Glacier changes are a vivid example of how environmental systems react to a changing climate. Distributed surface mass balance models, which translate the meteorological conditions on glaciers into local melting rates, help to attribute and detect glacier mass and volume responses to changes in the climate drivers. A well-calibrated model is a suitable test bed for sensitivity, detection, and attribution analyses for many scientific applications and often serves as a tool for quantifying the inherent uncertainties. Here, we present the open-source COupled Snowpack and Ice surface energy and mass balance model in PYthon (COSIPY), which provides a flexible and user-friendly framework for modeling distributed snow and glacier mass changes. The model has a modular structure so that the exchange of routines or parameterizations of physical processes is possible with little effort for the user. The framework consists of a computational kernel, which forms the runtime environment and takes care of the initialization, the input–output routines, and the parallelization, as well as the grid and data structures. This structure offers maximum flexibility without having to worry about the internal numerical flow. The adaptive subsurface scheme allows an efficient and fast calculation of the otherwise computationally demanding fundamental equations. The surface energy balance scheme uses established standard parameterizations for radiation as well as for the energy exchange between atmosphere and surface. The schemes are coupled by solving both surface energy balance and subsurface fluxes iteratively such that consistent surface skin temperature is returned at the interface. COSIPY uses a one-dimensional approach limited to the vertical fluxes of energy and matter but neglects any lateral processes. Accordingly, the model can be easily set up in parallel computational environments for calculating both energy balance and climatic surface mass balance of glacier surfaces based on flexible horizontal grids and with varying temporal resolution. The model is made available on a freely accessible site and can be used for non-profit purposes. Scientists are encouraged to actively participate in the extension and improvement of the model code.

Precipitation is a central quantity of hydrometeorological research and applications. Especially in complex terrain, such as in High Mountain Asia (HMA), surface precipitation observations are scarce. Gridded precipitation products are one way to overcome the limitations of ground truth observations. They can provide datasets continuous in both space and time. However, there are many products available, which use various methods for data generation and lead to different precipitation values. In our study we compare nine different gridded precipitation products from different origins (ERA5, ERA5-Land, ERA-interim, HAR v2 10 km, HAR v2 2 km, JRA-55, MERRA-2, GPCC and PRETIP) over a subregion of the Central Himalaya and the Southwest Tibetan Plateau, from May to September 2017. Total spatially averaged precipitation over the study period ranged from 411 mm (GPCC) to 781 mm (ERA-Interim) with a mean value of 623 mm and a standard deviation of 132 mm. We found that the gridded products and the few observations, with few exceptions, are consistent among each other regarding precipitation variability and rough amount within the study area. It became obvious that higher grid resolution can resolve extreme precipitation much better, leading to overall lower mean precipitation spatially, but higher extreme precipitation events. We also found that generally high terrain complexity leads to larger differences in the amount of precipitation between products. Due to the considerable differences between products in space and time, we suggest carefully selecting the product used as input for any research application based on the type of application and specific research question. While coarse products such as ERA-Interim or ERA5 that cover long periods but have coarse grid resolution have previously shown to be able to capture long-term trends and help with identifying climate change features, this study suggests that more regional applications, such as glacier mass-balance modeling, require higher spatial resolution, as is reproduced, for example, in HAR v2 10 km.

The COupled Snowpack and Ice surface energy and mass balance model in PYthon (COSIPY) was employed to investigate the relationship between the variability and sensitivity of the mass balance record of the Halji glacier, in the Himalayas, north-western Nepal, over a 40 year period since October 1981 to atmospheric drivers. COSIPY was forced with the atmospheric reanalysis dataset ERA5-Land that has been statistically downscaled to the location of an automatic weather station at the Halji glacier. Glacier mass balance simulations with air temperature and precipitation perturbations were executed and teleconnections investigated. For the mass-balance years 1982 to 2019, a mean annual glacier-wide climatic mass balance of −0.48 meters water equivalent per year (m w.e. a⁻¹) with large interannual variability (standard deviation 0.71 m w.e. a⁻¹) was simulated. This variability is dominated by temperature and precipitation patterns. The Halji glacier is mostly sensitive to summer temperature and monsoon-related precipitation perturbations, which is reflected in a strong correlation with albedo. According to the simulations, the climate sensitivity with respect to either positive or negative air temperature and precipitation changes is nonlinear: A mean temperature increase (decrease) of 1 K would result in a change of the glacier-wide climatic mass balance of −1.43 m w.e. a⁻¹ (0.99 m w.e. a⁻¹) while a precipitation increase (decrease) of 10% would cause a change of 0.45m w.e. a⁻¹ (−0.59 m w.e. a⁻¹). Out of 22 circulation and monsoon indexes, only the Webster and Yang Monsoon index and Polar/Eurasia index provide significant correlations with the glacier-wide climatic mass balance. Based on the strong dependency of the climatic mass balance from summer season conditions, we conclude that the snow–albedo feedback in summer is crucial for the Halji glacier. This finding is also reflected in the correlation of albedo with the Webster and Yang Monsoon index.

