Glaciological Processes Research Articles

Unveiling Antarctica's Heat: A Review of Geothermal Heat Flow Estimation and the Rise of Machine Learning

Antarctica, characterized by its extreme environment and sparse data availability, presents a formidable challenge for estimating geothermal heat flow, a key parameter influencing its geological and glaciological processes. This review paper analyzes existing literature surveys on predicting geothermal heat flow in Antarctica, focusing on various methodologies employed, data sources utilized, and challenges encountered. Highlight the transition from traditional techniques reliant on sparse direct measurements to adopting machine learning (ML) approaches leveraging diverse datasets. The review synthesizes insights from studies utilizing geological, geophysical, and remote sensing data, alongside advancements in ML algorithms, to improve the spatial resolution and accuracy of heat flow predictions. Through a comprehensive examination of the literature, identify key trends, knowledge gaps, and future directions for research in this critical domain.

Advances in monitoring glaciological processes in Kalallit Nunaat (Greenland) over the past decades

Greenland’s glaciers have been retreating, thinning and accelerating since the mid-1990s, with the mass loss from the Greenland Ice Sheet (GrIS) now being the largest contributor to global sea level rise. Monitoring changes in glacier dynamics using in-situ or remote sensing methods has been and remains therefore crucial to improve our understanding of glaciological processes and the response of glaciers to changes in climate. Over the past two decades, significant advances in technology have provided improvements in the way we observe glacier behavior and have helped to reduce uncertainties in future projections. This review focuses on advances in in-situ monitoring of glaciological processes, but also discusses novel methods in satellite remote sensing. We further highlight gaps in observing, measuring and monitoring glaciers in Greenland, which should be addressed in order to improve our understanding of glacier dynamics and to reduce in uncertainties in future sea level rise projections. In addition, we review coordination and inclusivity of science conducted in Greenland and provide suggestion that could foster increased collaboration and co-production.

Impacts of Climate Change on Mountaineering Routes in the Écrins Massif (Western Alps, France)

Mountaineering is one of the most affected sports activities due to the current climate crisis and the resulting changes in the physical environment of the high mountain areas. Some routes have disappeared while others are no longer accessible in summer (hazards, technical difficulties, etc.). Research has already been conducted on the impact of climate change on mountaineering and on adaptation. The Mont-Blanc massif and the Valais Alps have been used as study areas to understand the evolution of mountaineering routes over recent decades. A mapping of the geomorphological and glaciological processes affecting mountaineering routes has been conducted for the Valais. This study aims to apply this method to Écrins massif (French Alps), which had so far remained unstudied despite its emblematic character. The famous topo-guide "Le massif des Écrins - Les 100 plus belles courses et randonnées" (1974) was used as a basis for the study with 70 routes being selected according to their notoriety and visitor numbers. All the processes affecting the routes were mapped using GIS, and a statistical analysis was carried out to gain a better understanding of the evolution of the routes, together with semi-directive interviews with experts and practitioners. A new process – the early summer melting of ice and snow cover – was identified. As is the case in the Mont-Blanc massif and the Valais, a route is affected by 9 processes on average, and almost 25% of the routes can no longer be climbed in summer. The mapping method has been successfully applied to Écrins massif, providing new knowledge of the recent evolution of mountaineering routes in the Alps.

Array processing in cryoseismology: a comparison to network-based approaches at an Antarctic ice stream

Abstract. Seismicity at glaciers, ice sheets, and ice shelves provides observational constraint on a number of glaciological processes. Detecting and locating this seismicity, specifically icequakes, is a necessary first step in studying processes such as basal slip, crevassing, imaging ice fabric, and iceberg calving, for example. Most glacier deployments to date use conventional seismic networks, comprised of seismometers distributed over the entire area of interest. However, smaller-aperture seismic arrays can also be used, which are typically sensitive to seismicity distal from the array footprint and require a smaller number of instruments. Here, we investigate the potential of arrays and array-processing methods to detect and locate subsurface microseismicity at glaciers, benchmarking performance against conventional seismic-network-based methods for an example at an Antarctic ice stream. We also provide an array-processing recipe for body-wave cryoseismology applications. Results from an array and a network deployed at Rutford Ice Stream, Antarctica, show that arrays and networks both have strengths and weaknesses. Arrays can detect icequakes from further distances, whereas networks outperform arrays in more comprehensive studies of a particular process due to greater hypocentral constraint within the network extent. We also gain new insights into seismic behaviour at the Rutford Ice Stream. The array detects basal icequakes in what was previously interpreted to be an aseismic region of the bed, as well as new icequake observations downstream and at the ice stream shear margins, where it would be challenging to deploy instruments. Finally, we make some practical recommendations for future array deployments at glaciers.

