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Pigmented microalgae inhabiting snow and ice environments lower the albedo of glacier and ice-sheet surfaces, significantly enhancing surface melt. Our ability to accurately predict their role in glacier and ice-sheet surface mass balance is limited by the current lack of empirical data to constrain their representation in predictive models. Here we present new empirical optical properties for snow and ice algae and incorporate them in a radiative transfer model to investigate their impact on snow and ice surface albedo. We found ice algal cells to be more efficient absorbers than snow algal cells, but their blooms had comparable impact on surface albedo due to the different photic conditions of their habitats. We then used the model to reconstruct the effect of ice algae on bare ice albedo spectra collected at our field site in southern Greenland, where blooms dropped the albedo locally by between 3 and 43%, equivalent to 1–10 L m$^{-2}$ d$^{-1}$ of melted ice. Using the newly parametrized model, future studies could investigate biological albedo reduction and algal quantification from remote hyperspectral and multispectral imagery.

Light absorbing impurities (LAI) initiate powerful snow albedo feedbacks, yet due to a scarcity of observations and measurements, LAI radiative forcing is often neglected or poorly constrained in climate and hydrological models. To support physically-based modeling of LAI processes, daily measurements of dust and black carbon (BC) stratigraphy, optical grain size, snow density and spectral albedo were collected over the 2013 ablation season in the Rocky Mountains, CO. Surface impurity concentrations exhibited a wide range of values (0.02–6.0 mg g−1 pptw) with 98% of mass being deposited by three episodic dust events in April. Even minor dust loading initiated albedo decline, and the negative relationship between dust concentrations and albedo was log-linear. As melt progressed, individual dust layers coalesced and emerged at the snow surface, with minimal mass loss to meltwater scavenging. The observations show that the convergence of dust layers at the surface reduced albedo to 0.3 and snow depth declined ~50% faster than other years with similar depth but less dust. The rapid melt led to an unexpected reduction in both grain size and density in uppermost surface layers. BC concentrations co-varied with dust concentrations but were several orders of magnitude lower (<1–20 ppb).

Owing to drifting snow processes, snow accumulation and surface density in polar environments are variable in space and time. We present new field data of manual measurements, repeat terrestrial laser scanning and snow micro-penetrometry from Dronning Maud Land, Antarctica, showing the density of new snow accumulations. We combine these data with published drifting snow mass flux observations, to evaluate the performance of the 1-D, detailed, physics-based snow cover model SNOWPACK in representing drifting snow and surface density. For two sites in East Antarctica with multiple years of data, we found a coefficient of determination for the simulated drifting snow of r2 = 0.42 and r2 = 0.50, respectively. The field observations show the existence of low-density snow accumulations during low wind conditions. Successive high wind speed events generally erode these low-density layers while producing spatially variable erosion/deposition patterns with typical length scales of a few metres. We found that a model setup that is able to represent low-density snow accumulating during low wind speed conditions, as well as subsequent snow erosion and redeposition at higher densities during drifting snow events was mostly able to describe the observed temporal variability of surface density in the field.

An end of summer snowline (EOSS) photographic dataset for Aotearoa New Zealand contains over four decades of equilibrium line altitude (ELA) observations for more than 50 index glaciers. This dataset provides an opportunity to create a climatological ELA reference series that has several applications. Our work screened out EOSS sites that had low temporal coverage and also removed limited observations when the official survey did not take place. Snowline data from 41 of 50 glaciers in the EOSS dataset were retained and included in a normalised master snowline series that spans 1977–2020. Application of the regionally representative normalised master snowline series in monthly and seasonally resolved climate response function analyses showed consistently strong relationships with austral warm-season temperatures for land-based stations west of the Southern Alps and the central Tasman Sea. There is a trend towards higher regional snowlines since the 1990s that has been steepening in recent decades. If contemporary decadal normalised master snowline series trends are maintained, the average Southern Alps snowline elevation will be displaced at least 200 m higher than normal by the 2025–2034 decade. More frequent extremely high snowlines are expected to drive more extreme cumulative mass-balance losses that will reduce the glacierised area of Aotearoa New Zealand.

Albedo is a key factor in modulating the absorption of solar radiation on ice surfaces. Satellite measurements have shown a general reduction in albedo across the Greenland ice sheet over the past few decades, particularly along the western margin of the ice sheet, a region known as the Dark Zone (albedo < 0.45). Here we chose a combination of Landsat 4–8 and Sentinel 2 imagery to enable us to derive the longest record of albedo variations in the Dark Zone, running from 1984 to 2020. We developed a simple, pragmatic and efficient sensor transformation to provide a long time series of consistent, harmonized satellite imagery. Narrow to broadband conversion algorithms were developed from regression models of harmonized satellite data and in situ albedo from the Program for Monitoring of the Greenland Ice Sheet (PROMICE) automatic weather stations. The albedo derived from the harmonized Landsat and Sentinel 2 data shows that the maximum extent of the Dark Zone expanded rapidly between 2005 and 2007, increasing to ~280% of the average annual maximum extent of 2900 km2 to ~8000 km2 since. The Dark Zone is continuing to darken slowly, with the average annual minimum albedo decreasing at a rate of $\\sim \\!-0.0006 \\pm 0.0004 \\, {\\rm a}^{-1}$ (p = 0.16, 2001–2020).

