Quantifications of dendrochronological information from contrasting microdensitometric measuring circumstances of experimental wood samples

详细信息	查看全文 | 推荐本文 |

• 作者：S. Helama^a ; ^{samuli.helama@metla.fi} ; Y. Bé ; gin^b ; ^c ; M. Vartiainen^d ; H. Peltola^e ; T. Kolstr&ouml ; m^f ; ¹ ; J. Meril&auml ; inen^g
• 关键词：Dendroclimatology ; Dendrodensitometry ; Paleoclimatology ; Scots pine ; Tree-ring
• 刊名：Applied Radiation and Isotopes
• 出版年：2012
• 期刊代码：6_09698043
• 类别：ce
• 出版时间：June, 2012
• 卷：70
• 期：6
• 页码：1014-1023
• 文件大小：1051 K

摘要

We analyzed how the pretreatment method of Scots pine (m>Pinus sylvestrism> L.) wood specimens together with X-ray methodology applied for density analyses affect resulting tree-ring data and derived proxy-based climate information. We also evaluated whether these results from two contrasting laboratory circumstances could be homogenized by applying dendroclimatic statistical methods. For this study, we measured a pair of X-ray based microdensitometry datasets using double samples of subfossil and recent wood specimens. Dendrochronological information of earlywood and latewood series was examined to determine for alterations in the resulting data. We found that the level of overall density, its trend over cambial ages and the growth amplitude altered due to the sample pretreatment/density measuring exercise, which means that comparisons of heterogeneous datasets should be, in general, regarded cautiously. Dendrochronological standardization did, however, even out several potentially biasing influences from the differing overall densities and their trends. The two latewood (maximum) density chronologies yielded paleoclimatic reconstructions which both calibrated and verified satisfactorily with the instrumental warm-season (March-September) mean temperatures. The transfer functions were found to further equalize the differences between the two proxy records. We recommend (if no strictly homogenous data are available) reconciling similar data assemblages through transfer functions with multiple independent variables.