Stratospheric aerosol data records for the ESA Climate Change Initiative (CCI) and beyond

Monday, 19 March 2018: 10:15
Salon Vilaflor (Hotel Botanico)
Christine Bingen1, Charles E. Robert1, Kerstin Stebel2, Christoph Bruehl3, Filip Vanhellemont1, Nina Mateshvili1 and Jennifer Schallock3, (1)Royal Belgian Institute for Space Aeronomy, Brussels, Belgium, (2)Norwegian Institute for Air Research, ATMOS, Kjeller, Norway, (3)Max Planck Institute for Chemistry, Mainz, Germany
Abstract:
For the last 6 years, the ESA Climate Change Initiative (CCI) aimed at the development of climate data records for Essential Climate Variables (ECV) through eleven dedicated projects. The CCI projects operate in annual cycles of algorithm development, data production and validation, and emphasize the collaboration with major actors of the climate modelling community providing recommendations to tailor the data sets for climate modelling applications, as well as acting as independent validation teams.

An important ECV within CCI is aerosols (Aerosol_CCI), which covers both the tropospheric and stratospheric aerosols. For the stratospheric aerosols, the data records were mostly developed based on the GOMOS instrument that flew on-board ENVISAT (2002-2012). We make use of the AerGOM retrieval algorithm to extract aerosol extinction data. This algorithm was specifically developed to optimize the retrieval of aerosol species from GOMOS.

The main product of the CCI stratospheric aerosol dataset is the extinction, which is provided in the 350-750 nm range. The latest version of the CCI-GOMOS dataset also provides a very first version of a set of aerosol parameters derived from the particle size distribution. These aerosol time series are provided as gridded data resolving latitude, longitude and altitude, and span the whole ENVISAT period using 5-day time intervals.

This presentation proposes an overview of the stratospheric GOMOS data records developed for Aerosol_CCI, and shows the role played by the user community to make these climate records the best suited for climate modelling applications. Aspects of algorithm development are discussed, as well as the product validation using comparisons with EMAC simulations and intercomparisons with different sets of lidar measurements. From the results and lessons learned, we draw perspectives for the future.

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