The ice surface temperature (IST) derived from thermal infrared remote sensing is crucial for accurately monitoring ice or snow surface temperatures in the polar region. Generally, the remote sensing IST needs to be validated by the in situ IST to ensure its accuracy. However, due to the limited availability of in situ IST measurements, previous studies in the validation of remote sensing ISTs are scarce in the Antarctic ice sheet. This study utilizes ISTs from eight broadband radiation stations to assess the accuracy of the latest-released Moderate Resolution Imaging Spectroradiometer (MODIS) IST and Visible Infrared Imager Radiometer Suite (VIIRS) IST products, which were derived from two different algorithms, the Split-Window (SW-based) algorithm and the Temperature–Emissivity Separation (TES-based) algorithm, respectively. This study also explores the sources of uncertainty in the validation process. The results reveal prominent errors when directly validating remote sensing ISTs with the in situ ISTs, which can be attributed to incorrect cloud detection due to the similar spectral characteristics of cloud and snow. Hence, cloud pixels are misclassified as clear pixels in the satellite cloud mask during IST validation, which emphasizes the severe cloud contamination of remote sensing IST products. By using a cloud index (n) to remove the cloud contamination pixels in the remote sensing IST products, the overall uncertainties for the four products are about 2 to 3 K, with the maximum uncertainty (RMSE) reduced by 3.51 K and the bias decreased by 1.26 K. Furthermore, a progressive cold bias in the validation process was observed with decreasing temperature, likely due to atmospheric radiation between the radiometer and the snow surface being neglected in previous studies. Lastly, this study found that the cloud mask errors of satellites are more pronounced during the winter compared to that in summer, highlighting the need for caution when directly using remote sensing IST products, particularly during the polar night.

In cooperation with multiple institutes, the Institute for Marine and Atmospheric Research Utrecht (IMAU) at Utrecht University has operated automated weather stations (AWSs) at 19 locations on the Antarctic ice sheet from 1995 through 2022. Besides standard meteorological measurements (pressure, temperature, humidity, wind speed, and direction), these stations include measured short-wave and long-wave radiation components and surface height, thereby allowing for the reliable in situ quantification of the surface energy balance (SEB) and surface mass balance (SMB) at hourly and 2 h temporal resolution. This unique dataset can be used for climate model evaluation and development, for the validation of remote sensing products, for the quantification of long-term climatological changes, for the interpretation of ice cores, and for process understanding in general. This paper describes the dataset and the applied measurement corrections. The total dataset contains 157 station years of data, of which 54 % include both SEB and SMB observations, and is available at 10.1594/PANGAEA.974080 .

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.

Recently released Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) collection 6.1 (C6.1) products are useful for understanding ice-atmosphere interactions over East Antarctica, but their accuracy should be known prior to application. This study assessed Level 2 and Level 3 MODIS C6.1 LST products (MxD11_L2 and MxD11C1) in comparison with the radiance-derived in situ LSTs from 12 weather stations. Significant cloud-related issues were identified in both LST products. By utilizing a stricter filter based on automatic weather station cloud data, despite losing 29.4% of the data, accuracy of MODIS LST was greatly improved. The cloud-screened MODIS LST exhibited cold biases (-5.18 to -0.07 degrees C, and root mean square errors from 2.37 to 6.28 degrees C) than in situ LSTs at most stations, with smaller cold biases at inland stations, but larger ones at coastal regions and the edge of plateau. The accuracy was notably higher during warm periods (October-March) than during cold periods (April-September). The cloud-screened MODIS C6.1 LST did not show significant improvements over C5 (Collection 5) version across East Antarctica. Ice-crystal precipitation occurring during temperature inversions at the surface (Tair-Tsurface) played a crucial role in MODIS LST accuracy on inland plateau. In coastal regions, larger MODIS LST biases were observed when the original measurements were lower.

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.

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.

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.

A new meteorological dataset derived from records of Antarctic automatic weather stations (here called the AntAWS dataset) at 3 h, daily and monthly resolutions including quality control information is presented here. This dataset integrates the measurements of air temperature, air pressure, relative humidity, and wind speed and direction from 267 Antarctic AWSs obtained from 1980 to 2021. The AWS spatial distribution remains heterogeneous, with the majority of instruments located in near-coastal areas and only a few inland on the East Antarctic Plateau. Among these 267 AWSs, 63 have been operating for more than 20 years and 27 of them in excess of more than 30 years. Of the five meteorological parameters, the measurements of air temperature have the best continuity and the highest data integrity. The overarching aim of this comprehensive compilation of AWS observations is to make these data easily and widely accessible for efficient use in local, regional and continental studies; it may be accessed at https://doi.org/10.48567/key7-ch19 (Wang et al., 2022). This dataset is invaluable for improved characterization of the surface climatology across the Antarctic continent, to improve our understanding of Antarctic surface snow–atmosphere interactions including precipitation events associated with atmospheric rivers and to evaluate regional climate models or meteorological reanalysis products.

