AOD, land-cover, and meteorology over Southeastern Asia: a 13-year analysis.

Abstract:

Southeast Asia has been experiencing major haze events over the past decades. Even though they are due to large-scale biomass burning, recent studies have shown that these events are becoming worse, in terms of both frequency and impact on air pollution. Some of the heaviest events, which used to be associated only with strong El-Nino events (and other phenomenons leading to large-scale tropical drying) are now becoming more frequent over the continent (2002, 2004, 2006, 2009, 2013, and 2014).

Direct emissions from these fires (black carbon and organic carbon aerosols) and indirect pollutants (ozone) play a major role in both the derivation of human health, changes to radiative forcing, and cloud properties. Furthermore, given the meteorology and active convection in the tropics, such pollutants tend to spread widely, entering into the global-scale circulation patterns. Finally, local sources of pollution may contribute to increase the toxicity of the event.

The critical component regarding this burning is the rapid conversion of forests, agricultural lands, and associated waste products to other types or uses. As there is a long time record of land surface properties (reflectance, albedo, LAI and NDVI), any observed changes in the fires must be associated with observed changes in the large-scale properties of the land itself.

To this end, we use a principal component approach to determine the regions of the continent which are the most variable from 2001 through 2013. We then derive the relationships between these spatial and temporal regions of variability, the AOD (aerosol optical depth), the large-scale reanalysis meteorology, the remotely sensed precipitation, and solar radiation.

These different factors are further analyzed in a regime by regime basis. Some of the regimes are expected to be variable but statistically climatological, while the influence of others should be changing. This study is a precursor step to future modeling efforts for deriving bi-directional climate change influences.