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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Veröffentlicht in: | Global biogeochemical cycles 2019-10, Vol.33 (10), p.1289-1309 |
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creator | 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 |
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 (CO2) 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 CO2 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 CO2 in the atmosphere (CLM5). Overall, CLM5 simulations showed better agreement with observed ecosystem responses to experimental N and CO2 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.
Plain Language Summary
How do changes in the availability of nitrogen in soils or carbon dioxide in the atmosphere affect the amount of carbon that can be stored on land? Answering this question is critical, but it remains difficult for land models that are used to make climate change projections—in part because of limited understanding in how terrestrial ecosystems will respond to environmental change. Experimental manipulations that increase the availability of nitrogen or carbon dioxide, however, provide insights into how ecosystems are likely to respond to changes in resource availability. We expect that models |
doi_str_mv | 10.1029/2018GB006141 |
format | Article |
fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6919943</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2314490857</sourcerecordid><originalsourceid>FETCH-LOGICAL-c5499-c1def8319cb23411a159d8766183255c5708f33aa97acf53c853e79f233f693c3</originalsourceid><addsrcrecordid>eNp9ks9v0zAYhiMEYmVw44wiuIBEwJ9_JP44ILVV6ZA6cYCdLc9xVo_UDnFS6H-PS8Y0duD0Sfajx68-v1n2HMg7IBTfUwJyvSCkBA4Pshkg5wVSyh9mMyJlWZSUlSfZkxivCQEuBD7OThhI5FCJWaYW9hB8nX8d9OBMvrDebHe6_-781Yf8IqaRr351tnc76wfd5ufau25sExx8zIeQr_a6HfVg841OmvNQ2zafxzjuuj_I0-xRo9ton93M0-zi0-rb8qzYfFl_Xs43hREcsTBQ20YyQHNJGQfQILCWVVmCZFQIIyoiG8a0xkqbRjAjBbMVNpSxpkRm2Gn2cfJ24-XO1ial7XWruhRc9wcVtFP_3ni3VVdhr0oERM6S4OUkCHFwKho3WLM1wXtrBgVCEk6qBL2ZoO0999l8o45nhFcUEeUeEvv6JlEffow2DmrnorFtq70NY1QUpZDyCCf01T30Ooy9T_tSlAHnSKQ4Pv52okwfYuxtc5sAiDpWQd2tQsJf3N3ILfz37xNAJ-Cna-3hvzK1XixpahOy370Au2M</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2314490857</pqid></control><display><type>article</type><title>Beyond Static Benchmarking: Using Experimental Manipulations to Evaluate Land Model Assumptions</title><source>Wiley Free Content</source><source>Wiley-Blackwell AGU Digital Library</source><source>Wiley Online Library Journals Frontfile Complete</source><source>Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals</source><creator>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</creator><creatorcontrib>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 ; Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)</creatorcontrib><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 (CO2) 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 CO2 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 CO2 in the atmosphere (CLM5). Overall, CLM5 simulations showed better agreement with observed ecosystem responses to experimental N and CO2 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.
Plain Language Summary
How do changes in the availability of nitrogen in soils or carbon dioxide in the atmosphere affect the amount of carbon that can be stored on land? Answering this question is critical, but it remains difficult for land models that are used to make climate change projections—in part because of limited understanding in how terrestrial ecosystems will respond to environmental change. Experimental manipulations that increase the availability of nitrogen or carbon dioxide, however, provide insights into how ecosystems are likely to respond to changes in resource availability. We expect that models should exhibit similar responses to those observed in the real world. Our results show that over the course of model development later versions of the Community Land Model do a better job of simulating the global carbon cycle and capturing appropriate ecosystem responses to nitrogen and carbon dioxide enrichment. This improves our confidence in the future carbon cycle projections made by more recent versions of the Community Land Model. Our results also identify assumptions in the model that are not well supported by observations and can help to prioritize future model developments.
