In north-western North America, the so-called divergence problem (DP) is expressed in tree ring width (RW) as an unstable temperature signal in recent decades. Maximum latewood density (MXD), from the same region, shows minimal evidence of DP. While MXD is a superior proxy for summer temperatures, there are very few long MXD records from North America. Latewood blue intensity (LWB) measures similar wood properties as MXD, expresses a similar climate response, is much cheaper to generate and thereby could provide the means to profoundly expand the extant network of temperature sensitive tree-ring (TR) chronologies in North America. In this study, LWB is measured from 17 white spruce sites ( Picea glauca) in south-western Yukon to test whether LWB is immune to the temporal calibration instabilities observed in RW. A number of detrending methodologies are examined. The strongest calibration results for both RW and LWB are consistently returned using age-dependent spline (ADS) detrending within the signal-free (SF) framework. RW data calibrate best with June–July maximum temperatures (Tmax), explaining up to 28% variance, but all models fail validation and residual analysis. In comparison, LWB calibrates strongly (explaining 43–51% of May–August Tmax) and validates well. The reconstruction extends to 1337 CE, but uncertaintiesmore »
Machine-learning based reconstructions of primary and secondary climate variables from North American and European fossil pollen data
We test several quantitative algorithms as palaeoclimate reconstruction tools for North American and European fossil pollen data, using both classical methods and newer machine-learning approaches based on regression tree ensembles and artificial neural networks. We focus on the reconstruction of secondary climate variables (here, January temperature and annual water balance), as their comparatively small ecological influence compared to the primary variable (July temperature) presents special challenges to palaeo-reconstructions. We test the pollen–climate models using a novel and comprehensive cross-validation approach, running a series of
- Publication Date:
- NSF-PAR ID:
- 10153848
- Journal Name:
- Scientific Reports
- Volume:
- 9
- Issue:
- 1
- ISSN:
- 2045-2322
- Publisher:
- Nature Publishing Group
- Sponsoring Org:
- National Science Foundation
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