Causes and implications of persistent atmospheric carbon dioxide biases in Earth System Models
The strength of feedbacks between a changing climate and future CO2 concentrations is uncertain and difficult to predict using Earth System Models (ESMs). We analyzed emission‐driven simulations—in which atmospheric CO2levels were computed prognostically—for historical (1850–2005) and future periods...
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creator | Hoffman, F. M. Randerson, J. T. Arora, V. K. Bao, Q. Cadule, P. Ji, D. Jones, C. D. Kawamiya, M. Khatiwala, S. Lindsay, K. Obata, A. Shevliakova, E. Six, K. D. Tjiputra, J. F. Volodin, E. M. Wu, T. |
description | The strength of feedbacks between a changing climate and future CO2 concentrations is uncertain and difficult to predict using Earth System Models (ESMs). We analyzed emission‐driven simulations—in which atmospheric CO2levels were computed prognostically—for historical (1850–2005) and future periods (Representative Concentration Pathway (RCP) 8.5 for 2006–2100) produced by 15 ESMs for the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5). Comparison of ESM prognostic atmospheric CO2 over the historical period with observations indicated that ESMs, on average, had a small positive bias in predictions of contemporary atmospheric CO2. Weak ocean carbon uptake in many ESMs contributed to this bias, based on comparisons with observations of ocean and atmospheric anthropogenic carbon inventories. We found a significant linear relationship between contemporary atmospheric CO2 biases and future CO2levels for the multimodel ensemble. We used this relationship to create a contemporary CO2 tuned model (CCTM) estimate of the atmospheric CO2 trajectory for the 21st century. The CCTM yielded CO2estimates of 600±14 ppm at 2060 and 947±35 ppm at 2100, which were 21 ppm and 32 ppm below the multimodel mean during these two time periods. Using this emergent constraint approach, the likely ranges of future atmospheric CO2, CO2‐induced radiative forcing, and CO2‐induced temperature increases for the RCP 8.5 scenario were considerably narrowed compared to estimates from the full ESM ensemble. Our analysis provided evidence that much of the model‐to‐model variation in projected CO2 during the 21st century was tied to biases that existed during the observational era and that model differences in the representation of concentration‐carbon feedbacks and other slowly changing carbon cycle processes appear to be the primary driver of this variability. By improving models to more closely match the long‐term time series of CO2from Mauna Loa, our analysis suggests that uncertainties in future climate projections can be reduced.
Key Points
We analyzed emission‐driven simulations from 15 Earth System Models (ESMs)
Most ESMs had a small positive bias in contemporary atmospheric CO2 predictions
We used a linear relationship to create a trajectory of future atmospheric CO2 |
doi_str_mv | 10.1002/2013JG002381 |
format | Article |
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Key Points
We analyzed emission‐driven simulations from 15 Earth System Models (ESMs)
Most ESMs had a small positive bias in contemporary atmospheric CO2 predictions
We used a linear relationship to create a trajectory of future atmospheric CO2</description><identifier>ISSN: 2169-8953</identifier><identifier>EISSN: 2169-8961</identifier><identifier>DOI: 10.1002/2013JG002381</identifier><language>eng</language><publisher>Washington: Blackwell Publishing Ltd</publisher><subject>Anthropogenic factors ; Atmospherics ; Bias ; Carbon ; Carbon cycle ; Carbon dioxide ; Climate change ; Climate models ; climate warming ; climate-carbon cycle feedbacks ; Continental interfaces, environment ; Earth ; Emissions ; Feedback ; greenhouse gases ; Intergovernmental Panel on Climate Change (IPCC) ; Marine ; Mathematical models ; Ocean, Atmosphere ; Sciences of the Universe ; terrestrial and oceanic carbon sinks ; uncertainty quantification</subject><ispartof>Journal of geophysical research. Biogeosciences, 2014-02, Vol.119 (2), p.141-162</ispartof><rights>2013. The Authors.