# Climate change exacerbates snow-water-energy challenges for European ski tourism

Hugues François, Raphaëlle Samacoïts, David Neil Bird, Judith Köberl, Franz Prettenthaler, Samuel Morin

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Hugues François, Raphaëlle Samacoïts, David Neil Bird, Judith Köberl, Franz Prettenthaler, et al. Climate change exacerbates snow-water-energy challenges for European ski tourism. Nature Climate Change, 2023, pp.1-32. 10.1038/s41558-023-01759-5. hal-04192000

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Climate change exacerbates
snow-water-energy challenges for European
ski tourism

Hugues François¹†, Raphaëlle Samacoïts¹,²†, David Neil

Bird³, Judith Köberl³, Franz Prettenthaler³ and Samuel
Morin⁴*

¹Univ. Grenoble Alpes, INRAE, LESSEM, Grenoble, France.

²Météo-France, Direction de la Climatologie et des Services
Climatiques, Toulouse, France.

³JOANNEUM RESEARCH Forschungsgesellschaft mbH,
Institute for Climate, Energy Systems and Society, Graz, Austria.

⁴Univ. Grenoble Alpes, Université de Toulouse, Météo-France,
CNRS, Centre National de Recherches Météorologiques, Toulouse
and Grenoble, France.

*Corresponding author(s). E-mail(s): samuel.morin@meteo.fr;

Contributing authors: hugues.francois@inrae.fr;

raphaëlle.samacoits@meteo.fr; neil@birds-ccc.at;

judith.koeberl@joanneum.at; franz.prettenthaler@joanneum.at;

†These authors contributed equally to this work.

Abstract

Ski tourism is a substantial component of the economy of mountainous regions in Europe and highly vulnerable to snow scarcity, which increases due to climate change. However the climate change snow supply risk to ski tourism has not been quantified in a consistent way throughout Europe, including the influence and environmental footprint of snow-making. Here we show that the snow supply risk to ski tourism increases with global warming level, heterogeneously within and across mountain areas and countries. Without snowmaking, 53% and 98% of the 2,234 ski resorts studied in 28 European countries are projected at very high risk for snow supply under global warming of 2°C and 4°C, respectively. By contrast, assuming a snowmaking fractional coverage of 50%

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2 Climate change challenges European ski tourism

leads to a proportion of 27% and 71%, respectively, but with increasing
water and electricity demand (and related carbon footprint) of snow-
making. While it represents a modest fraction of the overall carbon
footprint of ski tourism, snowmaking is an inherent part of the ski tourism
industry and it epitomizes some of the key challenges at the nexus
between climate change adaptation, mitigation, and sustainable devel-
opment in the mountains, with their high social-ecological vulnerability.

Keywords: snow, tourism, ski, snowmaking, Europe, climate change, water, carbon footprint

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Due to climate change, the mountain seasonal snow cover decreases, at a rate which depends on the geographical location and the elevation [1-3]. Assessing the extent to which past and future changes in climate affect ski resort operations across Europe is critical to design meaningful development pathways for this key sector of the European tourism industry [4], particularly in the context of the European Green Deal [5]. This concerns both climate change adaptation and mitigation [6], and needs to take into account related pressures on the local environment and resources such as water and energy [7]. Indeed, ski tourism faces complex and intertwined sustainability challenges in the context of global change [8, 9]. Global tourism (not only ski tourism) is responsible for substantial greenhouse gas emissions, estimated to 8.1% of global emissions with 49.1% of this contribution caused by transport [10]. Tourism strongly influences local and regional social-ecological systems, sustaining livelihoods but also affecting biodiversity and use of resources [11]. The environmental impact of ski tourism is often viewed in terms of water and electricity demand for snowmaking, but only a few studies provide quantitative estimates, most often at the local scale [12, 13].

Snow scarce conditions are detrimental to ski resort operations, because
snow on ski pistes is the major ingredient of ski tourism supply. An occasional
lack of snow has always been contemplated by ski resort operators, due to
the strong interannual variability of snow conditions [1, 2, 14, 15]. A key con-
cern is how often and to what extent such snow scarce situations occur, as
recurring snow scarce winters threaten the long term economic sustainability
of ski resorts [14, 16–19]. Here we adopt the definition of risk of the Inter-
governmental Panel on Climate Change (IPCC) [20], i.e. the "potential for
adverse consequences for human or ecological systems", and conceptualize its
application to snow supply to ski tourism on Figure 1. Quantifying long term
climate change impacts and risks pertaining to the snow supply to European
ski tourism requires accounting not only for the evolution of climatic impact-
drivers (temperature increase, snow cover decline), the related hazards (the
frequency of warm and/or snow scarce winters), but also for their combina-
tion with the exposure (geographic location, elevation range) of individual ski
resorts, and the ski resorts’ individual and collective vulnerability.
<figure><img src="image_2.png" /><figcaption>Fig. 1 Snow supply risk to ski tourism in the context of climate change.</figcaption></figure>

Following the IPCC's conceptual framework, risk, which we define as the frequency of snow scarce conditions on ski pistes, results from the combination of hazard, exposure and vulnerability. Changes in the hazard are related to temperature rise and snow cover decline. Snow management is designed to reduce the snow supply risk, but is itself influenced by climatic impact-drivers. In turn, it induces pressure on environmental externalities, with related greenhouse gas emissions. The risk also influences demand-side factors, through skier satisfaction (influencing profitability of operations) and skier visits (contributing to greenhouse gas emissions through transportation, housing etc.). Profitability of operations are influenced by snow management costs but also influence investments in snowmaking and related path dependencies.

The risk on snow supply to ski tourism can thus be defined as the frequency of snow scarce conditions on ski pistes, taking into account snow grooming and snowmaking [1, 16, 21, 22]. Indeed, the climatic impact-driver "snow cover decline" directly leads to a rise in the snow supply risk to ski tourism (Figure 1). Snowmaking, by contrast, is primarily designed to reduce the snow supply risk to ski tourism by decreasing the frequency of snow scarce conditions on ski pistes through the addition of machine-made snow [14, 18, 19, 23]. There are multiple interactions between snowmaking, the local climate (snowmaking can only proceed if the temperature remains sufficiently low), the local environment (water resources, local ecosystems), the broader socio-economic functioning of ski tourism within mountain areas, and the influence of ski tourism on the climate through greenhouse gas emissions [7, 14, 18, 19, 23] (see Figure 1).

