Ice Mass Balance Research Articles

Ice Mass Balance in Liaodong Bay: Modeling and Observations

During the winters of 2009/2010 and 2020/2021, observations were carried out at an eastern port of Liaodong Bay to examine the variations in sea ice thickness and atmospheric conditions. The daily ice thickness (DIT) and the cumulative ice thickness (CIT) are the two main observation items related to the thickness of sea ice. For DIT, the sea ice thickness gradually decreases as the temperature increases, and the freezing rate a is 1.48 cm/(°C·d)1/2. For CIT, when the temperature is −12 °C, the maximum growth rate of ice thickness decreases from 3.5 cm/d to 1.5 cm/d as the ice thickness increases from 0 to 20 cm. The residual method was applied to calculate the oceanic heat flux, which is an important parameter of ice modeling, and both the analytic model (Stefan’s law) and numerical model (high-resolution thermodynamic snow-and-ice model) were utilized in this work. It was found that the accuracy of the simulation results was high when the growth coefficient of the analytic mode was 2.3 cm/(°C·d)1/2. With an oceanic heat flux of 2 W·m−2, the maximum error of the numerical model approached 60% in 2010 and 3.7% in 2021. However, using the oceanic heat flux calculated in this work, the maximum error can be significantly reduced to 4.2% in the winter of 2009/2010 and 1.5% in 2020/2021. Additionally, the oceanic heat flux in Liaodong Bay showed a decreasing trend with the increase in ice thickness and air temperature.

Seasonal and interannual variability of the landfast ice mass balance between 2009 and 2018 in Prydz Bay, East Antarctica

Abstract. Landfast ice (LFI) plays a crucial role for both the climate and the ecosystem of the Antarctic coastal regions. We investigate the snow and LFI mass balance in Prydz Bay using observations from 11 sea ice mass balance buoys (IMBs). The buoys were deployed offshore from the Chinese Zhongshan Station (ZS) and Australian Davis Station (DS), with the measurements covering the ice seasons of 2009–2010, 2013–2016, and 2018. The observed annual maximum ice thickness and snow depth were 1.59 ± 0.17 and 0.11–0.76 m off ZS and 1.64 ± 0.08 and 0.11–0.38 m off DS, respectively. Early in the ice growth season (May–September), the LFI basal growth rate near DS (0.6 ± 0.2 cm d−1) was larger than that around ZS (0.5 ± 0.2 cm d−1). This is attributed to cooler air temperature (AT) and lower oceanic heat flux at that time in the DS region. Air temperature anomalies were more important in regulating the LFI growth rate at that time because of thinner sea ice having a weaker thermal inertia relative to thick ice in later seasons. Interannual and local spatial variabilities for the seasonality of LFI mass balance identified at ZS are larger than at DS due to local differences in topography and katabatic wind regime. Snow ice contributed up to 27 % of the LFI total ice thickness at the offshore site close to ground icebergs off ZS because of the substantial snow accumulation. Offshore from ZS, the supercooled water was observed at the sites close to the Dålk Glacier from July to October, which reduced the oceanic heat flux and promoted the LFI growth. During late austral spring and summer, the increased oceanic heat flux led to a reduction of LFI growth at all investigated sites. The variability of LFI properties across the study domain prevailed at interannual timescales, over any trend during the recent decades. Based on the results derived from this study, we argue that an increased understanding of snow (on LFI) processes, local atmospheric and oceanic conditions, as well as coastal morphology and bathymetry, are required to improve the Antarctic LFI modeling.

