Research ArticleENVIRONMENTAL STUDIES

Reduced tree growth in the semiarid United States due to asymmetric responses to intensifying precipitation extremes

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Science Advances  02 Oct 2019:
Vol. 5, no. 10, eaaw0667
DOI: 10.1126/sciadv.aaw0667
  • Fig. 1 CVP and its historical and projected changes.

    (A and B) Historical (1981–2010) CVP from PRISM for the cool and warm seasons, respectively. The bounding box in (A) indicates the Southwest region used for subsequent regional analyses. (C and D) PRISM-estimated historical change in CVP (∆CVPhistorical) from the early 20th century (1901–1930) to the late 20th/early 21st century (1981–2010) for the cool and warm seasons. (E and F) U.S. Historical Climatology Network (USHCN)–estimated CVP change. (G and H) Projected changes in cool- and warm-season CVP (∆CVPprojected) from 1981–2010 to 2071–2100 based on the ensemble median from 32 CMIP5 models under RCP8.5. ∆CVPprojected from each individual model is shown in figs. S4 and S5. Details regarding the CMIP5 models are provided in Materials and Methods and the Supplementary Materials.

  • Fig. 2 Growth responses to six-month precipitation composites.

    Growth responses are shown for cool season precipitation (A and B) and warm season precipitation (C and D). Relationships between tree growth and precipitation were defined as follows: not precipitation limited (P > PFDR or linear slope less than zero), symmetric (PPFDR and AIClinear ≤ AICquadratic), a positive asymmetry (PPFDR and AIClinear > AICquadratic with concave-up quadratic model), and a negative asymmetry (PPFDR and AIClinear > AICquadratic with concave-down quadratic model). Response functions were calculated from 1895 (the start of the PRISM precipitation estimates) through the ending year of tree-ring measurements, which varied by site but was never earlier than 1970. (B and D) Proportion of sites characterized as each response type for all species with at least 25 sites in the tree-ring database. Species include Abies lasiocarpa (ABLA, 32 sites), J. occidentalis (JUOC, 25 sites), Picea engelmannii (PCEN, 40 sites), P. edulis (PIED, 86 sites), Pinus flexilis (PIFL, 27 sites), P. ponderosa (PIPO, 215 sites), P. menziesii (PSME, 197 sites), Q. alba (QUAL, 62 sites), Q. douglasii (QUDG, 29 sites), Q. macrocarpa (QUMA, 37 sites), Q. stellata (QUST, 57 sites), T. distichum (TADI, 34 sites), Tsuga canadaensis (TSCA, 52 sites), and Tsuga mertensiana (TSME, 46 sites). (E) Conceptual diagrams of the four response types in (A) to (D).

  • Fig. 3 Growth responses to 3-month seasonal precipitation composites.

    Growth responses are shown for prior autumn (September to November) precipitation (A and B), winter (December to February) precipitation (C and D), spring (March to May) precipitation (E and F), and summer (June to August) precipitation (G and H). All methods, symbols, and species are identical to those shown in Fig. 2.

  • Fig. 4 Responses of forest growth to seasonal precipitation extremes.

    Proportion of years with extremely low or high precipitation (lower or upper 20th percentile, respectively) that are also extremely low or high growth years after using a Cohen’s κ correction to account for chance co-occurrence. Responses to extreme precipitation were calculated from 1895 (the start of the PRISM precipitation estimates) through the ending year of tree-ring measurements, which varied by site but was never earlier than 1970. (A and B) Responses of growth to extremely dry and extremely wet cool seasons (prior October to March). (C and D) Responses of growth to extremely dry and extremely wet warm seasons (April to September). Sites outlined in dark gray indicate significant co-occurrence of extreme precipitation and extreme growth based on the binomial distribution, with the false discovery rate (FDR) controlled at the αglobal = 0.05 level (Materials and Methods). The number of sites where PPFDR is shown for each panel.

  • Fig. 5 Density plots showing the relationship between growth responses to extreme events and site-level mean precipitation from all sites (N = 1314).

