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Climate change affects human activities, including tourism across various sectors and time frames. The winter tourism industry, dependent on low temperatures, faces significant impacts. This paper reviews the implications of climate change on winter tourism, emphasising challenges for activities like skiing and snowboarding, which rely on consistent snowfall and low temperatures. As the climate changes, these once taken-for-granted conditions are no longer as commonplace. Through a comprehensive review supported by up-to-date satellite imagery, this paper presents evidence suggesting that the reliability of winter snow is decreasing, with findings revealing a progressive reduction in snow levels associated with temperature and precipitation changes in some regions. The analysis underscores the need for concerted efforts by stakeholders who must recognize the reality of diminishing snow availability and work towards understanding the specific changes in snow patterns. This should involve multi-risk and multi-instrument assessments, including ongoing satellite data monitoring to track snow cover changes. The practical implications for sports activities and the tourism industry reliant on snow involve addressing challenges by diversifying offerings. This includes developing alternative winter tourism activities less dependent on snow, such as winter hiking, nature walks, or cultural experiences. [Display omitted] • Snow coverage in winter is projected to shrink due to climate change. • Low and inconsistent levels of snow will impact the winter tourism industry. • Besides being unsustainable, artificial snowmaking is a short-term solution. • To survive, the winter tourism industry must adapt to the changing climate. [ABSTRACT FROM AUTHOR]

Planning under complex uncertainty often asks for plans that can adapt to changing future conditions. To inform plan development during this process, exploration methods have been used to explore the performance of candidate policies given uncertainties. Nevertheless, these methods hardly enable adaptation by themselves, so extra efforts are required to develop the final adaptive plans, hence compromising the overall decision-making efficiency. This paper introduces Reinforcement Learning (RL) that employs closed-loop control as a new exploration method that enables automated adaptive policy-making for planning under uncertainty. To investigate its performance, we compare RL with a widely-used exploration method, Multi-Objective Evolutionary Algorithm (MOEA), in two hypothetical problems via computational experiments. Our results indicate the complementarity of the two methods. RL makes better use of its exploration history, hence always providing higher efficiency and providing better policy robustness in the presence of parameter uncertainty. MOEA quantifies objective uncertainty in a more intuitive way, hence providing better robustness to objective uncertainty. These findings will help researchers choose appropriate methods in different applications. • Introduce reinforcement learning to decision-making under deep uncertainty. • Compare reinforcement learning and evolutionary algorithm. • Reinforcement learning is consistently more computationally efficient. • Reinforcement learning provides better robustness to parameter uncertainty. • Evolutionary algorithm provides better robustness to objective uncertainty. [ABSTRACT FROM AUTHOR]