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SUMMARY:Developing novel methods for interrogating tree ring anatomy for u
 se in modelling carbon sequestration - Dr Andrew D. Friend\, Department of
  Geography
DTSTART:20180213T130000Z
DTEND:20180213T140000Z
UID:TALK100786@talks.cam.ac.uk
CONTACT:Dr Vivien Gruar
DESCRIPTION:Tree wood anatomy records environmental\, particularly climate
 \, variations at annual and sub-annual timescales. Current methods exploit
  observations of ring width and maximum density in order to reconstruct pa
 st temperatures\, and are critical in putting current global warming in co
 ntext. These methods use sophisticated mathematical approaches that have e
 volved to overcome problems of variability and weak signals in the raw dat
 a.\n\nRather than extracting climate signals from observed tree growth pat
 terns\, we are concerned with a different set of objectives than tradition
 al ""dendro"" research. Namely\, the understanding of wood development and
  carbon uptake and sequestration by trees\, in other words how you relate 
 environmental signals to tree anatomy\, rather than the other way round. A
 s such we are developing process-based tree growth models. It seems that t
 he methods used in traditional ""dendroclimatological"" research result in
  great loss of information contained in raw tree ring data\, information t
 hat we would like to learn how to exploit.\n\nWe hope that you can help 1)
  in assessing what underpins current dendroclimatological methodologies an
 d\, especially\, 2) in developing novel tree ring anatomy data analysis me
 thods that will exploit the raw data in new ways that are better suited to
  our needs. Our project is rather exploratory. We have collaborations in t
 he US (Harvard) and Switzerland\, where raw data are being collected that 
 we wish to exploit. This work has the potential to revolutionise understan
 ding of controls on tree growth and the global carbon cycle\, and hence co
 ntribute to reducing uncertainties in future climate change.\n\nDesired ou
 tput is an R or Python script that can be used to analyse observational ra
 w data in a way that makes it more valuable for process-based model output
  - observation comparisons. The last collaboration with a student at the I
 nstitute of Mathematics was very successful and resulted in a peer-reviewe
 d publication (https://doi.org/10.3389/fpls.2017.00182). \n\nWe look forwa
 rd to hearing from you!\n
LOCATION:MR3 Centre for Mathematical Sciences
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