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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 most of the large-scale sea-ice volume variability, and (d) sea-ice thickness observations can improve the simulation of both the sea ice and near-surface air temperatures on seasonal time-scales in the Arctic and beyond. 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

Introducing a parameterization of the interactions between wind-driven snow depth changes and melt\ud pond evolution allows us to improve large scale models. In this paper we have implemented an explicit\ud melt pond scheme and, for the first time, a wind dependant snow redistribution model and new snow\ud thermophysics into a coupled ocean–sea ice model.\ud The comparison of long-term mean statistics of melt pond fractions against observations demonstrates\ud realistic melt pond cover on average over Arctic sea ice, but a clear underestimation of the pond coverage\ud on the multi-year ice (MYI) of the western Arctic Ocean. The latter shortcoming originates from the concealing\ud effect of persistent snow on forming ponds, impeding their growth. Analyzing a second simulation\ud with intensified snow drift enables the identification of two distinct modes of sensitivity in the\ud melt pond formation process. First, the larger proportion of wind-transported snow that is lost in leads\ud directly curtails the late spring snow volume on sea ice and facilitates the early development of melt\ud ponds on MYI. In contrast, a combination of higher air temperatures and thinner snow prior to the onset\ud of melting sometimes make the snow cover switch to a regime where it melts entirely and rapidly. In the\ud latter situation, seemingly more frequent on first-year ice (FYI), a smaller snow volume directly relates to\ud a reduced melt pond cover.\ud Notwithstanding, changes in snow and water accumulation on seasonal sea ice is naturally limited,\ud which lessens the impacts of wind-blown snow redistribution on FYI, as compared to those on MYI. At\ud the basin scale, the overall increased melt pond cover results in decreased ice volume via the ice-albedo\ud feedback in summer, which is experienced almost exclusively by MYI.