As of 2023, approximately ****** kilograms of ice per square meter have been lost from the glaciers worldwide compared to 1970. Such a downward trend has been intensifying recently, adding approximately 1,000 kilograms per square meter to the glacier loss each year.

In the Arctic region, global climate change is quickly transforming the environment. Melting of the Greenland ice sheet and loss of Arctic Ocean sea ice are two processes that have increased dramatically since the late 20th century. Here, we provide a four-part educational module focused on these two processes designed for upper middle school, high school and undergraduate students. Through this investigative module students learn critical science skills as they collect, analyze and draw conclusions from data and engage with some of the most urgent environmental questions of our time. Finally, they are challenged to think about how these changes are affecting their lives and the lives of others around the globe. The teaching module is described in Holzer and Rennermalm (2019) and includes video and data resources, and step-by-step instructions. This teaching module consists of four activities: Activity 1: My burning Feet (Engage) Activity 2: From burning feet to the Greenland Ice Sheet: Examining model estimates of Greenland ice sheet mass loss, its drivers, and its impact on global sea levels (Explore and Explain) Activity 3: Should I Move Inland? What About Others Around the World - Should They Move to Higher Ground? (Elaborate) Activity 4: How Does Melting Arctic Ice (sea ice & ice sheets) Impact the Climate Where I live? (Evaluate) The following materials are included in the module: • Teacher instructions of the entire module (Greenland Hydrology Teacher Notes.pdf) • Detailed teacher instructions for Activity 2 (Greenland hydrology activity 2 teacher instructions.pdf) • Lecture power point material (TeacherPPTs.pptx) • Student worksheets for each module (Greenland hydrology activity [1,2,3,4] wrksht.pdf) • Student report document for Activity 2 (Greenland Hydrology Student Report Document.pdf) • Data files for Activity 2 • Video resources (Welcome_To_Greenland.mov, Why_the_Arctic_matters.mov) • Videos featuring four scientists uncovering the secrets of Greenland ice sheet in four short videos (Scientist_in_Greenland_Asa.mov, Scientist_in_Greenland_Rohi.mov, Scientist_in_Greenland_Sasha.mov, Scientist_in_Greenland_Vena.mov) • An article about the module published in The Earth Scientist by Holzer and Rennermalm (2019) Part of Activity 2, students will analyze model estimates simulated with a regional climate model that also calculates ice sheet mass balance. The model is named Modèle Atmosphérique Régional (MAR) and is widely used among researchers to understand the changing Greenland ice sheet (see a detailed description of the MAR simulations in Methods). References: Holzer M and Rennermalm A (2019) Arctic Happenings – Global Impacts of the Melting Greenland Ice Sheet and Melting Sea Ice. The Earth Scientist (Winter), 6, https://www.nestanet.org/cms/content/publications/tes/archive

Between 1992 and 2017, global mean sea levels rose by **** millimeters. This is mainly due to the melting of inland glaciers on Greenland, which accounted for **** millimeters of the sea level rise. The melting of glaciers is not the only threat to sea level rise, the warming of oceans as a result of increasing global temperatures is causing the existing sea water to expand slightly.

The rate at which Earth is losing its icecaps is accelerating. In the *****, the average rate of loss was *** billion metric tons of ice per year, which is significantly higher than the ***** rate of ** billion metric tons of ice per year. The estimates for global sea level rise by 2100 is around *** meters.

This data set includes spatially distributed estimates of the debris thickness and sub-debris melt enhancement factors for every debris-covered glacier in the Randolph Glacier Inventory Version 6, excluding the ice sheets and Antarctic Periphery. The debris thickness estimates are derived using a novel approach that uses a combination of sub-debris melt inversion and surface temperature inversion methods. The sub-debris melt enhancement factors are estimated from the debris thickness using debris thickness-melt curves normalized by estimates of the clean-ice melt.

Melt from the Greenland and Antarctic ice sheets is increasingly contributing to sea-level rise. This ice sheet mass loss is primarily driven by the thinning, retreat, and acceleration of glaciers in contact with the ocean. Observations from the field and satellites indicate that glaciers are sensitive to changes at the ice-ocean interface and that the increase in submarine melting is likely to be driven by the discharge of meltwater from underneath the glacier known as subglacial meltwater plumes. The melting of glacier ice also directly adds a large volume of freshwater into the ocean, potentially causing significant changes in the circulation of ocean waters that regulate global heat transport, making ice-ocean interactions an important potential factor in climate change and variability. The ability to predict, and hence adequately respond to, climate change and sea-level rise therefore depends on our knowledge of the small-scale processes occurring in the vicinity of subglacial meltwater plumes at the ice-ocean interface. Currently, understanding of the underlying physics is incomplete; for example, different models of glacier-ocean interaction could yield melting rates that vary over a factor of five for the same heat supply from the ocean. It is then very difficult to assess the reliability of predictive models. This project will use comprehensive laboratory experiments to study how the melt rates of glaciers in the vicinity of plumes are affected by the ice roughness, ice geometry, ocean turbulence, and ocean density stratification at the ice-ocean interface. These experiments will then be used to develop new and improved predictive models of ice-sheet melting by the ocean. This project builds bridges between modern experimental fluid mechanics and glaciology with the goal of leading to advances in both fields. This project consists of a comprehensive experimental program designed for studying the melt rates of glacier ice under the combined influences of (1) turbulence occurring near and at the ice-ocean interface, (2) density stratification in the ambient water column, (3) irregularities in the bottom topology of an ice shelf, and (4) differing spatial distributions of multiple meltwater plumes. The objective of the experiments is to obtain high-resolution data of the velocity, density, and temperature near/at the ice-ocean interface, which will then be used to improve understanding of melt processes down to scales of millimeters, and to devise new, more robust numerical models of glacier evolution and sea-level rise. Specially, laser-based, optical techniques in experimental fluid mechanics (particle image velocity and laser-induced fluorescence) will be used to gather the data, and the experiments will be conducted using refractive-index matching techniques to eliminate changes in refractive indices that could otherwise bias the measurements. The experiments will be run inside a climate-controlled cold room to mimic field conditions (ocean temperature from 0-10 degrees C). The project will use 3D-printing to create different casting molds for making ice blocks with different types of roughness. The goal is to investigate how ice melt rate changes as a function of the properties of the plume, the ambient ocean water, and the geometric properties of the ice interface. Based on the experimental findings, this project will develop and test a new integral-plume-model coupled to a regional circulation model (MITgcm) that can be used to predict the effects of glacial melt on ocean circulation and sea-level rise.

