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Tree-rings tell of past climates. To do so, tree-ring chronologiescomprising numerous climate-sensitive living-tree and subfossil time-seriesneed to be "transferred" into palaeoclimate estimates using transferfunctions. The purpose of this study is to compare different types oftransfer functions, especially linear and nonlinear algorithms. Accordingly,multiple linear regression (MLR), linear scaling (LSC) and artificial neuralnetworks (ANN, nonlinear algorithm) were compared. Transfer functions werebuilt using a regional tree-ring chronology and instrumental temperatureobservations from Lapland (northern Finland and Sweden). In addition,conventional MLR was compared with a hybrid model whereby climate wasreconstructed separately for short- and long-period timescales prior tocombining the bands of timescales into a single hybrid model. The fidelityof the different reconstructions was validated against instrumental climatedata. The reconstructions by MLR and ANN showed reliable reconstructioncapabilities over the instrumental period (AD 1802–1998). LCS failed toreach reasonable verification statistics and did not qualify as a reliablereconstruction: this was due mainly to exaggeration of the low-frequencyclimatic variance. Over this instrumental period, the reconstructedlow-frequency amplitudes of climate variability were rather similar by MLRand ANN. Notably greater differences between the models were found over theactual reconstruction period (AD 802–1801). A marked temperature decline, asreconstructed by MLR, from the Medieval Warm Period (AD 931–1180) to theLittle Ice Age (AD 1601–1850), was evident in all the models. This declinewas approx. 0.5°C as reconstructed by MLR. Different ANN basedpalaeotemperatures showed simultaneous cooling of 0.2 to 0.5°C,depending on algorithm. The hybrid MLR did not seem to provide furtherbenefit above conventional MLR in our sample. The robustness of theconventional MLR over the calibration, verification and reconstructionperiods qualified it as a reasonable transfer function for our forest-limit(i.e., timberline) dataset. ANN appears a potential tool for otherenvironments and/or proxies having more complex and noisier climaticrelationships

annales geophysicae

Dendroclimatic transfer functions revisited: Little Ice Age and Medieval Warm Period summer temperatures reconstructed using artificial neural networks and linear algorithms