Coupled component modelling for inter- and transdisciplinary climate change impact research: Dimensions of integration and examples of interface design

Challenges to link climate change data provision and user needs: Perspective from the COST‐action VALUE

“…Besides scientific issues, a number of non‐scientific aspects hamper the downscaling interface to work properly. These are not climate data or downscaling methodology specific, but are issues common in inter‐ and transdisciplinary projects, such as different concepts and perspectives on data, different background knowledge, and different use of languages (e.g., Eppler, ; Strasser et al, ). In our view, the following three issues matter most at the downscaling interface: (a) knowledge‐based issues, (b) communication‐related issues, (c) structural issues.…”

Crossing numerical simulations of snow conditions with a spatially-resolved socio-economic database of ski resorts: A proof of concept in the French Alps

“…This is particularly well illustrated by the fact that the 2006-2007 season was both unfavorable in terms of natural snow conditions and skier days, while the 2010-2011 season was unfavorable in terms of natural snow conditions but did not show the same drop of skier days than 2006-2007, most probably because meteorological conditions and/or snowmaking strategies were more appropriate for good snow conditions on ski slopes. This work lays the foundation for a long-term tool addressing quantitatively the interactions between physical and socioeconomic drivers of the mountain touristic sector (Strasser et al, 2014). Nevertheless, several aspects of this work deserve to be significantly improved, in order to be able to address the questions left unanswered in this first assessment.…”

“…It shows the high potential of crossing meteorological and snowpack modeling with socio-economic information to produce synthetic assessments of the relationships between snow conditions and economic results of mountain ski resorts, although only natural snow conditions are considered so far in our analysis. Our work follows the logics of deeper integration of physical science and socio-economic science results allowing for transdisciplinary assessments of the relationships between these two interlinked drivers of human activities (Strasser et al, 2014). In addition, this work introduces a framework which will be expanded in the future to account explicitly for snow management techniques (including snowmaking and grooming) and allow a diversity of applications including climate projections of snow conditions in ski resorts.…”

A flexible approach to model coupling through probabilistic pooling

“…A growing number of integrated models are being developed by combining existing models as components (Schlueter et al, 2012;Strasser et al, 2014). Combining existing models supports the development of more comprehensive models while building on the confidence placed in simpler models.…”