On 11 April 2016 we observed high slushflow and wet snow avalanche activity at the environmental monitoring station Kobbefjord in W-Greenland. Snow avalanches released as a result of snow wetting induced by rain-on-snow in combination with a strong rise in air temperature. We exploit high-resolution satellite imagery covering pre- and post-event conditions for avalanche quantification and show that nearly 800 avalanches were triggered during this cycle. The nature of this extraordinary event is put into a longer temporal context by analysing several years of meteorological data and time-lapse imagery. We find that no event of similar size has occurred during the past 10 years of intense environmental monitoring in the study area. Meteorological reanalysis data reveal consistent relevant weather patterns for potential rain-on-snow events in the study area being warm fronts from Southwest with orographic lifting processes that triggered heavy precipitation.

Gaining knowledge of tidewater glacier (TWG) margin evolution, solid ice flux and their responses to climate over large spatio-temporal scales provides valuable context for the projection of future Greenland ice sheet (GrIS) change. Although studies of sector-wide responses of TWGs exist, studies at an ice-sheet-wide scale have only just become feasible. Here, we present a dataset of 224 annual TWG margins for 1984–2017 (n= 3801), showing that averaged over regional scales, normalised terminus change is linear. Regionally linear retreat trends were identified across most sectors of the GrIS starting in the mid-1990s, although in contrast to previous studies, the northeastern sector is shown to have experienced sustained retreat since the mid-1980s. Through cointegration analyses, individual glaciers are shown to have differing sensitivities to potential climate drivers, though on a sector-wide scale the northwest and southeast are shown to be especially sensitive to annual sea surface temperature and June–July–August air temperature, respectively. Although 92% of the analysed glaciers experience retreat across the GrIS, observed increases in absolute flux for the entire ice sheet can be explained by changes in just 11 of these TWGs.

The Arctic is getting warmer and wetter. Here, we document two independent examples of how associated extreme precipitation patterns have severe implications for high Arctic ecosystems. The events stand out in a 23-year record of continuous observations of a wide range of ecosystem parameters and act as an early indication of conditions projected to increase in the future. In NE Greenland, August 2015, one-quarter of the average annual precipitation fell during a 9-day intensive rain event. This ranked number one for daily sums during the 1996–2018 period and caused a strong and prolonged reduction in solar radiation decreasing CO₂uptake in the order of 18–23 g C m⁻², a reduction comparable to typical annual C budgets in Arctic tundra. In a different type of event, but also due to changed weather patterns, an extreme snow melt season in 2018 triggered a dramatic gully thermokarst causing rapid transformation in ecosystem functioning from consistent annual ecosystem CO₂uptake and low methane exchange to highly elevated methane release, net source of CO₂, and substantial export of organic carbon downstream as riverine and coastal input. In addition to climate warming alone, more frequent occurrence of extreme weather patterns will have large implications for otherwise undisturbed tundra ecosystems including their element transport and carbon interactions with the atmosphere and ocean.

Glaciers and ice caps (GICs) are important contributors of meltwater runoff and to global sea level rise. However, knowledge of GIC mass changes is largely restricted to the last few decades. Here we show the extent of 5327 Greenland GICs during Little Ice Age (LIA) termination (1900) and reveal that they have fragmented into 5467 glaciers in 2001, losing at least 587 km³ from their ablation areas, equating to 499 Gt at a rate of 4.34 Gt yr⁻¹. We estimate that the long‐term mean mass balance in glacier ablation areas has been at least −0.18 to −0.22 m w.e. yr⁻¹ and note the rate between 2000 and 2019 has been three times that. Glaciers with ice‐marginal lakes formed since the LIA termination have had the fastest changing mass balance. Considerable spatial variability in glacier changes suggest compounding regional and local factors present challenges for understanding glacier evolution.

Surface melt on the Greenland ice sheet has been increasing in intensity and extent over the last decades due to Arctic atmospheric warming. Surface melt depends on the surface energy balance, which includes the atmospheric forcing but also the thermal budget of the snow, firn and ice near the ice sheet surface. The temperature of the ice sheet subsurface has been used as an indicator of the thermal state of the ice sheet's surface. Here, we present a compilation of 4612 measurements of firn and ice temperature at 10 m below the surface (T_10 m) across the ice sheet, spanning from 1912 to 2022. The measurements are either instantaneous or monthly averages. We train an artificial neural network model (ANN) on 4597 of these point observations, weighted by their relative representativity, and use it to reconstruct T_10 m over the entire Greenland ice sheet for the period 1950–2022 at a monthly timescale. We use 10-year averages and mean annual values of air temperature and snowfall from the ERA5 reanalysis dataset as model input. The ANN indicates a Greenland-wide positive trend of T_10 m at 0.2 ^∘C per decade during the 1950–2022 period, with a cooling during 1950–1985 (−0.4 ^∘C per decade) followed by a warming during 1985–2022 (+0.7 ^∘ per decade). Regional climate models HIRHAM5, RACMO2.3p2 and MARv3.12 show mixed results compared to the observational T_10 m dataset, with mean differences ranging from −0.4 ^∘C (HIRHAM) to 1.2 ^∘C (MAR) and root mean squared differences ranging from 2.8 ^∘C (HIRHAM) to 4.7 ^∘C (MAR). The observation-based ANN also reveals an underestimation of the subsurface warming trends in climate models for the bare-ice and dry-snow areas. The subsurface warming brings the Greenland ice sheet surface closer to the melting point, reducing the amount of energy input required for melting. Our compilation documents the response of the ice sheet subsurface to atmospheric warming and will enable further improvements of models used for ice sheet mass loss assessment and reduce the uncertainty in projections.

Seasonal snow cover has important climatic and ecological implications for the ice-free regions of coastal Greenland. Here we present, for the first time, a dataset of quality-controlled snow depth measurements from nine locations in coastal Greenland with varying periods between 1997 and 2021. Using a simple modelling approach (Δsnow), we estimate snow water equivalent values solely based on the daily time series of snow depth. Snow pit measurements from two locations enable us to evaluate the Δsnow model. As there are very little in situ data available for Greenland, we then test the performance of the regional atmospheric climate model (RACMO2.3p2, 5.5 km spatial resolution) and reanalysis product (CARRA, 2.5 km spatial resolution) at the nine locations with snow observations. Using the combined information from all three data sources, we study spatio-temporal characteristics of the seasonal snow cover in coastal Greenland using the example of six ecologically relevant snow indicators (maximum snow water equivalent, melt onset, melt duration, snow cover duration, snow cover onset, and snow cover end date). In particular, we evaluate the ability of RACMO2.3p2 and CARRA to simulate these snow indicators at the nine different locations, perform a time series analysis of the indicators, and assess their spatial variability. The different locations have considerable spatial and temporal variability in snow cover characteristics, and seasonal maximum snow water equivalent (amount of liquid water stored in the snowpack) values range from less than 50 kg m⁻² to greater than 600 kg m⁻². The correlation coefficients between maximum snow water equivalent output from Δsnow and CARRA and RACMO are 0.73 and 0.48, respectively. Correlation coefficients are highest for maximum snow water equivalent and snow cover duration, and model and reanalysis output underestimate snow cover onset. We find little evidence of statistically significant (p<0.05) trends at varied periods between 1997 and 2021 except for the earlier onset of snowmelt in Zackenberg (−8 d per decade, p=0.02, based upon RACMO output). While we stress the need for context-specific validation, this study suggests that in most cases snow depth or snow water equivalent output from CARRA can describe spatial and temporal characteristics of seasonal snow cover, particularly changes in melt onset and snow cover end date.