Increasing precipitation variability and climate-growth responses of five tree species in North Carolina, USA

Abstract

We examined the effects of increasing summertime precipitation variability from 1950–2022 on the radial-growth responses of five tree species native to central North Carolina, USA. Tree-ring data were collected from chestnut oak, post oak, longleaf pine, shortleaf pine, and Virginia pine and processed following standard dendrochronology procedures. Adjusted latewood chronologies for each species were created and correlated with either monthly or multi-monthly combinations of summertime precipitation for above average (AA, >1 σ above mean), below average (BA, <-1 σ below mean), and near average (NA, <-1, and 1> σ) precipitation years. June–September precipitation variability and other summertime monthly combinations significantly increased during the study period, with a 10.2% increase in AA/BA years during the 21st century. Climate-growth correlations ranged from 0.40–0.51 using all years within the study period. However, using AA and BA years exclusively, climate-growth responses ranged from 0.44–0.71, with post oak and longleaf pine experiencing significantly higher correlations. No significant changes in climate-growth responses occurred for chestnut oak, shortleaf pine, and Virginia pine. These findings suggest the effects of increased precipitation variability on climate-growth responses are species-dependent and affected by the precipitation classification (i.e., AA or BA years). These responses help explain temporal variations in the strength of climate-growth responses, particularly for some species, and offer additional considerations for dendroclimatological research.

Reconstructing Historical Intense and Total Summer Rainfall in Central North Carolina Using Tree-Ring Data (1770–2020)

“…There are no values of IRE frequency below the −2.00 standard deviation threshold (Figure 5), likely because the IRE frequency distribution is skewed due to a fixed minimum value of IREs/summer (i.e., 1) and no theoretic maximum value. The differences in IRE precipitation model strength between the early and late analysis periods, particularly the greater occurrence of +1.00 and +2.00 standard deviation IRE frequency years during the early period, are supported by other research at this location in the Uwharrie Mountains [30]. When latewood-precipitation models were compared between models that include all precipitation years and reduced models that include only above-average (i.e., >+1.00 standard deviations) and below-average (i.e., <−1.00 standard deviations) precipitation years, the correlation between longleaf pine latewood and precipitation is significantly greater during above-average and below-average years when compared to the model that includes all years [30] suggesting the importance of non-average precipitation years on overall model strength.…”

“…The differences in IRE precipitation model strength between the early and late analysis periods, particularly the greater occurrence of +1.00 and +2.00 standard deviation IRE frequency years during the early period, are supported by other research at this location in the Uwharrie Mountains [30]. When latewood-precipitation models were compared between models that include all precipitation years and reduced models that include only above-average (i.e., >+1.00 standard deviations) and below-average (i.e., <−1.00 standard deviations) precipitation years, the correlation between longleaf pine latewood and precipitation is significantly greater during above-average and below-average years when compared to the model that includes all years [30] suggesting the importance of non-average precipitation years on overall model strength. Conversely, the shortleaf pine latewoodprecipitation models had no significant differences when comparing different precipitation combinations [30], suggesting that IRE precipitation-latewood growth responses are species dependent [12].…”

“…When latewood-precipitation models were compared between models that include all precipitation years and reduced models that include only above-average (i.e., >+1.00 standard deviations) and below-average (i.e., <−1.00 standard deviations) precipitation years, the correlation between longleaf pine latewood and precipitation is significantly greater during above-average and below-average years when compared to the model that includes all years [30] suggesting the importance of non-average precipitation years on overall model strength. Conversely, the shortleaf pine latewoodprecipitation models had no significant differences when comparing different precipitation combinations [30], suggesting that IRE precipitation-latewood growth responses are species dependent [12]. Here, our results suggest that IRE precipitation-latewood models perform better when the magnitude and frequency of intense rainfall events are greater.…”