J14.2A 'Amazonian Clouds' Microphysical Structures in Different Thermodynamic and Aerosol Conditions

Thursday, 14 January 2016: 8:45 AM
Room 357 ( New Orleans Ernest N. Morial Convention Center)
Micael A. Cecchini, INPE, São José dos Campos, Brazil

Manaus is a city located in northern Brazil (3o6' S, 60o1' W) in the Amazon rainforest. It has a population of around 2 million people and is isolated in a mostly remote region. As such, it is a good natural laboratory to study aerosol-cloud interactions. The low aerosol background concentrations and horizontal homogeneity due to the extension of the forest results in natural clouds that are in some ways similar to those found at or near the oceans. However, anthropogenic activities in Manaus city and some industries around it produce air pollution that affects the local meteorology. For instance, after the rush hour in the morning the city pollution propagates usually to the southwest due to the rather stable wind regime, producing the so-called Manaus plume. There are also frequent biomass burning events in the forest (either anthropogenic-induced or natural) which greatly enhance particle concentrations on the atmosphere. The main objective of the study is to describe Amazonian cloud microphysical structures that are affected or not by pollution, under different thermodynamic conditions. The GoAmazon2014/5 experiment is an international effort that aims to contribute to the knowledge of tropical forest systems, specifically the Amazon, with relation to the coupling of terrestrial, atmosphere and cloud processes. Two Intensive Operation Periods (IOP) took place in 2014, respectively during February-March (IOP1) and September-October (IOP2). In the IOP1 the Department of Energy Atmospheric Radiation Measurement program's Gulfstream-1 (G-1) research aircraft performed a total of 42 flight hours as part of the first phase of the Intensive Airborne Research in Amazon (IARA) campaign. IARA's phase 2 took place as the second IOP, jointly with the Aerosol, Cloud, Precipitation, and Radiation Interactions and Dynamics of Convective Cloud Systems (ACRIDICON) campaign. The latter consisted mainly of research flights with the High Altitude and Long-Range Research Aircraft (HALO), totaling 96 hours of measurements. Both G-1 and HALO aircrafts were equipped to observe both aerosol and cloud properties. The main objective of this work is to describe the microphysical properties of warm clouds that occur under different thermodynamic and aerosols conditions over the Amazon. More specifically, the relations between vertical velocity, aerosol concentration and hydrometeor size distribution will be explored. The conceptual model of the warm phase microphysics is important not only to understand Amazonian cloud characteristics but is also critical for further analyzes regarding the mixed-phase and ice processes in deep convective clouds. The G-1 flight patterns performed during the IARA campaign were focused on measurements in and around the Manaus pollution plume. Therefore, given the relatively stable wind regimes of the region, it is possible to compare the pollution-affected clouds to pristine ones based on the location of the aircraft relatively to the plume. Overall, the results show a picture consistent with enhanced water vapor competition inside the polluted region. The cloud droplet size distributions (DSDs) measured inside the plume presented generally larger droplet number concentrations (NC), lower mean diameters and higher liquid water contents (LWC). However, more detailed analyzes should be employed to highlight the intricacies of the process. For instance, while the plume influenced and clean DSD populations have overall differences, it is also interesting to observe that the frequency distribution of their properties have significant overlap. In this way, it is also important to understand how and why polluted and clean DSDs can be similar. This can be observed in Figure 1, where the mean DSDs are shown for different altitudes. While there are quantitative differences between the two populations (Figures 1a-c), they are quite similar with respect to the relative dispersion of liquid water with droplet size (Figures 1d,e) close to cloud base. This may be a result of a predominance of one growth mechanism (condensation), even if it is not the only one occurring. Figures 1c and 1f show that the differences between polluted and clean DSDs get clearer higher in the cloud, where the droplets had more time to grow overall. A general conclusion from this analysis is that the microphysical differences between a polluted and a clean cloud should get more pronounced throughout their life cycle because of the increasingly non-linear processes occurring. This is justified if the cloud is seen as a non-linear system where the aerosol effects on the condensational process alters its initial condition. The high endurance of the HALO aircrafts allowed for long-range flights and the observation of different types of clouds. Some flights sampled biomass-burning affected regions, while others focused on pristine, remote locations – including one flight near the Atlantic Ocean coast. The contrast between forested and deforested regions was also made possible. Preliminary analysis indicate that high aerosol loading suppressed droplet growth with altitude in the warm phase of the clouds with the biggest droplets observed in either remote forested or coastal regions. Both the G-1 and HALO observations form a dataset that covers most of the cloud regimes in Amazon, including the local wet and dry season differences, therefore motivating the development of a conceptual model. The latter should be able to relate the microphysical structure of the clouds to aerosol concentrations (and chemical properties) and instability conditions over the Amazon forest. It is understood that the appropriate description of the warm-phase microphysics is crucial to comprehend the other, more complex, precipitation formation mechanisms occurring inside the cloud. Figure 1 caption: Mean DSDs for plume (red) or clean (blue) conditions as a function of altitude. Figures 3a-c shows the mean DSDs in absolute number concentrations while Figures 1d-f show the mean normalized distribution of LWC (computed as an average from individual normalized DSDs). For the latter, all distributions integrate to 1 and each bin is the size-range contribution to the total water content. The legend in Figures 3a-c shows the corresponding total droplet concentrations.

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