Abstract
Abstract. The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) instrument aboard the European Space Agency (ESA) Envisat satellite operated from July 2002 to April 2012. The infrared limb emission measurements provide a unique dataset of day and night observations of polar stratospheric clouds (PSCs) up to both poles. A recent classification method for PSC types in infrared (IR) limb spectra using spectral measurements in different atmospheric window regions has been applied to the complete mission period of MIPAS. The method uses a simple probabilistic classifier based on Bayes' theorem with a strong independence assumption on a combination of a well-established two-colour ratio method and multiple 2-D probability density functions of brightness temperature differences. The Bayesian classifier distinguishes between solid particles of ice, nitric acid trihydrate (NAT), and liquid droplets of supercooled ternary solution (STS), as well as mixed types. A climatology of MIPAS PSC occurrence and specific PSC classes has been compiled. Comparisons with results from the classification scheme of the spaceborne lidar Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud-Aerosol-Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite show excellent correspondence in the spatial and temporal evolution for the area of PSC coverage (APSC) even for each PSC class. Probability density functions of the PSC temperature, retrieved for each class with respect to equilibrium temperature of ice and based on coincident temperatures from meteorological reanalyses, are in accordance with the microphysical knowledge of the formation processes with respect to temperature for all three PSC types. This paper represents unprecedented pole-covering day- and nighttime climatology of the PSC distributions and their composition of different particle types. The dataset allows analyses on the temporal and spatial development of the PSC formation process over multiple winters. At first view, a more general comparison of APSC and AICE retrieved from the observations and from the existence temperature for NAT and ice particles based on the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis temperature data shows the high potential of the climatology for the validation and improvement of PSC schemes in chemical transport and chemistry–climate models.
Highlights
The essential role of polar stratospheric clouds (PSCs) in the depletion of stratospheric ozone has been well established (e.g. Solomon, 1999)
We present a first comparison of the daily and height-resolved area of the hemisphere covered by PSC (APSC, in 106 km2 units), a secondary data product based on the classification results for both instruments where for CloudAerosol Lidar with Orthogonal Polarization (CALIOP) the newer version (2.0) has been used
For the individual cloud classes (ATYPEi), supercooled ternary solution (STS) for Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) compared to STS for CALIOP, MIPAS nitric acid trihydrate (NAT) to the sum of NAT mixtures and enhanced mixtures, and MIPAS ice to the CALIOP ice plus wave ice class, we found generally good consistency in the temporal evolution
Summary
The essential role of polar stratospheric clouds (PSCs) in the depletion of stratospheric ozone has been well established (e.g. Solomon, 1999). Measurements of the particle type of PSCs are highly desirable but are so far limited These measurements should cover the complete polar vortex and should last for multiple winters, which is only possible either passively in the mid-infrared or actively through lidar measurements from space. The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) (Fischer et al, 2008) on Envisat (2002–2012) and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) (Winker et al, 2009) on the Cloud-Aerosol-Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite (2006–present) retrieve PSC information from space beyond comparison with respect to horizontal, vertical, and temporal resolution and coverage These datasets have already motivated numerous PSC studies that both extend and challenge our present knowledge of PSC processes and modelling capabilities (e.g. Peter and Grooß, 2012, and references therein). First analyses of retrieved parameters from the climatology which are important for the evaluation of chemistry transport models (CTMs) and chemistry–climate models (CCMs) are presented, for example, time series of the area of the hemisphere covered by PSCs (APSC) and the three corresponding PSC types (AICE, ANAT, and ASTS)
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