University of Cambridge > Talks.cam > Cambridge Mathematics Placements Seminars > Developing novel methods for interrogating tree ring anatomy for use in modelling carbon sequestration

Developing novel methods for interrogating tree ring anatomy for use in modelling carbon sequestration

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If you have a question about this talk, please contact Dr Vivien Gruar.

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 past temperatures, and are critical in putting current global warming in context. These methods use sophisticated mathematical approaches that have evolved to overcome problems of variability and weak signals in the raw data.

Rather than extracting climate signals from observed tree growth patterns, we are concerned with a different set of objectives than traditional ””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. As such we are developing process-based tree growth models. It seems that the methods used in traditional ””dendroclimatological”” research result in great loss of information contained in raw tree ring data, information that we would like to learn how to exploit.

We hope that you can help 1) in assessing what underpins current dendroclimatological methodologies and, especially, 2) in developing novel tree ring anatomy data analysis methods 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 the US (Harvard) and Switzerland, where raw data are being collected that we wish to exploit. This work has the potential to revolutionise understanding of controls on tree growth and the global carbon cycle, and hence contribute to reducing uncertainties in future climate change.

Desired output is an R or Python script that can be used to analyse observational raw data in a way that makes it more valuable for process-based model output – observation comparisons. The last collaboration with a student at the Institute of Mathematics was very successful and resulted in a peer-reviewed publication (https://doi.org/10.3389/fpls.2017.00182).

We look forward to hearing from you!

This talk is part of the Cambridge Mathematics Placements Seminars series.

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