GAW Report No. 205 - IGAC Project

CHAPTER 1 - INTRODUCTIONFigure 18 - Concept of atmospheric modelling. Yellow boxes: A priori knowledge that is not calculated by the model itself.Green boxes: Forcings that may or may not be calculated by the model itself but are needed for accurate model output.Blue ovals: Main purpose (or output) of the model. Numerical Weather Prediction models (NWP) andGeneral Circulation models (GCMs) use prescribed fields of chemical species (e.g. aerosols) and calculate meteorology,while chemical transport models (CTMs) take meteorology as input and calculate the chemical composition of theatmosphere. Coupled climate-chemistry models (CCMs) calculate both meteorology and chemical composition and allowfor couplings between the two. This figure is not exhaustive but is meant to illustrate the most basic components onlyResolution is one of the most fundamental features of a model. As a model usuallycalculates only one value per parameter for each grid box, assuming that the value is constant allover the grid box, many patterns that vary on finer spatial scales than the grid box dimensioncannot be resolved. For example, some air pollutants have very short lifetimes compared toatmospheric transport timescales, and thus have rather uneven distributions, which reflect pointsources of emissions. Also, topography and land use vary, in general, on smaller scales than canbe resolved in a regional, let alone, global model. In order to resolve fine scale patterns in a modelit is necessary to run the model on a higher resolution (i.e. to use a smaller grid box size). When alarge domain has to be investigated this necessarily leads to a large number of grid boxes in themodel, and often exceeds the limitations with respect to computer power.Megacities affect their environment on very local scales, down to street level, but as strongemission sources they also have global environmental impacts. Conversely, global change willinfluence megacities related to both climate change and long-range transport of air pollution. Giventhe multi-scale character of the effects of and upon megacities, trade-offs have to be made inmodelling. Models having a sufficiently fine resolution for local air pollution studies cannot be runglobally, but only for a confined region of the atmosphere (so-called regional or local models). Theprovision of boundary conditions for these models is not trivial, and effects from the model domainon the areas beyond cannot be taken into account. On the other hand, global models that addresschanges in large-scale meteorological parameters and changes in the background concentrationsof long-lived air pollutants have too coarse a resolution to be applied to local studies. Althoughcomputer power is increasing, this problem cannot be easily solved because the ongoing (anddesirable) inclusion of ever more complex physical and chemical processes largely compensatesfor the increase in available computing power.Baklanov and Nuterman [2009] therefore note the importance of building a chain of modelsof different spatial scales with nesting of high-resolution/small-domain models into lowerresolution/larger-domainmodels. Their example describes the bridging from regional to localscales, using coupled systems of obstacle-resolved urban models and coarser-scale local models.It is shown how features from outside the obstacle-resolved model influence its results, thusjustifying a proper inclusion of information from the coarser scale. In general, scale interactions canplay an important role in both directions, i.e. not only from the larger scale to the smaller scale, but22