Defense in Meteorology
Student: Gabriel Ghiraldello Balestra
Program: Meteorology
Title: "Analyzing the effects of aerosols and humidity on the organization of shallow cloud fields in the Amazon"
Advisor: Prof. Dr. Micael Amore Cecchini
Judging Committee:
- Prof. Dr. Micael Amore Cecchini - Presidente e Orientador - IAG/USP
- Prof. Dr. Jorge Alberto Martins – UTFPR (por videoconferência)
- Prof. Dr. Thiago Souza Biscaro – INPE (por videoconferência)
Other Members:
- Profa. Dra. Rachel Ifanger Albrecht - IAG/USP
- Prof. Dr. Dirceu Luis Herdies – INPE
- Prof. Dr. Henrique de Melo Jorge Barbosa – UMBC
- Prof. Dr. Luiz Augusto Toledo Machado - IF/USP
Abstract:
The Amazon rainforest plays a fundamental role in the climatology of South America, influencing radiative balance, surface fluxes, and precipitation regimes. The presence of aerosols affects cloud microphysics by acting as cloud condensation nuclei (CCN), altering vapor diffusion processes, hydrometeor collision dynamics, and rainfall formation. In polluted environments, the suppression of warm rain can generate instability and favor the development of deeper clouds, while atmospheric humidity modulates the availability of water vapor for condensation and convective available potential energy (CAPE), influencing the transition from shallow to deep convection. The organization of cloud fields is a central aspect for understanding how these processes manifest at the field scale. This study investigated the effects of aerosol concentration and atmospheric humidity on the organization of convective cloud fields in the Amazon. Idealized numerical simulations were performed with the RAMS model, varying aerosol loading (100 to 3200 nuclei/mg of dry air) and humidity profiles (Low, Mid, High). From the model outputs, microphysical variables (e.g., liquid water and rain water paths), dynamical variables (e.g., vertical velocities), cloud statistics (e.g., mean radius), and six convective organization indices (SCAI, MCAI, COP, ABCOP, Iorg, and OIDRA) were analyzed. A normalized sensitivity analysis was applied to quantify the response of each index to gradual variations of the parameters. The results showed that the vertically integrated liquid water path increased with both forcings, but through distinct mechanisms: humidity promoted cloud deepening, increasing the column containing liquid water, while aerosols acted through the microphysical effect, increasing the number of droplets. The vertically integrated rain water path exhibited maxima at intermediate aerosol levels (200–800 nuclei/mg of dry air), indicating an optimal regime. Cloud top height and mean cloud radius also peaked under moderate aerosol and high humidity conditions, with evidence of convective aggregation between the fifth and eighth hours. The SCAI and MCAI indices were strongly correlated with cloud number, while COP and ABCOP were more sensitive to the horizontal size of the clusters. ABCOP stood out as the most consistent index with the expected physics for the Amazon, adequately capturing the diurnal cycle of convection and the optimal aerosol regimes. Iorg classified all scenes as organized, and OIDRA showed no clear behavior. The results indicate that the response of convection to aerosols and humidity is nonlinear and dependent on the chosen organization metric, with important implications for the parameterization of interactions in climate models. The effectiveness of ABCOP for the proposed context supports the idea that organization indices can be useful for parameterizing cloud fields in global climate models.
Keywords: Amazon, Convective organization, Numerical modeling, Aerosol, Humidity, Organization indices