An analysis of ice growth and temperature dynamics in two Canadian subarctic lakes

The seasonal dynamics of freshwater lake ice and its interactions with air and snow are studied in two small subarctic lakes with comparable surface areas but contrasting depths (4.3 versus 91 m). Two, 2.9 m long thermistor chain sensors (Snow and Ice Mass Balance Apparatuses), were used to remotely measure air, snow, ice, and water temperatures every 15-min between December 2021 and March 2022. Results showed that freeze-up occurred later in the deeper lake (Ryan Lake) and earlier in the shallow lake (Landing Lake). Ice growth was significantly faster in Ryan Lake than in Landing Lake, due to cold water temperatures (mean (Tw¯) =0.65 to 0.96°C) persisting beneath the ice. In Landing Lake, basal ice growth was hindered because of warm water temperatures (Tw¯=1.5 to 2.1°C) caused by heat released from lake sediments. Variability in air temperatures at both lakes had significant influences on the thermal regimes of ice and snow, particularly in Ryan Lake, where ice temperatures were more sensitive to rapid changes in air temperatures. This finding suggests that conductive heat transfer through the air-water continuum may be more sensitive to variability in air temperatures in deeper lakes with colder water temperatures than in shallow lakes with warmer water temperatures, if snow depths and densities are comparable. This study highlights the significance of lake morphology and rapid air temperature variability on influencing ice growth processes. Conclusions drawn aim to improve the representation of ice growth processes in regional and global climate models, and to improve ice safety for northern communities.

Sea ice and snow characteristics from year-long transects at the MOSAiC Central Observatory

Repeated transects have become the backbone of spatially distributed ice and snow thickness measurements crucial for understanding of ice mass balance. Here we detail the transects at the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) 2019–2020, which represent the first such measurements collected across an entire season. Compared with similar historical transects, the snow at MOSAiC was thin (mean depths of approximately 0.1–0.3 m), while the sea ice was relatively thick first-year ice (FYI) and second-year ice (SYI). SYI was of two distinct types: relatively thin level ice formed from surfaces with extensive melt pond cover, and relatively thick deformed ice. On level SYI, spatial signatures of refrozen melt ponds remained detectable in January. At the beginning of winter the thinnest ice also had the thinnest snow, with winter growth rates of thin ice (0.33 m month−1 for FYI, 0.24 m month−1 for previously ponded SYI) exceeding that of thick ice (0.2 m month−1). By January, FYI already had a greater modal ice thickness (1.1 m) than previously ponded SYI (0.9 m). By February, modal thickness of all SYI and FYI became indistinguishable at about 1.4 m. The largest modal thicknesses were measured in May at 1.7 m. Transects included deformed ice, where largest volumes of snow accumulated by April. The remaining snow on level ice exhibited typical spatial heterogeneity in the form of snow dunes. Spatial correlation length scales for snow and sea ice ranged from 20 to 40 m or 60 to 90 m, depending on the sampling direction, which suggests that the known anisotropy of snow dunes also manifests in spatial patterns in sea ice thickness. The diverse snow and ice thickness data obtained from the MOSAiC transects represent an invaluable resource for model and remote sensing product development.

Arctic sea ice mass balance in a new coupled ice–ocean model using a brittle rheology framework

Abstract. Sea ice is a key component of the Earth's climate system as it modulates the energy exchanges and associated feedback processes at the air–sea interface in polar regions. These exchanges have been suggested to strongly depend on openings in the sea ice cover, which are associated with fine-scale sea ice deformations, but the importance of these processes remains poorly understood as most numerical models struggle to represent these deformations without using very costly horizontal resolutions (≃ 5 km). In this study, we present results from a 12 km resolution ocean–sea ice coupled model, the first that uses a brittle rheology to represent the mechanical behaviour of sea ice. This rheology has been shown to reproduce observed characteristics and complexity of fine-scale sea ice deformations at relatively coarse resolutions. We evaluate and discuss the Arctic sea ice mass balance of this coupled model for the period 2000–2018. We first assess sea ice quantities relevant for climate (volume, extent, and drift) and find that they are consistent with satellite observations. We evaluate components of the mass balance for which observations are available, i.e. sea ice volume export through Fram Strait and winter mass balance in the Arctic marginal seas for the period 2003–2018. Model values show a good match with observations, remaining within the estimated uncertainty, and the interannual variability of the dynamic contribution to the winter mass balance is generally well captured. We discuss the relative contributions of dynamics and thermodynamics to the sea ice mass balance in the Arctic Basin for 2000–2018. Using the ability of the model to represent divergence motions at different scales, we investigate the role of leads and polynyas in ice production. We suggest a way to estimate the contribution of leads and polynyas to ice growth in winter, and we estimate this contribution to add up to 25 %–35 % of the total ice growth in pack ice from January to March. This contribution shows a significant increase over 2000–2018. This coupled framework opens up new opportunities to understand and quantify the interplay between small-scale sea ice dynamics and ocean properties.

