Skip to main content

Thank you for visiting nature.com. 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.

Hot extremes have become drier in the United States Southwest

Abstract

The impacts of summer heat extremes are mediated by humidity. Increases in temperatures due to human-caused climate change are generally expected to increase specific humidity; however, it remains unclear how humidity extremes may change, especially in climatologically dry (low-humidity) regions. Here we show that specific humidity on dry days during summer (defined here as July–September) has decreased over the past seven decades in the United States Southwest, and that the greatest decreases co-occur with the hottest temperatures. Hot, dry summers have anomalously low evapotranspiration that is linked to low summer soil moisture. The recent decrease in summer soil moisture is explained by declines in June soil moisture, whereas the interannual variability is controlled by summer precipitation. Future projections of hot, dry days in the Southwest are uncertain due to the large spread in the Coupled Model Intercomparison Project phase 6 (CMIP6) trends in soil moisture and precipitation through 2100.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Changes in the distribution of specific humidity as a function of increasing global mean temperature anomalies (GMTA) and local temperature.
Fig. 2: Decreases in specific humidity in the American Southwest are amplified on hot days.
Fig. 3: The observed and fitted amplification index from 1950 to 2019.
Fig. 4: Increased probability of hot, dry days linked to reduced evapotranspiration and soil moisture.
Fig. 5: CMIP6 projections of June column soil moisture, summer precipitation and summer surface soil moisture.

Data availability

Data from ISD are publicly available at https://www.ncdc.noaa.gov/isd/data-access. Data from ERA5 are publicly available at https://cds.climate.copernicus.eu. Data from JRA-55 are publicly available at https://rda.ucar.edu/datasets/ds628.1/ (monthly) and https://rda.ucar.edu/datasets/ds628.0/ (daily). Data from GPCC are publicly available at https://psl.noaa.gov/data/gridded/data.gpcc.html. Data from GLDAS2.0 are publicly available at https://ldas.gsfc.nasa.gov/data. Model output from CMIP6 is publicly available at https://esgf-node.llnl.gov/projects/cmip6/. Berkeley Earth global mean temperature is publicly available at http://berkeleyearth.lbl.gov/auto/Global/Land_and_Ocean_complete.txt. ERSSTv.5 data are publicly available at http://berkeleyearth.lbl.gov/auto/Global/Land_and_Ocean_complete.txt. The AMV index was calculated using the Climate Variability and Diagnostics Package, which is publicly available at https://www.cesm.ucar.edu/working_groups/CVC/cvdp/. The PDO index is publicly available at https://www.ncdc.noaa.gov/teleconnections/pdo/. The Niño 3.4 index is publicly available at https://www.cpc.ncep.noaa.gov/data/indices/oni.ascii.txt.

Code availability

Code to fit the non-crossing quantile smoothing splines model is available at https://github.com/karenamckinnon/humidity_variability. Code to perform the analysis and reproduce the figures in the paper is available at https://github.com/karenamckinnon/compound_extremes.

References

  1. 1.

    Seager, R. et al. Whither the 100th meridian? The once and future physical and human geography of America’s arid–humid divide. Part I: The story so far. Earth Interact. 22, 1–22 (2018).

    Google Scholar 

  2. 2.

    Bradshaw, L. S., Deeming, J. E., Burgan, R. E. & Cohen, J. D. The 1978 National Fire-Danger Rating System: Technical Documentation (USDA Forest Service, 1983).

  3. 3.

    Evett, R. R., Mohrle, C. R., Hall, B. L., Brown, T. J. & Stephens, S. L. The effect of monsoonal atmospheric moisture on lightning fire ignitions in southwestern North America. Agric. For. Meteorol. 148, 1478–1487 (2008).

    Article  Google Scholar 

  4. 4.

    Fosberg, M. A. Weather in Wildland Fire Management: The Fire Weather Index (USDA Forest Service, 1978).

  5. 5.

    Flannigan, M. & Harrington, J. A study of the relation of meteorological variables to monthly provincial area burned by wildfire in Canada (1953–80). J. Appl. Meteorol. 27, 441–452 (1988).

    Article  Google Scholar 

  6. 6.

    Williams, A. P. et al. Causes and implications of extreme atmospheric moisture demand during the record-breaking 2011 wildfire season in the southwestern United States. J. Appl. Meteorol. Climatol. 53, 2671–2684 (2014).

    Article  Google Scholar 

  7. 7.

    Friedrich, K. et al. Reservoir evaporation in the Western United States: current science, challenges, and future needs. Bull. Am. Meteorol. Soc. 99, 167–187 (2018).

    Article  Google Scholar 

  8. 8.

