A short history of the thermomechanical theory and modeling of glaciers and ice sheets

Blatter, Heinz; Greve, Ralf; Abe‐Ouchi, Ayako

doi:10.3189/002214311796406059

Cited by 10 publications

(12 citation statements)

References 103 publications

(117 reference statements)

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“…Such advancements include incorporation of higher‐order ice flow formulations and improvements in the simulation of key physical processes (e.g., inclusion of data assimilation and adaptive grids to maintain high spatial resolution; i.e., Aschwanden et al, ; Cornford et al, ; Fürst et al, ; Gillet‐Chaulet et al, ; Larour et al, ; Morlighem et al, ; Schoof & Hindmarsh, ; Seroussi et al, ). The development of efficient and stable numerical algorithms combined with increased computational resources have made it possible to study complex ice sheet systems at higher spatial and temporal resolutions, evidenced by a growing number of new‐generation ISMs (e.g., Aschwanden et al, ; Blatter et al, ; Gillet‐Chaulet et al, ; Larour, Seroussi, et al, ; Rutt et al, ; Zwinger et al, ). Recently, these models have become increasingly capable of reproducing observed changes at the continental ice sheet and the regional outlet glacier scales.…”

Section: Introductionmentioning

confidence: 99%

Quantification of Surface Forcing Requirements for a Greenland Ice Sheet Model Using Uncertainty Analyses

Schlegel

Larour

2019

Geophysical Research Letters

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The Greenland Ice Sheet is a substantial reservoir of almost 7 m of sea level equivalent, and on average, climate dictates 60% of its sea level contribution. Changes in ice discharge, driven by perturbations in outlet glacier ice dynamics, constitute the rest. Climate also affects ice discharge, since the flow of interior ice feeding the outlet glaciers evolves in response to surface changes over time. Here, using an ice sheet model and uncertainty quantification, we explore ice flow sensitivity to climate‐driven changes in ice surface topography on multidecadal timescales. We find that changes in surface forcing near large outlet glaciers can influence region‐wide ice flow. Improvements to climate products should be prioritized in these areas, especially in the Central West and Southeast. Results also suggest that over most of Greenland, surface forcing should be supplied at a spatial resolution of 21 km or finer to accurately simulate ice response to climate change.

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Section: Introductionmentioning

confidence: 99%

Quantification of Surface Forcing Requirements for a Greenland Ice Sheet Model Using Uncertainty Analyses

Schlegel

Larour

2019

Geophysical Research Letters

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“…Glaciological modeling has benefited from significant advancements over the last two decades, including the increased availability of ground‐ and space‐based observations [e.g., Bamber and Rivera , 2007; Arendt et al , 2009; Braithwaite , 2009; Zemp et al , 2009], accessibility of high resolution surface and subsurface topographic data [e.g., Arnold et al , 2006; Mottram et al , 2009; Le Brocq et al , 2010], introduction of climate reanalysis products [e.g., Hanna et al , 2005; Radić and Hock , 2006; Rye et al , 2010], and exponential growth in computing power [e.g., Blatter et al , 2010; Pollard , 2010]. This has resulted in a trend toward models of ever‐increasing complexity that are built around physical (rather than empirical) relationships and are implemented at ever‐higher spatial and temporal resolutions [e.g., Arnold and Rees , 2009; Ettema et al , 2009].…”

Section: Introductionmentioning

confidence: 99%

On the need for automated multiobjective optimization and uncertainty estimation of glacier mass balance models

et al. 2012

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[1] Physically based models are valuable tools for exploring the detailed spatial and temporal responses of glaciers and ice sheets to climate forcing. However, while the last two decades have seen considerable progress in the development of increasingly sophisticated numerical descriptions, very little attention has been given to simulation uncertainty. In particular, glaciological models have traditionally been calibrated (or "tuned") in order to identify a single set of parameters (e.g., snow density, surface albedo, temperature lapse rate) such that the model's behavior closely matches real-world observations. The present study disputes this classical approach by demonstrating that it is often difficult (if not impossible) to find a single "best" solution. Instead, multiple equally plausible parameter sets will usually exist. To address this limitation, we present a novel application of a calibration technique previously not used in glacial modeling -multiobjective optimization -designed to identify multiple optimal parameter sets that fit different characteristics of available observations, thereby enabling an assessment of the uncertainty associated with predictions. The strength and applicability of our approach is illustrated through the implementation of a surface mass balance model for two glaciers in Svalbard: Midre Lovénbreen and Kongsvegen. The model is forced using the ERA-40 reanalysis and calibrated against available mass balance measurements. The overall uncertainty range in modeled cumulative annual surface mass balance is À7.84 to À14.02 m w.e. for Midre Lovénbreen (over the period and À0.91 to +9.80 m w.e. for Kongsvegen (over the period [1987][1988][1989][1990][1991][1992][1993][1994][1995][1996][1997][1998][1999][2000][2001]. The calibrated model is used to extend the mass balance records of the two glaciers back to the beginning of the ERA-40 reanalysis, giving a cumulative loss of loss of 13.2 AE 3.3 m w.e. for Midre Lovénbreen and a cumulative gain of 5.3 AE 3.4 m w.e. for Kongsvegen over the period . The multiobjective optimization results also indicate that current mass balance models may contain structural inadequacies relating to how the mass balance gradient is simulated.Citation: Rye, C. J., I. C. Willis, N. S. Arnold, and J. Kohler (2012), On the need for automated multiobjective optimization and uncertainty estimation of glacier mass balance models,

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“…For a given geometric and climatic setting, the reliability of such forecasts depends, in part, on how accurately the ice-flow model represents the actual glacier dynamics. The dynamical sophistication of several models developed to date is briefly summarized in a recent paper by Blatter and others (2010).…”

Section: Introductionmentioning

confidence: 99%

Improvements to shear-deformational models of glacier dynamics through a longitudinal stress factor

Adhikari¹,

Marshall²

2011

J. Glaciol.

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In a two-dimensional (plane strain) glacier domain, gravity-driven ice flow is balanced by basal drag and the resistance associated with longitudinal stress gradients. The plane strain Stokes model accommodates both these resistances, whereas several simpler models only account for basal drag. Solving the Stokes equations is numerically challenging and computationally expensive, but simpler models may lead to unrealistic dynamical behaviour. Here, we propose a factor which can be introduced in shear-deformational flow models to yield results comparable to those from the plane strain Stokes model. As this factor adapts simpler models to capture the effects of missing dynamics, i.e. longitudinal stress gradients, we refer to it as the longitudinal stress (L L-)factor. We assess the usefulness of this factor for idealized domains with complex basal topography and evolving geometry. We apply the model to Haig Glacier, Canadian Rockies, in order to present an illustration of how simulations of glacier response to climate forcing can be improved through the introduction of the L-factor in a shear-deformational flow model.

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A short history of the thermomechanical theory and modeling of glaciers and ice sheets

Cited by 10 publications

References 103 publications

Quantification of Surface Forcing Requirements for a Greenland Ice Sheet Model Using Uncertainty Analyses

Quantification of Surface Forcing Requirements for a Greenland Ice Sheet Model Using Uncertainty Analyses

On the need for automated multiobjective optimization and uncertainty estimation of glacier mass balance models

Improvements to shear-deformational models of glacier dynamics through a longitudinal stress factor

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