Given the complex interaction between aerosol, cloud, atmospheric properties, it is difficult to extract their individual effects to observed rainfall amount. This research uses principle component analysis (PCA) that combines Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol and cloud products, NCEP Reanalysis atmospheric products, and rainrate estimates from the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) to assess the specific combinations of these inputs that most affect warm rain processes. Data collected during September 2006 over the South America, which includes the Amazon basin, are used as aerosols, clouds, and precipitation are all present in this region at this time. The goal of this research is to combine these observations into a smaller number of variables through PCA with each having a unique physical interpretation. In particular, we are concerned with PC variables whose weightings include aerosol optical thickness (AOT), as these may be an indicator of aerosol indirect effects. If they are indeed occurring, then PC values that include AOT should change as a function of rainrate. To emphasize the advantage of PCA, changes in aerosol, cloud, and atmospheric observations are compared to rainrate. Comparing no-rain, rain, and heavy rain (>5 mm h-1) samples, cloud thicknesses, humidity, and upward motion are all larger for the rain and heavy rain samples. However, no statistically significant difference in AOT exists, indicating that atmospheric conditions are more important to rainfall than aerosol concentrations as expected. If aerosols are affecting warm process clouds, it would be expected that stratiform precipitation would decrease as a function increasing aerosol concentration through either Twomey and/or semi-direct effects. PCA extracts the latter signal in a variable labeled PC2, which explains 15% of the total variance and is second in importance the variable (PC1) containing the broad atmospheric conditions. PC2 contains weightings showing that AOT is inversely proportional to low-level humidity and cloud optical thickness. Increasing AOT is also positively correlated with increasing low-level instability due to aerosol absorption. The nature of these weightings is strongly suggestive that PC2 is an indicator of the semi-direct effect with larger values associated with lower rainfall rates. PC weightings consistent with the Twomey effect (an anti-correlation between AOT and cloud droplet effective radius) are only present in PC13, which explains less than 1% of the total variance. Also, it does not vary significantly with rainrate. Thus, if the Twomey effect is occurring, it is highly non-linear and/or being overshadowed by other processes. Using the raw variables alone, these determinations could not be made; thus, we are able to show the advantage of using advanced statistical techniques such as PCA for analysis of aerosols impacts on precipitation in South America.
Atmospheric Chemistry & Physics Discussions
- Pub Date:
- October 2009