Key Points
Experimental manipulations provide critical insights into ecosystem responses to environmental change that can evaluate land models
Parametric and structural changes to the Community Land Model version 5 improve the simulated response to environmental change
Model assumptions related to nutrient acquisition strategies and trade‐offs between carbon and nitrogen limitation deserve further attention</description><identifier>ISSN: 0886-6236</identifier><identifier>EISSN: 1944-9224</identifier><identifier>EISSN: 1944-8224</identifier><identifier>DOI: 10.1029/2018GB006141</identifier><identifier>PMID: 31894175</identifier><language>eng</language><publisher>United States: Blackwell Publishing Ltd</publisher><subject>Atmosphere ; Atmospheric models ; Availability ; Benchmarks ; Biogeochemical Cycles, Processes, and Modeling ; Biogeochemical Kinetics and Reaction Modeling ; Biogeochemistry ; Biogeosciences ; Carbon ; Carbon cycle ; Carbon Cycling ; Carbon dioxide ; Carbon dioxide atmospheric concentrations ; Carbon dioxide concentration ; Carbon sequestration ; Climate change ; Climate models ; Climatology ; Communities ; Community Land Model ; Computer simulation ; Cryosphere ; Earth Sciences ; Earth System Modeling ; Ecosystems ; Ecosystems, Structure, Dynamics, and Modeling ; Ecosystems: Structure and Dynamics ; elevated CO2 ; Enrichment ; Environmental changes ; ENVIRONMENTAL SCIENCES ; Geodesy and Gravity ; Global Change ; Global Change from Geodesy ; gross primary productivity ; land model ; meta-analysis ; model validation ; Natural Hazards ; Nitrogen ; Nitrogen Cycling ; nitrogen enrichment ; nitrogen enrichment elevated CO2 land model biogeochemistry ; Nitrogen in soils ; Nutrient uptake ; Oceanography: Biological and Chemical ; Paleoceanography ; Performance evaluation ; Physical Modeling ; Plant physiology ; Primary production ; Resource availability ; Sciences of the Universe ; Soil ; Terrestrial ecosystems ; Terrestrial environments ; Uptake</subject><ispartof>Global biogeochemical cycles, 2019-10, Vol.33 (10), p.1289-1309</ispartof><rights>2019. The Authors.</rights><rights>2019. American Geophysical Union. All Rights Reserved.</rights><rights>Attribution</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5499-c1def8319cb23411a159d8766183255c5708f33aa97acf53c853e79f233f693c3</citedby><cites>FETCH-LOGICAL-c5499-c1def8319cb23411a159d8766183255c5708f33aa97acf53c853e79f233f693c3</cites><orcidid>0000-0002-3367-0065 ; 0000-0002-0057-9900 ; 0000-0003-0830-6437 ; 0000-0001-7116-1985 ; 0000-0002-2968-3023 ; 0000-0003-3260-9227 ; 0000-0001-7222-2268 ; 0000-0003-1282-7825 ; 0000-0003-4317-3983 ; 0000-0003-3557-7929 ; 0000000200579900 ; 0000000229683023 ; 0000000171161985 ; 0000000332609227 ; 0000000312827825 ; 0000000233670065 ; 0000000172222268 ; 0000000308306437 ; 0000000343173983 ; 0000000335577929</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1029%2F2018GB006141$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1029%2F2018GB006141$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>230,314,776,780,881,1411,1427,11493,27901,27902,45550,45551,46384,46443,46808,46867</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/31894175$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://cnrs.hal.science/hal-04729998$$DView record in HAL$$Hfree_for_read</backlink><backlink>$$Uhttps://www.osti.gov/servlets/purl/1580407$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Wieder, William R.</creatorcontrib><creatorcontrib>Lawrence, David M.</creatorcontrib><creatorcontrib>Fisher, Rosie A.</creatorcontrib><creatorcontrib>Bonan, Gordon B.</creatorcontrib><creatorcontrib>Cheng, Susan J.</creatorcontrib><creatorcontrib>Goodale, Christine L.</creatorcontrib><creatorcontrib>Grandy, A. Stuart</creatorcontrib><creatorcontrib>Koven, Charles D.</creatorcontrib><creatorcontrib>Lombardozzi, Danica L.</creatorcontrib><creatorcontrib>Oleson, Keith W.</creatorcontrib><creatorcontrib>Thomas, R. Quinn</creatorcontrib><creatorcontrib>Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)</creatorcontrib><title>Beyond Static Benchmarking: Using Experimental Manipulations to Evaluate Land Model Assumptions</title><title>Global biogeochemical cycles</title><addtitle>Global Biogeochem Cycles</addtitle><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 (CO2) 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 CO2 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 CO2 in the atmosphere (CLM5). Overall, CLM5 simulations showed better agreement with observed ecosystem responses to experimental N and CO2 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.
