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.

2021 North American heatwave amplified by climate change-driven nonlinear interactions


Heat conditions in North America in summer 2021 exceeded previous heatwaves by margins many would have considered impossible under current climate conditions. Associated severe impacts highlight the need for understanding the physical drivers of the heatwave and relations to climate change, to improve the projection and prediction of future extreme heat risks. Here, we find that slow- and fast-moving components of the atmospheric circulation interacted, along with regional soil moisture deficiency, to trigger a 5-sigma heat event. Its severity was amplified ~40% by nonlinear interactions between its drivers, probably driven in part by land–atmosphere feedbacks catalysed by long-term regional warming and soil drying. Since the 1950s, global warming has transformed the peak daily regional temperature anomaly of the event from virtually impossible to a presently estimated ~200-yearly occurrence. Its likelihood is projected to increase rapidly with further global warming, possibly becoming a 10-yearly occurrence in a climate 2 °C warmer than the pre-industrial period, which may be reached by 2050.

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

Access options

Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: Timing and location of the PNW heatwave and its associated atmospheric dynamical and land surface conditions.
Fig. 2: Nonlinear interactions of common drivers and their long-term trends.
Fig. 3: Modelled PNW monthly temperature variability and extreme event return periods, with versus without soil moisture interaction.
Fig. 4: 2021 heatwave likelihood estimates over recent decades and under future emissions pathways.

Data availability

All ERA5 output data used in this study are available from ECMWF at!/dataset/reanalysis-era5-single-levels. All CAM5_GOGA output used in this study is available at CMIP6 multimodel mean warming levels are available at

Code availability

All figures were produced using Python v.3.6 ( All code needed to reproduce the main figures is available at (ref. 69).


  1. Popovich, N. & Choi-Schagrin, W. Hidden toll of the Northwest heat wave: hundreds of extra deaths. The New York Times (11 August 2021).

  2. Excess Deaths Associated with COVID-19 (CDC, 2021);

  3. Heat-Related Deaths in B.C. Knowledge Update (BC Coroners Service, accessed August 2021);

  4. Schramm, P. J. et al. Heat-related emergency department visits during the Northwestern heat wave—United States, June 2021. MMWR Morb. Mortal. Wkly Rep. 70, 1020–1021 (2021).

    Google Scholar 

  5. American Housing Survey (AHS) (US Census Bureau, accessed August 2021);

  6. Tigchelaar, M., Battisti, D. S. & Spector, J. T. Work adaptations insufficient to address growing heat risk for U.S. agricultural workers. Environ. Res. Lett. 15, 094035 (2020).

  7. Map Archive (U.S. Drought Monitor, accessed August 2021);

  8. National Fire News (NICF, accessed August 2021);

  9. Silverman, H., Guy, M. & Sutton, J. Western wildfire smoke is contributing to New York City’s worst air quality in 15 years. CNN (21 July 2021);

  10. Meehl, G. A. & Tebaldi, C. More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305, 994–997 (2004).

    CAS  Google Scholar 

  11. Perkins-Kirkpatrick, S. E. & Lewis, S. C. Increasing trends in regional heatwaves. Nat. Commun. 11, 3357 (2020).

    CAS  Google Scholar 

  12. Philip, S. Y. et al. Rapid Attribution Analysis of the Extraordinary Heatwave on the Pacific Coast (World Weather Attribution, 2021);

  13. Coumou, D. & Robinson, A. Historic and future increase in the global land area affected by monthly heat extremes. Environ. Res. Lett. 8, 034018 (2013).

    Google Scholar 

  14. Power, S. B. & Delage, F. P. D. Setting and smashing extreme temperature records over the coming century. Nat. Clim. Change 9, 529–534 (2019).

    Google Scholar 

  15. Fischer, E. M., Sippel, S. & Knutti, R. Increasing probability of record-shattering climate extremes. Nat. Clim. Change 11, 689–695 (2021).

    Google Scholar 

  16. Thompson, V. et al. The 2021 western North America heat wave among the most extreme events ever recorded globally. Sci. Adv. 8, eabm6860 (2022).