Using Deep Learning to Model Elevation Differences between Radar and Laser Altimetry

Satellite and airborne observations of surface elevation are critical in understanding climatic and glaciological processes and quantifying their impact on changes in ice masses and sea level contribution. With the growing number of dedicated airborne campaigns and experimental and operational satellite missions, the science community has access to unprecedented and ever-increasing data. Combining elevation datasets allows potentially greater spatial-temporal coverage and improved accuracy; however, combining data from different sensor types and acquisition modes is difficult by differences in intrinsic sensor properties and processing methods. This study focuses on the combination of elevation measurements derived from ICESat-2 and Operation IceBridge LIDAR instruments and from CryoSat-2’s novel interferometric radar altimeter over Greenland. We develop a deep neural network based on sub-waveform information from CryoSat-2, elevation differences between radar and LIDAR, and additional inputs representing local geophysical information. A time series of maps are created showing observed LIDAR-radar differences and neural network model predictions. Mean LIDAR vs. interferometric radar adjustments and the broad spatial and temporal trends thereof are recreated by the neural network. The neural network also predicts radar-LIDAR differences with respect to waveform parameters better than a simple linear model; however, point level adjustments and the magnitudes of the spatial and temporal trends are underestimated.

The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0)

Abstract. We introduce the first version of the Stochastic Ice-sheet and Sea-level System Model (StISSM v1.0), which adds stochastic parameterizations within a state-of-the-art large-scale ice sheet model. In StISSM v1.0, stochastic parameterizations target climatic fields with internal variability, as well as glaciological processes exhibiting variability that cannot be resolved at the spatiotemporal resolution of ice sheet models: calving and subglacial hydrology. Because both climate and unresolved glaciological processes include internal variability, stochastic parameterizations allow StISSM v1.0 to account for the impacts of their high-frequency variability on ice dynamics and on the long-term evolution of modeled glaciers and ice sheets. StISSM v1.0 additionally includes statistical models to represent surface mass balance and oceanic forcing as autoregressive processes. Such models, once appropriately calibrated, allow users to sample irreducible uncertainty in climate prediction without the need for computationally expensive ensembles from climate models. When combined together, these novel features of StISSM v1.0 enable quantification of irreducible uncertainty in ice sheet model simulations and of ice sheet sensitivity to noisy forcings. We detail the implementation strategy of StISSM v1.0, evaluate its capabilities in idealized model experiments, demonstrate its applicability at the scale of a Greenland ice sheet simulation, and highlight priorities for future developments. Results from our test experiments demonstrate the complexity of ice sheet response to variability, such as asymmetric and/or non-zero mean responses to symmetric, zero-mean imposed variability. They also show differing levels of projection uncertainty for stochastic variability in different processes. These features are in line with results from stochastic experiments in climate and ocean models, as well as with the theoretical expected behavior of noise-forced non-linear systems.