Plain Language Summary
How do changes in the availability of nitrogen in soils or carbon dioxide in the atmosphere affect the amount of carbon that can be stored on land? Answering this question is critical, but it remains difficult for land models that are used to make climate change projections—in part because of limited understanding in how terrestrial ecosystems will respond to environmental change. Experimental manipulations that increase the availability of nitrogen or carbon dioxide, however, provide insights into how ecosystems are likely to respond to changes in resource availability. We expect that models should exhibit similar responses to those observed in the real world. Our results show that over the course of model development later versions of the Community Land Model do a better job of simulating the global carbon cycle and capturing appropriate ecosystem responses to nitrogen and carbon dioxide enrichment. This improves our confidence in the future carbon cycle projections made by more recent versions of the Community Land Model. Our results also identify assumptions in the model that are not well supported by observations and can help to prioritize future model developments.
Key Points
Experimental manipulations provide critical insights into ecosystem responses to environmental change that can evaluate land models
Parametric and structural changes to the Community Land Model version 5 improve the simulated response to environmental change
Model assumptions related to nutrient acquisition strategies and trade‐offs between carbon and nitrogen limitation deserve further attention</description><subject>Atmosphere</subject><subject>Atmospheric models</subject><subject>Availability</subject><subject>Benchmarks</subject><subject>Biogeochemical Cycles, Processes, and Modeling</subject><subject>Biogeochemical Kinetics and Reaction Modeling</subject><subject>Biogeochemistry</subject><subject>Biogeosciences</subject><subject>Carbon</subject><subject>Carbon cycle</subject><subject>Carbon Cycling</subject><subject>Carbon dioxide</subject><subject>Carbon dioxide atmospheric concentrations</subject><subject>Carbon dioxide concentration</subject><subject>Carbon sequestration</subject><subject>Climate change</subject><subject>Climate models</subject><subject>Climatology</subject><subject>Communities</subject><subject>Community Land Model</subject><subject>Computer simulation</subject><subject>Cryosphere</subject><subject>Earth Sciences</subject><subject>Earth System Modeling</subject><subject>Ecosystems</subject><subject>Ecosystems, Structure, Dynamics, and Modeling</subject><subject>Ecosystems: Structure and Dynamics</subject><subject>elevated CO2</subject><subject>Enrichment</subject><subject>Environmental changes</subject><subject>ENVIRONMENTAL SCIENCES</subject><subject>Geodesy and Gravity</subject><subject>Global Change</subject><subject>Global Change from Geodesy</subject><subject>gross primary productivity</subject><subject>land model</subject><subject>meta-analysis</subject><subject>model validation</subject><subject>Natural Hazards</subject><subject>Nitrogen</subject><subject>Nitrogen Cycling</subject><subject>nitrogen enrichment</subject><subject>nitrogen enrichment elevated CO2 land model biogeochemistry</subject><subject>Nitrogen in soils</subject><subject>Nutrient uptake</subject><subject>Oceanography: Biological and Chemical</subject><subject>Paleoceanography</subject><subject>Performance evaluation</subject><subject>Physical Modeling</subject><subject>Plant physiology</subject><subject>Primary production</subject><subject>Resource availability</subject><subject>Sciences of the Universe</subject><subject>Soil</subject><subject>Terrestrial ecosystems</subject><subject>Terrestrial environments</subject><subject>Uptake</subject><issn>0886-6236</issn><issn>1944-9224</issn><issn>1944-8224</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><recordid>eNp9ks9v0zAYhiMEYmVw44wiuIBEwJ9_JP44ILVV6ZA6cYCdLc9xVo_UDnFS6H-PS8Y0duD0Sfajx68-v1n2HMg7IBTfUwJyvSCkBA4Pshkg5wVSyh9mMyJlWZSUlSfZkxivCQEuBD7OThhI5FCJWaYW9hB8nX8d9OBMvrDebHe6_-781Yf8IqaRr351tnc76wfd5ufau25sExx8zIeQr_a6HfVg841OmvNQ2zafxzjuuj_I0-xRo9ton93M0-zi0-rb8qzYfFl_Xs43hREcsTBQ20YyQHNJGQfQILCWVVmCZFQIIyoiG8a0xkqbRjAjBbMVNpSxpkRm2Gn2cfJ24-XO1ial7XWruhRc9wcVtFP_3ni3VVdhr0oERM6S4OUkCHFwKho3WLM1wXtrBgVCEk6qBL2ZoO0999l8o45nhFcUEeUeEvv6JlEffow2DmrnorFtq70NY1QUpZDyCCf01T30Ooy9T_tSlAHnSKQ4Pv52okwfYuxtc5sAiDpWQd2tQsJf3N3ILfz37xNAJ-Cna-3hvzK1XixpahOy370Au2M</recordid><startdate>201910</startdate><enddate>201910</enddate><creator>Wieder, William R.