J. Holopainen

J. Meriläinen

L. M. Karimova

M. Eronen

M. Timonen

N. G. Makarenko

O. A. Kruglun

S. Helama

Annales Geophysicae

Crossref

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

Helama, Samuli

Makarenko, N.G.

Karimova, L.M.

Kruglun, O.A.

Timonen, M.

Holopainen, J.

Meriläinen, J.

Eronen, M.

Jukuri

Helama, S.

Helsingin yliopiston digitaalinen arkisto

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

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&deg;C as reconstructed by MLR. Different ANN based

palaeotemperatures showed simultaneous cooling of 0.2 to 0.5&deg;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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Dendroclimatic transfer functions revisited : Little Ice Age and Medieval Warm Period summer temperatures reconstructed using artificial neural networks and linear algorithms

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Jukuri

Helsingin yliopiston digitaalinen arkisto

Directory of Open Access Journals