Ensemble Kalman Filtering for Glacier Modeling

TL;DR

This paper demonstrates how Ensemble Kalman Filtering enhances glacier melt predictions by assimilating observational data, leading to improved sea level rise estimates and storm surge modeling.

Contribution

It introduces the application of EnKF to a two-stage ice sheet model, improving predictions despite initial condition inaccuracies and limited historical data.

Findings

EnKF improves glacier melt predictions with incorrect initial conditions.

Modern observations can correct deviations caused by sparse pre-satellite data.

Enhanced models enable better sea level rise and storm surge estimates.

Abstract

Working with a two-stage ice sheet model, we explore how statistical data assimilation methods can be used to improve predictions of glacier melt and relatedly, sea level rise. We find that the EnKF improves model runs initialized using incorrect initial conditions or parameters, providing us with better models of future glacier melt. We explore the necessary number of observations needed to produce an accurate model run. Further, we determine that the deviations from the truth in output that stem from having few data points in the pre-satellite era can be corrected with modern observation data. Finally, using data derived from our improved model we calculate sea level rise and model storm surges to understand the affect caused by sea level rise.