Large-Scale Monitoring of Glacier Surges by Integrating High-Temporal- and -Spatial-Resolution Satellite Observations: A Case Study in the Karakoram

Glacier surges have been increasingly reported from the mountain and high-latitude cryosphere. They represent active glaciological processes that affect the evolution of natural landscapes, and they possibly lead to catastrophic consequences, such as ice collapse, which threatens the downstream communities. Identifying and monitoring surge-type glaciers has been challenging due to the irregularity of the behavior and limitations on the spatiotemporal coverage of remote-sensing observations. With a focus on the Karakoram region, with concentrated surge-type glaciers, we present a new method to efficiently detect glacier-surging activities by integrating the high temporal resolution of MODIS imagery and the long-term archived medium spatial resolution of Landsat imagery. This method first detects the location and initial time of glacier surges by trend analysis (trend and breakpoint) from MODIS data, which is implemented by the Breaks for Additive Seasonal and Trend (BFAST) tool. The initial location and time information is then validated with the detailed surging features, such as the terminus-position changes from Landsat, and the thickness-change patterns from surface-elevation-change maps. Our method identified 74 surging events during 2000–2020 in the Karakoram, including three tributary-glacier surges, and seven newly detected surge-type glaciers. The surge-type glaciers tend to have longer lengths and smaller mean slopes compared with nonsurge-type glaciers. A comparison with previous studies demonstrated the method efficiency for detecting the surging of large-scale and mesoscale glaciers, with limitations on small and narrow glaciers due to the spatial-resolution limitation of MODIS images. For the 38 surge-type nondebris-covered glaciers, we provide details of the surging, which depict the high variability (heavy-tailed distribution) in the surging parameters in the region, and the concentration of the surge initiation during 2008–2010 and 2013–2015. The updated glacier-surging information solidifies the basis for a further investigation of the surging processes at polythermal glaciers, and for an improved assessment of the glacier-mass balance and monitoring of glacier hazards.

Glacier monitoring using real-aperture 94 GHz radar

AbstractClose-range sensors are employed to observe glaciological processes that operate over short timescales (e.g. iceberg calving, glacial lake outburst floods, diurnal surface melting). However, under poor weather conditions optical instruments fail while the operation of radar systems below 17 GHz do not have sufficient angular resolution to map glacier surfaces in detail. This letter reviews the potential of millimetre-wave radar at 94 GHz to obtain high-resolution 3-D measurements of glaciers under most weather conditions. We discuss the theory of 94 GHz radar for glaciology studies, demonstrate its potential to map a glacier calving front and summarise future research priorities.

A local model of snow–firn dynamics and application to the Colle Gnifetti site

Abstract. The regulating role of glaciers in catchment run-off is of fundamental importance in sustaining people living in low-lying areas. The reduction in glacierized areas under the effect of climate change disrupts the distribution and amount of run-off, threatening water supply, agriculture and hydropower. The prediction of these changes requires models that integrate hydrological, nivological and glaciological processes. In this work we propose a local model that combines the nivological and glaciological scales. The model describes the formation and evolution of the snowpack and the firn below it, under the influence of temperature, wind speed and precipitation. The model has been implemented in two versions: (1) a multi-layer one that considers separately each firn layer and (2) a single-layer one that models firn and underlying glacier ice as a single layer. The model was applied at the site of Colle Gnifetti (Monte Rosa massif, 4400–4550 ma.s.l.). We obtained an average reduction in annual snow accumulation due to wind erosion of 2×103 kgm-2yr-1 to be compared with a mean annual precipitation of about 2.7×103 kgm-2yr-1. The conserved accumulation is made up mainly of snow deposited between April and September, when temperatures above the melting point are also observed. End-of-year snow density, instead, increased an average of 65 kg m−3 when the contribution of wind to snow compaction was added. Observations show a high spatial and interannual variability in the characteristics of snow and firn at the site and a correlation of net balance with radiation and the number of melt layers. The computation of snowmelt in the model as a sole function of air temperature may therefore be one of the reasons for the observed mismatch between model and observations.

