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. One of the challenges inrepresenting warm rain processes in global climate models (GCMs) is relatedto the representation of the subgrid variability of cloud properties, such ascloud water and cloud droplet number concentration (CDNC), and the effectthereof on individual precipitation processes such as autoconversion. Thiseffect is conventionally treated by multiplying the resolved-scale warm rainprocess rates by an enhancement factor ( Eq ) which is derived fromintegrating over an assumed subgrid cloud water distribution. The assumedsubgrid cloud distribution remains highly uncertain. In this study, we derivethe subgrid variations of liquid-phase cloud properties over the tropicalocean using the satellite remote sensing products from Moderate ResolutionImaging Spectroradiometer (MODIS) and investigate the correspondingenhancement factors for the GCM parameterization of autoconversion rate. Wefind that the conventional approach of using only subgrid variability ofcloud water is insufficient and that the subgrid variability of CDNC, as wellas the correlation between the two, is also important for correctlysimulating the autoconversion process in GCMs. Using the MODIS data whichhave near-global data coverage, we find that Eq shows a strongdependence on cloud regimes due to the fact that the subgrid variability ofcloud water and CDNC is regime dependent. Our analysis shows a significantincrease of Eq from the stratocumulus (Sc) to cumulus (Cu) regions.Furthermore, the enhancement factor EN due to the subgrid variation ofCDNC is derived from satellite observation for the first time, and resultsreveal several regions downwind of biomass burning aerosols (e.g., Gulf ofGuinea, east coast of South Africa), air pollution (i.e., East China Sea),and active volcanos (e.g., Kilauea, Hawaii, and Ambae, Vanuatu), where the EN is comparable to or even larger than Eq , suggesting an importantrole of aerosol in influencing the EN . MODIS observations suggest thatthe subgrid variations of cloud liquid water path (LWP) and CDNC aregenerally positively correlated. As a result, the combined enhancementfactor, including the effect of LWP and CDNC correlation, is significantlysmaller than the simple product of Eq⋅EN . Given the importanceof warm rain processes in understanding the Earth's system dynamics and watercycle, we conclude that more observational studies are needed to provide abetter constraint on the warm rain processes in GCMs.

atmospheric chemistry and physics

Subgrid variations of the cloud water and droplet number concentration over the tropical ocean: satellite observations and implications for warm rain simulations in climate models