What Limits Predictive Certainty of Long-Term Carbon Uptake?
Abstract
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 leafmore »
- Authors:
-
[1];
[3];
- 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
- Publication Date:
- Research Org.:
- Brookhaven National Laboratory (BNL), Upton, NY (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1482088
- Alternate Identifier(s):
- OSTI ID: 1488674
- Report Number(s):
- BNL-209437-2018-JAAM
Journal ID: ISSN 2169-8953
- Grant/Contract Number:
- SC0012704
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Geophysical Research. Biogeosciences
- Additional Journal Information:
- Journal Volume: 123; Journal Issue: 12; Journal ID: ISSN 2169-8953
- Publisher:
- American Geophysical Union
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES
Citation Formats
Raczka, Brett, Dietze, Michael C., Serbin, Shawn P., and Davis, Kenneth J. What Limits Predictive Certainty of Long-Term Carbon Uptake?. United States: N. p., 2018.
Web. doi:10.1029/2018JG004504.
Raczka, Brett, Dietze, Michael C., Serbin, Shawn P., & Davis, Kenneth J. What Limits Predictive Certainty of Long-Term Carbon Uptake?. United States. https://doi.org/10.1029/2018JG004504
Raczka, Brett, Dietze, Michael C., Serbin, Shawn P., and Davis, Kenneth J. Sat .
"What Limits Predictive Certainty of Long-Term Carbon Uptake?". United States. https://doi.org/10.1029/2018JG004504. https://www.osti.gov/servlets/purl/1482088.
@article{osti_1482088,
title = {What Limits Predictive Certainty of Long-Term Carbon Uptake?},
author = {Raczka, Brett and Dietze, Michael C. and Serbin, Shawn P. and Davis, Kenneth J.},
abstractNote = {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.},
doi = {10.1029/2018JG004504},
journal = {Journal of Geophysical Research. Biogeosciences},
number = 12,
volume = 123,
place = {United States},
year = {Sat Dec 01 00:00:00 EST 2018},
month = {Sat Dec 01 00:00:00 EST 2018}
}
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