The determination of a new global GPS-derived surface velocity field and its application to the problem of 20th century sea-level rise

This project addresses the current uncertainty in 20th Century global sea level rise estimates (1.5-2.0 mm/yr). These estimates of global sea level rise and its water mass contributors are primarily derived from space geodetic (altimetry, time-variable gravity, etc.) and tide gauge measurements. Accurate measurements of these are, however, complicated by a lack of confidence in accurately correcting global tide gauge (TG) measurements for their vertical motion which prevent true sea level rise from being measured. Likewise, accurate, precise and geographically widespread surface velocity measurements are needed to constrain models of Earth's rebound from previous ice melting (glacial isostatic adjustment, GIA), for example in Antarctica. Uncertainty in GIA measurements are, perhaps most importantly, a limiting factor in obtaining accurate ice mass gain/loss estimates from GRACE (a recent satellite mission that 'weighs' the Earth). In both cases, Global Positioning System (GPS) time series offer important constraints and, indeed, have been installed in many of the critical locations. However, to date there is still uncertainty and bias in GPS time series at the 1-2mm/yr level in the vertical site velocity, mainly due to insufficient GPS signal modelling (e.g., troposphere) and resulting reference frame issues. However, recent advances in GPS error modelling mean that vertical site rates may now be obtained with a step-change in precision and accuracy compared to those currently routinely generated by the International GNSS Service. Reprocessing these raw data therefore allows even the earliest data to be used to produce coordinates of similar accuracy and precision as the most recent data. In addition, radical improvements in computational capabilities based on clustering technologies now allow what was previously impossible: the reprocessing of tens-of-thousand site-years of GPS data quickly and, therefore, in an experimental manner. Here, we propose to place improved constraints on GIA models and TG observations through a global reprocessing campaign featuring hundreds of sites globally, thereby allowing a significant advance in our understanding of global sea level rise estimates and climate-related driving mechanisms.