    Eamus, D., Boulain, N., Cleverly, J. & Breshears, D. D. Global change-type drought-induced tree mortality: vapor pressure deficit is more important than temperature per se in causing decline in tree health. Ecol. Evol. 3, 2711–2729 (2013).

    Article  Google Scholar 

  9. 9.

    Allen, C. D. et al. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 259, 660–684 (2010).

    Article  Google Scholar 

  10. 10.

    Williams, A. P. et al. Temperature as a potent driver of regional forest drought stress and tree mortality. Nat. Clim. Change 3, 292–297 (2013).

    Article  Google Scholar 

  11. 11.

    Abatzoglou, J. T. & Williams, A. P. Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl Acad. Sci. USA 113, 11770–11775 (2016).

    CAS  Article  Google Scholar 

  12. 12.

    Byrne, M. P. & O’Gorman, P. A. Understanding decreases in land relative humidity with global warming: conceptual model and GCM simulations. J. Clim. 29, 9045–9061 (2016).

    Article  Google Scholar 

  13. 13.

    Chadwick, R., Good, P. & Willett, K. A simple moisture advection model of specific humidity change over land in response to SST warming. J. Clim. 29, 7613–7632 (2016).

    Article  Google Scholar 

  14. 14.

    Brown, P. J. & DeGaetano, A. T. Trends in US surface humidity, 1930–2010. J. Appl. Meteorol. Climatol. 52, 147–163 (2013).

    Article  Google Scholar 

  15. 15.

    Coffel, E. D., Horton, R. M., Winter, J. M. & Mankin, J. S. Nonlinear increases in extreme temperatures paradoxically dampen increases in extreme humid-heat. Environ. Res. Lett. 14, 084003 (2019).

    Article  Google Scholar 

  16. 16.

    Rastogi, D., Lehner, F. & Ashfaq, M. Revisiting recent US heat waves in a warmer and more humid climate. Geophys. Res. Lett. 47, e2019GL086736 (2020).

    Google Scholar 

  17. 17.

    Smith, A., Lott, N. & Vose, R. The integrated surface database: recent developments and partnerships. Bull. Am. Meteorol. Soc. 92, 704–708 (2011).

    Article  Google Scholar 

  18. 18.

    Koenker, R., Ng, P. & Portnoy, S. Quantile smoothing splines. Biometrika 81, 673–680 (1994).

    Article  Google Scholar 

  19. 19.

    McKinnon, K. A. & Poppick, A. Estimating changes in the observed relationship between humidity and temperature using noncrossing quantile smoothing splines. J. Agric. Biol. Environ. Stat. 25, 292–314 (2020).

  20. 20.

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

    Article  Google Scholar 

  21. 21.

    Kobayashi, S. et al. The JRA-55 reanalysis: general specifications and basic characteristics. J. Meteorol. Soc. Jpn II 93, 5–48 (2015).

    Article  Google Scholar 

  22. 22.

    Sutton, R. T. & Hodson, D. L. Atlantic Ocean forcing of North American and European summer climate. Science 309, 115–118 (2005).

    CAS  Article  Google Scholar 

  23. 23.

    Kushnir, Y., Seager, R., Ting, M., Naik, N. & Nakamura, J. Mechanisms of tropical Atlantic SST influence on North American precipitation variability. J. Clim. 23, 5610–5628 (2010).

    Article  Google Scholar 

  24. 24.

    Ruprich-Robert, Y. et al. Impacts of the Atlantic multidecadal variability on North American summer climate and heat waves. J. Clim. 31, 3679–3700 (2018).

    Article  Google Scholar 

  25. 25.

    Teuling, A. et al. A regional perspective on trends in continental evaporation. Geophys. Res. Lett. 36, L02404 (2009).

  26. 26.

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

    CAS  Article  Google Scholar 

  27. 27.

    Vargas Zeppetello, L. R., Battisti, D. S. & Baker, M. B. The origin of soil moisture evaporation ‘regimes’. J. Clim. 32, 6939–6960 (2019).

    Article  Google Scholar 

  28. 28.

    Schneider, U., Fuchs, T., Meyer-Christoffer, A. & Rudolf, B. Global Precipitation Analysis Products of the GPCC (GPCC/DWD, 2008).

  29. 29.

    Rodell, M. et al. The global land data assimilation system. Bull. Am. Meteorol. Soc. 85, 381–394 (2004).

    Article  Google Scholar 

  30. 30.

    Cayan, D. R. et al. Future dryness in the southwest US and the hydrology of the early 21st century drought. Proc. Natl Acad. Sci. USA 107, 21271–21276 (2010).

    CAS  Article  Google Scholar 

  31. 31.

    Williams, A. P. et al. Large contribution from anthropogenic warming to an emerging North American megadrought. Science 368, 314–318 (2020).

