scholarly journals Results from the Ice Thickness Models Intercomparison eXperiment Phase 2 (ITMIX2)

2021 ◽  
Vol 8 ◽  
Author(s):  
Daniel Farinotti ◽  
Douglas J. Brinkerhoff ◽  
Johannes J. Fürst ◽  
Prateek Gantayat ◽  
Fabien Gillet-Chaulet ◽  
...  
Keyword(s):  
Spatial Information ◽  
Numerical Models ◽  
Spatial Scales ◽  
Thickness Distribution ◽  
Data Availability ◽  
Added Value ◽  
Ice Thickness ◽  
Test Case ◽  
Glacier Surface ◽  
The Mean

Knowing the ice thickness distribution of a glacier is of fundamental importance for a number of applications, ranging from the planning of glaciological fieldwork to the assessments of future sea-level change. Across spatial scales, however, this knowledge is limited by the paucity and discrete character of available thickness observations. To obtain a spatially coherent distribution of the glacier ice thickness, interpolation or numerical models have to be used. Whilst the first phase of the Ice Thickness Models Intercomparison eXperiment (ITMIX) focused on approaches that estimate such spatial information from characteristics of the glacier surface alone, ITMIX2 sought insights for the capability of the models to extract information from a limited number of thickness observations. The analyses were designed around 23 test cases comprising both real-world and synthetic glaciers, with each test case comprising a set of 16 different experiments mimicking possible scenarios of data availability. A total of 13 models participated in the experiments. The results show that the inter-model variability in the calculated local thickness is high, and that for unmeasured locations, deviations of 16% of the mean glacier thickness are typical (median estimate, three-quarters of the deviations within 37% of the mean glacier thickness). This notwithstanding, limited sets of ice thickness observations are shown to be effective in constraining the mean glacier thickness, demonstrating the value of even partial surveys. Whilst the results are only weakly affected by the spatial distribution of the observations, surveys that preferentially sample the lowest glacier elevations are found to cause a systematic underestimation of the thickness in several models. Conversely, a preferential sampling of the thickest glacier parts proves effective in reducing the deviations. The response to the availability of ice thickness observations is characteristic to each approach and varies across models. On average across models, the deviation between modeled and observed thickness increase by 8.5% of the mean ice thickness every time the distance to the closest observation increases by a factor of 10. No single best model emerges from the analyses, confirming the added value of using model ensembles.

Where shall we measure? Results from the second phase of the Ice Thickness Models Intercomparison eXperiment (ITMIX2)

2021 ◽  
Author(s):  
Daniel Farinotti ◽  
Keyword(s):  
Numerical Models ◽  
Thickness Distribution ◽  
Data Availability ◽  
Ice Thickness ◽  
Test Case ◽  
Second Phase ◽  
Ice Caps ◽  
Direct Measurements ◽  
Thickness Measurements ◽  
Weak Influence

<p>Knowing the ice thickness distribution of glaciers and ice caps is of critical importance for a number of studies. However, since measuring ice thickness directly is difficult and time consuming, the availability of such information is generally scarce. Here, we present results from the Second Phase of the Ice Thickness Models Intercomparison eXperiment (ITMIX2) which had a two-fold objective. First, it aimed at characterizing the capability of numerical models to use sparse thickness measurements to their advantage. Second, it aimed at identifying possible strategies for maximizing the information content gained through direct ice thickness surveys.</p><p>The experiment was designed around 23 test cases including both real-world and synthetic glaciers, and comprised a set of 16 different experiments per test case simulating different scenarios of data availability. Based on a total of 2,544 individual solutions submitted by 13 different models, our results show that for locations without direct measurements, the ice thickness can be predicted with typical deviations in the order of 16% of the mean ice thickness. Despite large scatter, even limited sets of ice thickness observations are found to be effective in constraining the glacier total volume, particularly when the thickest part of a glacier is surveyed. Other spatial distributions of the ice thickness observations have only a weak influence on the predicted thickness, although surveys restricted to the lowest glacier elevations often result in an underestimation of the glacier’s total volume. The response to the various scenarios of data availability is found to be specific to individual models, and while no single best approach emerges, an ensemble-approach based on a combination of models is shown to be beneficial in terms of accuracy and robustness.</p>

scholarly journals East Antarctic sea ice: observations and modelling

1998 ◽  
Vol 27 ◽  
pp. 427-432 ◽  
Author(s):  
Anthony P. Worby ◽  
Xingren Wu
Keyword(s):  
Sea Ice ◽  
Climate Models ◽  
Numerical Models ◽  
Global Climate ◽  
Thickness Distribution ◽  
Ice Thickness ◽  
Antarctic Sea Ice ◽  
Pack Ice ◽  
Concentration Sensitivity ◽  
Sensitivity Studies

