Some of the biggest uncertainties in weather forecasts and climate
models stem from the challenge of properly representing clouds and
convection (thermally-driven movement) in the atmosphere. Clouds can
significantly alter the Earth's albedo (reflectivity of the sun's rays
back to space), and thus affect the heating that drives atmosphere
dynamics. Part of the difficulty in representing the effects of clouds
is that small-scale dynamics have unknown but significant impacts on
large-scale dynamics, especially when the atmosphere and cloud structure
change drastically over long distances. A simple model coupling cloud
cover and sea surface temperature was developed. It compared well with
atmospheric data derived from a collection of observations in a region
of the Pacific Ocean where clouds transition from shallow (over cold
ocean) to deep (over warm ocean). Next, convection was represented as a
simple model of multiple rising plumes of warm air that could mix and
entrain surrounding air in a random way. The model produced a realistic
cloud transition compared to ship-based measurements over the same
region of the Pacific Ocean. These results showed promise that simple
models, which represent small-scale cloud dynamics, can be extended to
large-scale climate models where small-scale features are
underrepresented.
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