Accounting for Variability in Cloud Formation

ARM lidar data provide important information on variability in temperature and water vapor, helping models determine when clouds should form.

The Science

Clouds form when an air parcel becomes saturated. In nature, this phenomenon occurs at scales smaller than the grid boxes in most global climate models (GCMs). Therefore, cloud parameterizations in GCMs must make assumptions about subgrid scale variability in temperature and water vapor in order to produce partially cloudy grid boxes. Most GCMs specify a parameter, known as critical relative humidity (RHcrit), which serves as a threshold for cloud formation. Until this study, the lack of high-resolution observations has hindered estimates of how RHcrit varies with meteorological conditions.

The Impact

For the first time, RHcrit was determined for a variety of assumed GCM grid box sizes using high-resolution temperature and water vapor measurements from the Raman lidar at the Department of Energy’s Atmospheric Radiation Measurement (ARM) Climate Research Facility’s Southern Great Plains site. RHcrit was found to vary with height and over the diurnal cycle. There was little sensitivity in RHcrit to the horizontal resolutions studied (30 km to 120 km); however, larger sensitivity was found to the vertical grid spacing. The results indicate that using the same value of RHcrit for vertical grids of different resolution could be problematic, an important finding as models continue to increase resolution. Finally, the team evaluated a new parameterization that linked RHcrit to variance generated by small-scale turbulence in the boundary layer and found that it produced a realistic diurnal evolution of RHcrit, but that the contribution of gravity waves (not currently included in the parameterization) may also be important.


This exploratory study is aimed at understanding whether current noise levels of lidar-retrieved temperature and water vapor are sufficiently low to obtain a reasonable estimate of RHcrit. Lidar has the advantage in that it provides long-term, high-temporal resolution measurements of the lower tropospheric profile. However, lidar is inherently noisy, and any analysis of the higher-order moments will be contingent on the ability to quantify and remove this noise. This study used 45 days of data from the Raman lidar at the ARM Climate Research Facility’s Southern Great Plains Site in Lamont, Oklahoma, coinciding with the Midlatitude Continental Convective Clouds Experiment (MC3E).

Vertical profiles of RHcrit could be derived from the lidar with an uncertainty of a few percent. A large source of uncertainty in the lidar measurements was a cyclic behavior in the lidar signals caused by the heating and cooling units within the lidar enclosures that needed to be filtered out before analysis of higher-order moments could be performed. RHcrit tends to be smallest near the boundary-layer top and seems to be insensitive to the horizontal grid spacing at the scales investigated here (30 km to 120 km). However, larger sensitivity was found to the vertical grid spacing. RHcrit was observed to decrease by 10 percent as the vertical grid spacing quadrupled.

The lidar-retrieved RHcrit profiles were used to evaluate a parameterization that estimates RHcrit from variances diagnosed from the boundary-layer parameterization. The parameterization overestimates RHcrit by up to 10 percent, but captures the diurnal variability of RHcrit well, with lower values of RHcrit near the boundary-layer top. While the results show that the uncertainties associated with the retrievals are large, the lidar observations show promise in diagnosing and evaluating an important parameter to predict cloud fraction in climate and numerical weather prediction models.

Principal Investigator(s)

Kwinten Van Weverberg
UK Met Office


The data for this paper were obtained from the U.S. Department of Energy’s Atmospheric Radiation Measurement data archive (http://www.archive.arm.gov/armlogin/login.jsp).


Van Weverberg, K., I. A. Boutle, C. J. Morcrette, and R. K. Newsom. 2016. “Towards Retrieving Critical Relative Humidity from Ground-Based Remote-Sensing Observations,” Quarterly Journal of the Royal Meteorological Society, DOI: 10.1002/qj.2874.