Abstract. Black carbon (BC), brown carbon (BrC), and soil dust are the most important radiation-absorbing aerosols (RAAs). When RAAs are deposited on the snowpack, they lower the snow albedo, causing an increase in the solar radiation absorption. The climatic impact associated with the snow darkening induced by RAAs is highly uncertain. The Intergovernmental Panel on Climate Change (IPCC) Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) attributes low and medium confidence to radiative forcing (RF) from BrC and dust in snow, respectively. Therefore, the contribution of anthropogenic sources and carbonaceous aerosols to RAA RF in snow is not clear. Moreover, the snow albedo perturbation induced by a single RAA species depends on the presence of other light-absorbing impurities contained in the snowpack. In this work, we calculated the present-day RF of RAAs in snow starting from the deposition fields from a 5-year simulation with the GEOS-Chem global chemistry and transport model. RF was estimated taking into account the presence of BC, BrC, and mineral soil dust in snow, simultaneously. Modeled BC and black carbon equivalent (BCE) mixing ratios in snow and the fraction of light absorption due to non-BC compounds (fnon-BC) were compared with worldwide observations. We showed that BC, BCE, and fnon-BC, obtained from deposition and precipitation fluxes, reproduce the regional variability and order of magnitude of the observations. Global-average all-sky total RAA-, BC-, BrC-, and dust-snow RF were 0.068, 0.033, 0.0066, and 0.012 W m−2, respectively. At a global scale, non-BC compounds accounted for 40 % of RAA-snow RF, while anthropogenic RAAs contributed to the forcing for 56 %. With regard to non-BC compounds, the largest impact of BrC has been found during summer in the Arctic (+0.13 W m−2). In the middle latitudes of Asia, the forcing from dust in spring accounted for 50 % (+0.24 W m−2) of the total RAA RF. Uncertainties in absorbing optical properties, RAA mixing ratio in snow, snow grain dimension, and snow cover fraction resulted in an overall uncertainty of −50 %/+61 %, −57 %/+183 %, −63 %/+112 %, and −49 %/+77 % in BC-, BrC-, dust-, and total RAA-snow RF, respectively. Uncertainty upper bounds of BrC and dust were about 2 and 3 times larger than the upper bounds associated with BC. Higher BrC and dust uncertainties were mainly due to the presence of multiple absorbing impurities in the snow. Our results highlight that an improvement of the representation of RAAs in snow is desirable, given the potential high efficacy of this forcing.

Abstract. Italy is a territory characterized by complex topography with the Apennines mountain range crossing the entire peninsula and its highest peaks in central Italy. Using the latter as our area of interest and the snow seasons 2018/19, 2019/20 and 2020/21, the goal of this study is to investigate the ability of a simple single-layer and a more sophisticated multi-layer snow cover numerical model to reproduce the observed snow height, snow water equivalent and snow extent in the central Apennines, using for both models the same forecast weather data as meteorological forcing. We here consider two well-known ground surface and soil models: (i) Noah LSM, an Eulerian model which simulates the snowpack as a bulk single layer, and (ii) Alpine3D, a multi-layer Lagrangian model which simulates the snowpack stratification. We adopt the Weather Research and Forecasting (WRF) model to produce the meteorological data to drive both Noah LSM and Alpine3D at a regional scale with a spatial resolution of 3 km. While Noah LSM is already online-coupled with the WRF model, we develop here a dedicated offline coupling between WRF and Alpine3D. We validate the WRF simulations of surface meteorological variables in central Italy using a dense network of automatic weather stations, obtaining correlation coefficients higher than 0.68, except for wind speed, which suffered from the model underestimation of the real elevation. The performances of both WRF–Noah and WRF–Alpine3D are evaluated by comparing simulated and measured snow height, snow height variation and snow water equivalent, provided by a quality-controlled network of automatic and manual snow stations located in the central Apennines. We find that WRF–Alpine3D can predict better than WRF–Noah the snow height and the snow water equivalent, showing a correlation coefficient with the observations of 0.9 for the former and 0.7 for the latter. Both models show similar performances in reproducing the observed daily snow height variation; nevertheless WRF–Noah is slightly better at predicting large positive variations, while WRF–Alpine3D can slightly better simulate large negative variations. Finally we investigate the abilities of the models in simulating the snow cover area fraction, and we show that WRF–Noah and WRF–Alpine3D have almost equal skills, with both models overestimating it. The equal skills are also confirmed by Jaccard and the average symmetric surface distance indices.

This work presents a new approach for the estimation of snow extent, height and density in complex orography regions, which combines differential interferometric synthetic-apertureradar (DInSAR) data and snowpack numerical model data through artificial neural networks (ANNs). The estimation method, subdivided into classification and estimation, is based on two artificial neural networks trained by a DInSAR response model coupled with Alpine3D snow cover numerical model outputs. Auxiliary satellite training data from satellite visibleinfrared MODIS imager as well as digital elevation and land cover models are used to discriminate wet and dry snow areas. For snow cover classification the ANN-based estimation methodology is combined with fuzzy-logic and compared with a consolidated decision threshold approach using C-band SAR backscattering information. For snow height and density estimation, the proposed methodology is compared with an analytical inverse method and two model-based statistical techniques (linear regression and maximum likelihood). The validation is carried out in Central Apennines, a mountainous area in Italy with an extension of about 104 km 2 and peaks up to 2912 m, using in situ data collected between December 2018 and February 2019. Results show that the ANN-based technique has a snow cover area classification accuracy of more than 80% when compared MODIS maps. Estimation bias and root mean square error are equal to about 0.5 cm and 20 cm for snow height and to 5 kg/m 3 and 80 kg/m 3 for snow density. As expected, worse results are associated to low DInSAR coherence between two repeat passes and to snow melting periods.

Environmental context We present a chemical characterisation of the seasonal snowpack sampled for four consecutive years at the Calderone, the southernmost glacier still surviving in peninsular Italy. This debris covered glacier recently split into two little ice bodies, whose evolution could be influenced by the snowpack properties. In particular the impact of long-range aerosol advections on concentrations of impurities in the snowpack over the local background is discussed. Rationale The Calderone Glacier (Central Apennine, Gran Sasso d’Italia mountain group) is the southernmost glacial apparatus in Europe, split into two glacierets (Upper and Lower Calderone) since the end of the last millennium. Because of its location and altitude, this site is mainly characterised by the long-range transport of air masses which arise from different Mediterranean source regions. Therefore, the seasonal snowpack’s chemistry is strongly affected by the dry and wet deposition of contaminants associated with anthropogenic and natural sources. Methodology In the present study, the seasonal snowpack stratified on the Calderone glacier has been characterised for four consecutive years (2017–2020) in the same monitoring site (2700 m asl), where a snow pit has been dug yearly, to observe the modification of chemical and physical properties depending on local and long-range atmospheric contributions. We determined the concentrations and fluxes of major inorganic ions (MIs) by ion chromatography and of 31 trace elements (TEs) by triple quadrupole ICP-MS. Results Major and trace element concentration profiles along the snowpack allowed to discriminate the snow layers contaminated by long range advections from the uncontaminated ones. The uncontaminated snow layers’ concentrations were used to calculate regional background values. The results have been compared to other remote sites to assess their robustness. Discussion Different source contributions have been recognised using enrichment factors for the trace elements, particularly crustal, marine and anthropogenic. Deposited atmospheric aerosols, found in the snowpack as distinct layers generated during intense air mass advections, have been correlated to these contributions.