The novel GOME-type Ozone Profile Essential Climate Variable (GOP-ECV) data record covering the past 26 years

Kurzfassung

We present the Global Ozone Monitoring Experiment (GOME)-type Ozone Profile Essential Climate Variable (GOP-ECV) data record covering the 26-year period from July 1995 until October 2021. It is derived from a series of five nadir-viewing ultraviolet-visible(-near-infrared) GOME-type satellite instruments, including GOME/the second European Remote Sensing satellite (ERS-2), the Scanning Imaging Spectrometer for Atmospheric Chartography (SCIAMACHY)/the Environmental Satellite (ENVISAT), the Ozone Monitoring Instrument (OMI)/Aura, GOME-2/Meteorological Operational satellite A (MetOp-A), and GOME-2/Meteorological Operational satellite B (MetOp-B), which are merged into a single coherent long-term time series. It provides monthly mean ozone profiles at a spatial resolution of 5° × 5° latitude by longitude. The profiles are given as partial columns for 19 atmospheric layers ranging from the surface up to 80 km. The underlying profile retrieval algorithm is the Rutherford Appleton Laboratory scheme, which is sensitive to both tropospheric and stratospheric amounts of ozone. The merged profile record has been developed by the German Aerospace Center (DLR) in the framework of the European Space Agency Climate Change Initiative+ (ESA-CCI+) ozone project (Ozone_CCI+). Profiles from the individual instruments are first harmonized through careful inspection and elimination of inter-sensor deviations and drifts and then merged into a combined record. In a further step, the merged time series is harmonized with the GOME-type Total Ozone Essential Climate Variable (GTO-ECV) data record, which is based on nearly the same satellite sensors. GTO-ECV possesses excellent long-term stability, and, with the homogenization presented in this paper, an improvement in the robustness and stability of the merged profiles can be achieved. For this purpose, an altitude-dependent scaling is applied that utilizes ozone profile Jacobians obtained from a machine learning approach. We found that climatological ozone distributions derived for selected layers from the final GOP-ECV data record exhibit expected spatiotemporal patterns.