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Reduced frequency and size of late-twenty-first-century snowstorms over North America


Understanding how snowstorms may change in the future is critical for estimating impacts on water resources and the Earth and socioeconomic systems that depend on them. Here we use snowstorms as a marker to assess the mesoscale fingerprint of climate change, providing a description of potential changes in winter weather event occurrence, character and variability in central and eastern North America under a high anthropogenic emissions pathway. Snowstorms are segmented and tracked using high-resolution, snow water equivalent output from dynamically downscaled simulations which, unlike global climate models, can resolve important mesoscale features such as banded snow. Significant decreases are found in the frequency and size of snowstorms in a pseudo-global warming simulation, including those events that produce the most extreme snowfall accumulations. Early and late boreal winter months show particularly robust proportional decreases in snowstorms and snow water equivalent accumulations.

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Fig. 1: Seasonal comparisons between CTRL and PGW.
Fig. 2: Spatial climatology of snow events.
Fig. 3: Snow event counts.
Fig. 4: Weekly per cent difference between the two epochs.

Data availability

The dynamically downscaled simulation output is available from NCAR’s Research Data Archive40.

Code availability

The source code56 for the snow event identification and tracking is available from


  1. Changnon, S. A. & Changnon, D. A spatial and temporal analysis of damaging snowstorms in the United States. Nat. Hazards 37, 373–389 (2006).

    Article  Google Scholar 

  2. Squires, M. F. et al. The regional snowfall index. Bull. Am. Meteorol. Soc. 95, 1835–1848 (2014).

    Article  Google Scholar 

  3. Coleman, J. S. M. & Schwartz, R. M. An updated blizzard climatology of the contiguous United States (1959–2014): an examination of spatiotemporal trends. J. Appl. Meteor. Climatol. 56, 173–187 (2017).

    Article  Google Scholar 

  4. Kunkel, K. E. et al. Trends in 20th century U.S. snowfall using a quality-controlled data set. J. Atmos. Oceanic Technol. 26, 33–44 (2009).

    Article  Google Scholar 

  5. Kunkel, K. E. et al. Monitoring and understanding trends in extreme storms: state of knowledge. Bull. Am. Meteorol. Soc. 94, 499–514 (2013).

    Article  Google Scholar 

  6. Kunkel, K. E. et al. Trends and extremes in Northern Hemisphere snow characteristics. Curr. Clim. Change Rep. 2, 65–73 (2016).

    Article  Google Scholar 

  7. Krasting, J. P., Broccoli, A. J., Dixon, K. W. & Lanzante, J. R. Future changes in Northern Hemisphere snowfall. J. Clim. 26, 7813–7828 (2013).

    Article  Google Scholar 

  8. O’Gorman, P. A. Contrasting responses of mean and extreme snowfall to climate change. Nature 512, 416–418 (2014).

    Article  CAS  Google Scholar 

  9. Diffenbaugh, N. S., Scherer, M. & Ashfaq, M. Response of snow-dependent hydrologic extremes to continued global warming. Nat. Clim. Change 3, 379–384 (2012).

    Article  Google Scholar 

  10. Kapnick, S. B. & Delworth, T. L. Controls of global snow under a changed climate. J. Clim. 26, 5537–5562 (2013).

    Article  Google Scholar 

  11. Notaro, M., Lorenz, D., Hoving, C. & Schummer, M. Twenty-first-century projections of snowfall and winter severity across Central-Eastern North America. J. Clim. 27, 6526–6550 (2014).

    Article  Google Scholar 

  12. Danco, J. F., DeAngelis, A. M., Raney, B. K. & Broccoli, A. J. Effects of a warming climate on daily snowfall events in the Northern Hemisphere. J. Clim. 29, 6295–6318 (2016).

    Article  Google Scholar 

  13. Janoski, T. P., Broccoli, A. J., Kapnick, S. B. & Johnson, N. C. Effects of climate change on wind-driven heavy-snowfall events over eastern North America. J. Clim. 31, 9037–9054 (2018).

    Article  Google Scholar 

  14. Zarzycki, C. M. Projecting changes in societally impactful northeastern U.S. snowstorms. Geophys. Res. Lett. 45, 12067–12075 (2018).

    Article  Google Scholar 

  15. Liu, C. et al. Continental-scale convection-permitting modeling of the current and future climate of North America. Clim. Dynam. 49, 71–95 (2017).

    Article  Google Scholar 

  16. Rasmussen, R. et al. High-resolution coupled climate runoff simulations of seasonal snowfall over Colorado: a process study of current and warmer climate. J. Clim. 24, 3015–3048 (2011).

    Article  Google Scholar 

  17. Prein, A. F. et al. Simulating North American mesoscale convective systems with a convection-permitting climate model. Clim. Dynam. 1432-0894, 1–16 (2017).

    Google Scholar 

  18. Notaro, M., Bennington, V. & Vavrus, S. Dynamically downscaled projections of lake-effect snow in the Great Lakes basin. J. Clim. 28, 1661–1684 (2015).

