Cloud variability and climate signatures in MODIS level-3 data

Variability in the global cloud system is a key factor in understanding the global radiation budget, the hydrological cycle and in turn global climate variability. For this reason we must monitor the global climate system on a spatial and temporal scales that allows us to ascertain variability on all scales that influence the global cloud fields. This requires global observations on temporal scales that capture the day-to-day variability and on spatial scales that resolves geographical variability of the global cloud system. The Moderate Resolution Imaging Spectrometer (MODIS) instruments accomplish this by glimpsing nearly the entire globe 4 times a day at a 1km resolution. This spatiotemporal resolution gives us an unprecedented ability to analyze local and regional variability, thus allowing us to gain knowledge of how the global climate system is varying.

For example in the global time-averaged mean fraction of liquid clouds for the entire Terra and Aqua record we can clearly see the influences of island wakes in the surrounding maritime cloud regimes. Islands located in regions of a high cloud fraction, e.g. South Georgia Island, have wakes producing clear regions that reduce the total mean cloud fraction, but not the mean ice cloud fraction. This is due to the island's orographic influences being mostly confined to the lower atmosphere, where liquid clouds predominate, and not extending up to regions where ice clouds exist. The microphysics in the wake of the island are also altered. The effective radius of cloud drops in the wake is reduced, and thus the water path is reduced. This is expected because the clouds are less mature in the wake cloud then the surrounding stratus clouds.

The converse is true in regions where fewer clouds exist, e.g. the Canaries, or Hawaiian Islands. Here the wake clouds created downwind of the islands increase the water cloud fraction above that of the surrounding maritime regime.

As we can view islands as a land perturbation in a background water field we can also view lakes as a water perturbation in a background land field, giving an analogous situation to the above island example for lakes. We can see influences of lakes on the cloud properties over land. Lakes tend to reduce the cloud fraction in regions where thermal convection is produced, but can increase clouds in regions where lake and surface air temperatures greatly differ.

On larger spatiotemporal scales MODIS is able to detect variability and characteristics of regional cloud systems. An example of this is the South East Asian/Indian Monsoon. On level-3 scales we see the evolution of various monsoon components on a day-to-day and year-to-year basis, and from this we can draw relationships between cloud characteristics and monsoon onset and intensity. Using the cloud fraction in conjunction with the cloud top pressure we determine to a degree the amount and type of convection occurring in the region. When this information is coupled with the liquid and ice paths for the region we can gain some information about the voracity of the monsoon convection. Furthermore, with other cloud microphysical properties we can possibly draw some conclusions about the intensity of the monsoon as seen from MODIS and the cloud macro and microphysical properties.

On larger spatiotemporal scales MODIS shows trends and differences in the cloud regimes of systems and geographical regions. At this scale we attempt to draw relationships between cloud characteristics and geographical and environmental influences. For example land and sea influences in cloud characteristics and microphysics are evident and not just on synoptic scales. This is shown, as mentioned above, on scales the size of islands but are also visible on hemispheric scales.

The above examples demonstrate that by using MODIS level-3 statistics we are able to diagnose and attribute variability on many spatiotemporal scales. This allows for a greater understanding of the relationship between clouds and other climatological factors. Furthermore, if we understand the variability of the cloud field on most scales we can extrapolate knowledge about how the cloud field will change with various natural and anthropogenic influences.