[For more details, please click on the bold text, or expand all.]
Clouds are important: they strongly influence Earth's radiation budget and thereby affect Earth's temperature,
precipitation, and atmospheric circulation. Clouds are also
intriguingly complicated: they span a large range of spatial scales and involve many tightly
coupled processes, including dynamics, thermodynamics, radiation, microphysics, and aerosol chemistry.
Such complexity makes it very difficult to unravel the essential mechanisms of clouds and to predict
their responses and feedbacks in different climate regimes.
I use a hierarchy of models that span across various scales, including
idealized general circulation models (GCM), single column models (SCM),
and large-scale simulations (LES).
Idealized experiments are designed for these models for simplification
to the extent possible that the fundamental physical constraints remain valid.
Understanding on the essential mechanisms of clouds are thereby developed, and
simple analytical models that capture the key processes can be constructed accordingly.
This new knowledge can be applied in the actual climate change context, which helps us to
reduce the theoretical and modeling uncertainty on cloud responses and feedbacks.
My PhD thesis was jointly supervised by
Professor Tapio Schneider
Doctor Joao Teixeira.
First, I have investigated the subtropical low-cloud
response to climate change under large-scale and surface energetic constraints
with idealized LES experiments. Simple mechanisms of the trade-wind cumulus response
to CO2-driven warming are proposed.
Second, I have developed a closure for the physically consistent representation
of sub-grid scale (SGS) processes in GCMs, which aims to improve the cloud biases
due to the artificial separation between convection and turbulence.
I have worked with Doctors
and Colleen Kaul in the development of
the PyCLES model, a novel
Python-based LES code for simulating turbulence, convection, and cloud dynamics at high-order accuracy.
I have also worked with Doctor Florent Brient
on investigating why the simulated shallowness of present-day tropical low clouds is strongly
linked to the predicted magnitude of global warming among the ensemble of CMIP5 climate models.