Modelling atmospheric chemistry and long-range transport of emerging Asian pollutants

Modeling is a very important tool for scientific processes, requiring long-term dedication, desire, and continuous reflection. In this work, we discuss several aspects of modeling, and the reasons for doing it. We discuss two major modeling systems that have been built by us over the last 10 years. It is a long and arduous process but the reward of understanding can be enormous, as demonstrated in the examples shown in this work. We found that long-range transport of emerging Asian pollutants can be interpreted using a Lagrangian framework for wind analysis. More detailed processes still need to be modeled but an accurate representation of the wind structure is the most important thing above all others. Our long-term chemistry integrations reveal the capability of the IMS model in simulating tropospheric chemistry on a climate scale. These long-term integrations also show ways for further model development. Modeling is a quantitative process, and the understanding can be sustained only when theories are vigorously tested in the models and compared with high quality measurements. We should also not over look the importance of data visualization techniques. Humans feel more confident when they see things. Hence, modeling is an incredible journey, combining data collection, theoretical formulation, detailed computer coding and harnessing computer power. The best is yet to come.Comment: 30 pages, 18 figure

An emerging technique: multi-ice-core multi-parameter correlations with Antarctic sea-ice extent

ABSTRACT. Using results stemming from the International Trans-Antarctic Scientific Expedition (ITASE) ice-core array plus data from ice cores from the South Pole and Siple Dome we investigate the use of sodium (Na+), non-sea-salt sulfate (nssSO4 2–) and methylsulfonate (MS–) as proxies for Antarctic sea-ice extent (SIE). Maximum and mean annual chemistry concentrations for these three species correlate significantly with maximum, mean and minimum annual SIE, offering more information and clarification than single ice-core and single species approaches. Significant correlations greater than 90% exist between Na+ and maximum SIE; nssSO4 2– with minimum and mean SIE; and MS– with mean SIE. Correlations with SIE within large geographic regions are in the same direction for all ice-core sites for Na+ and nssSO4 2– but not MS–. All ice cores display an SIE correlation with nssSO4 2– and MS–, but not all correlate with Na+. This multi-core multi-parameter study provides the initial step in determining which chemical species can be used reliably and in which regions as a building block for embedding other ice-core records. Once established, the resulting temporal and spatial matrix can be used to relate ice extents, atmospheric patterns, biological productivity and site conditions