Environmental Controls on Tropical Sea Breeze Convection and Resulting Aerosol Redistribution

Abstract

Sea breeze fronts propagate inland from the coastline, driving convective initiation and aerosol redistribution. Forecasting sea breezes is challenging due to uncertainties in the initial conditions, as well as the covariance and interaction of various meteorological and surface parameters. Using the Regional Atmospheric Modeling System coupled to an interactive land‐surface model, we conduct an ensemble of 130 idealized cloud‐resolving simulations by simultaneously perturbing six atmospheric and four surface parameters describing the initial conditions. To identify the key parameters impacting the inland characteristics and the intensity of the sea breeze convection in a tropical rainforest, we apply statistical emulation and variance‐based sensitivity analysis techniques. This study extends a previous study which explored the impacts of various parameters on sea breeze characteristics in arid environments devoid of moist convection. Wind speed is identified as the main contributor to the inland extent, similar to the arid environment study. However, the relative impacts of surface properties on the inland extent are less significant in the moist environment where land‐surface heating can be suppressed via moist convective processes and vegetation‐atmosphere interactions. Two sea breeze‐initiated convection regimes are also identified: shallow and deep. Over the shallow regime, where convective available potential energy is limited, the inversion layer strength is the primary control of the convective intensity. Over the deep regime, boundary layer temperature exerts a robust control over the convective available potential energy and hence the convective intensity. The potential vertical redistribution of aerosols is closely related to the convective intensity.

Weakening of tropical sea breeze convective systems through interactions of aerosol, radiation, and soil moisture

“…Code and data availability. The source code and name list necessary to generate output data have been archived indefinitely in the Colorado State University Mountain Scholar (https://doi.org/10.25675/10217/199723, Park et al, 2020a).…”

Demystifying the complex nature of stratocumulus clouds with machine learning

“…Emulation is also used alongside ‘history matching’ to rule out values in the parameter ranges that contradict real world observations (Williamson et al ., 2013; Andrianakis et al ., 2017). Recently, emulation of cloud models was used to study the influence of environmental and aerosol conditions on cloud properties (Wellmann et al ., 2018; Glassmeier et al ., 2019; Hoffmann et al ., 2020; Park et al ., 2020). The ability to study how changing multiple parameters at once affects the output across the full range of uncertain values is particularly useful in modelling the many interacting cloud processes.…”

Weakened Sea‐Land Breeze in a Coastal Megacity Driven by Urbanization and Ocean Warming

“…The changes in the frequency, strength, and structure of SLB is attributed to the changes in land use and cover, sea surface temperature (SST) anomaly, stronger anthropogenic heat emissions, and regional extreme weather (Ding et al., 2004; Hu et al., 2022; Shen et al., 2022). The variations in SLB would contribute to the evolution of the coastal atmospheric boundary layer, regional transport of air pollutants, and local weather such as wind, temperature, and humidity (Igel et al., 2018; Kim et al., 2021; J. M. Park et al., 2020). This further leads to disruptions in the energy balance, which could affect the aerosol formation and removal processes (Flamant et al., 2018).…”