ANGEO Annales Geophysicae ANGEO Ann. Geophys. 1432-0576 Copernicus Publications Göttingen, Germany 10.5194/angeo-27-1097-2009 Dendroclimatic transfer functions revisited: Little Ice Age and Medieval Warm Period summer temperatures reconstructed using artificial neural networks and linear algorithms Helama S. 1 Makarenko N. G. 2 Karimova L. M. 3 Kruglun O. A. 3 Timonen M. 4 Holopainen J. 1 Meriläinen J. 5 Eronen M. 1 Department of Geology, University of Helsinki, Finland Pulkovo Astronomical Observatory of RAS, St. Petersburg, Russia Institute of Mathematics, Almaty, Kazakhstan Finnish Forest Research Institute, Rovaniemi Research Unit, Finland SAIMA Unit of the Savonlinna Department of Teacher Education, University of Joensuu, Finland 04 03 2009 27 3 1097 1111 Copyright: © 2009 S. Helama et al. 2009 This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/ This article is available from https://angeo.copernicus.org/articles/27/1097/2009/angeo-27-1097-2009.html The full text article is available as a PDF file from https://angeo.copernicus.org/articles/27/1097/2009/angeo-27-1097-2009.pdf

Tree-rings tell of past climates. To do so, tree-ring chronologies comprising numerous climate-sensitive living-tree and subfossil time-series need to be "transferred" into palaeoclimate estimates using transfer functions. The purpose of this study is to compare different types of transfer functions, especially linear and nonlinear algorithms. Accordingly, multiple linear regression (MLR), linear scaling (LSC) and artificial neural networks (ANN, nonlinear algorithm) were compared. Transfer functions were built using a regional tree-ring chronology and instrumental temperature observations from Lapland (northern Finland and Sweden). In addition, conventional MLR was compared with a hybrid model whereby climate was reconstructed separately for short- and long-period timescales prior to combining the bands of timescales into a single hybrid model. The fidelity of the different reconstructions was validated against instrumental climate data. The reconstructions by MLR and ANN showed reliable reconstruction capabilities over the instrumental period (AD 1802–1998). LCS failed to reach reasonable verification statistics and did not qualify as a reliable reconstruction: this was due mainly to exaggeration of the low-frequency climatic variance. Over this instrumental period, the reconstructed low-frequency amplitudes of climate variability were rather similar by MLR and ANN. Notably greater differences between the models were found over the actual reconstruction period (AD 802–1801). A marked temperature decline, as reconstructed by MLR, from the Medieval Warm Period (AD 931–1180) to the Little Ice Age (AD 1601–1850), was evident in all the models. This decline was approx. 0.5°C as reconstructed by MLR. Different ANN based palaeotemperatures showed simultaneous cooling of 0.2 to 0.5°C, depending on algorithm. The hybrid MLR did not seem to provide further benefit above conventional MLR in our sample. The robustness of the conventional MLR over the calibration, verification and reconstruction periods qualified it as a reasonable transfer function for our forest-limit (i.e., timberline) dataset. ANN appears a potential tool for other environments and/or proxies having more complex and noisier climatic relationships.

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