Brice Noël

Ice mass loss in Greenland attracts considerable public attention because the Greenland Ice Sheet (GrIS) is the largest single contributor to the mass component of global sea-level rise. To project the future evolution of the GrIS over the coming decades, accurate geophysical models are required. In particular, regional climate models, such as the Regional Atmospheric Climate Model RACMO2.3/RACMO2.3p2, are used to estimate the Surface Mass Balance (SMB) of the GrIS and surrounding areas. Data from the GRACE and GRACE Follow-On (GFO) satellite gravity missions provide a primary source of independent information for evaluating modelled mass anomalies integrated over individual GrIS drainage systems and Greenland as a whole.In this study, we compare Greenland-wide estimates of annual mass trends derived from RACMO2.3/2.3p2 with those obtained from GRACE/GFO level-2 data for the period 2003–2023. As input, we use monthly sets of GRACE/GFO-based spherical harmonic coefficients produces at four institutions: the Center of Space Research (CSR) at the University of Texas at Austin; the German Research Centre for Geosciences (GFZ) in Potsdam; the Jet Propulsion Laboratory (JPL) of the California Institute of Technology; and the Institute of Geodesy at Graz University of Technology (ITSG), Austria. The SMB-based estimates from RACMO2.3/2.3p2, combined with information on ice discharge (Mankoff et al, 2020), provide estimates of the total mass trends using the input-output method. We aim to ensure full consistency between the datasets being compared and their processing. Since the spatial resolution of RACMO model is substantially higher than that of GRACE/GFO data, we transform the RACMO-based mass anomalies into the spherical harmonic domain before further processing. In addition, we use fully consistent sets of months for both datasets to mitigate the potential impact of temporal gaps in GRACE/GFO data.The comparison conducted in this study allows us, first and foremost, to draw conclusions about the performance of RACMO2.3/2.3p2. For example, a recent study by Ran et al (Nature, 2024), based on elastic rock deformations measured by GPS during 2009–2015 showed that RACMO2.3p2 may underestimate meltwater runoff in “hot” years and overestimate it in “cold” years. By considering a substantially longer time interval, our study provides an independent evaluation of meltwater runoff modelled by RACMO2.3/2.3p2. At the same time, comparing GRACE/GFO-based estimates from different data processing centres with RACMO-based estimates enables us to assess the accuracy of the considered GRACE/GFO data products in the context of Greenland.

Accurately estimating the surface mass balance (SMB) of the Greenland Ice Sheet (GrIS) is essential to quantify its contribution to sea-level rise. The polar regional atmospheric climate model MAR is widely used to simulate GrIS SMB and to force ice sheet dynamics models, highlighting the need for a thorough evaluation. Here, we evaluate the latest MAR version (MARv3.14) over Greenland at 5 km spatial resolution and examine the impact of coarser resolutions (10–30 km) on the simulated SMB and its components. MAR outputs are compared to a range of independent observations, including in situ SMB measurements, automatic weather station (AWS) records of near-surface meteorological variables, satellite-derived melt extent, and albedo products. At 5 km, MAR reproduces the observed SMB with a root-mean-square error (RMSE) of 0.51 m and a correlation of 0.93. For near-surface meteorological variables and surface energy budget fluxes, the model RMSE is smaller than the corresponding observed natural variability (i.e., standard deviation), indicating non-significant model error. Prescribing bare-ice albedo improves the model performance in the ablation zone, while biases remain in the accumulation zone, suggesting that further improvements are required in the snow albedo scheme. In addition, simulated melt timing is consistent with satellite-based melt extent products. Sensitivity experiments reveal that discrepancies between simulations at different spatial resolutions are mostly limited to the ice sheet margins where strong SMB and topographic gradients occur, notably in the southeast of Greenland where precipitation peaks. Differences in integrated SMB generally remain small and mostly non-significant, while individual components, i.e., precipitation and runoff, exhibit larger resolution-dependent variations. We find that a resolution of at least 10 km is required to accurately capture the GrIS climate and SMB. Reducing computation time about 10-fold relative to 5 km simulation, a 10 km grid makes a good compromise for long-term climate projections.

