The demand side of climate services for real-time snow management in Alpine ski resorts: Some empirical insights and implications for climate services development

Köberl, Judith; François, Hugues; Cognard, Jonathan; Carmagnola, Carlo Maria; Prettenthaler, Franz; Damm, Andrea; Morin, Samuel

doi:10.1016/j.cliser.2021.100238

Cited by 8 publications

(2 citation statements)

References 18 publications

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“…Other studies [8] have proposed the co-creation of specific indices for each specific activity/destination. One such study described indices for beach and snow tourism [32], while others developed indices for skiing [33,34], and still others have focused on surfing [35]. Sports tourism, based either on attending a sports event or on practicing the sport, has experienced considerable growth in the last several decades.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring the Climatic Potential of Somo’s Surf Spot for Tourist Destination Management

et al. 2022

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Surfing is one of the most popular activities in coastal tourism resorts. However, the sport depends strongly on the met-ocean weather conditions, particularly on the surface wind-generated waves that reach the coast. This study provides examples of how users’ needs and user perspectives are considered by climate data specialists to develop needed, highly useful information addressing human and social needs. In this vein, the climate analysis of such data can provide input on the expected length of a surfing season, according to the surfer’s level of expertise. In addition, other water sports, such as SUP Wave and windsurfing, among others, might be indicated when surfing conditions are not optimal. Finally, the safety of surfers and other tourists who venture into the sea is also dependent on those conditions. We collaborated with the surfing community to define a series of indices for quantifying surfing days (SD), surfing days stratified by surfers’ skills (SDS), alternate offers (AOs), and surfers’ and swimmers’ safety (SuS and SwS). These are of general applications but require wind and wave data at a very fine scale as the input. To illustrate the potential of our indices, we applied them to the Somo beach (Cantabria, Spain). We downscaled a global wave hindcast dataset covering a 30-year period to a spatial resolution of 100 m to obtain wave-surfing information at Somo’s surf spot. The results confirmed Somo’s status as a year-round surf spot, with SD values of 229.5 days/year and monthly values between 22 days/month and 16 days/month. SDS showed different seasonal peaks according to the surfers’ skills. Beginners’ conditions occurred more often in the summer (18.1 days/month in July), intermediate surfers’ conditions appeared in the transitional seasons (14.1 days/month in April), and advanced and big-wave riders in the winter (15.1 days/month in January and 0.7 days/month, respectively). The AO index identified the SUP wave values of 216 days/year. Wind water sports presented values of 141.6 days/year; conversely, SUP sports were possible on only 7.4 days/year. SuS and SwS identified different seasonal hazard values, decreasing from the winter, autumn, and spring to minimum values in the summer.

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Section: Literature Reviewmentioning

confidence: 99%

Exploring the Climatic Potential of Somo’s Surf Spot for Tourist Destination Management

et al. 2022

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“…Snowpack is a natural "integrator" of the climatic conditions over multiple days/months, so even if daily temperature and precipitation forecasts do not match the corresponding observations, the differences may compensate over monthly/seasonal timescales and allow for reasonable monthly/seasonal snowpack forecasts. Several economic activities recognized a value in seasonal forecasts of mountain snow accumulation, either per se or as an indicator of the meltwater available in the season ahead: (i) public water managers, who could prepare strategies to mitigate the negative effects of extremely dry or extremely wet seasons; (ii) hydropower companies involved in reservoir management, who could use forecasts of the snowpack evolution to decide whether to release or save water in the reservoir; and (iii) mountain ski resort managers, for whom seasonal snowpack predictions are relevant to estimate the amount of artificial snow to be produced (Marke et al, 2015) and have high saving potential (Köberl et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Seasonal forecasting of snow resources at Alpine sites

Terzago

Bongiovanni²,

Hardenberg³

2023

Hydrol. Earth Syst. Sci.

