|Working Group I: The Scientific Basis|
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10.2.2 High Resolution and Variable Resolution AGCM Experiments
Though simulations of many centuries are required to fully integrate the global climate system, for many applications regional information on climate or climate change is required for at most several decades. Over these time-scales AGCM, simulations are feasible at resolutions of the order of 100 km globally, or 50 km locally, with variable resolution models. This suggests identifying periods of interest within AOGCM transient simulations and modelling these with a higher resolution or variable resolution AGCM to provide additional spatial detail (e.g., Bengtsson et. al., 1995; Cubasch et al., 1995; Dèquè and Piedelievre, 1995).
Here the AGCM is used to provide a reinterpretation of the atmospheric response
to the anomalous atmospheric forcing (from GHG and aerosols) experienced in
a transient AOGCM simulation. Hence, both this forcing and its accumulated effect
on the ocean surface have to be provided to the AGCM. In a typical experiment
(e.g., May and Roeckner, 2001), two time slices, say 1961 to 1990 and 2071 to
2100, are selected from a transient AOGCM simulation. The simulations include
prescribed time-dependent GHG and aerosol concentrations as in the corresponding
periods of the AOGCM run. Also prescribed as lower boundary conditions are the
time-dependent Sea Surface Temperature (SST) and sea-ice distributions simulated
by the AOGCM. The AGCM simulations are initialised using atmospheric and land-surface
conditions interpolated from the corresponding AOGCM fields.
The philosophy behind the use of high or variable resolution AGCM simulations is that, given the SST, sea ice, trace gas and aerosol forcing, relatively high-resolution information can be obtained globally or regionally without having to perform the whole transient simulation with high resolution models. The main theoretical advantage of this approach is that the resulting simulations are globally consistent, capturing remote responses to the impact of higher resolution. The use of higher resolution can lead to improved simulation of the general circulation in addition to providing regional detail (e.g., HIRETYCS, 1998; Stratton, 1999a).
In general, AGCMs will evolve their own planetary scale climatology. Therefore, in a climate change simulation they are providing a reinterpretation of the impact on the atmosphere of the sea surface and radiative forcings compared to that given by the driving AOGCM. This may lead to inconsistency with the AOGCM-derived forcing. This issue has yet to be explored but should be considered carefully when interpreting AGCM responses. It would be of less concern if a model simulation of the resolved planetary scale variables were asymptoting to a solution as resolution increased, i.e., if the solution would not change fundamentally in character with resolution but just add extra detail at the finer scales. Evidence shows that this is not the case at the current resolution of AOGCMs (Williamson, 1999).
A current weakness of high resolution AGCMs is that they generally use the
same formulations as at the coarse resolution for which these have been optimised
to reproduce current climate. Some processes may be represented less accurately
when finer scales are resolved and so the model formulations would need to be
optimised for use at higher resolution. Experience with high resolution GCMs
is still limited, so that, at present, increasing the resolution of an AGCM
generally both enhances and degrades different aspects of the simulations. With
global variable resolution models, this issue is further complicated as the
model physics parametrizations have to be designed in such a way that they can
be valid, and function correctly, over the range of resolutions covered by the
Use of high resolution and variable resolution global models is computationally very demanding, which poses limits to the increase in resolution obtainable with this method. However, it has been suggested that high-resolution AGCMs could be used to obtain forcing fields for higher resolution RCMs or statistical downscaling, thus effectively providing an intermediate step between AOGCMs and regional and empirical models.
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