The CryoGrid community model is a flexible toolbox for simulating the ground thermal regime and the ice–water balance for permafrost and glaciers, extending a well-established suite of permafrost models (CryoGrid 1, 2, and 3). The CryoGrid community model can accommodate a wide variety of application scenarios, which is achieved by fully modular structures through object-oriented programming. Different model components, characterized by their process representations and parameterizations, are realized as classes (i.e., objects) in CryoGrid. Standardized communication protocols between these classes ensure that they can be stacked vertically. For example, the CryoGrid community model features several classes with different complexity for the seasonal snow cover, which can be flexibly combined with a range of classes representing subsurface materials, each with their own set of process representations (e.g., soil with and without water balance, glacier ice). We present the CryoGrid architecture as well as the model physics and defining equations for the different model classes, focusing on one-dimensional model configurations which can also interact with external heat and water reservoirs. We illustrate the wide variety of simulation capabilities for a site on Svalbard, with point-scale permafrost simulations using, e.g., different soil freezing characteristics, drainage regimes, and snow representations, as well as simulations for glacier mass balance and a shallow water body. The CryoGrid community model is not intended as a static model framework but aims to provide developers with a flexible platform for efficient model development. In this study, we document both basic and advanced model functionalities to provide a baseline for the future development of novel cryosphere models.

Deposition of small amounts of airborne dust on glaciers causes positive radiative forcing and enhanced melting due to the reduction of surface albedo. To study the effects of dust deposition on the mass balance of Brúarjökull, an outlet glacier of the largest ice cap in Iceland, Vatnajökull, a study of dust deposition events in the year 2012 was carried out. The dust-mobilisation module FLEXDUST was used to calculate spatio-temporally resolved dust emissions from Iceland and the dispersion model FLEXPART was used to simulate atmospheric dust dispersion and deposition. We used albedo measurements at two automatic weather stations on Brúarjökull to evaluate the dust impacts. Both stations are situated in the accumulation area of the glacier, but the lower station is close to the equilibrium line. For this site ( ∼ 1210 m a.s.l.), the dispersion model produced 10 major dust deposition events and a total annual deposition of 20.5 g m⁻². At the station located higher on the glacier ( ∼ 1525 m a.s.l.), the model produced nine dust events, with one single event causing ∼ 5 g m⁻² of dust deposition and a total deposition of ∼ 10 g m⁻² yr⁻¹. The main dust source was found to be the Dyngjusandur floodplain north of Vatnajökull; northerly winds prevailed 80 % of the time at the lower station when dust events occurred. In all of the simulated dust events, a corresponding albedo drop was observed at the weather stations. The influence of the dust on the albedo was estimated using the regional climate model HIRHAM5 to simulate the albedo of a clean glacier surface without dust. By comparing the measured albedo to the modelled albedo, we determine the influence of dust events on the snow albedo and the surface energy balance. We estimate that the dust deposition caused an additional 1.1 m w.e. (water equivalent) of snowmelt (or 42 % of the 2.8 m w.e. total melt) compared to a hypothetical clean glacier surface at the lower station, and 0.6 m w.e. more melt (or 38 % of the 1.6 m w.e. melt in total) at the station located further upglacier. Our findings show that dust has a strong influence on the mass balance of glaciers in Iceland.

Like most ice caps and glaciers worldwide, Icelandic glaciers are retreating in a warming climate. Here, the evolution of Vatnajökull ice cap, Iceland, from 1980 to 2300 is simulated by forcing the Parallel Ice Sheet Model (PISM) with output from Regional Climate Models (RCMs). For climate simulations of the recent past, HARMONIE-AROME reanalysis-forced simulations are used, while for future climate conditions, high-resolution (5.5 km) simulations from the RCM HIRHAM5 are used in addition to available CORDEX simulations (12 km). The glacier evolution is modelled using the RCP 4.5 and RCP 8.5 scenarios until 2100. To extend the time series, the 2081–2100 climate forcing is repeated until 2300. For RCP 4.5, the ice cap loses 31–64% of its volume and 13–37% of its area by 2300 depending on the used model forcing. For RCP 8.5, the volume decrease is 51–94% and the area decrease is 24–80% by 2300. In addition, the effect of elevation feedbacks is investigated by adding a precipitation and temperature lapse rate to the HIRHAM5 simulations. By 2300, the lapse rate runs have a 9–14% smaller volume and a 9–20% smaller area than the runs without a lapse rate correction.

