Causes and Implications of Extreme Atmospheric Moisture Demand during the Record-Breaking 2011 Wildfire Season in the Southwestern United States

In 2011, exceptionally low atmospheric moisture content combined with moderately high temperatures to produce a record-high vapor pressure deficit (VPD) in the southwestern United States (SW). These conditions combined with record-low cold-season precipitation to cause widespread drought and extreme wildfires. Although interannual VPD variability is generally dominated by temperature, high VPD in 2011 was also driven by a lack of atmospheric moisture. The May–July 2011 dewpoint in the SW was 4.5 standard deviations below the long-term mean. Lack of atmospheric moisture was promoted by already very dry soils and amplified by a strong ocean-to-continent sea level pressure gradient and upper-level convergence that drove dry northerly winds and subsidence upwind of and over the SW. Subsidence drove divergence of rapid and dry surface winds over the SW, suppressing southerly moisture imports and removing moisture from already dry soils. Model projections developed for the fifth phase of the Coupled Model Intercomparison Project (CMIP5) suggest that by the 2050s warming trends will cause mean warm-season VPD to be comparable to the record-high VPD observed in 2011. CMIP5 projections also suggest increased interannual variability of VPD, independent of trends in background mean levels, as a result of increased variability of dewpoint, temperature, vapor pressure, and saturation vapor pressure. Increased variability in VPD translates to increased probability of 2011-type VPD anomalies, which would be superimposed on ever-greater background VPD levels. Although temperature will continue to be the primary driver of interannual VPD variability, 2011 served as an important reminder that atmospheric moisture content can also drive impactful VPD anomalies

Correlations between components of the water balance and burned area reveal new insights for predicting forest fire area in the southwest United States

We related measurements of annual burned area in the southwest United States during 1984–2013 to records of climate variability. Within forests, annual burned area correlated at least as strongly with spring–summer vapour pressure deficit (VPD) as with 14 other drought-related metrics, including more complex metrics that explicitly represent fuel moisture. Particularly strong correlations with VPD arise partly because this term dictates the atmospheric moisture demand. Additionally, VPD responds to moisture supply, which is difficult to measure and model regionally due to complex micrometeorology, land cover and terrain. Thus, VPD appears to be a simple and holistic indicator of regional water balance. Coupled with the well-known positive influence of prior-year cold season precipitation on fuel availability and connectivity, VPD may be utilised for burned area forecasts and also to infer future trends, though these are subject to other complicating factors such as land cover change and management. Assuming an aggressive greenhouse gas emissions scenario, climate models predict mean spring–summer VPD will exceed the highest recorded values in the southwest in nearly 40% of years by the middle of this century. These results forewarn of continued increases in burned forest area in the southwest United States, and likely elsewhere, when fuels are not limiting

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

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

Growth-Mortality Relationships in Piñon Pine (<i>Pinus edulis)</i> during Severe Droughts of the Past Century: Shifting Processes in Space and Time

<div><p>The processes leading to drought-associated tree mortality are poorly understood, particularly long-term predisposing factors, memory effects, and variability in mortality processes and thresholds in space and time. We use tree rings from four sites to investigate <i>Pinus edulis</i> mortality during two drought periods in the southwestern USA. We draw on recent sampling and archived collections to (1) analyze <i>P. edulis</i> growth patterns and mortality during the 1950s and 2000s droughts; (2) determine the influence of climate and competition on growth in trees that died and survived; and (3) derive regression models of growth-mortality risk and evaluate their performance across space and time. Recent growth was 53% higher in surviving vs. dying trees, with some sites exhibiting decades-long growth divergences associated with previous drought. Differential growth response to climate partly explained growth differences between live and dead trees, with responses wet/cool conditions most influencing eventual tree status. Competition constrained tree growth, and reduced trees’ ability to respond to favorable climate. The best predictors in growth-mortality models included long-term (15–30 year) average growth rate combined with a metric of growth variability and the number of abrupt growth increases over 15 and 10 years, respectively. The most parsimonious models had high discriminatory power (ROC>0.84) and correctly classified ∼70% of trees, suggesting that aspects of tree growth, especially over decades, can be powerful predictors of widespread drought-associated die-off. However, model discrimination varied across sites and drought events. Weaker growth-mortality relationships and higher growth at lower survival probabilities for some sites during the 2000s event suggest a shift in mortality processes from longer-term growth-related constraints to shorter-term processes, such as rapid metabolic decline even in vigorous trees due to acute drought stress, and/or increases in the attack rate of both chronically stressed and more vigorous trees by bark beetles.</p></div

The modeled relationship between tree growth, status, competition and climate in trees from 2000s sites.