    Google Scholar 

  17. Taleb, N. N. The Black Swan: The Impact of the Highly Improbable (Random House, 2007).

  18. Aven, T. On the meaning of a black swan in a risk context. Saf. Sci. 57, 44–51 (2013).

    Google Scholar 

  19. Lin, N. & Emanuel, K. Grey swan tropical cyclones. Nat. Clim. Change 6, 106–111 (2015).

    Google Scholar 

  20. Petoukhov, V., Rahmstorf, S., Petri, S. & Schellnhuber, H. J. Quasiresonant amplification of planetary waves and recent Northern Hemisphere weather extremes. Proc. Natl Acad. Sci. USA 110, 5336–5341 (2013).

    CAS  Google Scholar 

  21. Petoukhov, V. et al. Role of quasiresonant planetary wave dynamics in recent boreal spring-to-autumn extreme events. Proc. Natl Acad. Sci. USA 113, 6862–6867 (2016).

    CAS  Google Scholar 

  22. Screen, J. A. & Simmonds, I. Amplified mid-latitude planetary waves favour particular regional weather extremes. Nat. Clim. Change 4, 704–709 (2014).

    Google Scholar 

  23. Kornhuber, K. et al. Summertime planetary wave resonance in the Northern and Southern Hemispheres. J. Clim. 30, 6133–6150 (2017).

    Google Scholar 

  24. Kornhuber, K. et al. Amplified Rossby waves enhance risk of concurrent heatwaves in major breadbasket regions. Nat. Clim. Change 10, 48–53 (2019).

    Google Scholar 

  25. Mann, M. E. et al. Influence of anthropogenic climate change on planetary wave resonance and extreme weather events. Sci. Rep. 7, 45242 (2017).

    CAS  Google Scholar 

  26. Mann, M. E. et al. Projected changes in persistent extreme summer weather events: the role of quasi-resonant amplification. Sci. Adv. 4, eaat3272 (2018).

    CAS  Google Scholar 

  27. Kornhuber, K. & Tamarin-Brodsky, T. Future changes in northern hemisphere summer weather persistence linked to projected arctic warming. Geophys. Res. Lett. 48, e2020GL091603 (2021).

    Google Scholar 

  28. Hirschi, M. et al. Observational evidence for soil-moisture impact on hot extremes in southeastern Europe. Nat. Geosci. 4, 17–21 (2010).

    Google Scholar 

  29. Miralles, D. G., van den Berg, M. J., Teuling, A. J. & de Jeu, R. A. M. Soil moisture–temperature coupling: a multiscale observational analysis. Geophys. Res. Lett. 39, L21707 (2012).

  30. Miralles, D. G., Teuling, A. J., van Heerwaarden, C. C. & Vilà-Guerau de Arellano, J. Mega-heatwave temperatures due to combined soil desiccation and atmospheric heat accumulation. Nat. Geosci. 7, 345–349 (2014).

    CAS  Google Scholar 

  31. Rasmijn, L. M. et al. Future equivalent of 2010 Russian heatwave intensified by weakening soil moisture constraints. Nat. Clim. Change 8, 381–385 (2018).

    Google Scholar 

  32. Dirmeyer, P. A., Balsamo, G., Blyth, E. M., Morrison, R. & Cooper, H. M. Land–atmosphere interactions exacerbated the drought and heatwave over northern Europe during summer 2018. AGU Adv. 2, e2020AV000283 (2021).

    Google Scholar 

  33. Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: a review. Earth Sci. Rev. 99, 125–161 (2010).

    CAS  Google Scholar 

  34. Koster, R. D. et al. Regions of strong coupling between soil moisture and precipitation. Science 305, 1138–1140 (2004).

    CAS  Google Scholar 

  35. Cook, B. I., Smerdon, J. E., Seager, R. & Coats, S. Global warming and 21st century drying. Clim. Dynam. 43, 2607–2627 (2014).

    Google Scholar 

  36. Cook, B. I., Ault, T. R. & Smerdon, J. E. Unprecedented 21st century drought risk in the American Southwest and Central Plains. Sci. Adv. 1, e1400082 (2015).

