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Dendroclimatology has focused mainly on the tree growth response to atmospheric variables. However, the roots of trees directly sense the “underground climate,” which can be expected to be no less important to tree growth. Data from two meteorological stations approximately 140 km apart in southern Siberia were applied to characterize the spatiotemporal dynamics of soil temperature and the statistical relationships of soil temperature to the aboveground climate and tree-ring width (TRW) chronologies of Larix sibirica Ledeb. from three forest–steppe stands. Correlation analysis revealed a depth-dependent delay in the maximum correlation of TRW with soil temperature. Temperatures of both the air and soil (depths 20–80 cm) were shown to have strong and temporally stable correlations between stations. The maximum air temperature is inferred to have the most substantial impact during July–September (R = −0.46–−0.64) and early winter (R = 0.39–0.52). Tree-ring indices reached a maximum correlation with soil temperature at a depth of 40 cm (R = −0.49–−0.59 at 40 cm) during April–August. High correlations are favored by similar soil characteristics at meteorological stations and tree-ring sites. Cluster analysis of climate correlations for individual trees based on the K-means revealed groupings of trees driven by microsite conditions, competition, and age. The results support a possible advantage of soil temperature over air temperature for dendroclimatic analysis of larch growth in semiarid conditions during specific seasons.
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Frequencies of Multivariate Air Masses Drive Tree Growth
Abstract Midlatitude surface meteorological conditions are embedded within—and affected by—synoptic‐scale systems, including the movement and persistence of air masses (AMs). Changes in AM frequencies (number of daily occurrences) over the past several decades could have large effects on ecosystems: each organism is exposed to the synergistic effects of the entire suite of atmospheric variables acting upon it—an inherently multivariate environment—which is best captured using AMs. Utilizing a global‐scale AM classification system and a large network of tree‐ring chronologies, we investigate how variation in AM frequency impacts tree growth at over 900 locations. We find that AM frequencies are well‐correlated with tree growth, especially in the 12‐month period from July in the year prior to growth through June in the year of growth. The most impactful AMs are Dry‐Warm and Humid‐Cool AMs, which exhibit average correlations ofρ = −0.4 andρ = +0.4 with tree growth, respectively, for certain tree species, with correlations at some sites exceedingρ = ±0.8 in some seasons. Compared to empirical models based solely on temperature and precipitation, modeling using only AM frequencies proved superior at nearly 60% of the sites and for over 80% of the well‐sampled (n ≥ 10) species. These results should provide a foundation for using AMs to improve forecasts of tree growth, tree stress and wildfire potential. Long‐term reconstructions of AM frequencies back several centuries may also be feasible using tree‐ring data, which will help contextualize and temporally extend multivariate perspectives of climate change that utilize such air masses.
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- Award ID(s):
- 2001753
- PAR ID:
- 10419650
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Biogeosciences
- Volume:
- 128
- Issue:
- 3
- ISSN:
- 2169-8953
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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