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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 de-scribe 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.

Remote Sensing, 11 (8), ISSN:2072-4292

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

Alpine valleys are rarely closed systems, implying that the atmospheric boundary layer of a particular valley section is influenced by the surrounding terrain and large-scale flows. A detailed characterisation and quantification of these effects is required in order to design appropriate parameterisation schemes for complex terrains. The focus of this work is to improve the understanding of the effects of surrounding terrain (plains, valleys or tributaries) on the heat and mass budgets of the stable boundary layer of a valley section, under dry and weak large-scale wind conditions. Numerical simulations using idealised and real frameworks are performed to meet this goal. Several idealised terrains (configurations) were considered: an infinitely long valley (i.e. two-dimensional), and upstream valleys opening either on a plain (valley-plain), on a wider valley (draining) or on a narrower valley (pooling). In three-dimensional valleys, two main regimes can be identified for all configurations: a transient regime, before the down-valley flow develops, followed by a quasi-steady regime, when the down-valley flow is fully developed. The presence of a downstream valley reduces the along-valley temperature difference, therefore leading to weaker down-valley flows. As a result, the duration of the transient regime increases compared to the respective valley-plain configuration. Its duration is longest for the pooling configuration. For strong pooling the along-valley temperature difference can reverse, forcing up-valley flows from the narrower towards the wider valley. In this regime, the average cooling rate at the valley-scale is found to be a maximum and its magnitude is dependent on the configuration considered. Therefore pooling and draining induce colder and deeper boundary layers than the respective valley-plain configurations. In the quasisteady regime the cooling rate is smaller than during the transient regime, and almost independent of the configuration considered. Indeed, as the pooling character is more pronounced, the warming contribution from advection to the heat budget decreases because of weaker down-valley flows, and so does the cooling contribution from the surface sensible heat flux. The mass budget of the valley boundary layer was found to be controlled by a balance between the convergence of downslope flows at the top of the boundary layer and the divergence of the down-valley flow along the valley axis, with negligible contributions of subsidence far from the valley sidewalls. The mass budget highlighted the importance of the return current above the down-valley flow, which may contribute significantly to the inflow of air at the top of the boundary layer. A case-study of a persistent cold-air pool event which occurred in February 2015 in the Arve River Valley during the intensive observation period 1 (IOP1) of the PASSY- 2015 field campaign, allowed us to quantify the effects of neighbouring valleys on the heat and mass budgets of a real valley atmosphere. The cold-air pool persisted as a result of warm air advection at the valley top, associated with the passage of an upper-level ridge over Europe. The contributions from each tributary valley to the mass and heat budgets of the valley atmosphere were found to vary from day to day within the persistent stage of the cold-air pool, depending on the large-scale flow. Tributary flows had significant impact on the height of the inversion layer and the strength of the cold-air pool, transporting a significant amount of mass within the valley atmosphere throughout the night. The strong stratification of the near-surface atmosphere prevented the tributary flows from penetrating down to the valley floor. The evolution of the large-scale flow during the episode had a profound impact on the near-surface circulation of the valley. The channelling of the large-scale flow at night, can lead to the decrease of the horizontal temperature difference driving the near-surface down-valley flow, favouring the stagnation of the air close to the ground.

