Abstract
Abstract. This study analyses tropical rainfall variability (on a range of temporal and spatial scales) in a set of parallel Met Office Unified Model (MetUM) simulations at a range of horizontal resolutions, which are compared with two satellite-derived rainfall datasets. We focus on the shorter scales, i.e. from the native grid and time step of the model through sub-daily to seasonal, since previous studies have paid relatively little attention to sub-daily rainfall variability and how this feeds through to longer scales. We find that the behaviour of the deep convection parametrization in this model on the native grid and time step is largely independent of the grid-box size and time step length over which it operates. There is also little difference in the rainfall variability on larger/longer spatial/temporal scales. Tropical convection in the model on the native grid/time step is spatially and temporally intermittent, producing very large rainfall amounts interspersed with grid boxes/time steps of little or no rain. In contrast, switching off the deep convection parametrization, albeit at an unrealistic resolution for resolving tropical convection, results in very persistent (for limited periods), but very sporadic, rainfall. In both cases, spatial and temporal averaging smoothes out this intermittency. On the ∼ 100 km scale, for oceanic regions, the spectra of 3-hourly and daily mean rainfall in the configurations with parametrized convection agree fairly well with those from satellite-derived rainfall estimates, while at ∼ 10-day timescales the averages are overestimated, indicating a lack of intra-seasonal variability. Over tropical land the results are more varied, but the model often underestimates the daily mean rainfall (partly as a result of a poor diurnal cycle) but still lacks variability on intra-seasonal timescales. Ultimately, such work will shed light on how uncertainties in modelling small-/short-scale processes relate to uncertainty in climate change projections of rainfall distribution and variability, with a view to reducing such uncertainty through improved modelling of small-/short-scale processes.
Highlights
The realism of rainfall in a climate model is a key indicator of its skill in representing the underlying physical processes, and in increasing our confidence for projecting future changes in rainfall
There have been some improvements in some aspects of the representation of precipitation between the 3rd and 5th phases of the Climate Model Intercomparison Project (CMIP3, Meehl et al, 2007, and CMIP5, Taylor et al, 2012, respectively), as described, for example, in Koutroulis et al (2016), uncertainties in hydrological predictions from the current generation of models still pose a serious challenge to the reliability of projections across temporal and spatial scales (Trenberth, 2011)
The resultant heating applied produces an inversion at the top of the boundary layer on the time step that the diagnosis deems too strong to allow convection to initiate
Summary
The realism of rainfall in a climate model is a key indicator of its skill in representing the underlying physical processes, and in increasing our confidence for projecting future changes in rainfall. Correlations with time show that all configurations with parametrized convection show a strong lag-1 decrease in the auto-correlation of time step precipitation (Fig. 7e), which persists even when spatial averaging is applied (Fig. 7f), it reduces in magnitude as more grid boxes are averaged together in the finer-resolution models. This further demonstrates the intermittent nature of parametrized convection in MetUM-GA6, which is insensitive to horizontal resolution. Following averaging to the N48 grid and 3 h scale, the temporal and spatial coherence of tropical rainfall from all the model simulations is similar to that from the satellite-derived datasets
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