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High-resolution modelling of climatic hazards relevant for Canada's northern transportation sector.
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- المؤلفون: Teufel, B.; Sushama, L.
- المصدر:
Climate Dynamics; Nov2022, Vol. 59 Issue 9/10, p3135-3151, 17p
- الموضوع:
- معلومة اضافية
- الموضوع:
- نبذة مختصرة :
Infrastructure and transportation systems on which northern communities rely are exposed to a variety of climatic hazards over a broad range of scales. Efforts to adapt these systems to the rapidly warming Arctic climate require high-quality climate projections. Here, a state-of-the-art regional climate model is used to perform simulations at 4-km resolution over the eastern and central Canadian Arctic. These include, for the first time over this region, high-resolution climate projections extending to the year 2040. Validation shows that the model adequately simulates base climate variables, as well as variables hazardous to northern engineering and transportation systems, such as degrading permafrost, extreme rainfall, and extreme wind gust. Added value is found over coarser resolution simulations. A novel approach integrating climate model output and machine learning is used for deriving fog—an important, but complex hazard. Hotspots of change to climatic hazards over the next two decades (2021–2040) are identified. These include increases to short-duration rainfall intensity extremes exceeding 50%, suggesting Super–Clausius–Clapeyron scaling. Increases to extreme wind gust pressure are projected to reach 25% over some regions, while widespread increases in active layer thickness and ground temperature are expected. Overall fog frequency is projected to increase by around 10% over most of the study region by 2040, due to increasing frequency of high humidity conditions. Given that these changes are projected to be already underway, urgent action is required to successfully adapt northern transportation and engineering systems located in regions where the magnitude of hazards is projected to increase. [ABSTRACT FROM AUTHOR]
- نبذة مختصرة :
Copyright of Climate Dynamics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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