Coastal Tropical Convection in a Stochastic Modeling Framework

TL;DR

This paper introduces a stochastic modeling framework with a trigger function to better simulate coastal convection, capturing its occurrence and organization influenced by land-sea interactions, which are poorly represented in standard models.

Contribution

The paper develops a novel decision algorithm and integrates it into a stochastic cloud model to improve coastal convection simulation in global models.

Findings

The combined trigger function and stochastic model effectively simulate coastal deep convection.

The model captures observed spatial and temporal cloud organization in coastal regions.

Results suggest potential improvements for weather and climate model accuracy.

Abstract

Recent research has suggested that the overall dependence of convection near coasts on large-scale atmospheric conditions is weaker than over the open ocean or inland areas. This is due to the fact that in coastal regions convection is often supported by meso-scale land-sea interactions and the topography of coastal areas. As these effects are not resolved and not included in standard cumulus parametrization schemes, coastal convection is among the most poorly simulated phenomena in global models. To outline a possible parametrization framework for coastal convection we develop an idealized modeling approach and test its ability to capture the main characteristics of coastal convection. The new approach first develops a decision algorithm, or trigger function, for the existence of coastal convection. The function is then applied in a stochastic cloud model to increase the occurrence probability of deep convection when land-sea interactions are diagnosed to be important. The results suggest that the combination of the trigger function with a stochastic model is able to capture the occurrence of deep convection in atmospheric conditions often found for coastal convection. When coastal effects are deemed to be present the spatial and temporal organization of clouds that has been documented form observations is well captured by the model. The presented modeling approach has therefore potential to improve the representation of clouds and convection in global numerical weather forecasting and climate models.