    Google Scholar 

  37. Dirmeyer, P. A. et al. Projections of the shifting envelope of water cycle variability. Clim. Change 136, 587–600 (2016).

    Google Scholar 

  38. Seneviratne, S. I., Lüthi, D., Litschi, M. & Schär, C. Land–atmosphere coupling and climate change in Europe. Nature 443, 205–209 (2006).

    CAS  Google Scholar 

  39. Petoukhov, V. et al. Alberta wildfire 2016: apt contribution from anomalous planetary wave dynamics. Sci. Rep. 8, 12375 (2018).

    Google Scholar 

  40. Teng, H. & Branstator, G. Amplification of waveguide teleconnections in the boreal summer. Curr. Clim. Change Rep. 5, 421–432 (2019).

    Google Scholar 

  41. Neal, E., Huang, C. S. Y. & Nakamura, N. The 2021 Pacific Northwest heat wave and associated blocking: meteorology and the role of an upstream cyclone as a diabatic source of wave activity. Geophys. Res. Lett. 49, e2021GL097699 (2022).

  42. Wang, J. et al. Changing lengths of the four seasons by global warming. Geophys. Res. Lett. 48, e2020GL091753 (2021).

    Google Scholar 

  43. Berg, A. et al. Impact of soil moisture–atmosphere interactions on surface temperature distribution. J. Clim. 27, 7976–7993 (2014).

    Google Scholar 

  44. Swain, D. L., Singh, D., Touma, D. & Diffenbaugh, N. S. Attributing extreme events to climate change: a new frontier in a warming world. One Earth 2, 522–527 (2020).

    Google Scholar 

  45. van Oldenborgh, G. J. et al. Pathways and pitfalls in extreme event attribution. Clim. Change 166, 13 (2021).

    Google Scholar 

  46. Philip, S. et al. A protocol for probabilistic extreme event attribution analyses. Adv. Stat. Climatol. Meteorol. Oceanogr. 6, 177–203 (2020).

    Google Scholar 

  47. McKinnon, K. A., Rhines, A., Tingley, M. P. & Huybers, P. The changing shape of Northern Hemisphere summer temperature distributions. J. Geophys. Res. 121, 8849–8868 (2016).

    Google Scholar 

  48. Volodin, E. M. & Yurova, A. Y. Summer temperature standard deviation, skewness and strong positive temperature anomalies in the present day climate and under global warming conditions. Clim. Dynam. 40, 1387–1398 (2013).

    Google Scholar 

  49. Philip, S. Y. et al. Rapid attribution analysis of the extraordinary heatwave on the Pacific Coast of the US and Canada June 2021. Preprint at Earth Syst. Dynam. (2021).

  50. White, R. H., Kornhuber, K., Martius, O. & Wirth, V. From atmospheric waves to heatwaves: a waveguide perspective for understanding and predicting concurrent, persistent and extreme extratropical weather. Bull. Am. Meteorol. Soc. 103, E923–E935 (2021).

    Google Scholar 

  51. Xu, P. et al. Amplified waveguide teleconnections along the polar front jet favor summer temperature extremes over northern Eurasia. Geophys. Res. Lett. 48, e2021GL093735 (2021).

  52. Liu, Y., Sun, C. & Li, J. The boreal summer zonal wavenumber-3 trend pattern and its connection with surface enhanced warming. J. Clim. 35, 833–850 (2022).

    Google Scholar 

  53. Sun, X. et al. Enhanced jet stream waviness induced by suppressed tropical Pacific convection during boreal summer. Nat. Commun. 13, 1288 (2022).

    CAS  Google Scholar 

  54. Dirmeyer, P. A. The terrestrial segment of soil moisture–climate coupling. Geophys. Res. Lett. 38, L16702 (2011).

    Google Scholar 

  55. Schwingshackl, C., Hirschi, M. & Seneviratne, S. I. Quantifying spatiotemporal variations of soil moisture control on surface energy balance and near-surface air temperature. J. Clim. 30, 7105–7124 (2017).