The Chilean Patagonia constitutes one of the most important and extensive fjord systems worldwide, therefore can be used as a natural laboratory to elucidate the pathway of both organic and inorganic matter in the receiving environment. In this study we use data collected during an intensive oceanographic cruise along the Magellan Strait into the Almirantazgo Fjord in southern Patagonia to evaluate how different sources of dissolved inorganic carbon (DIC) and recycling may impact particulate organic carbon (POC) $δ$13C and influence the nutrients and carbonate system spatial distribution. The carbonate system presented large spatial heterogeneity. The lowest total alkalinity and DIC were associated to freshwater dilution observed near melting glaciers. The $δ$13CDIC analysis suggests that most DIC in the upper 50 m depth was not derived from terrestrial organic matter remineralization. 13C‐depleted riverine and ice‐melting DIC influence the DIC pool along the study area, but due to that DIC concentration from rivers and glaciers is relatively low, atmospheric carbon contribution or biological processes seem to be more relevant. Intense undersaturation of CO2 was observed in high chlorophyll waters. Respired DIC coming from the bottom waters seems to be almost insignificant for the inorganic carbon pool and therefore do not impact significantly the stable carbon isotopic composition of dissolved organic carbon and POC in the upper 50 m depth. Considering the combined effect of cold and low alkalinity waters due to ice melting, our results highlight the importance of these processes in determining corrosive waters for CaCO3 and local acidification processes associated to calving glacier in fjord ecosystems.

This is the data and metatada for modeled Long Wave Radiation - part of six modeled parameters that comprise the Taylor Valley Galcier Melt modeling Data contained and described in this document correspond to the physically-based surface energy balance model for the glaciers of Taylor Valley developed by the dataset owners. The spatial variability in ablation (ice melt and sublimation), runoff, and climate sensitivity of the glaciers was modeled using 16 years of meteorological and surface mass balance (the net mass gain or loss of ice on the surface of the glacier) observations collected in Taylor Valley (see figure). Â An unusual aspect of the model is the inclusion of transmission of solar radiation into the ice and subsequent drainage of some subsurface melt . Â Melt model was applied to the ablation zones of the glaciers of Taylor Valley, identified by colored areas. Mass balance stakes, meteorological stations, and stream gages shown for reference. These output files are part of a multi set comprising 5 outputs and one input dataset. Â The input dataset package is at http://mcmlter.org/content/glacier-melt-modeling-inputs-and-example-m-fi...

The assessment of meltwater sourcing from the clean and debris-covered glaciers is scarce in High Mountain Asia (HMA). The melting rate varies with the debris cover thickness and glacier orientation. The present study quantifies glacier melting rate attributed to varying thickness of debris cover in the Karakoram. We observed daily melting rates by installing ablation stakes over debris-free and debris-covered ice during a field expedition. The stakes were installed on glacier surface with debris cover thickness ranges between 0.5 and 40 cm at selected experimental sites during the ablation period (September to October 2018) and (July to August 2019). We selected three glaciers including Ghulkin, Hinarchi, and Hoper facing east, south, and north, respectively to assess the role of glacier orientation on melting rates. We observed that the debris-free ice melts faster than the debris-covered ice. Intriguingly, a thin debris layer of 0.5 cm does not enhance melting compared to the clean ice which is inconsistent with the earlier studies. The melting rate decreases as the thickness of debris cover increases at all the three selected glaciers. Furthermore, south-facing glacier featured the highest melting (on average ~ 25% more). However, the north and east-facing glaciers revealed almost same melting rates. For further information, please read the paper associated with this data: Muhammad, S., Tian, L., Ali, S., Latif, Y., Wazir, M.A., Goheer, M.A., Saifullah, M., Hussain, I. and Shiyin, L., 2020. Thin debris layers do not enhance melting of the Karakoram glaciers. Science of the Total Environment, 746, p.141119.