Accurately estimating the surface mass balance (SMB) of the Greenland Ice Sheet (GrIS) is essential to quantify its contribution to sea-level rise. The polar regional atmospheric climate model MAR is widely used to simulate GrIS SMB and to force ice sheet dynamics models, highlighting the need for a thorough evaluation. Here, we evaluate the latest MAR version (MARv3.14) over Greenland at 5 km spatial resolution and examine the impact of coarser resolutions (10–30 km) on the simulated SMB and its components. MAR outputs are compared to a range of independent observations, including in situ SMB measurements, automatic weather station (AWS) records of near-surface meteorological variables, satellite-derived melt extent, and albedo products. At 5 km, MAR reproduces the observed SMB with a root-mean-square error (RMSE) of 0.51 m and a correlation of 0.93. For near-surface meteorological variables and surface energy budget fluxes, the model RMSE is smaller than the corresponding observed natural variability (i.e., standard deviation), indicating non-significant model error. Prescribing bare-ice albedo improves the model performance in the ablation zone, while biases remain in the accumulation zone, suggesting that further improvements are required in the snow albedo scheme. In addition, simulated melt timing is consistent with satellite-based melt extent products. Sensitivity experiments reveal that discrepancies between simulations at different spatial resolutions are mostly limited to the ice sheet margins where strong SMB and topographic gradients occur, notably in the southeast of Greenland where precipitation peaks. Differences in integrated SMB generally remain small and mostly non-significant, while individual components, i.e., precipitation and runoff, exhibit larger resolution-dependent variations. We find that a resolution of at least 10 km is required to accurately capture the GrIS climate and SMB. Reducing computation time about 10-fold relative to 5 km simulation, a 10 km grid makes a good compromise for long-term climate projections.

Accurately estimating the surface mass balance (SMB) of the Greenland Ice Sheet (GrIS) is essential to quantify its contribution to sea-level rise. The polar regional atmospheric climate model MAR is widely used to simulate GrIS SMB and to force ice sheet dynamics models, highlighting the need for a thorough evaluation. Here, we evaluate the latest MAR version (MARv3.14) over Greenland at 5 km spatial resolution and examine the impact of coarser resolutions (10–30 km) on the simulated SMB and its components. MAR outputs are compared to a range of independent observations, including in situ SMB measurements, automatic weather station (AWS) records of near-surface meteorological variables, satellite-derived melt extent, and albedo products. At 5 km, MAR reproduces the observed SMB with a root-mean-square error (RMSE) of 0.51 m and a correlation of 0.93. For near-surface meteorological variables and surface energy budget fluxes, the model RMSE is smaller than the corresponding observed natural variability (i.e., standard deviation), indicating non-significant model error. Prescribing bare-ice albedo improves the model performance in the ablation zone, while biases remain in the accumulation zone, suggesting that further improvements are required in the snow albedo scheme. In addition, simulated melt timing is consistent with satellite-based melt extent products. Sensitivity experiments reveal that discrepancies between simulations at different spatial resolutions are mostly limited to the ice sheet margins where strong SMB and topographic gradients occur, notably in the southeast of Greenland where precipitation peaks. Differences in integrated SMB generally remain small and mostly non-significant, while individual components, i.e., precipitation and runoff, exhibit larger resolution-dependent variations. We find that a resolution of at least 10 km is required to accurately capture the GrIS climate and SMB. Reducing computation time about 10-fold relative to 5 km simulation, a 10 km grid makes a good compromise for long-term climate projections.

Intense surface melt and subsequent meltwater ponding are the key triggers for the disintegration of ice shelves in the Antarctic Peninsula (AP). Although a long-term decline in AP surface melt has been evident since the early 1990s, episodes of intense surface melt events have frequently been reported in recent years. Here, using 1979-2023 daily surface melt rates from regional climate models, we show that this declining trend has reversed, with November–February surface melt increasing significantly by 3% year⁻¹ (p < 0.05) between 2009 and 2022. Decomposition of trends based on circulation classification indicates that changes in the frequency of atmospheric circulation patterns cannot account for this enhancement. Instead, the thermodynamics—linked to variations in the surface energy budget independent of atmospheric circulation contribute 80% of the positive trend in AP surface melt. Analysis of extreme surface melt events further supports the dominant thermodynamic contribution.

