Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Patterns and trends of Northern Hemisphere snow mass from 1980 to 2018

A Publisher Correction to this article was published on 09 June 2020

This article has been updated


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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: March annual snow mass and its trend.
Fig. 2: Distribution and decadal trend of mean March hemispheric snow mass.
Fig. 3: Evolution of March SWE in East Siberia.

Similar content being viewed by others

Data availability

The data are available from ref. 47. The same dataset is also available from the GlobSnow service:; (daily data); (monthly data); (bias-corrected data); and (all auxiliary data).

Code availability

Codes are available from and

Change history


  1. Brown, R. D. & Mote, P. W. The response of Northern Hemisphere snow cover to a changing climate. J. Clim. 22, 2124–2145 (2009).

    ADS  Google Scholar 

  2. Derksen, C. & Brown, R. Spring snow cover extent reductions in the 2008–2012 period exceeding climate model projections. Geophys. Res. Lett. 39, L19504 (2012).

    ADS  Google Scholar 

  3. 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).

    ADS  Google Scholar 

  4. 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).

    ADS  CAS  PubMed  Google Scholar 

  5. IPCC. Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  6. Sturm, M., Goldstein, M. A. & Parr, C. Water and life from snow: a trillion dollar science question. Wat. Resour. Res. 53, 3534–3544 (2017).

    ADS  Google Scholar 

  7. 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).

    ADS  Google Scholar 

  8. 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).

    ADS  Google Scholar 

  9. Bormann, K. J., Brown, R. D., Derksen, C. & Painter, T. H. Estimating snow-cover trends from space. Nat. Clim. Chang. 8, 924–928 (2018).

    ADS  Google Scholar 

  10. 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).

    ADS  Google Scholar 

  11. Bintanja, R. & Andry, O. Towards a rain dominated Arctic. Nat. Clim. Chang. 7, 263–267 (2017).

    ADS  Google Scholar 

  12. 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).

    ADS  CAS  Google Scholar 

  13. 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).

    PubMed  PubMed Central  Google Scholar 

  14. 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).

    ADS  CAS  Google Scholar 

  15. 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).

    ADS  CAS  Google Scholar 

  16. Pulliainen, J. et al. Early snowmelt significantly enhances boreal springtime carbon uptake. Proc. Natl Acad. Sci. USA 114, 11081–11086 (2017).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  17. 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).

    ADS  Google Scholar 

  18. 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).

    ADS  Google Scholar 

  19. 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).

    Google Scholar 

  20. Frei, A. et al. A review of global satellite-derived snow products. Adv. Space Res. 50, 1007–1029 (2012).

    ADS  Google Scholar 

  21. Chang, A., Foster, J. & Hall, D. Nimbus-7 SMMR derived global snow cover parameters. Ann. Glaciol. 9, 39–44 (1987).

    ADS  Google Scholar 

  22. 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).

    ADS  Google Scholar 

  23. 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).

    ADS  Google Scholar 

  24. 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).

    ADS  Google Scholar 

  25. 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).

    ADS  Google Scholar 

  26. 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).

    ADS  Google Scholar 

  27. 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).

    ADS  Google Scholar 

  28. 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).

    ADS  Google Scholar 

  29. 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).

    ADS  Google Scholar 

  30. Mudryk, L. et al. Terrestrial Snow Cover. Arctic Report Card (2018).

  31. Metsämäki, S. et al. Introduction to GlobSnow Snow Extent products with considerations for accuracy assessment. Remote Sens. Environ. 156, 96–108 (2015).

    ADS  Google Scholar 

  32. Lehtinen, M. On Statistical Inversion Theory. Theory and Applications of Inverse Problems (Longman, 1988).

  33. Pulliainen, J., Kärnä, J.-P. & Hallikainen, M. Development of geophysical retrieval algorithms for the MIMR. IEEE Trans. Geosci. Remote Sens. 31, 268–277 (1993).

    ADS  Google Scholar 

  34. Kruopis, N. et al. Passive microwave measurements of snow-covered forest areas in EMAC’95. IEEE Trans. Geosci. Remote Sens. 37, 2699–2705 (1999).

    ADS  Google Scholar 

  35. 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).

    ADS  Google Scholar 

  36. 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).

    ADS  Google Scholar 

  37. 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).

    ADS  Google Scholar 

  38. 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).

    ADS  Google Scholar 

  39. Gelaro, R. et al. The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). J. Clim. 30, 5419–5454 (2017).

    ADS  PubMed  Google Scholar 

  40. 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; (Goddard Earth Sciences Data and Information Services Center, 2015).

  41. 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).

    Google Scholar 

  42. Brönnimann, S. et al. Observations for reanalyses. Bull. Am. Meteorol. Soc. 99, 1851–1866 (2018).

    ADS  Google Scholar 

  43. 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).

    Google Scholar 

  44. 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).

    Google Scholar 

  45. Mätzler, C. Passive microwave signatures of landscapes in winter. Meteorol. Atmos. Phys. 54, 241–260 (1994).

    ADS  Google Scholar 

  46. 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).

    ADS  Google Scholar 

  47. Luojus, K., Pulliainen, J., Takala, M., Lemmetyinen, J. & Moisander, M. GlobSnow v.3.0 snow water equivalent (SWE). Pangaea (2020).

Download references


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).

Author information

Authors and Affiliations



J.P. initiated the work and made a major contribution towards developing the methodology and writing the manuscript. K.L., C.D., L.M. and J.L. contributed to data analysis and writing of the manuscript, and supported the conclusions. M.S. contributed to the writing and editing/revising of the manuscript. J.I., M.T., J.C.,T.S. and J.N. contributed to the analysis and processing of satellite and in situ data.

Corresponding author

Correspondence to Jouni Pulliainen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Extended Data Fig. 1 Hemispheric SWE retrievals for March.

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.

Extended Data Fig. 2 Map showing the spatial distribution of the SWE estimation bias.

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.

Extended Data Fig. 3 Evolution of the annual bias in GlobSnow SWE estimates for March.

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.

Extended Data Fig. 4 Histograms showing hemispheric SWE retrieval accuracy for March.

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.

Extended Data Table 1 Monthly GlobSnow v3.0 estimates of snow mass
Extended Data Table 2 March snow mass from various data sources

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing