Transient Variations in Glacial Mass Near Upernavik Isstrøm (West Greenland) Detected by the Combined Use of GPS and GRACE Data

Abstract

The continuously operating Global Positioning System (GPS) sites mounted on bedrock around the coast of Greenland provide important geodetic data sets to quantify the solid Earth's response to historical and present‐day ice mass variations. However, the presence of colored noise and irregular seasonal signals makes it difficult to detect transient changes in GPS time series. Here we apply the multichannel singular spectral analysis to the combination of GPS data and Gravity Recovery and Climate Experiment (GRACE) data so that we can identify and fully utilize the spatial correlations from these two independent data sets. Using the GPS and GRACE data near Upernavik Isstrøm in West Greenland as an example, we demonstrate that this method successfully detects two transient signals in ice mass variations during 2008 and 2014. Our forward modeling of loading displacements due to changes in surface mass balance (SMB) and ice dynamics suggests that the transient change starting in mid‐2008 was due to the combined contributions from dynamically induced mass loss and SMB. The transient change starting in mid‐2011 was mainly due to ablation. Specifically, the ice melted more in 2012 and less in 2013 with little contribution from anomalies in accumulation.

Contributions of Greenland GPS Observed Deformation From Multisource Mass Loading Induced Seasonal and Transient Signals

“…We find that the GPS and SMB time series have evident uplifts in some stations during 2012–2015. Zhang et al (2017) found that the uplifts are caused by SMB for a few GPS stations. To further extract the transient signals and quantify the influence of SMB‐induced transient signals over entire Greenland, we apply M‐SSA (Rangelova et al, 2010, 2012; Walwer et al, 2016) to GPS and GRACE data.…”

“…Larger N/M (N is the length of data) ratio gives better results (Ghil et al, 2002). We follow the approach in Zhang et al (2017) to apply M‐SSA with a window width of 700 days to the time series. We adopt the MC‐SSA (Allen & Robertson, 1996; Allen & Smith, 1996) to confirm the number of significant PCs (Text S3).…”

“…M‐SSA is an improved SSA method (Chen et al, 2013; Ghil et al, 2002). It can effectively process multivariate data and has been successfully applied to GRACE data (Rangelova et al, 2010, 2012), GPS data (Walwer et al, 2016), and multivariate data set (Zhang et al, 2017). We briefly describe the theory in supporting information (Text S2), and further details can be obtained from Ghil et al (2002) and Walwer et al (2016).…”

A Novel ENSO Monitoring Method using Precipitable Water Vapor and Temperature in Southeast China

“…MSSA is an advanced adaptive data method for extracting the trend characteristics of a space and time series and the nonlinear oscillation embedded in the climate time series without prior information about functions or stochastic models. Therefore, it can be flexibly applied to various data and has widely been used in oceanography, meteorology, and climate research [10,50,51]. In this study, the MSSA is used to analyze the PWV and T time series and obtain their periodic signals and anomalous change trend.…”

“…The partial signal could be reconstructed from Equation 7. More details of the MSSA can be found in [10].…”