%0 Journal Article %1 medlyn2015using %A Medlyn, Belinda E. %A Zaehle, Sönke %A De Kauwe, Martin G. %A Walker, Anthony P. %A Dietze, Michael C. %A Hanson, Paul J. %A Hickler, Thomas %A Jain, Atul K. %A Luo, Yiqi %A Parton, William %A Prentice, I. Colin %A Thornton, Peter E. %A Wang, Shusen %A Wang, Ying-Ping %A Weng, Ensheng %A Iversen, Colleen M. %A McCarthy, Heather R. %A Warren, Jeffrey M. %A Oren, Ram %A Norby, Richard J. %D 2015 %I Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved. %J Nature Climate Change %K dukeface face facemds facemip ornlface %P 528-- %T Using ecosystem experiments to improve vegetation models %U https://doi.org/10.1038/nclimate2621 %V 5 %X Ecosystem responses to rising CO2 concentrations are a major source of uncertainty in climate change projections. Data from ecosystem-scale Free-Air CO2 Enrichment (FACE) experiments provide a unique opportunity to reduce this uncertainty. The recent FACE Model–Data Synthesis project aimed to use the information gathered in two forest FACE experiments to assess and improve land ecosystem models. A new 'assumption-centred' model intercomparison approach was used, in which participating models were evaluated against experimental data based on the ways in which they represent key ecological processes. By identifying and evaluating the main assumptions causing differences among models, the assumption-centred approach produced a clear roadmap for reducing model uncertainty. Here, we explain this approach and summarize the resulting research agenda. We encourage the application of this approach in other model intercomparison projects to fundamentally improve predictive understanding of the Earth system.

@article{medlyn2015using, abstract = {Ecosystem responses to rising CO2 concentrations are a major source of uncertainty in climate change projections. Data from ecosystem-scale Free-Air CO2 Enrichment (FACE) experiments provide a unique opportunity to reduce this uncertainty. The recent FACE Model–Data Synthesis project aimed to use the information gathered in two forest FACE experiments to assess and improve land ecosystem models. A new 'assumption-centred' model intercomparison approach was used, in which participating models were evaluated against experimental data based on the ways in which they represent key ecological processes. By identifying and evaluating the main assumptions causing differences among models, the assumption-centred approach produced a clear roadmap for reducing model uncertainty. Here, we explain this approach and summarize the resulting research agenda. We encourage the application of this approach in other model intercomparison projects to fundamentally improve predictive understanding of the Earth system.}, added-at = {2019-08-16T14:27:48.000+0200}, author = {Medlyn, Belinda E. and Zaehle, Sönke and De Kauwe, Martin G. and Walker, Anthony P. and Dietze, Michael C. and Hanson, Paul J. and Hickler, Thomas and Jain, Atul K. and Luo, Yiqi and Parton, William and Prentice, I. Colin and Thornton, Peter E. and Wang, Shusen and Wang, Ying-Ping and Weng, Ensheng and Iversen, Colleen M. and McCarthy, Heather R. and Warren, Jeffrey M. and Oren, Ram and Norby, Richard J.}, biburl = {https://www.bibsonomy.org/bibtex/2f44ed854ca3bd0cd67abf086b9b7e8ea/karinawilliams}, interhash = {d283abd0a45ae4363cf11bd678430627}, intrahash = {f44ed854ca3bd0cd67abf086b9b7e8ea}, journal = {Nature Climate Change}, keywords = {dukeface face facemds facemip ornlface}, month = may, pages = {528--}, publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.}, timestamp = {2019-08-16T14:27:48.000+0200}, title = {Using ecosystem experiments to improve vegetation models}, url = {https://doi.org/10.1038/nclimate2621}, volume = 5, year = 2015 }