Leveraging 35 years of <i>Pinus taeda</i> research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments

Thomas, R. Quinn; Brooks, Evan B.; Jersild, Annika L.; Ward, Eric J.; Wynne, Randolph H.; Albaugh, Timothy J.; Dinon-Aldridge, Heather; Burkhart, Harold E.; Domec, Jean-Christophe; Fox, Thomas R.; Gonzalez-Benecke, Carlos A.; Martin, Timothy A.; Noormets, Asko; Sampson, David A.; Teskey, Robert O.

doi:https://doi.org/10.5194/bg-14-3525-2017

Articles | Volume 14, issue 14

https://doi.org/10.5194/bg-14-3525-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/bg-14-3525-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 14, issue 14

Research article

|

26 Jul 2017

Research article |

| 26 Jul 2017

Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments

R. Quinn Thomas, Evan B. Brooks, Annika L. Jersild, Eric J. Ward, Randolph H. Wynne, Timothy J. Albaugh, Heather Dinon-Aldridge, Harold E. Burkhart, Jean-Christophe Domec, Thomas R. Fox, Carlos A. Gonzalez-Benecke, Timothy A. Martin, Asko Noormets, David A. Sampson, and Robert O. Teskey

Supplement

https://doi.org/10.5194/bg-14-3525-2017-supplement

Data sets

The Duke FACE study H. R. McCarthy, R. Oren,K. H. Johnsen, A. Gallet-Budynek, S. G. Pritchard, C. W. Cook, S. L. LaDeau, R. B. Jackson, and A. C. Finzi https://doi.org/10.1111/j.1469-8137.2009.03078.x

The PINEMAP studies Terrestrial Carbon (TerraC) Information System http://terrac.ifas.ufl.edu

The US-DK3 eddy-flux tower data Ameriflux database http://ameriflux.lbl.gov

SSURGO soils database USDA https://sdmdataaccess.sc.egov.usda.gov

The Waycross data C. Bryars, C. Maier, D. Zhao, M. Kane, B. Borders, R. Will,and R. Teskey https://doi.org/10.1016/j.foreco.2012.09.031

Download

Article (5025 KB)
Full-text XML

Short summary

To improve predictions of future forest productivity, we introduce an analytical approach that uses data from numerous research experiments that have occurred across the southeastern US to calibrate a mathematical forest model and estimate uncertainty in predictions. As a result, predictions using the model are consistent with a rich history of forest research in a region that supplies a large fraction of wood products to the world.