A global scale mechanistic model of photosynthetic capacity (LUNA V1.0)
By A. Ali, Chonggang Xu, Alistair Rogers, R. Fisher, S. Wullschleger, E. Massoud, Jasper A. Vrugt, Jordan D. Muss, Nathan G. McDowell, Joshua B. Fisher and 2 more. 2016
137 citations recorded by the source corpus.
Abstract. Although plant photosynthetic capacity as determined by the maximum carboxylation rate (i.e., Vc, max25) and the maximum electron transport rate (i.e., Jmax25) at a reference temperature (generally 25 °C) is known to vary considerably in space and time in response to environmental conditions, it is typically parameterized in Earth system models (ESMs) with tabulated values associated with plant functional types. In this study, we have developed a mechanistic model of leaf utilization of nitrogen for assimilation (LUNA) to predict photosynthetic capacity at the global scale under different environmental conditions. We adopt an optimality hypothesis to nitrogen allocation among light capture, electron transport, carboxylation and respiration. The LUNA model is able to reasonably capture the measured spatial and temporal patterns of photosynthetic capacity as it explains ∼ 55 % of the global variation in observed values of Vc, max25 and ∼ 65 % of the variation in the observed values of Jmax25. Model simulations with LUNA under current and future climate conditions demonstrate that modeled values of Vc, max25 are most affected in high-latitude regions under future climates. ESMs that relate the values of Vc, max25 or Jmax25 to plant functional types only are likely to substantially overestimate future global photosynthesis.
Subfields: Global and Planetary Change, Plant Science
View Paper: https://www.semanticscholar.org/paper/c496382f877ea13a1b3eb6be492527c85d41938c
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Global and Planetary Change
In Plant Science
Citations in the corpus
- The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty (2019, methodology): “Data Assimilation Capabilities The capabilities for conducting data assimilation with CLM5 using the Data Assimilation Research Testbed (DART, Anderson et al., 2009) continue to improve, particularly with respect to computational efficiency.”