Comparing different calibration methods (WA/WA-PLS regression and Bayesian modelling) and different-sized calibration sets in pollen-based quantitative climate reconstruction

We compare a Bayesian modelling-based technique with weighted averaging (WA) and weighted averaging-partial least squares (WA-PLS) regression in pollen-based summer temperature transfer function calibration. We test the methods using a new, 113-sample calibration set from Estonia, Lithuania and European Russia, and a Holocene fossil pollen sequence from Lake Kharinei, a previously studied lake in northeast European Russia. We find WA-PLS to outperform WA, probably because of smaller edge-effect biases in the ends of the calibration set gradient. The Bayesian-based calibration models show further improved performance compared with WA-PLS in leave-one-out cross-validation, while additional h-block cross-validation shows the Bayesian method to be little affected by spatial autocorrelation. Comparison with independent climate proxies reveals, however, some clear biases in the Bayesian palaeotemperature reconstructions, likely reflecting in part some specific limitations of our calibration set. As the selected prior parameters can significantly affect both Bayesian cross-validation performance and reconstructions, there is a clear need to further test the Bayesian method in different geographic contexts and over different timescales, with special attention given to the selection of the most realistic priors in each situation. In general, our finding that statistically well-performing transfer functions may produce clearly differing palaeotemperature reconstructions urges caution in transfer function-based inferences. We additionally test a spatially restricted, 58-sample subset of the full 113-sample calibration set. We find some reduced biases with the smaller set, likely because of complex, partially bimodal responses of several taxa along the longer temperature gradient, ill-suited for calibration methods assuming unimodal responses to climate.