Parametrization (climate)
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Parametrization - within a climate model refers to the method of replacing processes that are too small-scale or complex to be physically represented in the model by a simplified process. This can be contrasted with other processes - for example the large-scale motions of the atmosphere - that are explicitly resolved within the models.
Associated with these parameterizations are various parameters used in the simplified processes: the fall speed of raindrops, for example. Example include convective cloud parameterization, simplifications of the atmospheric radiative transfer on the basis of atmospheric radiative transfer codes, or cloud microphysics parameterizations.
Some processes are not parametrized but are represented directly: the basic equations for the fluid flow of the atmosphere.
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[edit] Convective clouds parameterization
A typical climate model gridbox has sides of about 100-300 km. A typical cumulus cloud has a scale of less than a kilometer, and would require a grid even finer than this to be represented physically by the equations of fluid motion. Therefore the processes that such clouds represent are parametrized, by processes of various sophistication. In the earliest models, if a column of air in a model gridbox was unstable (ie, the bottom warmer than the top) then it would be overturned, and the air in that vertical column mixed. More sophisticated schemes add enhancements, recognising that only some portions of the box might convect and that entrainment and other processes occur.
By contrast, the formation of large-scale (stratus-type) clouds is more physically based, they form when the relative humidity reaches some prescribed value. But again, sub grid scale processes need to be taken into account. Rather than assuming that clouds form at 100% relative humidity (in a real atmopheric air parcel, small supersaturations are required for cloud formation, i.e. relative humidities must be slightly greater than 100%, depending on the available CCN) the cloud fraction can be related to a "rh_crit" [1] (typically 95%), reflecting the sub grid scale variation that would occur in the real world.
[edit] Estimating the effect of parameters on climate sensitivity
The climateprediction.net project [2] attempted to estimate the sensitivity of the climate sensitivity of a version of HadCM3 (actually HadSM3) by varying various chosen parameters, such as vf1 (the ice fall speed through clouds), cw_land, cw_sea (this relates how much water there is in a cloud to when it starts raining), z0fsea (the roughness length of the sea surface), r_layers (related to the number and size of plant roots in the soil – and, consequently, to how water is taken up from the soil and into the atmosphere by plant transpiration). A fuller list is at [3].
[edit] Example of well constrained parameters
This would include things like the freezing point of water, direct radiative effect of CO2 and other items that are well constrained by lab experiments.
