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Discussion papers
https://doi.org/10.5194/acp-2018-1170
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2018-1170
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 21 Nov 2018

Submitted as: research article | 21 Nov 2018

Review status
This discussion paper is a preprint. It has been under review for the journal Atmospheric Chemistry and Physics (ACP). The revised manuscript was not accepted.

Interpreting the time variability of world-wide GPS and GOME/SCIAMACHY integrated water vapour retrievals, using reanalyses as auxiliary tools

Roeland Van Malderen1, Eric Pottiaux2, Gintautas Stankunavicius3, Steffen Beirle4, Thomas Wagner4, Hugues Brenot5, and Carine Bruyninx2 Roeland Van Malderen et al.
  • 1Royal Meteorological Institute of Belgium (RMIB), Uccle, Belgium
  • 2Royal Observatory of Belgium (ROB), Uccle, Belgium
  • 3Dep. of Hydrology and Climatology, Institute of Geosciences, Faculty of Chemistry and Geosciences, Vilnius University, Lithuania
  • 4Max Planck Institute for Chemistry (MPI-C), Mainz, Germany
  • 5Royal Belgium Institute for Space Aeronomy (BIRA), Uccle, Belgium

Abstract. This study investigates different aspects of the Integrated Water Vapour (IWV) variability at 118 globally distributed Global Positioning System (GPS) sites, using additionally UV/VIS satellite retrievals by GOME, SCIAMACHY and GOME-2 (denoted as GOMESCIA below), and ERA-Interim reanalysis output at these site locations. Apart from some spatial representativeness issues at especially coastal and island sites, those three datasets correlate rather well, the lowest correlation found between GPS and GOMESCIA (0.865 on average). In this paper, we first study the geographical distribution of the frequency distributions of the IWV time series, and subsequently analyse the seasonal IWV cycle and linear trend differences among the three different datasets. Finally, both the seasonal behaviour and the long-term variability are fitted together by means of a stepwise multiple linear regression of the station’s time series, with a selection of regionally dependent candidate explanatory variables. Overall, the variables that are most frequently used and explain the largest fractions of the IWV variability are the surface temperature and precipitation. Also the surface pressure and tropopause pressure (in particular for higher latitude sites) are important contributors to the IWV time variability. All these variables also seem to account for the sign of long-term trend in the IWV time series to a large extent, when considered as explanatory variable. Furthermore, the multiple linear regression linked the IWV variability at some particular regions to teleconnection patterns or climate/oceanic indices like the North Oscillation index for West USA, the El Niňo Southern Oscillation (ENSO) for East Asia, the East Atlantic (associated with the North Atlantic Oscillation, NAO) index for Europe.

Roeland Van Malderen et al.
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Interactive discussion
Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement
Roeland Van Malderen et al.
Roeland Van Malderen et al.
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Short summary
The study investigates the long-term time variability of the integrated water vapour retrieved by different techniques (GPS, UV/VIS satellites and numerical weather prediction reanalyses) for a global dataset of almost 120 sites and for the time period 1995–2010. A stepwise multiple linear regression technique is applied to ascribe the time variability of integrated water vapour to surface measurements at the sites, but also using teleconnection patterns or climate/oceanic indices.
The study investigates the long-term time variability of the integrated water vapour retrieved...
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