We studied methanotrophic activity in the water column in relation to heterotrophic bacterial production and efflux of methane (CH 4 ) from the lake surface in a small, stratified, humic, boreal lake (Valkea-Kotinen, southern Finland). During summer and winter stratification, the highest methanotrophic activities were in the metalimnion, where oxygen concentration was Ͻ6 mmol m Ϫ3 . During an incomplete spring turnover and summer stratification period, 3-5 times more CH 4 was consumed by methanotrophs in the water column than was released to the atmosphere. The highest CH 4 effluxes (1.2-5.1 mmol m Ϫ2 d Ϫ1 ) to the atmosphere occurred during the autumnal turnover despite observed methanotrophic activity in the whole water column. In winter, the amount of CH 4 consumed by methanotrophs (0.20 mol CH 4 m Ϫ2 during 6.5 months) was of the same order of magnitude as that during the ice-free period (0.22 mol CH 4 m Ϫ2 during 5.5 months). Annually ϳ80% of CH 4 diffused from the sediment was consumed by methanotrophs in the water column, and only 20% (0.11 mol CH 4 m Ϫ2 yr Ϫ1) was released to the atmosphere. During the ice-free period, bacterial production measured as [14 C]leucine uptake showed a bell-shaped relation to CH 4 concentration. The highest production was found in the metalimnion at CH 4 concentrations ranging from 5 to 10 mmol m Ϫ3. During summer stratification, net production of methanotrophs corresponded to 23-47% of total bacterial production, but during the autumn turnover, this proportion was higher (27-81%), indicating that methanotrophs offer a potentially significant source of carbon to zooplankton in stratified humic lakes.

Accurate snow depth observations are critical to assess water resources. More than a billion people rely on water from snow, most of which originates in the Northern Hemisphere mountain ranges. Yet, remote sensing observations of mountain snow depth are still lacking at the large scale. Here, we show the ability of Sentinel-1 to map snow depth in the Northern Hemisphere mountains at 1 km² resolution using an empirical change detection approach. An evaluation with measurements from ~4000 sites and reanalysis data demonstrates that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences. This is showcased with the contrasting snow depths between 2017 and 2018 in the US Sierra Nevada and European Alps. With Sentinel-1 continuity ensured until 2030 and likely beyond, these findings lay a foundation for quantifying the long-term vulnerability of mountain snow-water resources to climate change.

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Abstract. The conventional climate gridded datasets based on observations only are widely used in atmospheric sciences; our focus in this paper is on climate and hydrology. On the Norwegian mainland, seNorge2 provides high-resolution fields of daily total precipitation for applications requiring long-term datasets at regional or national level, where the challenge is to simulate small-scale processes often taking place in complex terrain. The dataset constitutes a valuable meteorological input for snow and hydrological simulations; it is updated daily and presented on a high-resolution grid (1 km of grid spacing). The climate archive goes back to 1957. The spatial interpolation scheme builds upon classical methods, such as optimal interpolation and successivecorrection schemes. An original approach based on (spatial) scale-separation concepts has been implemented which uses geographical coordinates and elevation as complementary information in the interpolation. seNorge2 daily precipitation fields represent local precipitation features at spatial scales of a few kilometers, depending on the station network density. In the surroundings of a station or in dense station areas, the predictions are quite accurate even for intense precipitation. For most of the grid points, the performances are comparable to or better than a state-of-the-art pan-European dataset (E-OBS), because of the higher effective resolution of seNorge2. However, in very data-sparse areas, such as in the mountainous region of southern Norway, seNorge2 underestimates precipitation because it does not make use of enough geographical information to compensate for the lack of observations. The evaluation of seNorge2 as the meteorological forcing for the seNorge snow model and the DDD (Distance Distribution Dynamics) rainfall-runoff model shows that both models have been able to make profitable use of seNorge2, partly because of the automatic calibration procedure they incorporate for precipitation. The seNorge2 dataset 1957-2015 is available at https://doi.org/10.5281/zenodo.845733. Daily updates from 2015 onwards are available at

The structure of the oceanic Arctic front west of Spitsbergen is investigated using data from high‐resolution CTD sections from September 1998‐2000. Below the fresher surface layer, the front appears as a temperature‐salinity front situated near the shelf break. No clear corresponding front in density is found. Our analysis suggests that barotropic front instability is a main factor in provoking subsurface cross‐front exchange. The subsurface heat loss in the West Spitsbergen Current due to this exchange is estimated to be of the same order of magnitude as the heat loss to the atmosphere in the surface layer.

Abstract. Daily maps of snow conditions have been produced in Norway with the seNorge snow model since 2004. The seNorge snow model operates with 1 × 1 km resolution, uses gridded observations of daily temperature and precipitation as its input forcing, and simulates, among others, snow water equivalent (SWE), snow depth (SD), and the snow bulk density (ρ). In this paper the set of equations contained in the seNorge model code is described and a thorough spatiotemporal statistical evaluation of the model performance from 1957-2011 is made using the two major sets of extensive in situ snow measurements that exist for Norway. The evaluation results show that the seNorge model generally overestimates both SWE and ρ, and that the overestimation of SWE increases with elevation throughout the snow season. However, the R 2 -values for model fit are 0.60 for (logtransformed) SWE and 0.45 for ρ, indicating that after removal of the detected systematic model biases (e.g. by recalibrating the model or expressing snow conditions in relative units) the model performs rather well. The seNorge model provides a relatively simple, not very data-demanding, yet nonetheless process-based method to construct snow maps of high spatiotemporal resolution. It is an especially well suited alternative for operational snow mapping in regions with rugged topography and large spatiotemporal variability in snow conditions, as is the case in the mountainous Norway.

Abstract.Using