# Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO 2

Anthony Walker, Martin De Kauwe, Ana Bastos, Soumaya Belmecheri, Katerina Georgiou, Ralph F. Keeling, Sean M. McMahon, Belinda E. Medlyn, David J. P. Moore, Richard J. Norby, et al.

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► To cite this version:

Anthony Walker, Martin De Kauwe, Ana Bastos, Soumaya Belmecheri, Katerina Georgiou, et al. "Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO 2." New Phytologist, 2021, 229 (5), pp.1-33. 10.1111/nph.16866. hal-03243579

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# Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2

## Accepted version

"This is the peer reviewed version of the following article:

Walker, A. P., De Kauwe, M. G., Bastos, A., Belmecheri, S., Georgiou, K., Keeling, R., et al. (2021). Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2. New Phytologist, 229(5), 2413-2445. doi:10.1111/NPH.16866

which has been published in final form at doi:10.1111/NPH.16866.

This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley's version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited."

Published in: New Phytologist

<figure><img src="image_2.png" /></figure>

This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 647204
<figure><img src="image_3.png" /></figure>

# Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2

## Accepted version

"This is the peer reviewed version of the following article:

Walker, A. P., De Kauwe, M. G., Bastos, A., Belmecheri, S., Georgiou, K., Keeling, R., et al. (2021). Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2. New Phytologist, 229(5), 2413-2445. doi:10.1111/NPH.16866

which has been published in final form at doi:10.1111/NPH.16866.

This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley's version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited."

Published in: New Phytologist

<figure><img src="image_4.png" /></figure>

This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 647204
<figure><img src="image_3.png" /></figure>

DR ANTHONY P WALKER (Orcid ID: 0000-0003-0557-5594)

DR MARTIN GERARD DE KAUWE (Orcid ID: 0000-0002-3399-9098)

DR RICHARD J NORBY (Orcid ID: 0000-0002-0238-9828)

DR SÖNKE ZAEHLE (Orcid ID: 0000-0001-5602-7956)

DR KRISTINA ANDERSON-TEIXEIRA (Orcid ID: 0000-0001-8461-9713)

DR DAVID ELLSWORTH (Orcid ID: 0000-0002-9699-2272)

DR SIMONE FATICHI (Orcid ID: 0000-0003-1361-6659)

DR LIANHONG GU (Orcid ID: 0000-0001-5756-8738)

DR COLLEEN M IVERSEN (Orcid ID: 0000-0001-8293-3450)

DR MINGKAI JIANG (Orcid ID: 0000-0002-9982-9518)

DR TREVOR F. KEENAN (Orcid ID: 0000-0002-3347-0258)

MR JÜRGEN KNAUER (Orcid ID: 0000-0002-4947-7067)

DR WILLIAM SMITH (Orcid ID: 0000-0002-5785-6489)

PROF. CHRISTIAN KOERNER (Orcid ID: 0000-0001-7768-7638)

MS MANON ELISA BLEUNIENN SABOT (Orcid ID: 0000-0002-9440-4553)

DR BENTON NEIL TAYLOR (Orcid ID: 0000-0002-9834-9192)

DR CÉSAR TERRER (Orcid ID: 0000-0002-5479-3486)

PROF. ANNA TRUGMAN (Orcid ID: 0000-0002-7903-9711)

Article type : Commissioned Material - Tansley Review

This article has been accepted for publication and undergone full peer review but has not been
through the copyediting, typesetting, pagination and proofreading process, which may lead to
differences between this version and the Version of Record. Please cite this article as doi:
10.1111/NPH.16866

This article is protected by copyright. All rights reserved
<figure><img src="image_5.png" /></figure>

DR ANTHONY P WALKER (Orcid ID: 0000-0003-0557-5594)

DR MARTIN GERARD DE KAUWE (Orcid ID: 0000-0002-3399-9098)

DR RICHARD J NORBY (Orcid ID: 0000-0002-0238-9828)

DR SÖNKE ZAEHLE (Orcid ID: 0000-0001-5602-7956)

DR KRISTINA ANDERSON-TEIXEIRA (Orcid ID: 0000-0001-8461-9713)

DR DAVID ELLSWORTH (Orcid ID: 0000-0002-9699-2272)

DR SIMONE FATICHI (Orcid ID: 0000-0003-1361-6659)

DR LIANHONG GU (Orcid ID: 0000-0001-5756-8738)

DR COLLEEN M IVERSEN (Orcid ID: 0000-0001-8293-3450)