</rights><rights>2014. American Geophysical Union. All Rights Reserved.</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c6198-2e2ff844276581be4a12a1b28c372ea5ff314e8562577ca8db8c901814eb648b3</citedby><cites>FETCH-LOGICAL-c6198-2e2ff844276581be4a12a1b28c372ea5ff314e8562577ca8db8c901814eb648b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2F2013JG002381$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2F2013JG002381$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,1417,1433,27924,27925,45574,45575,46409,46833</link.rule.ids><backlink>$$Uhttps://hal.science/hal-03211805$$DView record in HAL$$Hfree_for_read</backlink><backlink>$$Uhttps://www.osti.gov/biblio/1126524$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Hoffman, F. M.</creatorcontrib><creatorcontrib>Randerson, J. T.</creatorcontrib><creatorcontrib>Arora, V. K.</creatorcontrib><creatorcontrib>Bao, Q.</creatorcontrib><creatorcontrib>Cadule, P.</creatorcontrib><creatorcontrib>Ji, D.</creatorcontrib><creatorcontrib>Jones, C. D.</creatorcontrib><creatorcontrib>Kawamiya, M.</creatorcontrib><creatorcontrib>Khatiwala, S.</creatorcontrib><creatorcontrib>Lindsay, K.</creatorcontrib><creatorcontrib>Obata, A.</creatorcontrib><creatorcontrib>Shevliakova, E.</creatorcontrib><creatorcontrib>Six, K. D.</creatorcontrib><creatorcontrib>Tjiputra, J. F.</creatorcontrib><creatorcontrib>Volodin, E. M.</creatorcontrib><creatorcontrib>Wu, T.</creatorcontrib><creatorcontrib>Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)</creatorcontrib><title>Causes and implications of persistent atmospheric carbon dioxide biases in Earth System Models</title><title>Journal of geophysical research. Biogeosciences</title><addtitle>J. Geophys. Res. Biogeosci</addtitle><description>The strength of feedbacks between a changing climate and future CO2 concentrations is uncertain and difficult to predict using Earth System Models (ESMs). We analyzed emission‐driven simulations—in which atmospheric CO2levels were computed prognostically—for historical (1850–2005) and future periods (Representative Concentration Pathway (RCP) 8.5 for 2006–2100) produced by 15 ESMs for the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5). Comparison of ESM prognostic atmospheric CO2 over the historical period with observations indicated that ESMs, on average, had a small positive bias in predictions of contemporary atmospheric CO2. Weak ocean carbon uptake in many ESMs contributed to this bias, based on comparisons with observations of ocean and atmospheric anthropogenic carbon inventories. We found a significant linear relationship between contemporary atmospheric CO2 biases and future CO2levels for the multimodel ensemble. We used this relationship to create a contemporary CO2 tuned model (CCTM) estimate of the atmospheric CO2 trajectory for the 21st century. The CCTM yielded CO2estimates of 600±14 ppm at 2060 and 947±35 ppm at 2100, which were 21 ppm and 32 ppm below the multimodel mean during these two time periods. Using this emergent constraint approach, the likely ranges of future atmospheric CO2, CO2‐induced radiative forcing, and CO2‐induced temperature increases for the RCP 8.5 scenario were considerably narrowed compared to estimates from the full ESM ensemble. Our analysis provided evidence that much of the model‐to‐model variation in projected CO2 during the 21st century was tied to biases that existed during the observational era and that model differences in the representation of concentration‐carbon feedbacks and other slowly changing carbon cycle processes appear to be the primary driver of this variability. By improving models to more closely match the long‐term time series of CO2from Mauna Loa, our analysis suggests that uncertainties in future climate projections can be reduced.
