Using blue light reflectance from high-resolution images (6000 dpi) of Scots pine tree rings to reconstruct three centuries of Scottish summer temperatures

Abstract: Advances in scanner technology have made it possible to obtain high resolution (6000 dpi) images of tree samples. Due to the images’ increased capability of resolving anatomical wood structures, the new technology could be of benefit to dendroclimatology.  This study attempts to expand on Rydval et al.’s (2017) previous 800 years reconstruction of Scottish summer temperatures by assessing whether a higher image resolution of samples has the ability to improve the accuracy of the region’s temperature reconstruction. Two independent blue intensity (BI) chronologies, based on differing image resolutions (6000 dpi and 2400 dpi) of Scots pine samples, were developed and subjected to standard detrending procedures. Raw data from Rydval et al.’s (2017) prior study was used to develop the chronology which was based on the 2400 dpi images. On the other hand, newly acquired data was utilized for the other chronology. In order to resolve the primary question that this paper explores, the characteristics and strength of the two BI chronologies’ climatic signals were compared. In addition, the newly acquired data was used to develop a 318 years reconstruction of mean July/August temperatures for Scotland.  Calibrations against meteorological data indicated that the improved image resolution did not generate a positive effect on the chronology’s ability to retain a reliable climatic signal. The study’s findings were thus inconclusive in showing that a higher image resolution of Scots pine samples improves the accuracy of temperature reconstructions for Scotland. Future studies are encouraged to investigate the applicability of dendroclimatic computer softwares (i.e. CooRecorder) with regard to a high image resolution.  From a broader perspective, this study contributes to setting climate change in a more accurate long term spatiotemporal context. This is crucial in predicting future climate variability, as well as understanding the role and extent of anthropogenic forcing.