Krinner, Gerhard Derksen, Chris Essery, Richard Flanner, Mark Hagemann, Stefan Clark, Martyn Hall, Alex Rott, Helmut Brutel-Vuilmet, Claire Kim, Hyungjun Menard, Cecile B. Mudryk, Lawrence Thackeray, Chad Wang, Libo Arduini, Gabriele Balsamo, Gianpaolo Bartlett, Paul Boike, Julia Boone, Aaron Cheruy, Frederique Colin, Jeanne Cuntz, Matthias Dai, Yongjiu Decharme, Bertrand Derry, Jeff Ducharne, Agnes Dutra, Emanuel Fang, Xing Fierz, Charles Ghattas, Josephine Gusev, Yeugeniy Haverd, Vanessa Kontu, Anna Lafaysse, Matthieu Law, Rachel Lawrence, Dave Li, Weiping Marke, Thomas Marks, Danny Menegoz, Martin Nasonova, Olga Nitta, Tomoko Niwano, Masashi Pomeroy, John Raleigh, Mark S. Schaedler, Gerd Semenov, Vladimir Smirnova, Tanya G. Stacke, Tobias Strasser, Ulrich Svenson, Sean Turkov, Dmitry Wang, Tao Wever, Nander Yuan, Hua Zhou, Wenyan Zhu, Dan Menard, Cecile/F-7860-2014; Raleigh, Mark S/M-7687-2015; Krinner, Gerhard/A-6450-2011; Balsamo, Gianpaolo/M-5734-2019; Flanner, Mark/C-6139-2011; Nasonova, Olga/B-6093-2014; KIM, HYUNGJUN/I-5099-2014; Dutra, Emanuel/A-3774-2010; Turkov, Dmitry/Y-3186-2018; gusev, yeugeniy/G-4711-2014; Kontu, Anna/O-8886-2014; Zhu, Dan/J-4450-2019; Balsamo, Gianpaolo/I-3362-2013; Niwano, Masashi/N-6723-2016 Menard, Cecile/0000-0003-2166-9523; Raleigh, Mark S/0000-0002-1303-3472; Krinner, Gerhard/0000-0002-2959-5920; Balsamo, Gianpaolo/0000-0002-1745-3634; Flanner, Mark/0000-0003-4012-174X; KIM, HYUNGJUN/0000-0003-1083-8416; Dutra, Emanuel/0000-0002-0643-2643; Turkov, Dmitry/0000-0002-1813-757X; gusev, yeugeniy/0000-0003-3886-2143; Kontu, Anna/0000-0001-6880-6260; Zhu, Dan/0000-0002-5857-1899; Balsamo, Gianpaolo/0000-0002-1745-3634; Fang, Xing/0000-0002-4333-4815; Decharme, Bertrand/0000-0002-8661-1464; Niwano, Masashi/0000-0003-3121-3802 1991-9603; International audience; This paper describes ESM-SnowMIP, an international coordinated modelling effort to evaluate current snow schemes, including snow schemes that are included in Earth system models, in a wide variety of settings against local and global observations. The project aims to identify crucial processes and characteristics that need to be improved in snow models in the context of local-and global-scale modelling. A further objective of ESM-SnowMIP is to better quantify snow-related feedbacks in the Earth system. Although it is not part of the sixth phase of the Coupled Model Intercom-parison Project (CMIP6), ESM-SnowMIP is tightly linked to the CMIP6-endorsed Land Surface, Snow and Soil Moisture Model Intercomparison (LS3MIP).