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4 Climate change challenges European ski tourism

Europe (see Figure 2 for the included mountain ranges) is the largest global ski tourism market, with about 50% of the world's total ski resorts and over 80% of the world's ski resorts with more than 1 million skier visits (i.e. number of daily individual use of ski resorts facilities) per year [24]. It accounts for 60% of annual skier visits (43% only for the European Alps) corresponding to 209 million skier visits in 2019, before the Covid-19 outbreak [24], and a turnover exceeding 30 billion euros per year according to media reports [25]. Past studies dedicated to climate change impacts on ski tourism in Europe have either neglected the influence of snow management, and in particular snowmaking [4, 26–28], or focused on smaller geographical domains, e.g. at the scale of individual ski resorts or an aggregation at the national scale [16, 17, 29–31]. Also, many studies [27–29, 31] addressed the demand side risk resulting from tourists’ reactions to future snow scarce conditions rather than quantifying the risk to snow supply itself. These deficiencies have made it difficult to produce a consistent assessment at the scale of the entire continent. The environmental footprint of snowmaking has seldom been studied [12, 32], and never at the scale of the entire continent.

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# 1 Climate change ski tourism snow supply risk

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We study the climate change effects on the snow supply risk to ski tourism at the scale of the entire European ski tourism market. We use numerical simulations of snow conditions, with and without snowmaking, and under past and future climate conditions, at the scale of individual ski resorts (see Method and Figures 1 and 2). Expanding from previous work [16, 18, 33], we quantify daily snow conditions at ski resort scale as the fraction of the ski resort’s surface area with a snow mass (snow water equivalent) exceeding a threshold of 100 kg m⁻². This corresponds to 20 cm of snow with a density of 500 kg m⁻³, which is sufficient for alpine skiing. Averaging this daily value over the critical time period from December to February [14, 21] provides a yearly indicator of snow conditions, referred to as the snow reliability index. We define the meaning of ”snow scarce conditions” for each ski resort as the worst 20% years in terms of groomed natural snow encountered during the reference time period 1961-1990.

The simulation results used in this study are based on the approach implemented to compute the Copernicus Climate Change Service (CS3) Mountain Tourism Meteorological and Snow Indicators (MTMSI) dataset [34]. This dataset uses the pan-European near-surface meteorological reanalysis UERRA and an ensemble of adjusted EURO-CORDEX regional climate model simulations. Both feed the detailed snow cover model Crocus, which includes a representation of snow grooming and snowmaking [34, 35]. In the model, snowmaking depends on operating rules and meteorological conditions, in particular wet bulb air temperature ([34], see Methods and Extended Data Figure 1). The MTMSI dataset provides state-of-the-art estimates of relevant climate data for the ski tourism industry, yet is insufficient for sectoral risk assessment because it does not consider the location of individual ski resorts. We
<figure><img src="image_3.png" /><figcaption>Fig. 2 Distribution of ski resorts in mountain areas in Europe.</figcaption></figure>

Map of Europe, showing the 18 geographical entities (European Environmental Agency mountain areas, 6 subdomains for the European Alps) considered in this study and the corresponding total estimated surface area of ski pistes (total 984 km², as of November 2022). For each geographical entity, subpanels around the map show the vertical distribution of ski pistes, along with the hypsometry and the corresponding number and total surface areas of the ski resorts. The subplots also display the distribution of snow reliability values (from 0 to 100) of snow conditions in ski resorts during snow scarce winters, corresponding to the 20% worst winter seasons for the time periods 1961-1990 (left) and 1991-2015 (right).

address this gap by combining the data used to produce the MTMSI dataset with geographical information of 2,234 individual ski resorts across Europe (see Method and Extended Data Figures 1 and 2). This enables to compute snow cover conditions (including the snow reliability index - expanding its use when snowmaking is taken into account, see Extended Data Figure 3) and water demand for snowmaking, for various values of the snowmaking fractional
6 Climate change challenges European ski tourism

231 coverage (i.e. the fraction of a resort's ski piste area covered by snowmaking infrastructure) for each ski resort. The results of such calculations are 232 illustrated on Extended Data Figures 4, 5, 6 and 7 for contrasted ski resorts 233 located in the French/Swiss middle mountains (Metabief, Jura, France), in the 234 Austrian Alps, in the Nordic mountains and in British Isles, respectively. 235

236 The distribution of $Q_{20}^{ref}$ values for ski resorts in Europe, shown on Figure 2, reveals the strong heterogeneity in reference snow conditions within and 237 across mountain areas, due to the combined effect of the local climate and the 238 location of ski resorts, and provides the baseline upon which future changes can 239 be analyzed. It also shows the distribution of the ski resorts' 20th percentile 240 of snow reliability index values ($Q_{20}$) within a geographical entity for the time 241 period 1991-2015, highlighting that over past decades natural snow conditions 242 in ski resorts have generally declined, at a rate which differs depending on the 243 mountain area. 244

245 Based on future climate change scenarios, we investigate how the frequency 246 of snow scarce winters changes with the global warming level for various illus- 247 trative values of the snowmaking fractional coverage (0%, 25%, 50% and 75%). 248 The snow supply risk to the European ski tourism is displayed as a function 249 of the global warming level in the form of burning embers in Figure 3 at the 250 scale of mountain areas [36] (see Methods and Extended Data Figure 1). We 251 define the risk level as follows: a frequency of 30% of winters below the snow 252 scarcity threshold, corresponding to a 50% increase in the risk compared to 253 the reference value (20%), is referred to as "moderate" (yellow). A frequency 254 of 40%, corresponding to a doubling of the reference risk value, is referred to as 255 "high" (red). The risk reaches the "very high" (violet) domain at a frequency 256 of 50% and above, i.e. snow scarce conditions encountered at least once every 257 two years on average. 258