Tropical glaciers are excellent indicators of climate variability due to their fast response to temperature and precipitation variations. At same time, they supply freshwater to downstream populations. In this study, a hydro-glaciological model was adapted to analyze the influence of meteorological forcing on melting and discharge variations at Glacier 12 of Antisana volcano (4,735–5,720 m above sea level (a.s.l.), 1.68 km2, 0°29′S; 78°9′W). Energy fluxes and melting were calculated using a distributed surface energy balance model using 20 altitude bands from glacier snout to the summit at 30-min resolution for 684 days between 2011 and 2013. The discharge was computed using linear reservoirs for snow, firn, ice, and moraine zones. Meteorological variables were recorded at 4,750 m.a.s.l. in the ablation area and distributed through the altitudinal range using geometrical corrections, and measured lapse rate. The annual specific mass balance (−0.61 m of water equivalent -m w.e. y−1-) and the ablation gradient (22.76 kg m−2 m−1) agree with the values estimated from direct measurements. Sequential validations allowed the simulated discharge to reproduce hourly and daily discharge variability at the outlet of the catchment. The latter confirmed discharge simulated (0.187 m3 s−1) overestimates the streamflow measured. Hence it did not reflect the net meltwater production due to possible losses through the complex geology of the site. The lack of seasonality in cloud cover and incident short-wave radiation force the reflected short-wave radiation via albedo to drive melting energy from January to June and October to December. Whereas the wind speed was the most influencing variable during the July-September season. Results provide new insights on the behaviour of glaciers in the inner tropics since cloudiness and precipitation occur throughout the year yielding a constant short-wave attenuation and continuous variation of snow layer thickness.

In 2012, multiple lines of evidence suggest a record release of freshwater from the Greenland ice sheet (GrIS), the largest expanse of glacial ice in the Northern Hemisphere and a major source of meltwater and associated material to the surrounding North Atlantic and Arctic Oceans. Between July 8 and 12, satellite-derived estimates of surface melt increased from 40% to 97% of the ice sheet. This warming received widespread media attention and public interest, yet the scientific community lacks direct observations of meltwater controls on ice sheet movement and meltwater geochemistry for such a large-scale melting event. The overarching goal of this RAPID proposal is to quantify, in short order, the impact of a large-scale melting event on the ice sheet dynamics and meltwater biogeochemistry of a large, land-terminating glacier on the western Greenland margin. RAPID funds will enable the analysis and synthesis of a unique sample set collected during other fieldwork from the Leverett Glacier catchment, a large outlet glacier that discharges through a single proglacial river. Though the focus is on the 2012 episode, the investigators will also analyze samples and GrIS dynamics data from two field seasons (2011- 12), which will aid in interpretation of changes driven by the historic melting event owing to the highly contrasting annual freshwater discharge rates. The investigators will use time-series radiogenic (beryllium-7 and radon-222) and stable isotopes (oxygen and hydrogen), each with unique sources and constant production rates, from the Leverett glacier watershed to differentiate the fractions of meltwater sourced from recent surface snow, glacial ice and delayed flow meltwater. They will also investigate the composition and magnitude of nutrient and metal fluxes released from the subglacial environment and study how these fluxes evolved during July 2012. From 2011-12, parallel ice sheet geophysical data (GPS, satellite imagery) and meltwater biogeochemistry samples (carbon, macro- and micro-nutrients) have been collected by the PI and colleagues from universities in the United Kingdom. The proposed project will fully fund a Ph.D. student, and high school students and undergraduate interns will be recruited to assist with the laboratory aspect of the research. In order to increase the visibility of this project among the general public, the PI and student will co-author an article for the WHOI magazine Oceanus. This publication reaches a wide audience through both print- and web-based editions. Finally, the Ph.D. student will continue to report on the results of this study via an Expedition Blog called "Following the Ice" on Scientific American (http://blogs.scientificamerican.com/expeditions/tag/following-the-ice/).

The assessment of meltwater sourcing from the clean and debris-covered glaciers is scarce in High Mountain Asia (HMA). The melting rate varies with the debris cover thickness and glacier orientation. The present study quantifies glacier melting rate attributed to varying thickness of debris cover in the Karakoram. We observed the melting rates by installing ablation stakes on Hinarchi and Sachen glaciers over debris-free and debris-covered ice during a field expedition. The stakes were installed on clean ice and glacier surface with debris cover thickness ranges between 0.5 and 2 cm at selected experimental sites between 2700 and 3100 m a.s.l. during July 2021.

This data set contains annual surface melt onset and freeze onset dates across all glaciers in the Hindu Kush Himalayas (HKH) retrieved from time series synthetic aperture radar (SAR) imagery. The data set was based on analysis of C-band Sentinel-1 A/B SAR time series, comprising 32,741 Sentinel-1 A/B SAR images. The duration of annual glacier surface melt was determined for 105,432 mapped glaciers (83,102 km2 glacierized area) during the calendar years 2017-2020.

Drone-Assisted Glacier Melt Monitoring Market Outlook

According to our latest research, the global Drone-Assisted Glacier Melt Monitoring market size reached USD 487.6 million in 2024, with a robust compound annual growth rate (CAGR) of 14.7% projected through the forecast period. By 2033, the market is expected to attain a value of USD 1,531.2 million. This exceptional growth is driven by increasing demand for high-precision environmental monitoring, technological advancements in drone and sensor technologies, and heightened global awareness of climate change impacts on glacial regions.