Surface meltwater production impacts the mass balance of the Greenland and Antarctic ice sheets in several ways, both directly (e.g., through runoff in Greenland) and indirectly (e.g., through cryo-hydrologic warming and frontal melt of marine-terminating glaciers in Greenland and hydrofracturing of ice shelves in Antarctica). Despite its importance, the spatial and temporal patterns in melt rates on both ice sheets are still relatively poorly understood. In this contribution we review and contrast surface melt ‘weather ‘(i.e., short term, intra- and interdiurnal variability) and surface melt ‘climate’ (i.e., longer term, interannual variability and future melt) of both ice sheets. We find that in situ observations using suitably equipped (automatic or staffed) weather stations are invaluable for a complete understanding of the melt process, which represents the complex transport of energy by radiation, turbulence, and molecular conduction between the lower atmosphere, the ice/snow surface, and the subsurface ice/snow layers. We provide example time series of ice sheet melt ‘weather’ for the marginal Greenland ice sheet, where warm and humid air masses tend to increase surface melt rate, and for coastal East Antarctica, where the opposite happens. Apart from process understanding, these in situ observations, which especially in Antarctica are scarce in space and time, are also invaluable to validate, evaluate and calibrate satellite- and model-based estimates of ice sheet surface melt rate. We provide examples of modelled melt maps for both ice sheets, and melt projections for a high-warming, fossil-fuelled development scenario. Although important milestones in melt observations (both in-situ and remotely sensed) and melt models (both global and regional) have recently been reached, we identify multiple outstanding research questions pertaining to current and future ice sheet surface melt rates.

MODIS (Moderate Resolution Imaging Spectroradiometer) land surface temperature measurements in combination with in situ air temperature records from 119 meteorological stations are used to reconstruct a monthly near-surface air temperature product over the Antarctic Ice Sheet (AIS) by means of a neural network model. The product is generated on a regular grid of 0.05° × 0.05°, spanning from 2001 to 2018. Comparison with independent in situ air temperature measurements shows low uncertainty, with a mean bias of 0.09°C, a mean absolute error of 2.23°C, and a correlation coefficient of 97%. Furthermore, the performance of the reconstruction is better than ERA5 (the fifth-generation ECMWF reanalysis model) against in situ measurements. For the 2001–18 period, the MODIS-based near-surface air temperature product yields annual warming in the East Antarctica, but cooling in the Antarctic Peninsula and West Antarctica. However, they are not statistically significant. This product can also be used to investigate the impact of the Southern Hemisphere annual mode (SAM) on year-to-year variability of air temperature. The enhanced positive phase of SAM in recent decades in austral summer has a cooling effect on East and West Antarctica. In addition, the dataset has the potential application for climate model validation and data assimilation due to the independence of the input of a numerical weather prediction model.

A comprehensive compilation of observed records is needed for accurate quantification of surface mass balance (SMB) over Antarctica, which is a key challenge for calculation of Antarctic contribution to global sea level change. Here, we present the AntSMB dataset: a new quality-controlled dataset of a variety of published field measurements of the Antarctic Ice Sheet SMB by means of stakes, snow pits, ice cores, ultrasonic sounders, and ground-penetrating radar (GPR). The dataset collects 3579 individual multi-year-averaged observations, 687 annually resolved time series from 675 sites extending back over the past 1000 years, and daily resolved records covering 245 years from 32 sites across the whole ice sheet. These records are derived from ice cores, snow pits, stakes/stake farms, and ultrasonic sounders. Furthermore, GPR multi-year-averaged measurements are included in the dataset, covering an area of 22 025 km2. This is the first ice-sheet-scale compilation of SMB records at different temporal (daily, annual, and multi-year) resolutions from multiple types of measurement and is available at https://doi.org/10.11888/Glacio.tpdc.271148 (Wang et al., 2021). The database has potentially wide applications such as the investigation of temporal and spatial variability in SMB, model validation, assessment of remote sensing retrievals, and data assimilation. As a case of model estimation, records of the AntSMB dataset are used to assess the performance of ERA5 for temporal and spatial variability in SMB over Antarctica.