258 Burning embers for groomed natural snow only (left), and including snow- 259 making (right), consistent with the approach taken in the literature and in 260 recent IPCC reports [36–38] for displaying climate change risk with and with- 261 out adaptation, shows that, in all cases, the risk systematically increases with 262 higher global warming level (Figure 3). Further, for groomed natural snow 263 conditions only, in most geographical entities, a global warming level of 1.5°C 264 leads to a high to very high snow supply risk to ski tourism, with only a few 265 entities showing comparatively lower increases in the risk (e.g. Turkey, Central 266 European middle mountains 2). Three main patterns emerge from the burning 267 embers analysis taking snowmaking into account. For some mountain areas, 268 mainly in the Alps but also in the Nordic mountains and in Turkey, addi- 269 tional snowmaking fractional coverage reduces the risk. However the marginal 270 benefit decreases, especially between 50% and 75% (block A in Figure 3). For 271 other areas, snowmaking has a positive impact on the risk level up to a cer- 272 tain point, but increases in the snowmaking fractional coverage beyond 50% 273 reduce its efficiency and the risk increases (block B in Figure 3). Last, in some 274 areas, the risk level quickly reaches high values with increasing global warm- 275 ing level regardless of the snowmaking fractional coverage (block C in Figure 276
<figure><img src="image_4.png" /><figcaption>Fig. 3 Burning ember representation of the snow supply risk to ski tourism in European mountain areas.</figcaption></figure>

Burning embers risplay the risk value, i.e. the frequency of snow scarce winters, using snow conditions without snowmaking for 1961-1990 (corresponding to +0.6 °C global warming level since 1850-1900) as a reference. For each geographical entity, the left bar refers to natural snow only (including grooming) and the three other bars account for different levels of snowmaking fractional coverage. Circles display relative changes in water demand for snowmaking, compared to the situation for a fractional snowmaking coverage of 25% during the reference time period 1961-1990. European mountain areas are sorted according to the categories (A, B and C) introduced in the text.

3). Note that these general observations do not necessarily apply to all individual ski resorts within the mountain areas analyzed. This illustrates that the snow supply risk to ski tourism under climate change is context-dependent in multiple ways, including the influence of the snowmaking fractional coverage and the geographically and climatically variable efficiency of snowmaking itself [14, 39, 40]. At 2°C and 4°C global warming levels, respectively, without snowmaking, 53% and 98% of the 2,234 ski resorts studied are projected to experience a very high snow supply risk. Assuming a snowmaking fractional

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323 coverage of 50%, these figures decrease to 27% and 71%, respectively. Results

324 for individual mountain areas are provided in Extended Data Figure 8.

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## 2 Snowmaking water demand and energy footprint

<figure><img src="image_5.png" /><figcaption>Fig. 4 Water and electricity demand, and associated carbon footprint for electricity production, due to snowmaking.</figcaption></figure>

Water demand, corresponding electricity demand and associated carbon footprint for electricity production, due to snowmaking only, assuming a uniform 50% snowmaking fractional coverage, for the main 12 countries. Rows correspond to global warming levels compared to 1850-1900: +0.6°C (1961-1990 reference period, top), +2°C (middle), and +4°C (bottom). a) Water and electricity demand, from left to right, for November, November and December, and hydrological year (summer to summer; snow production occurs between November and February). Box plots, showing the percentiles 10, 20, 50, 80 and 90, are coloured according to the ratio between water demand for snowmaking and total annual precipitation on the ski pistes equipped with snowmaking (colouring also provided in the circles). b) Carbon footprint of electricity production for snowmaking. The colouring refers to the carbon footprint per skier visit.

We estimate on Figure 4 the water and electricity demand for snowmaking under past and future climate conditions, along with the corresponding carbon footprint for the related electricity production (see Methods). Here we
focus on the 12 main countries in terms of the total snowmaking amount, 369
comprising 95% of the ski resorts' surface area considered in this study. The 370
median value of the total annual water demand for snowmaking, for the refer- 371
ence period 1961-1990 with 50% snowmaking fractional coverage, is estimated 372
at 103 Mm³ (90 - 119 Mm³ range spanning the 10th to 90th percentiles). 373
Under these conditions, water demand for snowmaking at the European scale 374
would correspond to 13% of the total annual precipitation falling on ski piste 375
areas equipped with snowmaking facilities. For individual countries, this figure 376
varies between 10% and 32%. 377

Water demand changes substantially in a warmer climate, both in terms 378
of the total quantity and its seasonal distribution. Our results indicate a gen- 379
eral decrease of the water demand for snowmaking in November (decreases in 380
national values range from 0% to -53% at +2°C, and -5% to -96% at +4°C), 381
due to increasing temperatures leading to fewer time periods adequate for 382
snowmaking. Projections indicate an overall increase in annual water demand 383
with the global warming level (changes in national values range from +8% to 384
+25% at +2°C, and +14% to +42% at +4°C), driven by mountain regions 385
where climate conditions remain sufficiently cold for snowmaking. The cor- 386
responding estimates for snowmaking fractional coverages of 25% and 75%, 387
respectively, are provided on Extended Data Figure 9. 388

The median value of the total annual electricity demand for snowmak- 389
ing in these 12 countries is estimated at 309±103 GWh during the reference 390
time period 1961-1990, assuming a 50% snowmaking fractional coverage (see 391
Methods and Extended Data Figure 1). This value is in a comparable order 392
of magnitude to a prior estimate of 600 GWh over the European Alps [41]. 393
Using figures on skier visits from 2019, our estimates corresponds on average 394
to about 1.5±0.5 kWh per skier visit devoted to snowmaking. The total elec- 395	ricity demand for snowmaking is projected to increase on average by 18% and 396
24% for +2°C and +4°C global warming, respectively. 397

Based on the carbon intensity of electricity in 2019, the annual carbon 398
footprint of electricity use for snowmaking in the considered 12 countries is esti- 399
mated at 78±26 kt CO2eq for the reference period 1961-1990, and projected to 400
reach 93±31 kt CO2eq and 97±32 kt CO2eq at +2°C and +4 °C global warm- 401
ing level, respectively. Due to ongoing and future policy changes, the carbon 402
intensity of electricity production is expected to decrease in upcoming decades. 403
Our results illustrate how the carbon footprint of electricity used for snowmak- 404
ing would evolve in a warmer climate, based on the current energy mix. Due to 405
differences between European countries in both water demand for snowmak- 406
ing and the current carbon intensity of electricity production (ranging from 407
0.008 to 1.1 kg CO2eq kWh-1 on annual averages, see Methods), the carbon 408
footprint of electricity demand for snowmaking varies considerably between 409
countries, as shown in Figure 4. Mountainous entities with the highest car- 410
bon footprint for snowmaking combine a comparatively high water demand for 411
snowmaking with a high carbon intensity for electricity production.Per skier 412
visit, the carbon footprint of electricity used for snowmaking varies between 413
414
10 Climate change challenges European ski tourism

415 0.01 and 2.3 kg CO₂ eq. These values are consistent with previous estimates at
416 the national scale [42].