    CAS  Article  Google Scholar 

  32. 32.

    Ault, T. R. et al. A robust null hypothesis for the potential causes of megadrought in western North America. J. Clim. 31, 3–24 (2018).

    Article  Google Scholar 

  33. 33.

    Seager, R., Kushnir, Y., Herweijer, C., Naik, N. & Velez, J. Modeling of tropical forcing of persistent droughts and pluvials over western North America: 1856–2000. J. Clim. 18, 4065–4088 (2005).

    Article  Google Scholar 

  34. 34.

    Kumar, S., Newman, M., Wang, Y. & Livneh, B. Potential reemergence of seasonal soil moisture anomalies in North America. J. Clim. 32, 2707–2734 (2019).

    Article  Google Scholar 

  35. 35.

    Lehner, F., Deser, C., Simpson, I. R. & Terray, L. Attributing the US Southwest’s recent shift into drier conditions. Geophys. Res. Lett. 45, 6251–6261 (2018).

    Article  Google Scholar 

  36. 36.

    Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Article  Google Scholar 

  37. 37.

    O’Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).

    Article  Google Scholar 

  38. 38.

    Mankin, J. S., Smerdon, J. E., Cook, B. I., Williams, A. P. & Seager, R. The curious case of projected twenty-first-century drying but greening in the American West. J. Clim. 30, 8689–8710 (2017).

    Article  Google Scholar 

  39. 39.

    Cook, B. et al. Twenty-first century drought projections in the CMIP6 forcing scenarios. Earth’s Future 8, e2019EF001461 (2020).

  40. 40.

    Swann, A. L. Plants and drought in a changing climate. Curr. Clim. Change Rep. 4, 192–201 (2018).

    Article  Google Scholar 

  41. 41.

    MacDonald, G. M. Water, climate change, and sustainability in the southwest. Proc. Natl Acad. Sci. USA 107, 21256–21262 (2010).

    CAS  Article  Google Scholar 

  42. 42.

    Bolton, D. The computation of equivalent potential temperature. Mon. Weather Rev. 108, 1046–1053 (1980).

    Article  Google Scholar 

  43. 43.

    Zheng, W. et al. Improvement of daytime land surface skin temperature over arid regions in the NCEP GFS model and its impact on satellite data assimilation. J. Geophys. Res. 117, D06117 (2012).

  44. 44.

    Sheffield, J., Goteti, G. & Wood, E. F. Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J. Clim. 19, 3088–3111 (2006).

    Article  Google Scholar 

  45. 45.

    Rohde, R. et al. Berkeley Earth temperature averaging process. Geoinfor. Geostat. 1, 20–100 (2013).

    Google Scholar 

  46. 46.

    Huang, B. et al. Extended reconstructed sea surface temperature, version 5 (ERSSTv5): upgrades, validations, and intercomparisons. J. Clim. 30, 8179–8205 (2017).

    Article  Google Scholar 

  47. 47.

    Phillips, A. S., Deser, C. & Fasullo, J. Evaluating modes of variability in climate models. Eos Trans. Am. Geophys. Union 95, 453–455 (2014).

    Article  Google Scholar 

  48. 48.

    Trenberth, K. E. & Shea, D. J. Atlantic hurricanes and natural variability in 2005. Geophys. Res. Lett. 33, L12704 (2006).

  49. 49.

    Mantua, N. J., Hare, S. R., Zhang, Y., Wallace, J. M. & Francis, R. C. A Pacific interdecadal climate oscillation with impacts on salmon production. Bull. Am. Meteorol. Soc. 78, 1069–1080 (1997).

    Article  Google Scholar 

  50. 50.

    Lee, E. R., Noh, H. & Park, B. U. Model selection via Bayesian information criterion for quantile regression models. J. Am. Stat. Assoc. 109, 216–229 (2014).

    CAS  Article  Google Scholar 

  51. 51.

    Liu, Y. & Wu, Y. Stepwise multiple quantile regression estimation using non-crossing constraints. Stat. Its Interface 2, 299–310 (2009).

    Article  Google Scholar 

  52. 52.

    Bretherton, C. S., Widmann, M., Dymnikov, V. P., Wallace, J. M. & Bladé, I. The effective number of spatial degrees of freedom of a time-varying field. J. Clim. 12, 1990–2009 (1999).

    Article  Google Scholar 

Download references

Acknowledgements

K.A.M. acknowledges support from the National Science Foundation (award no. 1939988). I.R.S. was supported by the National Center for Atmospheric Research (NCAR), which is a major facility sponsored by the National Science Foundation under the Cooperative Agreement 1852977. JRA-55 data were provided by the Research Data Archive of the Computational and Information Systems Laboratory (CISL) at NCAR. Analysis was performed on the Casper cluster supported by by NCAR’s CISL. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups (listed in Methods) for producing and making available their model output. For CMIP, the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.