Accurately estimating the surface mass balance (SMB) of the Greenland Ice Sheet (GrIS) is essential to quantify its contribution to sea-level rise. The polar regional atmospheric climate model MAR is widely used to simulate GrIS SMB and to force ice sheet dynamics models, highlighting the need for a thorough evaluation. Here, we evaluate the latest MAR version (MARv3.14) over Greenland at 5 km spatial resolution and examine the impact of coarser resolutions (10–30 km) on the simulated SMB and its components. MAR outputs are compared to a range of independent observations, including in situ SMB measurements, automatic weather station (AWS) records of near-surface meteorological variables, satellite-derived melt extent, and albedo products. At 5 km, MAR reproduces the observed SMB with a root-mean-square error (RMSE) of 0.51 m and a correlation of 0.93. For near-surface meteorological variables and surface energy budget fluxes, the model RMSE is smaller than the corresponding observed natural variability (i.e., standard deviation), indicating non-significant model error. Prescribing bare-ice albedo improves the model performance in the ablation zone, while biases remain in the accumulation zone, suggesting that further improvements are required in the snow albedo scheme. In addition, simulated melt timing is consistent with satellite-based melt extent products. Sensitivity experiments reveal that discrepancies between simulations at different spatial resolutions are mostly limited to the ice sheet margins where strong SMB and topographic gradients occur, notably in the southeast of Greenland where precipitation peaks. Differences in integrated SMB generally remain small and mostly non-significant, while individual components, i.e., precipitation and runoff, exhibit larger resolution-dependent variations. We find that a resolution of at least 10 km is required to accurately capture the GrIS climate and SMB. Reducing computation time about 10-fold relative to 5 km simulation, a 10 km grid makes a good compromise for long-term climate projections.

Accurately estimating the surface mass balance (SMB) of the Greenland Ice Sheet (GrIS) is essential to quantify its contribution to sea-level rise. The polar regional atmospheric climate model MAR is widely used to simulate GrIS SMB and to force ice sheet dynamics models, highlighting the need for a thorough evaluation. Here, we evaluate the latest MAR version (MARv3.14) over Greenland at 5 km spatial resolution and examine the impact of coarser resolutions (10–30 km) on the simulated SMB and its components. MAR outputs are compared to a range of independent observations, including in situ SMB measurements, automatic weather station (AWS) records of near-surface meteorological variables, satellite-derived melt extent, and albedo products. At 5 km, MAR reproduces the observed SMB with a root-mean-square error (RMSE) of 0.51 m and a correlation of 0.93. For near-surface meteorological variables and surface energy budget fluxes, the model RMSE is smaller than the corresponding observed natural variability (i.e., standard deviation), indicating non-significant model error. Prescribing bare-ice albedo improves the model performance in the ablation zone, while biases remain in the accumulation zone, suggesting that further improvements are required in the snow albedo scheme. In addition, simulated melt timing is consistent with satellite-based melt extent products. Sensitivity experiments reveal that discrepancies between simulations at different spatial resolutions are mostly limited to the ice sheet margins where strong SMB and topographic gradients occur, notably in the southeast of Greenland where precipitation peaks. Differences in integrated SMB generally remain small and mostly non-significant, while individual components, i.e., precipitation and runoff, exhibit larger resolution-dependent variations. We find that a resolution of at least 10 km is required to accurately capture the GrIS climate and SMB. Reducing computation time about 10-fold relative to 5 km simulation, a 10 km grid makes a good compromise for long-term climate projections.

Like most ice caps and glaciers worldwide, Icelandic glaciers are losing mass and retreating as a result of a warming climate. Here, we link satellite observations and dynamic ice-flow modeling to produce robust long-term projections of the five largest Icelandic ice caps—Vatnajökull, Langjökull, Hofsjökull, Mýrdalsjökull, and Eyjafjallajökull—until 2300 under the RCP 8.5 scenario. The simulations are conducted using the Parallel Ice Sheet Model (PISM), forced by climatic mass balance fields from a regional climate model.PISM is initialized by constraining ice-flow parameters using satellite-derived surface velocities from Sentinel-1 for the period 2015–2020. Simulated velocities are compared to observations to identify the best-fitting parameter set, and simulated thickness and area changes are evaluated against available geodetic measurements and glacier outlines. These observational constraints are used to improve confidence in subsequent long-term projections.The calibrated ice-flow model is then used to simulate glacier evolution until 2100, and then the 2081–2100 climatic mass balance forcing is repeated to extend the simulations to 2300. The ice caps are projected to lose 15–30% of their volume and 7–22% of their area by 2100, increasing to 50–70% volume loss and 30-60% area loss by 2300.