    Article  Google Scholar 

  19. Cohen, J., Pfeiffer, K. & Francis, J. A. Warm arctic episodes linked with increased frequency of extreme winter weather in the United States. Nat. Commun. 9, 869 (2018).

    Article  CAS  Google Scholar 

  20. Chang, E. K. M. CMIP5 projection of significant reduction in extratropical cyclone activity over North America. J. Clim. 26, 9903–9922 (2013).

    Article  Google Scholar 

  21. Eichler, T. P., Gaggini, N. & Pan, Z. Impacts of global warming on Northern Hemisphere winter storm tracks in the CMIP5 model suite. J. Geophys. Res. Atmos. 118, 3919–3932 (2013).

    Article  Google Scholar 

  22. Tamarin-Brodsky, T. & Kaspi, Y. Enhanced poleward propagation of storms under climate change. Nat. Geosci. 10, 908–913 (2017).

    Article  CAS  Google Scholar 

  23. Vaughan, D.G. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 4 (IPCC, Cambridge Univ. Press, 2013).

  24. Suriano, Z. J. & Leathers, D. J. Twenty-first century snowfall projections within the eastern Great Lakes region: detecting the presence of a lake-induced snowfall signal in GCMs. Int. J. Climatol. 36, 2200–2209 (2016).

    Article  Google Scholar 

  25. Brown, R. D. & Robinson, D. A. Northern Hemisphere spring snow cover variability and change over 1922–2010 including an assessment of uncertainty. Cryosphere 5, 219–229 (2011).

    Article  Google Scholar 

  26. Collins, M. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) Ch. 12 (IPCC, Cambridge Univ. Press, 2013).

  27. Déry, S. J. & Brown, R. D. Recent Northern Hemisphere snow cover extent trends and implications for the snow-albedo feedback. Geophys. Res. Lett. 34, L22504 (2007).

    Article  Google Scholar 

  28. Choi, G., Robinson, D. A. & Kang, S. Changing Northern Hemisphere snow seasons. J. Clim. 23, 5305–5310 (2010).

    Article  Google Scholar 

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

    Article  Google Scholar 

  30. Brutel-Vuilmet, C., Ménégoz, M. & Krinner, G. An analysis of present and future seasonal Northern Hemisphere land snow cover simulated by CMIP5 coupled climate models. Cryosphere Discuss. 6, 3317–3348 (2012).

    Article  Google Scholar 

  31. Colle, B. A. et al. Historical evaluation and future prediction of eastern North American and western Atlantic extratropical cyclones in the CMIP5 models during the cool season. J. Clim. 26, 6882–6903 (2013).

    Article  Google Scholar 

  32. Ma, C.-G. & Chang, E. K. M. Impacts of storm-track variations on wintertime extreme weather events over the continental United States. J. Clim. 30, 4601–4624 (2017).

    Article  Google Scholar 

  33. Zhou, B., Wang, Z., Shi, Y., Xu, Y. & Han, Z. Historical and future changes of snowfall events in China under a warming background. J. Clim. 31, 5873–5889 (2018).

    Article  Google Scholar 

  34. IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) (Cambridge Univ. Press, 2014).

  35. Mankin, J. S. et al. The potential for snow to supply human water demand in the present and future. Environ. Res. Lett. 10, 114016 (2015).

    Article  Google Scholar 

  36. Impacts, Risks, and Adaptation in the United States: Fourth National Climate Assessment Vol. II (eds Reidmiller, D. R. et al.) 24–32 (USGCRP, 2018).

  37. Trenberth, K. E. Changes in precipitation with climate change. Clim. Res. 47, 123–138 (2011).

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

  39. Climate Science Special Report: Fourth National Climate Assessment Vol. I (eds Wuebbles, D. J. et al.) (USGCRP, 2017).

  40. High Resolution WRF Simulations of the Current and Future Climate of North America (NCAR, accessed 1 April 2018);

  41. Prein, A. F. et al. Increased rainfall volume from future convective storms in the U.S. Nat. Clim. Change 7, 880 (2017).

    Article  Google Scholar 

  42. Déqué, M. et al. An intercomparison of regional climate simulations for Europe: assessing uncertainties in model projections. Clim. Change 81, 53–70 (2007).

    Article  Google Scholar 

  43. Michaelis, A. C., Willison, J., Lackmann, G. M. & Robinson, W. A. Changes in winter North Atlantic extratropical cyclones in high-resolution regional pseudo-global warming simulations. J. Clim. 30, 6905–6925 (2017).

    Article  Google Scholar 

  44. Musselman, K. N. et al. Projected increases and shifts in rain-on-snow flood risk over western North America. Nat. Clim. Change 8, 808–812 (2018).

    Article  Google Scholar 

  45. Michaelis, A. C. & Lackmann, G. M. Climatological changes in the extratropical transition of tropical cyclones in high-resolution global simulations. J. Clim. 32, 8733–8753 (2019).

    Article  Google Scholar 

  46. Scaff, L. et al. Simulating the convective precipitation diurnal cycle in North America’s current and future climate. Clim. Dynam. (2019).