DR MINGKAI JIANG (Orcid ID: 0000-0002-9982-9518)

DR TREVOR F. KEENAN (Orcid ID: 0000-0002-3347-0258)

MR JÜRGEN KNAUER (Orcid ID: 0000-0002-4947-7067)

DR WILLIAM SMITH (Orcid ID: 0000-0002-5785-6489)

PROF. CHRISTIAN KOERNER (Orcid ID: 0000-0001-7768-7638)

MS MANON ELISA BLEUNIENN SABOT (Orcid ID: 0000-0002-9440-4553)

DR BENTON NEIL TAYLOR (Orcid ID: 0000-0002-9834-9192)

DR CÉSAR TERRER (Orcid ID: 0000-0002-5479-3486)

PROF. ANNA TRUGMAN (Orcid ID: 0000-0002-7903-9711)

Article type : Commissioned Material - Tansley Review

This article has been accepted for publication and undergone full peer review but has not been
through the copyediting, typesetting, pagination and proofreading process, which may lead to
differences between this version and the Version of Record. Please cite this article as doi:
10.1111/NPH.16866

This article is protected by copyright. All rights reserved
Integrating the evidence for a terrestrial carbon sink caused by
increasing atmospheric CO2

Anthony P. Walker<sup>1,*</sup>, Martin G. De Kauwe<sup>2,3,4</sup>, Ana Bastos<sup>5</sup>, Soumaya Belmecheri<sup>6,7</sup>, Katerina Georgiou<sup>8</sup>, Ralph Keeling<sup>9</sup>, Sean M. McMahon<sup>10</sup>, Belinda E. Medlyn<sup>11</sup>, David J. P. Moore<sup>12</sup>, Richard J. Norby<sup>1</sup>, Sönke Zaehle<sup>13</sup>, Kristina J. Anderson-Teixeira<sup>14,15</sup>, Giovanna Battipaglia<sup>16</sup>, Roel J. W. Brienen<sup>17</sup>, Kristine G. Cabugao<sup>1</sup>, Maxime Cailleret<sup>18,19</sup>, Elliott Campbell<sup>20</sup>, Josep Canadell<sup>21</sup>, Philippe Ciais<sup>22</sup>, Matthew E. Craig<sup>1</sup>, David Ellsworth<sup>11</sup>, Graham Farquhar<sup>23</sup>, Simone Fatichi<sup>24,25</sup>, Joshua B. Fisher<sup>26</sup>, David Frank<sup>6</sup>, Heather Graven<sup>27</sup>, Lianhong Gu<sup>1</sup>, Vanessa Haverd<sup>21</sup>, Kelly Heilman<sup>6</sup>, Martin Heimann<sup>13</sup>, Bruce A. Hungate<sup>28</sup>, Colleen M. Iversen<sup>1</sup>, Fortunat Joos<sup>29</sup>, Mingkai Jiang<sup>11</sup>, Trevor F. Keenan<sup>30,31</sup>, Jürgen Knauer<sup>21</sup>, Christian Körner<sup>32</sup>, Victor O. Leshyk<sup>28</sup>, Sebastian Leuzinger<sup>33</sup>, Yao Liu<sup>1</sup>, Natasha MacBean<sup>34</sup>, Yadvinder Malhi<sup>35</sup>, Tim McVicar<sup>36,37</sup>, Josep Penuelas<sup>38,39</sup>, Julia Pongratz<sup>40,41</sup>, A. Shafer Powell<sup>1</sup>, Terhi Riutta<sup>35</sup>, Manon E. B. Sabot<sup>2,3,4</sup>, Juergen Schleucher<sup>42</sup>, Stephen Sitch<sup>43</sup>, William K. Smith<sup>12</sup>, Benjamin Sulman<sup>1</sup>, Benton Taylor<sup>10</sup>, César Terrer<sup>44</sup>, Margaret S. Torn<sup>31</sup>, Kathleen Treseder<sup>45</sup>, Anna T. Trugman<sup>46</sup>, Susan E. Trumbore<sup>13</sup>, Phillip J. van Mantgem<sup>47</sup>, Steve L. Voelker<sup>48</sup>, Mary E. Whelan<sup>49</sup>, Pieter A. Zuidema<sup>50</sup>

This manuscript has been co-authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doi-public-access-plan).

