Snow and ice albedo plays a fundamental role in climate change amplification. Its importance is by modulating absorbed sunlight; the largest average melt energy source. Further, the presence or lack of light absorbing impurities including living matter and meltwater effects can strongly influence snow and ice heating rates. Through multiple consecutive satellite missions, cryosphere albedo has been mapped globally and continuously for more than four decades now.This work examines a 42 year record of cryosphere albedo by joining the satellite climate records of snow and ice albedo from AVHRR 1982 to present, NASA MODIS 1999 to present, and EU Copernicus Sentinel-3 2017 to present. The long-term stability of the climate records is examined using independent field data from Greenland and Antarctica. Additionally, the work presents long term trends in snow and ice albedo in relation to the competing effects of surface melting, snowfall and rainfall.

The surface elevation of the Greenland Ice Sheet is constantly changing due to the interplay between surface mass balance processes and ice dynamics, each exhibiting distinct spatiotemporal patterns. Here, we employ satellite and airborne altimetry data with fine spatial (1 km) and temporal (monthly) resolutions to document this spatiotemporal evolution from January 2003 to August 2023. To estimate elevation changes of the Greenland Ice Sheet (GIS), we utilize radar altimetry data from CryoSat-2 and EnviSat, laser altimetry data from the ICESat and ICESat-2, and laser altimetry data from NASA's Operation IceBridge Airborne Topographic Mapper. We produce continuous monthly ice surface elevation changes from January 2003 to August 2023 on a 1 km grid covering the entire GIS. We estimate cumulative ice loss of 4352 Gt +/- 315 Gt (12.1 +/- 0.9 mm sea level equivalent) during this period, excluding peripheral glaciers. Between 2003 and 2023, the ice sheet land-terminating margin underwent a significant cumulative thinning of several meters. Ocean-terminating glaciers exhibited thinning between 20-40 m, with Jakobshavn Isbr ae experiencing an exceptional thinning of nearly 70 m. This dataset of fine-resolution altimetry data in both space and time will support studies of ice mass loss and will be useful for GIS modeling. To validate our monthly mass changes of the Greenland ice sheet, we use mass change from satellite gravimetry and mass change from the input-output method. On multiannual timescales, there is a strong correlation between the time series, with R values ranging from 0.88 to 0.92 (10.5061/dryad.s4mw6m9dh, Khan et al., 2025)

The glaciers on the High Russian Arctic archipielago Novaya Zemlya have been losing roughly 10 Gt/yr over the past decade, 5 Gt/yr more than in the one before. While earlier research pointed to ocean discharge as driver of the acceleration, we present new results that show that foehn events, triggered by atmospheric rivers, led to the most severe melt events in the recent times. We use output of the regional atmospheric model MAR, together with geodetic observations from CryoSat-2, and reanalysis data (CARRA, ERA5, MERRA-2) to show that roughly 70 % of the melt occurs during atmospheric rivers episodes. Between 1990 and 2022, 45 of the 54 days with more than 1 Gt melt were accompanied by foehn winds. We conclude that the representation of atmospheric rivers and foehn winds in models is crucial for accurate projections of the future glacier evolution.

The rapid demise of ice sheets and glaciers worldwide has increased the need for mass balance observations at a temporal and spatial resolution, where they can both help us understand the physical processes and also serve as validation or calibration for remote sensing data products or regional climate model output. Here we present the latest developments in measuring crucial components of the surface mass balance at automatic weather stations, including snow water equivalent, snow height in the vicinity of the station, sufficiently accurate transmitted position and elevation of the station, snow compaction and non-stake ice sheet ablation. Immediate access to the observations is key to certain applications, such as numerical weather forecasts. Hence, we also present the complications of providing near real-time data transmission and quality-checking as well as obstacles to a wider distribution on the WMO Global Telecommunication System (GTS).

