Models for climate prediction

For models to be successful they need to include an adequate description of the feedbacks we have listed. The water vapour feedback and its regional distribution depend on the detailed processes of evaporation, condensation and advection (the transfer of heat by horizontal air flow) of water vapour, and on the way in which convection processes (responsible for showers and thunderstorms) are affected by higher surface temperatures. All these processes are already well included in weather forecasting models and water vapour feedback has been very thoroughly studied. The most important of the others are cloud-radiation feedback and ocean-circulation feedback. How are these incorporated into the models?

For modelling purposes, clouds divide into two types - layer clouds present on scales larger than the grid size and convective clouds generally on smaller scales than a grid box. For the introduction of layer clouds, early weather forecasting and climate models employed comparatively simple schemes. A typical scheme would generate cloud at specified levels whenever the relative humidity exceeded a critical value, chosen for broad agreement between model-generated cloud cover and that observed from climatological records. More recent models parameterise the processes of condensation, freezing, precipitation and cloud formation much more completely. They also take into account detailed cloud properties (e.g. water droplets or ice crystals and droplet number and size) that enable their radiative properties (e.g. their reflectivity and transmissivity) to be specified sufficiently well for the influence of clouds on the atmosphere's overall energy budget to be properly described. The most sophisticated models also include allowance for the effect of aerosols on cloud properties (Figure 3.9) - denoted the indirect aerosol effect in Figure 3.11. The effects of convective clouds are incorporated as part of the model's scheme for the parameterisa-tion of convection.

The amount and sign (positive or negative) of the average cloud-radiation feedback in a particular climate model is dependent on many aspects of the model's formulation as well as on the particular scheme used for the description of cloud formation. Different climate models, therefore, can show average cloud-radiation feedback which can be either positive or negative (see box); further, the feedback can show substantial regional variation. For instance, models differ in their treatment of low cloud such that in some models the amount of low cloud increases with increased greenhouse gases, but in other models it decreases. Although considerable progress has been made in recent years in the observations of clouds and their representation in models, uncertainty

Snow-covered surfaces like the Arctic and Antarctica reflect 70% of the sunlight that hits them, but the polar regions don't have a large impact on the overall albedo of the Earth because the high latitudes get little sunlight to start with. Snow covering North America and Eurasia in the springtime, as the Sun returns in full force, has a much greater effect on the climate.

Snow-covered surfaces like the Arctic and Antarctica reflect 70% of the sunlight that hits them, but the polar regions don't have a large impact on the overall albedo of the Earth because the high latitudes get little sunlight to start with. Snow covering North America and Eurasia in the springtime, as the Sun returns in full force, has a much greater effect on the climate.

regarding cloud-radiation feedback continues to be the main reason for the wide uncertainty range in what is called climate sensitivity (see Chapter 6) or the change in global average surface temperature due to a doubling of carbon dioxide concentration.

The remaining feedback that is of great importance is that due to the effects of the ocean circulation. Compared with a global atmospheric model for weather forecasting, the most important elaboration of a climate model is the inclusion of the effects of the ocean. Early climate models only included the ocean very crudely; they represented it by a simple slab some 50 or 100 m deep, the approximate depth of the 'mixed layer' of ocean which responds to the seasonal heating and cooling at the Earth's surface. In such models, adjustments had to be made to allow for the transport of heat by ocean currents. When running the model with a perturbation such as increased carbon dioxide, it was not possible to make allowance for any changes in that transport which might occur. Such models therefore possessed severe limitations.

For an adequate description of the influence of the ocean it is necessary to model the ocean circulation and its coupling to the atmospheric circulation. Figure 5.17 shows the ingredients of such a model. For the atmospheric part of the model, in order to accommodate long runs on available computers, the size of the grid has to be substantially larger, typically 100-300 km in the horizontal. Otherwise it is essentially the same as the global model for weather forecasting described earlier. The formulation of the dynamics and physics of the ocean part of the model is similar to that of its atmospheric counterpart. The effects of water vapour are of course peculiar to the atmosphere, but the salinity (the salt content) of the oceans has to be included as a parameter together with its considerable effects on the water density. Because dynamical systems, e.g. large scale eddies in the oceans, are of smaller scale than their atmospheric counterparts, the grid size of the ocean component is typically about half that of the atmospheric component. On the other hand, because changes in the ocean are slower, the time step for model integration can be greater for the ocean component.

At the ocean-atmosphere interface there are exchanges of heat, water and momentum (exchange of momentum leads to friction) between the two fluids. The importance of water in the atmosphere and its influence on the atmospheric circulation have already been shown. The distribution of fresh water precipitated from the atmosphere as rain or snow also has a large influence on the ocean's circulation through its effect on the distribution of salt in the ocean, which in turn affects the ocean density. It is not surprising, therefore, to find that the 'climate' described by the model is quite sensitive to the size and the distribution of water exchanges at the interface.

Before the model can be used for prediction it has to be run for a considerable time until it reaches a steady 'climate'. The 'climate' of the model, when it is run unperturbed by increasing greenhouse gases, should be as close as possible to the current actual climate. If the exchanges listed above are not correctly described, this will not be the case. Much effort has gone into model descriptions of these exchanges. Until about the year 2000, many coupled models

Radiation u

Atmosphere: Density Motion Water vapour


Exchange of: Momentum Water

U tl


Density (incl. salinity) Motion

Sea ice


Figure 5.17 Component elements and parameters of a coupled atmosphere-ocean model including the exchanges at the atmosphere-ocean interface.

introduced artificial adjustments to the fluxes at the surface of heat, water and momentum so as to ensure that the model's 'climate' was as identical as possible to the current climate. However, since that time the ocean component of the model has been improved especially through introducing higher resolution (100 km or less), so that models are now able to provide an adequate description of the climate with no such adjustments.

Before leaving the oceans, there is a particular feedback that should be mentioned between the hydrological cycle and the deep ocean circulation (see box below). Changes in rainfall, by altering the ocean salinity, can interact with the ocean circulation. This could affect the climate, particularly of the North Atlantic region; it may also have been responsible for some dramatic climate changes in the past (see Chapter 4).

The most important feedbacks belong to the atmospheric and the ocean components of the model. They are the largest components, and, because they are both fluids and have to be dynamically coupled together, their incorporation into the model is highly demanding. However, another feedback to be modelled is the ice-albedo feedback, which arises from the variations of sea-ice and of snow.

Sea-ice covers a large part of the polar regions in the winter. It is moved about by both the surface wind and the ocean circulation. So that the ice-albedo feedback can be properly described, the growth, decay and dynamics of sea-ice have to be included in the model. Land ice is also included, essentially as a boundary condition - a fixed quantity - because its coverage changes little from year to year. However, the model needs to show whether there are likely to be changes in ice volume, even though these are small, in order to find out their effect on sea level (Chapter 7 considers the impacts of sea-level change).

Interactions with the land surface must also be adequately described. The most important properties for the model are land surface wetness or, more precisely, soil moisture content (which will determine the amount of evaporation) and albedo (reflectivity to solar radiation). The models keep track of the changes in soil moisture through evaporation and precipitation. The albedo depends on soil type, vegetation, snow cover and surface wetness.

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