Alpine valleys are rarely closed systems, implying that the atmospheric boundary layer of a particular valley is influenced by the surrounding terrain and large-scale flows. A detailed characterisation and quantification of these effects is required in order to design appropriate parameterisation schemes for complex terrains. The focus of this work is to improve the understanding of the effects of surrounding terrain (plains, valleys or tributaries) on the heat and mass budgets of the stable boundary layer of a valley, under dry and weak large-scale wind conditions. Numerical simulations using idealised and real frameworks are performed to meet this goal. Several idealised terrains (configurations) were considered: an infinitely long valley (i.e. two-dimensional), and upstream valleys opening either on a plain (valley-plain), on a wider valley (draining) or on a narrower valley (pooling). In three-dimensional valleys, two main regimes can be identified for all configurations: a transient regime, before the down-valley flow develops, followed by a quasi-steady regime, when the down-valley flow is fully developed. The presence of a downstream valley reduces the along-valley temperature difference, therefore leading to weaker down-valley flows. As a result, the duration of the transient regime increases compared to the respective valley-plain configuration. Its duration is longest for pooling configuration. For strong pooling the along-valley temperature difference can reverse, forcing up-valley flows from the narrower towards the wider valley. In this regime, the volume-averaged cooling rate is found maximum and its magnitude dependent on the configuration considered. Therefore pooling and draining induce colder and deeper boundary layers than the respective valley-plain configurations. In the quasi-steady regime the cooling rate is smaller than in the transient regime, and almost independent of the configuration considered. Indeed, as the pooling character is more pronounced, the warming contribution from advection to the heat budget decreases because of weaker down-valley flows, and so does the cooling contribution from the surface sensible heat flux. The mass budget of the valley boundary layer was found to be controlled by a balance between the convergence of downslope flows at the boundary layer top and the divergence of down-valley flows along the valley axis, with negligible contributions of subsidence far from the slopes. The mass budget highlighted the importance of the return current above the down-valley flow, which may contribute significantly to the inflow of air at the top of the boundary layer. A case-study of a persistent cold-air pool event which occurred in February 2015 in the Arve River Valley during the intensive observation period 1 of the PASSY-2015 field campaign, allowed to quantify the effects of neighbouring valleys on the heat and mass budgets of a real valley atmosphere. The cold-air pool persisted because of warm air advection at the valley top, associated with the passage of an upper-level ridge over Europe. The contributions from each tributary valley to the mass and heat budgets of the valley atmosphere were found to vary from day to day within the persistent stage of the cold-air pool, depending on the large-scale flow. Tributary flows had significant impact on the height of the inversion layer and the strength of the cold-air pool, transporting a significant amount of mass within the valley atmosphere throughout the night. The strong stratification of the near-surface atmosphere prevented the tributary flows from penetrating down to the valley floor. The evolution of the large-scale flow during the episode had a profound impact on the near-surface circulation of the valley. The channelling of the large-scale flow at night, can lead to the decrease of the horizontal temperature difference driving the near-surface down-valley flow, favouring the stagnation of the air close to the ground., La dynamique de la couche limite atmosphérique d'une vallée alpine est influencée par le relief environnant et par l’écoulement de grande échelle qui la surmonte. La paramétrisation de cette circulation atmosphérique requiert donc de caractériser finement ces effets. C'est l’objectif de ce travail de thèse : comprendre l’influence du relief environnant une vallée sur les bilans de masse et de chaleur au travers d’une section de cette vallée, par conditions stables et sèches et lorsque le vent synoptique est faible mais non négligeable. Le travail s’appuie sur des simulations numériques.Plusieurs vallées idéalisées ont tout d’abord été considérées: une vallée infiniment longue (bidimensionnelle) et une vallée tridimensionnelle, qualifiée de supérieure, ouvrant soit sur une plaine (cas “vallée-plaine”), soit sur une autre vallée, qualifiée d’inférieure. Cette seconde vallée est soit plus large (cas “drainage”) ou plus étroite (cas “quasi-stagnation”).Dans les vallées tridimensionnelles, deux régimes principaux ont été identifiés, quelle que soit le cas considéré : un régime transitoire, avant que le vent de vallée (descendant) ne se développe, puis un régime quasi-stationnaire, quand le vent de vallée est complètement développé. La présence d’une vallée inférieure réduit la variation de température le long de la vallée, de sorte que le vent de vallée y est plus faible que dans le cas vallée-plaine. En conséquence, la durée du régime transitoire augmente et est maximum pour le cas quasi-stagnation. Lorsque la vallée inférieure est très étroite, la variation de température peut même changer de signe, conduisant à un vent de vallée montant, de la vallée inférieure vers la vallée supérieure. Durant ce régime transitoire, le taux de refroidissement moyenné sur le volume de la vallée est maximum, sa valeur dépendant du cas considéré. En conclusion, les cas drainage et quasi-stagnation conduisent à une couche limite dans la vallée supérieure plus froide et plus profonde que dans le cas vallée-plaine.Dans le régime quasi-stationnaire, le taux de refroidissement moyenné sur le volume de la vallée est plus faible que dans le régime transitoire et varie peu en fonction du cas considéré. En effet, lorsque la vallée inférieure devient plus étroite, le réchauffement lié aux effets advectifs diminue car la vitesse du vent de vallée diminue, de sorte que la contribution (refroidissante) du flux de chaleur sensible diminue également. La conservation de la masse dans la couche limite de la vallée supérieure est assurée par un équilibre entre la convergence des vents de pente au sommet de la couche limite (alimenté par un courant de retour au-dessus (et en sens inverse) du vent de vallée descendant) et la divergence du vent de vallée, les effets de subsidence loin des parois de la vallée jouant un rôle négligeable.Le cas réaliste de la vallée de l’Arve autour de Passy durant une période d’observation intensive de la campagne de mesures PASSY-2015 a permis de caractériser l’impact des vallées environnant Passy sur les bilans de masse et de chaleur dans la vallée. Une couche d’air froid persistante se forme en fond de vallée, suite à l’advection d’air chaud associée au passage d’une crête anticyclonique au-dessus de l’Europe. Les écoulements le long des vallées tributaires présentent une grande variabilité durant la phase persistante de l’épisode, dépendant de la variabilité de l’écoulement à grande échelle, et ont un impact majeur sur l’intensité de la couche d’air froid et la hauteur de l’inversion qui la surmonte. La forte stratification près du sol conduit à leur décollement au-dessus du fond de vallée, les empêchant d'y pénétrer. L’évolution de l’écoulement à grande échelle durant l’épisode a un profond impact sur la dynamique proche du fond de vallée. Durant la nuit en effet, la canalisation de cet écoulement réduit la variation de température le long de la vallée contrôlant le vent de vallée, favorisant la stagnation de l’air.