417 Recent studies estimate that the operations of a ski resort (snow manage-
418 ment, ski lift operations etc.), including electricity demand for snowmaking,
419 correspond to 2% to 4% of the total carbon footprint of the destination [42, 43].
420 In contrast, more than 50% of the carbon footprint, sometimes even exceed-
421 ing 80%, is induced by arrival, departure and on-site transportation of skiers
422 [42, 43]. However, while the carbon footprint of ski resort operations represents
423 a small share of a destination's total footprint, both are strongly interrelated
424 as ski-related tourism activities are often the main driver for tourist visits.
425 Ski tourism operators, together with other mountain tourism stakeholders, are
426 thus jointly responsible for the broader emissions (e.g. accommodation, trans-
427 portation) related to ski tourism, even if their operations make a small direct
428 contribution.

429

3 Discussion
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432 All estimates provided in this study rely on state-of-the-art reanalyses and
433 climate projections of the snow cover reliability and a detailed inventory of
434 ski resort locations in Europe (see Methods and Extended Data Figure 1).
435 This study comes with inevitable limitations and simplifications implied by
436 our pan-European approach in a context where detailed data are often missing
437 (e.g. data on the snowmaking fractional coverage of individual ski resorts,
438 and the technology implemented). Our estimates of the water demand for
439 snowmaking assume current technologies and practices of the industry. They
440 neither reflect any limitations in water availability under the current or future
441 climate, nor possible changes in technologies and practices of the industry.
442 Nevertheless, based on a series of reasonable hypotheses and assumptions, we
443 provide a consistent depiction of the current and future state of the snow
444 supply risk pertaining to the ski tourism industry in Europe, and some key
445 related environmental externalities.
446

446 Although a warmer climate will systematically increase the snow supply
447 risk pertaining to this industry, its magnitude depends on the region. Our
448 results highlight not only the general trend and rising challenges, but also
449 the variability across mountain regions of Europe in this respect, which is
450 further modulated at the local scale due to the strong variability within moun-
451 tain regions [17, 18]. This work provides insights, which are primarily relevant
452 for regional scale mountain development policy [21, 44]. Our methods and
453 results also provide a basis for producing local climate change impact stud-
454 ies in Europe at the ski-resort level. However, their local application would
455 require the use of additional local information. This includes the use of a
456 detailed map of the ski resort including the location and characteristics of
457 snowmaking units, an analysis of the availability and use of water under current
458 and future climate conditions, the availability of energy resources for sustain-
459 ing snowmaking operations, and impact studies on local ecosystems [18, 32].
460
Such information is critical for informing climate action in mountainous areas, 461
across all relevant scales of decision [44–46]. Our results are also relevant in 462
the context of strengthening EU regulations on climate risk disclosure for all 463
sectors. For planning purposes, such information relevant to the challenges of 464
the ski tourism supply side (operating conditions) needs to be combined with 465
future trajectories on the ski tourism demand side (i.e., skiers preferences and 466
potential changes in practices), which are also heavily dependent on snow con- 467
ditions and broader socio-economic perspectives including the diversification 468
of mountain economies and avoiding maladaptation [1, 6, 7, 19, 27, 44, 47, 48]. 469

The presented results illustrate the interaction between climate change 470 impacts and risks to a given sector (here snow supply to ski tourism) and 471 broader environmental and climate challenges. Indeed, reaching strong reduc- 472 tions in greenhouse gas emissions for ski tourism destinations will chiefly 473 depend on massive reductions of the carbon footprint of transportation and 474 accommodation [6]. This holds implications for tourism development strate- 475 gies. In particular, it remains questionable whether far-reaching policies 476 required to limit global warming to +2°C are compatible with carbon intensive 477 tourism activities in their current form, in particular regarding transporta- 478 tion, housing, and the carbon intensity of electricity production [7, 19]. Even 479 if a substantial fraction of European ski resorts is projected to still be able to 480 operate at 2°C global warming, the ability of ski tourism destinations, as a 481 whole, to achieve their share of greenhouse gas emission reductions required 482 to remain below this global warming level is a major challenge. 483
484

## Acknowledgments

We are grateful to all organizations and individuals involved in the produc- 487 tion and distribution of the datasets used for this work. Work towards this 488 publication has benefited from funding from the European Union’s Horizon 489 2020 research and innovation programme under grant agreement No 730203. 490 We thank N. Ribot (Geospatial Solutions) for support on database manage- 491 ment and E. Maldonado and F. Bray (INRAE) for operating the DBMS 492 tools. Figures 2 and 3 and Extended Data Figures 1 and 8 use images from 493 flaticon.com. 494

## Author contributions

H.F. and S.M. conceived the original idea for the paper. R.S. and H.F. carried 498 out the calculations, with input from N.B. regarding carbon intensity calcu- 499 lations. S.M., H.F. and R.S. led the analysis. All authors contributed to the 500 analysis, drafting, reviewing and editing the paper. 501

## Other information

Samuel Morin is the author to whom correspondence and requests for materials 505 should be addressed. 506
507 This version of the article has been accepted for publication, after peer
508 review but is not the Version of Record and does not reflect post-acceptance
509 improvements, or any corrections. The Version of Record is available online
510 at: https://doi.org/10.1038/s41558-023-01759-5.

