Xiaohui Feng

A quantitative assessment of a terrestrial biosphere model's data needs across North American biomes

Terrestrial biosphere models are designed to synthesize our current understanding of how ecosystems function, test competing hypotheses of ecosystem function against observations, and predict responses to novel conditions such as those expected under climate change. Reducing uncertainties in such models can improve both basic scientific understanding and our predictive capacity, but rarely are ecosystem models employed in the design of field campaigns. We provide a synthesis of carbon cycle uncertainty analyses conducted using the Predictive Ecosystem Analyzer ecoinformatics workflow with the Ecosystem Demography model v2. This work is a synthesis of multiple projects, using Bayesian data assimilation techniques to incorporate field data and trait databases across temperate forests, grasslands, agriculture, short rotation forestry, boreal forests, and tundra. We report on a number of data needs that span a wide array of diverse biomes, such as the need for better constraint on growth respiration, mortality, stomatal conductance, and water uptake. We also identify data needs that are biome specific, such as photosynthetic quantum efficiency at high latitudes. We recommend that future data collection efforts balance the bias of past measurements toward aboveground processes in temperate biomes with the sensitivities of different processes as represented by ecosystem models. ©2014. American Geophysical Union. All Rights Reserved.

Scale dependence in the effects of leaf ecophysiological traits on photosynthesis: Bayesian parameterization of photosynthesis models

Relationships between leaf traits and carbon assimilation rates are commonly used to predict primary productivity at scales from the leaf to the globe. We addressed how the shape and magnitude of these relationships vary across temporal, spatial and taxonomic scales to improve estimates of carbon dynamics. Photosynthetic CO2 and light response curves, leaf nitrogen (N), chlorophyll (Chl) concentration and specific leaf area (SLA) of 25 grassland species were measured. In addition, C3 and C4 photosynthesis models were parameterized using a novel hierarchical Bayesian approach to quantify the effects of leaf traits on photosynthetic capacity and parameters at different scales. The effects of plant physiological traits on photosynthetic capacity and parameters varied among species, plant functional types and taxonomic scales. Relationships in the grassland biome were significantly different from the global average. Within-species variability in photosynthetic parameters through the growing season could be attributed to the seasonal changes of leaf traits, especially leaf N and Chl, but these responses followed qualitatively different relationships from the across-species relationship. The results suggest that one broad-scale relationship is not sufficient to characterize ecosystem condition and change at multiple scales. Applying trait relationships without articulating the scales may cause substantial carbon flux estimation errors.