Informace o publikaci
Different maximum latewood density and blue intensity measurements techniques reveal similar results
| Autoři | |
|---|---|
| Rok publikování | 2018 |
| Druh | Článek v odborném periodiku |
| Časopis / Zdroj | DENDROCHRONOLOGIA |
| Fakulta / Pracoviště MU | |
| Citace | |
| www | https://www.sciencedirect.com/science/article/pii/S1125786517300450 |
| Doi | http://dx.doi.org/10.1016/j.dendro.2018.03.005 |
| Klíčová slova | Climate reconstruction; Dendrochronology; Maximum Wood Density; Blue intensity; Norway spruce; Tatra Mountains |
| Popis | Annually resolved and absolutely dated Maximum Latewood Density (MXD) and Blue Intensity (BI) measurements are frequently used for reconstructing summer temperature variability over the last centuries to millennia. A direct comparison of the outcome of both methods using similar material is needed due to how quickly this method is being adopted. The application of slightly different measuring systems (hardware) and analysis tools (software) in tandem with different wood samples and preparation procedures further challenges any straightforward assessment. Here we process 26 Norway spruce samples from the upper timberline in the Polish Tatra Mountains with the six most frequently used MXD and BI applications. Although offset is found in the raw MXD and BI data (0.04-0.13 g/cm(3) and 0.45-1.58 dimensionless blue intensity), interannual and longer-term fluctuations are significantly (p < 0.01) positively correlated between all MXD and BI time-series. Our results emphasize the potential of faster and cheaper, as well as overall more user-friendly techniques to generate reliable MXD surrogates for high-frequency dendroclimatological studies. Although the correlations between MXD and BI were lower than within MXD and BI, the results of growth-climate response performed for both proxies show only marginal differences. The obtained level-offset further questions the suitability of joining different density surrogates for developing long-term composite chronologies to reconstruct low-frequency climate variability. |