Sensitivity analysis of convective clouds and precipitation to biomass burning aerosols in the Maritime Continent

C. Bayu Risanto, Avelino F. Arellano

Department of Hydrology and Atmospheric Sciences, The Univrersity of Arizona, Tucson, Arizona

The role of aerosols emitted from biomass burning activities remains to be a challenge despite observational and modeling efforts, especially in Indonesia, a large contributor to aerosol emissions from fires due to a rapidly changing landscape, complex topography, and maritime influence. The studies on the interaction between fire aerosols, convective clouds and precipitation are very limited in the Maritime Continent. This study is to investigate the dominant large-scale interactions of aerosols in the region by using available satellite retrievals of meteorology and aerosols properties from VIIRS and TMI (TRMM), MODIS, MISR, and CALIOP, and modeled simulations and analysis from MERRA-2. The study will conduct sensitivity analyses of convective clouds and precipitation to biomass burning aerosols in Sumatra and Borneo based on a hypothesis that biomass-burning aerosols reduces precipitation during warm ENSO years but generate more cloudy conditions due to cloud longer lifetime. This work leverages on methodologies and approaches reported from a few studies in the region and in other fire regions. The study will combine several satellite observations to quantify emergent patterns of these interactions under a probabilistic framework. The study first conducts a set of cluster analysis on fire events occurring during the dry season (May to December) from 2000 to 2016 through a series of hierarchical classification according to the following variables: a) magnitude of fire occurrence; b) spatial and temporal distribution of aerosol loading; c) magnitude of liquid water path; c) presence of convective clouds; d) dominant climatic conditions (warm versus cold ENSO); e) cloud properties; and f) magnitude of rain rates. Statistical summaries (PDFs) of these variables will be produced. Second, the study will carry out multiple regression and principal component analyses on each cluster to investigate the covariations of the dominant modes of variations between these variables within a cluster and across clusters. Inference will be based on probabilistic understanding of these events and supplemented by joint sensitivity estimates between variables. Third, the study will compare and contrast these sets of clusters and the sensitivities derived from satellite data analyses with MERRA-2 model output. Comparison and evaluation of MERRA-2 shall focus on emergent relationships (sensitivities) to assess the ability of MERRA-2 to capture these large-scale interactions.

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