Vertical structure of turbulence and fluxes across cloud mesoscale
organizations from the WP-3D Orion aircraft during ATOMIC
Abstract
In-situ measurements of the trade cumulus boundary layer turbulence
structure are compared across large-scale circulation conditions and
cloud horizontal organizations during the EUREC4A-ATOMIC campaign. The
vertical structure of turbulent (e.g. vertical velocity variance, total
kinetic energy) and flux (e.g. sensible, latent, and buoyancy)
quantities are derived and investigated using the WP-3D aircraft stacked
level legs (cloud modules).The 16 cloud modules aboard the P-3 were
split into three groups according to cloud top height and
column-integrated TKE and vertical velocity variance. These groups map
onto qualitative cloud features related to object size and clustering
over a scale of 100 km. This grouping also correlates to the large scale
forcings of surface windspeed and low-level divergence on the scale of a
few hundred km. The ratio cloud top to trade inversion base height is
consistent across the groups at around 1.18. The altitude of maximum
turbulence is 0.75-0.85 of cloud top height. The consistency of these
ratios across the groups may point to the underlying coupling between
convection, dissipation, and boundary layer thermodynamic structure. The
following picture of turbulence and cloud organization is proposed: (1)
light surface winds and turbulence which decreases from the sub-cloud
mixed layer (ML) with height generates clouds with generally uniform
spacing and smaller features, then (2) as the surface winds increase,
convective aggregation occurs, and finally (3), if surface convergence
occurs, convection and turbulence reach higher altitudes, producing
higher clouds which may precipitate and create colds pools. Observations
are compared to a CAM simulation is run over the study period, nudged by
ERA5 winds and surface pressure. CAM produces higher column integrated
turbulent kinetic energy and larger maximum values on the days where
higher cloud tops are observed from the aircraft, which is likely a
factor that influences the development of deeper clouds in the model.
However, CAM places the peak turbulence 500 m lower than observed,
suggesting there may be a bias in CAM representation of turbulence and
moisture transport. CAM also does not capture the large LHFs seen for
two of the days in which lower cloud tops are observed, which could
result in insufficient lower free tropospheric moistening in the model
during this type of cloud organization. A large and consistent bias
between the model and observations for all cloud groups is the negative
SHFs produced in CAM near 1500 m. This is not observed in the
measurements. This leads to a net negative buoyancy flux not observed
and provides evidence of a specific shortcoming that can be addressed as
part of the needed improvement in the representation of clouds by
large-scale models.