Database of daily Lagrangian Arctic sea ice parcel drift tracks with coincident ice and atmospheric conditions

Since the early 2000s, sea ice has experienced an increased rate of decline in thickness, extent and age. This new regime, coined the ‘New Arctic’, is accompanied by a reshuffling of energy flows at the surface. Understanding of the magnitude and nature of this reshuffling and the feedbacks therein remains limited. A novel database is presented that combines satellite observations, model output, and reanalysis data with sea ice parcel drift tracks in a Lagrangian framework. This dataset consists of daily time series of sea ice parcel locations, sea ice and snow conditions, and atmospheric states, including remotely sensed surface energy budget terms. Additionally, flags indicate when sea ice parcels travel within cyclones, recording cyclone intensity and distance from the cyclone center. The quality of the ice parcel database was evaluated by comparison with sea ice mass balance buoys and correlations are high, which highlights the reliability of this database in capturing the seasonal changes and evolution of sea ice. This database has multiple applications for the scientific community; it can be used to study the processes that influence individual sea ice parcel time series, or to explore generalized summary statistics and trends across the Arctic.

The evolution of the Fram Strait sea ice volume export decomposed by age: estimating with parameter-optimized sea ice-ocean model outputs

Sea ice export through the Fram Strait is crucial in the dynamic evolution of Arctic sea ice and can further modulate Arctic sea ice mass balance as well as the ocean thermohaline circulation. In this study, based on outputs from a parameter-optimized and fully physical ocean–sea ice coupled model and sea ice age observation, we estimate sea ice volume (SIV) flux and its age evolution via the Fram Strait. The estimate of mean annual SIV flux is about 1605 315 km3 yr−1 without a significant trend for 1979–2021. Combining with sea ice age data, the variation of the sea ice age and its corresponding SIV flux are obtained for 1984–2020. The SIV flux of 1st-year ice significantly increases as expected, but it still contributes very little to the total flux in the 2010s with a proportion of 3.5%. SIV fluxes of different ages in multi-year ice present diverse variations. The proportions of 2nd-year ice and 3rd-year ice in the annual SIV flux show an extreme increase from 6.8% and 25.0% in the 1980s to 49.0% and 38.8% in the 2010s, respectively, while the proportions of 4th-year ice and 5th-year and older (5+ year) ice significantly decrease from 22.8% and 45.0% in the 1980s to 7.1% and 1.6% in the 2010s, respectively. Meanwhile, the prevailing age of annual volume export via Fram Strait shifts from 4th-year and 5+ year ice to 2nd-year and 3rd-year ice around 2007/2008. It is worth noticing that the variation in Fram Strait ice export modulates the variation in Arctic SIV prior to 2008, but the reverse is true after 2008, indicating a decreasing influence of Fram Strait SIV export on Artic SIV variability with decreasing sea ice age. The results are beneficial to promote the understanding of the evolution of Fram Strait SIV export under the warming Arctic.

Summertime surface mass balance and energy balance of Urumqi Glacier No. 1, Chinese Tien Shan, modeled by linking COSIMA and in-situ measured meteorological records

To get a better overview of atmosphere-driven mass changes at Urumqi Glacier No.1, Chinese Tien Shan, the surface energy budget and mass balance is modeled by linking the COupled Snowpack and Ice surface energy and MAss balance model (COSIMA) with in-situ measured meteorological records for the ablation period in 2018. The COSIMA is calibrated by manual optimization and the modeled results agree well with the in-situ surface temperature, snow height and seasonal mass balance. Our results reveal that Urumqi Glacier No.1 experienced a significant mass loss, with an average value of − 0.77 m w.e. over the ablation period 2018. The surface energy budget components can be classified into two categories: radiation (shortwave and longwave) and turbulent fluxes. Surface melt and solid precipitation were dominated components of mass balance. The COSIMA can reproduce the glaciological mass balance compared with other models. Sensitivity analysis showed that the mass balance was more sensitive to the temperature than precipitation, and mass loss caused by temperature increase of 1 K needed to be compensated by at least 40% precipitation increase. Air temperature during the ablation period was more important than annual precipitation in controlling mass balance changes on Urumqi Glacier No. 1. These findings will enhance our understanding of the mechanisms underlying mass balance processes of ablation period and their contribution to the acceleration of glacier retreat in Tien Shan.

