Validating a continental European charcoal calibration dataset

Adolf, Carole; Doyon, Fabienne; Klimmek, Fabian; Tinner, Willy (2018). Validating a continental European charcoal calibration dataset. The Holocene, 28(10), pp. 1642-1652. Sage 10.1177/0959683618782607

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Large-scale training sets enabling quantitative reconstructions of past fire parameters are needed to better assess potential effects of increased fire hazard under global warming conditions. The aim of this article is to validate recently developed continental regression equations for the reconstruction of fire number, intensity and size. These transfer functions were built by linking satellite data and charcoal collected in annually sampled sediment traps. We apply these European regression equations to four annually layered lakes located on a North-South gradient in Europe. Down-core annual microscopic charcoal (MIC) and macroscopic charcoal (MAC) influx values were compared with satellite-derived time series of fire number, fire intensity and area burned. Results show that the match between predicted and observed values improves when the overall mean and median of sampled years (12 and 9 years) are considered. Especially, the comparisons of median values show a very good agreement between charcoal-inferred and satellite-observed fire-regime parameters. MIC-based predictions underestimate the variability of the observed fire parameters and MAC-based predictions overestimate it. Our results imply that median values of the fire parameters can be reconstructed well by using MIC and MAC, while it is more difficult to infer the variability of fire-regime parameters. However, when MIC- and MAC-based predictions are pooled together, the fit between observed and predicted values increases for both medians and variability. This finding suggests that MIC and MAC are complementary proxies, thus best sedimentary fire reconstructions may be achieved when they are used together. We conclude that sediment traps can be used for the construction of continental-scale training sets and that their results can be applied to Holocene sedimentary charcoal sequences.

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