Accurately quantifying all the components of the surface energy balance (SEB) is a prerequisite for the reliable estimation of surface melt and the surface mass balance over ice and snow. This study quantifies the SEB closure by comparing the energy available for surface melt, determined from continuous measurements of radiative fluxes and turbulent heat fluxes, to the surface ablation measured on the Greenland ice sheet between 2003 and 2023. We find that the measured daily energy available for surface melt exceeds the observed surface melt by on average 18 +/- 30 W m-2 for snow and 12 +/- 54 W m-2 for ice conditions (mean +/- SD), which corresponds to 46 and 10% of the average energy available for surface melt, respectively. When the surface is not melting, the daily SEB is on average closed within 5 W m-2. Based on the inter-comparison of different ablation sensors and radiometers installed on different stations, and on the evaluation of modelled turbulent heat fluxes, we conclude that measurement uncertainties prevent a better daily to sub-daily SEB closure. These results highlight the need and challenges in obtaining accurate long-term in situ SEB observations for the proper evaluation of climate models and for the validation of remote sensing products.

The variability of the Antarctic and Greenland ice sheets occurs on various timescales and is important for projections of sea level rise; however, there are substantial uncertainties concerning future ice-sheet mass changes. In this Review, we explore the degree to which short-term fluctuations and extreme glaciological events reflect the ice sheets’ long-term evolution and response to ongoing climate change. Short-term (decadal or shorter) variations in atmospheric or oceanic conditions can trigger amplifying feedbacks that increase the sensitivity of ice sheets to climate change. For example, variability in ocean-induced and atmosphere-induced melting can trigger ice thinning, retreat and/or collapse of ice shelves, grounding-line retreat, and ice flow acceleration. The Antarctic Ice Sheet is especially prone to increased melting and ice sheet collapse from warm ocean currents, which could be accentuated with increased climate variability. In Greenland both high and low melt anomalies have been observed since 2012, highlighting the influence of increased interannual climate variability on extreme glaciological events and ice sheet evolution. Failing to adequately account for such variability can result in biased projections of multi-decadal ice mass loss. Therefore, future research should aim to improve climate and ocean observations and models, and develop sophisticated ice sheet models that are directly constrained by observational records and can capture ice dynamical changes across various timescales. The different contributions of long-term and short-term variability to the evolution of ice sheets lead to substantial uncertainties in ice sheet models. This Review describes the response of ice sheets to oceanic, atmospheric and hydrological processes across a range of timescales.

The NASA MODIS MOD10A1 snow albedo product has enabled numerous glaciological applications. The temporal consistency of MODIS albedo is critical to obtaining reliable results from this 22-year time series. The orbit of Terra began to drift toward earlier acquisition times after the final inclination adjustment maneuver to maintain its nominal orbit by NASA on 27 February 2020, which may introduce biases that compromise the accuracy of quantitative time series analysis as the drift continues. Here, we evaluate the impact of Terra's orbital drift by comparing the differences between the Terra MODIS albedo and albedo products derived from Aqua MODIS, harmonized Landsat and Sentinel 2, Sentinel 3, and PROMICE (Programme for Monitoring of the Greenland Ice Sheet) ground measurements over the Greenland ice sheet. Our results suggest that the influence of orbital drift on albedo is small (+0.01 in 2020), but potentially biased for time series analysis. Our analysis also finds that the drift effect that causes earlier image acquisition time may lead to more apparently cloudy pixels and thus effectively reduce the Terra MODIS temporal resolution over Greenland.

AbstractRecently, climate extremes have been grabbing attention as important drivers of environmental change. Here, we assemble an observational inventory of energy and mass fluxes to quantify the ice loss from glaciers on the Russian High Arctic archipelago of Novaya Zemlya. Satellite altimetry reveals that 70 ± 19% of the 149 ± 29 Gt mass loss between 2011 and 2022 occurred in just four high-melt years. We find that 71 ± 3% of the melt, including the top melt cases, are driven by extreme energy imports from atmospheric rivers. The majority of ice loss occurs on leeward slopes due to foehn winds. 45 of the 54 high-melt days (>1 Gt d−1) in 1990 to 2022 show a combination of atmospheric rivers and foehn winds. Therefore, the frequency and intensity of atmospheric rivers demand accurate representation for reliable future glacier melt projections for the Russian High Arctic.