Evaluation of the relationships and uncertainties of airborne and ground-based sea ice surface temperature measurements against remotely sensed temperature records

ABSTRACT Sea ice surface temperature (IST) is an important indicator of environmental changes in the Arctic Ocean. In this study, the relative performance of four mainstream IST records, i.e. airborne IST, infrared radiometer measured IST (IR IST), longwave radiation derived IST (LWR IST), and snow and ice mass balance array buoy derived IST (Buoy IST), were evaluated against the MODIS IST product. Bias, standard deviation (STD), and root mean square error (RMSE) were used to evaluate the data quality. Results revealed that airborne IST had the best accuracy, which was 0.21 K colder than MODIS IST, with STD of 1.46 K and RMSE of 1.47 K. Ground-based ISTs were biased with each other but all warmer than the MODIS IST. The IR IST had the best overall accuracy (bias = 0.55 K; STD = 1.52 K; RMSE = 1.61 K), while the LWR IST was the noisiest measurement with the largest outlier data percent. Besides, co-located IR and LWR ISTs were more consistent than any type of evaluated IST against MODIS IST (correlation coefficient = 0.99). Airborne and IR ISTs are thus the premier choice for monitoring the rapidly changing Arctic sea ice, together with satellite observations.

A Theory of Abrupt Climate Changes: Their Genesis and Anatomy

We combine our ice-sheet and climate models to formulate a deductive theory of abrupt climate changes pertaining to Heinrich/Dansgaard–Oeschger (H/DO) cycles and the last deglaciation punctuated by the Younger Dryas (YD). Since they are all accompanied by ice-rafted debris, we posit their common origin in the calving of the ice sheet due to a thermal switch at its bed, which naturally endows abruptness to these climate signals of the millennial timescale characteristics of the ice-mass balance. To distinguish the H/DO cycles, we differentiate the thermal triggers by geothermal-heat/surface-melt in the calving of inland/coastal ice, which provide their respective freshwater sources. Since surface-melt requires post-H warmth during the glacial, but is already operative in the Holocene, the DO cycles are encased within the H cycle during the glacial, but self-sustaining in the Holocene. They otherwise share the same time signature, thus resolving this seeming puzzle of commonality without invoking unknown climate forcing. The DO cycles transcend deglaciation to produce the observed sequence, but the calving-induced freshwater flux needs to be boosted by the rerouting of continental meltwater to cause YD. We discern a key process of an eddying ocean in its millennial adjustment toward maximum entropy production (MEP), which would melt the H-induced sea ice to cause an abrupt post-H warming followed by a gradual cooling that anchors the DO cycles to form the hierarchical Bond cycle. Since the modelled anatomies resemble the observed ones, our theory may provide a robust and unified account of abrupt climate changes.

A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval

Abstract. As changes to Earth's polar climate accelerate, the need for robust, long–term sea ice thickness observation datasets for monitoring those changes and for verification of global climate models is clear. By linking an algorithm for retrieving snow–ice interface temperature from passive microwave satellite data to a thermodynamic sea ice energy balance relation known as Stefan's law, we have developed a retrieval method for estimating thermodynamic sea ice thickness growth from space: Stefan's Law Integrated Conducted Energy (SLICE). With an initial condition at the beginning of the sea ice growth season, the method can model basin-wide absolute sea ice thickness by combining the one-dimensional SLICE retrieval with an ice motion dataset. The advantages of the SLICE retrieval method include daily basin-wide coverage, lack of atmospheric reanalysis product input requirement, and a potential for use beginning in 1987. Validation of the retrieval against measurements from 10 ice mass balance buoys shows a mean correlation of 0.89 and a mean bias of 0.06 m over the course of an entire sea ice growth season. Despite its simplifications and assumptions relative to models like the Pan-Arctic Ice–Ocean Modeling and Assimilation System (PIOMAS), basin-wide SLICE performs nearly as well as PIOMAS when compared against CryoSat-2 and Operation IceBridge using a linear correlation between collocated points.