This study investigates strategies for calibration of surface mass balance (SMB) models in the Monte Sarmiento Massif (MSM), Tierra del Fuego, with the goal of achieving realistic simulations of the regional SMB. Applied calibration strategies range from a local single-glacier calibration to a regional calibration with the inclusion of a snowdrift parameterization. We apply four SMB models of different complexity. In this way, we examine the model transferability in space, the benefit of regional mass change observations and the advantage of increasing the complexity level regarding included processes. Measurements include ablation and ice thickness observations at Schiaparelli Glacier as well as elevation changes and flow velocity from satellite data for the entire study site. Performance of simulated SMB is validated against geodetic mass changes and stake observations of surface melting. Results show that transferring SMB models in space is a challenge, and common practices can produce distinctly biased estimates. Model performance can be significantly improved by the use of remotely sensed regional observations. Furthermore, we have shown that snowdrift does play an important role in the SMB in the Cordillera Darwin, where strong and consistent winds prevail. The massif-wide average annual SMB between 2000 and 2022 falls between −0.28 and −0.07 m w.e. yr⁻¹, depending on the applied model. The SMB is mainly controlled by surface melting and snowfall. The model intercomparison does not indicate one obviously best-suited model for SMB simulations in the MSM.

The complex topography and size of High Mountain Asia (HMA) result in large differences in glacier mass-balance variability and climate sensitivity. Current understanding of these sensitivities is limited by simplifications in past studies’ model structure. This study overcomes this limitation by using a mass-balance model to investigate the climatic mass-balance variability and climate sensitivity of 16 glaciers covering major mountain ranges in HMA. Generally, glaciers in the southeast have higher mass turnover while glaciers at the margins of HMA show higher interannual mass-balance variability. All glaciers are most sensitive to temperature perturbations in summer. The climatic mass balance of 15 glaciers is most sensitive to precipitation perturbations in summer or spring and summer, even if the seasonal accumulation peak is not in summer. Only one glacier's mass balance (Chhota Shigri Glacier) is most sensitive to precipitation perturbations in winter. Glaciers with high mass turnover and high summer-precipitation ratio are more sensitive to temperature perturbations. Sensitivity experiments reveal that besides the non-linearity of mass-balance temperature sensitivity, mass-balance precipitation sensitivity is non-linear as well. Furthermore, resolving the diurnal cycle of albedo, (re)freezing and the differentiation between liquid and solid precipitation are important to assess climate sensitivity of glaciers in HMA.

Originating in the Tian Shan mountains, Urumqi River plays a key role in terms of water supply to downstream areas. In its headwaters, Urumqi Glacier No. 1 (UG1) is the largest glacier contributing to water discharge. Assessing its response to the changing climatic conditions in the area is of major importance to quantify future water availability. We here apply COSIPY, a COupled Snowpack and Ice surface energy and mass balance model in PYthon, to UG1, implementing a new albedo parameterization which integrates site-specific bare-ice albedo values on a pixel-by-pixel basis observed by remote sensing. We assess model performance threefold: quantitatively based on long-term measurement data of (1) surface mass balance (SMB) and (2) water discharge as well as qualitatively (3) comparing simulated snow line altitudes to such imated on the basis of time-lapse photography. Comparison of the modeled SMB with annually-averaged data from ablation stakes reveals that COSIPY including the new albedo parameterization accounts for 57.6% of the variance observed in the measurements. The original albedo parameterization performs only slightly inferior (57.1%). Glacier-wide comparison between modeled and glaciological SMB shows high agreement. In terms of discharge prediction, COSIPY reproduces onset and duration of the discharge season well. Estimated discharge from the whole catchment shows shortcomings in exactly matching the measured times series, but interannual variability is captured.