Cloud-Radiation Interactions and their Contributions to Convective Self-Aggregation

2021 
This study investigates the direct radiative-convective processes that drive and maintain aggregation within convection permitting elongated channel (and smaller square) simulations of the UK Met Office Unified Model. Our simulations are configured using three fixed sea surface temperatures (SSTs) following the Radiative-Convective Equilibrium Model Intercomparison Project (RCEMIP) protocol. By defining cloud types based on the profile of condensed water, we study the importance of radiative interactions with each cloud type to aggregation. We eliminate the SST dependence of the vertically-integrated frozen moist static energy (FMSE) variance budget framework by normalizing FMSE between hypothetical upper and lower limits based on SST. The elongated channel simulations reach similar degrees of aggregation across SSTs, despite shortwave and longwave interactions with FMSE contributing less to aggregation as SST increases. High-cloud longwave interactions are the main drivers and maintainers of aggregation. Their influence decreases with SST as high clouds become less abundant. This SST-dependence is consistent with changes in grid spacing and the critical humidity threshold for condensation (RHcrit). However, the domain-mean longwave-FMSE feedback would likely decrease as grid spacing and RHcrit are reduced by lowering the condensed water path and cloud top height of high-cloud, and altering the distribution of different cloud types. Shortwave interactions with water vapor are key maintainers of aggregation and are dependent on SST and the degree of aggregation itself. The analysis method used provides a new framework to compare the effects of radiative-convective processes on self-aggregation across different SSTs and model configurations to help improve our understanding of self-aggregation.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    40
    References
    0
    Citations
    NaN
    KQI
    []
    Baidu
    map