The importance of monitoring sea ice for studies of global climate has been well noted for several decades. Observations have shown that sea ice exhibits large seasonal variability in extent, concentration and thickness. These changes have a significant impact on climate, and the potential nature of many of these connections has been revealed in studies with numerical models. An accurate representation of the sea-ice distribution (including ice extent, concentration and thickness) in climate models is therefore important for modelling global climate change. This work presents an overview of the observed sea-ice characteristics in the East Antarctic pack ice (60-150° E) and outlines possible improvements to the simulation of sea ice over this region by modifying the ice-thickness parameterisation in a coupled sea-ice-atmosphere model, using observational data of ice thickness and concentration. Sensitivity studies indicate that the simulation of East Antarctic sea ice can be improved by modifying both the “lead parameterisation” and “rafting scheme” to be ice-thickness dependent. The modelled results are currently out of phase with the observed data, and the addition of a multilevel ice-thickness distribution would improve the simulation significantly.

scholarly journals Seasonal development of the sea-ice thickness distribution in East Antarctica: measurements from upward-looking sonar

2001 ◽  
Vol 33 ◽  
pp. 177-180 ◽  
Author(s):  
A. P. Worby ◽  
G. M. Bush ◽  
I. Allison
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
East Antarctica ◽  
Seasonal Development ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Seasonal Evolution ◽  
Pack Ice ◽  
The Mean ◽  
Prototype Instrument

AbstractUpward-looking sonar (ULS) data are presented from a prototype instrument deployed at 63° 18’ S, 107°49’ E in 1994. These data show the seasonal evolution of the ice-draft distribution from May when predominantly thin ice is present, through October when substantially thicker ice has been formed by deformation. The mean ice draft reaches a maximum in August at 1.21 m, the same month in which ship-based observations from the same region show a peak in ice thickness. The observed distribution from ULS data is only for drafts > 0.3 m due to data losses caused by the low acoustic reflectivity of actively forming ice. The spring distributions show very little development of drafts > 3.0 m, and it is hypothesized that this is due to the cyclical nature of deformation in the East Antarctic pack-ice zone, and that periods of sustained pressure required to form very thick ice are uncommon in this region

scholarly journals A method for sea ice thickness and concentration analysis based on SAR data and a thermodynamic model

2012 ◽  
Vol 6 (6) ◽  
pp. 1507-1526 ◽  
Author(s):  
J. Karvonen ◽  
B. Cheng ◽  
T. Vihma ◽  
M. Arkett ◽  
T. Carrieres
Keyword(s):  
Spatial Distribution ◽  
Sea Ice ◽  
Thickness Distribution ◽  
Added Value ◽  
Ice Thickness ◽  
Motion Field ◽  
Sea Ice Thickness ◽  
Automated Method ◽  
Basin Scale ◽  
Sar Data

Abstract. An analysis of ice thickness distribution is a challenge, particularly in a seasonal sea ice zone with a strongly dynamic ice motion field, such as the Gulf of St. Lawrence off Canada. We present a novel automated method for ice concentration and thickness analysis combining modeling of sea ice thermodynamics and detection of ice motion on the basis of space-borne Synthetic Aperture Radar (SAR) data. Thermodynamic evolution of sea ice thickness in the Gulf of St. Lawrence was simulated for two winters, 2002–2003 and 2008–2009. The basin-scale ice thickness was controlled by atmospheric forcing, but the spatial distribution of ice thickness and concentration could not be explained by thermodynamics only. SAR data were applied to detect ice motion and ice surface structure during these two winters. The SAR analysis is based on estimation of ice motion between SAR image pairs and analysis of the local SAR texture statistics. Including SAR data analysis brought a significant added value to the results based on thermodynamics only. Our novel method combining the thermodynamic modeling and SAR yielded results that well match with the distribution of observations based on airborne Electromagnetic Induction (EM) method. Compared to the present operational method of producing ice charts for the Gulf of St. Lawrence, which is based on visual interpretation of SAR data, the new method reveals much more detailed and physically based information on spatial distribution of ice thickness. The algorithms can be run automatically, and the final products can then be used by ice analysts for operational ice service. The method is globally applicable to all seas where SAR data are available.

scholarly journals Exploratory studies into seasonal flow forecasting potential for large lakes