We developed a Handheld Integrating Sphere Snow Grain Sizer (HISSGraS) for field use to measure the specific surface area (SSA) of snow. In addition to snow samples, HISSGraS can directly measure snow surfaces and snow pit walls. The basic measurement principle is the same as that of the IceCube SSA instrument. The retrieval algorithm for SSA from reflectance employs two conversion equations formulated using spherical and nonspherical grain shape models. We observed SSAs using HISSGraS, IceCube and the gas adsorption method in a snowfield in Hokkaido, Japan. Intercomparison of the results confirmed that with HISSGraS direct measurement, SSA profile observations can be completed in just ~1/10 the time required for measurement of snow samples. Our results also suggest that HISSGraS and IceCube have similar accuracy when the same snow samples are measured using the same grain shape model. However, SSAs of near-surface snow layers measured using the three techniques exhibited some biases, possibly due to rapid snow metamorphism or melting during measurement and some technical issues with optical techniques. When excluding SSA data for the surface layer, which metamorphosed remarkably during measurement, IceCube- and HISSGraS-derived SSAs correlated strongly with those obtained by gas adsorption and HISSGraS accuracy is 21–34%.

The accumulation area ratio (AAR) of a glacier reflects its current state of equilibrium, or disequilibrium, with climate and its vulnerability to future climate change. Here, we present an inventory of glacier-specific annual accumulation areas and equilibrium line altitudes (ELAs) for over 3000 glaciers in Alaska and northwest Canada (88% of the regional glacier area) from 2018 to 2022 derived from Sentinel-2 imagery. We find that the 5 year average AAR of the entire study area is 0.41, with an inter-annual range of 0.25–0.49. More than 1000 glaciers, representing 8% of the investigated glacier area, were found to have effectively no accumulation area. Summer temperature and winter precipitation from ERA5-Land explained nearly 50% of the inter-annual ELA variability across the entire study region ( ${R}^2=0.47$). An analysis of future climate scenarios (SSP2-4.5) projects that ELAs will rise by ∼170 m on average by the end of the 21st century. Such changes would result in a loss of 25% of the modern accumulation area, leaving a total of 1900 glaciers (22% of the investigated area) with no accumulation area. These results highlight the current state of glacier disequilibrium with modern climate, as well as glacier vulnerability to projected future warming.

This study investigates black carbon (BC) concentrations in the seasonal snowpack on the Godwin-Austen Glacier and in surface snow at K2 Camps 1 and 2 (Karakoram Range), assessing their impact on snowmelt during the 2019 ablation season. Potential BC and moisture sources were identified through back-trajectory analysis and atmospheric reanalyses. Variations in water stable isotopes (δ1⁸O and δ2H) in the snowpack were analysed to confirm its representativeness as a climatic record for the 2018–19 accumulation season. The average BC concentration in the snow pits (12 ng g−1) generated 66 mm w.e. (or 53 mm w.e. excluding the basal zone) of meltwater. Surface snow at K2 Camp 1 showed BC concentrations of 7 ng g−1, consistent with those on the snowpack surface, suggesting it may reflect local BC levels in late February 2019. In contrast, higher concentrations at K2 Camp 2 (26 ng g−1) were potentially linked to expedition activities.

The timing of snowmelt onset (SMO) is a critical climate indicator in the Arctic. However, spaceborne, in-situ measurements, and model simulations yield different estimates for the timing. Understanding these discrepancies is essential for identifying the physical mechanisms driving SMO. In this study, SMO, snow, and sea ice thermodynamics were simulated using a single-column snow/ice model (HIGHTSI) along trajectories of 42 ice mass balance buoys operating in the period of 2010 to 2015. The results were compared with passive microwave remote sensing and ice mass balance observations. The modeled surface-SMO has a high inter-annual correlation (0.94) with the ice mass balance-derived results but occurred on average 5 days earlier than observations. The remote-sensing-derived Early-SMO was 12 days before the ice mass balance-derived surface-SMO, while the Continuous-SMO showed a 5 day lag. The modeled average snow depth, ice thickness, and snow/ice temperature captured the recorded seasonal variations. The modeled snow/ice temperature showed seasonal biases of 0.4°C/0.5°C between May–September, and −2.7°C/−4.6°C between October–April, respectively. The corresponding biases for average snow depth and ice thickness were −0.05 m/−0.15 m and 0.03 m/0.14 m, respectively. Accurate representation of air temperature forcing and solar radiation absorption is crucial for realistic simulation of SMO.

At high elevations on the Greenland ice sheet meltwater percolates and refreezes in place, and hence does not contribute to mass loss. However, meltwater generation and associated surface runoff is occurring from increasingly higher altitudes, causing changes in firn stratigraphy that have led to the presence of near-surface ice slabs. These ice slabs force meltwater to flow laterally instead of percolating downwards. Here we present a simple, physics-based quasi-2-D model to simulate lateral meltwater runoff and superimposed ice (SI) formation on top of ice slabs. Using an Eulerian Darcy flow scheme, the model calculates how far meltwater can travel within a melt season and when it appears at the snow surface. Results show that lateral flow is a highly efficient runoff mechanism, as lateral outflow exceeds locally generated meltwater in all model gridcells, with total meltwater discharge sometimes reaching more than 30 times the average amount of in situ generated melt. SI formation, an important process in the formation and thickening of the ice slabs, can retain up to 40% of the available meltwater, and generally delays the appearance of visible runoff. Validating the model against field- or remote-sensing data remains challenging, but the results presented here are a first step towards a more comprehensive understanding and description of the hydrological system in the accumulation zone of the southwestern Greenland ice sheet.