<footnote>* Corresponding author: walkerap@ornl.gov, +1 865 576 9365</footnote>

<footnote n="1">¹ Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA</footnote>
Integrating the evidence for a terrestrial carbon sink caused by
increasing atmospheric CO2

Anthony P. Walker<sup>1,*</sup>, Martin G. De Kauwe<sup>2,3,4</sup>, Ana Bastos<sup>5</sup>, Soumaya Belmecheri<sup>6,7</sup>, Katerina Georgiou<sup>8</sup>, Ralph Keeling<sup>9</sup>, Sean M. McMahon<sup>10</sup>, Belinda E. Medlyn<sup>11</sup>, David J. P. Moore<sup>12</sup>, Richard J. Norby<sup>1</sup>, Sönke Zaehle<sup>13</sup>, Kristina J. Anderson-Teixeira<sup>14,15</sup>, Giovanna Battipaglia<sup>16</sup>, Roel J. W. Brienen<sup>17</sup>, Kristine G. Cabugao<sup>1</sup>, Maxime Cailleret<sup>18,19</sup>, Elliott Campbell<sup>20</sup>, Josep Canadell<sup>21</sup>, Philippe Ciais<sup>22</sup>, Matthew E. Craig<sup>1</sup>, David Ellsworth<sup>11</sup>, Graham Farquhar<sup>23</sup>, Simone Fatichi<sup>24,25</sup>, Joshua B. Fisher<sup>26</sup>, David Frank<sup>6</sup>, Heather Graven<sup>27</sup>, Lianhong Gu<sup>1</sup>, Vanessa Haverd<sup>21</sup>, Kelly Heilman<sup>6</sup>, Martin Heimann<sup>13</sup>, Bruce A. Hungate<sup>28</sup>, Colleen M. Iversen<sup>1</sup>, Fortunat Joos<sup>29</sup>, Mingkai Jiang<sup>11</sup>, Trevor F. Keenan<sup>30,31</sup>, Jürgen Knauer<sup>21</sup>, Christian Körner<sup>32</sup>, Victor O. Leshyk<sup>28</sup>, Sebastian Leuzinger<sup>33</sup>, Yao Liu<sup>1</sup>, Natasha MacBean<sup>34</sup>, Yadvinder Malhi<sup>35</sup>, Tim McVicar<sup>36,37</sup>, Josep Penuelas<sup>38,39</sup>, Julia Pongratz<sup>40,41</sup>, A. Shafer Powell<sup>1</sup>, Terhi Riutta<sup>35</sup>, Manon E. B. Sabot<sup>2,3,4</sup>, Juergen Schleucher<sup>42</sup>, Stephen Sitch<sup>43</sup>, William K. Smith<sup>12</sup>, Benjamin Sulman<sup>1</sup>, Benton Taylor<sup>10</sup>, César Terrer<sup>44</sup>, Margaret S. Torn<sup>31</sup>, Kathleen Treseder<sup>45</sup>, Anna T. Trugman<sup>46</sup>, Susan E. Trumbore<sup>13</sup>, Phillip J. van Mantgem<sup>47</sup>, Steve L. Voelker<sup>48</sup>, Mary E. Whelan<sup>49</sup>, Pieter A. Zuidema<sup>50</sup>

This manuscript has been co-authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doi-public-access-plan).

<footnote>* Corresponding author: walkerap@ornl.gov, +1 865 576 9365</footnote>

<footnote n="1">¹ Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA</footnote>
² ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, NSW 2052, Australia

³ Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia

⁴ Evolution and Ecology Research Centre, University of New South Wales, Sydney, NSW 2052, Australia

⁵ Ludwig Maximilians University of Munich, Luisenstr. 37, 80333, Germany

⁶ University of Arizona, Laboratory of Tree Ring research, 1215 E Lowell St, Tucson, AZ 85721, USA

⁷ US National Science Foundation, 2415 Eisenhower Ave, Alexandria, VA 22314, USA

⁸ Department of Earth System Science, Stanford University, Stanford, CA 94305, USA

⁹ Scripps Institution of Oceanography, UC San Diego, La Jolla, CA 92093, USA

¹⁰ Smithsonian Environmental Research Center, Edgewater, MD 21037, USA

¹¹ Hawkesbury Institute for the Environment, Western Sydney University. Locked Bag 1797 Penrith NSW 2751 Australia

¹² School of Natural Resources and the Environment, 1064 East Lowell Street, Tucson, AZ 85721, USA

¹³ Biogeochemical Integration Department, Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, 07745 Jena, Germany

¹⁴ Conservation Ecology Center, Smithsonian Conservation Biology Institute, MRC 5535, Front Royal, VA, 22630 USA

