资源环境科技发展态势分析平台

Snow cover is an important indicator of climate change but constraints on observational data quality can limit interpretation of spatial and temporal variability, especially in mountain areas. This issue was addressed using archived data from the Snow Survey of Great Britain to infer key climate relationships which were then used to reference larger-scale patterns of change. Data analysis using nonlinear (logistic) regression showed average changes in yearly snow cover were strongly related to mean temperature rather than precipitation values. Inferred change shows long-term decline in average yearly snow cover with greatest declines in some mountain areas, notably in northern England, that can be related to their position on the most temperature-sensitive segment of the logistic curve. Further declines in snow cover were projected in the future: a central ensemble projection from HadRM3 climate model showed average yearly snow cover predominantly confined to Great Britain mountain areas by the 2050s. However, inter-annual variability means some years can deviate significantly from average snow cover patterns. Site-based analysis showed this variability has distinctive geographical variations and different influences for mountains compared to adjacent valleys. Comparison of inter-annual variability with Lamb weather-type frequency and North Atlantic Oscillation index shows the influence of large-scale airflow patterns on snow cover duration. Most notable is the role of northwesterly and northerly flows in explaining snowy years on mountains exposed to that direction, compared to influence of easterly flows at lower levels. Future changes will therefore depend on dominant annual/decadal circulation patterns in addition to long-term declines from climate warming.