Atmospheric processes

In the mid-high latitude region, variations of stable isotopic compositions of atmospheric precipitation (δ18Op and δDp) were commonly regarded as reflecting the “temperature effect”. However, some studies have indicated that changes in moisture sources are important controlling factors for δ18Op. To clarify whether there are connections between δ18Op and variations of moisture sources in Southwest France (SW-France), whose implications for speleothem δ18O are of great importance, we have used among the longest isotopic time-series from SW-France (Le Mas and Villars stations) and a 5 days’ reconstruction of air mass history during the 1997–2016 A.D period based on the HYSPLIT tracking model. We found the percentage of initial moisture sources (PIMS) as important factors controlling the oxygen isotope composition of precipitation in SW-France, whether monthly or inter-annual timescale was considered. Additionally, we observed that the δ18Op preserved the signal of local temperature, supporting the “temperature effect”, while no evidence for its “amount effect” has been observed. These quantified links between PIMS/local-temperature and δ18Op appear useful references to understand the link between stable oxygen isotopes and climate parameters. Our long-term monitoring of δ18Op, d-excess, and moisture sources reveals decadal trends, highlighting a tight coupling in hydrologic systems and relatively fast changes on rainfall sources controlled by atmospheric circulations in SW-France.

Modes of climate variability strongly impact our climate and thus human society. Nevertheless, the statistical properties of these modes remain poorly known due to the short time frame of instrumental measurements. Reconstructing these modes further back in time using statistical learning methods applied to proxy records is useful for improving our understanding of their behaviour. For doing so, several statistical methods exist, among which principal component regression is one of the most widely used in paleoclimatology. Here, we provide the software ClimIndRec to the climate community; it is based on four regression methods (principal component regression, PCR; partial least squares, PLS; elastic net, Enet; random forest, RF) and cross-validation (CV) algorithms, and enables the systematic reconstruction of a given climate index. A prerequisite is that there are proxy records in the database that overlap in time with its observed variations. The relative efficiency of the methods can vary, according to the statistical properties of the mode and the proxy records used. Here, we assess the sensitivity to the reconstruction technique. ClimIndRec is modular as it allows different inputs like the proxy database or the regression method. As an example, it is here applied to the reconstruction of the North Atlantic Oscillation by using the PAGES 2k database. In order to identify the most reliable reconstruction among those given by the different methods, we use the modularity of ClimIndRec to investigate the sensitivity of the methodological setup to other properties such as the number and the nature of the proxy records used as predictors or the targeted reconstruction period. We obtain the best reconstruction of the North Atlantic Oscillation (NAO) using the random forest approach. It shows significant correlation with former reconstructions, but exhibits higher validation scores.