¹⁵ Center for Tropical Forest Science-Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Balboa, Ancon, Panama, Republic of Panama

¹⁶ Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Università della Campania "L. Vanvitelli", Italy

¹⁷ School of Geography, University of Leeds, Leeds, LS6 9JT, UK

¹⁸ INRAE, Aix-Marseille Université, UMR RECOVER, 3275 route de Cézanne, 13182 Aix-en-Provence Cedex 5, France

¹⁹ Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Zürcherstrasse 111, 8903 Birmensdorf, Switzerland
² ARC Centre of Excellence for Climate Extremes, University of New South Wales, Sydney, NSW 2052, Australia

³ Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia

⁴ Evolution and Ecology Research Centre, University of New South Wales, Sydney, NSW 2052, Australia

⁵ Ludwig Maximilians University of Munich, Luisenstr. 37, 80333, Germany

⁶ University of Arizona, Laboratory of Tree Ring research, 1215 E Lowell St, Tucson, AZ 85721, USA

⁷ US National Science Foundation, 2415 Eisenhower Ave, Alexandria, VA 22314, USA

⁸ Department of Earth System Science, Stanford University, Stanford, CA 94305, USA

⁹ Scripps Institution of Oceanography, UC San Diego, La Jolla, CA 92093, USA

¹⁰ Smithsonian Environmental Research Center, Edgewater, MD 21037, USA

¹¹ Hawkesbury Institute for the Environment, Western Sydney University. Locked Bag 1797 Penrith NSW 2751 Australia

¹² School of Natural Resources and the Environment, 1064 East Lowell Street, Tucson, AZ 85721, USA

¹³ Biogeochemical Integration Department, Max Planck Institute for Biogeochemistry, Hans-Knöll-Str. 10, 07745 Jena, Germany

¹⁴ Conservation Ecology Center, Smithsonian Conservation Biology Institute, MRC 5535, Front Royal, VA, 22630 USA

¹⁵ Center for Tropical Forest Science-Forest Global Earth Observatory, Smithsonian Tropical Research Institute, Balboa, Ancon, Panama, Republic of Panama

¹⁶ Department of Environmental, Biological and Pharmaceutical Sciences and Technologies, Università della Campania "L. Vanvitelli", Italy

¹⁷ School of Geography, University of Leeds, Leeds, LS6 9JT, UK

¹⁸ INRAE, Aix-Marseille Université, UMR RECOVER, 3275 route de Cézanne, 13182 Aix-en-Provence Cedex 5, France

¹⁹ Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Zürcherstrasse 111, 8903 Birmensdorf, Switzerland
²⁰ Department of Geography, University of California Santa Barbara, Santa Barbara, CA, 93106 USA

²¹ CSIRO Oceans and Atmosphere. GPO Box 1700, ACT 2601, Australia

²² Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ,
Université Paris-Saclay, F-91191 Gif-sur-Yvette, France

²³ Plant Sciences, Research School of Biology, The Australian National University, ACT 2601,
Australia

²⁴ Department of Civil and Environmental Engineering, National University of Singapore; 1
Engineering Drive 2, 117576, Singapore

²⁵ Institute of Environmental Engineering, ETH Zurich, Stefano-Francini Platz 5, 8093, Zurich,
Switzerland

²⁶ Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA,
91109, USA

²⁷ Imperial College London, Department of Physics, South Kensington Campus, London SW7 2AZ,
UK

²⁸ Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff AZ 86011, USA

²⁹ Climate and Environmental Physics, Physics Institute and Oeschger Centre for Climate Change
Research, University of Bern, Sidlerstr. 5, CH-3012 Bern, Switzerland

³⁰ Department of Environmental Science, Policy and Management, UC Berkeley, Berkeley CA
94720, USA

³¹ Earth and Environmental Sciences Area, Lawrence Berkeley National Lab., Berkeley CA 94720,
USA

³² Department of Environmental Sciences, Botany, University of Basel, 4056 Basel, Switzerland

³³ Institute for Applied Ecology, Auckland University of Technology, Auckland, New Zealand

³⁴ Department of Geography, Indiana University, Bloomington, IN 47405, USA

³⁵ School of Geography and the Environment, University of Oxford, Oxford, OX1 3QY, UK

³⁶ CSIRO Land and Water, Black Mountain, Canberra, Australia

³⁷ Australian Research Council Centre of Excellence for Climate Extremes, Canberra, Australia