</creator><creator>Lawrence, David M.</creator><creator>Fisher, Rosie A.</creator><creator>Bonan, Gordon B.</creator><creator>Cheng, Susan J.</creator><creator>Goodale, Christine L.</creator><creator>Grandy, A. Stuart</creator><creator>Koven, Charles D.</creator><creator>Lombardozzi, Danica L.</creator><creator>Oleson, Keith W.</creator><creator>Thomas, R. Quinn</creator><general>Blackwell Publishing Ltd</general><general>American Geophysical Union</general><general>American Geophysical Union (AGU)</general><general>John Wiley and Sons Inc</general><scope>24P</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7TG</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>KL.</scope><scope>L.G</scope><scope>7S9</scope><scope>L.6</scope><scope>1XC</scope><scope>VOOES</scope><scope>OIOZB</scope><scope>OTOTI</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-3367-0065</orcidid><orcidid>https://orcid.org/0000-0002-0057-9900</orcidid><orcidid>https://orcid.org/0000-0003-0830-6437</orcidid><orcidid>https://orcid.org/0000-0001-7116-1985</orcidid><orcidid>https://orcid.org/0000-0002-2968-3023</orcidid><orcidid>https://orcid.org/0000-0003-3260-9227</orcidid><orcidid>https://orcid.org/0000-0001-7222-2268</orcidid><orcidid>https://orcid.org/0000-0003-1282-7825</orcidid><orcidid>https://orcid.org/0000-0003-4317-3983</orcidid><orcidid>https://orcid.org/0000-0003-3557-7929</orcidid><orcidid>https://orcid.org/0000000200579900</orcidid><orcidid>https://orcid.org/0000000229683023</orcidid><orcidid>https://orcid.org/0000000171161985</orcidid><orcidid>https://orcid.org/0000000332609227</orcidid><orcidid>https://orcid.org/0000000312827825</orcidid><orcidid>https://orcid.org/0000000233670065</orcidid><orcidid>https://orcid.org/0000000172222268</orcidid><orcidid>https://orcid.org/0000000308306437</orcidid><orcidid>https://orcid.org/0000000343173983</orcidid><orcidid>https://orcid.org/0000000335577929</orcidid></search><sort><creationdate>201910</creationdate><title>Beyond Static Benchmarking: Using Experimental Manipulations to Evaluate Land Model Assumptions</title><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></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5499-c1def8319cb23411a159d8766183255c5708f33aa97acf53c853e79f233f693c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Atmosphere</topic><topic>Atmospheric models</topic><topic>Availability</topic><topic>Benchmarks</topic><topic>Biogeochemical Cycles, Processes, and Modeling</topic><topic>Biogeochemical Kinetics and Reaction Modeling</topic><topic>Biogeochemistry</topic><topic>Biogeosciences</topic><topic>Carbon</topic><topic>Carbon cycle</topic><topic>Carbon Cycling</topic><topic>Carbon dioxide</topic><topic>Carbon dioxide atmospheric concentrations</topic><topic>Carbon dioxide concentration</topic><topic>Carbon sequestration</topic><topic>Climate change</topic><topic>Climate models</topic><topic>Climatology</topic><topic>Communities</topic><topic>Community Land Model</topic><topic>Computer simulation</topic><topic>Cryosphere</topic><topic>Earth Sciences</topic><topic>Earth System Modeling</topic><topic>Ecosystems</topic><topic>Ecosystems, Structure, Dynamics, and Modeling</topic><topic>Ecosystems: Structure and Dynamics</topic><topic>elevated CO2</topic><topic>Enrichment</topic><topic>Environmental changes</topic><topic>ENVIRONMENTAL SCIENCES</topic><topic>Geodesy and Gravity</topic><topic>Global Change</topic><topic>Global