Warming surface temperatures have driven a substantial reduction in the extent and duration of Northern Hemisphere snow cover1,2,3. These changes in snow cover affect Earth’s climate system via the surface energy budget, and influence freshwater resources across a large proportion of the Northern Hemisphere4,5,6. In contrast to snow extent, reliable quantitative knowledge on seasonal snow mass and its trend is lacking7,8,9. Here we use the new GlobSnow 3.0 dataset to show that the 1980–2018 annual maximum snow mass in the Northern Hemisphere was, on average, 3,062 ± 35 billion tonnes (gigatonnes). Our quantification is for March (the month that most closely corresponds to peak snow mass), covers non-alpine regions above 40° N and, crucially, includes a bias correction based on in-field snow observations. We compare our GlobSnow 3.0 estimates with three independent estimates of snow mass, each with and without the bias correction. Across the four datasets, the bias correction decreased the range from 2,433–3,380 gigatonnes (mean 2,867) to 2,846–3,062 gigatonnes (mean 2,938)—a reduction in uncertainty from 33% to 7.4%. On the basis of our bias-corrected GlobSnow 3.0 estimates, we find different continental trends over the 39-year satellite record. For example, snow mass decreased by 46 gigatonnes per decade across North America but had a negligible trend across Eurasia; both continents exhibit high regional variability. Our results enable a better estimation of the role of seasonal snow mass in Earth’s energy, water and carbon budgets.
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The data are available from ref. 47. The same dataset is also available from the GlobSnow service: http://www.globsnow.info/swe/archive_v3.0/; http://www.globsnow.info/swe/archive_v3.0/L3A_daily_SWE/ (daily data); http://www.globsnow.info/swe/archive_v3.0/L3B_monthly_SWE/ (monthly data); http://www.globsnow.info/swe/archive_v3.0/L3B_monthly_biascorrected_SWE/ (bias-corrected data); and http://www.globsnow.info/swe/archive_v3.0/auxiliary_data/ (all auxiliary data).
Codes are available from https://github.com/fmidev/GlobSnow3.0 and http://www.globsnow.info/swe/archive_v3.0/source_codes/.
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This work is supported by the European Space Agency (ESA) Snow Climate Change Initiative (CCI) project (grant 4000124098/18/I-NB); the ESA SnowPEx project (4000111278/14/I-LG); the Academy of Finland Centre of Excellence (118780); the Academy of Finland ASTRA-Snow project (325397); and the Nordic Centre of Excellence Climate Change Effects on the Epidemiology of Infectious Diseases and the Impacts on Northern Societies (CLINF; 76413).
The authors declare no competing interests.
Peer review information Nature thanks Fanny Larue and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
a, Scatterplot of GlobSnow v3.0 SWE estimates versus interval-stratified in situ (landscape) SWE data for all snow course observations, with ± standard deviations. As the original GlobSnow approach is based on microwave radiometry, it tends to underestimate SWE with high levels of SWE (more than 150 mm) owing to saturation of the microwave signal. The bias-correction approach mitigates this problem. b, Histogram showing bias-corrected GlobSnow v3.0 estimates of mean March SWE (x axis) across the period 1980–2018 for all grid cells with mean SWE values of more than 0 mm.
a, Kriging-interpolated map for March calculated from biases observed at the locations of 2,636 snow courses. b, The same map, but also indicating the locations of snow courses (black dots). c, Weather stations that report snow depth (black dots). A–E are the dedicated areas that we use to investigate regional trends.
In other words, the figure shows the evolution of the systematic SWE estimation error for the period of maximum snow mass in the Northern Hemisphere. a, We calculated bias for the March observations of a given year, separately for each snow course (locations shown in Extended Data Fig. 2). We then averaged these snow-course-stratified biases over both continents, with results shown here. The P-values for trend lines are 0.89 and 0.81 for Eurasia and North America (Canada), respectively, indicating negligible trends. The other assessed SWE datasets are not directly applicable to the trend analysis in Eurasia, as the bias compared with snow course observations changes systematically with time (P-values of bias trend lines in Eurasia are 6.7 × 10−3 for Crocus v7, 2.7 × 10−3 for Brown and 2.7 × 10−3 for MERRA2). b, About 400 snow courses for Eurasia and 200 for North America provide observations throughout the investigated time period.
a, RMSE. b, Bias determined for each of 2,636 snow courses. c, Residual errors for all 100,651 observations from March throughout the GlobSnow v3.0 time series.
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Pulliainen, J., Luojus, K., Derksen, C. et al. Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018. Nature 581, 294–298 (2020). https://doi.org/10.1038/s41586-020-2258-0
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