Ice losses from the Greenland and Antarctic ice sheets have accelerated since the 1990s, accounting for a significant increase in the global mean sea level. Here, we present a new 29-year record of ice sheet mass balance from 1992 to 2020 from the Ice Sheet Mass Balance Inter-comparison Exercise (IMBIE). We compare and combine 50 independent estimates of ice sheet mass balance derived from satellite observations of temporal changes in ice sheet flow, in ice sheet volume, and in Earth's gravity field. Between 1992 and 2020, the ice sheets contributed 21.0±1.9 mm to global mean sea level, with the rate of mass loss rising from 105 Gt yr ⁻¹ between 1992 and 1996 to 372 Gt yr ⁻¹ between 2016 and 2020. In Greenland, the rate of mass loss is 169±9 Gt yr ⁻¹ between 1992 and 2020, but there are large inter-annual variations in mass balance, with mass loss ranging from 86 Gt yr ⁻¹ in 2017 to 444 Gt yr ⁻¹ in 2019 due to large variability in surface mass balance. In Antarctica, ice losses continue to be dominated by mass loss from West Antarctica (82±9 Gt yr ⁻¹ ) and, to a lesser extent, from the Antarctic Peninsula (13±5 Gt yr ⁻¹ ). East Antarctica remains close to a state of balance, with a small gain of 3±15 Gt yr ⁻¹ , but is the most uncertain component of Antarctica's mass balance. The dataset is publicly available at https://doi.org/10.5285/77B64C55-7166-4A06-9DEF-2E400398E452 (IMBIE Team, 2021).

We compare the performance of five different regional climate models (RCMs) (COSMO-CLM², HIRHAM5, MAR3.10, MetUM, and RACMO2.3p2), forced by ERA-Interim reanalysis, in simulating the near-surface climate and surface mass balance (SMB) of Antarctica. All models simulate Antarctic climate well when compared with daily observed temperature and pressure, with nudged models matching daily observations slightly better than free-running models. The ensemble mean annual SMB over the Antarctic ice sheet (AIS) including ice shelves is 2329±94 Gt yr⁻¹ over the common 1987–2015 period covered by all models. There is large interannual variability, consistent between models due to variability in the driving ERA-Interim reanalysis. Mean annual SMB is sensitive to the chosen period; over our 30-year climatological mean period (1980 to 2010), the ensemble mean is 2483 Gt yr⁻¹. However, individual model estimates vary from 1961±70 to 2519±118 Gt yr⁻¹. The largest spatial differences between model SMB estimates are in West Antarctica, the Antarctic Peninsula, and around the Transantarctic Mountains. We find no significant trend in Antarctic SMB over either period. Antarctic ice sheet (AIS) mass loss is currently equivalent to around 0.5 mm yr⁻¹ of global mean sea level rise (Shepherd et al., 2020), but our results indicate some uncertainty in the SMB contribution based on RCMs. We compare modelled SMB with a large dataset of observations, which, though biased by undersampling, indicates that many of the biases in SMB are common between models. A drifting-snow scheme improves modelled SMB on ice sheet surface slopes with an elevation between 1000 and 2000 m, where strong katabatic winds form. Different ice masks have a substantial impact on the integrated total SMB and along with model resolution are factored into our analysis. Targeting undersampled regions with high precipitation for observational campaigns will be key to improving future estimates of SMB in Antarctica.

We present here surface water vapor isotopic measurements conducted from June to August 2010 at the NEEM (North Greenland Eemian Drilling Project) camp, NW Greenland (77.45° N, 51.05° W, 2484 m a.s.l.). Measurements were conducted at 9 different heights from 0.1 m to 13.5 m above the snow surface using two different types of cavity-enhanced near-infrared absorption spectroscopy analyzers. For each instrument specific protocols were developed for calibration and drift corrections. The inter-comparison of corrected results from different instruments reveals excellent reproducibility, stability, and precision with a standard deviations of ~ 0.23‰ for δ¹⁸O and ~ 1.4‰ for δD. Diurnal and intraseasonal variations show strong relationships between changes in local surface humidity and water vapor isotopic composition, and with local and synoptic weather conditions. This variability probably results from the interplay between local moisture fluxes, linked with firn–air exchanges, boundary layer dynamics, and large-scale moisture advection. Particularly remarkable are several episodes characterized by high (> 40‰) surface water vapor deuterium excess. Air mass back-trajectory calculations from atmospheric analyses and water tagging in the LMDZiso (Laboratory of Meteorology Dynamics Zoom-isotopic) atmospheric model reveal that these events are associated with predominant Arctic air mass origin. The analysis suggests that high deuterium excess levels are a result of strong kinetic fractionation during evaporation at the sea-ice margin.

ICESat has provided surface elevation measurements of the ice sheets since the launch in January 2003, resulting in a unique dataset for monitoring the changes of the cryosphere. Here, we present a novel method for determining the mass balance of the Greenland ice sheet, derived from ICESat altimetry data. Three different methods for deriving elevation changes from the ICESat altimetry dataset are used. This multi-method approach provides a method to assess the complexity of deriving elevation changes from this dataset. The altimetry alone can not provide an estimate of the mass balance of the Greenland ice sheet. Firn dynamics and surface densities are important factors that contribute to the mass change derived from remote-sensing altimetry. The volume change derived from ICESat data is corrected for changes in firn compaction over the observation period, vertical bedrock movement and an intercampaign elevation bias in the ICESat data. Subsequently, the corrected volume change is converted into mass change by the application of a simple surface density model, in which some of the ice dynamics are accounted for. The firn compaction and density models are driven by the HIRHAM5 regional climate model, forced by the ERA-Interim re-analysis product, at the lateral boundaries. We find annual mass loss estimates of the Greenland ice sheet in the range of 191 ± 23 Gt yr⁻¹ to 240 ± 28 Gt yr⁻¹ for the period October 2003 to March 2008. These results are in good agreement with several other studies of the Greenland ice sheet mass balance, based on different remote-sensing techniques.

Within the last decade, the Greenland ice sheet (GrIS) and its surroundings have experienced record high surface temperatures (Mote, 2007 and Box et al., 2010), ice sheet melt extent (Fettweis et al., 2011) and record-low summer sea-ice extent (Nghiem et al., 2007). Using three independent data sets, we derive, for the first time, consistent ice-mass trends and temporal variations within seven major drainage basins from gravity fields from the Gravity Recovery and Climate Experiment (GRACE; Tapley et al., 2004), surface-ice velocities from Inteferometric Synthetic Aperture Radar (InSAR; Rignot and Kanagaratnam, 2006) together with output of the regional atmospheric climate modelling (RACMO2/GR; Ettema et al., 2009), and surface-elevation changes from the Ice, cloud and land elevation satellite (ICESat; Sørensen et al., 2011). We show that changing ice discharge (D), surface melting and subsequent run-off (M/R) and precipitation (P) all contribute, in a complex and regionally variable interplay, to the increasingly negative mass balance of the GrIS observed within the last decade. Interannual variability in P along the northwest and west coasts of the GrIS largely explains the apparent regional mass loss increase during 2002–2010, and obscures increasing M/R and D since the 1990s. In winter 2002/2003 and 2008/2009, accumulation anomalies in the east and southeast temporarily outweighed the losses by M/R and D that prevailed during 2003–2008, and after summer 2010. Overall, for all basins of the GrIS, the decadal variability of anomalies in P, M/R and D between 1958 and 2010 (w.r.t. 1961–1990) was significantly exceeded by the regional trends observed during the GRACE period (2002–2011)