On the Statistical Significance of Climatic Trends Estimated From GPS Tropospheric Time Series

Alshawaf, Fadwa; Zus, Florian; Balidakis, Kyriakos; Deng, Zhiguo; Hoseini, Mostafa; Dick, Galina; Wickert, Jens

doi:10.1029/2018jd028703

Cited by 31 publications

(35 citation statements)

References 41 publications

(68 reference statements)

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“…Although the GPS-based wet delay is converted into water vapor using the atmospheric weighted mean temperature that is calculated using reanalysis data, the quality of the IWV products does not change compared to using radiosonde temperature measurements (Ning et al 2016b). We inspected a subset of GPS observations north of 658N (Deng et al 2016;Alshawaf et al 2018), which covers 35 sites ( Fig. S2).…”

Section: Cfsrmentioning

confidence: 99%

Trends of Vertically Integrated Water Vapor over the Arctic during 1979–2016: Consistent Moistening All Over?

et al. 2019

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Arctic trends of integrated water vapor were analyzed based on four reanalyses and radiosonde data over 1979–2016. Averaged over the region north of 70°N, the Arctic experiences a robust moistening trend that is smallest in March (0.07 ± 0.06 mm decade−1) and largest in August (0.33 ± 0.18 mm decade−1), according to the reanalyses’ median and over the 38 years. While the absolute trends are largest in summer, the relative ones are largest in winter. Superimposed on the trend is a pronounced interannual variability. Analyzing overlapping 30-yr subsets of the entire period, the maximum trend has shifted toward autumn (September–October), which is related to an accelerated trend over the Barents and Kara Seas. The spatial trend patterns suggest that the Arctic has become wetter overall, but the trends and their statistical significance vary depending on the region and season, and drying even occurs over a few regions. Although the reanalyses are consistent in their spatiotemporal trend patterns, they substantially disagree on the trend magnitudes. The summer and the Nordic and Barents Seas, the central Arctic Ocean, and north-central Siberia are the season and regions of greatest differences among the reanalyses. We discussed various factors that contribute to the differences, in particular, varying sea level pressure trends, which lead to regional differences in moisture transport, evaporation trends, and differences in data assimilation. The trends from the reanalyses show a close agreement with the radiosonde data in terms of spatiotemporal patterns. However, the scarce and nonuniform distribution of the stations hampers the assessment of central Arctic trends.

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Section: Cfsrmentioning

confidence: 99%

Trends of Vertically Integrated Water Vapor over the Arctic during 1979–2016: Consistent Moistening All Over?

et al. 2019

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“…CC BY 4.0 License. series over Europe, Alshawaf et al (2018) found that the number of years of daily data required to estimate a IWV trend above 0.3 mm/decade is between 28 and 40 years. As we have only 15 years available for most of the stations, our time series is too short to draw firm conclusions on the presence or magnitude of a trend.…”

Section: Long-term Time Variability Of the Iwv Retrievalsmentioning

confidence: 99%

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

Malderen

Pottiaux

Stankūnavičius

et al. 2018

Preprint

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<p><strong>Abstract.</strong> 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.</p>

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“…Atmospheric water vapor is an important parameter for the climate research (Alshawaf et al, 2018;Balidakis et al, 2018) and a valuable input for numerical weather model (NWM; Gutman et al, 2004;Oigawa et al, 2018). Retrieving precipitable water vapor (PWV) using Global Navigation Satellite System (GNSS) with 1-to 2-mm accuracy has been demonstrated at ground-based stations (Wang et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Retrieving Precipitable Water Vapor From Shipborne Multi‐GNSS Observations

Wang

Semmling

et al. 2019

Geophysical Research Letters

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Precipitable water vapor (PWV) is an important parameter for climate research and a crucial factor to achieve high accuracy in satellite geodesy and satellite altimetry. Currently Global Navigation Satellite System (GNSS) PWV retrieval using static Precise Point Positioning is limited to ground stations. We demonstrated the PWV retrieval using kinematic Precise Point Positioning method with shipborne GNSS observations during a 20‐day experiment in 2016 in Fram Strait, the region of the Arctic Ocean between Greenland and Svalbard. The shipborne GNSS PWV shows an agreement of ~1.1 mm with numerical weather model data and radiosonde observations, and a root‐mean‐square of ~1.7 mm compared to Satellite with ARgos and ALtiKa PWV. An improvement of 10% is demonstrated with the multi‐GNSS compared to the Global Positioning System solution. The PWV retrieval was conducted under different sea state from calm water up to gale. Such shipborne GNSS PWV has the promising potential to improve numerical weather forecasts and satellite altimetry.

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On the Statistical Significance of Climatic Trends Estimated From GPS Tropospheric Time Series

Cited by 31 publications

References 41 publications

Trends of Vertically Integrated Water Vapor over the Arctic during 1979–2016: Consistent Moistening All Over?

Trends of Vertically Integrated Water Vapor over the Arctic during 1979–2016: Consistent Moistening All Over?

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

Retrieving Precipitable Water Vapor From Shipborne Multi‐GNSS Observations

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