1Earth System Science Center, UAHuntsville, Huntsville, AL, USA
2Department of Atmospheric Science, UAHuntsville, Huntsville, AL, USA
Received: 13 May 2009 – Published in Atmos. Chem. Phys. Discuss.: 12 Oct 2009
Abstract. Given the complex interaction between aerosol, cloud, and 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 if aerosols affect warm rain processes. Data collected during September 2006 over the Amazon basin in South America during the biomass-burning season are used. The goal of this research is to combine these observations into a smaller number of variables through PCA with each new variable 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.
Revised: 26 Feb 2010 – Accepted: 02 Mar 2010 – Published: 05 Mar 2010
To emphasize the advantage of PCA, changes in aerosol, cloud, and atmospheric observations are compared to rainrate. Comparing no-rain, rain, and heavy rain only (>5 mm h−1) samples, we find that cloud thicknesses, humidity, and upward motion are all greater during rain and heavy rain conditions. However, no statistically significant difference in AOT exists between each sample, 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 higher order PC variables that explain less than 1% of the total variance, and do not vary significantly as a function of rainrate. 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.
Jones, T. A. and Christopher, S. A.: Statistical properties of aerosol-cloud-precipitation interactions in South America, Atmos. Chem. Phys., 10, 2287-2305, doi:10.5194/acp-10-2287-2010, 2010.