Past and Future High-resolution Global Glacier Mass Changes

Introduction

World-wide glaciers are losing mass which affects global sea-level, river runoff, freshwater influx to the oceans, glacier-related hazards, and landscape changes, with implications for human livelihoods and ecosystems. Hence, accurate estimates of past, current, and future glacier mass variations at a high temporal and spatial resolution are key to effective adaptation strategies.

Challenges in Current Models

However, previous mass-balance reconstructions and projections have relied on scarce observations with limited spatial and/or temporal resolution. Additionally, these models have been:

1. Overparameterized
2. Insufficiently constrained
3. Highly simplified

The simplifications have been necessitated by the high computational costs incurred by the global scale.

Project Overview: GLACMASS

GLACMASS will propel the current state-of-the-art of global-scale glacier reconstruction and projection forward in unprecedented ways. It aims to deliver a fundamentally novel and internally consistent physically-based modelling framework that:

• Draws, for the first time on a global scale, on both data assimilation and modern machine learning techniques.
• Utilizes emerging global-scale glacier-related satellite-derived data.

Objectives

The framework will be used to:

• Reconstruct multi-decadal past glacier changes.
• Make policy-relevant multi-century projections of mass and area changes of all >200,000 glaciers outside the ice sheets with unprecedented accuracy, spatiotemporal detail, and computational efficiency.
• Nowcast present mass changes in a near-real-time fashion for selected regions.

Methodology

The model framework will fuse output from a novel physically-based glacier evolution model with all relevant observations available for each glacier. These observations include:

• In-situ measurements
• Geodetic data
• Gravimetry-derived mass balances
• Snowlines and other relevant observations

This approach will simultaneously exploit the untapped strengths of different types of observational data sets in an optimal manner.