International audience; Wintertime anticyclonic conditions, associated with clear sky and cold nights, trigger the formation of persistent layers of stable air over the ground. In an urban area, these persistent layers lead to poor air quality, especially when the terrain is mountainous. This is particularly the case in the Arve River Valley near the city of Passy, located 20 km downstream of Chamonix-Mont-Blanc, where air quality stands among the poorest ones in France.Beyond the monitoring of air quality, as performed by the Auvergne-Rhône-Alpes air quality agency or within the scientific project DECOMBIO led by the Institute for Geosciences and the Environment (IGE), knowledge of the atmospheric dynamics at the valley scale should be gained to understand how pollutants are dispersed. This is the motivation of the Passy project, which started in 2014. It relies on the Passy-2015 field experiment, whereof presentation, along with the discussion of a few results, is the purpose of the present paper. The objective of this field experiment is to document the atmospheric dynamics in the Arve River Valley during wintertime pollution episodes.The work conducted during the Passy project and the analysis of the Passy-2015 field experiment will benefit from a several-year long collaboration among the different partners. The knowledge thus gained will contribute to refine weather forecast and air quality prediction in the Arve River Valley and, more generally, in mountain urban areas under stable conditions. From an operational perspective, our goal is to improve our ability to forecast critical events such as low temperatures, ice and fog formation, pollution events or locations subject to high pollutant concentration.; Les conditions anticycloniques hivernales (ciel clair, nuits froides) conduisent à la formation de couches stables persistantes qui favorisent les épisodes de pollution, particulièrement en terrain montagneux. La vallée de l’Arve est très sensible à ce phénomène, en particulier près de la ville de Passy (Haute-Savoie), située à 20 kilomètres en aval de Chamonix-Mont-Blanc, où la qualité de l’air est l’une des moins bonnes de France.Au-delà du suivi de la qualité de l’air, tel que réalisé par Atmo Auvergne-Rhône-Alpes ou par le projet DECOMBIO piloté par l’Institut des Géosciences et de l’Environnement (IGE), il est primordial d’améliorer la connaissance de la dynamique atmosphérique à l’échelle de la vallée en conditions stables pour mieux comprendre comment, couplée au cycle et à la géographie des émissions, elle pilote la dispersion des polluants. C’est la motivation du projet Passy, initié en 2014. Ce projet s’appuie sur les observations de la campagne Passy-2015, présentées dans cet article avec quelques premiers résultats. L’objectif général de cette campagne est de documenter la dynamique atmosphérique au sein de la vallée de l’Arve lors des épisodes de pollution hivernale.Les travaux menés dans le cadre du projet et de l’analyse des données de la campagne s’inscrivent au sein d’une collaboration sur plusieurs années entre les différents partenaires. Ils contribueront à affiner la prévision du temps et de la qualité de l’air dans ce type de vallée, et plus généralement en conditions stables. Il s’agit en particulier d’améliorer la capacité à prévoir des phénomènes critiques, comme les températures minimales, le verglas, le brouillard, les évènements de pollution ou encore les zones de pollution intense.

Different practices are currently being used to couple the different components of global models for weather forecasts (several days to weeks ahead) and climate applications (several decades to centuries). This report provides a summary of the coupling methods currently used for Numerical Weather Prediction (NWP) and climate applications. We have reviewed the different approaches employed in the community and used them as a motivation to design novel experiments aimed at improving the coupling between the atmosphere, ocean and sea ice. The results of these experiments, together with prior results from the literature, have guided the recommendations provided in this report. To improve coupling methodology in NWP and climate models, we recommend using fluxes that are consistent at the interface between the atmosphere, the ocean and the sea ice. More specifically, we show that accounting for the differences in horizontal resolution between the different components of the Earth System when computing the surface fluxes, or accounting for the different sea ice thickness categories can lead to small but detectable differences in the representation of key variables like Arctic sea ice extent and volume. Even if the differences are small, computing the fluxes in a consistent way is more physical and could lead to a better representation of the atmospheric boundary layer and consequently of near-surface weather. We further show that the representation of key physical processes such as snow over sea ice is essential for a realistic representation of near surface temperature in coupled models and it is particularly important for NWP. Representing processes such as snow over sea ice is key to reduce model biases in the polar regions and hence to improve short term predictions and the representation of the model climate. We expect that the results presented in this report will help weather and climate model developments in the future.

