Abstract. Within the project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water), long-term tropospheric water vapour isotopologue data records are provided for ten globally distributed ground-based mid-infrared remote sensing stations of the NDACC (Network for the Detection of Atmospheric Composition Change). We present a new method allowing for an extensive and straightforward characterisation of the complex nature of such isotopologue remote sensing datasets. We demonstrate that the MUSICA humidity profiles are representative for most of the troposphere with a vertical resolution ranging from about 2 km (in the lower troposphere) to 8 km (in the upper troposphere) and with an estimated precision of better than 10 %. We find that the sensitivity with respect to the isotopologue composition is limited to the lower and middle troposphere, whereby we estimate a precision of about 30 ‰ for the ratio between the two isotopologues HD 16 O and H 16 2 O. The measurement noise, the applied atmospheric temperature profiles, the uncertainty in the spectral baseline, and the cross-dependence on humidity are the leading error sources. We introduce an a posteriori correction method of the cross-dependence on humidity, and we recommend applying it to isotopologue ratio remote sensing datasets in general. In addition, we present mid-infrared CO 2 retrievals and use them for demonstrating the MUSICA network-wide data consistency.In order to indicate the potential of long-term isotopologue remote sensing data if provided with a well-documented quality, we present a climatology and compare it to simulations of an isotope incorporated AGCM (Atmospheric General Circulation Model). We identify differences in the multiyear mean and seasonal cycles that significantly exceed the estimated errors, thereby indicating deficits in the modeled atmospheric water cycle.

Atmospheric carbon dioxide was monitored continously by infrared analysis near Barrow, Alaska, from September, 1965 to July, 1966. The purpose of these measures was to determine the fluctuations of carbon dioxide under the snow cover. Average daily surface values of carbon dioxide as measured at the tundra surface increased a few days after the first snowfall. High and variable values occurred until early December and then decreased to lower, relatively stable concentrations that persisted to early May. At that time, the carbon dioxide concentrations under the snow increased until the snow left the tundra in late June. Tundra surface temperature and wind speed four meters above the ground near the study area were monitored simultaneously with the carbon dioxide measurements. There was a general positive correlation between surface temperature and carbon dioxide, and an inverse relationship with wind speed.

Abstract. Within the NDACC (Network for the Detection of Atmospheric Composition Change), more than 20 FTIR (Fourier-transform infrared) spectrometers, spread worldwide, provide long-term data records of many atmospheric trace gases. We present a method that uses measured and modelled XCO 2 for assessing the consistency of these NDACC data records. Our XCO 2 retrieval setup is kept simple so that it can easily be adopted for any NDACC/FTIR-like measurement made since the late 1950s. By a comparison to coincident TCCON (Total Carbon Column Observing Network) measurements, we empirically demonstrate the useful quality of this suggested NDACC XCO 2 product (empirically obtained scatter between TCCON and NDACC is about 4 ‰ for daily mean as well as monthly mean comparisons, and the bias is 25 ‰). Our XCO 2 model is a simple regression model fitted to CarbonTracker results and the Mauna Loa CO 2 in situ records. A comparison to TCCON data suggests an uncertainty of the model for monthly mean data of below 3 ‰. We apply the method to the NDACC/FTIR spectra that are used within the project MUSICA (multi-platform remote sensing of isotopologues for investigating the cycle of atmospheric water) and demonstrate that there is a good consistency for these globally representative set of spectra measured since 1996: the scatter between the modelled and measured XCO 2 on a yearly time scale is only 3 ‰.

Abstract. We report on the ground-based FTIR (Fourier transform infrared) tropospheric water vapour isotopologue remote sensing data that have been recently made available via the database of NDACC (Network for the Detection of Atmospheric Composition Change; ftp://ftp.cpc.ncep.noaa.gov/ndacc/MUSICA/) and via doi:10.5281/zenodo.48902. Currently, data are available for 12 globally distributed stations. They have been centrally retrieved and quality-filtered in the framework of the MUSICA project (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water). We explain particularities of retrieving the water vapour isotopologue state (vertical distribution of H 16 2 O, H 18 2 O, and HD 16 O) and reveal the need for a new metadata template for archiving FTIR isotopologue data. We describe the format of different data components and give recommendations for correct data usage. Data are provided as two data types. The first type is best-suited for tropospheric water vapour distribution studies disregarding different isotopologues (comparison with radiosonde data, analyses of water vapour variability and trends, etc.). The second type is needed for analysing moisture pathways by means of {H 2 O, δD}-pair distributions.

Abstract. Water vapour is a critical component of the Earth system. Techniques to acquire and improve measurements of atmospheric water vapour and its isotopes are under active development. This work presents a detailed intercomparison of water vapour total column measurements taken between 2006 and 2014 at a Canadian High Arctic research site (Eureka, Nunavut). Instruments include radiosondes, sun photometers, a microwave radiometer, and emission and solar absorption Fourier transform infrared (FTIR) spectrometers. Close agreement is observed between all combination of datasets, with mean differences ≤ 1.0 kg m −2 and correlation coefficients ≥ 0.98. The one exception in the observed high correlation is the comparison between the microwave radiometer and a radiosonde product, which had a correlation coefficient of 0.92.A variety of biases affecting Eureka instruments are revealed and discussed. A subset of Eureka radiosonde measurements was processed by the Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN) for this study. Comparisons reveal a small dry bias in the standard radiosonde measurement water vapour total columns of approximately 4 %. A recently produced solar absorption FTIR spectrometer dataset resulting from the MU-SICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water) retrieval technique is shown to offer accurate measurements of water vapour total columns (e.g. average agreement within −5.2 % of GRUAN and −6.5 % of a co-located emission FTIR spectrometer). However, comparisons show a small wet bias of approximately 6 % at the high-latitude Eureka site. In addition, a new dataset derived from Atmospheric Emitted Radiance Interferometer (AERI) measurements is shown to provide accurate water vapour measurements (e.g. average agreement was within 4 % of GRUAN), which usefully enables measurements to be taken during day and night (especially valuable during polar night).