Clouds & Climate contribution to HyMEX
Stephan de Roode, Harm Jonker and Pier Siebesma
The life cycle of deep convection
Deep convective clouds develop during relatively high surface temperatures. A
necessary constraint for their rapid rise up to heights well above 10 km is the
availability of a sufficiently large amount of moisture, as this provides the 'fuel' for
the convective clouds by its heating capacity due to condensation and freezing of
cloud water. This, in turn, leads to the formation of intense precipitation which can
severely hinder traffic. An accurate prediction of the onset and evolution of deep
convective clouds is therefore compelling, but is as of yet still hindered by a lack of
knowledge concerning cloud microphysics (Morrison et al., 2009; Dawson, 2010;
Van Weverberg, 2011). The major uncertainties in the life cycle of deep convection
concern the formation of cold pools caused by evaporative cooling of precipitation
during its early stage, and the timing of freezing of cloud liquid water droplets.
One of the main aims of this proposal is to apply an improved version of the Dutch
Atmospheric LES model (DALES, Heus et al. 2010) for the short-term forecasting
(~3-6 hr) of deep convection. Currently, in a collaborative project between the KNMI
and TU Delft, DALES is used to verify whether it can be more successfully applied
for short-term weather predictions of low clouds like shallow cumulus or
stratocumulus over the Netherlands than a regular weather-forecast model. The main
advantage of the LES model being that it has a much finer grid resolution than a
weather forecast model, such that turbulent transport can be explicitly resolved up to
the grid-size scales (on the order of ~ 10-50 m). Although DALES has been recently
adapted to make it suitable for the representation of the dynamics deep convection
(Böing et al., 2010), this version still suffers from uncertainty in the most optimal
choice for the parameterization constants used in the cloud microphysics routine.
The evaporation of precipitating water falling out of convective clouds leads to a local
cooling and the subsequent formation of downdrafts which transport this air towards
the ground leaving clear signatures of so-called cold pool structures. The amount of
precipitating water that evaporates is key to the further evolution of the cloud system.
A study with a Large-Eddy Simulation (LES) model showed that newly developing
clouds tended to appear along the edges of the spreading cold pool areas
(Khairoutdinov et al., 2009). This work also demonstrated that an artificially switchoff of the evaporation of precipitating water results in a reduction of the cloud amount
compared to the reference run. As an explanation, if all precipitation reaches the
ground surface less moisture in the atmosphere will be available for newly developing
clouds. The accuracy of the prediction of deep convective cloud systems is hampered
by a large uncertainty in the parameters used in the calculation of the evaporation
rate.
In an ascending cloud plume the temperature gradually decreases with height, and if
the cloud grows sufficiently tall the temperature may drop below 0 0C. In a
temperature range of ~ -20 0C up to the normal freezing temperature cloud droplets
typically may remain in the liquid, so called 'supercooled' phase. It can be readily
understood that the precise timing of the freezing process is important for cloud
dynamics, as the latent heat release that is associated with phase changes will enhance
the updraft velocity (Carey and Rutledge 2000). In addition, the presence of
supercooled raindrops provides an instantaneous source and plentiful supply of large
precipitation sized ice.
Current state-of-the-art radar remote sensing devices like radars, microwave
radiometers and lidars are indispensible to alleviate the problem of uncertainty in
microphyics model constants. We aim to use data obtained from these, and other,
instruments to study in detail the evolution of deep convective cloud systems as
observed during the HyMEX. In particular, we would like to assess the spatial and
temporal variation of the precipitation field. In addition, the data will be used to
analyse the timing of the freezing process in the cloud. The improvement in the skill
of modified parameterizations will be quantified by running DALES using initial
profiles and boundary conditions from the HyMEX observations. In turn,
observations of precipitation, cloud liquid water and surface fluxes of heat and
moisture will be used for model validation.
References
Dawson, D. T., M. Xue, J, A. Milbrandt, M. K. Yau, 2010: Comparison of
evaporation and cold pool development between single-moment and multimoment
bulk microphysics schemes in idealized simulations of tornadic thunderstorms. Mon.
Wea. Rev., 138, 1152–1171. doi: 10.1175/2009MWR2956.1.
Böing, S. J., H. J. J. Jonker, A. P. Siebesma, and W. W. Grabowski, 2010: Influence
of subcloud-layer structures on the transition to deep convection, 19th Symposium on
Boundary-Layers and Turbulence, Keystone, CO, USA, 2-6 August 2010.
Carey, L. D., and S. A. Rutledge, 2000: The relation between precipitation and
lightning in tropical island convection: A C-ban polarimetric study. Mon. Wea. Rev.,
128, 2687-2710.
Heus, T., C. C. van Heerwaarden, H. J. J. Jonker, A. P. Siebesma, S. Axelsen, K. van
den Dries, O. Geoffroy, A. F. Moene, D. Pino, S. R de Roode and J. Vilà-Guerau de
Arellano, 2010: Formulation of and numerical studies with the Dutch Atmospheric
Large-Eddy Simulation (DALES). Geosci. Model Development, 3, 415-444,
doi:10.5194/gmd-3-415-2010.
Khairoutdinov, M. F., S. K. Krueger, C.-H. Moeng, P. A. Bogenschutz, D. A.
Randall, 2009: Large-Eddy Simulation of maritime deep tropical convection. J. Adv.
Modeling Earth Systems, 1, DOI:10.3894/JAMES.2009.1.15
Morrison, H., G. Thompson, V. Tatarskii. 2009: Impact of cloud microphysics on the
development of trailing stratiform precipitation in a simulated squall line: Comparison
of one- and two-moment schemes. Mon. Wea. Rev., 137, 991-1007.
Van Weverberg, K., N. P. M. van Lipzig, L. Delobbe, 2011: The impact of size
distribution assumptions in a bulk one-moment microphysics scheme on simulated
surface precipitation and storm dynamics during a low-topped supercell case in
Belgium. Mon. Wea. Rev., 139, 1131–1147. doi: 10.1175/2010MWR3481.1