IAGOS Adjusted Water Vapor Climatologies

Content The files contain adjusted mean water vapor values based on the IAGOS ICH measurements. The data are provided to a 5x5° grid for different levels of potential temperatureand on pressure. The dataset contains the adjusted water vapor and an error estimate, which is based on the measurement uncertainity as well as the uncertainty of the adjusmtent methdology.   Data Coverage Mean values are derived from measurements in the period 1996 to 2022   Data Creation The adjustment methodology is decribed in detail in the following publication, Section 4:  Konjari, P. and Rolf, C. and Hegglin, M. I. and Rohs, S. and Li, Y. and Zahn, A. and Bönisch, H. and Krämer, M. and Petzold, A., Water Vapor climatologies in the extra-tropical Upper Troposphere and Lower Stratosphere derived from a Synthesis of Passenger and  Research Aircraft Measurements, EGUsphere,  doi: 10.5194/egusphere-2024-2360   Application The adjusted water vapor climatologies can be used to further study the spatial and seasonal variability over water vapor in the UTLS.  For comparison of the adjusted IAGOS data with other datasets, the data coverage of the adjusted data should be considered. For this purpose, for every bin, the amount of data for every single month of the respective years is provided in the variable 'num_measurements' (dimension: longitude x latitude x theta x nyear x month, with nyear=0 for 1996).   Caveats: Because a sufficient amount of data in the order of several thousand measurement points is needed to derive the adjusted mean values, only multi-annual means are provided, and no trends can be derived Uncertainties of the adjustment methodology: The uncertainty of the water vapor data can exceed 10 % and reach up to 20 % for dry stratospheric air masses (10 ppmv). These uncertainties, provided within the variable 'H2O_gas_err', should always be considered in the analyses. Due to  temporal inhomogenety in the amount of data per year and bin (latitude x longitude x Theta), the temporal coverage differs from bin to bin. Hence, differences between bins might also occur because of this reason.