Abstract/Summary

Many mountainous environments and ecosystems around the world are responding rapidly to ongoing climate change. Long-term climatological time-series from such regions are crucial for developing improving understanding of the mechanisms driving such changes and ultimately delivering more reliable future impact projections to environmental managers and other decision makers. Whilst it is already established that high elevation regions tend to be comparatively under-sampled, detailed spatial and other patterns in the coverage of mountain climatological data have not yet been comprehensively assessed on a global basis. To begin to address this deficiency, we analyse the coverage of mountainous records from the Global Historical Climatological Network-Daily (GHCNd) inventory with respect to space, time, and elevation. Three key climate-related variables—air temperature, precipitation, and snow depth—are considered across 292 named mountain ranges. Several additional datasets are also introduced to characterize data coverage relative to topographic, hydrological, and socio-economic factors. Spatial mountain data coverage is found to be highly uneven, with station densities in several “Water Tower Units” that were previously identified as having great hydrological importance to society being especially low. Several mountainous regions whose elevational distribution is severely undersampled by GHCNd stations are identified, and mountain station density is shown to be only weakly related to the human population or economic output of the corresponding downstream catchments. Finally, we demonstrate the capabilities of a script (which is provided in the Supplementary Material) to produce detailed assessments of individual records' temporal coverage and measurement quality information. Overall, our contribution should help international authorities and regional stakeholders identify areas, variables, and other monitoring-related considerations that should be prioritized for infrastructure and capacity investment. Finally, the transparent and reproducible approach taken will enable the analysis to be rapidly repeated for subsequent versions of GHCNd, and could act as a basis for similar analyses using other spatial reporting boundaries and/or environmental monitoring station networks.