This deliverable provides an assessment of the impact of new and/or improved model components developed within APPLICATE WP2 into numerical weather prediction (NWP) systems. Based on these conclusions, it provides recommendations about priorities to pursue for further model developments and components to include in the next generation of NWP systems. The APPLICATE developments considered in this deliverable are: the inclusion of dynamical and/or thermodynamic sea ice models. the extension of single-layer snow schemes on land to multi-layer snow schemes. the inclusion of snow schemes on top of sea ice. an improved version of the eddy-diffusivity mass-flux scheme which detects decoupled stratocumulus. Introducing an interactive sea ice model into numerical weather predictions leads to a robust increase in forecast quality for near-surface temperature close to the sea ice edge in the ARPEGE/AROME prediction system. Both bias and standard deviation are substantially reduced. The temperature gradient across the sea ice edge is more realistic. Exploiting both observed sea surface temperatures and sea ice fraction to initialize the model is essential to optimize the benefits from the interactive sea ice model. Similarly, extracting boundary conditions from a global model which also uses an interactive sea ice model improves substantially the performance of the regional prediction system. The dynamic coupling of the ice cover in IFS allows for capturing accurately rapid changes in sea ice concentration, especially along the sea ice edge as illustrated on a case study. A substantial impact is seen in the near surface temperature (up to 6°C) when the large-scale meteorological situation is favourable. At high latitudes, near-surface temperature tends to be overestimated during cold events occurring in clear-sky conditions. Introducing a multi-layer snow scheme over land into the IFS prediction systems leads to a better representation of these cold events at forecast day 2 over the Sodankylä observational supersite. The diurnal cycle in near surface temperature is also better captured with the multi-layer snow scheme than with the previous single-layer scheme, with a reduced warm bias at the daily minimum and cold bias at the daily maximum. Including this multi-layer snow scheme over sea ice allows for a marginally better simulated temperature in the lower atmosphere for a case study from the SHEBA campaign. However, during periods of liquid-phase cloud cover, systematic errors in the representation of such clouds leads to the increase of the errors of skin temperature in the simulation using the multi-layer snow scheme. These results highlight the key role of compensating errors in the surface energy balance and the need for improved representation of Arctic boundary layer processes. A revised version of the eddy-diffusivity mass-flux scheme was proposed to identify decoupled stratocumulus. This scheme detects and simulates cloud layers that would not have been represented before, as seen in an ASCOS Arctic stratocumulus case. The root mean square error for clouds and radiative properties are drastically reduced with this improved scheme.

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort., Remote Sensing, 10 (12), ISSN:2072-4292

In this work a new multi-layer snow scheme for the ECMWF Integrated Forecasting System (IFS) is evaluated and the impact on global short- and medium-range weather forecasts analyzed. The work described in the abstract has been presented at the European Geophysical Union (EGU) General Assembly 2019.

This deliverable provides an assessment of the impact of new and/or improved model components developed within APPLICATE WP2 into numerical weather prediction (NWP) systems. Based on these conclusions, it provides recommendations about priorities to pursue for further model developments and components to include in the next generation of NWP systems. The APPLICATE developments considered in this deliverable are: the inclusion of dynamical and/or thermodynamic sea ice models. the extension of single-layer snow schemes on land to multi-layer snow schemes. the inclusion of snow schemes on top of sea ice. an improved version of the eddy-diffusivity mass-flux scheme which detects decoupled stratocumulus. Introducing an interactive sea ice model into numerical weather predictions leads to a robust increase in forecast quality for near-surface temperature close to the sea ice edge in the ARPEGE/AROME prediction system. Both bias and standard deviation are substantially reduced. The temperature gradient across the sea ice edge is more realistic. Exploiting both observed sea surface temperatures and sea ice fraction to initialize the model is essential to optimize the benefits from the interactive sea ice model. Similarly, extracting boundary conditions from a global model which also uses an interactive sea ice model improves substantially the performance of the regional prediction system. The dynamic coupling of the ice cover in IFS allows for capturing accurately rapid changes in sea ice concentration, especially along the sea ice edge as illustrated on a case study. A substantial impact is seen in the near surface temperature (up to 6°C) when the large-scale meteorological situation is favourable. At high latitudes, near-surface temperature tends to be overestimated during cold events occurring in clear-sky conditions. Introducing a multi-layer snow scheme over land into the IFS prediction systems leads to a better representation of these cold events at forecast day 2 over the Sodankylä observational supersite. The diurnal cycle in near surface temperature is also better captured with the multi-layer snow scheme than with the previous single-layer scheme, with a reduced warm bias at the daily minimum and cold bias at the daily maximum. Including this multi-layer snow scheme over sea ice allows for a marginally better simulated temperature in the lower atmosphere for a case study from the SHEBA campaign. However, during periods of liquid-phase cloud cover, systematic errors in the representation of such clouds leads to the increase of the errors of skin temperature in the simulation using the multi-layer snow scheme. These results highlight the key role of compensating errors in the surface energy balance and the need for improved representation of Arctic boundary layer processes. A revised version of the eddy-diffusivity mass-flux scheme was proposed to identify decoupled stratocumulus. This scheme detects and simulates cloud layers that would not have been represented before, as seen in an ASCOS Arctic stratocumulus case. The root mean square error for clouds and radiative properties are drastically reduced with this improved scheme.

