The impact of surface heterogeneity on the diurnal cycle of deep convection

Abstract

Despite some recent improvements, major deficiencies remain in model simulations using parameterised convection in capturing both the phase and amplitude of the daily cycle of precipitation in tropical regions. The difficulties are particularly acute in regions of heterogeneous surface conditions, since the simulations need not only to respond appropriately to the local forcing from surface fluxes but also to capture the interactions with near‐surface mesoscale circulations. Here, we examine such a situation by means of idealised cloud‐resolving simulations of deep convection over a heterogeneous surface, performed using the cloud‐resolving simulation known as the Met Office NERC Cloud model (MONC). In these simulations, we show that precipitation forms preferentially over dry and warm patches (“DRY”) compared with wet and cold patches (“WET”), with the peak precipitation rates differing by a factor of approximately 4. The initiation of precipitation occurs approximately 1.5 hr earlier in the DRY patches compared with the WET patches. Moreover, within the WET and DRY patches there are marked differences in the spatial distribution of the precipitation. These cloud‐resolving simulations are then used as a benchmark to assess the behaviour of simulations using parameterised convection, performed using the idealised configuration of the Met Office Unified Model (MetUM). The MetUM simulations produce a response with some qualitative similarities to the cloud‐resolving simulations. In particular, although the simulations with parameterised convection initiate precipitation too early, they are capable of capturing the relative amounts of daily‐mean precipitation in the DRY and WET patches. We propose that the cloud‐resolving simulations could be used further to investigate the impact of fully interactive surface schemes and as benchmark simulations to evaluate new parameterisation schemes.

Assessing Memory in Convection Schemes Using Idealized Tests

“…Under steady‐state conditions we investigated here (RCE), the importance of the temporal dependence of convection on its own past state (i.e., the prognosticity of the memory variable) may not be as apparent compared to transient scenarios. Nonetheless, the memory time scales revealed in our experiments (∼12 hr in the UW‐ org scheme) are very similar to that of the diurnal cycle as well as the moisture adjustment time scale observed over the tropical oceans (Bretherton, Peters, & Back, 2004), suggesting that our experiments have likely isolated issues related to the inability of some memory‐less schemes in the correct simulation of diurnal cycles (Daleu et al., 2020; Harvey et al., 2022). Lastly, our SCM setup necessarily means that no insights about convective organization can be provided, which limits the interpretation of certain results.…”

Daytime convective development over land: The role of surface forcing

“…In nature, horizontal gradients of the surface characteristics (soil and/or vegetation type, availability of surface water, etc.) can lead to significant mesoscale circulations and spatial variability of the LCL height (e.g., Wilde et al, 1985;van Heerwaarden and Guerau de Arellano, 2008;Harvey et al, 2022;and references in those papers). Finally, large-scale advection, included in a simplistic way in periodic-domain LES-type simulations, might lead to a variety of physical processes (e.g., fronts, baroclinic circulations, etc.)…”

Weaker land–atmosphere coupling in global storm-resolving simulation

“…Furthermore, focusing on the feedback between soil moisture heterogeneity and convection, Taylor et al ( 30 ) found negative feedback in observations and positive feedback in coarse-resolution global climate simulations. Large-eddy simulations ( 31 – 33 ) and km-scale models with explicit convection ( 34 , 35 ) can well reproduce the triggering of convection by such soil moisture heterogeneities. By looking at the partitioning of tropical precipitation between land and ocean, Hohenegger and Stevens ( 36 ) deduced a negative feedback between water storage and precipitation in observations and a positive feedback in the ensemble mean of coarse-resolution global climate models used for the Coupled Model Intercomparison Project phase 6 (CMIP6).…”