    (A and B) Relationship between log-transformed cool-season mean precipitation and, respectively, the proportion of years with extremely low and high cool-season precipitation that are also years with extremely low and high growth, after correcting for chance agreement using Cohen’s κ. (C and D) Relationship between log-transformed warm-season mean precipitation and, respectively, the proportion of years with extremely low and high warm-season precipitation that are also years with extremely low and high growth, after correcting for chance agreement using Cohen’s κ. The linear relationship between log-transformed mean precipitation and growth responses to extremes is shown with blue lines (cool season) and red lines (warm season) for those with significant (P < 0.05) relationships, along with Spearman’s rank correlation coefficient (ρ).

  • Fig. 6 Simulated implications of changing precipitation variability for tree growth in the southwestern United States (bounding box in Fig. 1A).

    (A) Cumulative and probability density functions (lines and bars, respectively) of the gamma distribution fit to regional cool-season precipitation. The 1901–1930 precipitation distribution was fit based on historical mean regional precipitation from PRISM, whereas 1981–2010 precipitation distribution was simulated with the same mean as 1901–1930 but with the observed increase in variability (∆CVPhistorical). (B) Violin and box plots of 10,000 cool-season precipitation realizations derived from the gamma distributions in (A), which were used to simulate growth anomalies based on the observed sensitivity of regional growth to precipitation. The empirical cumulative density functions from these growth simulations are shown in (C), with accompanying violin and box plots in (D).

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/10/eaaw0667/DC1

    Fig. S1. ITRDB sites used in these analyses (N = 1314).

    Fig. S2. Comparison of historical CVP change between PRISM (ΔCVPPRISM) and the UHCN (ΔCVPUSHCN).

    Fig. S3. Historical changes in PRISM-derived CVP of the Southwest.

    Fig. S4. Projected changes in cool-season CVP (1981–2010 versus 2071–2100) from each downscaled CMIP5 model.

    Fig. S5. Projected changes in warm-season CVP (1981–2010 versus 2071–2100) from each downscaled CMIP5 model.

    Fig. S6. Effect of species on the sensitivity of growth to precipitation extremes based on linear mixed effects models with random intercepts.

    Fig. S7. Same as Fig. 4, but for the 3-month meteorological seasons.

    Fig. S8. Model and diagnostics for the relationship between cool-season precipitation and growth in the Southwest.

    Fig. S9. Same as Fig. 6, but with a linear, rather than asymmetric, growth relationship to precipitation.

    Fig. S10. Number of multiyear runs (in the 10,000-year simulations) with simulated mean regional growth anomalies at least 1 SD below the historical mean.

    Table S1. Coefficients (with SE and P values) from linear mixed effects models of site-level low and high seasonal extreme responses (κ).

    Table S2. Downscaled CMIP5 models.

  • Supplementary Materials

    This PDF file includes:

    • Fig. S1. ITRDB sites used in these analyses (N = 1314).
    • Fig. S2. Comparison of historical CVP change between PRISM (ΔCVPPRISM) and the UHCN (ΔCVPUSHCN).
    • Fig. S3. Historical changes in PRISM-derived CVP of the Southwest.
    • Fig. S4. Projected changes in cool-season CVP (1981–2010 versus 2071–2100) from each downscaled CMIP5 model.
    • Fig. S5. Projected changes in warm-season CVP (1981–2010 versus 2071–2100) from each downscaled CMIP5 model.
    • Fig. S6. Effect of species on the sensitivity of growth to precipitation extremes based on linear mixed effects models with random intercepts.
    • Fig. S7. Same as Fig. 4, but for the 3-month meteorological seasons.
    • Fig. S8. Model and diagnostics for the relationship between cool-season precipitation and growth in the Southwest.
    • Fig. S9. Same as Fig. 6, but with a linear, rather than asymmetric, growth relationship to precipitation.
    • Fig. S10. Number of multiyear runs (in the 10,000-year simulations) with simulated mean regional growth anomalies at least 1 SD below the historical mean.
    • Table S1. Coefficients (with SE and P values) from linear mixed effects models of site-level low and high seasonal extreme responses (κ).
    • Table S2. Downscaled CMIP5 models.

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