Different practices are currently being used to couple the different components of global models for weather forecasts (several days to weeks ahead) and climate applications (several decades to centuries). This report provides a summary of the coupling methods currently used for Numerical Weather Prediction (NWP) and climate applications. We have reviewed the different approaches employed in the community and used them as a motivation to design novel experiments aimed at improving the coupling between the atmosphere, ocean and sea ice. The results of these experiments, together with prior results from the literature, have guided the recommendations provided in this report. To improve coupling methodology in NWP and climate models, we recommend using fluxes that are consistent at the interface between the atmosphere, the ocean and the sea ice. More specifically, we show that accounting for the differences in horizontal resolution between the different components of the Earth System when computing the surface fluxes, or accounting for the different sea ice thickness categories can lead to small but detectable differences in the representation of key variables like Arctic sea ice extent and volume. Even if the differences are small, computing the fluxes in a consistent way is more physical and could lead to a better representation of the atmospheric boundary layer and consequently of near-surface weather. We further show that the representation of key physical processes such as snow over sea ice is essential for a realistic representation of near surface temperature in coupled models and it is particularly important for NWP. Representing processes such as snow over sea ice is key to reduce model biases in the polar regions and hence to improve short term predictions and the representation of the model climate. We expect that the results presented in this report will help weather and climate model developments in the future.

In such a remote and harsh environment as the Arctic, the monitoring of essential climate variables is expensive and, therefore, sporadic. In turn, satellites provide observations constrained to the surface. Also, because of technical restrictions, satellites cannot sample, at least not year-round, a set of essential variables, such as the sea ice thickness. To overcome this difficulty, numerical models are key tools for studying and predicting the Arctic weather and climate. Numerical models aim at reproducing the interactions between different climate components such as the land, atmosphere, ocean, and sea ice. Such interactions are complex and described with non-linear functions. This is one reason numerical models are in constant improvement. The primary goals of Work Package 2 are to promote improvements in numerical models and establish the impact of these improvements on model results, both for the study of the Arctic climate and numerical weather prediction. This deliverable presents a set of model enhancements that are implemented and tested in fully coupled models (AWI-CM1, HadGEM3-GC3.1, EC-Earth3 PRIMAVERA, and ECMWF IFS CY45R1) and forced-mode (NEMO3.6-LIM3 and GELATO). All model developments aim to improve the representation of physical processes that take place in the sea ice or snow. These consist of a better representation of the turbulent exchanges of heat and momentum between the atmosphere and sea ice (form drag), a description of the melt ponds that appear on the sea ice surface during the melt season (melt ponds), a parameterization which takes into account the effect of the sea ice attached to the shore and ocean floor (landfast ice), a scheme which accounts for the size of the floes that form the sea ice cover (floe size distribution), and an improved representation of the snow both over land and sea ice (multilayer snow scheme). We assess the benefit of the model developments based on the following five aspects: (i) changes in various components of the Arctic surface energy budget, (ii) changes in the transfer of momentum from the atmosphere to the ocean, (iii) the overall realism of the simulated climate system, (iv) effects on the Arctic Ocean circulation, and (v) changes in the Arctic climate sensitivity. Common results emerged from the form drag experiments: increased sea ice drift speed in the marginal ice zone, a general decrease in ice thickness, and a marginal decrease of ice concentration at the ice edge in summer. The form drag parameterization improved the large-scale atmospheric and ocean-driven ocean circulation. The melt pond parameterization shows a clear impact on the albedo and sea ice variability and reinforces that a reduced sea ice regime in the Arctic impacts the large-scale, density-driven ocean circulation. The multilayer snow scheme makes the models more sensitive to the surface thermodynamic forcing than the control run with a single layer of snow and shows a more realistic albedo. In numerical weather prediction, the multilayer scheme leads to an improved prediction of the 2-m temperature diurnal cycle over land. Over sea ice, the new scheme reduces large positive biases of outgoing longwave radiation. Fast ice developments lead to more realistic sea ice conditions supported by evidence from observational data. Parameterization of floe size distribution reveals sea ice growth caused by large ice floes in the marginal ice zone during summer.

Presentation at the November 2020 around-the-clock International Verification Methods Workshop Online (2020-IVMW-O)

In this work a new multi-layer snow scheme for the ECMWF Integrated Forecasting System (IFS) is evaluated and the impact on global short- and medium-range weather forecasts analyzed. The work described in the abstract has been presented at the European Geophysical Union (EGU) General Assembly 2019.

