Abstract
Abstract. A convolution-based method of spectral nudging of atmospheric fields is developed in the Australian Community Climate and Earth Systems Simulator (ACCESS) version 1.3 which uses the UK Met Office Unified Model version 7.3 as its atmospheric component. The use of convolutions allow for flexibility in application to different atmospheric grids. An approximation using one-dimensional convolutions is applied, improving the time taken by the nudging scheme by 10–30 times compared with a version using a two-dimensional convolution, without measurably degrading its performance. Care needs to be taken in the order of the convolutions and the frequency of nudging to obtain the best outcome. The spectral nudging scheme is benchmarked against a Newtonian relaxation method, nudging winds and air temperature towards ERA-Interim reanalyses. We find that the convolution approach can produce results that are competitive with Newtonian relaxation in both the effectiveness and efficiency of the scheme, while giving the added flexibility of choosing which length scales to nudge.
Highlights
Atmospheric modelling is a discipline that has impacts in many fields of scientific study as well as everyday life
To determine the performance of the spectral filter, we look at the nudged runs compared with ERA-Interim reanalysis (ERAI), as well as comparing with a control simulation without nudging
This paper has introduced the use of spectral nudging in the Unified Model (UM) and ACCESS
Summary
Atmospheric modelling is a discipline that has impacts in many fields of scientific study as well as everyday life. Global climate models are powerful tools, but they have limitations due to grid resolution, approximations to atmospheric physical processes (e.g. convection and turbulent mixing), and because of incomplete or imperfect data sets such as for representing land use. Since the atmosphere is a chaotic system, the simulated synoptic patterns deviate from observations over time. This makes it more difficult to evaluate modelled behaviour, since the advection of tracers depends on the synoptic-scale atmospheric circulation. To reduce biases caused by these issues, it is useful to introduce a correction to align the model more closely with a host model, often an observational product such as the ERA-Interim reanalysis (ERAI; Dee et al, 2011). The process of adjusting dynamical variables of a model towards a host model is commonly known as nudging (Kida et al, 1991; Telford et al, 2008)
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