2018 ◽  
Vol 22 (1) ◽  
pp. 127-141 ◽  
Author(s):  
Kevin Sene ◽  
Wlodek Tych ◽  
Keith Beven
Keyword(s):  
Water Balance ◽  
Lake Victoria ◽  
Spatial Scales ◽  
Data Availability ◽  
Climate Indices ◽  
Test Case ◽  
Large Lakes ◽  
Analytical Approximations ◽  
Seasonal Flow ◽  
The Individual

Abstract. In seasonal flow forecasting applications, one factor which can help predictability is a significant hydrological response time between rainfall and flows. On account of storage influences, large lakes therefore provide a useful test case although, due to the spatial scales involved, there are a number of modelling challenges related to data availability and understanding the individual components in the water balance. Here some possible model structures are investigated using a range of stochastic regression and transfer function techniques with additional insights gained from simple analytical approximations. The methods were evaluated using records for two of the largest lakes in the world – Lake Malawi and Lake Victoria – with forecast skill demonstrated several months ahead using water balance models formulated in terms of net inflows. In both cases slight improvements were obtained for lead times up to 4–5 months from including climate indices in the data assimilation component. The paper concludes with a discussion of the relevance of the results to operational flow forecasting systems for other large lakes.

Evaluating Simplified Numerical Models of Debris-Covered Glaciers

2021 ◽  
Author(s):  
James C. Ferguson ◽  
Tobias Bolch ◽  
Andreas Vieli
Keyword(s):  
Numerical Models ◽  
Spatial Scales ◽  
Global Scale ◽  
Glacier Surface ◽  
Ice Dynamics ◽  
Surface Mass ◽  
Wide Range ◽  
Debris Cover ◽  
Debris Transport ◽  
Modelling Studies

<p>The transient response of debris-covered glaciers to a changing climate is governed by nonlinear feedbacks between ice dynamics, debris transport, and glacier geometry and that act over a wide range of temporal and spatial scales. Current numerical models that are able to accurately represent the relevant physical processes are computationally expensive since they must track the debris transport not only at the glacier surface but also englacially. This makes such models difficult to use for simulations at the regional to global scale.</p><p>In order to address this challenge, we developed a fully coupled numerical model that solves both englacial debris transport and ice flow and includes the effect of debris cover on surface ablation. We use this model to evaluate different simplified approaches to modelling debris-covered glaciers. These simplifications include parametrized 1-D debris transport models, parametrized models of surface mass balance that include debris cover effects, and zero-dimensional models. We compare the model performances using a number of tests with an idealized synthetic glacier geometry and a range of forcings, thereby allowing for an evaluation of the relative merits of each approach. A key goal of this work is to provide guidance and tools for modelling studies involving debris cover at the regional to global scale.</p>

scholarly journals Using surface velocities to calculate ice thickness and bed topography: a case study at Columbia Glacier, Alaska, USA

2012 ◽  
Vol 58 (212) ◽  
pp. 1151-1164 ◽  
Author(s):  
R.W. Mcnabb ◽  
R. Hock ◽  
S. O’Neel ◽  
L.A. Rasmussen ◽  
Y. Ahn ◽  
...  
Keyword(s):  
Thickness Distribution ◽  
Computational Time ◽  
Ice Thickness ◽  
Thickness Change ◽  
Bed Topography ◽  
Surface Data ◽  
Direct Measurements ◽  
The Mean ◽  
Time Required

AbstractInformation about glacier volume and ice thickness distribution is essential for many glaciological applications, but direct measurements of ice thickness can be difficult and costly. We present a new method that calculates ice thickness via an estimate of ice flux. We solve the familiar continuity equation between adjacent flowlines, which decreases the computational time required compared to a solution on the whole grid. We test the method on Columbia Glacier, a large tidewater glacier in Alaska, USA, and compare calculated and measured ice thicknesses, with favorable results. This shows the potential of this method for estimating ice thickness distribution of glaciers for which only surface data are available. We find that both the mean thickness and volume of Columbia Glacier were approximately halved over the period 1957–2007, from 281 m to 143 m, and from 294 km3 to 134 km3, respectively. Using bedrock slope and considering how waves of thickness change propagate through the glacier, we conduct a brief analysis of the instability of Columbia Glacier, which leads us to conclude that the rapid portion of the retreat may be nearing an end.