    Google Scholar 

  56. Mueller, B. & Seneviratne, S. I. Hot days induced by precipitation deficits at the global scale. Proc. Natl Acad. Sci. USA 109, 12398–12403 (2012).

    CAS  Google Scholar 

  57. Hersbach, H. et al. The ERA5 global reanalysis. Q. J. Roy. Meteor. Soc. 146, 1999–2049 (2020).

    Google Scholar 

  58. Lee, D. E., Ting, M., Vigaud, N., Kushnir, Y. & Barnston, A. G. Atlantic multidecadal variability as a modulator of precipitation variability in the Southwest United States. J. Clim. 31, 5525–5542 (2018).

    Google Scholar 

  59. Pomposi, C., Giannini, A., Kushnir, Y. & Lee, D. E. Understanding Pacific Ocean influence on interannual precipitation variability in the Sahel. Geophys. Res. Lett. 43, 9234–9242 (2016).

    Google Scholar 

  60. Neale, R. B. et al. The mean climate of the Community Atmosphere Model (CAM4) in forced SST and fully coupled experiments. J. Clim. 26, 5150–5168 (2013).

    Google Scholar 

  61. Titchner, H. A. & Rayner, N. A. The Met Office Hadley Centre sea ice and sea surface temperature data set, version 2: 1. Sea ice concentrations. J. Geophys. Res. 119, 2864–2889 (2014).

    Google Scholar 

  62. Hauser, M., Orth, R. & Seneviratne, S. I. Investigating soil moisture–climate interactions with prescribed soil moisture experiments: an assessment with the Community Earth System Model (version 1.2). Geosci. Mod. Dev. 10, 1665–1677 (2017).

    Google Scholar 

  63. Humphrey, V. et al. Soil moisture–atmosphere feedback dominates land carbon uptake variability. Nature 592, 65–69 (2021).

    CAS  Google Scholar 

  64. Hauser, M. mathause/cmip_temperatures: version 0.2.1. Zenodo (2021).

  65. Coles, S. An Introduction to Statistical Modeling of Extreme Values (Springer, 2001).

  66. Paciorek, C. climextRemes: tools for analyzing climate extremes. Zenodo (2019).

  67. Bell, B. et al. The ERA5 global reanalysis: preliminary extension to 1950. Q. J. Roy. Meteor. Soc. 147, 4186–4227 (2021).

    Google Scholar 

  68. Data. GISS: GISS surface temperature analysis (GISTEMP v4) (NASA, accessed January 2022);

  69. Bartusek, S. sambartusek/PNW_heatwave_2021: PNW_heatwave_2021. Zenodo (2022).

Download references


We are thankful to Y. Wu, R. Horton, D. Singh, C. Raymond, C. Rogers and R. Seager for valuable feedback on this work. We thank D. Lee for configuring, running and making output available from CAM5–GOGA. Support for this work was provided by NSF-AGS-1934358 (S.B., K.K. and M.T.) and NOAA NA20OAR4310379 (M.T.).

Author information

Authors and Affiliations



M.T. initiated and supervised the project. S.B. and K.K. analysed data with input from M.T. S.B. generated figures and wrote the first draft of the manuscript with input from K.K. and M.T. All authors discussed and edited the manuscript.

Corresponding author

Correspondence to Samuel Bartusek.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Climate Change thanks Rong Fu, Mark Risser and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Atmospheric dynamics during June 2021 leading to the anomalous geopotential heights associated with the PNW heatwave.

See Text S1 for further discussion. (af): 500hPa Geopotential height (filled contours), 300hPa meridional wind speed (red and blue contours), and outgoing longwave radiation (OLR; green and dark brown contours) anomalies averaged over 9-day periods centred on the annotated date. For clarity, the meridional wind field is only shown poleward of 20°N and the OLR field is only shown within 90°E–100°W (roughly the Pacific Ocean). For example, (a) shows the 9-day mean surrounding 06/05, when geopotential heights were high in the PNW accompanying a heatwave, with centres of low and high geopotential height extending westward over the Pacific forming a tripole. By 06/10 (b)) the tripole had expanded longitudinally, placing negative geopotential height over the PNW, and begun to constitute part of a wavenumber-4 pattern in meridional wind and geopotential height encircling the midlatitudes. Over 06/10–06/20 (c–e)) this wavenumber-4 pattern moved slightly northward and shifted phase longitudinally, eventually placing high geopotential height over the PNW. Throughout the last two weeks of June (d–f)) the wavenumber-4 pattern persisted and amplified, causing extreme temperatures and dry soils in central Europe, Siberia, and the PNW, and was reinforced by a Rossby wavetrain emanating from the subtropical western Pacific.

