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Three-dimensional city models are increasingly being used for analyses and simulations. To enable such applications, it is necessary to standardise semantically richer city models and, in some cases, to connect the models with external data sources. In this study, we describe the development of a new Swedish specification for 3D city models, denoted as 3CIM, which is a joint effort between the three largest cities in Sweden—Stockholm, Gothenburg, and Malmö. Technically, 3CIM is an extension of the OGC standard CityGML 2.0, implemented as an application domain extension (ADE). The ADE is semantically thin, mainly extending CityGML 2.0 to harmonise with national standards; in contrast, 3CIM is mainly based on linkages to external databases, registers, and operational systems for the semantic part. The current version, 3CIM 1.0, includes various themes, including Bridge, Building, Utility, City Furniture, Transportation, Tunnel, Vegetation, and Water. Three test areas were created with 3CIM data, one in each city. These data were evaluated in several use-cases, including visualisation as well as daylight, noise, and flooding simulations. The conclusion from these use-cases is that the 3CIM data, together with the linked external data sources, allow for the inclusion of the necessary information for the visualisation and simulations, but extract, transform, and load (ETL) processes are required to tailor the input data. The next step is to implement 3CIM within the three cities, which will entail several challenges, as discussed at the end of the paper.

We present a 1986 through March 2020 estimate of Greenland Ice Sheet ice discharge. Our data include all discharging ice that flows faster than 100 m yr−1 and are generated through an automatic and adaptable method, as opposed to conventional handpicked gates. We position gates near the present-year termini and estimate problematic bed topography (ice thickness) values where necessary. In addition to using annual time-varying ice thickness, our time series uses velocity maps that begin with sparse spatial and temporal coverage and end with near-complete spatial coverage and 12 d updates to velocity. The 2010 through 2019 average ice discharge through the flux gates is ∼487±49 Gt yr−1. The 10 % uncertainty stems primarily from uncertain ice bed location (ice thickness). We attribute the ∼50 Gt yr−1 differences among our results and previous studies to our use of updated bed topography from BedMachine v3. Discharge is approximately steady from 1986 to 2000, increases sharply from 2000 to 2005, and then is approximately steady again. However, regional and glacier variability is more pronounced, with recent decreases at most major glaciers and in all but one region offset by increases in the northwest region through 2017 and in the southeast from 2017 through March 2020. As part of the journal's living archive option and our goal to make an operational product, all input data, code, and results from this study will be updated as needed (when new input data are available, as new features are added, or to fix bugs) and made available at https://doi.org/10.22008/promice/data/ice_discharge (Mankoff, 2020a) and at https://github.com/mankoff/ice_discharge (last access: 6 June 2020, Mankoff, 2020e).

The Antarctic Ice Sheet is an important indicator of climate change and driver of sea-level rise. Here we combine satellite observations of its changing volume, flow and gravitational attraction with modelling of its surface mass balance to show that it lost 2,720 ± 1,390 billion tonnes of ice between 1992 and 2017, which corresponds to an increase in mean sea level of 7.6 ± 3.9 millimetres (errors are one standard deviation). Over this period, ocean-driven melting has caused rates of ice loss from West Antarctica to increase from 53 ± 29 billion to 159 ± 26 billion tonnes per year; ice-shelf collapse has increased the rate of ice loss from the Antarctic Peninsula from 7 ± 13 billion to 33 ± 16 billion tonnes per year. We find large variations in and among model estimates of surface mass balance and glacial isostatic adjustment for East Antarctica, with its average rate of mass gain over the period 1992–2017 (5 ± 46 billion tonnes per year) being the least certain.

