A mechanistic nitrogen limitation model for CLM(ED)

Friday, 19 December 2014
Ashehad Ashween Ali1, Chonggang Xu2, Nathan G McDowell2, Alistair Rogers3, Stan D Wullschleger4, Rosie Fisher5 and Jasper A Vrugt6, (1)Organization Not Listed, Washington, DC, United States, (2)Los Alamos National Laboratory, Los Alamos, NM, United States, (3)Brookhaven National Laboratory, Upton, NY, United States, (4)Oak Ridge National Laboratory, Oak Ridge, TN, United States, (5)National Center for Atmospheric Research, Boulder, CO, United States, (6)University of California Irvine, Irvine, CA, United States
Photosynthetic capacity is a key plant trait that determines the rate of photosynthesis; however, in Earth System Models it is either a fixed value or derived from a linear function of leaf nitrogen content. A mechanistic leaf nitrogen allocation model have been developed for a DOE-sponsored Community Land Model coupled to the Ecosystem Demography model (CLM-ED) to predict the photosynthetic capacity [Vc,max25 (µmol CO2 m-2 s-1)] under different environmental conditions at the global scale. We collected more than 800 data points of photosynthetic capacity (Vc,max25) for 124 species from 57 studies with the corresponding leaf nitrogen content and environmental conditions (temperature, radiation, humidity and day length) from literature and the NGEE arctic site (Barrow). Based on the data, we found that environmental control of Vc,max25 is about 4 times stronger than the leaf nitrogen content. Using the Markov-Chain Monte Carlo simulation approach, we fitted the collected data to our newly developed nitrogen allocation model, which predict the leaf nitrogen investment in different components including structure, storage, respiration, light capture, carboxylation and electron transport at different environmental conditions. Our results showed that our nitrogen allocation model explained 52% of variance in observed Vc,max25 and 65% variance in observed Jmax25 using a single set of fitted model parameters for all species. Across the growing season, we found that the modeled Vc,max25 explained 49% of the variability in measured Vc,max25. In the context of future global warming, our model predicts that a temperature increase by 5oC  and the doubling of atmospheric carbon dioxide reduced the Vc,max25 by 5%, 11%, respectively.