Detecting the Sea-Level Fingerprint of Polar Ice Mass Changes: Testing a New Method for Estimating the Sources of Global Sea Level Change

Twentieth and 21st century rates of globally averaged sea level (SL) change have commonly been estimated using subsets of tide gauge records and satellite altimetry data. However, these estimates ignore the information embedded in the geographic variability of the measurements. It is now well known, for example, that the rapid melting of an individual ice sheet or glacier will produce a unique geometry, or fingerprint, of SL change. In principle, a suite of such fingerprints, together with a network of modern SL observations with sufficient geographic distribution and signal-to-noise properties, may be used to infer recent sources of meltwater flux. We outline a new formalism based on a Kalman filter for estimating the individual SL contributions to global SL change using tide gauge and satellite altimetry measurements. The Kalman filter is well-suited to such an estimate because: 1) it naturally accommodates missing data, a significant factor for sparse sections of the tide gauge record; 2) it is able to optimally estimate non-stationary trends and the associated uncertainty; and 3) its recursive nature reduces the potentially onerous computational memory requirements caused by large volumes of tide gauge or satellite altimetry data. We first explore the feasibility of extracting source information from SL records by applying the new methodology in a series of detection experiments with synthetic tide gauge and altimetry data sets. Our synthetic data sets are constructed by combining de-trended tide gauge or satellite altimetry records with sea-level fingerprints computed for a variety of Greenland Ice Sheet (GIS) and West Antarctic Ice Sheet (WAIS) melt scenarios. Additional contributions to SL change, such as the signal due to on-going glacial isostatic adjustment in response to the last ice age and dynamic sea level changes due to thermal expansion of the ocean are also included in the synthetics. We apply our modified Kalman filter to various subsets of synthetic data, constructed using a suite of ice sheet melting scenarios, to assess the accuracy with which individual contributions of meltwater to 20th and 21st century sea level change are detectable. With this insight in hand, we end by reporting on the first application of the new methodology to the existing global data base of tide gauge and satellite altimetry records.