Surface melt on the Greenland ice sheet has been increasing in intensity and extent over the last decades due to Arctic atmospheric warming. Surface melt depends on the surface energy balance, which includes the atmospheric forcing but also the thermal budget of the snow, firn and ice near the ice sheet surface. The temperature of the ice sheet subsurface has been used as an indicator of the thermal state of the ice sheet's surface. Here, we present a compilation of 4612 measurements of firn and ice temperature at 10 m below the surface (T10 m) across the ice sheet, spanning from 1912 to 2022. The measurements are either instantaneous or monthly averages. We train an artificial neural network model (ANN) on 4597 of these point observations, weighted by their relative representativity, and use it to reconstruct T10 m over the entire Greenland ice sheet for the period 1950–2022 at a monthly timescale. We use 10-year averages and mean annual values of air temperature and snowfall from the ERA5 reanalysis dataset as model input. The ANN indicates a Greenland-wide positive trend of T10 m at 0.2 ∘C per decade during the 1950–2022 period, with a cooling during 1950–1985 (−0.4 ∘C per decade) followed by a warming during 1985–2022 (+0.7 ∘ per decade). Regional climate models HIRHAM5, RACMO2.3p2 and MARv3.12 show mixed results compared to the observational T10 m dataset, with mean differences ranging from −0.4 ∘C (HIRHAM) to 1.2 ∘C (MAR) and root mean squared differences ranging from 2.8 ∘C (HIRHAM) to 4.7 ∘C (MAR). The observation-based ANN also reveals an underestimation of the subsurface warming trends in climate models for the bare-ice and dry-snow areas. The subsurface warming brings the Greenland ice sheet surface closer to the melting point, reducing the amount of energy input required for melting. Our compilation documents the response of the ice sheet subsurface to atmospheric warming and will enable further improvements of models used for ice sheet mass loss assessment and reduce the uncertainty in projections.

Our limited knowledge of the marine mammal fauna in northernmost Greenland and Canada, specifically north of 80°N, relies largely on opportunistic observations collected during expeditions with different objectives. The narwhal (Monodon monoceros) migrates long distances in response to ice formation and decay and is notoriously skittish, avoiding areas with ice breakers. Scattered observations from the past 20 years, assessed together with historical observations after 1881, suggest that there is a population of narwhals that uses Hall Basin and its adjacent fjord systems—for example, Nares Strait—as a summer ground. Dating the tusks and bones that have been found shows that narwhals were present in this area as far back as nearly 7000 years ago. The wintering locations of these narwhals remain unknown, highlighting the need to investigate whether they are vulnerable to hunting activities in north-west Greenland. By gaining a better understanding of the narwhals’ winter behaviour and potential hunting risks, we can develop more informed conservation and management strategies for this population.

<p>With temperatures in the Arctic rising rapidly at a rate of 3-4 times the global mean, monitoring the state of the Greenland ice sheet has never been more relevant.</p> <p>In-situ observations from the Arctic, in particular from the Greenland ice sheet, are scarce due to the cost and difficulty of maintaining instrumentation in the harsh and remote environment. Yet, the fate of the ice sheet concerns 100s of millions of people living in coastal zones worldwide. To gain understanding of the ice sheet processes leading to sea level rise and increase our ability to capture those in climate models, there is an urgent need to collect in-situ observations from the ice sheet surface. Similarly, ground-truthing observations are necessary for validation and calibration of satellite-derived estimates of ice sheet change.</p> <p>The Geological Survey of Denmark and Greenland (GEUS), along with partner institutions Asiaq and DTU Space, currently operates a combined network of 40 automatic weather stations (AWS) on ice in Greenland, mainly through the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and the Greenland Climate Network (GC-Net).</p> <p>GEUS has implemented a new pipeline providing near-real-time hourly weather observations from the PROMICE and GC-Net stations to its users and the World Meteorological Organization (WMO) for use in numerical weather prediction. The satellite-transmitted AWS data is processed and submitted to the WMO via the Danish Meteorological Institute with a latency of 7 minutes after observations are recorded.</p> <p>Here, we present the recent advances in our AWS instrumentation, data processing and database solution to invite discussion on how we can best meet the community needs for in-situ observations from the ice sheet.</p>