One of the primary growth factors fueling the expansion of the Drone-Assisted Glacier Melt Monitoring market is the urgent need for accurate, timely, and cost-effective data on glacier dynamics. Traditional glacier monitoring methods, such as ground-based surveys and manned aerial observations, are limited by accessibility, safety risks, and high operational costs. Drones, equipped with advanced sensors, provide a transformative solution by enabling frequent, high-resolution data collection over vast and often hazardous terrains. This capability supports more detailed and actionable insights into glacier melt rates, ice flow patterns, and surface changes, which are critical for climate modeling, water resource management, and disaster risk mitigation.

The rapid evolution and integration of sensor technologies in drones represent another significant driver for this market. The adoption of thermal, LiDAR, multispectral, and optical sensors has dramatically enhanced the accuracy and range of data that can be captured during glacier monitoring missions. These technological advancements allow for comprehensive analyses that combine surface mapping, volumetric measurements, and climate research, all in a single drone flight. Additionally, improvements in drone endurance, payload capacity, and autonomous navigation further expand the scope and efficiency of glacier monitoring operations, making them accessible to a broader range of end-users, including research institutes, government agencies, and environmental organizations.

A third major growth factor is the increasing global emphasis on sustainable environmental practices and climate change mitigation. As the effects of glacier melt become more pronounced—impacting sea levels, freshwater resources, and ecosystem stability—governments and international organizations are investing heavily in monitoring and research initiatives. The availability of real-time, high-precision data from drone-assisted monitoring programs is crucial for shaping policy decisions, developing adaptive strategies, and raising public awareness. This trend is expected to further accelerate market growth, particularly as regulatory frameworks evolve to support the integration of drone technologies in environmental monitoring.

From a regional perspective, North America and Europe currently dominate the Drone-Assisted Glacier Melt Monitoring market, accounting for the largest shares due to their advanced technological infrastructure, significant research funding, and established environmental monitoring programs. However, the Asia Pacific region is poised for the fastest growth, driven by increasing investments in climate research, expanding glacier monitoring initiatives in the Himalayas, and supportive government policies. Latin America and the Middle East & Africa are also witnessing steady adoption, primarily in regions with critical water resources tied to glacial melt. These developments underscore the global relevance and expanding reach of drone-assisted glacier monitoring solutions.

Drone Type Analysis

The drone type segment in the Drone-Assisted Glacier Melt Monitoring market is classified into fixed-wing, rotary-wing, and hybrid drones. Fixed-wing drones have gained significant traction due to their superior endurance, higher altitude capabilities, and extended coverage, making them ideal for monitoring large glacier expanses over long durations. These drones are particularly suited for missions requiring comprehensive surface mapping and volumetric measurements, as they can cover hundreds of square kilometers in a single flight. Their ability to carry heavier and more advanced sensor payloads further enhances their utility for research institutes and government agencies conducting in-depth glacier studies.

Rotary-wing drones, including quadcopters and hexacopters,

seasonal gain-of-ice period

We compare the volume flux divergence of Antarctic ice shelves in 2007 and 2008 with 1979 to 2010 surface accumulation and 2003 to 2008 thinning to determine their rates of melting and mass balance. Basal melt of 1325 ± 235 gigatons per year (Gt/year) exceeds a calving flux of 1089 ± 139 Gt/year, making ice-shelf melting the largest ablation process in Antarctica. The giant cold-cavity Ross, Filchner, and Ronne ice shelves covering two-thirds of the total ice-shelf area account for only 15% of net melting. Half of the meltwater comes from 10 small, warm-cavity Southeast Pacific ice shelves occupying 8% of the area. A similar high melt/area ratio is found for six East Antarctic ice shelves, implying undocumented strong ocean thermal forcing on their deep grounding lines.

Methods 1. Ice shelf thickness Ice thickness is from BEDMAP-2 (1) and NASA Operation IceBridge (OIB) (18-19), which are available, respectively, at www.antarctica.ac.uk › Projects AZ › Bedmap2 and at the National Snow and Ice Data Center nsidc.org/data/icebridge/ data_summaries.html. BEDMAP-2 merges measurements of ice thickness from airborne radio echo sounding with estimates derived from radar-altimetry observations of surface elevation from 1994 (20). The altimetry product uses the most inland grounding line positions from InSAR (21), MOA (36), or ASAID (37) to minimize the omission of floating sectors. Ice thickness may be erroneously high where ice is not in hydrostatic equilibrium, e.g., in a transition region. Along most glaciers, MOA and ASAID grounding lines (GL) have lateral errors up to 50 km (21), which impact the calculation of ice thickness, volume flux, and basal melt rate. Here, we only rely on a systematic, precise mapping of GL with InSAR (available at nsidc.org/data/docs/measures/ nsidc0498_rignot/), and minimize the risk of including grounded ice sectors. Special cases: As GL ice thickness is not well known on Larsen D-G, we use balance discharge from RACMO2 for the GL flux. At the GL of Larsen C, Rayner/Thyer, Edward VIII, and the thickest parts of Shackleton (Denman Glacier) and Moscow University Ice Shelves, ice thickness assumes hydrostatic equilibrium. For Larsen B, we use ice velocity from 2000 and ice-shelf thickness from 1994, pre-dating its 2002 collapse. For the GL of Ross East, Nansen, Aviator, Mariner, Ninnis, Mertz, Dibble, Holmes, Totten, Wilma/Robert/Downer, Rayner/Thyer and Shirase in East Antarctica (EAIS), and Land, Nickerson, Sulzberger and Swinburne in West Antarctica (WAIS), we use (20); for David Glacier, we use OIB. For ice-front fluxes, we use BEDMAP-2, except for Rayner/Thyer, where ice thickness uses hydrostatic equilibrium.

