Influence of precipitation seasonality on pinon pine cellulose delta D values

The influence of seasonal to interannual climate variations on cellulose hy drogen isotopic composition (delta D) was assessed by analysing tree rings and needles of pinon pine (Pinus edulis and P. monophylla). Sites spanned a gradient of decreasing summer precipitation, from New Mexico to Arizona to Nevada. Tree rings were divided into earlywood, latewood and whole-year in crements, and annual cohorts of needles were collected. The study period (1 989-96) included two La Nina events (1989, 1996) and a prolonged El Nino ev ent (1991-95). Winter and spring moisture conditions were strongly related to October-March Southern Oscillation Index (SOI) in New Mexico and Arizona , with above-average precipitation occurring in El Nino years. Wood delta D values at these sites were correlated with winter and spring moisture cond itions. Needle delta D values were correlated with summer moisture conditio ns in New Mexico and with winter moisture and SOI in Arizona. Low cellulose delta D values observed from 1991 to 1993 in both wood and needles occurre d during wet El Nino years, whereas high delta D values in needles were pre sent during the dry, La Nina years of 1989 and 1996. North-eastern Nevada d oes not receive precipitation anomalies related to ENSO, and thus cellulose delta D values did not reflect the ENSO pattern observed at the other site s. Cellulose delta D values were strongly, inversely correlated with relati ve humidity variations at all sites, as predicted by a mechanistic model. C ontrary to predictions from the same model and observations from more mesic areas, time series of cellulose delta D values were not directly correlate d with interannual or seasonal variations in precipitation delta D values o r temperature at any of the sites. On a regional basis, however, mean delta D values in needles and wood were correlated with mean annual temperature and delta D values of precipitation. This suggests that temporal averaging may bias relationships between biological systems and climate.