The Greenland Climate Network (GC-Net) consists of 31 automatic weather stations (AWSs) at 30 sites across the Greenland Ice Sheet. The first site was initiated in 1990, and the project has operated almost continuously since 1995 under the leadership of the late Konrad Steffen. The GC-Net AWS measured air temperature, relative humidity, wind speed, atmospheric pressure, downward and reflected shortwave irradiance, net radiation, and ice and firn temperatures. The majority of the GC-Net sites were located in the ice sheet accumulation area (17 AWSs), while 11 AWSs were located in the ablation area, and two sites (three AWSs) were located close to the equilibrium line altitude. Additionally, three AWSs of similar design to the GC-Net AWS were installed by Konrad Steffen's team on the Larsen C ice shelf, Antarctica. After more than 3 decades of operation, the GC-Net AWSs are being decommissioned and replaced by new AWSs operated by the Geological Survey of Denmark and Greenland (GEUS). Therefore, making a reassessment of the historical GC-Net AWS data is necessary. We present a full reprocessing of the historical GC-Net AWS dataset with increased attention to the filtering of erroneous measurements, data correction and derivation of additional variables: continuous surface height, instrument heights, surface albedo, turbulent heat fluxes, and 10 m ice and firn temperatures. This new augmented GC-Net level-1 (L1) AWS dataset is now available at https://doi.org/10.22008/FK2/VVXGUT (Steffen et al., 2023) and will continue to be refined. The processing scripts, latest data and a data user forum are available at https://github.com/GEUS-Glaciology-and-Climate/GC-Net-level-1-data-processing (last access: 30 November 2023). In addition to the AWS data, a comprehensive compilation of valuable metadata is provided: maintenance reports, yearly pictures of the stations and the station positions through time. This unique dataset provides more than 320 station years of high-quality atmospheric data and is available following FAIR (findable, accessible, interoperable, reusable) data and code practices.

Turbulent heat fluxes, that is, the sensible heat flux and latent heat flux, are important sources/sinks of energy for surface melt over glaciers and ice sheets. Therefore, credible simulations of for example, future Greenland Ice Sheet mass loss need an accurate description of these fluxes. However, the parameterization of surface turbulent heat fluxes in climate models requires knowledge about the surface roughness lengths for momentum, heat and moisture, which are currently either unknown or tuned to indirect observations. In this study we take advantage of a large data set of eddy covariance observations acquired during multiple years and at multiple sites over the Greenland Ice Sheet. These in‐situ observations are used to develop an improved parameterization for the roughness length for momentum, and update the parameterization for the roughness lengths for heat and moisture over rough ice surfaces. The newly derived parameterizations are implemented in a surface energy balance model that is used to compute surface melt. Sensitivity experiments confirm the high sensitivity of surface melt to the chosen roughness length models. The new parameterization models the sensible heat flux to within 10 W m −2 , and the cumulative ice ablation within 10% at three out of four sites. Two case studies demonstrate the important contribution of the turbulent heat fluxes to surface ablation. The presented roughness parameterizations can be implemented in climate models to improve the physical representation of surface roughness over rough snow and ice surfaces, which is expected to improve the modeled turbulent heat fluxes and thus surface melt.

