Interpreting inter- and intra-annual environmental signals in tree-ring δ18O using isotope-enabled modeling

Abstract

Interpreting tree-ring oxygen isotope composition (δ18Oring) is complicated by the combined influences of source water (δ18Osw) and relative humidity (RH). This study investigates intra- and interannual δ18Oring signals in Scots pine stands in southern and northern Finland over a 10-year period (2010–19). We applied correlation analysis and process-based intra-annual δ18O and δ13C modeling to disentangle RH and δ18Osw signals in δ18Oring. Growth models for xylogenesis were used to date the analyzed tree-ring subsections. Dual isotope modeling provided additional constraints to evaluate the uncertainties caused by xylogenesis. Our results show that δ18Oring signals were dominated by RH, due to its much higher relative variability compared with that of δ18Osw. Generally, correlations were stronger at interannual than intra-annual resolution. Modeling indicated that additional factors complicate the interpretation of intra-annual δ18Oring signals beyond the combined effects of RH and δ18Osw. We show that seasonal variations in the proportion of oxygen exchange with source water during the pathway to tree-ring cellulose may explain the lower RH signal at intra-annual resolution. Incorporating variable oxygen exchange improved model performance and aligned modeled δ18Oring more closely with observations. Despite the encouraging modeling results using growth models for dating tree-ring subsections, we recognize that time integration and alignment will continue to challenge the interpretation of intra-annual isotope signals. Our study demonstrates that combining empirical data analysis with mechanistic modeling is essential for resolving the environmental drivers of δ18Oring and for extending interpretations beyond site-specific conditions. Our findings are particularly relevant as intra-annual δ18O analysis becomes more common, underscoring the importance of time integration and dating tree-ring subsections, highlighting future research needs (e.g., varying oxygen exchange) and advancing their use for climate reconstruction.