Extended Data Fig. 2 PNW land–atmosphere anomalies during the 2021 heatwave.

Mean conditions over the whole 9-day heatwave period (06/25–07/03; left column), its first half (06/25–06/29; middle column), and its second half (06/29–07/03; right column), for 2 m temperature (T2M) (top row), T2M anomalies (second row), soil moisture (SM) anomalies (third row), and evaporative fraction (EF) anomalies (bottom row). EF is calculated from daily-mean latent heat flux (LHF) and sensible heat flux (SHF) as LHF/(SHF + LHF). Many of the regions of hottest (absolute) T2M and hottest T2M, driest SM, and lowest EF (high SHF vs. total HF) anomalies during this heatwave overlapped, particularly in the center of the region: across northern Oregon, eastern Washington, northern Idaho, and central southern British Columbia (the Interior Plateau). However, some of the largest T2M anomalies were associated with high EF (high LHF vs. total HF) anomalies instead—mostly in the Coastal and Cascade mountains on the British Columbia coast and the Cariboo and Monashee mountains between British Columbia and Alberta. This pattern is very consistent with climatological daily correlation between EF and T2M anomalies (see Extended Data Fig. 6): areas where EF and T2M are anticorrelated (both typically and during this event) tend to be warmer, non-mountain areas with relatively low soil moisture and more arid and/or Mediterranean continental climates (that is, across much of eastern Oregon and Washington (the Columbia Plateau), Idaho, and British Columbia’s Interior Plateau. Therefore, overall, throughout the heatwave (06/25–07/03), the spatial anticorrelation between EF and T2M anomalies was very weak, reflecting the diversity of land types and land–atmosphere coupling regimes across the large region (yielding r = –0.04). However, where T2M was both anomalously and climatologically high, EF and T2M were more tightly anticorrelated. Masking to retain only land regions under the 850hPa level, the spatial correlation was –0.24, with p < 0.0001 (significance tested non-parametrically, accounting for spatial autocorrelation).

Extended Data Fig. 3 2-metre temperature anomaly, tendency, and latent versus sensible heat flux partitioning.

Two-day averages throughout 6/24–7/1, focusing on the heating phase of the event. The second-to-last row identifies points where the two-day average upward latent heat flux (LHF) was diminished and sensible heat flux (SHF) was enhanced (exhibiting negative and positive anomalies relative to 1981–2010, respectively, which tended to show strong persistence throughout the season). The last row further subselects points where the temperature tendency was also positive.

Extended Data Fig. 4 SW–warming relationship stratified by flux partitioning.

Points are daily averages for each land gridcell in the PNW region, over the heatwave period (06/25–07/02), with net SW (downward) anomaly plotted against 2-metre temperature anomaly. Orange dots represent daily averages at each point within the evolving mask shown in the second-to-last row of Extended Data Fig. 3, that is where (upward) sensible heat flux (SHF) was enhanced and latent heat flux (LHF) was diminished. Blue dots show all other land gridcells in the region. (KDE) contours are shown for each group of gridcells, considering only points with net anomalous shortwave radiation > 0, so that points not relevant to heating do not bias the KDE characterization.

Extended Data Fig. 5 Temperature tendency budget analysis at 850 hPa.

