Global Spatiotemporal Variability of Integrated Water Vapor Derived from GPS, GOME/SCIAMACHY and ERA-Interim: Annual Cycle, Frequency Distribution and Linear Trends

Roeland Van Malderen,Thomas Wagner,Carine Bruyninx,Eric Pottiaux,Gintautas Stankunavicius,Hugues Brenot,Steffen Beirle,Jonathan Jones

doi:10.3390/rs14041050

Abstract

Atmospheric water vapor plays a prominent role in climate change and atmospheric, meteorological, and hydrological processes. Because of its high spatiotemporal variability, precise quantification of water vapor is challenging. This study investigates Integrated Water Vapor (IWV) variability for the period 1995–2010 at 118 globally distributed Global Positioning System (GPS) sites, using additional UV/VIS satellite retrievals by GOME, SCIAMACHY, and GOME-2 (denoted as GOMESCIA below), plus ERA-Interim reanalysis output. Apart from spatial representativeness differences, particularly at coastal and island sites, all three IWV datasets correlate well with the lowest mean correlation coefficient of 0.878 (averaged over all the sites) between GPS and GOMESCIA. We confirm the dominance of standard lognormal distribution of the IWV time series, which can be explained by the combination of a lower mode (dry season characterized by a standard lognormal distribution with a low median value) and an upper mode (wet season characterized by a reverse lognormal distribution with high median value) in European, Western American, and subtropical sites. Despite the relatively short length of the time series, we found a good consistency in the sign of the continental IWV trends, not only between the different datasets, but also compared to temperature and precipitation trends.

Highlights

Introduction iationsBeing the most important natural greenhouse gas, water vapor plays a crucial role in climate change
The Global Positioning System (GPS)-Integrated Water Vapor (IWV) dataset is treated as the reference dataset, because of its high reliability, with root-mean-square values of 1–3 mm [6,35], and due to the spatial coverage being limited to the GPS site locations
Comparisons between GOMESCIA and ERA-Interim IWV at the GPS site locations are provided in the supplementary material (Figures S1–S3)

Summary

Datasets and Methodology

To study IWV variability, we use IWV retrievals from GPS continually observing reference stations (CORS), a merged dataset of IWV measured from 3 different satellite instruments (GOME, SCIAMACHY, GOME-2) and from the ERA-Interim [23] reanalysis model. In Appendix A, we compare GPS IWV values and trends estimated by using different sources of auxiliary meteorological parameters (ERA-Interim, NCEP/NCAR reanalysis, SYNOP stations) and different calculation methods of Tm (from the linear empirical relationship with the surface temperature as outlined above). We use the IWV “Climate” product from the European Space Agency (ESA) GOME-Evolution project, described in Beirle et al [33] It is provided as a monthly mean IWV 1◦ × 1◦ global grid from July 1995 to December 2015. We considered those monthly mean IWV time series at the pixels closest to the GPS stations This “Climate” product merges the IWV retrievals from three satellite spectrometers in the red spectral range with the same differential optical absorption spectroscopy (DOAS) technique: Global Ozone Monitoring Experiment (GOME, July 1995–July 2011), the Scanning. A temporal stability of about 1% per decade is achieved for the GOMESCIA climate product [33]

ERA-Interim Reanalysis Model

Dataset Comparison

Seasonal Behavior

Frequency Distribution

Linear Trends

Discussion