Change from Geodesy</topic><topic>gross primary productivity</topic><topic>land model</topic><topic>meta-analysis</topic><topic>model validation</topic><topic>Natural Hazards</topic><topic>Nitrogen</topic><topic>Nitrogen Cycling</topic><topic>nitrogen enrichment</topic><topic>nitrogen enrichment elevated CO2 land model biogeochemistry</topic><topic>Nitrogen in soils</topic><topic>Nutrient uptake</topic><topic>Oceanography: Biological and Chemical</topic><topic>Paleoceanography</topic><topic>Performance evaluation</topic><topic>Physical Modeling</topic><topic>Plant physiology</topic><topic>Primary production</topic><topic>Resource availability</topic><topic>Sciences of the Universe</topic><topic>Soil</topic><topic>Terrestrial ecosystems</topic><topic>Terrestrial environments</topic><topic>Uptake</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wieder, William R.</creatorcontrib><creatorcontrib>Lawrence, David M.</creatorcontrib><creatorcontrib>Fisher, Rosie A.</creatorcontrib><creatorcontrib>Bonan, Gordon B.</creatorcontrib><creatorcontrib>Cheng, Susan J.</creatorcontrib><creatorcontrib>Goodale, Christine L.</creatorcontrib><creatorcontrib>Grandy, A. Stuart</creatorcontrib><creatorcontrib>Koven, Charles D.</creatorcontrib><creatorcontrib>Lombardozzi, Danica L.</creatorcontrib><creatorcontrib>Oleson, Keith W.</creatorcontrib><creatorcontrib>Thomas, R. Quinn</creatorcontrib><creatorcontrib>Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><collection>OSTI.GOV - Hybrid</collection><collection>OSTI.GOV</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Global biogeochemical cycles</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wieder, William R.</au><au>Lawrence, David M.</au><au>Fisher, Rosie A.</au><au>Bonan, Gordon B.</au><au>Cheng, Susan J.</au><au>Goodale, Christine L.</au><au>Grandy, A. Stuart</au><au>Koven, Charles D.</au><au>Lombardozzi, Danica L.</au><au>Oleson, Keith W.</au><au>Thomas, R. Quinn</au><aucorp>Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Beyond Static Benchmarking: Using Experimental Manipulations to Evaluate Land Model Assumptions</atitle><jtitle>Global biogeochemical cycles</jtitle><addtitle>Global Biogeochem Cycles</addtitle><date>2019-10</date><risdate>2019</risdate><volume>33</volume><issue>10</issue><spage>1289</spage><epage>1309</epage><pages>1289-1309</pages><issn>0886-6236</issn><eissn>1944-9224</eissn><eissn>1944-8224</eissn><abstract>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 (CO2) 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 CO2 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 CO2 in the atmosphere (CLM5). Overall, CLM5 simulations showed better agreement with observed ecosystem responses to experimental N and CO2 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.
Plain Language Summary
How do changes in the availability of nitrogen in soils or carbon dioxide in the atmosphere affect the amount of carbon that can be stored on land? Answering this question is critical, but it remains difficult for land models that are used to make climate change projections—in part because of limited understanding in how terrestrial ecosystems will respond to environmental change. Experimental manipulations that increase the availability of nitrogen or carbon dioxide, however, provide insights into how ecosystems are likely to respond to changes in resource availability. We expect that models should exhibit similar responses to those observed in the real world. Our results show that over the course of model development later versions of the Community Land Model do a better job of simulating the global carbon cycle and capturing appropriate ecosystem responses to nitrogen and carbon dioxide enrichment. This improves our confidence in the future carbon cycle projections made by more recent versions of the Community Land Model. Our results also identify assumptions in the model that are not well supported by observations and can help to prioritize future model developments.