Proper orthogonal decomposition of ice velocity identifies drivers of flow variability at Sermeq Kujalleq (Jakobshavn Isbræ)

Abstract. The increasing volume and spatio-temporal resolution of satellite-derived ice velocity data have created new exploratory opportunities for the quantitative analysis of glacier dynamics. One potential technique, proper orthogonal decomposition (POD), also known as empirical orthogonal functions, has proven to be a powerful and flexible technique for revealing coherent structures in a wide variety of environmental flows. In this study we investigate the applicability of POD to an openly available TanDEM-X/TerraSAR-X-derived ice velocity dataset from Sermeq Kujalleq (Jakobshavn Isbræ), Greenland. We find three dominant modes with annual periodicity that we argue are explained by glaciological processes. The primary dominant mode is interpreted as relating to the stress reconfiguration at the glacier terminus, known to be an important control on the glacier's dynamics. The second and third largest modes together relate to the development of the spatially heterogenous glacier hydrological system and are primarily driven by the pressurisation and efficiency of the subglacial hydrological system. During the melt season, variations in the velocity shown in these two subsidiary modes are explained by the drainage of nearby supraglacial melt ponds, as identified with a Google Earth Engine Moderate Resolution Imaging Spectroradiometer (MODIS) dynamic thresholding technique. By isolating statistical structures within velocity datasets and through their comparison to glaciological theory and complementary datasets, POD indicates which glaciological processes are responsible for the changing bulk velocity signal, as observed from space. With the proliferation of optical- and radar-derived velocity products (e.g. MEaSUREs, ESA CCI, PROMICE), we suggest POD, and potentially other modal decomposition techniques, will become increasingly useful in future studies of ice dynamics.

Nunataks as barriers to ice flow: implications for palaeo ice sheet reconstructions

Abstract. Numerical models predict that discharge from the polar ice sheets will become the largest contributor to sea-level rise over the coming centuries. However, the predicted amount of ice discharge and associated thinning depends on how well ice sheet models reproduce glaciological processes, such as ice flow in regions of large topographic relief, where ice flows around bedrock summits (i.e. nunataks) and through outlet glaciers. The ability of ice sheet models to capture long-term ice loss is best tested by comparing model simulations against geological data. A benchmark for such models is ice surface elevation change, which has been constrained empirically at nunataks and along margins of outlet glaciers using cosmogenic exposure dating. However, the usefulness of this approach in quantifying ice sheet thinning relies on how well such records represent changes in regional ice surface elevation. Here we examine how ice surface elevations respond to the presence of strong topographic relief that acts as an obstacle by modelling ice flow around and between idealised nunataks during periods of imposed ice sheet thinning. We find that, for realistic Antarctic conditions, a single nunatak can exert an impact on ice thickness over 20 km away from its summit, with its most prominent effect being a local increase (decrease) of the ice surface elevation of hundreds of metres upstream (downstream) of the obstacle. A direct consequence of this differential surface response for cosmogenic exposure dating is a delay in the time of bedrock exposure upstream relative to downstream of a nunatak summit. A nunatak elongated transversely to ice flow is able to increase ice retention and therefore impose steeper ice surface gradients, while efficient ice drainage through outlet glaciers produces gentler gradients. Such differences, however, are not typically captured by continent-wide ice sheet models due to their coarse grid resolutions. Their inability to capture site-specific surface elevation changes appears to be a key reason for the observed mismatches between the timing of ice-free conditions from cosmogenic exposure dating and model simulations. We conclude that a model grid refinement over complex topography and information about sample position relative to ice flow near the nunatak are necessary to improve data–model comparisons of ice surface elevation and therefore the ability of models to simulate ice discharge in regions of large topographic relief.

Unsupervised Deep Clustering of Seismic Data: Monitoring the Ross Ice Shelf, Antarctica

AbstractAdvances in machine learning (ML) techniques and computational capacity have yielded state‐of‐the‐art methodologies for processing, sorting, and analyzing large seismic data sets. In this study, we consider an application of ML for automatically identifying dominant types of impulsive seismicity contained in observations from a 34‐station broadband seismic array deployed on the Ross Ice Shelf (RIS), Antarctica from 2014 to 2017. The RIS seismic data contain signals and noise generated by many glaciological processes that are useful for monitoring the integrity and dynamics of ice shelves. Deep clustering was employed to efficiently investigate these signals. Deep clustering automatically groups signals into hypothetical classes without the need for manual labeling, allowing for the comparison of their signal characteristics and spatial and temporal distribution with potential source mechanisms. The method uses spectrograms as input and encodes their salient features into a lower‐dimensional latent representation using an autoencoder, a type of deep neural network. For comparison, two clustering methods are applied to the latent data: a Gaussian mixture model (GMM) and deep embedded clustering (DEC). Eight classes of dominant seismic signals were identified and compared with environmental data such as temperature, wind speed, tides, and sea ice concentration. The greatest seismicity levels occurred at the RIS front during the 2016 El Niño summer, and near grounding zones near the front throughout the deployment. We demonstrate the spatial and temporal association of certain classes of seismicity with seasonal changes at the RIS front, and with tidally driven seismicity at Roosevelt Island.