³⁸ CSIC, Global Ecology CREAF-CSIC-UAB, Bellaterra, 08193 Barcelona, Catalonia, Spain
²⁰ Department of Geography, University of California Santa Barbara, Santa Barbara, CA, 93106 USA

²¹ CSIRO Oceans and Atmosphere. GPO Box 1700, ACT 2601, Australia

²² Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ,
Université Paris-Saclay, F-91191 Gif-sur-Yvette, France

²³ Plant Sciences, Research School of Biology, The Australian National University, ACT 2601,
Australia

²⁴ Department of Civil and Environmental Engineering, National University of Singapore; 1
Engineering Drive 2, 117576, Singapore

²⁵ Institute of Environmental Engineering, ETH Zurich, Stefano-Francini Platz 5, 8093, Zurich,
Switzerland

²⁶ Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA,
91109, USA

²⁷ Imperial College London, Department of Physics, South Kensington Campus, London SW7 2AZ,
UK

²⁸ Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff AZ 86011, USA

²⁹ Climate and Environmental Physics, Physics Institute and Oeschger Centre for Climate Change
Research, University of Bern, Sidlerstr. 5, CH-3012 Bern, Switzerland

³⁰ Department of Environmental Science, Policy and Management, UC Berkeley, Berkeley CA
94720, USA

³¹ Earth and Environmental Sciences Area, Lawrence Berkeley National Lab., Berkeley CA 94720,
USA

³² Department of Environmental Sciences, Botany, University of Basel, 4056 Basel, Switzerland

³³ Institute for Applied Ecology, Auckland University of Technology, Auckland, New Zealand

³⁴ Department of Geography, Indiana University, Bloomington, IN 47405, USA

³⁵ School of Geography and the Environment, University of Oxford, Oxford, OX1 3QY, UK

³⁶ CSIRO Land and Water, Black Mountain, Canberra, Australia

³⁷ Australian Research Council Centre of Excellence for Climate Extremes, Canberra, Australia

³⁸ CSIC, Global Ecology CREAF-CSIC-UAB, Bellaterra, 08193 Barcelona, Catalonia, Spain
<sup>39</sup> CREAF, Cerdanyola del Vallès, 08193 Barcelona, Catalonia, Spain

<sup>40</sup> Ludwig Maximilians University of Munich, Luisenstr. 37, 80333, Germany

<sup>41</sup> Max Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, Germany

<sup>42</sup> Department of Medical Biochemistry & Biophysics, Umeå University, 901 87 Umea, Sweden

<sup>43</sup> College of Life and Environmental Sciences, Laver Building, University of Exeter, Exeter, EX4 4QF, UK

<sup>44</sup> Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, USA

<sup>45</sup> Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, CA 92697, USA

<sup>46</sup> Department of Geography, 1832 Ellison Hall, Santa Barbara, CA 93016

<sup>47</sup> U.S. Geological Survey, Western Ecological Research Center, Arcata, CA 95521, USA

<sup>48</sup> Department of Environmental and Forest Biology, State University of New York College of Environmental Science and Forestry, Syracuse, NY 13210, USA

<sup>49</sup> Department of Environmental Sciences, Rutgers University, 14 College Farm Road, New Brunswick, NJ 08901, USA

<sup>50</sup> Forest Ecology and Forest Management group, Wageningen University, PO Box 47, 6700 AA Wageningen, The Netherlands

Received: 17 March 2020

Accepted: 6 July 2020

# Contents

Summary

I. Introduction

II. Theory – a hierarchy of mechanism

III. The evidence
<sup>39</sup> CREAF, Cerdanyola del Vallès, 08193 Barcelona, Catalonia, Spain

<sup>40</sup> Ludwig Maximilians University of Munich, Luisenstr. 37, 80333, Germany

<sup>41</sup> Max Planck Institute for Meteorology, Bundesstr. 53, 20146 Hamburg, Germany

<sup>42</sup> Department of Medical Biochemistry & Biophysics, Umeå University, 901 87 Umea, Sweden

<sup>43</sup> College of Life and Environmental Sciences, Laver Building, University of Exeter, Exeter, EX4 4QF, UK

<sup>44</sup> Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, CA, USA

<sup>45</sup> Department of Ecology and Evolutionary Biology, University of California Irvine, Irvine, CA 92697, USA

<sup>46</sup> Department of Geography, 1832 Ellison Hall, Santa Barbara, CA 93016

<sup>47</sup> U.S. Geological Survey, Western Ecological Research Center, Arcata, CA 95521, USA