Abstract. The surface melting of the Greenland ice sheet has been increasing over the last decades due to Arctic atmospheric warming. Surface melt depends on the energy balance which includes the atmospheric forcing but also the thermal budget of the snow, firn and ice near the ice sheet surface. We present a compilation of more than 4500 measurements of ice, snow and firn temperature at 10 m below the surface (T10m) across Greenland spanning from 1912 to 2022. The measurements are either instantaneous or monthly averages. We train an Artificial Neural Network model (ANN) on these observations, weighted by their relative representativity, and use it to reconstruct T10m over the entire Greenland ice sheet. We use 10 year averages and mean annual values of air temperature and snowfall from the ERA5 reanalysis dataset (1950–2022) as model input. The ANN indicates a Greenland-wide positive trend of T10m at 0.2 °C decade-1 during the 1950–2022 period, with a cooling during 1950–1985 (-0.3 °C decade-1) followed by a warming during 1985–2022 (+0.7 °C decade-1). Regional climate models HIRHAM5, RACMO2.3p2 and MARv3.12 show mixed results compared to the observational T10m dataset with mean differences ranging from -0.4 °C (HIRHAM) to 1.3 °C (MAR) and root mean squared differences ranging from 2.8 °C (HIRHAM) to 4.7 °C (MAR). The corresponding values for the ANN are -0.2 °C and 1.7 °C. The observation-based ANN also reveals an underestimation of the subsurface warming trends in climate models for the bare ice and dry snow areas. The subsurface warming brings the Greenland ice sheet surface closer to the melting point, reducing the amount of summer energy input required for melting. Our compilation documents the response of the ice sheet subsurface to atmospheric warming and will enable further improvements of models used for ice sheet mass loss assessment and reduce the uncertainty in projections.

Greenland rainfall has come into focus as a climate change indicator and from a variety of emerging cryospheric impacts. This study first evaluates rainfall in five state-of-the-art numerical prediction systems (NPSs) (CARRA, ERA5, NHM-SMAP, RACMO, MAR) using in situ rainfall data from two regions spanning from land onto the ice sheet. The new EU Copernicus Climate Change Service (C3S) Arctic Regional ReAnalysis (CARRA), with a relatively fine (2.5 km) horizontal grid spacing and extensive within-model-domain observational initialization, has the lowest average bias and highest explained variance relative to the field data. ERA5 inland wet bias versus CARRA is consistent with the field data and other research and is presumably due to more ERA5 topographic smoothing. A CARRA climatology 1991-2021 has rainfall increasing by more than one-third for the ice sheet and its peripheral ice masses. CARRA and in situ data illuminate extreme (above 300 mm per day) local rainfall episodes. A detailed examination CARRA data reveals the interplay of mass conservation that splits flow around southern Greenland and condensational buoyancy generation that maintains along-flow updraft 'rapids' 2 km above sea level, which produce rain bands within an atmospheric river interacting with Greenland. CARRA resolves gravity wave oscillations that initiate as a result of buoyancy offshore, which then amplify from terrain-forced uplift. In a detailed case study, CARRA resolves orographic intensification of rainfall by up to a factor of four, which is consistent with the field data.

The Greenland ice sheet mass loss is one of the main sources of contemporary sea-level rise. The mass loss is primarily caused by surface melt and the resulting runoff. During the melt season, the ice sheet’s surface receives energy from sunlight absorption and sensible heating, which subsequently heats the subsurface snow and ice. The energy from the previous melt season can also enhance melting in the following summer as less heating is required to bring the snow and ice to the melting point. Subsurface temperatures are therefore both a result and a driver of the timing and magnitude of surface melt on the ice sheet. We present a dataset of more than 3900 measurements of ice, snow and firn temperature at 10 m depth across the Greenland ice sheet spanning the years from 1912 to 2022. We construct an artificial neural network (ANN) model that takes as input the ERA5 reanalysis monthly near-surface air temperature and snowfall for the 1954-2022 period and train it on our compilation of observed 10-meter temperature. We use our dataset and the ANN to evaluate three broadly used regional climate models (RACMO, MAR and HIRHAM). Our ANN model provides an unprecedented and observation-based description of the recent warming of the ice sheet’s near-surface and our evaluation of the three climate models highlights future development for the models. Overall, these findings improve our understanding of the ice sheet’s response to recent atmospheric warming and will help reduce uncertainties of ice sheet surface mass balance estimates.

We present an update of the Snow and Ice (SICE) property retrieval algorithm based on the spectral measurements of Ocean and Land Color Instrument (OLCI) onboard Sentinel-3 satellites combined with the asymptotic radiative transfer theory valid for weakly absorbing turbid media. The main improvements include the introduction of a new atmospheric correction, retrieval of snow impurity load and properties, retrievals for partially snow-covered ground and also accounting for various thresholds to be used to assess the retrieval quality. The technique can be applied to various optical sensors (satellite and ground-based) operated in the visible and near infrared regions of electromagnetic spectra.