1. Ice shelf velocity Ice-shelf vector velocity data are from a mosaic of InSAR data from six sensors (22). Flow speeds are highest along the coast and on ice shelves. The error in speed is lowest in fast-moving areas mapped with multiple sensors and highest in slow-moving areas mapped using only Advanced Land Observing System (ALOS) Polarimetric Advanced L-band Synthetic Aperture Radar (PALSAR) data. The average errors in flow speed and direction are, respectively, 4 m/yr and 1.7. The velocity data are available online at nsidc.org/data/docs/measures/nsidc0484_rignot.

2. Drainage boundaries Drainage boundaries on continental ice are traditionally drawn using a digital elevation model of the ice sheet, assuming steady-state ice flow along the lines of steepest surface slope. This approach is not reliable on ice shelves due to their small surface slope. We use flow vector direction to delineate drainage boundaries between adjacent ice shelves. This approach helps to differentiate the ice flow into the Filchner Ice Shelf (East Antarctica Ice Sheet (EAIS)) from Academy Glacier versus ice flow into the Ronne Ice Shelf (West Antarctica Ice Sheet (WAIS)) from the Foundation Ice Stream. We also separate ice flow into Ross West (WAIS) versus Ross East (EAIS) and ice flow into Brunt-Stancomb versus Riiser-Larsen ice shelves. The transition between EAIS and WAIS is thus defined at the boundaries between Foundation Ice Stream and Academy Glacier in the Weddell Sea sector, and Mercer Ice Stream and Scott Glacier in the Ross Sea sector.

3. Ice-front positions We identify ice-front positions in a radar mosaic of ALOS PALSAR data for the years 2007-2008 at a 150-m posting. The results are compared for consistency and quality control with MOA 2009, updated from (36). As an ice front migrates with ice flow and calving events, an exact agreement is not expected, but the comparison helps identify and resolve discrepancies. In the case of broken ice shelves, where icebergs are partially detached and glued together with an ice mélange of iceberg debris, sea ice, and blown snow, ice front delineation uses clues from both radar and visible imagery. The 2007-2008 ice-front positions do not coincide with the boundaries of BEDMAP-2 because the data sets are from slightly different time periods. As a result, our ice front flux gates are slightly upstream of the 2007-2008 ice front positions. The area in-between the ice-front flux gates and the actual ice front positions is 2% of the total ice shelf area. For ice walls and smaller ice shelves excluded from our survey, we assume a 50/50 partitioning between calving and basal melt to balance the incoming flux, as in the case of tidewater glaciers (39-40).

4. Ice shelf areas, rises, and islands Inflow from ice rises and ice islands along the ice shelf perimeter is included in the GL flux. Ice rises, rumples, and islands within the ice shelf perimeters are included in the SMB input but excluded from the ice shelf area used to calculate total meltwater production.

5. Grounding line fluxes We have compared our GL fluxes with the balance fluxes calculated using RACMO2 (16). GL fluxes are within error bars of the balance fluxes except in a few areas known to be thinning rapidly (23). This verification provides an evaluation of the quality of the thickness data at the grounding line and helps justify the selection of alternative ice thickness estimates, as per the discussion in section 1, “Special cases”.

6. Surface mass balance (SMB) We employ SMB products from the University of Utrecht’s Regional Atmospheric Climate Model (RACMO2) validated with in-situ data (16). An error rate has been quantified for each basin based on error propagation (17). We use an average SMB for the time period 1979-2010 to obtain a long-term average SMB. Employing SMB values for 2007-2008, the time period of velocity mapping, would introduce significant noise and assume that ice shelf velocities respond instantaneously to annual fluctuations in snow input.

7. Basal melt rates The actual basal melt rate, B, in meters per year is deduced from the equation of mass conservation (15): ∂H/∂t = SMB – B - ∇ (H v), where H is the ice thickness, v is the ice velocity vector, SMB the surface mass balance, and ∂H/∂t the rate of ice shelf thickening (positive for ice shelf growth). To take into account the spatial resolution of the thickness data, we calculate the derivative terms of the mass conservation equation with a 10-km baseline, and the final melt rate map is smoothed with a 10-km filter. As a result, we miss points along the ice shelf perimeters when mapping the freeze/melt distribution; but this does not affect the estimation of area-average melt rates, B, expressed in Gt/yr, because that calculation is based on the total inflow and outflow within the ice shelf perimeters, not the integration of point values. We also calculate melt rates Bss for ∂H/∂t = 0, i.e., the amount of freezing and melting that would be required to maintain the ice shelves in a steady state of velocity and thickness in 2007-2008. For this calculation, we still use velocity data for 2007-2008. In reality, some of these glaciers have been accelerating in recent decades, e.g., several glaciers draining into the Amundsen Sea. For these glaciers, it would have been preferable to use ice velocities from an earlier time period, e.g., 1975, when the system seemed closer to steady state. As we do not have complete velocity and thickness data for that time period, we focus instead on the most complete data set. The spatial pattern of the melt rate B appears noisy on some ice shelves, in particular on Brunt-Stancomb or Ross. Part of this signal is real and associated with rifts, cracks, and vertical undulations in surface elevation present on those shelves. Part of the signal is caused by the time difference between ice thickness and ice velocity data and the advection of heterogeneities in ice thickness along flow. Furthermore, basal melting is expected to be non-uniform across such zones, with melting dominant along the rift sides and freezing dominant at the rift center. We first calculate the basal melt rates in meters per year over the surveyed areas from the GL flux, ice front flux, SMB, and ∂H/∂t. The result is then applied to the actual ice shelf area to deduce the total ice shelf meltwater production. We then re-calculate SMB and ∂H/∂t over the actual ice shelf areas instead of the surveyed area; the grounding line fluxes are unchanged because surveyed and actual areas share identical GL positions. The ice front fluxes are, however, corrected for the adjustment in SMB and ∂H/∂t to ensure closure of the mass balance equation. This correction amounts to 30 Gt/yr, i.e., < 3% of the total ice front flux, which covers 99.5% of the Antarctic ice shelf area.

