredients are already partly in orbit. The newly launched EarthCARE satellite carries a high spectral resolution lidar whose horizontal resolution of about 280 meters appears sufficient for the method, though its 100-meter vertical resolution would by itself introduce humidity errors of roughly half a gram per kilogram, a penalty that could be reduced with sufficient averaging given the natural variability of cloud base. The authors suggest that future instruments, perhaps adapted from technologies like the GEDI lidar on the International Space Station, could be designed with cloud base retrieval in mind, potentially alongside coincident measurements of wind speed and air-sea temperature difference. Even before dedicated satellites fly, cloud base information from existing lidars could be folded into reanalyses and into the statistical flux retrieval frameworks already in use, adding a physics-based constraint to approaches that have until now been purely empirical. If it scales, a humble measurement of where the clouds begin could sharpen our accounting of the planet’s largest heat engine.

Subject of Research: Remote estimation of near-surface specific humidity over convective oceans from cloud base height lidar observations

Article Title: Estimating near-surface specific humidity over convective oceanic regions from cloud base height observations

Article References: Estimating near-surface specific humidity over convective oceanic regions from cloud base height observations. (n.d.). https://doi.org/10.5194/amt-19-5973-2026

Image Credits: AI Generated

DOI: 10.5194/amt-19-5973-2026

Keywords: specific humidity, cloud base height, lidar, air-sea fluxes, evaporation, EUREC4A, trade-wind cumulus, boundary layer, satellite remote sensing, lifting condensation level, surface energy budget, EarthCARE

The research, led by scientists from the National Institute of Polar Research in Japan and Nagoya University, draws on measurements collected during the 64th Japanese Antarctic Research Expedition, known as JARE64, aboard the research icebreaker R/V Shirase. Between December 2022 and March 2023, as the vessel threaded its way across the Southern Ocean toward Antarctica, instruments aboard the ship continuously recorded cloud properties, atmospheric temperature and humidity, surface radiation, and aerosol concentrations. This sustained campaign produced one of the most comprehensive observational benchmarks ever assembled for evaluating how well models reproduce clouds and radiation over a region where ground truth is extraordinarily scarce. Professor Jun Inoue of the research team notes that numerical models have long shown poor skill in reproducing clouds, particularly over the Southern Ocean and Antarctica, where cloud-related biases propagate into errors in the surface energy budget through distortions of the radiative budget.

To test how well existing tools perform against these new measurements, the team evaluated three widely used datasets: the ERA5 and MERRA-2 atmospheric reanalyses, which blend observations with numerical weather prediction models to create continuous global records, and the CAM-ATRAS climate model, which simulates the atmosphere from first physical principles. Reanalyses are the workhorses of climate research, providing researchers with gridded estimates of atmospheric conditions even in data-sparse regions, so any systematic flaws in their cloud representation can quietly contaminate a wide range of downstream studies. The comparison covered the full span of the expedition, allowing the scientists to assess cloud occurrence, cloud phase, and the downward longwave radiation that reaches the ocean or sea ice surface across a broad swath of the Southern Ocean in a single coherent framework.

The results revealed a pattern of errors that is both subtle and consequential. All three datasets broadly captured the large-scale patterns of cloudiness over the Southern Ocean, which might initially suggest that models are performing reasonably well. But the details told a different story. ERA5 and MERRA-2 consistently overestimated how often low-level clouds occurred, painting the boundary layer as cloudier than it really was. CAM-ATRAS, by contrast, came closest to the observations, particularly in reproducing both the frequency of clouds and their phase, that is, whether they were composed of liquid water droplets or ice crystals. Cloud phase matters enormously in this environment, because supercooled liquid clouds emit radiation differently than ice-containing clouds and persist under conditions that models often misjudge.

The most surprising finding, however, was that despite simulating abundant low-level clouds, all three datasets underestimated the amount of downward longwave radiation reaching the surface. Downward longwave radiation is the infrared heat emitted by clouds and atmospheric gases back toward the ground, and over the Southern Ocean it is a critical component of the surface energy budget, helping to determine sea surface temperatures, sea ice formation, and the exchange of heat between ocean and atmosphere. If models produce plenty of clouds yet still deliver too little heat to the surface, something fundamental is wrong with the physical character of those simulated clouds, not merely their quantity. This discrepancy, the researchers realized, pointed toward deeper problems in how the models represent cloud microphysics and the thermal structure of the atmosphere.

