Over the past 50 years, humanity has benefited much from improved weather predictions. As infrastructures age and expand, reliance on accurate numerical weather prediction (NWP) will increase. Similarly, most countries now seem ready to act on global climate model (GCM) predictions of climatic change.

Since NWP models and GCMs are necessarily global, the Earth-Atmosphere system must be discretized into cells; the largest models have over 10,000,000 of them. The sizes of these cells are much larger than the scales at which many important physical processes occur. Current NWP models and GCMs have computational requirements that are amongst the world’s largest. If they are to keep pace with the mounting demands placed on them, key unresolved processes will have to be accounted for with more sophistication than the simple, yet complicated, parametrizations used presently.

This lecture emphasizes the quintessential parametrization problem: cloud-radiation interactions. Most cloud particles have diameters much smaller than 0.1 mm, some occasionally evolve into precipitation, and cloud systems often cover areas large enough to determine global radiation budgets. Indeed, uncertainties regarding the modelling of cloud particles are responsible for the lion’s share of uncertainty in estimates of global climatic change. Overviews will be given of both conventional methods of representing clouds in global models as well as avant-garde multi-scale modelling approaches.

Howard W. Barker is a Research Scientist in the Cloud Physics and Severe Weather Section at Environment and Climate Change Canada. His research involves application of satellite data to improve the representations of cloud and radiative transfer in large-scale weather and climate models, development of radiative transfer models for use in large-scale models, and simulation of satellite measurements that are aimed at remote sensing of clouds. He is also an Adjunct Professor at McMaster University’s Geography and Earth Sciences Department.