<sup>48</sup> Department of Environmental and Forest Biology, State University of New York College of Environmental Science and Forestry, Syracuse, NY 13210, USA

<sup>49</sup> Department of Environmental Sciences, Rutgers University, 14 College Farm Road, New Brunswick, NJ 08901, USA

<sup>50</sup> Forest Ecology and Forest Management group, Wageningen University, PO Box 47, 6700 AA Wageningen, The Netherlands

Received: 17 March 2020

Accepted: 6 July 2020

# Contents

Summary

I. Introduction

II. Theory – a hierarchy of mechanism

III. The evidence
IV. Synthesis

V. Conclusions

Acknowledgements

Author contributions

References
IV. Synthesis

V. Conclusions

Acknowledgements

Author contributions

References
## Summary

Atmospheric carbon dioxide concentration ([CO₂]) is increasing, which increases leaf-scale photosynthesis and intrinsic water-use efficiency. These direct responses have the potential to increase plant growth, vegetation biomass, and soil organic matter; transferring carbon from the atmosphere into terrestrial ecosystems (a carbon sink). A substantial global terrestrial carbon sink would slow the rate of [CO₂] increase and thus climate change. However, ecosystem CO₂-responses are complex or confounded by concurrent changes in multiple agents of global change and evidence for a [CO₂]-driven terrestrial carbon sink can appear contradictory. Here we synthesise theory and broad, multi-disciplinary evidence for the effects of increasing [CO₂] (iCO₂) on the global terrestrial carbon sink. Evidence suggests a substantial increase in global photosynthesis since pre-industry. Established theory, supported by experiments, indicates that iCO₂ is likely responsible for about half of the increase. Global carbon budgeting, atmospheric data, and forest inventories indicate a historical carbon sink, and these apparent iCO₂-responses are high in comparison with experiments and theory. Plant mortality and soil carbon iCO₂-responses are highly uncertain. In conclusion, a range of evidence supports a positive terrestrial carbon sink in response to iCO₂, albeit with uncertain magnitude and strong suggestion of a role for additional agents of global change.

**Key words:** global carbon-cycle, land-atmosphere feedback, carbon dioxide, terrestrial ecosystems, free-air CO₂ enrichment (FACE), CO₂-fertilisation hypothesis, CO₂-fertilization, beta factor

## ORCID:

<table><tr><td>Kristina J.</td><td><b>Anderson-Teixeira</b></td><td>0000-0001-8461-9713</td></tr><tr><td>Ana</td><td><b>Bastos</b></td><td>0000-0002-7368-7806</td></tr><tr><td>Giovanna</td><td><b>Battipaglia</b></td><td>0000-0003-1741-3509</td></tr><tr><td>Soumaya</td><td><b>Belmecheri</b></td><td>0000-0003-1258-2741</td></tr><tr><td>Roel J.W.</td><td><b>Brienen</b></td><td>0000-0002-5397-5755</td></tr><tr><td>Maxime</td><td><b>Cailleret</b></td><td>0000-0001-6561-1943</td></tr><tr><td>Philippe</td><td><b>Ciais</b></td><td>0000-0001-8560-4943</td></tr></table>
# Summary

Atmospheric carbon dioxide concentration ([CO₂]) is increasing, which increases leaf-scale photosynthesis and intrinsic water-use efficiency. These direct responses have the potential to increase plant growth, vegetation biomass, and soil organic matter; transferring carbon from the atmosphere into terrestrial ecosystems (a carbon sink). A substantial global terrestrial carbon sink would slow the rate of [CO₂] increase and thus climate change. However, ecosystem CO₂-responses are complex or confounded by concurrent changes in multiple agents of global change and evidence for a [CO₂]-driven terrestrial carbon sink can appear contradictory. Here we synthesise theory and broad, multi-disciplinary evidence for the effects of increasing [CO₂] (iCO₂) on the global terrestrial carbon sink. Evidence suggests a substantial increase in global photosynthesis since pre-industry. Established theory, supported by experiments, indicates that iCO₂ is likely responsible for about half of the increase. Global carbon budgeting, atmospheric data, and forest inventories indicate a historical carbon sink, and these apparent iCO₂-responses are high in comparison with experiments and theory. Plant mortality and soil carbon iCO₂-responses are highly uncertain. In conclusion, a range of evidence supports a positive terrestrial carbon sink in response to iCO₂, albeit with uncertain magnitude and strong suggestion of a role for additional agents of global change.

