I was the NCAR liaison for the
Climate Process Team (CPT)
on Low-Latitude Cloud Feedbacks on Climate Sensitivity.
This was a multi-institution NSF/NOAA-sponsored project (10/03-9/06) to try to better
understand the differences in cloud feedbacks on climate sensitivity
in three leading US climate models (NCAR, GFDL, and NASA-GMAO),
and to use recent findings from observations and process models to reduce uncertainties
in climate sensitivity by improving the representation of cloud microphysics,
turbulence and moist convection, and radiative transfer in cloudy atmospheres in these models.
We provide one-year full-time resolution datasets of single-column outputs at 20 locations proposed by the CPT. The datasets are in NetCDF format and can be downloaded from this website. We also provide plots of key diagnostics.
Comparison of the NCAR and GFDL models in subsidence regions
The focus of our research is on the role of low tropical clouds in affecting climate sensitivity.
Comparison of climate simulations between the Community Atmosphere Model (CAM3) and the GFDL Atmospheric Model (AM2)
indicates that these models have a very different response to a warming due to increased carbon dioxide.
Low clouds in regions off the west coast of tropical continents play a particular important role in modulating
sea surface temperatures in these regions. Stratiform cloud cover off the coasts of southern California,
northern South America, north and south Africa reduce shortwave radiation reaching the sea surface by order 50 to 80 Wm-2.
Subgrid scale cloud variability: subcolumn generator and McICA method implementation
To take into account the subgrid scale cloud variability in CAM, we have implemented
a subcolumn column generator.
The idea of the cloud generator is that the Subgrid scale cloud variability can be represented
by a set of subcolumns that are generated randomly.
We are testing the subcolumn generator on radiation calculations using
the Monte Carlo Independant Column Approximation (McICA).
The McICA is a method developed to improve the treatment of cloud variability in climate models.
This method is essentially a sampling strategy that yields unbiased estimates of heating rate profiles
with respect to the full ICA,
but that speeds the calculation up compared to the full ICA.