511

512 Competing interests

513

514 The authors declare no competing interests.

515

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      <td>565</td>
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      <td>566</td>
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      <td>567</td>
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      <td>568</td>
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      <td>569</td>
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      <td>570</td>
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      <td>571</td>
    </tr>
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      <td></td>
      <td>572</td>
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      <td></td>
      <td>573</td>
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      <td>574</td>
    </tr>
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      <td></td>
      <td>575</td>
    </tr>
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      <td></td>
      <td>576</td>
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      <td></td>
      <td>577</td>
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      <td>578</td>
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      <td>579</td>
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      <td></td>
      <td>580</td>
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      <td></td>
      <td>581</td>
    </tr>
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      <td>582</td>
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      <td></td>
      <td>583</td>
    </tr>
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      <td></td>
      <td>584</td>
    </tr>
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      <td></td>
      <td>585</td>
    </tr>
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      <td></td>
      <td>586</td>
    </tr>
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      <td></td>
      <td>587</td>
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      <td></td>
      <td>588</td>
    </tr>
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      <td></td>
      <td>589</td>
    </tr>
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      <td></td>
      <td>590</td>
    </tr>
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      <td></td>
      <td>591</td>
    </tr>
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      <td>592</td>
    </tr>
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      <td>593</td>
    </tr>
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      <td>594</td>
    </tr>
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      <td>595</td>
    </tr>
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      <td>596</td>
    </tr>
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      <td>597</td>
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    </tr>
  </tbody>
</table>
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680
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684
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  </tr>
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    <td>692</td>
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    <td>693</td>
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    <td>694</td>
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    <td>695</td>
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    <td>696</td>
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    <td>697</td>
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    <td>698</td>
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    <td>707</td>
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    <td>708</td>
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    <td>709</td>
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    <td>717</td>
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    <td>718</td>
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    <td>719</td>
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    <td>720</td>
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    <td>721</td>
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    <td>722</td>
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    <td>723</td>
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    <td>724</td>
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    <td>725</td>
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    <td>727</td>
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    <td>728</td>
    <td></td>
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    <td>729</td>
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    <td>730</td>
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    <td>731</td>
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    <td>732</td>
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    <td>733</td>
    <td></td>
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    <td>734</td>
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    <td>735</td>
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    <td>736</td>
    <td></td>
  </tr>
</table>
# 4 Methods 737

738

739

This study uses meteorological and snow cover simulations underpinning the Copernicus Climate Change Service (CS3) Mountain Tourism Meteorological and Snow Indicators (MTMSI) dataset [1]. Results are provided for all countries of the European Union, candidate countries and members of the European Free Trade Association. Altogether, the dataset covers all EU member states, Albania, Andorra, Montenegro, North Macedonia, Serbia, Turkey, the United Kingdom, Iceland, Liechtenstein, Norway and Switzerland. Within each country, the data is provided for each NUTS-3 (Nomenclature des unités territoriales statistiques) area, by elevation steps of 100 m, on flat terrain. Meteorological data are extracted from the UERRA European reanalysis at 5.5 km resolution, spanning the time period 1961-2015. This data was used to adjust 21 pairs (3 RCP2.6, 9 RCP4.5 and RCP8.5) of global (GCM) and regional (RCM) climate models from the EURO-CORDEX dataset, using the ADAMONT adjustment method [1]. See Extended Data Figure 1. 740
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Using the UERRA near-surface atmospheric reanalysis and the adjusted climate change projections, we performed snow cover simulations using the detailed model Crocus, with and without snowmaking [1, 2]. Crocus is a multi-layer snowpack model embedded in the ISBA land surface model within the SURFEX model [3]. Beyond natural snow processes including surface energy balance and snow metamorphism, Crocus makes it possible to account for grooming and snowmaking [4], based on physical representations of these snow management practices and operational rules [4–6]. For the simulations with snowmaking, the maximum wind speed threshold was set to 4.2 m s⁻¹, the density of machine-made snow to 600 kg m⁻³, the production rate of machine made snow to 1.2 10⁻³ kg m⁻² s⁻¹, and the wet-bulb temperature threshold for snowmaking to -5°C, consistent with previous studies [1, 5]. In the simulations, between November 1 and December 15, up to 150 kg m⁻² machine-made snow is produced, weather conditions permitting and regardless of natural snowfalls during the period, which corresponds to 25 cm snow depth. Between December 15 and February 28, snow is produced if meteorologically possible so as to maintain a total snow depth of 60 cm. After March 1, no more snow is produced. These threshold values are consistent with typical practices of ski resort operators [2]. All simulations were carried out for flat terrain topographic configuration. The simulation results include not only snow cover data (total depth, snow water equivalent) but also the water demand for snowmaking corresponding to the simulation results. 756
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We use the OpenSkiMap (https://openskimap.org) global database of ski resorts, which is based on the OpenStreetMap (https://www.openstreetmap. 780
781
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783 org) collaborative geospatial database. Specifically, we use the three files pro- 784 vided by OpenSkiMap (Ski Areas, Lifts and Runs). Information from these 785 three files was combined, involving careful checks and occasional removal of 786 lifts from our analysis, which are not associated directly with a ski piste, in 787 order to provide a consistent set of ski resorts and ski lifts, together forming a 788 series of 2,959 ski resorts in Europe. See Extended Data Figure 1. 789

790

790 **European mountain areas**
791 The 15 European Environmental Agency (EEA) European mountain areas 792 (https://www.eea.europa.eu/data-and-maps/data/european-mountain-areas) 793 form the core spatial structure of this work. The Atlantic islands, where 794 there is no resort, and the Eastern Mediterranean islands, where there is only 795 one resort, were excluded from the analysis. Thus we focus on 13 European 796 mountain areas. In this study, the "Alps" mountain area is split by countries 797 due to its dominant position in terms of ski tourism in Europe. 798