Greenland runoff, from ice mass loss and increasing rainfall, is increasing. That runoff, as discharge, impacts the physical, chemical, and biological properties of the adjacent fjords. However, where and when the discharge occurs is not readily available in an open database. Here we provide data sets of high-resolution Greenland hydrologic outlets, basins, and streams, as well as a daily 1958 through 2019 time series of Greenland liquid water discharge for each outlet. The data include 24 507 ice marginal outlets and upstream basins and 29 635 land coast outlets and upstream basins, derived from the 100 m ArcticDEM and 150 m BedMachine. At each outlet there are daily discharge data for 22 645 d – ice sheet runoff routed subglacially to ice margin outlets and land runoff routed to coast outlets – from two regional climate models (RCMs; MAR and RACMO). Our sensitivity study of how outlet location changes for every inland cell based on subglacial routing assumptions shows that most inland cells where runoff occurs are not highly sensitive to those routing assumptions, and outflow location does not move far. We compare RCM results with 10 gauges from streams with discharge rates spanning 4 orders of magnitude. Results show that for daily discharge at the individual basin scale the 5 % to 95 % prediction interval between modeled discharge and observations generally falls within plus or minus a factor of 5 (half an order of magnitude, or +500 %/-80 %). Results from this study are available at https://doi.org/10.22008/promice/freshwater (Mankoff, 2020a) and code is available at http://github.com/mankoff/freshwater (last access: 6 November 2020) (Mankoff, 2020b).

We present a 1986 through 2017 estimate of Greenland Ice Sheet ice discharge. Our data include all discharging ice that flows faster than 100 m yr−1 and are generated through an automatic and adaptable method, as opposed to conventional hand-picked gates. We position gates near the present-year termini and estimate problematic bed topography (ice thickness) values where necessary. In addition to using annual time-varying ice thickness, our time series uses velocity maps that begin with sparse spatial and temporal coverage and end with near-complete spatial coverage and 6 d updates to velocity. The 2010 through 2017 average ice discharge through the flux gates is ∼488±49 Gt yr−1. The 10 % uncertainty stems primarily from uncertain ice bed location (ice thickness). We attribute the ∼50 Gt yr−1 differences among our results and previous studies to our use of updated bed topography from BedMachine v3. Discharge is approximately steady from 1986 to 2000, increases sharply from 2000 to 2005, and then is approximately steady again. However, regional and glacier variability is more pronounced, with recent decreases at most major glaciers and in all but one region offset by increases in the NW (northwestern) region. As part of the journal's living archive option, all input data, code, and results from this study will be updated when new input data are accessible and made freely available at https://doi.org/10.22008/promice/data/ice_discharge.

The Greenland Ice Sheet has been a major contributor to global sea-level rise in recent decades 1 , 2 , and it is expected to continue to be so 3 . Although increases in glacier flow 4 – 6 and surface melting 7 – 9 have been driven by oceanic 10 – 12 and atmospheric 13 , 14 warming, the magnitude and trajectory of the ice sheet’s mass imbalance remain uncertain. Here we compare and combine 26 individual satellite measurements of changes in the ice sheet’s volume, flow and gravitational potential to produce a reconciled estimate of its mass balance. The ice sheet was close to a state of balance in the 1990s, but annual losses have risen since then, peaking at 345 ± 66 billion tonnes per year in 2011. In all, Greenland lost 3,902 ± 342 billion tonnes of ice between 1992 and 2018, causing the mean sea level to rise by 10.8 ± 0.9 millimetres. Using three regional climate models, we show that the reduced surface mass balance has driven 1,964 ± 565 billion tonnes (50.3 per cent) of the ice loss owing to increased meltwater runoff. The remaining 1,938 ± 541 billion tonnes (49.7 per cent) of ice loss was due to increased glacier dynamical imbalance, which rose from 46 ± 37 billion tonnes per year in the 1990s to 87 ± 25 billion tonnes per year since then. The total rate of ice loss slowed to 222 ± 30 billion tonnes per year between 2013 and 2017, on average, as atmospheric circulation favoured cooler conditions 15 and ocean temperatures fell at the terminus of Jakobshavn Isbræ 16 . Cumulative ice losses from Greenland as a whole have been close to the rates predicted by the Intergovernmental Panel on Climate Change for their high-end climate warming scenario 17 , which forecast an additional 70 to 130 millimetres of global sea-level rise by 2100 compared with their central estimate. Three techniques for estimating mass losses from the Greenland Ice Sheet produce comparable results for the period 1992–2018 that approach the trajectory of the highest rates of sea-level rise projected by the IPCC.