Energy and glacier mass balance of Fürkeleferner, Italy: past, present, and future

The energy and mass balance of mountain glaciers translate into volume changes that play out as area changes over time. From this, together with former moraines during maximum advances, information on past climate conditions and the climatic drivers behind during glacier advances can be obtained. Here, we use the distributed COupled Snowpack and Ice surface energy and mass balance model in PYthon (COSIPY) to simulate the present state of an Italian glacier, named Fürkeleferner, for the mass balance years 2013–2017. Next, we investigate the local climate during the time of the last “Little Ice Age” (LIA) maximum glacier advance using COSIPY together with the LIA glacier outline retrieved from moraine mapping and a digital elevation model (DEM) adapted for the glacier’s geometry at the time of the LIA as a benchmark. Furthermore, the glacier’s sensitivity to future air temperature increase of +1 K and +2 K is investigated using the same model. For all simulations, meteorological data of closely located climate stations are used to force the model. We show the individual monthly contribution of individual energy and mass balance components. Refreezing during the summer months is an important component of the energy and mass balance, on average about 9 % relative to total annual ablation. The results from simulating past climate show a 2.8 times larger glacier area for Fürkeleferner during the LIA than today. This further implies a 2.5 K colder climate, assuming that the amount of precipitation was 10 %–20 % in excess of today’s value. Concerning further temperature increase of 2 K, the glacier would only consist of the ablation area implying sustained mass loss and eventual total mass loss. Even under current climatic conditions, the glacier area would have to decrease to 17 % of its current area to be in a steady state. We discuss the reliability of the results by comparing simulated present mass balance to measured mass balances of neighboring glaciers in the European Alps and with short-term measurements on Fürkeleferner itself. In conclusion, we are able to show how the glacier responds to past and future climate change and determine the climatic drivers behind.

Pan-Arctic melt pond fraction trend, variability, and contribution to sea ice changes

Because the Pan-Arctic melt pond significantly influences the mass balance of sea ice, analyzing changes in the Pan-Arctic melt pond can offer insights into the Arctic sea ice and environment. In this study, the MODIS level 2 surface reflectance product (MOD09A1) was used to calculate variability and trends in the Pan-Arctic melt pond fraction (MPF) from May 9 to August 29 during the period 2000–2019, along with its relationship with September sea ice extent (SIE). The results showed that as a result of advances in Arctic ice melt onset and the extension of the melting period, the MPF as a whole showed an insignificant upward trend but that the MPF in the multiyear ice area increased significantly. Melt pond distribution is closely related to atmospheric circulation over the Arctic. Significant positive and negative geopotential height anomalies at different locations in the Arctic will cause poleward winds from different directions, bringing warm, humid air and leading to an increase in MPF in different regions of the Arctic. There is a strong negative correlation between integrated MPF and September SIE, concentrated in the northern part of the Beaufort Sea. On June 18, the integrated MPF had the strongest correlation with September SIE, at −0.96. The use of linear regression and support vector machine combined with integrated MPF and 850-hpa average wind, Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) indexes to forecast September SIE shows that the support vector machine is more suitable for forecasting September SIE. When the forecast factor is only the integrated MPF, the MPF is integrated from May 9 to June 10 has the forecast accuracy is the highest, with a considerable forecast skill. When the average May–June AO is combined with the integrated MPF, the forecast skill will be further improved.