See Text S2 for further discussion. Top row, left: Temperature (at 850 hPa and 2 metres) and horizontal and vertical wind (at 850 hPa) anomalies averaged during the 2021 PNW heatwave (06/24–07/03). The green box, blue box, and yellow contour outline the subregions highlighted in the right column (the green box shows the region focused on in the main results). Bottom two rows, left: Spatial patterns of contributions from various (grouped) terms in the 850 hPa temperature tendency budget, averaged throughout the heatwave warming phase (06/24–06/29). The residual ‘diabatic’ term is calculated as the total tendency minus the sum of all non-diabatic terms, and indicates processes not accounted for by the non-diabatic terms that may in part be attributed to land–atmosphere processes. Fields are smoothed with a running 4-gridcell (~1°) window in both directions. Right column: Temporal evolution of grouped terms in the budget throughout 06/23–07/01, averaged within the green, yellow, and blue outlined areas (in top row of maps). Solid lines show the total heating, horizontal heat advection, the sum of vertical heat advection and adiabatic expansion/compression, and the residual term. Additionally, the dashed translucent red line shows the residual term only where the long-term daily correlation between latent heat flux (LHF) and soil moisture (SM) exceeds 0.2 (see Extended Data Fig. 6), that is, where land–atmosphere interactions may be more likely to cause positive feedbacks on temperature extremes. 2-metre and 850hPa temperature anomalies in each sub-region are shown on the right axes.

Extended Data Fig. 6 Climatologies and trends of PNW temperature variability and land–atmosphere quantities.

Top row: 1981–2010 June–July climatologies (top panels) and 1979–2020 linear trends (bottom panels) of 2 m temperature (T2M), T2M variability (within-year standard deviation and skewness of daily anomalies), soil moisture (SM), and evaporative fraction (EF, calculated from daily latent heat flux [LHF] and sensible heat flux [SHF] as LHF/[LHF + SHF]). Bottom row: Climatologies and trends of four metrics of land–atmosphere coupling: the first three (correlations between LHF and SHF, LHF and SM, and EF and SM) represent the terrestrial component, while EF and T2M correlation represents the total feedback pathway. Correlation climatologies are created by correlating two variables (with June–July 1979–2020 trends removed) against each other throughout all June–July 1981–2010 days. Trends are between correlations within June–July of individual years (1979–2020). While SM and T2M are nearly everywhere anticorrelated, these metrics show where soil moisture deficit may causally affect T2M: LHF/SHF anticorrelation, LHF/SM correlation, EF/SM correlation, and EF/T2M anticorrelation indicate moisture-limited (versus energy-limited) regimes with potentially stronger land–atmosphere coupling, typical of transitional climate zones. If evapotranspiration is moisture-limited, under heating EF may decrease (SHF’s partition of flux increases), allowing for positive land–atmosphere feedbacks by further increasing T2M, decreasing SM, increasing SHF and decreasing LHF. Climatologically, such areas extend from the drier interior central West to the Columbia Plateau in eastern Washington and into interior British Columbia (bottom row, top panels). Trends indicate that much of the PNW has undergone strengthening in at least the terrestrial component of land–atmosphere coupling—most notably where soil moisture is climatologically moderate as opposed to extremely low, including much of BC’s Interior Plateau, much of the Cascade Range region (including near Portland and Seattle) and to the east of the Columbia Plateau. In some of these areas, T2M itself has become more coupled to EF, potentially signifying strengthened feedbacks—but such trends have not conclusively emerged overall. The spatial pattern of strengthening land–atmosphere coupling corresponds relatively well with warming, drying, and decreasing EF, and in some places with increasing T2M variability (areas of increasing T2M standard deviation and skewness correspond better to land–atmosphere correlation trends than to SM or EF trends alone).

Extended Data Fig. 7 Fit and validation for non-stationary location, stationary-scale historical GEV fit.

Same as Fig. 4 but showing results from a GEV distribution fit with stationary-scale parameter (location parameter is still non-stationary). Bootstrapped 95% confidence intervals are shaded as in Fig. 4.

Supplementary information

Supplementary Information

Supplementary Texts 1 and 2, Figs. 1–11 and Table 1.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Bartusek, S., Kornhuber, K. & Ting, M. 2021 North American heatwave amplified by climate change-driven nonlinear interactions. Nat. Clim. Chang. (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


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