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/.
Brown, R. D. & Mote, P. W. The response of Northern Hemisphere snow cover to a changing climate. J. Clim. 22, 2124–2145 (2009).
Derksen, C. & Brown, R. Spring snow cover extent reductions in the 2008–2012 period exceeding climate model projections. Geophys. Res. Lett. 39, L19504 (2012).
Hori, M. et al. A 38-year (1978–2015) Northern Hemisphere daily snow cover extent product derived using consistent objective criteria from satellite-borne optical sensors. Remote Sens. Environ. 191, 402–418 (2017).
Barnett, T. P., Adam, J. C. & Lettenmaier, D. P. Potential impacts of a warming climate on water availability in snow-dominated regions. Nature 438, 303–309 (2005).
IPCC. Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).
Sturm, M., Goldstein, M. A. & Parr, C. Water and life from snow: a trillion dollar science question. Wat. Resour. Res. 53, 3534–3544 (2017).
Liu, J., Li, Z., Huang, L. & Tian, B. Hemispeheric scale comparison of monthly passive microwave snow water equivalent products. J. Appl. Remote Sens. 8, 084688 (2014).
Mudryk, L. R., Derksen, C., Kushner, P. J. & Brown, R. Characterization of Northern Hemisphere snow water equivalent datasets, 1981–2010. J. Clim. 28, 8037–8051 (2015).
Bormann, K. J., Brown, R. D., Derksen, C. & Painter, T. H. Estimating snow-cover trends from space. Nat. Clim. Chang. 8, 924–928 (2018).
Henderson, G. R., Peings, Y., Furtado, J. C. & Kushner, P. J. Snow-atmosphere coupling in the Northern Hemisphere. Nat. Clim. Chang. 8, 954–963 (2018).
Bintanja, R. & Andry, O. Towards a rain dominated Arctic. Nat. Clim. Chang. 7, 263–267 (2017).
Flanner, M. G., Shell, K. M., Barlage, M., Perovich, D. K. & Tschudi, M. A. Radiative forcing and albedo feedback from the Northern Hemisphere cryosphere between 1979 and 2008. Nat. Geosci. 4, 151–155 (2011).
Bokhorst, S. et al. Changing Arctic snow cover: a review of recent developments and assessment of future needs for observations, modelling and impacts. Ambio 45, 516–537 (2016).
Natali, S. et al. Large loss of CO2 in winter observed across the northern permafrost region. Nat. Clim. Chang. 9, 852–857 (2019); correction 9, 1005 (2019).
Kittler, F. et al. Long-term drainage reduces CO2 uptake and CH4 emissions in a Siberian permafrost ecosystem. Glob. Biogeochem. Cycles 31, 1704–1717 (2017).
Pulliainen, J. et al. Early snowmelt significantly enhances boreal springtime carbon uptake. Proc. Natl Acad. Sci. USA 114, 11081–11086 (2017).
Wrzesien, M. et al. A new estimate of North American mountain snow accumulation from regional climate model simulations. Geophys. Res. Lett. 45, 1423–1432 (2018).
de Rosnay, P., Balsamo, G., Albergel, C., Muñoz-Sabater, J. & Isaksen, L. Initialisation of land surface variables for numerical weather prediction. Surv. Geophys. 35, 607–621 (2014).
Jeong, D., Sushama, L. & Khaliq, M. Attribution of spring snow water equivalent (SWE) changes over the northern hemisphere to anthropogenic effects. Clim. Dyn. 48, 3645–3658 (2017).
Frei, A. et al. A review of global satellite-derived snow products. Adv. Space Res. 50, 1007–1029 (2012).
Chang, A., Foster, J. & Hall, D. Nimbus-7 SMMR derived global snow cover parameters. Ann. Glaciol. 9, 39–44 (1987).
Kelly, R., Chang, A., Tsang, L. & Foster, J. A prototype AMSR-E global snow area and snow depth algorithm. IEEE Trans. Geosci. Remote Sens. 41, 230–242 (2003).
Derksen, C., Walker, A. & Goodison, B. Evaluation of passive microwave snow water equivalent retrievals across the boreal forest/tundra transition of western Canada. Remote Sens. Environ. 96, 315–327 (2005).
Pulliainen, J. Mapping of snow water equivalent and snow depth in boreal and sub-arctic zones by assimilating space-borne microwave radiometer data and ground-based observations. Remote Sens. Environ. 101, 257–269 (2006).
