• Close window

CLIPC: Constructing Europe's Climate Information Portal

CLIPC provides access to Europe's climate data and information.

Use case downhill skiing tourism

Starting point

The European Commission aims to (fictitiously) initiate a funding programme for regions which rely heavily on winter tourism, because climate changeclimate change
Climate change refers to a change in the state of the climate that can be identified (e.g., by using statistical tests) by changes in the mean and/or the variability of its properties, and that persists for an extended period, typically decades or longer. Climate change may be due to natural internal processes or external forcings such as modulations of the solar cycles, volcanic eruptions and persistent anthropogenic changes in the composition of the atmosphere or in land use. Note that the United Nations Framework Convention on Climate Change (UNFCCC), in its Article 1, defines climate change as: 'a change of climate which is attributed directly or indirectly to human activity that alters the composition of the global atmosphere and which is in addition to natural climate variability observed over comparable time periods'. The UNFCCC thus makes a distinction between climate change attributable to human activities altering the atmospheric composition, and climate variability attributa
is expected to negatively affect skiing conditions there. The (ficititious) European Winter Tourism Federation (EWTF) is lobbying on behalf of these regions and wants to demonstrate how many regions are possibly affected and which regions are especially in need of funding. Therefore, the EWTF contracts a consultant to identify those skiing regions which are projected to be most affected by warming winter climate.

 Basic approach

The analysis comprises two tasks:

  • Identify cells where climatic conditions for winter tourism are deteriorating in the future.
  • Identify cells which have a high concentration of skiing infrastructure, e.g. kilometres of ski lifts.

Basic indicators

  • Number of icing days (current and future projection)
  • Length of ski lifts in kilometres (current)

Extended approach

Instead of using the very standard ‘icing days per year’ indicator, the user

  • defines the period with good temperature conditions for skiing in a more advanced way and
  • calculates how much snow is falling in this period

This is done for a particularly vulnerable skiing region (based on the basic approach).

Extended indicators

  • Daily average temperature (current and future projection)
  • Daily precipitation (current and future projection)

CLIP-C tools featured

Basic version:  Map viewer, ScenarioScenario
A plausible description of how the future may develop based on a coherent and internally consistent set of assumptions about key driving forces (e.g., rate of technological change, prices) and relationships. Note that scenarios are neither predictions nor forecasts, but are useful to provide a view of the implications of developments and actions. See also Climate scenario, Emission scenario, Representative Concentration Pathways and SRES scenarios.
viewer, Compare tool, Combine tool
Extended version: Processing tool and histogram feature (in addition to above)

Contact person

Johannes Lückenkötter (TUDO)
johannes.lueckenkoetter(at)tu-dortmund.de     Tel. +49-231-755-2127

Tjark Bornemann (TUDO)
tjark.bornemann(at)tu-dortmund.de                Tel. +49-231-755-2374

Analytical steps in detail
Basic approach

  1. Calculate change indicator for icing days
    Load ‘icing days’ indicator twice into the Combine Tool: on the left side select the 2080-2100 time period and on the right side the 2000-2020 time period.
    Subtract the values of the 2000-2020 dataset from the 2080-2100 dataset.
    Save the result as new indicator ‘Icing days change’.

  2. Combine Icing Days Change and Length of Ski Lifts indicators
    Load the new indicator ‘Icing Days Change’ and ‘Length of Ski Lifts’ into the Combine Tool. Because they have different units (days vs. km), both indicators need to first be normalised (select the min-max option) so that they both have values ranging from 0 to 1.
    Combine the two indicators by using the ‘add’ function with a weight of 0.5 for each indicator.
    Interpret the map of the new combined indicator and show on the map which areas are most in need for funding because they are at the same time suffering from deteriorating future climate conditions and have a high concentration of  winter tourism (skiing) infrastructure.

Save the result as new indicator ‘Winter Tourism VulnerabilityVulnerability
The propensity or predisposition to be adversely affected. Vulnerability encompasses a variety of concepts including sensitivity or susceptibility to harm and lack of capacity to cope and adapt.

Extended approach

The user zooms in on one particular region that according to the basic approach’s results is identified as potentially severely impacted, let’s say a skiing resort area in the Pyrrenees mountains in Spain.

  1. Identify the beginning of the snow season
    In the Processing Tool calculate average monthly icing days for October to April for the time period 2000-2020. In the Map Viewer load this newly created indicator and click on cells of the Pyrenees skiing region and look at the values for each month. Identify in which month the cells for the first time have on average at least 15 icing days. Likewise determine the month in which the cells have for the last time at least 15 icing days. Conclude that the ‘core winter/snow season’ is from the middle of the first month to the middle of the last month, e.g. December 15 – February 15.

  2. Calculate tailor-made snow indicators
    In the Processing Tool load the ‘Daily precipitation’ indicator, select the RCP 8.5 scenario and add all precipitation values between e.g. Dec 15 – Feb 15 (see above). Do this for every year between 2001-2020 and between 2081-2100. The add the thus calculated values for each of the two time periods. This would yield a proxy indicator for mm equivalent of snow that falls in those time periods (which are constantly below freezing).

    Afterwards, in the Combine Tool load the newly created indicators: on the left side select the 2001-2020 time period and on the right side the 2081-2100 time period. Subtract the values of the 2001-2020 dataset from the 2081-2100 dataset.

  3. Calculate combined skiing climate indicator
    In the Combine Tool load the new snow fall change indicator and the icing days change for 2000-2020 (because both aspects i.e. length of time with low temperatures and snow fall are important for skiing tourism), select normalisation none, weight 0.5 for each indicator. Executive. Save the resulting new indicator as ‘ClimateForSkiing_2001-2020’.

    Repeat the same procedure for the two 2081-2100 indicators and save result as ‘ClimateForSkiing_2040-2060’.

  4. Calculate a climate change indicator
    In the Combine Tool load the two newly created Climate For Skiing indicators and subtract the values of the 2001-2020 indicator from the 2081-2100 indicator.  Save the result as new indicator ‘ClimateChangeForSkiing_2001-2100’.

  5. Combine the climate change and ski lift indicators
    Load the two Climate Change For Skiing and the Length of Ski Lifts indicators into the Combine Tool. Select normalisation min-max, then combine by using the ‘add’ function with a weight of 0.5 for each indicator.
    Save the result as new indicator ‘Snow fall change’.

Interpret the values and the map of the new combined indicator.
Save the result as new indicator ‘Winter tourism vulnerability advanced’.