In such a remote and harsh environment as the Arctic, the monitoring of essential climate variables is expensive and, therefore, sporadic. In turn, satellites provide observations constrained to the surface. Also, because of technical restrictions, satellites cannot sample, at least not year-round, a set of essential variables, such as the sea ice thickness. To overcome this difficulty, numerical models are key tools for studying and predicting the Arctic weather and climate. Numerical models aim at reproducing the interactions between different climate components such as the land, atmosphere, ocean, and sea ice. Such interactions are complex and described with non-linear functions. This is one reason numerical models are in constant improvement. The primary goals of Work Package 2 are to promote improvements in numerical models and establish the impact of these improvements on model results, both for the study of the Arctic climate and numerical weather prediction. This deliverable presents a set of model enhancements that are implemented and tested in fully coupled models (AWI-CM1, HadGEM3-GC3.1, EC-Earth3 PRIMAVERA, and ECMWF IFS CY45R1) and forced-mode (NEMO3.6-LIM3 and GELATO). All model developments aim to improve the representation of physical processes that take place in the sea ice or snow. These consist of a better representation of the turbulent exchanges of heat and momentum between the atmosphere and sea ice (form drag), a description of the melt ponds that appear on the sea ice surface during the melt season (melt ponds), a parameterization which takes into account the effect of the sea ice attached to the shore and ocean floor (landfast ice), a scheme which accounts for the size of the floes that form the sea ice cover (floe size distribution), and an improved representation of the snow both over land and sea ice (multilayer snow scheme). We assess the benefit of the model developments based on the following five aspects: (i) changes in various components of the Arctic surface energy budget, (ii) changes in the transfer of momentum from the atmosphere to the ocean, (iii) the overall realism of the simulated climate system, (iv) effects on the Arctic Ocean circulation, and (v) changes in the Arctic climate sensitivity. Common results emerged from the form drag experiments: increased sea ice drift speed in the marginal ice zone, a general decrease in ice thickness, and a marginal decrease of ice concentration at the ice edge in summer. The form drag parameterization improved the large-scale atmospheric and ocean-driven ocean circulation. The melt pond parameterization shows a clear impact on the albedo and sea ice variability and reinforces that a reduced sea ice regime in the Arctic impacts the large-scale, density-driven ocean circulation. The multilayer snow scheme makes the models more sensitive to the surface thermodynamic forcing than the control run with a single layer of snow and shows a more realistic albedo. In numerical weather prediction, the multilayer scheme leads to an improved prediction of the 2-m temperature diurnal cycle over land. Over sea ice, the new scheme reduces large positive biases of outgoing longwave radiation. Fast ice developments lead to more realistic sea ice conditions supported by evidence from observational data. Parameterization of floe size distribution reveals sea ice growth caused by large ice floes in the marginal ice zone during summer.

Energy exchange at the snow‐atmosphere interface in winter is important for the evolution of temperature at the surface and within the snow, preconditioning the snowpack for melt during spring. This study illustrates a set of diagnostic tools that are useful for evaluating the energy exchange at the Earth's surface in an Earth System Model, from a process‐based perspective, using in situ observations. In particular, a new way to measure model improvement using the response of the surface temperature and other surface energy budget (SEB) terms to radiative forcing is presented. These process‐oriented diagnostics also provide a measure of the coupling strength between the incoming radiation and the various terms in the SEB, which can be used to ensure that improvements in predictions of user‐relevant properties, such as 2 m temperature, are happening for the right reasons. Correctly capturing such process relationships is a necessary step toward achieving more skilful weather forecasts and climate projections. These diagnostic techniques are applied to assess the impact of a new multi‐layer snow scheme in the European Centre for Medium‐Range Weather Forecasts'‐Integrated Forecast System at two high‐Arctic sites (Summit, Greenland and Sodankylä, Finland). A previous study showed that it will enhance 2 m temperature forecast skill across the Northern Hemisphere in boreal winter compared to forecasts with the single layer model, reducing a warm bias. In this study we use the diagnostics to show that the bias is improved for the right reasons.

These data support the journal article : An Urban Scheme for the ECMWF Integrated Forecasting System: Single-Column and Global Offline Evaluation (Journal of Advances in Modeling Earth Systems). The files provided are as follows: 3D_FIELD_PARIS.nc - Provides an example 3D profile from the Single Column Model including temperature, sensible heat flux and PBL height. GLOBAL_JUL_AVE.zip - Provides the global 2m temperature from three different surface only simulations at ~1 km resolution for July 2019, these include a control, urban-world and using a more realistic urban map. OBSERVATIONS.nc - Includes the 2m air temperature from SYNOP sites used to optimise the urban parameters in the SCM. SCM_DIURNAL_JAN/JUL.nc - Provides the 2m air temperature diurnal cycle for January and July estimated by the SCM using optimised parameters over 8 sites. SCM_OUTPUT.zip - Provides the January and July SCM output of 2m air temperature using optimised parameters over 8 sites. For more information please contact joe.mcnorton@ecmwf.int