799

800 **Ski resort geospatial modelling**
801 We combine various geospatial data sources to generate spatial information 802 for ski resorts (see Extended Data Figures 1 and 2). Using the European 803 Digital Elevation Model (EU-DEM), version 1.1, with a spatial resolution 804 of 25 m (http://land.copernicus.eu/pan-european/satellite-derived-products/ 805 eu-dem/eu-dem-v1.1/view) and the ski-lift catalogue from OpenSkiMap 806 (Extended Data Figure 2a), we compute, for each ski lift in each ski resort, 807 the geographical domain accessible downhill from the top of all the ski lifts 808 (Extended Data Figure 2b) and reaching the bottom of one of the ski lifts 809 (Extended Data Figure 2c). Combining these two areas results in the gravita- 810 tional envelope of the ski resort (Extended Data Figures 2d and 2e) [5, 7–9]. 811 Further, we apply a geospatial modelling framework to infer the location where 812 snowmaking is applied within a given ski resort, depending on the snowmak- 813 ing fractional coverage value (0%, 25%, 50% and 75%, see Extended Data 814 Figure 2f) [2, 8]. Each pixel of the ski resort gravitational domain is associated 815 to a NUTS-3 region (Extended Data Figure 2h) and a 100 m elevation band 816 (Extended Data Figure 2f) from the climate and snow cover dataset, for flat 817 terrain conditions. 818

818

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The limitations of the MTMSI dataset regarding the available elevation bands for each NUTS-3 region compared to the actual elevation ranges or the ski resorts made it necessary to reassign some values. For the resorts fully included in mountain NUTS-3 regions, but with missing elevation bands, we assigned the minimum or the maximum available NUTS-3 elevation, depending on whether the missing elevations are at the top or at the bottom of the ski resort, in the limit of a 300 m difference and with minimum elevation not less than 500 m. Resorts outside the NUTS-3 areas, for which climate data are available, were not processed further. This led to 2,625 ski resorts, within which only those belonging to EEA mountain areas were considered further, resulting in a final selection of 2,234 ski resorts (see Extended Data Figure 1).
Resort-level snow cover reliability calculation 829

The snow cover reliability indicator for a given ski resort on a given day corresponds to the fraction of the surface area (fraction of the pixels) of the gravitational envelope exceeding a snow mass threshold of 100 kg m⁻² (corresponding to 100 mm w.e., i.e. 20 cm of snow depth for a typical snow density of 500 kg m⁻³). The average of the daily values for the months from December to February is used as the snow cover reliability indicator applied in this work. Crocus model runs provide water demand for snowmaking, which are aggregated for each ski resort. Consistent with previous research, we assume that 10% of the surface area of the gravitational envelope is actually covered with ski pistes [5]. While the full envelope is taken into account for the calculation of the snow reliability index, the 10% ratio is applied only for the calculation of the water volume demand.

Defining whether conditions are snow scarce or not, in a given ski resort, when snowmaking is taken into account 843

Snowmaking is implemented in ski resorts in order to secure snow conditions suitable for skiing. Therefore, snow conditions taking snowmaking into account can be considered snow scarce if the snow reliability index value is lower than the snowmaking fractional coverage considered. For assessing the risk level when snowmaking is taken into account, we therefore define the snow reliability index threshold $Q_{threshold}$ as (Equation (1)):

$$Q_{threshold} = \max(Q_{20}^{ref}, X_{snowmaking}) \qquad (1)$$

where $X_{snowmaking}$ is the snowmaking fractional coverage value considered (25%, 50% or 75%). We exemplify on Extended Data Figure 3 how this calculation is designed and implemented. The snow supply risk to ski tourism is thus expressed as the proportion of snow reliability index values below $Q_{threshold}$. By design, the risk level is equal to 20% for the reference period without snowmaking. This is illustrated on Extended Data Figures 4, 5, 6 and 7.

Computation of the snow supply risk and burning embers 862

The snow supply risk to ski tourism is defined as the frequency of snow scarce conditions. These are defined as the 20th percentile of the snow reliability index during the reference period 1961-1990 without snowmaking, based on Crocus model runs driven by the UERRA 5.5 km reanalysis, for each ski resort. The frequency of snow scarce conditions is estimated for each ski resort for 20-year time periods for each GCM/RCM pair, centered for each pair on the time period when the GCM simulation reaches a global warming level for the values +1.5°C, +2°C, +3°C and +4°C with respect to the time period 1850-1900. Note that, in contrast to the discrepancy identified between RCM and GCM simulated temperature changes over Europe in summer, there is a good overall consistency for the winter season, which is the main seasonal focus of our study [10, 11]. The global warming level dataset, providing information for all of the
20 Climate change challenges European ski tourism

875 GCM results used in this study, is obtained from the Santander Meteorology
876 Group (UC-CSIC) ( https://github.com/SantanderMetGroup/ATLAS/blob/
877 main/warming-levels/CMIP5_Atlas_WarmingLevels.csv ).

878 For each ski resort, the average value of the frequency of snow reliability
879 index values below the $Q_{20}^{ref}$, across all GCM/RCM pairs corresponding to a
880 given global warming level, is used to relate the risk value to this particular
881 warming level. This resort-level risk value is then averaged for each mountain
882 area, using the surface area of the ski resort as a weighting factor. The corre-
883 sponding risk value for each mountain area and global warming level is then
884 used to build the burning ember diagram. Ultimately, burning embers [12]
885 represent the snow supply risk to ski tourism under different global warming
886 levels, with and without snowmaking.

887 Due to the lack of a European database on the fraction per ski resort
888 equipped with snowmaking, we apply a homogeneous 25%, 50% and 75% snow-
889 making fractional coverage to all ski resorts in Europe, notwithstanding actual
890 heterogeneities in snowmaking fractional coverages within and across mountain
891 areas and countries [9].

892 By design, the risk level is equal to 20% for the reference period without
893 snowmaking. Hence, a frequency of 20% of snow scarce winters corresponds
894 to an undetectable change in risk level (displayed in white on Figure 3). A
895 frequency of 30%, corresponding to a 50% increase in the frequency of snow
896 scarce conditions compared to the reference value (20%), is referred to as
897 "moderate" risk (yellow), while a doubling in the frequency (40%) is referred
898 to as "high" risk (red). The risk reaches the "very high" (violet) domain at a
899 frequency of 50% and above, which corresponds to encountering at least once
900 every two years a situation of snow scarcity, which was only encountered once
901 every 5 years during the reference period.