Key Points
Experimental manipulations provide critical insights into ecosystem responses to environmental change that can evaluate land models
Parametric and structural changes to the Community Land Model version 5 improve the simulated response to environmental change
Model assumptions related to nutrient acquisition strategies and trade‐offs between carbon and nitrogen limitation deserve further attention</abstract><cop>United States</cop><pub>Blackwell Publishing Ltd</pub><pmid>31894175</pmid><doi>10.1029/2018GB006141</doi><tpages>21</tpages><orcidid>https://orcid.org/0000-0002-3367-0065</orcidid><orcidid>https://orcid.org/0000-0002-0057-9900</orcidid><orcidid>https://orcid.org/0000-0003-0830-6437</orcidid><orcidid>https://orcid.org/0000-0001-7116-1985</orcidid><orcidid>https://orcid.org/0000-0002-2968-3023</orcidid><orcidid>https://orcid.org/0000-0003-3260-9227</orcidid><orcidid>https://orcid.org/0000-0001-7222-2268</orcidid><orcidid>https://orcid.org/0000-0003-1282-7825</orcidid><orcidid>https://orcid.org/0000-0003-4317-3983</orcidid><orcidid>https://orcid.org/0000-0003-3557-7929</orcidid><orcidid>https://orcid.org/0000000200579900</orcidid><orcidid>https://orcid.org/0000000229683023</orcidid><orcidid>https://orcid.org/0000000171161985</orcidid><orcidid>https://orcid.org/0000000332609227</orcidid><orcidid>https://orcid.org/0000000312827825</orcidid><orcidid>https://orcid.org/0000000233670065</orcidid><orcidid>https://orcid.org/0000000172222268</orcidid><orcidid>https://orcid.org/0000000308306437</orcidid><orcidid>https://orcid.org/0000000343173983</orcidid><orcidid>https://orcid.org/0000000335577929</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0886-6236 |
ispartof | Global biogeochemical cycles, 2019-10, Vol.33 (10), p.1289-1309 |
issn | 0886-6236 1944-9224 1944-8224 |
language | eng |
recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_6919943 |
source | Wiley Free Content; Wiley-Blackwell AGU Digital Library; Wiley Online Library Journals Frontfile Complete; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
subjects | Atmosphere Atmospheric models Availability Benchmarks Biogeochemical Cycles, Processes, and Modeling Biogeochemical Kinetics and Reaction Modeling Biogeochemistry Biogeosciences Carbon Carbon cycle Carbon Cycling Carbon dioxide Carbon dioxide atmospheric concentrations Carbon dioxide concentration Carbon sequestration Climate change Climate models Climatology Communities Community Land Model Computer simulation Cryosphere Earth Sciences Earth System Modeling Ecosystems Ecosystems, Structure, Dynamics, and Modeling Ecosystems: Structure and Dynamics elevated CO2 Enrichment Environmental changes ENVIRONMENTAL SCIENCES Geodesy and Gravity Global Change Global Change from Geodesy gross primary productivity land model meta-analysis model validation Natural Hazards Nitrogen Nitrogen Cycling nitrogen enrichment nitrogen enrichment elevated CO2 land model biogeochemistry Nitrogen in soils Nutrient uptake Oceanography: Biological and Chemical Paleoceanography Performance evaluation Physical Modeling Plant physiology Primary production Resource availability Sciences of the Universe Soil Terrestrial ecosystems Terrestrial environments Uptake |
title | Beyond Static Benchmarking: Using Experimental Manipulations to Evaluate Land Model Assumptions |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-08T20%3A49%3A46IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Beyond%20Static%20Benchmarking:%20Using%20Experimental%20Manipulations%20to%20Evaluate%20Land%20Model%20Assumptions&rft.jtitle=Global%20biogeochemical%20cycles&rft.au=Wieder,%20William%20R.&rft.aucorp=Lawrence%20Berkeley%20National%20Laboratory%20(LBNL),%20Berkeley,%20CA%20(United%20States)&rft.date=2019-10&rft.volume=33&rft.issue=10&rft.spage=1289&rft.epage=1309&rft.pages=1289-1309&rft.issn=0886-6236&rft.eissn=1944-9224&rft_id=info:doi/10.1029/2018GB006141&rft_dat=%3Cproquest_pubme%3E2314490857%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2314490857&rft_id=info:pmid/31894175&rfr_iscdi=true |