Coverage of in situ climatological observations in the world's mountains

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 underlying mechanisms responsible for such changes, and generating more reliable future impact projections for 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 records associated with the mountainous subset of 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. To characterise data coverage relative to topographic, hydrological, and socio-economic factors, several additional datasets were introduced. Spatial mountain data coverage is highly uneven, and there are several mountain ranges whose elevational range is severely under-sampled by GHCNd stations. Crucially, the three "Water Tower Units" previously identified as having the greatest hydrological importance to society appear to have extremely low station densities. Mountain station density is weakly related to the human population or economic output of the corresponding downstream catchments. A script we developed enables detailed assessments of record temporal coverage and measurement quality information. This contribution should help international authorities and more regional stakeholders to identify areas, variables, and other aspects that should be prioritised for investment in infrastructure and capacity. Finally, the transparent and reproducible approach taken throughout will enable the work to be rapidly repeated for subsequent versions of GHCNd, and may furthermore enable similar analyses to be efficiently conducted on other spatial reporting boundaries and/or environmental monitoring station networks.