Key Points
We analyzed emission‐driven simulations from 15 Earth System Models (ESMs)
Most ESMs had a small positive bias in contemporary atmospheric CO2 predictions
We used a linear relationship to create a trajectory of future atmospheric CO2</description><subject>Anthropogenic factors</subject><subject>Atmospherics</subject><subject>Bias</subject><subject>Carbon</subject><subject>Carbon cycle</subject><subject>Carbon dioxide</subject><subject>Climate change</subject><subject>Climate models</subject><subject>climate warming</subject><subject>climate-carbon cycle feedbacks</subject><subject>Continental interfaces, environment</subject><subject>Earth</subject><subject>Emissions</subject><subject>Feedback</subject><subject>greenhouse gases</subject><subject>Intergovernmental Panel on Climate Change (IPCC)</subject><subject>Marine</subject><subject>Mathematical models</subject><subject>Ocean, Atmosphere</subject><subject>Sciences of the Universe</subject><subject>terrestrial and oceanic carbon sinks</subject><subject>uncertainty quantification</subject><issn>2169-8953</issn><issn>2169-8961</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2014</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><recordid>eNqNkdFr1TAYxYsoOObe_AOCvijYmS9p0vRxXLbOcedgu-KbIU1Tbmbb1CRXd_97UzouwwdZXvJx-J3Dl5wsewv4FDAmnwkGelWniQp4kR0R4FUuKg4vDzOjr7OTEO5xOiJJAEfZj5XaBROQGltkh6m3WkXrxoBchybjgw3RjBGpOLgwbY23GmnlGzei1roH2xrUWDUH2BGdKx-36G6fLAO6dq3pw5vsVaf6YE4e7-Ps28X5ZnWZr2_qL6uzda45VCInhnSdKApSciagMYUCoqAhQtOSGMW6jkJhBOOElaVWom2ErjCIJDa8EA09zt4tuS5EK4O20eitduNodJQAhDNSJOjjAm1VLydvB-X30ikrL8_WctYwJQACs9-Q2A8LO3n3a2dClIMN2vS9Go3bBQm8BFbxgj8DZQRTwQQpE_r-H_Te7fyYfiYFUpHeXxRz4KeF0t6F4E13WBawnLuWT7tOOF3wP7Y3-_-y8qq-rZPARHLli2tu-OHgUv6n5CUtmfz-tZbsDl_cbla13NC_qzO2LA</recordid><startdate>201402</startdate><enddate>201402</enddate><creator>Hoffman, F. M.</creator><creator>Randerson, J. T.</creator><creator>Arora, V. K.</creator><creator>Bao, Q.</creator><creator>Cadule, P.</creator><creator>Ji, D.</creator><creator>Jones, C. D.</creator><creator>Kawamiya, M.</creator><creator>Khatiwala, S.</creator><creator>Lindsay, K.</creator><creator>Obata, A.</creator><creator>Shevliakova, E.</creator><creator>Six, K. D.</creator><creator>Tjiputra, J. F.</creator><creator>Volodin, E. M.</creator><creator>Wu, T.</creator><general>Blackwell Publishing Ltd</general><general>American Geophysical Union</general><scope>BSCLL</scope><scope>24P</scope><scope>WIN</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>KR7</scope><scope>L7M</scope><scope>1XC</scope><scope>VOOES</scope><scope>OTOTI</scope></search><sort><creationdate>201402</creationdate><title>Causes and implications of persistent atmospheric carbon dioxide biases in Earth System Models</title><author>Hoffman, F. M. ; Randerson, J. T. ; Arora, V. K. ; Bao, Q. ; Cadule, P. ; Ji, D. ; Jones, C. D. ; Kawamiya, M. ; Khatiwala, S. ; Lindsay, K. ; Obata, A. ; Shevliakova, E. ; Six, K. D. ; Tjiputra, J. F. ; Volodin, E. 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M.</creatorcontrib><creatorcontrib>Randerson, J. T.</creatorcontrib><creatorcontrib>Arora, V. K.</creatorcontrib><creatorcontrib>Bao, Q.</creatorcontrib><creatorcontrib>Cadule, P.</creatorcontrib><creatorcontrib>Ji, D.</creatorcontrib><creatorcontrib>Jones, C. D.</creatorcontrib><creatorcontrib>Kawamiya, M.</creatorcontrib><creatorcontrib>Khatiwala, S.</creatorcontrib><creatorcontrib>Lindsay, K.</creatorcontrib><creatorcontrib>Obata, A.</creatorcontrib><creatorcontrib>Shevliakova, E.</creatorcontrib><creatorcontrib>Six, K. D.</creatorcontrib><creatorcontrib>Tjiputra, J. F.</creatorcontrib><creatorcontrib>Volodin, E. M.</creatorcontrib><creatorcontrib>Wu, T.</creatorcontrib><creatorcontrib>Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)</creatorcontrib><collection>Istex</collection><collection>Wiley Online Library Open Access</collection><collection>Wiley Online Library (Open Access Collection)</collection><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Water Resources 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>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><collection>OSTI.GOV</collection><jtitle>Journal of geophysical research. Biogeosciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hoffman, F. M.