The singing firn

AbstractAntarctic firn presents an exotic seismological environment in which the behaviors of propagating waves can be significantly at odds with those in other Earth media. We present a condensed view of the nascent field of ambient noise seismology in Antarctic firn-covered media, and highlight multiple unusual and information-rich observations framed through the lens of the firn's important role as a buffer for air temperature anomalies and a complex contributor to ice mass balance. We summarize key results from several recent papers depicting novel wind-excited firn resonances and point to the plethora of ways these observations could facilitate imaging and monitoring of glacial systems at single, isolated seismometers. Finally, we propose significant instrumental and computational objectives necessary to constrain resonance excitation mechanisms and broadly apply these observations as useful monitoring tools in Antarctica.

A full year of turbulence measurements from a drift campaign in the Arctic Ocean 2019–2020

Ocean turbulent mixing is a key process in the global climate system, regulating ocean circulation and the uptake and redistribution of heat, carbon, nutrients, oxygen and other tracers. In polar oceans, turbulent heat transport additionally affects the sea ice mass balance. Due to the inaccessibility of polar regions, direct observations of turbulent mixing are sparse in the Arctic Ocean. During the year-long drift expedition “Multidisciplinary drifting Observatory for the Study of Arctic Climate” (MOSAiC) from September 2019 to September 2020, we obtained an unprecedented data set of vertical profiles of turbulent dissipation rate and water column properties, including oxygen concentration and fluorescence. Nearly 1,700 profiles, covering the upper ocean down to approximately 400 m, were collected in sets of 3 or more consecutive profiles every day, and complemented with several intensive sampling periods. This data set allows for the systematic assessment of upper ocean mixing in the Arctic, and the quantification of turbulent heat and nutrient fluxes, and can help to better constrain turbulence parameterizations in ocean circulation models.

Seasonality and timing of sea ice mass balance and heat fluxes in the Arctic transpolar drift during 2019–2020

Sea ice growth and decay are critical processes in the Arctic climate system, but comprehensive observations are very sparse. We analyzed data from 23 sea ice mass balance buoys (IMBs) deployed during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in 2019–2020 to investigate the seasonality and timing of sea ice thermodynamic mass balance in the Arctic Transpolar Drift. The data reveal four stages of the ice season: (I) onset of ice basal freezing, mid-October to November; (II) rapid ice growth, December–March; (III) slow ice growth, April–May; and (IV) melting, June onward. Ice basal growth ranged from 0.64 to 1.38 m at a rate of 0.004–0.006 m d–1, depending mainly on initial ice thickness. Compared to a buoy deployed close to the MOSAiC setup site in September 2012, total ice growth was about twice as high, due to the relatively thin initial ice thickness at the MOSAiC sites. Ice growth from the top, caused by surface flooding and subsequent snow-ice formation, was observed at two sites and likely linked to dynamic processes. Snow reached a maximum depth of 0.25 ± 0.08 m by May 2, 2020, and had melted completely by June 25, 2020. The relatively early onset of ice basal melt on June 7 (±10 d), 2019, can be partly attributed to the unusually rapid advection of the MOSAiC floes towards Fram Strait. The oceanic heat flux, calculated based on the heat balance at the ice bottom, was 2.8 ± 1.1 W m–2 in December–April, and increased gradually from May onward, reaching 10.0 ± 2.6 W m–2 by mid-June 2020. Subsequently, under-ice melt ponds formed at most sites in connection with increasing ice permeability. Our analysis provides crucial information on the Arctic sea ice mass balance for future studies related to MOSAiC and beyond.

Quantifying false bottoms and under-ice meltwater layers beneath Arctic summer sea ice with fine-scale observations