Author information

Affiliations

Authors

Contributions

K.A.M. conceived the study, performed the analysis and wrote the manuscript. A.P. contributed to the development of the method and provided commentary on the manuscript. I.R.S. provided feedback on the analysis and manuscript. Correspondence should be addressed to K.A.M.

Corresponding author

Correspondence to Karen A. McKinnon.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review informationNature Climate Change thanks Tim Cowan, Clemens Schwingshackl 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 Changes in hot, dry days for JRA-55, ERA5, ISD1979, and ERA5 at locations of Integrated Surface Database stations.

As in Fig. 2, but with data from (a, b) JRA-55, (c, d) ERA5, (e, f) Integrated Surface Database (ISD) stations from 1979–2019, and (g, h) ERA5 subset to the locations of the ISD stations in (e). Trends are calculated over the 1958–2019 period for JRA-55, and over the 1979–2019 period for all other maps. The ERA5-based trends in panel (d) uniquely do not show the amplification of drying at hot temperatures. To test whether the difference is due to the different starting point of the ERA5 trends, we recalculate trends in the ISD stations for the 1979–2019 period (panels e and f), which still show the amplification. To test whether the difference is due to the specific locations of the ISD stations, we subset the ERA5 trends to the locations of the ISD stations (panels g and h); this subset does not show the amplification behaviour. In the line plots (panels b, d, f, h), thin grey lines show the trends at each gridbox, and the thick black line is the area-weighted average across gridboxes. Note the different y-scales across the line plots.

Extended Data Fig. 2 Vertical and horizontal divergence do not explain the amplification index.

a, The composite vertical profile of July–August-September specific humidity from ERA5 on years in the top tercile (33%) of the amplification index minus the bottom tercile. b, The composite July–August-September vertically integrated moisture divergence from ERA5 on years in the top tercile of the amplification index minus the bottom tercile. c, The time series of the amplification index (orange) and the Southwest-average vertically integrated moisture divergence (teal). (d-i) As in (b)-(c), but for July, August, and September vertically integrated moisture divergence separately. Vertically integrated moisture divergence is the total per day.

Extended Data Fig. 3 Runoff covaries with surface soil moisture, but its contribution to the water balance is small.

As in Fig. 4, but for runoff from ERA5. Runoff is the total per day.

Extended Data Fig. 4 The CMIP6 models can have large biases in their mean state of precipitation, soil moisture, and evapotranspiration in the Southwest.

The distribution of July–August-September (a) average precipitation, (b) surface soil moisture, and (c) evapotranspiration from the 28 CMIP6 models used for Fig. 5 (blue histograms) and ERA5 (red vertical line). The CMIP6 estimates are based on 1979–2014, and the ERA5 estimates are based on 1979–2019; the end date in CMIP6 is the end of the historical scenario simulations. The 95% range shown for ERA5 is calculated by performing a bootstrap of the seasonal mean values with replacement, and provides an estimate of the uncertainty of the mean value due to sampling of internal variability. Surface soil moisture in the CMIP6 models is calculated over the top 10cm, whereas the top soil layer in ERA5 is 7cm; as such, the ERA5 surface soil moisture is multiplied by 10/7 for this comparison.

Extended Data Fig. 5 Changes in hot, dry extremes in Integrated Surface Database stations from 1950–2019.

As in Fig. 2, but with the limited number of Integrated Surface Database stations with data beginning in 1950. Trends are calculated over the 1950–2019 period.

Extended Data Fig. 6 The complex seasonal structure of specific humidity is captured by ten harmonics.

The empirical seasonal cycle of specific humidity (blue), and three different estimates of the seasonal cycle using ten seasonal harmonics (orange), a low-pass Butterworth filter with a frequency cutoff of 1/30 day−1, and a 15-day moving average at two Integrated Surface Database stations. Tucson International Airport (a) is influenced by the North American monsoon, and shows a rapid change in specific humidity at its onset in July, whereas the seasonal cycle in specific humidity at Stockton Metropolitan Airport (b) does not show such rapid changes.

Extended Data Fig. 7 The amplification index using more extreme cutoffs for hot and dry days.

The ISD1973 amplification index used in the main text (blue) compared to a version calculated with more extreme temperature (90th-100th percentile) and specific humidity (less than 5th percentile) cutoffs (orange).

Supplementary information

Supplementary Information

Supplementary Table 1.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

McKinnon, K.A., Poppick, A. & Simpson, I.R. Hot extremes have become drier in the United States Southwest. Nat. Clim. Chang. 11, 598–604 (2021). https://doi.org/10.1038/s41558-021-01076-9

Download citation

Search

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