</au><au>Randerson, J. T.</au><au>Arora, V. K.</au><au>Bao, Q.</au><au>Cadule, P.</au><au>Ji, D.</au><au>Jones, C. D.</au><au>Kawamiya, M.</au><au>Khatiwala, S.</au><au>Lindsay, K.</au><au>Obata, A.</au><au>Shevliakova, E.</au><au>Six, K. D.</au><au>Tjiputra, J. F.</au><au>Volodin, E. M.</au><au>Wu, T.</au><aucorp>Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Causes and implications of persistent atmospheric carbon dioxide biases in Earth System Models</atitle><jtitle>Journal of geophysical research. Biogeosciences</jtitle><addtitle>J. Geophys. Res. Biogeosci</addtitle><date>2014-02</date><risdate>2014</risdate><volume>119</volume><issue>2</issue><spage>141</spage><epage>162</epage><pages>141-162</pages><issn>2169-8953</issn><eissn>2169-8961</eissn><abstract>The strength of feedbacks between a changing climate and future CO2 concentrations is uncertain and difficult to predict using Earth System Models (ESMs). We analyzed emission‐driven simulations—in which atmospheric CO2levels were computed prognostically—for historical (1850–2005) and future periods (Representative Concentration Pathway (RCP) 8.5 for 2006–2100) produced by 15 ESMs for the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5). Comparison of ESM prognostic atmospheric CO2 over the historical period with observations indicated that ESMs, on average, had a small positive bias in predictions of contemporary atmospheric CO2. Weak ocean carbon uptake in many ESMs contributed to this bias, based on comparisons with observations of ocean and atmospheric anthropogenic carbon inventories. We found a significant linear relationship between contemporary atmospheric CO2 biases and future CO2levels for the multimodel ensemble. We used this relationship to create a contemporary CO2 tuned model (CCTM) estimate of the atmospheric CO2 trajectory for the 21st century. The CCTM yielded CO2estimates of 600±14 ppm at 2060 and 947±35 ppm at 2100, which were 21 ppm and 32 ppm below the multimodel mean during these two time periods. Using this emergent constraint approach, the likely ranges of future atmospheric CO2, CO2‐induced radiative forcing, and CO2‐induced temperature increases for the RCP 8.5 scenario were considerably narrowed compared to estimates from the full ESM ensemble. Our analysis provided evidence that much of the model‐to‐model variation in projected CO2 during the 21st century was tied to biases that existed during the observational era and that model differences in the representation of concentration‐carbon feedbacks and other slowly changing carbon cycle processes appear to be the primary driver of this variability. By improving models to more closely match the long‐term time series of CO2from Mauna Loa, our analysis suggests that uncertainties in future climate projections can be reduced.
Key Points
We analyzed emission‐driven simulations from 15 Earth System Models (ESMs)
Most ESMs had a small positive bias in contemporary atmospheric CO2 predictions
We used a linear relationship to create a trajectory of future atmospheric CO2</abstract><cop>Washington</cop><pub>Blackwell Publishing Ltd</pub><doi>10.1002/2013JG002381</doi><tpages>22</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Anthropogenic factors Atmospherics Bias Carbon Carbon cycle Carbon dioxide Climate change Climate models climate warming climate-carbon cycle feedbacks Continental interfaces, environment Earth Emissions Feedback greenhouse gases Intergovernmental Panel on Climate Change (IPCC) Marine Mathematical models Ocean, Atmosphere Sciences of the Universe terrestrial and oceanic carbon sinks uncertainty quantification |
title | Causes and implications of persistent atmospheric carbon dioxide biases in Earth System Models |
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