8. Adjustments for ice shelf thickening Ice shelf thickening ∂H/∂t is derived using corrected ICESat-1 altimetry data for the period 2003-2008, and surface mass balance and firn correction data posted at dx.doi.org/10.1594/PANGAEA.775984. The analysis follows the method in (23). Firn depth corrections provided for 100 ice shelves are interpolated to all ice shelves using inverse distance weighting.

This is the data and metatada for the micromet inputs - the parameter and support data to produce six modeled parameters that comprise the Taylor Valley Galcier Melt modeling datasets Data contained and described in this document correspond to the physically-based surface energy balance model for the glaciers of Taylor Valley developed by the dataset owners. The spatial variability in ablation (ice melt and sublimation), runoff, and climate sensitivity of the glaciers was modeled using 16 years of meteorological and surface mass balance (the net mass gain or loss of ice on the surface of the glacier) observations collected in Taylor Valley (see figure).  An unusual aspect of the model is the inclusion of transmission of solar radiation into the ice and subsequent drainage of some subsurface melt .  Melt model was applied to the ablation zones of the glaciers of Taylor Valley, identified by colored areas. Mass balance stakes, meteorological stations, and stream gages shown for reference. This dataset package includes input files necessary to run the simulations, see the companion output dataset packages to re-use micromet data. In here you will find: The 250m Digital Elevation Model (DEIM) used in the modeling process as ascii - spotdem250.txt, by Matthew Hoffman The landcover data used in the modeling process as ascii - tv_landcover_met.txt -by Matthew Hoffman. The locations of met stations used to inform the MicroMet model can be found in met_station_locations.xlsx (Excel format) An example MATLAB script for visualizing MicroMet generated met data grids is also included, here. grid_viz_example.m The parameter file used to run MicroMet through snowmodel is snowmodel.par.Â

Supplementary datasets for:

Geyman, E.C., van Pelt, W., Maloof, A.C., Faste Aas, H., and Kohler, J., 2021. "Historical glacier change on Svalbard predicts doubling of mass loss by 2100." Nature.

Abstract:

The melting of glaciers and ice caps accounts for about one third of current sea level rise, exceeding the mass loss from the more voluminous Greenland or Antarctic Ice Sheets. The Arctic archipelago of Svalbard, which hosts spatial climate gradients that are larger than the expected temporal shifts over the next century, is a natural laboratory to constrain the climate sensitivity of glaciers and predict their response to future warming. Leveraging an archive of historical aerial images from 1936 and 1938, we use structure-from-motion (SfM) photogrammetry to reconstruct the 3D geometry of 1,594 glaciers across Svalbard. We compare these reconstructions to modern ice elevation data to derive the spatial pattern of mass balance over a >70-year timespan, allowing us to see through the noise of annual and decadal variability to quantify how variables such as temperature and precipitation control ice loss. We find a robust temperature dependence of melt rates, whereby a 1°C rise in mean summer temperature corresponds to a decrease in area-normalized mass balance of -0.28 m yr^-1 of water equivalent. Finally, we design a space-for-time substitution to make first-order predictions of 21st century glacier change across Svalbard. Even in the most modest scenario (a ~1.4°C rise in mean summer temperature by 2100), we predict average glacier thinning rates in 2010-2100 of -0.67 m yr^-1, approximately twice the 1936-2010 rates.

Dataset description:

This dataset contains the digital elevation models (DEMs), elevation change maps, point clouds, orthophotos, and vector outlines of glacier extents based on the Norwegian Polar Institute's collection of 5,507 high-oblique aerial images captured over Svalbard in 1936/1938. The photographs were analyzed through structure-from-motion (SfM) photogrammetry to generate 3D models. We also provide an .xlsx spreadsheet containing glacier-by-glacier statistics of ice loss and climate fields. Note that all of the raster and point cloud files listed below have been georeferenced in Metashape using the ground control points (GCPs) illustrated in Main Text, Fig. 2e, but have not undergone the co-registration and bias-correction following the methods of Nuth & Kaab (2011), which was done on a glacier-by-glacier basis. However, the glacier change budgets in the .xlsx file [#5 below] do reflect the values from the glacier-by-glacier co-registered and bias-corrected DEMs. See below for descriptions of each dataset (each number below corresponds to a different zipped folder).