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Abstract. Climate warming in mountain regions is resulting in glacier shrinking, seasonal snow cover reduction, and changes in the amount and seasonality of meltwater runoff, with consequences on water availability. Droughts are expected to become more severe in the future with economical and environmental losses both locally and downstream. Effective adaptation strategies involve multiple timescales, and seasonal forecasts can help in the optimization of the available snow and water resources with a lead time of several months. We developed a prototype to generate seasonal forecasts of snow depth and snow water equivalent with a starting date of 1 November and a lead time of 7 months, so up to 31 May of the following year. The prototype has been co-designed with end users in the field of water management, hydropower production and mountain ski tourism, meeting their needs in terms of indicators, time resolution of the forecasts and visualization of the forecast outputs. In this paper we present the modelling chain, based on the seasonal forecasts of the ECMWF and Météo-France seasonal prediction systems, made available through the Copernicus Climate Change Service (C3S) Climate Data Store. Seasonal forecasts of precipitation, near-surface air temperature, radiative fluxes, wind and relative humidity are bias-corrected and downscaled to three sites in the Western Italian Alps and finally used as input for the physically based multi-layer snow model SNOWPACK. Precipitation is bias-corrected with a quantile mapping method using ERA5 reanalysis as a reference and then downscaled with the RainFARM stochastic procedure in order to allow an estimate of uncertainties due to the downscaling method. The impacts of precipitation bias adjustment and downscaling on the forecast skill have been investigated. The skill of the prototype in predicting the deviation of monthly snow depth with respect to the normal conditions from November to May in each season of the hindcast period 1995–2015 is demonstrated using both deterministic and probabilistic metrics. Forecast skills are determined with respect to a simple forecasting method based on the climatology, and station measurements are used as reference data. The prototype shows good skills at predicting the tercile category, i.e. snow depth below and above normal, in the winter (lead times: 2–3–4 months) and spring (lead times: 5–6–7 months) ahead: snow depth is predicted with higher accuracy (Brier skill score) and higher discrimination (area under the relative operating characteristics (ROC) curve skill score) with respect to a simple forecasting method based on the climatology. Ensemble mean monthly snow depth forecasts are significantly correlated with observations not only at short lead times of 1 and 2 months (November and December) but also at lead times of 5 and 6 months (March and April) when employing the ECMWFS5 forcing. Moreover the prototype shows skill at predicting extremely dry seasons, i.e. seasons with snow depth below the 10th percentile, while skills at predicting snow depth above the 90th percentile are model-, station- and score-dependent. The bias correction of precipitation forecasts is essential in the case of large biases in the global seasonal forecast system (MFS6) to reconstruct a realistic snow depth climatology; however, no remarkable differences are found among the skill scores when the precipitation input is bias-corrected, downscaled, or bias-corrected and downscaled, compared to the case in which raw data are employed, suggesting that skill scores are weakly sensitive to the treatment of the precipitation input.

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Exploring the landscape of seasonal forecast provision by Global Producing Centres

et al. 2022

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Despite the growing demand for seasonal climate forecasts, there is limited understanding of the landscape of organisations providing this critically important climate information. This study attempts to fill this gap by presenting results from an in-depth dialogue with the organisations entrusted with the provision of seasonal forecasts by the World Meteorological Organisation, known as the Global Producing Centres for Long-Range Forecasts (GPCs-LRF). The results provide an overview and detailed description of the organisational setup, mandate, target audience of GPCs-LRF and their interactions with other centres. Looking beyond the GPCs-LRF to other centres providing seasonal forecasts, some of which have been rapidly taking prominent places in this landscape, revealed a heterogeneous and still maturing community of practice, with an increasing number of players and emerging efforts to produce multi-model ensemble forecasts. The dialogues pointed at the need to not only improve climate models and produce more skilful climate forecasts, but also to improve the transformation of the forecasts into useful and usable products. Finally, using the lenses of credibility, salience and legitimacy, we explore ways to bridge the fragmentation of the information offered across the organisations considered and the people involved in the delivery and use of seasonal forecasts. The paper concludes by suggesting ways to address the boundary crossing between science, policy and society in the context of seasonal climate prediction.

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The demand side of climate services for real-time snow management in Alpine ski resorts: Some empirical insights and implications for climate services development

Cited by 8 publications

References 18 publications

Exploring the Climatic Potential of Somo’s Surf Spot for Tourist Destination Management

Exploring the Climatic Potential of Somo’s Surf Spot for Tourist Destination Management

Seasonal forecasting of snow resources at Alpine sites

Exploring the landscape of seasonal forecast provision by Global Producing Centres

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