Temperature as a potent driver of regional forest drought stress and tree mortality

Journal name:
Nature Climate Change
Year published:
Published online


As the climate changes, drought may reduce tree productivity and survival across many forest ecosystems; however, the relative influence of specific climate parameters on forest decline is poorly understood. We derive a forest drought-stress index (FDSI) for the southwestern United States using a comprehensive tree-ring data set representing AD 1000–2007. The FDSI is approximately equally influenced by the warm-season vapour-pressure deficit (largely controlled by temperature) and cold-season precipitation, together explaining 82% of the FDSI variability. Correspondence between the FDSI and measures of forest productivity, mortality, bark-beetle outbreak and wildfire validate the FDSI as a holistic forest-vigour indicator. If the vapour-pressure deficit continues increasing as projected by climate models, the mean forest drought-stress by the 2050s will exceed that of the most severe droughts in the past 1,000 years. Collectively, the results foreshadow twenty-first-century changes in forest structures and compositions, with transition of forests in the southwestern United States, and perhaps water-limited forests globally, towards distributions unfamiliar to modern civilization.

At a glance


  1. Correlation between the FDSI and climate.
    Figure 1: Correlation between the FDSI and climate.

    a, The annual FDSI derived from tree ring-width index records (red, 1896–2007) and estimated with climate data using equation (1) (black, 1896–2012). See Supplementary Fig. S3 for estimated confidence ranges in the FDSI values. b, Curves show correlation between the estimated and the actual FDSI, allowing predictive contributions of the warm-season VPD and cold-season precipitation to vary from 0 to 100% and 100 to 0%, respectively. The straight lines connect optimal correlations with axes. Grey areas represent 95% confidence intervals.

  2. Measurements of forest productivity and mortality overlaid on the FDSI (red, right
y axis).
    Figure 2: Measurements of forest productivity and mortality overlaid on the FDSI (red, right y axis).

    a, The annual average late-June to early-August NDVI calculated from satellite (1981–1999: AVHRR, 2000–2012: MODIS) imagery. b, Annual forest inventory and analysis measurements of the percentage of standing dead trees in the SWUS for the three most common conifer species. Error bars represent standard deviation of the percentage dead when each year’s forest inventory and analysis measurements are randomly resampled 1,000 times (Supplementary Information). c, Aerial-survey-derived estimates of the area where ≥10 trees per acre were killed by bark-beetle attack. d, Satellite-derived moderately and severely burned forest and woodland area in the SWUS. See Supplementary Information and Supplementary Fig. S4 for methods to calculate burned area. The inset shows the percentage of years within a given FDSI class that were top-10% fire-scar years during AD1650–1899 (the horizontal line is at the expected frequency of 10%, bins are 0.25 FDSI units wide). In all panels, the FDSI values for 2008–2012 (open red squares) were estimated by applying climate data to equation (1). Note the inverted y axes for the FDSI in bd.

  3. Eleven-year smoothed FDSI for AD[thinsp]1000-2012.
    Figure 3: Eleven-year smoothed FDSI for AD1000–2012.

    Black area: 95% confidence range of the FDSI, representing the range of FDSI values expected if all 335 chronologies were available. Vertical grey areas highlight drought events.

  4. Observed and modelled climate and forest drought-stress.
    Figure 4: Observed and modelled climate and forest drought-stress.

    ad, The warm-season VPD (a), warm-season Tmax (b), cold-season precipitation (c) and the FDSI (d). Black: observed records. Coloured bold lines: CMIP3 ensemble mean values. Shading around time series: inner 50% of CMIP3 values. Green time series: 2042–2069 dynamically downscaled NARCCAP ensemble mean values. Horizontal brown line and shading in d show the mean and 95% confidence FDSI values of the most severe 50% of years during the 1572–1587 megadrought. The horizontal grey lines show the anomaly in standard deviations from the observed 1896–2007 mean (right y axis). See Supplementary Figs S5 and S6 for individual model projections and alternative emissions scenarios.

  5. Extreme drought stress.
    Figure 5: Extreme drought stress.

    a, Cumulative distribution functions of tree-ring derived FDSI during AD1000–2007 (black) and model-projected FDSI during AD2000–2100 for the A2 (red), A1B (green) and B1 (blue) emissions scenarios. Brown line: mean FDSI during the most extreme half of the 1572–1587 megadrought. b, Fifty-year running frequency of annual FDSI values more negative than the mean FDSI during the most negative half of the years during the 1572–1587 megadrought. The colours in b represent the same as in a. Shaded areas: 95% confidence ranges for tree-ring-derived values and inner-quartile values for model ensemble projections.

  6. Where have trees died?
    Figure 6: Where have trees died?

    The x axis represents a long-term drought-stress gradient among SWUS forest grid cells. The grid cells with the most severe long-term drought-stress are on the left side of plots. a, The percentage of grid cells in each drought-stress class with ≥10 trees per acre killed by bark beetles during 1997–2011. b,The percentage of grid cells in each drought-stress class where moderate or severe wildfire occurred during 1984–2011. Horizontal dotted black lines in a and b indicate expected percentages if these mortality processes were spatially uniform. c, Probability distribution function of the average FDSI during 1896–2007 among SWUS forest grid cells. The site-specific FDSI (x axis) estimated using equation (1). The methods are described in the Supplementary Information. Grey and white shading is intended to assist with interpretation.


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Author information


  1. Earth & Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

    • A. Park Williams,
    • Chandana Gangodagamage &
    • Nate G. McDowell
  2. US Geological Survey, Fort Collins Science Center, Jemez Mountains Field Station, Los Alamos, New Mexico 87544, USA

    • Craig D. Allen
  3. School of Geography and Development, University of Arizona, Tucson, Arizona 85721, USA

    • Alison K. Macalady,
    • Daniel Griffin &
    • Connie A. Woodhouse
  4. Laboratory of Tree-ring Research, University of Arizona, Tucson, Arizona 85721, USA

    • Alison K. Macalady,
    • Daniel Griffin,
    • Connie A. Woodhouse,
    • David M. Meko,
    • Thomas W. Swetnam &
    • Jeffrey S. Dean
  5. Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

    • Sara A. Rauscher
  6. Lamont-Doherty Earth Observatory of Columbia University, Palisades, New York 10964, USA

    • Richard Seager &
    • Edward R. Cook
  7. Laboratory of Tree-Ring Science, Department of Geography, The University of Tennessee, Knoxville, Tennessee 37996, USA

    • Henri D. Grissino-Mayer
  8. Space Data Systems, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA

    • Michael Cai


A.P.W., C.D.A., A.K.M., D.G., C.A.W., D.M.M., T.W.S., S.A.R., R.S., M.C. and N.G.M. conceived and designed the experiments. A.P.W. performed the experiments. A.P.W. and E.R.C. analysed the data. A.K.M., D.G., C.A.W., C.G., D.M.M., T.W.S., S.A.R., H.D.G-M., J.S.D. and E.R.C. contributed data. A.P.W., C.D.A., A.K.M., D.G., C.A.W., D.M.M., T.W.S., S.A.R., R.S., H.D.G-M. and N.G.M. wrote the paper.

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