scholarly journals Impact of spatial aliasing on sea-ice thickness measurements

2015 ◽  
Vol 56 (69) ◽  
pp. 353-362 ◽  
Author(s):  
Cathleen Geiger ◽  
Hans-Reinhard Müller ◽  
Jesse P. Samluk ◽  
E. Rachel Bernstein ◽  
Jacqueline Richter-Menge
Keyword(s):  
Sea Ice ◽  
Spatial Scales ◽  
Thickness Distribution ◽  
Ice Thickness ◽  
Mathematical Functions ◽  
Sea Ice Thickness ◽  
Spatial Aliasing ◽  
Monitoring And Forecasting ◽  
Non Gaussian ◽  
The Impact

AbstractWe explore spatial aliasing of non-Gaussian distributions of sea-ice thickness. Using a heuristic model and >1000 measurements, we show how different instrument footprint sizes and shapes can cluster thickness distributions into artificial modes, thereby distorting frequency distribution, making it difficult to compare and communicate information across spatial scales. This problem has not been dealt with systematically in sea ice until now, largely because it appears to incur no significant change in integrated thickness which often serves as a volume proxy. Concomitantly, demands are increasing for thickness distribution as a resource for modeling, monitoring and forecasting air–sea fluxes and growing human infrastructure needs in a changing polar environment. New demands include the characterization of uncertainties both regionally and seasonally for spaceborne, airborne, in situ and underwater measurements. To serve these growing needs, we quantify the impact of spatial aliasing by computing resolution error (Er) over a range of horizontal scales (x) from 5 to 500 m. Results are summarized through a power law (Er= bxm) with distinct exponents (m) from 0.3 to 0.5 using example mathematical functions including Gaussian, inverse linear and running mean filters. Recommendations and visualizations are provided to encourage discussion, new data acquisitions, analysis methods and metadata formats.

scholarly journals The bedrock topography of Gries- and Findelengletscher

2018 ◽  
Vol 73 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Nadine Feiger ◽  
Matthias Huss ◽  
Silvan Leinss ◽  
Leo Sold ◽  
Daniel Farinotti
Keyword(s):  
Spatial Interpolation ◽  
Thickness Distribution ◽  
Estimation Method ◽  
Ice Thickness ◽  
Glacier Surface ◽  
Ice Flow ◽  
Bedrock Topography ◽  
Abstract Knowledge ◽  
Ground Penetrating ◽  
Item Item

Abstract. Knowledge of the ice thickness distribution of glaciers is important for glaciological and hydrological applications. In this contribution, we present two updated bedrock topographies and ice thickness distributions for Gries- and Findelengletscher, Switzerland. The results are based on ground-penetrating radar (GPR) measurements collected in spring 2015 and already-existing data. The GPR data are analysed using ReflexW software and interpolated by using the ice thickness estimation method (ITEM). ITEM calculates the thickness distribution by using principles of ice flow dynamics and characteristics of the glacier surface. We show that using such a technique has a significance advantage compared to a direct interpolation of the measurements, especially for glacier areas that are sparsely covered by GPR data. The uncertainties deriving from both the interpretation of the GPR signal and the spatial interpolation through ITEM are quantified separately, showing that, in our case, GPR signal interpretation is a major source of uncertainty. The results show a total glacier volume of 0.28±0.06 and 1.00±0.34 km3 for Gries- and Findelengletscher, respectively, with corresponding average ice thicknesses of 56.8±12.7 and 56.3±19.6 m.

scholarly journals Exploratory studies into seasonal flow forecasting potential for large lakes

2017 ◽  
Author(s):  
Kevin Sene ◽  
Wlodek Tych ◽  
Keith Beven
Keyword(s):  
Water Balance ◽  
Lake Victoria ◽  
Spatial Scales ◽  
Data Availability ◽  
Climate Indices ◽  
Test Case ◽  
Large Lakes ◽  
Analytical Approximations ◽  
Seasonal Flow ◽  
The Individual

Abstract. In seasonal flow forecasting applications, one factor which can help predictability is a significant hydrological response time between rainfall and flows. On account of storage influences, large lakes therefore provide a useful test case although, due to the spatial scales involved, there are a number of modelling challenges related to data availability and understanding the individual components in the water balance. Here some possible model structures are investigated using a 10 range of stochastic regression and transfer function techniques with additional insights gained from simple analytical approximations. The methods were evaluated using records for two of the largest lakes in the world – Lake Malawi and Lake Victoria – with forecast skill demonstrated several months ahead using water balance models formulated in terms of net inflows. In both cases slight improvements were obtained for lead times up to 4–5 months from including climate indices in the data assimilation component. The paper concludes with a discussion of the relevance of the results to operational flow 15 forecasting systems for other large lakes.

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