The Arctic is undergoing increased warming compared to the global mean, which has major implications for freshwater runoff into the oceans from seasonal snow and glaciers. Here, we present high-resolution (2.5 km) simulations of glacier mass balance, runoff, and snow conditions on Svalbard from 1991–2022, one of the fastest warming regions in the world. The simulations are created using the CryoGrid community model forced by Copernicus Arctic Regional ReAnalysis (CARRA) (1991–2021) and AROME-ARCTIC forecasts (2016–2022). Updates to the water percolation and runoff schemes are implemented in the CryoGrid model for the simulations. In situ observations available for Svalbard, including automatic weather station data, stake measurements, and discharge observations, are used to carefully evaluate the quality of the simulations and model forcing. We find a slightly negative climatic mass balance (CMB) over the simulation period of −0.08 $\mathrm{m}\mathrm{w}.\mathrm{e}.{\mathrm{yr}}^{-\mathrm{1}}$ but with no statistically significant trend. The most negative annual CMB is found for Nordenskiöldland (−0.73 $\mathrm{m}\mathrm{w}.\mathrm{e}.{\mathrm{yr}}^{-\mathrm{1}}$ ), with a significant negative trend of −0.27 $\mathrm{m}\mathrm{w}.\mathrm{e}.$ per decade for the region. Although there is no trend in the annual CMB, we do find a significant increasing trend in the runoff from glaciers of 0.14 $\mathrm{m}\mathrm{w}.\mathrm{e}.$ per decade. The average runoff was found to be 0.8 $\mathrm{m}\mathrm{w}.\mathrm{e}.{\mathrm{yr}}^{-\mathrm{1}}$ . We also find a significant negative trend in the refreezing of −0.13 $\mathrm{m}\mathrm{w}.\mathrm{e}.$ per decade. Using AROME-ARCTIC forcing, we find that 2021/22 has the most negative CMB and highest runoff over the 1991–2022 simulation period investigated in this study. We find the simulated climatic mass balance and runoff using CARRA and AROME-ARCTIC forcing are similar and differ by only 0.1 $\mathrm{m}\mathrm{w}.\mathrm{e}.{\mathrm{yr}}^{-\mathrm{1}}$ in climatic mass balance and by 0.2 $\mathrm{m}\mathrm{w}.\mathrm{e}.{\mathrm{yr}}^{-\mathrm{1}}$ in glacier runoff when averaged over all of Svalbard. There is, however, a clear difference over Nordenskiöldland, where AROME-ARCTIC simulates significantly higher mass balance and significantly lower runoff. This indicates that AROME-ARCTIC may provide similar high-quality predictions of the total mass balance of Svalbard as CARRA, but regional uncertainties should be taken into consideration. The simulations produced for this study are made publicly available at a daily and monthly resolution, and these high-resolution simulations may be re-used in a wide range of applications including studies on glacial runoff, ocean currents, and ecosystems.

A simulation of the surface climate of Vatnajökull ice cap, Iceland, carried out with the regional climate model HIRHAM5 for the period 1980–2014, is used to estimate the evolution of the glacier surface mass balance (SMB). This simulation uses a new snow albedo parameterization that allows albedo to exponentially decay with time and is surface temperature dependent. The albedo scheme utilizes a new background map of the ice albedo created from observed MODIS data. The simulation is evaluated against observed daily values of weather parameters from five automatic weather stations (AWSs) from the period 2001–2014, as well as in situ SMB measurements from the period 1995–2014. The model agrees well with observations at the AWS sites, albeit with a general underestimation of the net radiation. This is due to an underestimation of the incoming radiation and a general overestimation of the albedo. The average modelled albedo is overestimated in the ablation zone, which we attribute to an overestimation of the thickness of the snow layer and not taking the surface darkening from dirt and volcanic ash deposition during dust storms and volcanic eruptions into account. A comparison with the specific summer, winter, and net mass balance for the whole of Vatnajökull (1995–2014) shows a good overall fit during the summer, with a small mass balance underestimation of 0.04 m w.e. on average, whereas the winter mass balance is overestimated by on average 0.5 m w.e. due to too large precipitation at the highest areas of the ice cap. A simple correction of the accumulation at the highest points of the glacier reduces this to 0.15 m w.e. Here, we use HIRHAM5 to simulate the evolution of the SMB of Vatnajökull for the period 1981–2014 and show that the model provides a reasonable representation of the SMB for this period. However, a major source of uncertainty in the representation of the SMB is the representation of the albedo, and processes currently not accounted for in RCMs, such as dust storms, are an important source of uncertainty in estimates of snow melt rate.