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Beyond Static Benchmarking: Using Experimental Manipulations to Evaluate Land Model Assumptions

Land models are often used to simulate terrestrial responses to future environmental changes, but these models are not commonly evaluated with data from experimental manipulations. Results from experimental manipulations can identify and evaluate model assumptions that are consistent with appropriat...

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Autores principales: Wieder, William R., Lawrence, David M., Fisher, Rosie A., Bonan, Gordon B., Cheng, Susan J., Goodale, Christine L., Grandy, A. Stuart, Koven, Charles D., Lombardozzi, Danica L., Oleson, Keith W., Thomas, R. Quinn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6919943/
https://www.ncbi.nlm.nih.gov/pubmed/31894175
http://dx.doi.org/10.1029/2018GB006141
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author Wieder, William R.
Lawrence, David M.
Fisher, Rosie A.
Bonan, Gordon B.
Cheng, Susan J.
Goodale, Christine L.
Grandy, A. Stuart
Koven, Charles D.
Lombardozzi, Danica L.
Oleson, Keith W.
Thomas, R. Quinn
author_facet Wieder, William R.
Lawrence, David M.
Fisher, Rosie A.
Bonan, Gordon B.
Cheng, Susan J.
Goodale, Christine L.
Grandy, A. Stuart
Koven, Charles D.
Lombardozzi, Danica L.
Oleson, Keith W.
Thomas, R. Quinn
author_sort Wieder, William R.
collection PubMed
description Land models are often used to simulate terrestrial responses to future environmental changes, but these models are not commonly evaluated with data from experimental manipulations. Results from experimental manipulations can identify and evaluate model assumptions that are consistent with appropriate ecosystem responses to future environmental change. We conducted simulations using three coupled carbon‐nitrogen versions of the Community Land Model (CLM, versions 4, 4.5, and—the newly developed—5), and compared the simulated response to nitrogen (N) and atmospheric carbon dioxide (CO(2)) enrichment with meta‐analyses of observations from similar experimental manipulations. In control simulations, successive versions of CLM showed a poleward increase in gross primary productivity and an overall bias reduction, compared to FLUXNET‐MTE observations. Simulations with N and CO(2) enrichment demonstrate that CLM transitioned from a model that exhibited strong nitrogen limitation of the terrestrial carbon cycle (CLM4) to a model that showed greater responsiveness to elevated concentrations of CO(2) in the atmosphere (CLM5). Overall, CLM5 simulations showed better agreement with observed ecosystem responses to experimental N and CO(2) enrichment than previous versions of the model. These simulations also exposed shortcomings in structural assumptions and parameterizations. Specifically, no version of CLM captures changes in plant physiology, allocation, and nutrient uptake that are likely important aspects of terrestrial ecosystems' responses to environmental change. These highlight priority areas that should be addressed in future model developments. Moving forward, incorporating results from experimental manipulations into model benchmarking tools that are used to evaluate model performance will help increase confidence in terrestrial carbon cycle projections.
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spelling pubmed-69199432019-12-30 Beyond Static Benchmarking: Using Experimental Manipulations to Evaluate Land Model Assumptions Wieder, William R. Lawrence, David M. Fisher, Rosie A. Bonan, Gordon B. Cheng, Susan J. Goodale, Christine L. Grandy, A. Stuart Koven, Charles D. Lombardozzi, Danica L. Oleson, Keith W. Thomas, R. Quinn Global Biogeochem Cycles Research Articles Land models are often used to simulate terrestrial responses to future environmental changes, but these models are not commonly evaluated with data from experimental manipulations. Results from experimental manipulations can identify and evaluate model assumptions that are consistent with appropriate ecosystem responses to future environmental change. We conducted simulations using three coupled carbon‐nitrogen versions of the Community Land Model (CLM, versions 4, 4.5, and—the newly developed—5), and compared the simulated response to nitrogen (N) and atmospheric carbon dioxide (CO(2)) enrichment with meta‐analyses of observations from similar experimental manipulations. In control simulations, successive versions of CLM showed a poleward increase in gross primary productivity and an overall bias reduction, compared to FLUXNET‐MTE observations. Simulations with N and CO(2) enrichment demonstrate that CLM transitioned from a model that exhibited strong nitrogen limitation of the terrestrial carbon cycle (CLM4) to a model that showed greater responsiveness to elevated concentrations of CO(2) in the atmosphere (CLM5). Overall, CLM5 simulations showed better agreement with observed ecosystem responses to experimental N and CO(2) enrichment than previous versions of the model. These simulations also exposed shortcomings in structural assumptions and parameterizations. Specifically, no version of CLM captures changes in plant physiology, allocation, and nutrient uptake that are likely important aspects of terrestrial ecosystems' responses to environmental change. These highlight priority areas that should be addressed in future model developments. Moving forward, incorporating results from experimental manipulations into model benchmarking tools that are used to evaluate model performance will help increase confidence in terrestrial carbon cycle projections. John Wiley and Sons Inc. 2019-10-28 2019-10 /pmc/articles/PMC6919943/ /pubmed/31894175 http://dx.doi.org/10.1029/2018GB006141 Text en © 2019. The Authors. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Wieder, William R.
Lawrence, David M.
Fisher, Rosie A.
Bonan, Gordon B.
Cheng, Susan J.
Goodale, Christine L.
Grandy, A. Stuart
Koven, Charles D.
Lombardozzi, Danica L.
Oleson, Keith W.
Thomas, R. Quinn
Beyond Static Benchmarking: Using Experimental Manipulations to Evaluate Land Model Assumptions
title Beyond Static Benchmarking: Using Experimental Manipulations to Evaluate Land Model Assumptions
title_full Beyond Static Benchmarking: Using Experimental Manipulations to Evaluate Land Model Assumptions
title_fullStr Beyond Static Benchmarking: Using Experimental Manipulations to Evaluate Land Model Assumptions
title_full_unstemmed Beyond Static Benchmarking: Using Experimental Manipulations to Evaluate Land Model Assumptions
title_short Beyond Static Benchmarking: Using Experimental Manipulations to Evaluate Land Model Assumptions
title_sort beyond static benchmarking: using experimental manipulations to evaluate land model assumptions
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6919943/
https://www.ncbi.nlm.nih.gov/pubmed/31894175
http://dx.doi.org/10.1029/2018GB006141
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