Abstract
Abstract. To provide estimates of past glacier mass changes over the course of the 20th century, an adequate initial state is required. However, empirical evidence about past glacier states at regional or global scales is largely incomplete, both spatially and temporally, calling for the use of automated numerical methods. This study presents a new way to initialize the Open Global Glacier Model from past climate information and present-day glacier states. We use synthetic experiments to show that even with these perfectly known but incomplete boundary conditions, the problem of model initialization is an ill-posed inverse problem leading to nonunique solutions, and we propose an ensemble approach as a way forward. The method works as follows: we generate a large set of physically plausible glacier candidates for a given year in the past (e.g., 1850 in the Alps), all of which are then modeled forward to the date of the observed glacier outline and evaluated by comparing the results of the forward runs to the present-day states. We test the approach on 2660 Alpine glaciers and determine error estimates of the method from the synthetic experiments. The results show that the solution is often nonunique, as many of the reconstructed initial states converge towards the observed state in the year of observation. We find that the median state of the best 5 % of all acceptable states is a reasonable best estimate. The accuracy of the method depends on the type of the considered observation for the evaluation (glacier length, area, or geometry). Trying to find past states from only present-day length instead of the full geometry leads to a sharp increase in uncertainty. Our study thus also provides quantitative information on how well the reconstructed initial glacier states are constrained through the limited information available to us. We analyze which glacier characteristics influence the reconstructability of a glacier, and we discuss ways to develop the method further for real-world applications.
Highlights
Glaciers contributed significantly to past sea-level rise (SLR; e.g., Gregory et al, 2013; Slangen et al, 2017a; WCRP Global Sea Level Budget Group, 2018; Wouters et al, 2019; Zemp et al, 2019), and they will continue to be a major contributor in the 21st century (e.g., Church et al, 2013; Slangen et al, 2017b; Hock et al, 2019)
We present a new method estimating past glacier states and apply it to synthetic numerical experiments, and we show the obstacles that need to be overcome before applying our method to real-world problems
All candidates of the 5th percentile are in close proximity to the synthetic experiment
Summary
Glaciers contributed significantly to past sea-level rise (SLR; e.g., Gregory et al, 2013; Slangen et al, 2017a; WCRP Global Sea Level Budget Group, 2018; Wouters et al, 2019; Zemp et al, 2019), and they will continue to be a major contributor in the 21st century (e.g., Church et al, 2013; Slangen et al, 2017b; Hock et al, 2019). Reconstructions of past glacier mass change are necessary to determine the budget of past sea-level change (Gregory et al, 2013) and to increase the confidence in projections (by allowing the quantification of the agreement with observations; Marzeion et al, 2015); they enable us to quantify the pattern of the ongoing adjustment of glaciers to present-day climate. Estimates of global glacier mass change are based on in situ measurements in mass and length changes (e.g., Zemp et al, 2015; Leclercq et al, 2011), on remote sensing techniques (e.g., Gardner et al, 2013; Jacob et al, 2012; Bamber et al, 2018; Wouters et al, 2019), or on mass balance modeling driven by climate observations (Marzeion et al, 2012, 2015). Reconstructing glacier change based on climate model output allows us to test the skill of climate models (Goosse et al, 2018)
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