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The representation of snow is a crucial aspect of land-surface modelling, as it has a strong influence on energy and water balances. Snow schemes with multiple layers have been shown to better describe the snowpack evolution and bring improvements to soil freezing and some hydrological processes. In this paper, the wider hydrological impact of the multi-layer snow scheme, implemented in the ECLand model, was analyzed globally on hundreds of catchments. ERA5-forced reanalysis simulations of ECLand were coupled to CaMa-Flood, as the hydrodynamic model to produce river discharge. Different sensitivity experiments were conducted to evaluate the impact of the ECLand snow and soil freezing scheme changes on the terrestrial hydrological processes, with particular focus on permafrost. It was found that the default multi-layer snow scheme can generally improve the river discharge simulation, with the exception of permafrost catchments, where snowmelt-driven floods are largely underestimated, due to the lack of surface runoff. It was also found that appropriate changes in the snow vertical discretization, destructive metamorphism, snow-soil thermal conductivity and soil freeze temperature could lead to large river discharge improvements in permafrost by adjusting the evolution of soil temperature, infiltration and the partitioning between surface and subsurface runoff.

The Arctic environment is changing, increasing the vulnerability of local communities and ecosystems, and impacting its socio-economic landscape. In this context, weather and climate prediction systems can be powerful tools to support strategic planning and decision-making at different time horizons. This article presents several success stories from the H2020 project APPLICATE on how to advance Arctic weather and seasonal climate prediction, synthesizing the key lessons learned throughout the project and p roviding recommendations for future model and forecast system development.

Numerical systems used for weather and climate predictions have substantially improved over past decades. We argue that despite a continued need for further addressing remaining limitations of their key components, numerical prediction systems have reached a sufficient level of maturity to examine and critically assess the suitability of Earth’s current observing systems – remote and in situ, for prediction purposes; and that they can provide evidence-based support for the deployment of future observational networks. We illustrate this point by presenting recent, co-ordinated international efforts focused on Arctic observing systems, led in the framework of the Year of Polar Prediction and the H2020 project APPLICATE. The Arctic, one of the world’s most rapidly changing regions, is relatively poorly covered in terms of in situ data but richly covered in terms of satellite data. In this study, we demonstrate that existing state-of-the-art datasets and targeted sensitivity experiments produced with numerical prediction systems can inform us of the added value of existing or even hypothetical Arctic observations, in the context of predictions from hourly to interannual time-scales. Furthermore, we argue that these datasets and experiments can also inform us how the uptake of Arctic observations in numerical prediction systems can be enhanced to maximise predictive skill. Based on these efforts we suggest that (a) conventional in situ observations in the Arctic play a particularly important role in initializing numerical weather forecasts during the winter season, (b) observations from satellite microwave sounders play a particularly important role during the summer season, and their enhanced usage over snow and sea ice is expected to further improve their impact on predictive skill in the Arctic region and beyond, (c) the deployment of a small number of in situ sea-ice thickness monitoring devices at strategic sampling sites in the Arctic could be sufficient to monitor

Numerical systems used for weather and climate predictions have substantially improved over past decades. We argue that despite a continued need for further addressing remaining limitations of their key components, numerical prediction systems have reached a sufficient level of maturity to examine and critically assess the suitability of Earth's current observing systems – remote and in situ, for prediction purposes; and that they can provide evidence-based support for the deployment of future observational networks. We illustrate this point by presenting recent, co-ordinated international efforts focused on Arctic observing systems, led in the framework of the Year of Polar Prediction and the H2020 project APPLICATE. The Arctic, one of the world's most rapidly changing regions, is relatively poorly covered in terms of in situ data but richly covered in terms of satellite data. In this study, we demonstrate that existing state-of-the-art datasets and targeted sensitivity experiments produced with numerical prediction systems can inform us of the added value of existing or even hypothetical Arctic observations, in the context of predictions from hourly to interannual time-scales. Furthermore, we argue that these datasets and experiments can also inform us how the uptake of Arctic observations in numerical prediction systems can be enhanced to maximise predictive skill. Based on these efforts we suggest that (a) conventional in situ observations in the Arctic play a particularly important role in initializing numerical weather forecasts during the winter season, (b) observations from satellite microwave sounders play a particularly important role during the summer season, and their enhanced usage over snow and sea ice is expected to further improve their impact on predictive skill in the Arctic region and beyond, (c) the deployment of a small number of in situ sea-ice thickness monitoring devices at strategic sampling sites in the Arctic could be sufficient to monitor, This study was supported by the APPLICATE project (727862), which was funded by the European Union’s Horizon 2020 research and innovation programme. It was also supported by the Norwegian Research Council project no. 280573 “Advanced models and weather prediction in the Arctic: enhanced capacity from observations and polar process representations (ALERTNESS)”., Peer Reviewed, Article signat per 23 autors/es: Irina Sandu (1), François Massonnet (2), Guillian van Achter (2), Juan C. Acosta Navarro (3), Gabriele Arduini (1), Peter Bauer (1), Ed Blockley (4), Niels Bormann (1), Matthieu Chevallier (5), Jonathan Day (1), Mohamed Dahoui (1), Thierry Fichefet (2), Daniela Flocco (6), Thomas Jung (7), Ed Hawkins (6), Stephane Laroche (8), Heather Lawrence (1,4), Jorn Kristianssen (9), Eduardo Moreno-Chamarro (3), Pablo Ortega (3), Emmanuel Poan (8), Leandro Ponsoni (2), Roger Randriamampianina (9) // (1) European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, UK (2) Université Catholique de Louvain, Brussels, Belgium (3) Barcelona Supercomputing Centre, Barcelona, Spain (4) Met Office, Exeter, UK (5) Meteo-France, Toulouse, France (6) University of Reading, Reading, UK (7) Alfred Wegener Institute, Bremerhaven, Germany (8) Environment and Climate Change, Gatineau, Quebec Canada (9) Met Norway, Oslo, Norway, Postprint (published version)