ALBERT

Description: This study investigates how variations in Arctic sea ice and sea surface conditions influence δ18O of present-day Arctic precipitation. This is done using the model isoCAM3, an isotope-equipped version of the National Center for Atmospheric Research Community Atmosphere Model version 3. Four sensitivity experiments and one control simulation are performed with prescribed sea surface temperature (SST) and sea ice. Each of the four experiments simulates the atmospheric and isotopic response to Arctic oceanic conditions for selected years after the beginning of the satellite era in 1979. Changes in sea ice extent and SSTs have different impacts in Greenland and the rest of the Arctic. The simulated changes in central Arctic sea ice do not influence δ18O of Greenland precipitation, only anomalies of Baffin Bay sea ice. However, this does not exclude the fact that simulations based on other sea ice and sea surface temperature distributions might yield changes in the δ18O of precipitation in Greenland. For the Arctic, δ18O of precipitation and water vapour is sensitive to local changes in sea ice and sea surface temperature and the changes in water vapour are surface based. Reduced sea ice extent yields more enriched isotope values, whereas increased sea ice extent yields more depleted isotope values. The distribution of the sea ice and sea surface conditions is found to be essential for the spatial distribution of the simulated changes in δ18O.

Description: The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing. In this work, we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400–1020 nm, we derived important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on a spatial grid of 300 m. The algorithm also incorporated cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long-term snow property records essential for climate monitoring and data assimilation studies—especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo.

Description: This study investigates how variations in Arctic sea ice cover influence δ18O of presentday Arctic precipitation. This is done using the model isoCAM3, an isotope-equipped version of the National Center for Atmospheric Research Community Atmosphere Model version 3. Four sensitivity experiments and one control simulation are performed with prescribed SSTs and sea ice. Each of 5 the four experiments simulates the atmospheric and isotopic response to Arctic oceanic conditions for selected years after the beginning of the satellite era in 1979. Results show that δ18O of precipitation is sensitive to local changes of sea ice concentration. Reduced sea ice extent yields more enriched isotope values while increased sea ice extent yields more depleted isotope values. The configuration of the sea ice cover is essential for the spatial distribution 10 of the simulated changes in δ18O. The experiments of this study show no changes of δ18O for central Greenland. However, this does not exclude that simulations based on other sea ice configurations might yield changes in Greenland δ18O.

Description: We report high resolution measurements of the stable isotope ratios of ancient ice (δ18O, δD) from the North Greenland Eemian deep ice core (NEEM, 77.45° N, 51.06° E). The record covers the period 8–130 ky b2k (y before 2000) with a temporal resolution of ≈0.5 and 7 y at the top and the bottom of the core respectively and contains important climate events such as the 8.2 ky event, the last glacial termination and a series of glacial stadials and interstadials. At its bottom part the record contains ice from the Eemian interglacial. Isotope ratios are calibrated on the SMOW/SLAP scale and reported on the GICC05 (Greenland Ice Core Chronology 2005) and AICC2012 (Antarctic Ice Core Chronology 2012) time scales interpolated accordingly. We also provide estimates for measurement precision and accuracy for both δ18O and δD.

Description: Stable water isotopes from polar ice cores are invaluable high-resolution climate proxy records. Recent studies have aimed to improve our understanding of how the climate signal is stored in the stable water isotope record by addressing the influence of post-depositional processes on the isotopic composition of surface snow. In this study, the relationship between surface snow metamorphism and water isotopes during precipitation-free periods is explored using measurements of snow-specific surface area (SSA). Continuous daily SSA measurements from the East Greenland Ice Core Project site (EastGRIP) during the summer seasons of 2017, 2018 and 2019 are used to develop an empirical decay model to describe events of rapid decrease in SSA linked to snow metamorphism. We find that SSA decay during precipitation-free periods at the EastGRIP site is best described by the exponential equation SSA(t)Combining double low line(SSA0-22).e-αt+22, and has a dependency on wind speed. The relationship between surface snow SSA and snow isotopic composition is primarily explored using empirical orthogonal function analysis. A coherence between SSA and deuterium excess is apparent during 2017 and 2019, suggesting that processes driving change in SSA also influence snow deuterium excess. By contrast, 2018 was characterised by a covariance between SSA and 18O highlighting the inter-Annual variability in surface regimes. Moreover, we observed changes in isotopic composition consistent with fractionation effects associated with sublimation and vapour diffusion during periods of rapid decrease in SSA. Our findings support recent studies which provide evidence of isotopic fractionation during sublimation, and show that snow deuterium excess is modified during snow metamorphism.

