Temporal properties of spatially aggregated meteorological time series

Large-scale crop simulations with process-based models rely on meteorological input data of coarse spatial resolution. We assess how spatial aggregation of meteorological data to coarser resolutions affects the data's temporal properties. This is largely unknown as is the impact which this aggregation effect (AE) has on simulations which use such aggregated data as input. In simulations of crop yield AE may exceed 10% in single years. We hypothesize that AE should be analysed with regard to both temporal and spatial input data properties. For this purpose, we analysed changes in temporal multifractal properties of meteorological variables due to spatial averaging from 1 to 100 km resolution. Results show that temporal properties of the time series were affected depending on the meteorological variable. We argue that the magnitude of this effect depends on local orography and climate. Similar impact of spatial aggregation on temporal properties can therefore be expected in regions of comparable orography and climate. These changes in multifractal properties potentially affect results of continuous dynamic simulations.