Analysis of the Spatiotemporal Changes of Ice Sheet Mass and Driving Factors in Greenland

Abstract

With the warming of the global climate, the mass loss of the Greenland ice sheet is intensifying, having a profound impact on the rising of the global sea level. Here, we used Gravity Recovery and Climate Experiment (GRACE) RL06 data to retrieve the time series variations of ice sheet mass in Greenland from January 2003 to December 2015. Meanwhile, the spatial changes of ice sheet mass and its relationship with land surface temperature are studied by means of Theil–Sen median trend analysis, the Mann–Kendall (MK) test, empirical orthogonal function (EOF) analysis, and wavelet transform analysis. The results showed: (1) in terms of time, we found that the total mass of ice sheet decreases steadily at a speed of −195 ± 21 Gt/yr and an acceleration of −11 ± 2 Gt/yr2 from 2003 to 2015. This mass loss was relatively stable in the two years after 2012, and then continued a decreasing trend; (2) in terms of space, the mass loss areas of the Greenland ice sheet mainly concentrates in the southeastern, southwestern, and northwestern regions, and the southeastern region mass losses have a maximum rate of more than 27 cm/yr (equivalent water height), while the northeastern region show a minimum rate of less than 3 cm/yr, showing significant changes as a whole. In addition, using spatial distribution and the time coefficients of the first two models obtained by EOF decomposition, ice sheet quality in the southeastern and northwestern regions of Greenland show different significant changes in different periods from 2003 to 2015, while the other regions showed relatively stable changes; (3) in terms of driving factors temperature, there is an anti-phase relationship between ice sheet mass change and land surface temperature by the mean XWT-based semblance value of −0.34 in a significant oscillation period variation of 12 months. Meanwhile, XWT-based semblance values have the largest relative change in 2005 and 2012, and the smallest relative change in 2009 and 2010, indicating that the influence of land surface temperature on ice sheet mass significantly varies in different years.

Proper orthogonal decomposition of ice velocity identifies drivers of flow variability at Sermeq Kujalleq (Jakobshavn Isbræ)

“…Previously, POD has been applied to a variety of polar science problems and dynamic fields: daily melt maps of the Antarctic Peninsula (Datta et al, 2019), the mass of the Greenland Ice Sheet as measured by the GRACE satellite mission (Bian et al, 2019), ablation stake measurements of surface mass balance (Mernild et al, 2017), and accumulation from a regional climate model to inform estimates of glacial isostasy (Nield et al, 2012). In ice dynamic studies, however, the technique has been underutilised.…”

Comparisons of GRACE and GLDAS derived hydrological loading and the impacts on the GPS time series in Europe

“…Since April 2002, the global and regional land water mass redistribution has been constrained by the monthly GRACE gravity changes and monitored at a spatial resolution of about 330 km (Wahr et al, 1998). Compared with the Center for Space Research (CSR) Level 2 RL05 data, the RL06 data (http://icgem.gfz-potsdam.de/grace/level-2/csr/rl06) has some advantages in periodic change, accuracy, and spatial resolution characteristics (Göttl et al, 2018;Bian et al, 2019). Therefore, the data of CSR RL06 is used in this study.…”

“…In addition, with the Gravity Recovery and Climate Experiment (GRACE) satellite, some scholars have begun to use GRACE to monitor the hydrological load deformations, albeit spectral inconsistencies have been reported between the two products (Ferreira et al 2020). They are used to modify the periodic signals in the GPS time series, reduce the impact of the seasonal load deformation on them, and so improve the GPS positioning accuracy (Lavallée and Blewitt, 2002;Van Dam et al, 2007;Tregoning et al, 2009;Fu and Freymueller, 2012a;Fu and Freymueller, 2012b;Hao et al, 2016;Han et al, 2005;Ding et al, 2018;Li et al, 2019). At the same time, some scholars have combined GPS, GRACE and the hydrological model to study the load effect of the surface quality.…”

The role of snow and ice thickness on river ice process in Songhua River basin, Northeast China

“…Empirical orthogonal function (EOF) decomposition is commonly used in the climatic and hydrological analysis (Bian et al, 2019;Yang et al, 2017), whose basic principle is to decompose the field containing p 120 spatial points (variable) to decompose over time. If the sample size is n, then the data value xij including specific spatial point i and specific time j in the field can be regarded as the linear combination of spatial function Sik and time function tkj (k = 1,2,..., p), and the equation is listed as below.…”