**Key words:** global carbon-cycle, land-atmosphere feedback, carbon dioxide, terrestrial ecosystems, free-air CO₂ enrichment (FACE), CO₂-fertilisation hypothesis, CO₂-fertilization, beta factor

## ORCID:

<table><tr><td>Kristina J.</td><td><b>Anderson-Teixeira</b></td><td>0000-0001-8461-9713</td></tr><tr><td>Ana</td><td><b>Bastos</b></td><td>0000-0002-7368-7806</td></tr><tr><td>Giovanna</td><td><b>Battipaglia</b></td><td>0000-0003-1741-3509</td></tr><tr><td>Soumaya</td><td><b>Belmecheri</b></td><td>0000-0003-1258-2741</td></tr><tr><td>Roel J.W.</td><td><b>Brienen</b></td><td>0000-0002-5397-5755</td></tr><tr><td>Maxime</td><td><b>Cailleret</b></td><td>0000-0001-6561-1943</td></tr><tr><td>Philippe</td><td><b>Ciais</b></td><td>0000-0001-8560-4943</td></tr></table>
<table><tr><td>Matthew</td><td>E.</td><td>Craig</td><td>0000-0002-8890-7920</td></tr><tr><td>Martin</td><td>G.</td><td>De Kauwe</td><td>0000-0002-3399-9098</td></tr><tr><td>David</td><td></td><td>Ellsworth</td><td>0000-0002-9699-2272</td></tr><tr><td>Graham</td><td></td><td>Farquhar</td><td>0000-0002-7065-1971</td></tr><tr><td>Simone</td><td></td><td>Fatichi</td><td>0000-0003-1361-6659</td></tr><tr><td>Joshua</td><td>B.</td><td>Fisher</td><td>0000-0003-4734-9085</td></tr><tr><td>Heather</td><td></td><td>Graven</td><td>0000-0003-3934-2502</td></tr><tr><td>Vanessa</td><td></td><td>Haverd</td><td>0000-0003-4359-5895</td></tr><tr><td>Kelly</td><td></td><td>Heilman</td><td>0000-0001-5932-1317</td></tr><tr><td>Martin</td><td></td><td>Heimann</td><td>0000-0001-6296-5113</td></tr><tr><td>Bruce</td><td>A</td><td>Hungate</td><td>0000-0002-7337-1887</td></tr><tr><td>Colleen</td><td></td><td>Iversen</td><td>0000-0001-8293-3450</td></tr><tr><td>Fortunat</td><td></td><td>Joos</td><td>0000-0002-9483-6030</td></tr><tr><td>Mingkai</td><td></td><td>Jiang</td><td>0000-0002-9982-9518</td></tr><tr><td>Trevor</td><td>F.</td><td>Keenan</td><td>0000-0002-3347-0258</td></tr><tr><td>Jürgen</td><td></td><td>Knauer</td><td>0000-0002-4947-7067</td></tr><tr><td>William</td><td>K</td><td>Smith</td><td>0000-0002-5785-6489</td></tr><tr><td>Christian</td><td></td><td>Körner</td><td>0000-0001-7768-7638</td></tr><tr><td>Sebastian</td><td></td><td>Leuzinger</td><td>0000-0001-9306-5281</td></tr><tr><td>Yao</td><td></td><td>Liu</td><td>0000-0003-2783-3291</td></tr><tr><td>Natasha</td><td></td><td>MacBean</td><td>0000-0001-6797-4836</td></tr><tr><td>Sean</td><td></td><td>McMahon</td><td>0000-0001-8302-6908</td></tr><tr><td>Tim</td><td></td><td>McVicar</td><td>0000-0002-0877-8285</td></tr><tr><td>Belinda</td><td></td><td>Medlyn</td><td>0000-0001-5728-9827</td></tr><tr><td>David</td><td>J.P.</td><td>Moore</td><td>0000-0002-6462-3288</td></tr><tr><td>Richard</td><td>J.</td><td>Norby</td><td>0000-0002-0238-9828</td></tr></table>