  47. Dee, D. P. et al. The era-interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553–597 (2011).

    Article  Google Scholar 

  48. Powers, J. G. et al. The weather research and forecasting model: overview, system efforts, and future directions. Bull. Am. Meteorol. Soc. 98, 1717–1737 (2017).

    Article  Google Scholar 

  49. Rasmussen, K., Prein, A., Rasmussen, R., Ikeda, K. & Liu, C. Changes in the convective population and thermodynamic environments in convection-permitting regional climate simulations over the United States. Clim. Dynam. 0930–7575, 1–26 (2017).

    Google Scholar 

  50. Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim. Change 109, 213–241 (2011).

    Article  CAS  Google Scholar 

  51. Carroll, T. et al. NOHRSC Operations and the Simulation of Snow Cover Properties for the Conterminous U.S. (NOAA, 2001);

  52. Barrett, A. National Operational Hydrologic Remote Sensing Center Snow Data Assimilation System (SNODAS) Products at NSIDC (NSIDC, 2003);

  53. Snow Data Assimilation System (SNODAS) Data Products at NSIDC Version 1 (NSIDC, accessed 5 December 2019);

  54. Clow, D. W., Nanus, L., Verdin, K. L. & Schmidt, J. Evaluation of SNODAS snow depth and snow water equivalent estimates for the Colorado Rocky Mountains, USA. Hydrol. Process. 26, 2583–2591 (2012).

    Article  Google Scholar 

  55. Markowski, P. & Richardson, Y. Mesoscale Meteorology in Midlatitudes Ch. 1 (John Wiley & Sons, 2010).

  56. Haberlie, A. M. ahaberlie/Future_Snow: 2020.2 (Zenodo, 2020);

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We thank K. Ikeda and A. Prein for their assistance in accessing and interpreting the Liu et al.15 output. This research was supported by National Science Foundation grant no. ATM-1637225.

Author information

Authors and Affiliations



W.A. conceived the study. A.H. and W.A. led on the design of the study and analysed the data. A.H. constructed the tracking algorithm. V.G. led on the simulation output verification. All authors contributed to writing the manuscript, with W.A. as lead manuscript author and A.H. as lead author for the Methods.

Corresponding author

Correspondence to Walker S. Ashley.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Martin Baxter, Anthony Broccoli 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

Extended Data Fig. 1 Comparison to climatology.

October–April 2003–2013 a) SNODAS average SWE (mm) and b) the absolute difference between WRF-CTRL and SNODAS mean SWE shown as a per cent of the total mean SNODAS SWE. As discussed in the manuscript, 2004–05 season was omitted from analysis due to missing WRF data. Hatched areas on both figures indicate locations where SNODAS data were not available or both WRF-CTRL and SNODAS did not record any SWE during the analysis period.

Extended Data Fig. 2 Event detection and tracking.

Demonstration of the various sources of climatological calculations from CTRL, namely, a) total accumulation of snow (liquid water equivalent) from January 13th 2001 to January 16th 2001, b) a ‘slice’ within a qualifying swath with the lowest (0.1mm / 3-hr), 50th percentile (0.46mm / 3-hr), and 90th percentile (2.08mm / 3-hr) snowfall totals (liquid water equivalent) denoted by the colour fill. The black outline is the spatial extent of the swath (that is, where the swath produced at least 0.1mm / 3-hr liquid water equivalent totals), c) the spatial extent of the occurrence of 3-hr liquid water equivalent snowfall totals exceeding the 50th percentile within this swath, and d) the spatial extent of the occurrence of 3-hr liquid water equivalent snowfall totals exceeding the 90th percentile within this swath.

Extended Data Fig. 3 Mean seasonal SWE accumulations.

Mean seasonal SWE accumulations (2004–2005 excluded) for (a) CTRL, (b) PGW, and accumulated SWE (c) differences and (d) per cent differences.

Extended Data Fig. 4 Spatial climatology of 90th percentile snow events.

Mean annual (a, b) swath counts, and (c) swath count difference and (d) per cent difference between (a) CTRL and (b) PGW for only 90th percentile snow events. The areas in grey experienced no qualifying swaths during the study period.

Extended Data Fig. 5 Per cent change in midwinter moderate and extreme intensity snowstorms.

Per cent difference in (a) 50th and (b) 90th percentile swath event counts between CTRL and PGW for the months of January and February.

Supplementary information

Supplementary Information

Supplementary Table 1.

Source data

Source Data Fig. 1

Data used in construction of seasonal comparisons between CTRL and PGW.

Source Data Fig. 3

Data used in construction of seasonal and subseasonal variability in swath counts and snow water equivalent (SWE) per season (October–April) for CTRL and PGW, panels a–d.

Source Data Fig. 4

Data used in construction of weekly per cent difference between the two epochs.

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Ashley, W.S., Haberlie, A.M. & Gensini, V.A. Reduced frequency and size of late-twenty-first-century snowstorms over North America. Nat. Clim. Chang. 10, 539–544 (2020).

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