Abstract. The 0 ∘C temperature threshold is critical for many meteorological and hydrological processes driven by melting and freezing in the atmosphere, surface, and sub-surface and by the associated precipitation varying between rain, freezing rain, wet snow, and snow. This threshold is especially important in cold regions such as Canada, because it is linked with freeze–thaw, snowmelt, and permafrost. This study develops a Canada-wide perspective on near-0 ∘C conditions using hourly surface temperature and precipitation type observations from 92 climate stations for the period from 1981 to 2011. In addition, nine stations from various climatic regions are selected for further analysis. Near-0 ∘C conditions are defined as periods when the surface temperature is between −2 and 2 ∘C. Near-0 ∘C conditions occur often across all regions of the country, although the annual number of days and hours and the duration of these events varies dramatically. Various types of precipitation (e.g., rain, freezing rain, wet snow, and ice pellets) sometimes occur with these temperatures. Near-0 ∘C conditions and the reported precipitation type occurrences tend to be higher in Atlantic Canada, although high values also occur in other regions. Trends of most temperature-based and precipitation-based indicators show little or no change despite a systematic warming in annual surface temperatures over Canada. Over the annual cycle, near-0 ∘C temperatures and precipitation often exhibit a pattern: short durations occur around summer, driven by the diurnal cycle, and a tendency toward longer durations around winter, associated with storms. There is also a tendency for near-0 ∘C surface temperatures to occur more often than expected relative to other temperature windows at some stations due, at least in part, to diabatic cooling and heating that take place with melting and freezing, respectively, in the atmosphere and at the surface.

Abstract. In mountainous terrain, the snowpack is strongly affected by incoming shortwave and longwave radiation. In this study, a thorough evaluation of the solar and longwave downwelling irradiance products (DSSF and DSLF) derived from the Meteosat Second Generation satellite was undertaken in the French Alps and the Pyrenees. The satellite-derived products were compared with forecast fields from the meteorological model AROME and with analysis fields from the SAFRAN system. A new satellite-derived product (DSLFnew) was developed by combining satellite observations and AROME forecasts. An evaluation against in situ measurements showed lower errors for DSSF than AROME and SAFRAN in terms of solar irradiances. For longwave irradiances, we were not able to select the best product due to contrasted results falling in the range of uncertainty of the sensors. Spatial comparisons of the different datasets over the Alpine and Pyrenean domains highlighted a better representation of the spatial variability of solar fluxes by DSSF and AROME than SAFRAN. We also showed that the altitudinal gradient of longwave irradiance is too strong for DSLFnew and too weak for SAFRAN. These datasets were then used as radiative forcing together with AROME near-surface forecasts to drive distributed snowpack simulations by the model Crocus in the French Alps and the Pyrenees. An evaluation against in situ snow depth measurements showed higher biases when using satellite-derived products, despite their quality. This effect is attributed to some error compensations in the atmospheric forcing and the snowpack model. However, satellite-derived irradiance products are judged beneficial for snowpack modelling in mountains, when the error compensations are solved.

Abstract. From 19 to 22 June 2013, intense rainfall and concurrent snowmelt led to devastating floods in the Canadian Rockies, foothills and downstream areas of southern Alberta and southeastern British Columbia, Canada. Such an event is typical of late-spring floods in cold-region mountain headwater, combining intense precipitation with rapid melting of late-lying snowpack, and represents a challenge for hydrological forecasting systems. This study investigated the factors governing the ability to predict such an event. Three sources of uncertainty, other than the hydrological model processes and parameters, were considered: (i) the resolution of the atmospheric forcings, (ii) the snow and soil moisture initial conditions (ICs) and (iii) the representation of the soil texture. The Global Environmental Multiscale hydrological modeling platform (GEM-Hydro), running at a 1 km grid spacing, was used to simulate hydrometeorological conditions in the main headwater basins of southern Alberta during this event. The GEM atmospheric model and the Canadian Precipitation Analysis (CaPA) system were combined to generate atmospheric forcing at 10, 2.5 and 1 km over southern Alberta. Gridded estimates of snow water equivalent (SWE) from the Snow Data Assimilation System (SNODAS) were used to replace the model SWE at peak snow accumulation and generate alternative snow and soil moisture ICs before the event. Two global soil texture datasets were also used. Overall 12 simulations of the flooding event were carried out. Results show that the resolution of the atmospheric forcing affected primarily the flood volume and peak flow in all river basins due to a more accurate estimation of intensity and total amount of precipitation during the flooding event provided by CaPA analysis at convection-permitting scales (2.5 and 1 km). Basin-averaged snowmelt also changed with the resolution due to changes in near-surface wind and resulting turbulent fluxes contributing to snowmelt. Snow ICs were the main sources of uncertainty for half of the headwater basins. Finally, the soil texture had less impact and only affected peak flow magnitude and timing for some stations. These results highlight the need to combine atmospheric forcing at convection-permitting scales with high-quality snow ICs to provide accurate streamflow predictions during late-spring floods in cold-region mountain river basins. The predictive improvement by inclusion of high-elevation weather stations in the precipitation analysis and the need for accurate mountain snow information suggest the necessity of integrated observation and prediction systems for forecasting extreme events in mountain river basins.