The roughness of a natural surface is an important parameter in atmospheric models, as it determines the intensity of turbulent transfer between the atmosphere and the surface. Unfortunately, this parameter is often poorly known, especially in remote areas where neither high-resolution elevation models nor eddy-covariance measurements are available. In this study, we take advantage of the measurements of the ICESat-2 satellite laser altimeter. We use the geolocated photons product (ATL03) to retrieve a 1-m resolution surface elevation product over the K-transect (West Greenland ice sheet). In combination with a bulk drag partitioning model, the retrieved surface elevation is used to estimate the aerodynamic roughness length (z0m) of the surface. We demonstrate the high precision of the retrieved ICESat-2 elevation using co-located UAV photogrammetry, and then evaluate the modelled aerodynamic roughness against multiple in situ eddy-covariance observations. The results point out the importance to use a bulk drag model over a more empirical formulation. The currently available ATL03 geolocated photons are used to map the aerodynamic roughness along the K-transect (2018-2020). We find a considerable spatiotemporal variability in z0m, ranging between 10−4 m for a smooth snow surface to more than 10−1 m for rough crevassed areas, which confirms the need to incorporate a variable aerodynamic roughness in atmospheric models over ice sheets.

This study uses meteorological records from Automatic Weather Stations (AWSs) to estimate the performance of global reanalysis products for monthly air temperature, relative humidity and wind speed over the Greenland Ice Sheet (GrIS). These products include the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5), ECMWF Interim Reanalysis (ERAI), Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Climate Forecast System Reanalysis Version 1/Climate Forecast System Version 2 (CFSRv1/CFSRv2), and Japanese 55-year Reanalysis (JRA-55). The global reanalysis products generally perform better in summer than in winter, and their qualities vary by glaciological regime. No reanalysis is clearly identified as the optimal dataset for all meteorological parameters, seasons and regions. For all reanalyses, warm biases are observed in the accumulation zone, but cold biases are observed in the ablation area of the GrIS. ERAI, ERA5 and JRA-55 underestimate relative humidity during any month. While MERRA-2 overestimates wind speeds, underestimates are found for the other reanalyses excluding JRA-55 during all months. Despite the robust agreement between the AWS time series for all three variables and each reanalysis product averaged over the ice sheet, sudden jumps occur in annual mean wind speed in CFSR, and in annual relative humidity in JRA-55 from 2010 to 2011.

Warm and dry föhn winds on the Antarctic Peninsula (AP) cause surface melt that can destabilize vulnerable ice shelves. Topographic funneling of these downslope winds through mountain passes and canyons can produce localized wind‐induced melt that is difficult to quantify without direct measurements. Our Föhn Detection Algorithm (FöhnDA) identifies the surface föhn signature that causes melt from measurement by 12 Automatic Weather Stations on the AP, that train a machine learning model to detect föhn in 5 km Regional Atmospheric Climate Model 2 (RACMO2.3p2) simulations and in the ERA5 reanalysis model. We estimate the fraction of AP surface melt attributed to föhn and possibly katabatic winds and identify the drivers of melt, temporal variability, and long‐term trends and evolution from 1979–2018. We find that föhn wind‐induced melt accounts for 3.1% of the total melt on the AP and can be as high at 18% close to the mountains where the winds funnel through mountain canyons. Föhn‐induced surface melt does not significantly increase from 1979–2018, despite a warmer atmosphere and more positive Southern Annular Mode. However, a significant increase (+0.1 Gt y‐1) and subsequent decrease/stabilization occur in 1979–1998 and 1999–2018, consistent with the AP warming and cooling trends during the same time periods. Föhn occurrence, more than föhn strength, drives the annual variability in föhn‐induced melt. Long‐term föhn‐induced melt trends and evolution are attributable to seasonal changes in föhn occurrence, with increased occurrence in summer, and decreased occurrence in fall, winter, and early spring over the past 20 years.

Surface melt on the coastal Antarctic ice sheet (AIS) determines the viability of its ice shelves and the stability of the grounded ice sheet, but very few in situ melt rate estimates exist to date. Here we present a benchmark dataset of in situ surface melt rates and energy balance from nine sites in the eastern Antarctic Peninsula (AP) and coastal Dronning Maud Land (DML), East Antarctica, seven of which are located on AIS ice shelves. Meteorological time series from eight automatic and one staffed weather station (Neumayer), ranging in length from 15 months to almost 24 years, serve as input for an energy-balance model to obtain consistent surface melt rates and energy-balance results. We find that surface melt rates exhibit large temporal, spatial and process variability. Intermittent summer melt in coastal DML is primarily driven by absorption of shortwave radiation, while non-summer melt events in the eastern AP occur during fohn events that force a large downward directed turbulent flux of sensible heat. We use the in situ surface melt rate dataset to evaluate melt rates from the regional atmospheric climate model RACMO2 and validate a melt product from the QuikSCAT satellite.