Kwinten Van Weverberg — Met Office - UK
Boutle Ian — Met Office
Cyril Julien Morcrette — Met Office - UK
Rob K Newsom — Pacific Northwest National Laboratory
Category
General topics – Clouds
Description
Time-height cross sections of the lidar-retrieved and noise-filtered (a) water vapour variance, (b) temperature variance and (c) critical relative humidity for the 9th of May 2011 at the ARM SGP-site, assuming a grid box length of 120 km and a vertical layer thickness of 200m. Only grid points with an integral time scale of larger than 20s and a signal-to-noise ratio larger than 1 are shown. Variances and the critical relative humidity were calculated every 10 minutes.
Nearly all parameterizations of large-scale cloud require the specification of the critical relative humidity (RHcrit). This is the grid box-mean relative humidity at which the sub-grid fluctuations in temperature and water vapour become so large that part of a sub-saturated gridbox becomes saturated and cloud starts to form.
Until recently, the lack of high-resolution observations of temperature and moisture variability has hindered a reasonable estimate of the RHcrit from observations.
However, with the advent of ground-based measurements from Raman lidar, it becomes possible to obtain long records of temperature and moisture (co-)variances with sub-minute sample rates.
Lidar observations are inherently noisy and any analysis of higher-order moments will be very dependent on the ability to quantify and remove this noise. We present an exploratory study aimed at understanding whether current noise levels of lidar-retrieved temperature and water vapour are sufficient to obtain a reasonable estimate of the RHcrit.
We show that vertical profiles of RHcrit can be derived for a grid box length of about 30 km (120 km) with an uncertainty of about 4 (2) %. RHcrit tends to be smallest near the boundary layer top and seems to be fairly insensitive to the horizontal grid spacing at the scales investigated here (30 - 120 km).
However, larger sensitivity was found to the vertical grid spacing. As the grid spacing decreases from 400 to 100 m, RHcrit is observed to increase by about 5-8 %, which is more than the uncertainty in the RHcrit retrievals.
By way of example, the lidar-retrieved RHcrit profiles were used to evaluate a newly developed parameterization of RHcrit that uses information about the temperature and moisture (co-)variance diagnosed from the boundary-layer parameterization.
For a 6 week GCM-simulation coinciding with the Midlatitude Continental Convective Clouds Experiment (MC3E), it is shown that this parameterization captures the diurnal evolution of RHcrit fairly well, with lower values of RHcrit near the evolving boundary-layer top. However, RHcrit tends to be somewhat too high during the daytime and much too low in the morning, compared to the lidar-retrieved values of RHcrit.
While we show that the uncertainties associated with the retrievals are large, information obtained from lidar seems very promising for diagnosing and evaluating a very important parameter for predicting cloud fraction in climate and numerical weather prediction models.
