The effects of climate change on natural environments are well-documented and far-reaching. While research indicates that tourism industries are sensitive to changes in climate, limited research exists on the quantifiable impact of climate change on tourism demand. To fill this research gap, Gongmei Yu co-authored a study that creates and tests a Modified Climate Index for Tourism (MCIT), which combines various tourism-related climate elements to measure climate as a tourism resource. This tool uses over 50 years of hourly temperature, wind, and other significant weather data from Orlando, Florida, and King Salmon, Alaska, rather than relying on daily averages.
This study shows that the MCIT produced more meaningful results than previous methods that relied on average temperature alone. Using the MCIT, researchers found that both locations experienced rain showers and thunderstorms as more limiting to tourism activities than temperature throughout the year. Researchers also applied the MCIT to past climate data, and found meaningful results capturing tourism-related variations and trends. Specifically, the results show that the lengthening warm season in Alaska creates favorable tourism conditions, and that the increasing summer temperatures in Orlando are decreasing ideal climate conditions. Researchers note, however, that the impact of climate change varies from season to season. For example, while the conditions for tourism have deteriorated in Florida during the summer, they have improved in the winter.
By incorporating variables more relevant to tourism activities and addressing the overriding nature of some conditions, such as fog or rain, this index better captures the relationship between climate change and tourism than previous methods. The MCIT has the potential to provide valuable information for long-term planning in areas dependent on tourism. Used with climate models, this tool could better predict the impact of future climate change on tourism.