The Programme for Monitoring of the Greenland Ice Sheet (PROMICE) has been measuring climate and ice sheet properties since 2007. Currently, the PROMICE automatic weather station network includes 25 instrumented sites in Greenland. Accurate measurements of the surface and near-surface atmospheric conditions in a changing climate are important for reliable present and future assessment of changes in the Greenland Ice Sheet. Here, we present the PROMICE vision, methodology, and each link in the production chain for obtaining and sharing quality-checked data. In this paper, we mainly focus on the critical components for calculating the surface energy balance and surface mass balance. A user-contributable dynamic web-based database of known data quality issues is associated with the data products at https://github.com/GEUS-Glaciology-and-Climate/PROMICE-AWS-data-issues/ (last access: 7 April 2021). As part of the living data option, the datasets presented and described here are available at https://doi.org/10.22008/promice/data/aws (Fausto et al., 2019).

The influence of surface melt on the flow of Greenland's largest outlet glaciers remains poorly known and in situ observations are few. We use field observations to link surface meltwater forcing to glacier-wide diurnal velocity variations on East Greenland's Helheim Glacier over two summer melt seasons. We observe diurnal variations in glacier speed that peak ~6.5 h after daily maximum insolation and extend from the terminus region to the equilibrium line. Both the amplitude of the diurnal speed variation and its sensitivity to daily melt are largest at the glacier terminus and decrease up-glacier, suggesting that the magnitude of the response is controlled not only by melt input volume and temporal variability, but also by background effective pressure, which approaches zero at the terminus. Our results provide evidence that basal lubrication by meltwater drives diurnal velocity variations at Greenland's marine-terminating glaciers in a similar manner to alpine glaciers and Greenland's land-terminating outlet glaciers.

The dynamics of the Greenland Ice Sheet are affected by surface meltwater reaching the base of the ice, altering ice contact with the bedrock. Lack of understanding of this evolution hampers the ability to predict the effects of increasing temperatures on the Greenland Ice Sheet mass balance. Here we present a unique high-resolution study of ice velocity response to surface melting based on data from a COSMO-SkyMed satellite campaign over Upernavik Isstrøm (Northwest Greenland) for two months around the end of the 2014 melt season. We show that the velocity variations, due to both short-term (days) and seasonal variations in surface melt rates, are increasing in relative strength farther from the glacier terminus. Furthermore, we observe how ice dynamic response to frontal retreat, reaching several kilometres inland, can obscure the meltwater-induced velocity change close to the terminus. Future studies should consider the flow velocity dependence on the distance to the terminus, and local geometry, to distinguish subglacial hydrologic system changes from frontal processes and local basal conditions.

Ice-marginal lakes form at the edge of the Greenland Ice Sheet and its surrounding peripheral glaciers and ice caps (PGIC), where outflowing glacial meltwater is trapped by a moraine, or by the ice itself, and create a reservoir that is in contact with the adjacent ice. While glacial meltwater is typically assumed to flow directly into the ocean, ice-marginal lakes temporarily store a portion of this runoff, influencing glacier dynamics and ablation, ecosystems, and downstream hydrology. Their presence, and change in abundance and size, remain under-represented in projections of sea level change and glacier mass loss. Here, we present an eight-year (2016–2023) inventory of 2918 automatically classified ice-marginal lakes (≧0.05 km2) across Greenland, tracking changes in lake abundance, surface extent, and summer surface temperature over time. Fluctuations in lake abundance were most pronounced at the north (22 %) and northeast (14 %) PGIC margins and the southwest Ice Sheet margin (8 %). Over the study period, an increase in surface lake area was evident at 283 lakes, a decreasing trend was evident at 240 lakes, and 1373 remained stable (±0.05 km2). The northeast region contained the largest lakes, with a median size of 0.40 km2 at the ice sheet margin and 0.24 km2 at PGIC margins. Average summer surface temperatures fluctuated between 3.8 °C (2018) and 5.3 °C (2023), with spatial and temporal trends identified with possible links to lake setting and size. Validation against manually identified lakes showed 64 % agreement, yielding an error estimate of −809 lakes (36 %), while lake area uncertainty was ±5 %. Surface temperature estimates showed strong agreement with in situ measurements (r2=0.87, RMSE =1.68 °C, error ±1.2 °C). This dataset provides a crucial foundation for quantifying meltwater storage at ice margins and refining sea level contribution projections while supporting research on glacier-lake interactions, Arctic ecology, and environmental management. The inventory series is openly accessible on the GEUS Dataverse (https://doi.org/10.22008/FK2/MBKW9N, How et al., 2025) with full metadata and documentation, and a reproducible processing workflow.