During the Arctic melt season, relatively fresh meltwater layers can accumulate under sea ice as a result of snow and ice melt, far from terrestrial freshwater inputs. Such under-ice meltwater layers, sometimes referred to as under-ice melt ponds, have been suggested to play a role in the summer sea ice mass balance both by isolating the sea ice from saltier water below, and by driving formation of ‘false bottoms’ below the sea ice. Such layers form at the interface of the fresher under-ice layer and the colder, saltier seawater below. During the Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC) expedition in the Central Arctic, we observed the presence of under-ice meltwater layers and false bottoms throughout July 2020 at primarily first-year ice locations. Here, we examine the distribution, prevalence, and drivers of under-ice ponds and the resulting false bottoms during this period. The average thickness of observed false bottoms and freshwater equivalent of under-ice meltwater layers was 0.08 m, with false bottom ice comprised of 74–87% FYI melt and 13–26% snow melt. Additionally, we explore these results using a 1D model to understand the role of dynamic influences on decoupling the ice from the seawater below. The model comparison suggests that the ice-ocean friction velocity was likely exceptionally low, with implications for air-ice-ocean momentum transfer. Overall, the prevalence of false bottoms was similar to or higher than noted during other observational campaigns, indicating that these features may in fact be common in the Arctic during the melt season. These results have implications for the broader ice-ocean system, as under-ice meltwater layers and false bottoms provide a source of ice growth during the melt season, potentially reduce fluxes between the ice and the ocean, isolate sea ice primary producers from pelagic nutrient sources, and may alter light transmission to the ocean below.

Multi-centennial mass balance of perennial ice deposits in Alpine caves mirrors the evolution of glaciers during the Late Holocene

Mid-latitude alpine caves preserve a record of past solid precipitation during winter, locally spanning several centuries to millennia. Dating organic macro-remains trapped in ice layers allows the determination of timing and duration of past periods of positive and negative ice mass balance. We present here the largest comparative study of ice cave sites yet published, using Bayesian age-modelling on a database comprising 107 radiocarbon dates, spread over eight caves in the Austrian Alps. We show that periods of positive mass balance coincide with past glacier advances. We find organic and macro-remain rich layers dated to the Medieval Climate Anomaly (between 850 and 1200 CE) marking widespread ice retreat. We demonstrate positive ice mass balance at all studied sites for the Little Ice Age, coinciding with the largest glacier advances in the Holocene between 1400 and 1850 CE. At the sites with records spanning over 2000 years, positive mass balance is also observed during the periods from 300 BCE to 100 CE and 600–800 CE. These subterranean ice deposits show widespread evidence of accelerated negative mass balances in recent years and their record is under imminent threat of disappearing.

Inversion of a Stokes glacier flow model emulated by deep learning

AbstractData assimilation in high-order ice flow modeling is a challenging and computationally costly task, yet crucial to find ice thickness and ice flow parameter distributions that are consistent with ice flow mechanics and mass balance while best matching observations. Failing to find these distributions that are required as initial conditions leads to a disequilibrium between mass balance and ice flow, resulting in nonphysical transient effects in the prognostic model. Here we tackle this problem by inverting an emulator of the Stokes ice flow model based on deep learning. By substituting the ice flow equations using a convolutional neural network emulator, we simplify, make more robust and dramatically speed up the solving of the underlying optimization problem thanks to automatic differentiation, stochastic gradient methods and implementation of graphics processing unit (GPU). We demonstrate this process by simultaneously inferring the ice thickness distribution, ice flow parametrization and ice surface of ten of the largest glaciers in Switzerland. As a result, we obtain a high degree of assimilation while guaranteeing an equilibrium between mass-balance and ice flow mechanics. The code runs very efficiently (optimizing one large-size glacier at 100 m takes < 1 min on a laptop) while it is open-source and publicly available.

The internal melting of landfast sea ice in Prydz Bay, East Antarctica

Summertime internal melting of Antarctic sea ice is common due to the penetration of solar radiation below the snow and ice surface. We focus on the role of internal melting and heat conduction in generating gap layers within the ice. These often occur approximately 0.1 m below the ice surface. In a small-scale survey over land-fast sea ice in Prydz Bay, East Antarctica, we observed, for the first time, gap layers 0.6–1.0 m below the surface for both first-year ice and multi-year ice. A 1D snow/ice thermodynamic model successfully simulated snow and ice mass balance and the evolution of the gap layers. Their spatial distribution was largely controlled by snow thickness and ice thickness. A C-shaped ice temperature profile with the lowest values in the middle of the ice layer resulted in heat flux convergence causing downward progression of the internal melt layer. Multidecadal (1979–2019) seasonal simulations showed decreasing air temperature favored a postposed internal melting onset, reduced total internal melt, and delayed potential ice breakup, which indicated a higher chance for local coastal ice to be shifted from first-year ice to multi-year ice.