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Kondratyev, K. Ya., V. I. Korzov, V. V. Mukhenberg, and L. N. Dyachenko. 1982. "The short wave albedo and the surface emissivity." In P. S. Eagleson, ea., Land surface processes in atmospheric general circulation models, pp. 463-514. Cambridge University Press, Cambridge, UK.

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Mitchell, J. E B., and J. A. Bolton. 1983. "Some differences between the Met. 0.20 5- and 11-layer model annual cycle integrations." Proceedings of ECMWF Workshop on Intercomparison of Large-Scale Models Used for Extended Range Forecasts, Reading, UK., pp. 193-223.

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The current indications from simulations using general circulation models are that the climatic effects of any changes by man's management of the surface vegetation appear likely to be significant mainly on a regional scale. The effects are difficult to quantify, but there are likely to be secondary effects of considerable local significance in terms of water resources, runoff, and erosion. General circulation models offer a way of objectively quantifying these global and regional climatic responses, so that investment in improving their accuracy and reliability is essential.

Current models are able to predict the weather for short periods (about ten days at most). For longer periods the more realistic models generate a simulated climate with similar properties to the real atmosphere. The most realistic models should run indefinitely without producing infeasible conditions, but a great deal more development is needed before they will provide useful predictions over long periods. Besides those parameters actually being investigated, it is instructive to check values of other parameters generated over time by the model for comparisons with real values.

In general circulation models feedback loops are very important. This is the way in which variation of a parameter, due to, say, a change in vegetation, can affect the behaviour of the atmospheric model. The two types of feedback loop, positive and negative, are important since they affect the stability of the system. Positive feedback tends to reinforce the initial process. This leads to even greater effects and so destabilizes or even destroys the system. Negative feedback opposes the initial process, tending to damp down its effects and so stabilizes the system. With so complex a system as the global circulation of the atmosphere the numerous positive and negative feedback loops may be expected generally to balance each other. Changes in surface vegetation alter several surface parameters and so affect feedback loops. Important examples of land surface parameters are the surface albedo or reflectivity, which determines the fraction of solar radiation absorbed, and the surface moisture availability, which affects the partitioning of energy between thermal and latent heat.

There are very real dangers in modelling the atmospheric processes that the simplifications used may emphasize one type of feedback more than another. The query was raised that many of the effects shown are positive feedbacks, not negative, yet the atmosphere appears to be a very stable system. It was agreed that there are many negative feedback systems leading to stability but that, for instance, over the last 20 years anomalies of rainfall have been occurring over the Sahelian regions.

Some model simulations have demonstrated that a relatively small change in evaporation due to vegetation change has resulted in a knock-on effect causing a relatively large change in rainfall. Equilibrium at these new levels was apparently established within ten years (Henderson-Sellers and Gornitz 1984). However, negative feedbacks were undoubtedly omitted, such as small but significant changes in sea temperatures, a slight change in the Walker circulation, a decrease in cloudiness, feedback from surrounding terrestrial areas, and biosphere responses resulting from the inevitable change in vegetation. Similarly, some positive feedbacks were omitted, such as that caused by changes in runoff. This example demonstrates the importance of improving GCMs until they include and mimic all the features that significantly affect the output.

The grid scale used for GCMs precludes the incorporation of the fine scale pattern of the land surface, although this may have a significant effect upon the simulation. This deficiency is most pronounced in the treatment of the runoff process where such important parameters as slope, aspect, elevation, vegetation, soil type, canalization, as well as rainfall inhomogeneity, are omitted. A reduction of grid size might lead to a marginal improvement; similarly an increase in the number of vertical layers might improve the incorporation of inversions in the lower atmosphere. However, resolution is intimately linked with the time step of the model and in the final analysis to the capacity of the computer.

At the moment GCMs ought not to be regarded as predictive, since further refinement is needed before reliable predictive outputs are obtained. However, they already enable us to rank the parameters used in the models. To a degree, the recommendations for further investigations of parameters specified by Dr. Rowntree are based on such tests of sensitivity. The models not only indicate the level of accuracy needed for the parameters but also distinguish the precision required for different regions. They also lend support to the International Satellite Land Surface Climatology Project (ISLSCP).

Perhaps the most suspect data is that on grid square vegetation type. Even within one international agency or between national data sets there are serious disagreements often due to uncorrelated (independent) definitions. Thus although published atlases of vegetation and soil may be used as data files, the information available is often contradictory. In fact, it would be valuable to see how sensitive GCMs are to these differences. Since the surveys and classifications used are for other purposes, it may be necessary to collect GCM orientated data sets on vegetation types (correlated with albedo and aerodynamic roughness, perhaps) using satellite remote sensing.

Land surface topography that can generate gravity waves in the atmosphere is grossly simplified in most models, resulting in a generally "smooth" grid scale topography. Similarly, with the size of grid scales currently in use, the effect on momentum transfer of the aerodynamic roughness of different vegetation types is at sub grid scale. The problem also arises of averaging the values for a mosaic of vegetation types over the large grid areas of, say, 2° by 2°. When it becomes necessary to incorporate the aerodynamic roughness of vegetation in GCMs, correlation with canopy height and vegetation type will probably be sufficient.

Actual measured values are probably more vital for the albedo of land surfaces, as these may be poorly correlated with vegetation type due to the effects of soil moisture and foliage moisture status. An accuracy of between 1% and 5% is required by GCMs. This suggests that data are needed as a function of time of day, wavelength, cloud cover, and season. One study showing that a tall yellow-brown grass cover had half the albedo of short green grass illustrates the significance of the density and depth of foliage in trapping radiation rather than merely its colour. Measurements need to be taken from large homogeneous areas while "grid square vegetation" needs to be given a characteristic rather than average albedo. Long wave (>0.7 m) should be separated from short wave radiation, but at this stage further refinement is unnecessary.

It is evident that for the successful development of GCM techniques for better simulation of the effects of the manipulation of the land surface by man, more infor mation on surface parameters is needed. However, unless they are by-products of other hydrological or meterological projects conducted in their own right, it is difficult to define possible sponsors or, indeed, even find scientists interested in simply collecting the data for the modellers. A solution may be found through closer integration of the mesoscale experiment (mentioned in chap. 8) with satellite remote sensing. The former would provide the ground truth for the latter, while the satellite data might allow the micrometeorological and hydrological observations to be extended beyond the 50 km x 50 km experimental area. This approach would bring the mesoscale experiments significantly closer to the objectiveness of the ISLSCP programme. Such an experiment is intimately bound to the fortunes of GCMs, since these models are of increasing interest to the climatological and meteorological community. Previously the emphasis has been on two- to five-day forecasts and there has been little incentive to improve GCMs for this purpose. However, it is now evident that the response time in GCM simulations of land surface changes can be much faster than was suspected. Improving GCMs and obtaining more realistic values of parameters take time and must be justified by improved performance.

Several reasons can be given for the need for faster computers to allow the use of finer grids and better vertical resolution of the atmosphere. For instance, convectional atmospheric events are difficult to model despite their significant interaction with land surface processes, partly because their dimensions are much smaller than the grid sizes currently in use. However, a finer grid means that the length of time that is simulated is shorter, unless a faster computer is used. While for many transfer processes a finer grid scale also needs a finer time scale, the current trend is towards the use of coarser grid scales with a simulated time of 1,000 days or more rather than 30 to 60 days. A realistic aim would be for a 1° x 1° grid with a simulation for up to 1,000 or even 10,000 days.

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