902
903 Water and electricity demand for snowmaking

904 Water demand values are generated as a diagnostic of the snow cover sim-
905 ulations. Water demand for snowmaking is illustrated in Extended Data
906 Figures 4, 5, 6 and 7. See Extended Data Figure 8b for more informa-
907 tion on the calculation of the uncertainty pertaining to the water demand
908 for various global warming levels and snowmaking fractional coverage
909 values. Here we assume an electricity consumption of 3±1 kWh per m³
910 of water converted to snow. This value is consistent with recent reports
911 from the ski tourism industry and national and regional reports from
912 local environment and tourism authorities (https://www.wko.at/branchen/
913 transport-verkehr/seilbahnen/Oekologie_und_Umwelt.html, http://www.
914 observatoire.savoie.equipement-agriculture.gouv.fr/Atlas/4-hydro.htm, https:
915 //side.developpement-durable.gouv.fr/ACCIDR/doc/SYRACUSE/152732).

916 In fact, this value depends on the technology used and on the temperature of
917 snow production. However, in lack of a detailed inventory of the technology
918 deployed in all ski resorts of Europe, which would also require a more detailed
919 modelling framework than currently exists, we consider this central value
920
surrounded by a range from 2 to 4 kWh per m³ for this study with a central estimate of 3 kWh m⁻³. Since this is a constant parameter in our study, all of the results, in terms of electricity demand and corresponding greenhouse gas emissions, can be updated by means of a simple "rule of three" using a different value of the electricity demand for water conversion to snow. This value takes into account the entire snowmaking process, including snowguns operations, local water pumping etc.

## Carbon footprint of electricity consumption for snowmaking

The carbon footprints are calculated using amounts of electricity generation by type of production (e.g. hard coal, run-of-river hydropower, etc.) and country as recorded on the ENTSO-E Transparency Platform for 2018 to 2022 (https://transparency.entsoe.eu/dashboard/show). The central estimates were computed using the figures for the year 2019, deemed representative of the situation prior to the outbreak of Covid-19. Data for the years from 2018 to 2022 were used for uncertainty analysis, based on the 6 main countries in terms of water and electricity demand, and for which carbon intensity factors were calculated. Combining electricity demand time series (from past and future climate conditions) with a sampling of the carbon intensity data from these 5 years leads to an estimate of the uncertainty due to the interannual variability of carbon intensity. The multiannual standard deviation around the mean of the median shows, for the UERRA reanalysis reference time period 1961-1990, varies between 8.8% (Norway, expected to increase up to the values of 12.3% and 13.6% at +2°C and +4°C global warming levels, respectively) and 1.2% (Sweden, expected to reach 0.6% and 1.0% at +2°C and +4°C) of the mean value. Values for Italy and Austria, where the mean value is the highest, reach 2.5% and 3.7% respectively. This uncertainty is thus much lower than the uncertainty due to the electricity demand for snowmaking, considered in this study. Production for each generation type and country are multiplied by country dependent emission factors (kg CO₂eq kWh⁻¹) from the ecoinvent database [13], taking into account life cycle assessment (LCA) components. The emissions are calculated on an hourly basis and an average emission factor by country and month is used for the conversion of monthly electricity demand for snowmaking into equivalents of CO₂ emissions. Emissions from imported electricity are not included, which is a methodological limitation of this study. The emissions presented are for electricity consumption due to snowmaking only. They do not include emissions for snow grooming and maintenance, snow making equipment and its installation and maintenance and ski lift operation.

## Data availability

The MTMSI dataset is available on the Copernicus Climate Data Store following this doi: https://doi.org/10.24381/cds.2fe6a082 [14] under the Copernicus licence. We use the OpenSkiMap (https://openskimap.org) global database of ski resorts, which is based on

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967 the OpenStreetMap (https://www.openstreetmap.org) collaborative
968 geospatial database. The description of the 15 European Environ-
969 mental Agency (EEA) European mountain areas is available online:
970 https://www.eea.europa.eu/data-and-maps/data/european-mountain-areas.
971 The European Digital Elevation Model (EU-DEM), version 1.1, with a
972 spatial resolution of 25 m, is available online http://land.copernicus.eu/
973 pan-european/satellite-derived-products/eu-dem/eu-dem-v1.1/view. The
974 global warming level dataset is available from the Santander Meteorol-
975 ogy Group (UC-CSIC) (https://github.com/SantanderMetGroup/ATLAS/
976 blob/main/warming-levels/CMIP5_Atlas_WarmingLevels.csv). The carbon
977 footprints are calculated using hourly amounts of electricity generation
978 by type of production (e.g. hard coal, run-of-river hydropower, etc.) and
979 country as recorded on the ENTSO-E Transparency Platform for 2019
980 (https://transparency.entsoe.eu/dashboard/show).

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982 Code availability

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984 The Crocus snow cover model used for this work is developed inside the
985 open-source SURFEX project (http://www.umr-cnrm.fr/surfex/, last access :
986 7 June 2020). For reproducibility of results, the version used in this work is
987 tagged as "C3S-European-Tourism-MTMSI-2019" on the SURFEX git repos-
988 itory. The computer code necessary to reproduce the main results is provided
989 at the Zenodo repository (10.5281/zenodo.8047168) [15].

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991 Methods references

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993 References

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995 [1] Morin, S. et al. Pan-European meteorological and snow indicators of
996 climate change impact on ski tourism. Climate Services 22, 100215 (2021).
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999 [2] Spandre, P., François, H., George-Marcelpoil, E. & Morin, S. Panel based
1000 assessment of snow management operations in French ski resorts. Journal
1001 of Outdoor Recreation and Tourism (2016).
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1004 [3] Vionnet, V. et al. The detailed snowpack scheme Crocus and its
1005 implementation in SURFEX v7.2. Geosci. Model. Dev. 5, 773–791 (2012).
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1008 [4] Spandre, P. et al. Integration of snow management in a detailed snowpack
1009 model. Cold Regions Science and Technology (2016).
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1012 [5] Spandre, P. et al. Climate controls on snow reliability in French Alps ski
1013 resorts. Sci. Rep. 9 (2019).