To investigate the role of aerosols, the tiny airborne particles on which cloud droplets and ice crystals form, the team first compared aerosol concentrations in the reanalysis datasets against the shipboard measurements. The reanalyses, it turned out, contained higher aerosol concentrations than were actually observed over the Southern Ocean, a region famously remote from industrial pollution sources and often used as a natural laboratory for studying pristine marine clouds. The researchers then ran sensitivity experiments with CAM-ATRAS, artificially increasing aerosol emissions across the Southern Hemisphere to see how the added particles would influence cloud formation and surface radiation. The experiments showed that higher aerosol concentrations did indeed produce more low-level clouds, but the effect on surface radiation was limited. Aerosol loading alone, in other words, could not explain the radiation deficit.

The true culprit lay in the physical properties of the simulated clouds rather than in their abundance. In the models, the clouds contained excessive ice, and ice-rich clouds emit less infrared heat downward than the warmer, liquid-dominated clouds actually observed over the Southern Ocean. This microphysical flaw reduced the longwave radiation reaching the surface even as the models churned out plenty of cloud cover. Yet even correcting for this did not close the gap entirely. The study demonstrated that biases in cloud representation alone cannot account for the underestimated downward longwave radiation; the numerical models also carry an inherent cold temperature bias, systematically simulating atmospheric temperatures that are too low, which further suppresses the infrared emission reaching the surface. Both problems, the excessive ice and the cold bias, conspire to starve the modeled surface of heat.

The implications of this diagnosis reach well beyond the Southern Ocean. For decades, climate models have exhibited persistent biases in this region, including the notorious double bias in which too little sunlight is absorbed and the Southern Ocean remains too cold in simulations, with knock-on effects for global heat uptake and projections of future warming. The new findings indicate that accurately representing cloud phase and atmospheric temperature is more important than simply reproducing how often clouds appear. A model can generate the right amount of cloudiness and still get the energy budget badly wrong if those clouds are made of the wrong stuff and sit in an atmosphere that is too cold. This reframing of the problem gives model developers a concrete target: improving cloud microphysics schemes, refining aerosol-cloud interactions, and correcting background temperature biases will do more for simulation fidelity than tuning cloud frequency alone.

Progress, the researchers emphasize, will depend on both better observations and better use of the observations that already exist. Because measurements over Antarctica and the surrounding ocean remain sparse, numerical models still carry substantial uncertainties in their representation of the polar atmosphere. Assistant Professor Kazutoshi Sato, who is affiliated with the National Institute of Polar Research, points out that incorporating existing but currently underutilized observations into numerical models may offer an effective solution. He highlights the example of the PANSY radar at Japan’s Syowa Station, a sophisticated atmospheric radar whose measurements are not yet routinely assimilated into numerical weather prediction systems. Assimilating such data, he suggests, could help reduce model biases and improve forecast accuracy across the region. Expanded observational campaigns over the Southern Ocean and Antarctica, particularly of fundamental variables such as temperature, will be equally essential for rooting out the cold bias identified in this study.

By tracing persistent cloud biases to their physical roots, the JARE64 analysis marks an important step toward more trustworthy climate projections. The Southern Ocean will remain a linchpin of Earth’s climate system, absorbing heat and carbon while moderating the pace of global warming, and the clouds above it will continue to set the terms of that bargain. Models that misrepresent the phase of those clouds and the temperature of the air around them will misjudge how much heat the surface receives, with consequences for sea ice, ocean circulation, and the trajectory of climate change itself. The voyage of the R/V Shirase has shown that the path to better predictions runs through the details: the ice crystals in a cloud, the droplets on an aerosol particle, and the degrees of warmth in a column of air above one of the most inhospitable oceans on Earth.

Subject of Research: Evaluation of low-level cloud, temperature, and radiation simulations over the Southern Ocean using ship-based Antarctic expedition observations, reanalysis datasets, and a climate model

Article Title: Antarctic expedition reveals why Southern Ocean clouds remain one of climate science's greatest challenges

Article References: Antarctic expedition reveals why Southern Ocean clouds remain one of climate science's greatest challenges. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: Southern Ocean, clouds, climate models, Antarctica, downward longwave radiation, cloud phase, aerosols, reanalysis, JARE64, surface energy budget, cloud microphysics, cold bias