S. Caldararu, D. W. Purves, and P.I. Palmer
Phenology is essential to our understanding of biogeochemical cycles and the climate system. We develop a global mechanistic model of leaf phenology based on the hypothesis that phenology is a strategy for optimal carbon gain at the canopy level so that trees adjust leaf gains and losses in response to environmental factors such as light, temperature and soil moisture, to achieve maximum carbon assimilation. We fit this model to five years of satellite observations of leaf area index (LAI) using a Bayesian fitting algorithm. We show that our model is able to reproduce phenological patterns for all vegetation types and use it to explore variations in growing season length and the climate factors that limit leaf growth for different biomes. Phenology in wet tropical areas is limited by leaf age physiological constraints while at higher latitude leaf seasonality is limited by low temperature and light availability. Leaf growth in grassland regions is limited by water availability but often in combination with other factors. This model will advance the current understanding of phenology for ecosystem carbon models and our ability to predict future phenological behaviour.
Oleksandra Hararuk and Matthew J. Smith. Importance of modelling microbial dynamics in soil: evaluation of conventional and microbial soil models informed by observations across various plant functional types, 15 December 2014.
Y.P. Wang, B.C. Chen, W.R. Weider, M. Leite, B.E. Medlyn, M. Rasmussen, M.J. Smith, F.B. Augusto, F. Hoffman, and Y.Q. Luo. Oscillatory behavior of two nonlinear microbial models of soil carbon decomposition, Biogeosciences, European Geosciences Union, 7 April 2014.
Yiqi Luo, Trevor F. Keenan, and Matthew J. Smith. Predictability of the terrestrial carbon cycle, Global Change Biology, Wiley, October 2014.
Katherine E Todd-Brown, Yiqi Luo, James Tremper Randerson, Stephen D. Allison, and Matthew J. Smith. Understanding the Dynamics of Soil Carbon in CMIP5 Models, 15 December 2014.
Oleksandra Hararuk, Matthew J. Smith, and Yiqi Luo. Microbial models with data-driven parameters predict stronger soil carbon responses to climate change, Global Change Biology, Wiley, 1 December 2014.
Silvia Caldararu, Paul Palmer, and Drew Purves. Inferring Amazon leaf demography from satellite observations of leaf area index, Biogeosciences, European Geosciences Union, April 2012.
Y.P.Wang, B.C.Chen, W.R.Weider, Y.Q.Luo, B.E.Medlyn, M.Rasmussen, M.J.Smith, F.B.Agusto, and F.Hoffman. Oscillatory behaviour of two nonlinear microbial models of soil carbon decomposition, Biogeosciences Discussions, European Geosciences Union, December 2013.