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1015 [6] Spandre, P. et al. Winter tourism under climate change in the Pyrenees
1016 and the French Alps: relevance of snowmaking as a technical adaptation.
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The Cryosphere 13, 1325–1347 (2019). 1013

[7] François, H., Morin, S., Lafaysse, M. & George-Marcelpoil, E. Crossing numerical simulations of snow conditions with a spatially-resolved socio-economic database of ski resorts: A proof of concept in the French Alps. Cold Regions Science and Technology 108, 98–112 (2014). 1015
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[8] François, H., Morin, S., Spandre, P., Lafaysse, M. & George-Marcelpoil, E. Croisement de simulations numériques des conditions d’enneigement avec une base de données socioéconomiques spatialisée des stations de sports d’hiver : description de l’approche et application aux Alpes françaises. La Houille Blanche 4 (2016). 1019
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[9] Berard-Chenu, L., François, H., George, E. & Morin, S. Past changes in natural and managed snow reliability of French Alps ski resorts from 1961 to 2019. The Cryosphere 16, 863–881 (2022). 1025
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[10] Taranu, I. S., Somot, S., Alias, A., Boé, J. & Delire, C. Mechanisms behind large-scale inconsistencies between regional and global climate model-based projections over Europe. Climate Dynamics (2022). 1029
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[11] Ribes, A. et al. An updated assessment of past and future warming over France based on a regional observational constraint. Earth System Dynamics 13, 1397–1415 (2022). 1033
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[12] Zommers, Z. et al. Burning embers: towards more transparent and robust climate-change risk assessments. Nature Reviews Earth & Environment 1, 516–529 (2020). 1037
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[13] Wernet, G. et al. The ecoinvent database version 3 (part i): overview and methodology. Int. J. Life Cycle Assess. (2016). 1041
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[14] Morin, S., Samacoïts, R., François, H. & Abegg, B. Mountain tourism meteorological and snow indicators for europe from 1950 to 2100 derived from reanalysis and climate projection. https://doi.org/10.24381/cds.2fe6a082 (2020). 1044
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[15] François, H. & Samacoïts, R. European ski resorts snow reliability. https://doi.org/10.5281/zenodo.8047168 (2023). 1048
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1059 Extended Data

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<figure><img src="image_6.png" /></figure>

The method brings together advanced spatial modelling of the ski resorts (left), modelling of snow conditions in ski resorts taking into account grooming and snowmaking (middle) and estimates of the carbon intensity of snowmaking (right). Results, primarily computed for individual ski resorts and providing annual values, are aggregated at the scale of EEA mountain areas and countries, for past climate conditions and for various global warming levels.
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>
<figure><img src="image_9.png" /><figcaption>Fig. 4 Simulation results for the Metabief ski resort (French Jura mountain regions).</figcaption></figure>

Simulation results for the Metabief ski resort (French Jura mountain regions). All panels display past simulations (based on the UERRA reanalysis), and climate projections (historical from 1950 to 2005 and future projections for RCP2.6, RCP4.5 and RCP8.5 for 2006 to 2100). Panels are organized horizontally as a function of snowmaking fractional coverage (0% on the top row, panels a and b; 25% for panels c, d and e; 50% for panels f, g, and h; 75% for panels i, j and k). Panels on the left hand side (a, c, f and i) display the time series of the snow reliability index values. Individual annual values are displayed for UERRA reanalysis. For climate projections, the panels show the median (solid line) and 20/80 percentile values (colored area) for all simulations of the same RCP, on 15-years sliding windows. The black solid line displays the same information based on UERRA reanalysis. Panels in the middle column (b, d, g and j) display the frequency of years below the snow reliability threshold $Q_{threshold}$ (computed based on the values obtained using the UERRA reanalysis from 1961 to 1990). Here, the solid line refers to the mean of the frequency values obtained for each GCM/RCM pair for each 15-years periods, and the colored area displays the standard deviation around the mean. Panels on the right side (e, h and k) display the computed water demand for snowmaking. The solid line refers to the median and the colored area spans the 20/80 percentile range, on 15-years sliding windows.

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28 Climate change challenges European ski tourism

<figure><img src="image_10.png" /><figcaption>Fig. 5 Simulation results for a given ski resort in the Austrian Alps.</figcaption></figure>

Same as Extended Data Figure 4 but for a ski resort located in the Austrian Alps.

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<figure><img src="image_11.png" /><figcaption>Fig. 6 Simulation results for a given ski resort in Norway.</figcaption></figure>

Same as Extended Data Figure 4 but for a ski resort located in Norway.

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<figure><img src="image_12.png" /><figcaption>Fig. 7 Simulation results for a given ski resort in the British Isles.</figcaption></figure>

Same as Extended Data Figure 4 but for a ski resort located in the British Isles.

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<figure><img src="image_13.png" /><figcaption>Fig. 8 Numerical values of the snow supply risk levels and proportion of individual ski resorts belonging to the "very high risk" category.</figcaption></figure>

Numerical values of the snow supply risk levels from Figure 3, including the proportion of individual ski resorts belonging to the "very high risk" category for each mountain area, for various global warming levels and snowmaking fractional coverage values (a) and changes in water demand for snowmaking (b), used in the burning embers diagram displayed in Figure 3. The uncertainty shown in panel (b) is calculated based on the standard deviation around the mean of the water demand calculated for each level of climate warming with respect to the reference mean value (mean water demand for a snowmaking fractional coverage of 25% during the reference time period 1961-1990).
<figure><img src="image_14.png" /></figure>

25%

a) Water demand (Mm³) and electricity demand (GWh) for snowmaking

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b) Carbon footprint of electricity production for snowmaking

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UErrA, 1951-1990 (+0.6°C of GWL)

+2°C of GWL

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75%

c) Water demand (Mm³) and electricity demand (GWh) for snowmaking

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Fig. 9 Water and electricity demand and associated carbon footprint for electricity production, due to snowmaking only, assuming a uniform snowmaking fractional coverage of 25% and 75%.

Same as Figure 4 but for 25% snowmaking fractional coverage (a and b) and 75% snowmaking fractional coverage (c and d).