Estimating the sources of global sea level rise with data assimilation techniques

Abstract

A rapidly melting ice sheet produces a distinctive geometry, or fingerprint, of sea level (SL) change. Thus, a network of SL observationsmay, in principle, be used to infer sources ofmeltwater flux.We outline a formalism, based on amodified Kalman smoother, for using tide gauge observations to estimate the individual sources of global SL change.We also report on a series of detection experiments based on synthetic SL data that explore the feasibility of extracting source information from SL records. The Kalman smoother technique iteratively calculates the maximum-likelihood estimate of Greenland ice sheet (GIS) andWestAntarctic ice sheet (WAIS) melt at each time step, and it accommodates data gapswhile also permitting the estimation of nonlinear trends. Our synthetic tests indicate that when all tide gauge records are used in the analysis, it should be possible to estimate GIS and WAIS melt rates greater than ̃0.3 and ̃0.4 mm of equivalent eustatic sea level rise per year, respectively. We have also implemented a multimodel Kalman filter that allows us to account rigorously for additional contributions to SL changes and their associated uncertainty. The multimodel filter uses 72 glacial isostatic adjustment models and 3 ocean dynamic models to estimate the most likely models for these processes given the synthetic observations. We conclude that ourmodified Kalman smoother procedure provides a powerful method for inferring melt rates in a warming world.