Precipitation life cycle in trade wind cumuli

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Trade wind cumulus clouds play a vital role in the Earth's radiation budget and produce up to 20% of the total precipitation in the tropics. However, we still don't know how they will respond to global warming. Precipitation from trade wind cumuli can alter cloud macroscopic properties and the boundary layer structure and dynamics. Precipitation development in models is very uncertain, being dependent on simulation setup and microphysics. In particular, the autoconversion scheme dramatically affects precipitation flux, cloud structure, and organization. Currently, no evaluations of the different autoconversion schemes with observations reduced the uncertainties in rain processes. In ground-based observations, radar reflectivity based approaches detect precipitation at an advanced stage, hindering the identification of factors causing rain formation. Precipitation can also impact convection organization and circulation intensity with massive effects on climate sensitivity. Evaporation of precipitation determines the intensity of cold pools and influence the cloud field organization. It is hence key to quantify evaporation rates and their spatiotemporal variability. Parametrizations of evaporation below cloud base are available but strongly depend on the drop size distribution of raindrops. Also, in the observations, evaporation rates are hard to observe directly. The goal of this proposal is to answer the question of what and how triggers precipitation formation in trade wind cumulus clouds in observations and exploit these data to constrain rain formation process in LES models. Moreover, this research aims to quantify the spatiotemporal distribution of evaporation below cloud base in the trades, understanding on which parameters it mainly depends. The project is complementary to the upcoming EUREC4A field campaign and exploits the long-term dataset of the Barbados Cloud Observatory (BCO). Characterization of precipitation onset and development will be achieved through synergetic ground-based observations and novel approaches based on the skewness of the cloud radar Doppler spectrum to detect rain onset. The characterization of precipitation in observations will be used to evaluate the rain formation process in the ICON-LEM and DHARMA-LES models statistically, showing where the model reproduce the observed mean behaviors and where and why biases occur. The analysis will be developed in the observational space by means of the PAMTRA forward simulator.Evaporation rates will be retrieved exploiting the synergy of observations available within EUREC4A and at BCO. The spatiotemporal variability of the observed profiles will be characterized and used as a constraint to verify and improve the description of evaporation rates in LES models.This research will contribute to the Grand Challenge on Clouds, circulation, and climate sensitivity identified by the World Climate Research as high priority research.