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Using adjusted Blue Intensity data to attain high-quality summer temperature information: A case study from Central Scandinavia
- Björklund, Jesper, Gunnarson, Björn E, Seftigen, Kristina, Zhang, Peng, Linderholm, Hans W
- Pinus sylvestris, case studies, dead wood, differential staining, earlywood, heartwood, latewood, sapwood, summer, temperature, variance, Scandinavia
- The inexpensive Blue Intensity proxy has been considered a complement or surrogate to maximum latewood density (MXD), but is associated with biases from differential staining between sapwood and heartwood and also between deadwood samples and living-wood samples that compromise centennial-scale information. Here, we show that, with some minor adjustments, ΔBlue Intensity (ΔBI) is comparable with MXD or ΔDensity (Δ = the difference or contrast between latewood and earlywood density) in dendroclimatological reconstructions of summer temperatures in the Central Scandinavian region, using Pinus sylvestris L. (Scots pine), on annual and multi-centennial timescales. By using ΔBI, this bias is significantly reduced, but the contrast between earlywood and latewood in BI is altered with degree of staining, while for density it is not. Darker deadwood samples have a reduced contrast compared with the lighter living-wood samples that make ΔBI and ΔDensity chronologies diverge. Here, we quantify this behaviour in BI and offer an adjustment that can reduce this bias. The adjustment can be derived on independent samples, so in future work on BI, parallel density measurements are not necessary. We apply this methodology to two Central Scandinavian Scots pine chronologies that averaged into a composite is able to reconstruct summer temperatures with an explained variance in excess of 60% in each verification period using a split sample calibration verification procedure. Although the amount of data used to derive this contrast adjustment produces desirable results, more tests are needed to confirm its performance, and we suggest that future work on the BI proxy should aim for a small subset of parallel BI and density measurements while the bulk of the data is only measured with the BI technique. This is to ensure that the adjustment is continuously updated with new data and that the conclusions derived here are robust.