Seasonal Cold-Wave Propagation Into the Near-Surface Ice of Debris-Covered Khumbu Glacier, Nepal

Meltwater from high-elevation debris-covered glaciers—particularly those located in the greater Himalaya and Andes—shapes the water supply of major rivers and nourishes substantial terrestrial, estuarine, and marine habitats (Kraaijenbrink et al., 2017; Immerzeel et al., 2020). However, the relative inaccessibility and high elevation of such glaciers results in a paucity of data relating to their fundamental physical properties and processes, limiting the information available to constrain and evaluate numerical models of their behaviour and project future change. Knowledge of the subsurface properties of such glaciers is particularly deficient because it is largely obscured to satellite and airborne remote sensing; englacial investigations therefore commonly require direct access (Miles et al., 2020). Of the physical properties of glaciers, ice temperature exerts an important control over glaciological processes, such as glacier motion, and their modelled behaviour. For example, ice viscosity is sensitive to temperature such that, under the same stress, ice approaching the melting point deforms 5–10 times more rapidly than it would at −10°C (Deeley and Woodward, 1908; Cuffey and Paterson, 2010). Basal motion depends on lubrication facilitated by the presence of meltwater at the ice-bed interface and/or within the pore space of a subglacial sediment layer. Measurements of near-surface ice temperatures are important for modelling the surface energy balance and projecting the future mass-balance response of glaciers to anticipated climate change. This is especially the case for glaciers with a thick supraglacial debris layer that insulates the underlying ice (according to debris layer thickness and lithology), reducing ablation and potentially extending glacier longevity (Nicholson and Benn, 2006; Nicholson and Benn, 2013; Anderson and Anderson, 2016). Here, we present a one-year time series of near-surface ice temperatures, measured between 1.5 and 7.0 m below the ice surface, in a borehole drilled by hot water into the debris-covered tongue of Khumbu Glacier, Nepal.

Modeling of meltwater runoff on the example of the Nura river section from Samarkand to Intumak reservoirs

The Nura River basin is characterized by an uneven intra-annual distribution of runoff, 90 % of the river runoff falls on the spring flood, while it should be noted that a distinctive characteristic feature of the Nura River basin is that the flood runoff is formed due to snow melt runoff. Information about meltwater runoff is necessary when predicting a flood situation for a short and long-term period in industrial production, since this is a very urgent problem for the territory of Kazakhstan. Thawed runoff is one of the main factors in the formation of river flow, with heavy snowmelt-it is one of the main reasons for the formation of flooding, flooding and flooding. In this regard, the modeling of melt runoff is an urgent and significant task. Snow cover plays an important role in the interaction of climatic, hydrological and glaciological processes. Snow is a powerful climate-forming factor that has a significant impact on the formation of weather conditions. One of the most important characteristics of the snow cover is the water supply, or water equivalent, also called melt runoff. Therefore, the subject of the study was the modeling of melt runoff. The purpose of the article is forecasting and modeling of melt runoff on the example of the Nura River section from the Samarkand to Intumak reservoirs with the study of data for the period from 01.11.2013 to 01.04.2014 and the forecast of modeling from 1.04.2014 to 06.04.2014. The study period was not chosen by chance. Since during the analysis of data and characteristics for this site, it was the period of 2014 that was one of the most high-water years. In this connection, the above-mentioned study period was chosen. The research was carried out on the basis of the data of the RSE "KazHydromet" on hydrological and meteorological indicators and a digital relief model.

