On polar ice sheets, water vapor interacts with surface snow, and through the exchange of water molecules, imprints an isotopic climate signal into the ice sheet. This exchange is not well understood due to sparse observations in the atmosphere. There are currently no published vertical profiles of water isotopes above ice sheets that span the planetary boundary layer and portions of the free troposphere. Here, we present a novel dataset of water-vapor isotopes ( δ18O , δD, dxs) and meteorological variables taken by fixed-wing uncrewed aircraft on the northeast Greenland Ice Sheet (GIS). During June-July (2022), we collected 105 profiles of water-vapor isotopes and meteorological variables up to 1500 m above ground level. Concurrently, surface snow samples were collected at 12-hour intervals, allowing connection to surface-snow processes. We pair observations with modeling output from a regional climate model as well as an atmospheric transport and water-isotope distillation model. Climate model output of mean temperature and specific humidity agrees well with observations, with a mean difference of +0.095 °C and -0.043 g/kg (-2.91 %), respectively. We find evidence that along an air parcel pathway, the distillation model is not removing enough water prior to onsite arrival. Below the mean temperature inversion (~200m), water-isotope observations indicate a kinetic fractionating process, likely the result of mixing sublimated vapor from the ice sheet surface along with an unknown fraction of katabatic wind vapor. Modeled dxs does not agree well with observations, a result that requires substantial future analysis of kinetic fractionation processes along the entire moisture pathway.

A document by Raf Antwerpen. Click on the document to view its contents.

A number of insitu and passive microwave satellite sensors have observed Arctic sea ice and Greenland Ice Sheet (GrIS) mass loss trends over recent decades. Along with sea and land ice declines, above-freezing, near-surface air temperatures are observed earlier in boreal spring and later in autumn thus extending periods of melt beyond the core of summer (JJA). Little is known about whether lengthening periods of open ocean proximate to the ice sheet, for instance, demonstrably effect unseasonal GrIS melt events. Here, a new Baffin Bay sea ice advance dataset is utilized to determine dates of sea ice growth along Greenland’s west coast for the 2011-2015 period. Preceding, multi-scale ocean-atmospheric conditions, including at the Baffin-GrIS interface, are analyzed and linked to unseasonal melt events observed at a series of on-ice automatic weather stations (AWS) along the K-transect in southwest Greenland. The local marine versus synoptic influence on the above and below freezing surface air temperature events is assessed through analyses involving AWS winds, pressure, and humidity observations. These surface observations are further compared against Modele Atmospherique Regional (MAR), Regional Atmospheric Climate Model (RACMO), and ERA-Interim reanalysis fields to understand the airmass origins and (thermo)dynamic drivers of the melt events. Results suggest that the K-transect transition season melt events, primarily in the ablation zone, are strongly affected by ridging atmospheric circulation patterns that transport warm, moist air from lower latitude land-ocean areas toward west Greenland. While local conduction of oceanic surface heat appears to impact coastal air temperatures, consistent with previous studies, marine air incursions from Baffin waters onto the ice sheet are likely obstructed by barrier flows and the pressure gradient-driven katabatic regime off of central Greenland.

Projections of the sea level contribution from the Greenland and Antarctic ice sheets rely on atmospheric and oceanic drivers obtained from climate models. The Earth System Models participating in the Coupled Model Intercomparison Project phase 6 (CMIP6) generally project greater future warming compared with the previous CMIP5 effort. Here we use four CMIP6 models and a selection of CMIP5 models to force multiple ice sheet models as part of the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6). We find that the projected sea level contribution at 2100 from the ice sheet model ensemble under the CMIP6 scenarios falls within the CMIP5 range for the Antarctic ice sheet but is significantly increased for Greenland. Warmer atmosphere in CMIP6 models results in higher Greenland mass loss due to surface melt. For Antarctica, CMIP6 forcing is similar to CMIP5 and mass gain from increased snowfall counteracts increased loss due to ocean warming.

Sea-level rise (SLR) is a long-lasting consequence of climate change because global anthropogenic warming takes centuries to millennia to equilibrate. SLR projections based on climate models support policy analysis, risk assessment and adaptation planning today, despite their large uncertainties. The central range of the SLR distribution is estimated by process-based models. However, risk-averse practitioners often require information about plausible future conditions that lie in the tails of the SLR distribution, which are poorly defined by existing models. Here, a community effort combining scientist and practitioners, builds on a framework of discussing physical evidence to quantify high-end global SLR for practice. The approach is complementary to the IPCC AR6 report and provides further physically plausible high-end scenarios. High-end estimates for the different SLR components are developed for two climate scenarios at two timescales. For global warming of +2 ˚C in 2100 (SSP1-2.6) relative to pre-industrial values our high-end global SLR estimates are up to 0.9 m in 2100 and 2.5 m in 2300. Similarly, for +5 ˚C (SSP5-8.5) we estimate up to 1.6 m in 2100 and up to 10.4 m in 2300. The large and growing differences between the scenarios beyond 2100 emphasize the long-term benefits of mitigation. However, even a modest 2 ˚C warming may cause multi-meter SLR on centennial time scales with profound consequences for coastal areas. Earlier high-end assessments focused on instability mechanisms in Antarctica, while we emphasize the timing of ice-shelf collapse around Antarctica, which is highly uncertain due to low understanding of the driving processes.

Accurate estimates of surface mass balance over the Greenland ice sheet (GrIS) would contribute to understanding the cause of recent changes and would help to better estimate the future contribution of the GrIS to sea-level rise. Given the limitations of in-situ measurement, modeling, and remote sensing, it is critical to explore the opportunity to merge the available data to better characterize the spatial and temporal variation of the GrIS SMB. This work utilizes a particle batch smoother data assimilation technique that yields SMB estimates that benefit from the snow/ice model Crocus and a 16-day albedo product derived from satellite remote sensing data. Comparison of the results against in-situ SMB measurements from a transect located nearby the town of Kangerlussuaq shows that the assimilation of the albedo product reduces the root mean square error (RMSE) of the posterior estimates of SMB by 51% and reduces bias by 95%.