<p>Summary of the linear mixed-effects model with the formula RW∼PPT<i>_Cool</i> + (PPT<i>_Cool</i>)²+ VPD<i>_MJJ</i> + (VPD<i>_MJJ</i>)²+ CI + Tree Status + Site + CI:Tree Status + PPT<i>_Cool</i>:CI + PPT<i>_Cool</i>:Tree Status + VPD<i>_MJJ</i>:CI + VPD<i>_MJJ</i>:Tree Status + PPT<i>_Cool</i>:Site + (PPT<i>_Cool</i>)²:Site + VPD<i>_MJJ</i>:Site + (VPD<i>_MJJ</i>)²:Site + CI:Site + Tree Status:Site + PPT<i>_Cool</i>:CI:Site + PPT<i>_Cool</i>:Tree Status:Site + VPD<i>_MJJ</i>:Tree Status:Site + VPD<i>_MJJ</i>:CI:Tree Status, random  =  (∼1 | TreeID). A correlation term and variance weights were also included in the model in order to account for residual autocorrelation of growth between years and variance heterogeneity of residuals by TreeID and across fitted values <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092770#pone.0092770-Pinheiro1" target="_blank">[65]</a>. Growth was modeled from 1980, with the end of the modeled period varying depending on the outer growth year of the dead tree in each pair. Model parameters with estimates of (p<0.05) are in boldface type. The reference level for Tree Status is Dead. Contrasts were applied to calculate coefficients and significances associated with each site.</p

Best-ranked growth-mortality models.

<p>Variables include average growth (RW), mean sensitivity (Sens), growth trend (Trend), and abrupt growth changes (AbruptIncreases), with the number of years over which variables were averaged indicated after variable type. The best single-variable models in different growth categories, along with a model containing recent average growth as the only predictor variable (log(RW3)), are shown for comparison. <b>Δ</b>AIC is the difference in AIC between the best-ranked model and the model shown in each table row, with smaller values indicating more parsimonious model fit. ROC is a threshold independent measure of model discrimination, where 0.5 suggests no discrimination and values above 0.8 suggest excellent discrimination. Correct classification rates (CCR) are based on a bootstrapped internal validation with 1000 iterations in which 60% of the data was used for model fitting and 40% was used for model validation. Trees were classified as living if model output was greater than the empirically defined threshold <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092770#pone.0092770-Fawcett1" target="_blank">[70]</a>. ROC<i>_boot</i> is an average of the ROC statistics generated in the model-fitting portion of the bootstrapping routine. The kappa statistic measures the proportional improvement of the model classification over a random assignment of tree status <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092770#pone.0092770-Fleiss1" target="_blank">[99]</a>, and was also estimated by taking an average of kappa statistics generated in the bootstrapping routine.</p

Predicted probabilities of mortality associated with the best-ranked logistic regression model.

<p>The figure shows predicted survival probabilities associated with the best-ranked growth-mortality model in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092770#pone-0092770-t004" target="_blank">Table 4</a>. Values for mean sensitivity over 15 years (Sens15) and the count of abrupt growth increases over 10 years (AbruptIncreases10) are held at their mean for the dataset in (A); AbruptIncreases10 and log(RW30) (average 30-year growth rate) are held at their mean in (B); log(RW30) and Sens15 are held at their mean in (C).</p

Location, characteristics and sample sizes for each study site.

<p>Location, characteristics and sample sizes for each study site.</p

Growth chronologies and death dates from piñon target trees.

<p>Live (black) and dead (grey) tree ring-width index chronologies for TRP2000 (A), WRK2000 (B), BNM2000 (C), SEV2000 (D), BNM50 (E), and SEV50 (F). Tukey’s biweight robust mean was used to calculate chronology values from individual index series. A smoothing spline (df = 40) (thicker lines) is overlain on the annual mean value chronologies (thinner lines). A horizontal dashed line indicates the number of trees contributing to chronologies in each year. Bar plots of outside ring dates for dead trees at each site are shown in the small panels within each larger time series panel. The transparent grey boxes show SW drought events (as defined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092770#pone.0092770-Williams1" target="_blank">[5]</a>) preceding the 2000s (A–D) and 1950s (E–F) mortality events. The period 1945–1964 was the sixth strongest drought event since 1000 A.D., and the period 1899–1904 was the seventeenth strongest.</p

The modeled relationship between tree growth, status, and climate in trees from 1950s sites.

<p>Summary of the linear mixed-effects model with the formula RW∼PPT<i>_Cool</i> + (PPT<i>_Cool</i>)²+ VPD<i>_MJJ</i> + (VPD<i>_MJJ</i>)²+ Tree Status + Site + PPT<i>_Cool</i>:Tree Status + VPD<i>_MJJ</i>:Tree Status + PPT<i>_Cool</i>:Site + (PPT<i>_Cool</i>)²:Site + (VPD<i>_MJJ</i>)²:Site + Tree Status:Site + PPT<i>_cool</i>:Tree Status:Site + VPD<i>_MJJ</i>:Tree Status:Site, random  =  (∼1 | TreeID). A correlation term and variance weights were also included in the model in order to account for residual autocorrelation of growth between years and variance heterogeneity of residuals by TreeID and across fitted values <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0092770#pone.0092770-Pinheiro1" target="_blank">[65]</a>. Growth was modeled from 1930, with the end of the modeled period varying depending on the outer growth year of the dead tree in each pair. Model parameters with estimates of (p<0.05) are in boldface type. The reference level for Tree Status is Dead. Contrasts were applied to calculate coefficients and significances associated with each site.</p