| 16 Jan 2026
Estimating near-surface specific humidity over convective oceanic regions from cloud base height observations
Abstract. The surface moisture flux is a large term in the surface energy balance and difficult to estimate remotely. The main difficulty for its remote estimation is a poor ability to measure near-surface humidity. Current methods to retrieve near-surface specific humidity approach the problem statistically and have errors of approximately 1 g kg−1 even in global, annual averages. Using extensive measurements from the EUREC4A field campaign (ElUcidating the RolE of Clouds, Circulation Coupling in Climate), we demonstrate that remote sensing measurements of cloud base height can provide useful estimates of near-surface humidity over convective oceanic regions where optically-thick clouds do not prevent lidar sampling. First applying the method to 171 coincident radiosonde and ceilometer pairings collected from a research vessel from January 18 to February 14, 2020 yields skillful predictions of near-surface specific humidity regarding the mean (mean bias 0.33 g kg−1 compared to observed) and its variability (r = 0.76). We then apply this method using an airborne lidar to estimate cloud base height from above. In two representative case studies, we find similar skill in the predicted humidity, with low mean biases (−0.06 and −0.03 g kg−1 compared to observed) with substantial variability captured (r = 0.61 and r = 0.57, respectively). Besides estimates of cloud base height, we highlight two main error sources: (i) the relative humidity lapse rate below cloud base and (ii) the temperature difference between the sea surface and near-surface air, which would need to be calibrated if using this method to develop an operational product to estimate the near-surface specific humidity from downward-looking spaceborne lidar. This proof of concept raises the potential for application over convective oceanic regions where lidar sampling of cloud base is possible. This method could provide a physics-based augmentation to existing, more empirical approaches and therefore provide an additional observational constraint on the surface energy budget.
General comments
The authors present a new approach to estimate air specific humidity (qa) over convective oceanic regions by exploiting the physical connection between cloud base height and near-surface relative humidity in oceanic regions where optically-thick clouds do not prevent lidar sampling.
In my view the manuscript is well structured and detailed. Overall, the authors introduced the need for a specific humidity estimate from cloud base height observations, they described their methodology and results in detail. They addressed the benefits and limitations of the method they introduced in a satisfactory manner.
If I would add something to the discussion it would be the relation to wind speed and how it affects in-situ and satellite retrievals directly or indirectly, given that the wind speed is an essential meteorological parameter for estimating latent heat flux.
Specific comments
Lines 30-41 The authors most likely refer to the COARE3.5 bulk algorithm which is one (though widely used) of many bulk algorithms to estimate latent heat flux. I think it should be noted here that the variations in the definition of the exchange coefficient can lead to latent heat flux estimates that differ by more than 2Wm-2 especially at very low (<2m/s) and at high wind speeds (>17m/s). Also, the atmospheric stability is very important which is introduced into the stability functions in the form of z/L (where z is the measurement height and L is the Monin-Obukhov length) and is affected by the humidity estimate. The z/L could be used in a similar manner as the Buoyancy flux in Figure 1.
Lines 249-251 the consideration of the cool skin effect is essential in estimating specific humidity and latent heat flux. Just for reference, the developers of COARE just released a new treatment of the cool skin (https://doi.org/10.1029/2025JC023539). Omitting adjusting for the cool skin can lead to mean changes higher than 6Wm-2 (Cronin et al. 2019, doi: 10.3389/fmars.2019.00430), provided that the bulk formula applied “expects” the skin temperature as input. In this case the authors consider a representative value, but for the operational product I think that a computational approach should be considered (cool skin, warm layer adjustments to the bulk sea surface temperature).
Line 293 “Because the air–sea temperature difference varies only modestly across most of the global ocean, it can be estimated statistically.” Though the statement can be true on monthly mean and basin-scale it has significant limitations e.g. in western boundary currents, upwelling zones and frontal regions where the variability is large to name one. In addition statistical methods complement rather than replace direct measurements if we want to achieve target accuracies (2-5Wm-2 on daily/annual time scales) for surface turbulent fluxes. Note also that the accuracy for relative humidity would be 0.5-1% under common conditions, with critical challenges at very low wind speeds (up to 3% for u<3ms-1).
Line 301-306 the authors claim that one of the goals for the method application is to establish large scale latent heat flux climatologies. What I am missing from this outlook is the relation to the exchange coefficient (discussed in the introduction briefly) and thus the wind speed. Especially, the estimation of latent heat flux is challenging at very low and high wind speeds. At very low wind speeds many bulk formulae including COARE include a convective gustiness term in order to allow for convergence. My suggestion would be to add some discussion about the limitations (or not) of the introduced method at low wind speeds.
Technical corrections
Line 51. “Gentemann et al. (2020) detail approaches to this problem” should be something like “Gentemann et al. (2020) provides detailed approaches to this problem”?
Line 171, add a comma after “Similarly, …”
Figure 4 caption add comma after first parenthesis: “…cloud base height, h (using the first, major peak of the distribution), from R/V Meteor (n=171, dark blue), and BCO (n=118, medium blue) measurements…”