Helping to Make Sense of Regional Climate Modeling: Professional Development for Scientists and Decision-Makers Anytime, Anywhere

Walton, Peter J. ; Yarker, Morgan B. ; Mesquita, Michel D. S. ; Otto, Friederike E. L.

Année de publication
2016
Résumé
Globally, decision-makers are increasingly using high-resolution climate models to support policy and planning; however, many of these users do not have the knowledge needed to use them appropriately. This problem is compounded by not having access to quality learning opportunities to better understand how to apply the models and interpret results. This paper discusses and proposes an educational framework based on two independent online courses on regional climate modeling, which addresses the accessibility issue and provides guidance to climate science professors, researchers, and institutions who want to create their own online courses. The role of e-learning as an educational tool is well documented, highlighting the benefits of improved personal efficiency through “anywhere, anytime” learning with the flexibility to support professional development across different sectors. In addition, improved global Internet means increased accessibility. However, e-learning’s function as a tool to support understanding of atmospheric physics and high-resolution climate modeling has not been widely discussed. To date, few courses, if any, support understanding that takes full advantage of e-learning best practices. There is a growing need for climate literacy to help inform decision-making on a range of scales, from individual households to corporate CEOs. And while there is a plethora of climate information online, educational theory suggests that people need to be guided in how to convert this information into applicable knowledge. Here, we present how the experience of the courses we designed and ran independent of each other, both engaging learners with better understanding benefits and limitations of regional climate modeling, lead to a framework of designing e-learning for climate modeling.

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