Abstract
Abstract. CloudSat estimates that 1773 km3 of snow falls, on average, each year over the world's mountains. This amounts to 5 % of the global snowfall accumulations. This study synthetizes mountain snowfall estimates over the four continents containing mountains (Eurasia, North America, South America and Africa), comparing snowfall estimates from a new satellite cloud-radar-based dataset to those from four widely used reanalyses: Modern-Era Retrospective analysis for Research and Applications (MERRA), MERRA-2, Japanese 55-year Reanalysis (JRA-55), and European Center for Medium-Range Weather Forecasts Re-Analysis (ERA-Interim). Globally, the fraction of snow that falls in the world's mountains is very similar between all these independent datasets (4 %–5 %), providing confidence in this estimate. The fraction of snow that falls in the mountains compared to the continent as a whole is also very similar between the different datasets. However, the total of snow that falls globally and over each continent – the critical factor governing freshwater availability in these regions – varies widely between datasets. The consensus in fractions and the dissimilarities in magnitude could indicate that large-scale forcings may be similar in the five datasets, while local orographic enhancements at smaller scales may not be captured. This may have significant implications for our ability to diagnose regional trends in snowfall and its impacts on snowpack in rapidly evolving alpine environments.
Highlights
Falling snow transfers moisture and latent energy between the atmosphere and the surface
This study considers four modern reanalyses: Modern-Era Retrospective analysis for Research and Applications (MERRA), MERRA-2, ERA-Interim and JRA-55
The production of MERRA data ended in February 2016, as MERRA-2 is the preferred dataset, while CloudSat started in 2007
Summary
Falling snow transfers moisture and latent energy between the atmosphere and the surface. Even when these areas have relatively dense gauge networks, such as the CONUS (contiguous United States) mountains, gridded datasets have their limitations, most notably gauge under catchment issues and large snowfall accumulation gradients in complex terrain that are often insufficiently sampled by existing in situ networks (Henn et al, 2018) Given these shortcomings in snowfall surface observations, studies on snowfall in remote locations commonly rely on reanalyses (e.g., Bromwich et al, 2011). CloudSat has substantially extended the spatial extent of precipitation measurements compared to existing gauge or radar networks These instruments have greatly enhanced the observations of light precipitation, including snowfall over oceans, over remote high-latitude regions and over inaccessible land areas (e.g., Behrangi et al, 2016; Milani et al, 2018; Smalley et al, 2015; Norin et al, 2017; Lemonnier et al, 2019a, b). We derive mountain snowfall from five datasets (CloudSat 2CSP, MERRA, MERRA-2, ERA-Interim and JRA-55) to answer the following questions
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