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Svalbard-wide datasets [all georeferenced Svalbard-wide datasets are in the coordinate system UTM 33N]:

1. Svalbard-wide 1936 DEM (20 m and 50 m resolution) [georeferenced .tif file]

2. Svalbard-wide 1936 orthophotomosaic (20 m resolution) [georeferenced .tif file]

3. Svalbard-wide dh (1936-2010) (20 m and 50 m resolution) [georeferenced .tif file]

4. Shapefile of 1936 glacier extents [ESRI .shp file]

5. Glacier-by-glacier statistics [.xlsx file]

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Regional-datasets:

Due to file size limitations, the high-resolution (5 m) datasets are split into the 8 regions illustrated in Main Text, Fig. 2d:

Zone 1 - South Spitsbergen

Zone 2 - Barentsoya-Edgeoya

Zone 3 - Austfonna

Zone 4 - Vestfonna

Zone 5 - Northeast Spitsbergen

Zone 6 - Central Spitsbergen

Zone 7 - Northwest Spitsbergen

Zone 8 - North Spitsbergen

6. Regional 1936 DEMs (5 m resolution) [georeferenced .tif files]

7. Regional dh (1936-2010) (5 m resolution) [georeferenced .tif files]

8. Local 1936 orthomosaics (5 m resolution) [georeferenced .tif files]

9. Unprocessed point clouds [.laz files]. These files represent the raw 3D point clouds (x,y,z) generated in Agisoft Metashape for each of the 17 local models described in Extended Data Figure 3.

10. Thumbnail-sized copies of the 5,507 historical aerial images (1936 and 1938) analyzed in this study, along with a .csv file labeling the approximate location of each photograph.

Ice shelf basal melting is the primary mechanism driving mass loss from the Antarctic Ice Sheet, yet it is unknown how the localized melt enhancement from subglacial discharge will impact future Antarctic glacial retreat. Here, we develop a parameterization of ice shelf basal melt that accounts for both ocean and subglacial discharge forcing and apply it in future projections of Denman and Scott Glaciers, East Antarctica, through 2300. In forward simulations, subglacial discharge accelerates retreat of these systems into the deepest continental trench on Earth by 25 years. During this retreat, Denman Glacier alone contributes 0.33 mm/yr to global sea level rise, comparable to half of the contemporary sea level contribution of the entire Antarctic Ice Sheet. Our results stress the importance of resolving complex interactions between the ice, ocean, and subglacial environments in future Antarctic Ice Sheet projections. In this data publication, we present the model output and results ass..., Ice sheet model results: Direct results taken from the Ice-sheet and Sea-level System Model (ISSM, Larour et al. 2012) with no processing applied, provided yearly as *.mat files. Ice sheet model initial state: Initial state (ice geometry, mesh information, inversion results, etc.) of the ice sheet model containing Denman and Scott Galciers with no processing applied, provided as a *.mat file. The contents of the *.mat file is a MATLAB variable of class "model", which is compatible with ISSM. Melt parameterization script: Documented MATLAB script ready to run with the provided data sets. Ocean temperature and salinity timeseries: Bottom ocean temperature (°C) and salinity (PSU) timeseries (January 1st, 2017 through December 31, 2299) extracted from an East Antarctic configuration of the ocean component of the MITgcm (Pelle et al., 2021). Temperature and salinity were averaged bi-weekly along the ice fronts of Denman and Scott Glaciers (see white dashed contour in figure 1a of the main m..., Ice sheet modeling results and initial states are compatible with the open source, NASA funded Ice-sheet and Sea-level System Model (ISSM, Larour et al. 2012), which is freely available for download here. In addition, the data files provided in the publication are available as *.mat files, which are compatible with MATLAB but can be accessed using most scripting languages., # Data from: Subglacial discharge accelerates future retreat of Denman and Scott Glaciers, East Antarctica

https://doi.org/10.7280/D1X12S Journal: Science Advances

Principle Investigator:

• Tyler Pelle, Scripps Institution of Oceanography, University of California San Diego, tpelle@ucsd.edu

Co-Authors:

• Dr. Jamin Greenbaum, Scripps Institution of Oceanography, University of California San Diego, jsgreenbaum@ucsd.edu
• Dr. Christine Dow, Department of Geography and Environmental Management, University of Waterloo, christine.dow@uwaterloo.ca
• Dr. Adrian Jenkins, Department of Geography and Environmental Sciences, Northumbria University, adrian2.jenkins@northumbria.ac.uk
• Dr. Mathieu Morlighem, Department of Earth Sciences, Dartmouth College, [Mathieu.Morlighem@dartmouth.edu](mailto:Mathieu.Morlighem@da...

Adapted by Ministry for the Environment and Statistics New Zealand to provide for environmental reporting transparency. Dataset used to develop the "Annual glacier ice volumes" indicator (available at https://www.stats.govt.nz/indicators/annual-glacier-ice-volumes).

This dataset measures the total volume of ice in glaciers greater than one hectare in area throughout New Zealand between 1978 and 2020.

Glaciers are iconic indicators of climate change (Mackintosh et al., 2017). Glacier fluctuations are amongst the clearest signals of climate change because glaciers are highly sensitive indicators of the earth’s surface energy balance (Chinn, 1996). The amount of loss seen in two recent extreme mass-loss events for New Zealand glaciers was more likely to have occurred due to anthropogenic climate change (Vargo et al., 2020).

