What Limits Predictive Certainty of Long-Term Carbon Uptake?

Raczka, Brett; Dietze, Michael C.; Serbin, Shawn P.; Davis, Kenneth J.

doi:10.1029/2018JG004504

Title: What Limits Predictive Certainty of Long-Term Carbon Uptake?

Journal Article · Sat Dec 01 00:00:00 EST 2018 · Journal of Geophysical Research. Biogeosciences

DOI:https://doi.org/10.1029/2018JG004504· OSTI ID:1482088

^[1]; Dietze, Michael C. ^[2];

^[3]; Davis, Kenneth J. ^[4]

Pennsylvania State Univ., University Park, PA (United States). Dept. of Meteorology and Atmospheric Science; Univ. of Utah, Salt Lake City, UT (United States)
Boston Univ., Boston, MA (United States). Dept. of Earth and Environment
Brookhaven National Lab. (BNL), Upton, NY (United States). Environmental and Climate Sciences Dept.
Pennsylvania State Univ., University Park, PA (United States). Dept. of Meteorology and Atmospheric Science

Terrestrial biosphere models can help identify physical processes that control carbon dynamics, including land-atmosphere CO₂ fluxes, and have the potential to project the terrestrial ecosystem response to changing climate. It is important to identify ecosystem processes most responsible for model predictive uncertainty and design improved model representation and observational system studies to reduce that uncertainty. Here we identified model parameters that contribute the most uncertainty to long-term (~100 years) projections of net ecosystem exchange, net primary production, and aboveground biomass within a mechanistic terrestrial biosphere model (Ecosystem Demography, version 2.1) ED2. An uncertainty analysis identified parameters that represent the quantum efficiency of light to photosynthetic conversion, leaf respiration and soil-plant water transfer as the highest contributors to model uncertainty regardless of time frame (annual, decadal, and centennial) and output (e.g., net ecosystem exchange, net primary production, aboveground biomass). Contrary to expectations, the contribution of successional processes related to reproduction, competition, and mortality did not increase as the time scale increased. These findings suggest that uncertainty in the parameters governing short-term ecosystem processes remains the most significant bottleneck to reducing predictive uncertainty. Key actions to reduce parameter uncertainty include more leaf-level trait measurements across multiple sites for quantum efficiency and leaf respiration rate. Further, the empirical representation of soil-plant water transfer should be replaced with a mechanistic, hydraulic representation of water flow, which can be constrained with direct measurements. This analysis focused on aboveground ecosystem processes. The impact of belowground carbon cycling, initial conditions, and meteorological forcing should be addressed in future studies.

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Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Grant/Contract Number:: SC0012704

OSTI ID:: 1482088

Alternate ID(s):: OSTI ID: 1488674

Report Number(s):: BNL-209437-2018-JAAM

Journal Information:: Journal of Geophysical Research. Biogeosciences, Vol. 123, Issue 12; ISSN 2169-8953

Publisher:: American Geophysical UnionCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 20 works

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