Climate Change and Tourism - UNEP - Division of Technology ...

UNWTO, 9 July 2008
Advances in Climate Change Science and Implications for the Tourism Sector
Box 2 Using a climate change analogue approach to examine climate change vulnerability
of the New England (USA) ski industry 169
The record warm winter of 2001–2002 (+8° C warmer than climatically normal temperatures
for the 1961–1990 baseline period) is representative of projected future average winter climate
conditions in the New England region under a high greenhouse gas emission scenario for the
2040–2069 period. This winter was used as a climate change analogue to examine how a wide
range of ski area performance indicators (ski season length, hours of snow-making operation,
total skier visits, visitation by time of year, average season passes sold, and operating profit as a
percentage of total gross revenue) were affected by anomalously warm conditions. Comparison of
ski area performance indicators for the 2001–2002 analogue year with the climatically normal (for
1961–1990 period) years of 2000–2001 and 2004–2005, revealed substantial differences. The ski
season length was 11% shorter in the analogue year. Snow-making hours increased by 12% in the
analogue season and the amount of power utilized for the purpose of creating snow was almost
35% higher than during average ski seasons. Visitation during the climate change analogue season
was 12% lower versus the climatically normal winters. The combination of increased snow-making
costs, decreased season length, and lower visitation rates, caused financial strain that reduced
operating profits for the 2001–2002 season by 19% versus climatically average seasons. Analysis
of impact on different sized ski resorts (categorized by lift capacity) further revealed that smaller
ski areas experienced larger impacts.
Tourism decision makers need clear and reliable conclusions of impact assessments. To answer this need,
tourism research must adequately portray the differences between climate change scenarios produced by
different climate models and downscale GCM scenarios to regional and local levels in order to describe
the variability of climate, to present the probability of extreme events, and, above all, to ‘translate’
model outputs into indicators that are important for the tourism sector (e.g., number of rainy days,
length of showers, thermal comfort, duration of the snow season, forest fire frequency and intensity).
One of the key recommendations of the “Climate and Tourism on the Colorado Plateau Workshop” 170 ,
that involved representatives from research, industry, and environmental organizations, was for better
communication and characterization of certainties and uncertainties in climate variability and change
projections to the tourism industry and for the development of indicators catered toward local tourism
decision-making. Additional workshops like this one would be highly valuable in identifying regionally
specific weather and climate information needs of the tourism industry.
Projecting future climate change at local scales is difficult because the resolution of GCMs is too coarse
to take into account local features of climate. Climate modellers and meteorologists utilize several
techniques to produce regional-local scale climate scenarios, including Regional Climate Models
(RCMs), statistical weather generators and weather forecast simulations. RCMs are higher resolution
climate models that are usually better able to reproduce regional climate processes than GCMs. The
application of RCM scenarios in climate change impact assessments has been restricted because of the
limited range of GHG emission scenarios available. Weather generators are inexpensive computational
tools that replicate the statistical attributes of a local climate and can be used to produce site-specific,
multiple-year climate change scenarios at the daily timescale. 171 Tourism researchers have used these
techniques in climate change impact assessments. In order to obtain even more precise local results,
an additional technique can be to introduce some human experience in the downscaling process.
Local weather analysts, who are experts on local specificities, can be involved to take into account
localized micro-climate effects (e.g., coastal effects of marine winds on a tourist location). The IPCC has
provided guidelines for best practice in downscaling 172 , nonetheless, downscaling generally increases
the uncertainties and margins of error associated with climate change scenarios.
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