Sulfur dioxide (SO2) occurs naturally in the stratosphere, where it is oxidised to form aerosols which in turn have an important effect on the Earth’s climate. The main natural source of stratospheric SO2 is volcanic eruptions. One possible geo-engineering approach to mitigate anthropogenic climate change is the deliberate release of SO2 into the stratosphere. It is therefore important for us to understand the behaviour of SO2 in the atmosphere in as much detail as possible.
The Microwave Limb Sounder (MLS) instrument (http://mls.jpl.nasa.gov) on the Aura satellite has operated since 2004, returning almost continuous measurements of the chemistry of the stratosphere and upper troposphere. Sulfur dioxide (SO2) is one of the large suite of chemical species that MLS is capable of detecting. For most of the mission, the atmosphere contained too little SO2 for MLS to detect. However on a number of occasions moderate-sized volcanic eruptions have injected substantial quantities of SO2 into the upper troposphere and lower stratosphere (UTLS) where they can be recorded by MLS. The largest of these eruptions were of Sarychev in the Kuril islands in June 2009 and of Mt. Kasatochi in Alaska, in August 2008 . Other eruptions include Calbuco in 2015 (Fig. 1) and Raikoke in 2019.
The main quantitative questions regarding the sulfur dioxide injected into the atmosphere from a volcanic eruption are:
- What mass of SO2 was injected?
- At what height (or, over what range of heights) was it injected?
- At what time (or, over what range of times) was it injected?
Plume dispersion modelling using the FLEXPART  model (https://www.flexpart.eu/) is one way to answer these questions. The model allows a specified mass of a chemical to be placed in the atmosphere at a given time and place; the model then uses wind data to disperse the chemical. The results can then be compared with satellite data (Fig. 1). The process of finding the initial conditions for the model which best match the satellite data is an inverse problem which may be approached in a variety of ways. One way is to run FLEXPART forwards in time repeatedly, adjusting the initial conditions using a Markov-chain Monte Carlo approach. An alternative is to run FLEXPART backwards in time starting from the locations where satellite data are available.
These kinds of approaches have been tried with nadir-sounder data (which have good horizontal resolution and no vertical resolution) but have not been tried with limb sounder data (which have good vertical and poor horizontal resolution). The ultimate aims of this project would be
- To quantify the effects on the atmosphere of those eruptions which occurred during the Aura mission
- To improve our understanding of the nature of the information on the eruptions contained in limb sounder data, compared to that in nadir sounder data.
The above will form the core of the project; it could be extended in a variety of ways, such as
- Use of co-located MLS SO2 data and aerosol data from CALIPSO/CALIOP  to examine directly the relationship between SO2 and the sulfate aerosol into which it is converted.
- Use of larger and more sophisticated models (UM-UKCA and/or ECMWF-ICBG, both with the Leeds-developed GLOMAP module  ) to model the conversion of SO2 to aerosol, for comparison with combined MLS and CALIOP data
 H. C. Pumphrey, W. G. Read, N. J. Livesey, and K. Yang. Observations of volcanic SO2 from MLS on Aura. Atmos. Meas. Tech., 8:195–209, 2015. (http://doi.org/10.5194/amt-8-195-2015)
 Stohl, A., Forster, C., Frank, A., Seibert, P., and Wotawa, G.: Technical note: The Lagrangian particle dispersion model FLEXPART version 6.2, Atmos. Chem. Phys., 5, 2461–2474, 2005.
 Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., Hunt, W. H., and Young, S. A.: Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms, Journal of Atmospheric and Oceanic Technology, 26, 2310–2323, https://doi.org/10.1175/2009JTECHA1281.1, 2009.
 Bellouin, N., Mann, G. W. et al., “Impact of the modal aerosol scheme GLOMAP-mode on aerosol forcing in the HadGEM general circulation model”, Atmos. Chem. Phys., 13, 3027-3044, 2013