Glaciers provide an important natural resource that supports power generation, primary production, and water resources. Glaciers act as a reservoir of water and are vital for plants and animals dependent on downstream rivers and lakes, particularly throughout drier seasons. Glaciers regulate downstream water temperature, which is important for many aquatic species, including Taonga species. Changes to ice storage and melting can affect ecological and hydropower resources downstream, as well as important cultural values and tourism. Melting glaciers also add to coastal sea level rise, further contributing to the impacts of climate change.

Climate change is causing summer snowlines to rise and glaciers to retreat. A recent survey of all glacier ice in New Zealand found that the North Island glaciers had declined in area by 25 percent since 1988. For glaciers situated close to the limits of where they can exist, like those on Mt Ruapehu (the only North Island glacierised site today), even moderate warming scenarios predicted for the coming decades may lead to their extinction (Eaves & Brook, 2020). Mt Ruapehu is in the Tongariro National Park, which has been awarded UNESCO World Heritage status for its cultural and natural values. Ruapehu’s glaciers serve as a cultural reference point for local iwi. For example, the Whangaehu River, which has been recognised as indivisible and a living being, emerges from the Whangaehu Glacier on the east flank of Mt Ruapehu. The loss of glaciers will have a negative impact on culture and historical kōrero.

Between 1978 and 2020 the total volume of glacial ice in New Zealand decreased by 35 percent and the rate of annual loss increased.

The total volume of ice in glaciers in New Zealand decreased from 53.3km3 in 1978 to 34.6km3 in 2020.

The highest annual ice loss occurred in 2018 with 2.7km3 lost. The second highest annual ice loss occurred in both 2019 and 2011, with 2.5km3 lost.

More information on this dataset and how it relates to our environmental reporting indicators and topics can be found in the attached data quality pdf. Summary report available at https://environment.govt.nz/publications/environment-aotearoa-2022/.

References

Chinn, T. J. (1996). New Zealand glacier responses to climate change of the past century. _New Zealand Journal of Geology and Geophysics, 39_(3), 415–428. ++https://doi.org/10.1080/00288306.1996.9514723++

Eaves, S. R., & Brook, M. S. (2020). Glaciers and glaciation of North Island, New Zealand. _New Zealand Journal of Geology and Geophysics, 64_(1), 1–20. ++https://doi.org/10.1080/00288306.2020.1811354++

Mackintosh, A. N., Anderson, B. M., Lorrey, A. M., Renwick, J. A., Frei, P., & Dean, S. M. (2017). Regional cooling caused recent New Zealand glacier advances in a period of global warming. _Nature Communications, 8_(1). ++https://doi.org/10.1038/ncomms14202++

Vargo, L. J., Anderson, B. M., Dadić, R., Horgan, H. J., Mackintosh, A. N., King, A. D., & Lorrey, A. M. (2020). Anthropogenic warming forces extreme annual glacier mass loss. Nature Climate Change, 10(9), 856–861. ++https://doi.org/10.1038/s41558-020-0849-2++

Though much attention has been focused in recent years on the melting of ice from Greenland and Antarctica, nearly half of the ice volume currently being lost to the ocean is actually coming from other mountain glaciers and ice caps. Ice loss from a group of islands in northern Canada accounts for much of that volume. In a study published in April 2011 in the journal Nature, a team of researchers led by Alex Gardner of the University of Michigan found that land ice in both the northern and southern Canadian Arctic Archipelago has declined sharply. The maps above show ice loss from surface melting for the northern portion of the archipelago from 2004-2006 (left) and 2007-2009 (right). Blue indicates ice gain, and red indicates ice loss. In the six years studied, the Canadian Arctic Archipelago lost an average of approximately 61 gigatons of ice per year. (A gigaton is a billion tons of ice.) The research team also found the rate of ice loss was accelerating. From 2004 to 2006, the average mass loss was roughly 31 gigatons per year; from 2007 to 2009, the loss increased to 92 gigatons per year. Gardner and colleagues used three independent methods to assess ice mass, all of which showed the same trends. The team used a model to estimate the surface mass balance of ice and the amount of ice discharged. They also compiled and analyzed measurements from NASA's Ice, Cloud and Land Elevation Satellite (ICESat) to assess changes in the surface height of ice. Finally, they gathered observations from NASA's Gravity Recovery and Climate Experiment (GRACE) to determine changes in the gravity field in the region, an indicator of the amount of ice gained or lost. The Canadian Arctic Archipelago generally receives little precipitation, and the amount of snowfall changes little from year to year. But the rate of snow and ice melting varies considerably, so changes in ice mass come largely from changes in summertime melt. During the 2004 to 2009 study period, the Canadian Arctic Archipelago experienced four of its five warmest years since 1960, likely fueling the melting. Gardner notes that from 2001 to 2004, the sum of melting from all mountain glaciers and ice caps around the world (but not the Greenland and Antarctic ice sheets) contributed an estimated 1 millimeter per year to global sea level rise. Recent estimates suggest the Greenland and Antarctic ice sheets add another 1.3 millimeters per year to sea level. "This means 1 percent of the land ice volume--mountain glaciers and ice caps--account for about half of all ice loss to the world's oceans," Gardner said. "Most of the ice loss is coming from the Canadian Arctic Archipelago, Alaska, Patagonia, the Himalayas, and the smaller ice masses surrounding the main Greenland and Antarctic ice sheets."