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Title: What limits predictive certainty of long term carbon uptake? Quantifying parametric uncertainty at a site in Northern Wisconsin

Abstract

Terrestrial biosphere models can help identify physical processes that control carbon dynamics, including land-atmosphere CO 2 fluxes, and have the potential to project the terrestrial ecosystem response to changing climate. Observations are critical to evaluate model performance and to guide improvements to the representation of ecological processes within the models. Here we identify model parameters that contribute the most uncertainty to long term (~100 years) projections of net ecosystem exchange (NEE), net primary production (NPP) and above ground biomass (AGB) within a mechanistically detailed model (Ecosystem Demography, version 2.1). Through an uncertainty analysis that constrains parameters using observations of plant traits and site observations of NEE and AGB, we find quantum efficiency and leaf respiration rate parameters as the highest contributors to model uncertainty regardless of time frame (annual, decadal, centennial) and predictive variable (e.g. NEE, NPP, AGB). Key actions for model improvement include additional measurements of quantum efficiency and leaf respiration rate, both of which can be observed directly at the leaf level. We caution that the order in which parameters contributed to model uncertainty was sensitive to the inclusion of parameter random effects (across-site variation) in the analysis. Our finding was dependent upon the availability of site-to-site traitmore » observations and whether to account for that variability within the parameter uncertainty. Here, this analysis focused on parametric uncertainty; including contributions from initial conditions, meteorological driver data and process error in future studies will provide a more complete picture of the sources of uncertainty and observations needed to improve long-term predictions.« less

Authors:
 [1]; ORCiD logo [2];  [3];  [4]
  1. Pennsylvania State Univ., University Park, PA (United States); Univ. of Utah, Salt Lake City, UT (United States)
  2. Brookhaven National Lab. (BNL), Upton, NY (United States)
  3. Pennsylvania State Univ., University Park, PA (United States)
  4. Boston Univ., Boston, MA (United States)
Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1482088
Report Number(s):
BNL-209437-2018-JAAM
Journal ID: ISSN 2169-8953
Grant/Contract Number:  
SC0012704
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Geophysical Research. Biogeosciences
Additional Journal Information:
Journal Name: Journal of Geophysical Research. Biogeosciences; Journal ID: ISSN 2169-8953
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES

Citation Formats

Raczka, Brett, Serbin, Shawn P., Davis, Kenneth J., and Dietze, Michael C.. What limits predictive certainty of long term carbon uptake? Quantifying parametric uncertainty at a site in Northern Wisconsin. United States: N. p., 2018. Web. doi:10.1029/2018JG004504.
Raczka, Brett, Serbin, Shawn P., Davis, Kenneth J., & Dietze, Michael C.. What limits predictive certainty of long term carbon uptake? Quantifying parametric uncertainty at a site in Northern Wisconsin. United States. doi:10.1029/2018JG004504.
Raczka, Brett, Serbin, Shawn P., Davis, Kenneth J., and Dietze, Michael C.. Tue . "What limits predictive certainty of long term carbon uptake? Quantifying parametric uncertainty at a site in Northern Wisconsin". United States. doi:10.1029/2018JG004504.
@article{osti_1482088,
title = {What limits predictive certainty of long term carbon uptake? Quantifying parametric uncertainty at a site in Northern Wisconsin},
author = {Raczka, Brett and Serbin, Shawn P. and Davis, Kenneth J. and Dietze, Michael C.},
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. Observations are critical to evaluate model performance and to guide improvements to the representation of ecological processes within the models. Here we identify model parameters that contribute the most uncertainty to long term (~100 years) projections of net ecosystem exchange (NEE), net primary production (NPP) and above ground biomass (AGB) within a mechanistically detailed model (Ecosystem Demography, version 2.1). Through an uncertainty analysis that constrains parameters using observations of plant traits and site observations of NEE and AGB, we find quantum efficiency and leaf respiration rate parameters as the highest contributors to model uncertainty regardless of time frame (annual, decadal, centennial) and predictive variable (e.g. NEE, NPP, AGB). Key actions for model improvement include additional measurements of quantum efficiency and leaf respiration rate, both of which can be observed directly at the leaf level. We caution that the order in which parameters contributed to model uncertainty was sensitive to the inclusion of parameter random effects (across-site variation) in the analysis. Our finding was dependent upon the availability of site-to-site trait observations and whether to account for that variability within the parameter uncertainty. Here, this analysis focused on parametric uncertainty; including contributions from initial conditions, meteorological driver data and process error in future studies will provide a more complete picture of the sources of uncertainty and observations needed to improve long-term predictions.},
doi = {10.1029/2018JG004504},
journal = {Journal of Geophysical Research. Biogeosciences},
number = ,
volume = ,
place = {United States},
year = {Tue Nov 13 00:00:00 EST 2018},
month = {Tue Nov 13 00:00:00 EST 2018}
}

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