Energy exchange at the snow‐atmosphere interface in winter is important for the evolution of temperature at the surface and within the snow, preconditioning the snowpack for melt during spring. This study illustrates a set of diagnostic tools that are useful for evaluating the energy exchange at the Earth's surface in an Earth System Model, from a process‐based perspective, using in situ observations. In particular, a new way to measure model improvement using the response of the surface temperature and other surface energy budget (SEB) terms to radiative forcing is presented. These process‐oriented diagnostics also provide a measure of the coupling strength between the incoming radiation and the various terms in the SEB, which can be used to ensure that improvements in predictions of user‐relevant properties, such as 2 m temperature, are happening for the right reasons. Correctly capturing such process relationships is a necessary step toward achieving more skilful weather forecasts and climate projections. These diagnostic techniques are applied to assess the impact of a new multi‐layer snow scheme in the European Centre for Medium‐Range Weather Forecasts'‐Integrated Forecast System at two high‐Arctic sites (Summit, Greenland and Sodankylä, Finland). A previous study showed that it will enhance 2 m temperature forecast skill across the Northern Hemisphere in boreal winter compared to forecasts with the single layer model, reducing a warm bias. In this study we use the diagnostics to show that the bias is improved for the right reasons.

Dataset used for the evaluation of a new multi-layer snow scheme developed for the ECMWF Integrated Forecasting System (IFS). The dataset includes offline experiments to be compared with in situ observations and time-series and statistics of different quantities (snow depth, 2-metre temperature, cloud cover etc.) used to evaluate the impact on coupled land-atmosphere weather forecasts, as reported in the manuscript "Impact of a multi-layer snow scheme on near-surface weather forecasts" by Arduini et al. (2019). The file readme_data.txt contains details on the content of each file., {"references":["Arduini et al. (2019): Impact of a multi-layer snow scheme on near-surface weather forecasts"]}

© Copyright 2022 American Meteorological Society (AMS). For permission to reuse any portion of this Work, please contact permissions@ametsoc.org. Any use of material in this Work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act (17 U.S. Code § 107) or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC § 108) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a website or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. All AMS journals and monograph publications are registered with the Copyright Clearance Center (https://www.copyright.com). Additional details are provided in the AMS Copyright Policy statement, available on the AMS website (https://www.ametsoc.org/PUBSCopyrightPolicy). 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 providing recommendations for future model and forecast system development. The results discussed in this article were supported by the project APPLICATE (727862), funded by the European Union's Horizon 2020 research and innovation programme. PO was additionally supported by the Spanish fellowship RYC-2017-22772. Peer Reviewed Article signat per 29 autors/es: Pablo Ortega (1), Edward W. Blockley (2), Morten Køltzow (3), François Massonnet (4), Irina Sandu (5), Gunilla Svensson (6), Juan C. Acosta Navarro (1), Gabriele Arduini (5), Lauriane Batté (7), Eric Bazile (7), Matthieu Chevallier (8), Rubén Cruz-García (1), Jonathan J. Day (5), Thierry Fichefet (4), Daniela Flocco (9), Mukesh Gupta (4), Kerstin Hartung (6,10), Ed Hawkins (9), Claudia Hinrichs (11), Linus Magnusson (5), Eduardo Moreno-Chamarro (1), Sergio Pérez-Montero (1), Leandro Ponsoni (4), Tido Semmler (11), Doug Smith (2), Jean Sterlin (4), Michael Tjernström (6), Ilona Välisuo (7,12), and Thomas Jung (11,13) // (1) Barcelona Supercomputing Center, Barcelona, Spain | (2) Met Office, Exeter, UK | (3) Norwegian Meteorological Institute, Oslo, Norway | (4) Université catholique de Louvain, Earth and Life Institute, Georges Lemaître Centre for Earth and Climate Research, Louvain-la-Neuve, Belgium | (5) European Centre for Medium-Range Weather Forecasts, Reading, UK | (6) Department of Meteorology, Stockholm University, Stockholm, Sweden | (7) CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France | (8) Météo-France, Toulouse, France | (9) National Centre for Atmospheric Science, Department of Meteorology, University of Reading, Reading, UK. | (10) Now at: Deutsches Zentrum für Luft- und Raumfahrt, Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany | (11) Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany | (12) Now at: Meteorology Unit, Finnish Meteorological Institute, Helsinki, Finland | (13) Department of Physics and Electrical Engineering, University of Bremen, Bremen, Germany