Role of dense shelf water in the development of Antarctic submarine canyon morphology

Increased ocean heat supply to the Antarctic continental shelves is projected to cause accelerated ice sheet loss and contribute significantly to global sea-level rise over coming decades. Changes in temperature or salinity of dense shelf waters around Antarctica, resulting from increased glacial meltwater input, have the potential to significantly impact the location and structure of the global Meridional Overturning Circulation, with seabed irregularities such as submarine canyons, driving these flows toward the abyss. Submarine canyons also influence the location of intruding warm water currents by acting as preferential routes for rising Circumpolar Deep Water. These global changes have implications for large-scale effects to atmospheric and oceanic circulation. The ability for numerical modellers to predict these future behaviours is dependent upon our ability to understand both modern and past oceanic, sedimentological and glaciological processes. This knowledge allows ocean models to better predict the flux and pathways of Circumpolar Deep Water delivery to the shelf, and consequently to ice shelf cavities where melt is concentrated. Here we seek to understand how dense shelf water and other continental slope processes influence submarine canyon morphology by analysing newly collected geophysical and oceanographic data from a region of significant and prolonged dense shelf water export, the Hillary Canyon in the Ross Sea. We find that cascading flows of dense shelf water do not contribute to significant gully incision at the shelf edge during interglacial periods, however, are strong enough to prevent gully infilling and contribute to canyon-levee aggradation down-slope. We find buried paleo-gullies beneath gullies incising the modern seafloor. Paleo-gullies occur as single gullies and in complexes indicating that gully activity was continuous over multiple glacial cycles and formed an important role in the development of the shelf edge and upper slope. Glacial cycles likely drive large-scale shifts in canyon head processes with periods of intense seafloor erosion and significant gully incision likely occurring when ice grounded near to the shelf edge, during glacial and deglacial periods, when sediment-laden subglacial meltwater was released at the shelf edge. We put slope morphology observed at the Hillary Canyon head into global perspective to show that cascading flows of dense shelf water do not exert consistent patterns of erosion on high-latitude continental margins.

Investigating mass balance of Parvati glacier in Himalaya using satellite imagery based model

Accurate assessment of glacier mass loss is essential for understanding the glacier sensitivity to climate change and the ramifications of glacier retreat or surge. The glacier melt affects the runoff and water availability, on which the drinking and irrigation water supplies and generation of hydroelectric energy depend upon. The excessive glacial retreat may cause flood, glacial lake outburst flood, avalanches and sea level rise which are likely to affect the lives and livelihood of the people and damage the infrastructure. Here, we present a remote sensing based modeling framework to improve the understanding of accumulation and ablation processes and to quantify the glacier mass balance using multispectral satellite imageries, as several glacierized regions of the world are still poorly monitored because the field measurements for continuous monitoring on a large scale or in a complex harsh terrain are costly, time consuming and difficult. The developed modeling framework has been applied to the Parvati glacier in the western Himalaya to investigate glaciological processes and estimate the surface mass loss using 19 years of satellite images from 1998 to 2016. It spreads over 425.318 km2 and more than 50% of the area is accumulation area. The study shows that the Parvati glacier is not in equilibrium and its behavioural response changes year to year characterized with high rate of mass loss. The value of accumulation area ratio varies between 0.33 and 0.70 with an average value of 0.55, indicating a negative mass loss. The mean specific mass loss is − 0.49 ± 0.11 m w.e. and the total mass loss is 3.95 Gt., indicating strong influence of climate change and effect on river flows and water availability.

Toward the Acoustic Detection of Two‐Phase Flow Patterns and Helmholtz Resonators in Englacial Drainage Systems

AbstractPassive acoustic monitoring has revolutionized the characterization of industrial processes and the acoustic wavefield in various environments. However, cryospheric acoustic phenomena remain largely unknown, especially at medium and small scales. Furthermore, the englacial drainage system is poorly documented, even though it is fundamental for understanding water routing through the glacier body. Here I present the first‐of‐its‐kind in situ records of periodic or sustained acoustic signals generated by water drainage through crevasses at the calving front of a glacier, in this case a Greenlandic tidewater glacier. The generative mechanisms of gurgling and bubbling noise are explained as flow‐induced sounds that are excited by intermittent air‐water two‐phase flow and Helmholtz resonance, respectively. This paper demonstrates that there is the tremendous potential to study near‐surface glacier systems using acoustic methods and detect different flow patterns in englacial conduits from their acoustic signatures, both of which can significantly advance our understanding of glaciological processes.