Derksen, C. et al. Northwest territories and Nunavit snow characteristics from a subarctic traverse: implications for passive microwave remote sensing. J. Hydrometeorol. 10, 448–463 (2009).
Lemmetyinen, J. et al. A comparison of airborne microwave brightness temperatures and snowpack properties across the boreal forests of Finland and western Canada. IEEE Trans. Geosci. Remote Sens. 47, 965–978 (2009).
Kontu, A., Lemmetyinen, J., Vehviläinen, J., Leppänen, L. & Pulliainen, J. Coupling SNOWPACK-modeled grain size parameters with the HUT snow emission model. Remote Sens. Environ. 194, 33–47 (2017).
Takala, M. et al. Estimating northern hemisphere snow water equivalent for climate research through assimilation of space-borne radiometer data and ground-based measurements. Remote Sens. Environ. 115, 3517–3529 (2011).
Brun, E. et al. Simulation of northern Eurasian local snow depth, mass, and density using a detailed snowpack model and meteorological reanalyses. J. Hydrometeorol. 14, 203–219 (2013).
Mudryk, L. et al. Terrestrial Snow Cover. Arctic Report Card https://www.arctic.noaa.gov/Report-Card (2018).
Metsämäki, S. et al. Introduction to GlobSnow Snow Extent products with considerations for accuracy assessment. Remote Sens. Environ. 156, 96–108 (2015).
Lehtinen, M. On Statistical Inversion Theory. Theory and Applications of Inverse Problems (Longman, 1988).
Pulliainen, J., Kärnä, J.-P. & Hallikainen, M. Development of geophysical retrieval algorithms for the MIMR. IEEE Trans. Geosci. Remote Sens. 31, 268–277 (1993).
Kruopis, N. et al. Passive microwave measurements of snow-covered forest areas in EMAC’95. IEEE Trans. Geosci. Remote Sens. 37, 2699–2705 (1999).
Pulliainen, J., Grandell, J. & Hallikainen, M. HUT snow emission model and its applicability to snow water equivalent retrieval. IEEE Trans. Geosci. Remote Sens. 37, 1378–1390 (1999).
Lemmetyinen, J. et al. Correcting for the influence of frozen lakes in satellite microwave radiometer observations through application of a microwave emission model. Remote Sens. Environ. 115, 3695–3706 (2011).
Cohen, J. et al. The effect of boreal forest canopy in satellite snow mapping—a multisensor analysis. IEEE Trans. Geosci. Remote Sens. 53, 6593–6607 (2015).
Brun, E. et al. Simulation of northern Eurasian local snow depths, mass and density using a detailed snowpack model and meteorological reanalysis. J. Hydrometeorol. 14, 203–219 (2013).
Gelaro, R. et al. The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).
Global Modeling and Assimilation Office. MERRA-2 tavg1_2d_lnd_Nx: 2d, 1-hourly, time-averaged, single-level, assimilation, land surface diagnostics V5.12.4, Greenbelt, MD, USA; https://doi.org/10.5067/RKPHT8KC1Y1T (Goddard Earth Sciences Data and Information Services Center, 2015).
Brown, R., Brasnett, B. & Robinson, D. Gridded North American monthly snow depth and snow water equivalent for GCM evaluation. Atmos.-Ocean 41, 1–14 (2003).
Brönnimann, S. et al. Observations for reanalyses. Bull. Am. Meteorol. Soc. 99, 1851–1866 (2018).
Brown, R. D., Fang, B. & Mudryk, L. Update of Canadian historical snow survey data and analysis of snow water equivalent trends, 1967–2016. Atmos.-Ocean 59, 149–156 (2019).
Neumann, N., Derksen, C., Smith, C. & Goodison, B. Characterizing local scale snow cover using point measurements during winter season. Atmos.-Ocean 44, 257–269 (2006).
Mätzler, C. Passive microwave signatures of landscapes in winter. Meteorol. Atmos. Phys. 54, 241–260 (1994).
Yue, S., Pilon, P., Phinney, B. & Cavadias, G. The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrol. Processes 16, 1807–1829 (2002).
Luojus, K., Pulliainen, J., Takala, M., Lemmetyinen, J. & Moisander, M. GlobSnow v.3.0 snow water equivalent (SWE). Pangaea https://doi.org/10.1594/PANGAEA.911944 (2020).
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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