Modelling non-stationary tree growth responses to global warming
Tree-rings are a key proxy archive for reconstructing high resolution climate variability over the past 1-2ka at regional to global scales. Skillful reconstructions require a stationary relationship between tree growth and climate (Hutton’s principle of uniformitarianism), which is commonly evaluated by statistical calibration/verification trials against instrumental measurements. This association, however, weakened during the second half of the 20th century, when tree-ring width and density chronologies from Northern Hemisphere forests were not able to track the rapidly increasing temperatures. This so-called “divergence” problem was identified in the 1990s to be a large-scale phenomenon, and not only questions the reliability of tree-ring based temperature reconstruction, but also affects our understanding of the Earth’s climate sensitivity to anthropogenic greenhouse gases. A conclusive explanation for this central problem of contemporary paleoclimate research is, however, still missing. Here, I propose to develop a process model that simulates year-to-year and long-term variations in both tree-ring width and density of different conifer species growing under different climate regimes. Evidence from this model will be combined with data from a new, hemispheric scale network of tree-ring width and density chronologies, as well as in-situ monitoring data, to train the model, validate synthetic timeseries, and analyze spatially varying influences of climatological, air chemical and ecological drivers on tree growth. Model-data fusion and inverse modelling techniques will be applied to quantify the non-linear mechanisms underlying divergence, and to deduce methodological recommendations that can be applied by any paleoclimatologist, working with different species and in different regions of the Northern Hemisphere, to mitigate late 20th century divergence and thus improve their climate reconstructions.