PCA study of the interannual variability of GPS heights and environmental parameters

Time series of GPS coordinates longer than two decades are now available at many stations around the world. The objective of our study is to investigate large networks of GPS stations to identify and analyze spatially coherent signals present in the coordinate time series and, at the same locations, to identify and analyze common patterns in the series of environmental parameters and climate indexes. The study is confined to Europe and the Mediterranean area, where 107 GPS stations were selected from the Nevada Geodetic Laboratory (NGL) archive on the basis of the completeness and length of the data series. The parameters of interest for this study are the stations height (H), the atmospheric surface pressure (AP), the terrestrial water storage (TWS) and the various climate indexes, such as NAO (North Atlantic Oscillation), AO (Artic Oscillation), SCAND (Scandinavian Index) and MEI (Multivariate ENSO Index). The Empirical Orthogonal Function (EOF) is the methodology adopted to extract the main patterns of space/time variability of these parameters. We also focus on the coupled modes of space/time interannual variability between pairs of variables using the singular value decomposition (SVD) methodology. The coupled variability between all the afore mentioned parameters is investigated. It shall be pointed out that EOF and SVD are mathematical tools providing common modes on the one hand, and statistical correlations between pairs of parameters on the other. Therefore, these methodologies do not allow to directly infer the physical mechanisms responsible for the observed behaviors which should be explained through appropriate modelling. Our study has identified, over Europe and the Mediterranean, main modes of variability in the time series of GPS heights, atmospheric pressure and terrestrial water storage. For example, regarding the station heights, the EOF1 explains about 30% of the variance and the spatial pattern is coherent over the entire study area. The SVD analysis of coupled parameters, namely H-AP, TWS-AP and H-TWS, showed that most of the common variability is explained by the first 3 modes. In particular, 70% for the H-AP, 67% for the TWS-AP and 49% for the H-TWS pair. Moreover, we correlated the stations heights with the NAO, AO, SCAND and MEI indexes to investigate the possible influence of climate variability on the height behavior. To do so, the stations heights were represented using the first three EOFs to reduce the potential effect of local anomalies. More than 30 stations, over the total of 107, show significant correlations up to about 0.3 with the AO and SCAND indexes. The correlation coefficients with MEI turn out to be significant and up to 0.5 for about half of the stations.