Holocene sea-level variability from Chesapeake Bay Tidal Marshes, USA

We reconstructed the last 10,000 years of Holocene relative sea-level rise (RSLR) from sediment core records near Chesapeake Bay, eastern United States, including new marsh records from the Potomac and Rappahannock Rivers, Virginia. Results show mean RSLR rates of 2.6 mm yr−1 from 10 to 8 kilo-annum (ka) due to combined final ice-sheet melting during deglaciation and glacio-isostatic adjustment (GIA subsidence). Mean RSLR rates from ~6 ka to present were 1.4 mm yr−1 due mainly to GIA, consistent with other East Coast marsh records and geophysical models. However, a progressively slower mean rate (<1.0 mm yr−1) characterized the last 1000 years when a multi-century-long period of tidal marsh development occurred during the ‘Medieval Climate Anomaly’ (MCA) and ‘Little Ice Age’ (LIA) in the Chesapeake Bay region and other East Coast marshes. This decrease was most likely due to climatic and glaciological processes and, correcting for GIA, represents a fall in global mean sea level (GMSL) near the end of Holocene Neoglacial cooling. These pre-historical climate- and GIA-driven Chesapeake Bay sea-level changes contrast sharply with those based on Chesapeake Bay tide-gauge rates (3.1–4.5 mm yr−1) (back to 1903). After subtracting the GIA subsidence component, these rates can be attributed to long-term (millennial) global factors of accelerated ocean thermal expansion (~1.0 mm yr−1) and mass loss from alpine glaciers and Greenland and Antarctic Ice Sheets (1.5–2.0 mm yr−1).

Surface Expression of Basal and Englacial Features, Properties, and Processes of the Greenland Ice Sheet

AbstractRadar‐sounding surveys measuring ice thickness in Greenland have enabled an increasingly “complete” knowledge of basal topography and glaciological processes. Where such observations are spatially limited, bed elevation has been interpolated through mass conservation or kriging. Ordinary kriging fails to resolve anisotropy in bed geometry, however, leaving complex topography misrepresented in elevation models of the ice sheet bed. Here, we demonstrate the potential of new high‐resolution (≤5 m) surface topography data (ArcticDEM) to provide enhanced insight into basal and englacial geometry and processes. Notable surface features, quantified via residual surface elevation, are observed coincident with documented subglacial channels, and new, smaller‐scale tributaries (<2,000 m in width) and valley‐like structures are clearly identified. Residual surface elevation also allows the extent of basal ice units to be mapped, which in conjunction with radar data indicate that they act as “false bottoms,” likely due to a rheological contrast in the ice column.

Anomaly Detection in Paleoclimate Records Using Permutation Entropy

Permutation entropy techniques can be useful for identifying anomalies in paleoclimate data records, including noise, outliers, and post-processing issues. We demonstrate this using weighted and unweighted permutation entropy with water-isotope records containing data from a deep polar ice core. In one region of these isotope records, our previous calculations (See Garland et al. 2018) revealed an abrupt change in the complexity of the traces: specifically, in the amount of new information that appeared at every time step. We conjectured that this effect was due to noise introduced by an older laboratory instrument. In this paper, we validate that conjecture by reanalyzing a section of the ice core using a more advanced version of the laboratory instrument. The anomalous noise levels are absent from the permutation entropy traces of the new data. In other sections of the core, we show that permutation entropy techniques can be used to identify anomalies in the data that are not associated with climatic or glaciological processes, but rather effects occurring during field work, laboratory analysis, or data post-processing. These examples make it clear that permutation entropy is a useful forensic tool for identifying sections of data that require targeted reanalysis—and can even be useful for guiding that analysis.