Measuring shallow convective mass flux profiles in the trade wind region

Mass flux is a key quantity in parameterizations of shallow convection. To estimate the shallow convective mass flux as accurately as possible, and to test these parameterizations, observations of this parameter are necessary. In this study, we show how much the mass flux varies and how this can be used to test factors that may be responsible for its variation. Therefore, we analyze long term Doppler radar and Doppler lidar measurements at the Barbados Cloud Observatory over a time period of 30 months, which results in a mean mass flux profile with a peak value of 0.03 kg m−2 s−1 at an altitude of ~730 m, similar to observations from Ghate et al. (2011) at the Azores Islands. By combining Doppler radar and Doppler lidar measurements, we find that the cloud base mass flux depends mainly on the cloud fraction and refutes an idea based on large eddy simulations, that the velocity scale is in major control of the shallow cumulus mass flux. This indicates that the large scale conditions might play a more important role than what one would deduce from simulations using prescribed large-scale forcings.

Estimating the Shallow Convective Mass Flux from the Subcloud-Layer Mass Budget

This paper develops a method to estimate the shallow-convective mass flux M at the top of the subcloud layer as a residual of the subcloud-layer mass budget. The ability of the mass-budget estimate to reproduce the mass flux diagnosed directly from the cloud-core area fraction and vertical velocity is tested using real-case large-eddy simulations over the tropical Atlantic. We find that M reproduces well the magnitude, diurnal cycle, and day-to-day variability of the core-sampled mass flux, with an average root-mean-square error of less than 30% of the mean. The average M across the four winter days analyzed is 12 mm s−1, where the entrainment rate E contributes on average 14 mm s−1 and the large-scale vertical velocity W contributes −2 mm s−1. We find that day-to-day variations in M are mostly explained by variations in W, whereas E is very similar among the different days analyzed. Instead E exhibits a pronounced diurnal cycle, with a minimum of about 10 mm s−1 around sunset and a maximum of about 18 mm s−1 around sunrise. Application of the method to dropsonde data from an airborne field campaign in August 2016 yields the first measurements of the mass flux derived from the mass budget, and supports the result that the variability in M is mostly due to the variability in W. Our analyses thus suggest a strong coupling between the day-to-day variability in shallow convective mixing (as measured by M) and the large-scale circulation (as measured by W). Application of the method to the EUREC4A field campaign will help evaluate this coupling, and assess its implications for cloud-base cloudiness.

Measuring the variability of the shallow convective mass flux profiles in the tropical trade wind region

<p>Mass flux is a key parameter to represent shallow convection in global circulation models. To estimate the shallow convective mass flux as accurately as possible, observations of this parameter are necessary. Prior studies from Ghate et al. (2011) and Lamer et al. (2015) used Doppler radar measurements over a few months to identify a typical shallow convective mass flux profile based on cloud fraction and vertical velocity. In this study, we extend their observations by using long term remote sensing measurements at the Barbados Cloud Observatory (13° 09’ N, 59° 25’ W) over a time period of 30 months and check a hypothesis by Grant (2001), who proposed that the cloud base mass flux is just proportional to the sub-cloud convective velocity scale. Therefore, we analyze Doppler radar and Doppler lidar measurements to identify the variation of the vertical velocity in the cloud and sub-cloud layer, respectively. Furthermore, we show that the in-cloud mass flux is mainly influenced by the cloud fraction and provide a linear equation, which can be used to roughly calculate the mass flux in the trade wind region based on the cloud fraction.</p><p> </p><p>References:<br>Ghate,  V.  P.,  M.  A.  Miller,  and  L.  DiPretore,  2011:   Vertical  velocity structure of marine boundary layer trade wind cumulus clouds. Journal  of  Geophysical  Research: Atmospheres, 116  (D16), doi:10.1029/2010JD015344.</p><p>Grant,  A.  L.  M.,  2001:   Cloud-base  fluxes  in  the  cumulus-capped boundary layer. Quarterly Journal of the Royal Meteorological Society, 127 (572), 407–421, doi:10.1002/qj.49712757209.</p><p>Lamer, K., P. Kollias, and L. Nuijens, 2015:  Observations of the variability  of  shallow  trade  wind  cumulus  cloudiness  and  mass  flux. Journal of Geophysical Research: Atmospheres, 120  (12), 6161–6178, doi:10.1002/2014JD022950.</p>

A detailed look at the cumulus-valve mechanism and its potential implications for cloud-base cloudiness

<p>Most uncertainty in the warming response of trade-wind cumuli in climate models occurs near cloud base and is associated with model diversity in the strength of shallow convective mixing. In contrast to climate models, cloud-base cloudiness in large-eddy simulations (LES) and in observations is relatively insensitive to changes in the environment. The cumulus-valve mechanism provides a conceptual framework for understanding changes in cloud-base cloudiness in response to changes in the shallow-convective mass flux (M)—an important measure for convective mixing. The mechanism assumes that M keeps the mixed-layer top close to the lifting condensation level, which could explain a larger cloud-base cloudiness with larger M if the increase in M was mostly due to an increasing area fraction of cumuli. Here we use real-case LES over the tropical Atlantic to understand if cloud-base cloudiness increases with increasing M.</p><p>We find that M explains a lot of the variations in cloud-base cloudiness (correlation coefficient R=0.86), but the maximum relative humidity at the mixed-layer top (RH<sub>max</sub>) needs to be considered additionally to explain the nighttime behavior of cloud-base cloudiness (R=0.95). The coupling of M and RH<sub>max</sub> through adjustments in the sub-cloud layer depth is crucial for regulating cloud-base cloudiness. Inability of GCMs to adjust the sub-cloud layer depth in response to a change in M may likely contribute to their overestimated trade-cumulus cloud feedback. The simulated relationships will be compared to measurements from the EUREC<sup>4</sup>A field campaign.</p>