Description: Dataset containing surface snow measurements of snow specific surface area (SSA), snow density and snow accumulation. Surface samples were taken from the surface 2.5cm of snow. SSA measurements were determined using an Ice Cube measuring device (Zuanon, 2013). Snow density was measured from the SSA samples with a fixed volume. Snow accumulation describes the change in surface height at each sample site. All measured parameters have 10 daily samples taken at 10m intervals over a 90m transect. Sampling was carried out daily between May and August of 2016-2019, at approximately 24hr time intervals. All measurements were taken at the East Greenland Ice Core Project site (EastGRIP) situated in the accumulation zone of the Greenland Ice Sheet.

Description: Throughout the summer seasons 2017 to 2019 snow profiles were taken repeatedly. The sample positions are aligned along a 300 m wind-parallel transect at the EastGRIP ice core deep drilling site. The snow was collected at 6 positions with a 20 – 50 m spacing. Snow was sampled using carbon fiber tubes of 1-m length that were pushed gently into the snow. A maximum compaction of 1 cm was observed during extraction. The snow cores were carefully removed from the carbon fiber tubes on the cutting table. The core was then cut into slices of 1 cm thickness for the upper 10 cm and 2 cm thickness for the lower 90 cm. The samples were placed into Whirl-Pak® bags and closed airtight. The samples were shipped frozen to the Alfred-Wegener-Institut and stored at -25 °C. Prior to measurements, the samples were melted in the sample bags at room temperature. For the measurement of the isotopic composition, the instrument Picarro L2130-i were used. The measurement set-up followed the Van-Geldern protocol (Van Geldern and Barth, 2012). Each sample was injected 4 times unless otherwise noted in the comment column. As a measure of accuracy, we calculated the combined standard uncertainty (Magnusson, et al., 2017) including the long-term reproducibility and bias of our laboratory by measuring a quality check standard in each measurement run and including the uncertainty of the certified standards. The combined uncertainty for δ18O is 0.11 ‰ and for δ2H is 0.8 ‰. Deuterium excess is calculated as both 1) d = dD - 8*d18O; (Merlivat and Jouzel, 1979) and 2) dln = ln(dD + 1) - 8.47(ln(d18O+1)) - 28.5(ln(d18O+1))2; (Uemura et al., 2012).

Description: Snow height information derived from digital elevation models (DEMs) from the summer season of 2019 at the EastGRIP deep drilling site (75° 38'N, 36° 00'W, ~2,700 m altitude) in the accumulation zone of the Greenland Ice Sheet. The DEMs cover an area of 400 m^2 (x = 40 m, y = 10 m). A representative area of 20 cm width from y = 1.9 to y = 2.1 m along the 40 m transect was chosen in order to study the snow height evolution throughout the 2019 season (mid-May to the beginning of August). The largest gap between consecutive DEMs is three days and happened once. Gaps of two days occurred five times and a one-day gap seven times. A 20-point moving average is used to provide snow height information for stable water isotope data sampled in the same transect.

Description: Four data sets of water isotopologues and their products based on a time-resolved array of 1-m snow profiles extracted from the EastGRIP site on the Greenland Ice Sheet. This data product is derived from two data sets from the same EastGRIP site (Behrens et al, 2023; Steen-Larsen et al., 2023). Snow profiles were extracted during the summer field seasons of 2017, 2018, and 2019. They were extracted along a wind-aligned transect approximately 300 m long. All snow profiles were taken 1-cm resolution from 0-10 cm, and 2-cm resolution from 10-100 cm. The 1-sigma uncertainty of the d18O data is 0.11 ‰. The 1-sigma uncertainty of the dD data is 0.8 ‰. Most snow profiles are not the full 100 cm depth due to compression and loss during extraction. Each extraction date represents a time when snow cores were taken at up to six different tracks spaced 50 m apart along a line perpendicular to the prevailing (westward) wind direction. Taken together, these profiles represent a large area of the ice sheet, mitigating stratigraphic and other noise in the isotopic content. Starting from the initial extraction date on 5 May 2017, each subsequent snow profile extraction along a given track was performed approximately 1-m upwind of the previous extraction. Changes in snow height were measured between each extraction and incorporated into each profile. Changes in snow height between years is not accounted for in this data set. A depth-age model was developed for each profile by matching maximum and minimum d18O values to maximum and minimum month mean 2-m air temperature from nearby PROMICE weather data (Fausto et al., 2021), respectively. Two different depth-age models were applied to the deepest parts of each profile without clear maxima or minima tie points. The first model adopts a mean accumulation rate (MAcR) for the approximate time range representing the snow in question. The accumulation rate used here is derived from changes in surface height derived from the Fausto et al. (2021) data set. The second depth-age model adjusts the MAcR results in time based on matching d18O-values with expected transitional d18O values. The two final products are both interpolated to a regular depth grid of 1-cm resolution and a regular time grid of 1-day resolution. Deuterium excess is calculated as both 1) d = dD - 8*d18O; (Merlivat and Jouzel, 1979) and 2) dln = ln(dD + 1) - 8.47(ln(d18O+1)) - 28.5(ln(d18O+1))2; (Uemura et al., 2012).