Globally modelling explicit convection: how does it compare with the nested limited area model approach at high horizontal resolutions?

The difficulty to accurately represent atmospheric convection in numerical weather forecasts contributes to persistent biases in weather and climate simulations – particularly tropical precipitation. Convection-permitting global forecasts are an improvement on global models with parametrized convection schemes, however it is not yet clear whether they improve forecast skill to match or improve upon the current approach of nesting a convection-permitting high-resolution regional model inside a global model with parameterized convection. This is far less computationally expensive than running a global convection-permitting model.

To test this, the Met Office is coordinating a UK K-scale project nesting high resolution (2.2 km) limited area models (LAMs) within global models that have between 5 and 10 km grid resolution. We compare these nested regional models with two different global simulations, run with parameterised and explicit convection science configurations. The 2.2 km resolution LAMs encompass a variety of domains focussing on both tropical land and ocean regions.

Our current work seeks to investigate if and where we see differences in model evolution between the high-resolution nested LAM approach and the explicit convection global driving model.  We focus on an active MJO event in January 2018 where enhanced convection propagated across the Indian Ocean and impacted the Maritime continent. For high-impact events such as this, do we see a marked change in the model forecast when explicitly simulating convection globally rather than in a regional limited area model (as currently used in operational forecasts)? Further, are differences between the global convection permitting and LAM forecasts more pronounced over ocean-dominated regions where the amplitude of the diurnal cycle of convection is smaller?

This talk will summarise our findings in the context of the wider K-scale project, evaluating how our recent work contributes to the development of more accurate weather forecasts.