Workpackage 3:

Satellite data analyses

Combined analyses of key species (SO2, COS), UTS sulfate aerosol distribution and properties and UTS cloud properties from various satellite instruments together with chemistry-transport modelling and respective CCM simulations will allow to study the sulfur budget of the lower stratosphere, the transport pathways from the troposphere into the stratosphere, and the transformation into aerosols on a global scale.

3.1 Analyses of global SO2 and COS from MIPAS data and CTM simulations

MIPAS/ENVISAT observations of SO2 and COS together with chemical transport modelling will be used to investigate the varying budget of background stratospheric sulfur over the past 10 years (ENVISAT era). This will involve detailed analysis and characterisation of atmospheric profile retrievals of SO2 and COS from MIPAS/ENVISAT level 1 data, including a comprehensive error analysis and validation.

A global climatology of SO2 and COS, covering the UTS, will be built from the data from the MIPAS/ENVISAT measurement period (2002-2012). Chemical transport modelling constrained by this new data will be used to investigate the budget and variability of the stratospheric sulfur loading and the pathways into the stratosphere. To this end we will constrain upper tropospheric sulfur in the model by observations and use the models to calculate the transport and chemical transformation pathways of SO2 and COS from the upper troposphere into the stratosphere and their contribution to the stratospheric aerosol loading. The model calculations will be continued during the tropical campaign, constrained by the MIPAS climatology, to enable comparisons with new observations performed within StratoClim.

3.2 Global sulfate aerosols distribution and properties from infrared sounders

We will characterise the global aerosol distribution in the UTS based on data from satellite IR sounding instruments, which have a number of advantages for aerosol retrievals, such as: good sensitivity to aerosol type, observations at day and night time, low dependency on particle shape and surface reflectivity, and the ability to obtain the atmospheric gaseous composition simultaneously. The latter is very interesting for the measurement of SO2 as precursor of sulfate aerosols. The instrumental specifications of the most recent sounders, such as IASI A and B or TANSO-FTS, and new developments in data processing methodology, make them perfectly appropriate to measure particles and aerosol precursors as demonstrated by recent studies from IASI (Clarisse et al., 2010) and TANSO-FTS (Herbin et al., 2012).

We will characterise sulfate aerosols from the high resolution infrared instruments IASI and TANSO-FTS, and eventually ACE-FTS. The results will be linked with the SO2 and COS distributions derived from MIPAS (WP 3.1) and will provide data in support of the modelling activities described in WPs 4 and 5.

3.3 Sulfate aerosols and clouds in the UTS from space-borne lidar combined with Lagrangian modelling

Recent results have highlighted how the space borne lidar CALIOP can detect volcanic and anthropogenic aerosols at the tropopause and in the stratosphere (Vernier et al., 2011a, b). We will combine these observations with other satellite measurements to first document the spatial variability of upper troposphere/lower stratosphere aerosols, focusing on local enhancements from volcanic eruptions and convection. In parallel, we will use the same CALIOP observations to document the spatial extent and location of high tropospheric clouds, singling out those resulting from in-situ formation by using satellite infrared imagery and back-trajectories to exclude those created as part of convective systems. We will then run microphysical models including various candidate nucleation, growth and coagulation processes, and identify those processes that result in the best representation of the observed cloud systems.

3.4 UTS clouds: altitude of anvils and overshoots from geostationary satellite data

We will analyse observations from geostationary satellites to improve the determination of anvil altitude and of penetrating overshoots. The principle will be to exploit existing cloud classifications and cloud tracking methods for geostationary weather satellites that allow to follow the life cycle of meso-scale convective systems and then to use the A-train satellite observations, e.g. from CALIOP, in a learning procedure to adjust a new algorithm that will characterise the top of the clouds based on the spatio-temporal observations provided by the geostationary satellites. The method will be first developed for the SEVIRI instrument and will be extended to satellites using the same channels over the Indian Ocean, including a MSG successor to Meteosat 7. Within StratoClim, this work will provide detailed information about the distribution of detraining clouds in the TTL that will be used by the modelling activities described in WPs 4 and 5.