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Land ice discharge from the Antarctic continent into the ocean is restrained by ice shelves, floating extensions of grounded ice that buttress the glacier outflow. The ongoing thinning of these ice shelves – largely due to enhanced melting at their base in response to global warming – is known to accelerate the release of glacier meltwater into the world oceans, augmenting global sea level. Mechanisms of ocean heat intrusion under the ice base are therefore crucial to project the future of Antarctic ice shelves. Furthermore, ice shelves are weakened by the presence of kilometer-wide full-thickness ice rifts, which are observed all around Antarctica. However, their impact on ocean circulation around and below ice shelves has been largely unexplored as ocean models are commonly characterized by resolutions that are too coarse to resolve their presence. Here, we apply the Massachusetts Institute of Technology general circulation model at high resolution to investigate the sensitivity of sub-shelf ocean dynamics and ice-shelf melting to the presence of a kilometer-wide rift in proximity of the ice front. We find that (a) the rift curtails water and heat intrusion beneath the ice-shelf base and (b) the basal melting of a rifted ice shelf is on average 20 % lower than for an intact ice shelf under identical forcing. Notably, we calculate a significant reduction in melting rates of up to 30 % near the grounding line of a rifted ice shelf. We therefore posit that rifts and their impact on the sub-shelf dynamics are important to consider in order to accurately reproduce and project pathways of heat intrusion into the ice-shelf cavity. [ABSTRACT FROM AUTHOR]

This paper provides the first description of the open-source Glacier Energy and Mass Balance model. GEMB models the ice sheet and glacier surface–atmospheric energy and mass exchange, as well as the firn state. It is a column model (no horizontal communication) of intermediate complexity that includes those processes deemed most relevant to glacier studies. GEMB prioritizes computational efficiency to accommodate the very long (thousands of years) spin-ups necessary for initializing deep firn columns and sensitivity experiments needed to characterize model uncertainty on continental scales. The model is one-way coupled with the atmosphere, which allows the model to be run offline with a diversity of climate forcing but neglects feedback to the atmosphere. GEMB provides numerous parameterization choices for various key processes (e.g., albedo, subsurface shortwave absorption, and compaction), making it well suited for uncertainty quantification and model exploration. The model is evaluated against the current state of the art and in situ observations and is shown to perform well. [ABSTRACT FROM AUTHOR]

This paper provides the first description of version 1.0 of the open-source Glacier Energy and Mass Balance model. GEMB models the ice sheet and glacier surface-atmospheric energy and mass exchange, and firn state. It is a column model (no horizontal communication) of intermediate complexity that includes those processes deemed most relevant to glacier studies with the goal of retaining computational efficiency that can accommodate the very long (thousands of years) spin-ups necessary for initializing deep firn columns and for running sensitivity experiments to characterize model uncertainty on continental scales. The model is one-way coupled with the atmosphere which allows the model to be run off-line with a diversity of climate forcing but neglects feedback to the atmosphere. GEMB provides numerous parameterization choices for various key processes (e.g. albedo, subsurface shortwave absorption, and compaction), making it well suited for uncertainty quantification and model exploration. The model is evaluated against the current state-of-the-art and in situ observations and is shown to perform well. [ABSTRACT FROM AUTHOR]

This paper provides the first description of version 1.0 of the open-source Glacier Energy and Mass Balance model. GEMB models the ice sheet and glacier surface-atmospheric energy and mass exchange, and firn state. It is a column model (no horizontal communication) of intermediate complexity that includes those processes deemed most relevant to glacier studies with the goal of retaining computational efficiency that can accommodate the very long (thousands of years) spin-ups necessary for initializing deep firn columns and for running sensitivity experiments to characterize model uncertainty on continental scales. The model is one-way coupled with the atmosphere which allows the model to be run off-line with a diversity of climate forcing but neglects feedback to the atmosphere. GEMB provides numerous parameterization choices for various key processes (e.g. albedo, subsurface shortwave absorption, and compaction), making it well suited for uncertainty quantification and model exploration. The model is evaluated against the current state-of-the-art and in situ observations and is shown to perform well. [ABSTRACT FROM AUTHOR]

There is no consensus on how quickly the earth's ice sheets are melting due to global warming, nor on the ramifications to sea level rise. Due to its potential effects on coastal populations and global economies, sea level rise is a grave concern, making ice melt rates an important area of study. The ice‐sheet science community consists of two groups that perform related but distinct kinds of research: a data community, and a model building community. The data community characterizes past and current states of the ice sheets by assembling data from field and satellite observations. The modeling community forecasts the rate of ice‐sheet decline with computational models validated against observations. Although observational data and models depend on one another, these two groups are not well integrated. Better coordination between data collection efforts and modeling efforts is imperative if we are to improve our understanding of ice sheet loss rates. We present a new science gateway, GHub, a collaboration space for ice sheet scientists. This web‐accessible gateway will host datasets and modeling workflows, and provide access to codes that enable tool building by the ice sheet science community. Using GHub, we will collect and centralize existing datasets, creating data products that more completely catalog the ice sheets of Greenland and Antarctica. We will build workflows for model validation and uncertainty quantification, extending existing ice sheet models. Finally, we will host existing community codes, enabling scientists to build new tools utilizing them. With this new cyberinfrastructure, ice sheet scientists will gain integrated tools to quantify the rate and extent of sea level rise, benefitting human societies around the globe. [ABSTRACT FROM AUTHOR]

Assessing the impact of uncertainties in ice-sheet models is a major and challenging issue that needs to be faced by the ice-sheet community to provide more robust and reliable model- based projections of ice-sheet mass balance. In recent years, uncertainty quantification (UQ) has been increasingly used to characterize and explore uncertainty in ice-sheet models and improve the robustness of their projections. A typical UQ analysis involves first the (probabilistic) characterization of the sources of uncertainty followed by the propagation and sensitivity analysis of these sources of uncertainty. Previous studies concerned with UQ in ice-sheet models have generally focused on the last two steps but paid relatively little attention to the preliminary and critical step of the characterization of uncertainty. Sources of uncertainty in ice-sheet models, like uncertainties in ice-sheet geometry or surface mass balance, typically vary in space and potentially in time. For that reason, they are more adequately described as spatio(-temporal) random fields, which account naturally for spatial (and temporal) correlation. As a means of improving the characterization of the sources of uncertainties in ice-sheet models, we propose in this paper to represent them as Gaussian random fields with Matérn covariance function. The class of Matérn covariance functions provides a flexible model able to capture statistical dependence between locations with different degrees of spatial correlation or smoothness properties. Samples from a Gaussian random field with Matérn covariance function can be generated efficiently by solving a certain stochastic partial differential equation. Discretization of this stochastic partial differential equation by the finite element method results in a sparse approximation known as a Gaussian Markov random field. We solve this equation efficiently using the finite element method within the Ice-sheet and Sea-level System Model (ISSM). In addition, spatio-temporal samples can be generated by combining an autoregressive temporal model and the Matérn field. The implementation is tested on a set of synthetic experiments to verify that it captures well the desired spatial and temporal correlations. Finally, we demonstrate the interest of this sampling capability in an illustration concerned with assessing the impact of various sources of uncertainties on the Pine Island Glacier, West Antarctica. We find that both larger spatial and temporal correlations lengths will likely result in increased uncertainty in the projections. [ABSTRACT FROM AUTHOR]