<table><tr><td>Matthew</td><td>E.</td><td>Craig</td><td>0000-0002-8890-7920</td></tr><tr><td>Martin</td><td>G.</td><td>De Kauwe</td><td>0000-0002-3399-9098</td></tr><tr><td>David</td><td></td><td>Ellsworth</td><td>0000-0002-9699-2272</td></tr><tr><td>Graham</td><td></td><td>Farquhar</td><td>0000-0002-7065-1971</td></tr><tr><td>Simone</td><td></td><td>Fatichi</td><td>0000-0003-1361-6659</td></tr><tr><td>Joshua</td><td>B.</td><td>Fisher</td><td>0000-0003-4734-9085</td></tr><tr><td>Heather</td><td></td><td>Graven</td><td>0000-0003-3934-2502</td></tr><tr><td>Vanessa</td><td></td><td>Haverd</td><td>0000-0003-4359-5895</td></tr><tr><td>Kelly</td><td></td><td>Heilman</td><td>0000-0001-5932-1317</td></tr><tr><td>Martin</td><td></td><td>Heimann</td><td>0000-0001-6296-5113</td></tr><tr><td>Bruce</td><td>A</td><td>Hungate</td><td>0000-0002-7337-1887</td></tr><tr><td>Colleen</td><td></td><td>Iversen</td><td>0000-0001-8293-3450</td></tr><tr><td>Fortunat</td><td></td><td>Joos</td><td>0000-0002-9483-6030</td></tr><tr><td>Mingkai</td><td></td><td>Jiang</td><td>0000-0002-9982-9518</td></tr><tr><td>Trevor</td><td>F.</td><td>Keenan</td><td>0000-0002-3347-0258</td></tr><tr><td>Jürgen</td><td></td><td>Knauer</td><td>0000-0002-4947-7067</td></tr><tr><td>William</td><td>K</td><td>Smith</td><td>0000-0002-5785-6489</td></tr><tr><td>Christian</td><td></td><td>Körner</td><td>0000-0001-7768-7638</td></tr><tr><td>Sebastian</td><td></td><td>Leuzinger</td><td>0000-0001-9306-5281</td></tr><tr><td>Yao</td><td></td><td>Liu</td><td>0000-0003-2783-3291</td></tr><tr><td>Natasha</td><td></td><td>MacBean</td><td>0000-0001-6797-4836</td></tr><tr><td>Sean</td><td></td><td>McMahon</td><td>0000-0001-8302-6908</td></tr><tr><td>Tim</td><td></td><td>McVicar</td><td>0000-0002-0877-8285</td></tr><tr><td>Belinda</td><td></td><td>Medlyn</td><td>0000-0001-5728-9827</td></tr><tr><td>David</td><td>J.P.</td><td>Moore</td><td>0000-0002-6462-3288</td></tr><tr><td>Richard</td><td>J.</td><td>Norby</td><td>0000-0002-0238-9828</td></tr></table>
<table>
  <tr>
    <td>Josep</td>
    <td></td>
    <td><b>Penuelas</b></td>
    <td>0000-0002-7215-0150</td>
  </tr>
  <tr>
    <td>Julia</td>
    <td></td>
    <td><b>Pongratz</b></td>
    <td>0000-0003-0372-3960</td>
  </tr>
  <tr>
    <td>A.</td>
    <td>Shafer</td>
    <td><b>Powell</b></td>
    <td>0000-0002-9622-0061</td>
  </tr>
  <tr>
    <td>Manon</td>
    <td>E. B.</td>
    <td><b>Sabot</b></td>
    <td>0000-0002-9440-4553</td>
  </tr>
  <tr>
    <td>Juergen</td>
    <td></td>
    <td><b>Schleucher</b></td>
    <td>0000-0002-4815-3466</td>
  </tr>
  <tr>
    <td>Stephen</td>
    <td></td>
    <td><b>Sitch</b></td>
    <td>0000-0003-1821-8561</td>
  </tr>
  <tr>
    <td>Ben</td>
    <td></td>
    <td><b>Sulman</b></td>
    <td>0000-0002-3265-6691</td>
  </tr>
  <tr>
    <td>Benton</td>
    <td></td>
    <td><b>Taylor</b></td>
    <td>0000-0002-9834-9192</td>
  </tr>
  <tr>
    <td>César</td>
    <td></td>
    <td><b>Terrer</b></td>
    <td>0000-0002-5479-3486</td>
  </tr>
  <tr>
    <td>S.</td>
    <td>Margaret</td>
    <td><b>Torn</b></td>
    <td>0000-0002-8174-0099</td>
  </tr>
  <tr>
    <td>Kathleen</td>
    <td></td>
    <td><b>Treseder</b></td>
    <td>0000-0003-2847-6935</td>
  </tr>
  <tr>
    <td>Anna</td>
    <td>T.</td>
    <td><b>Trugman</b></td>
    <td>0000-0002-7903-9711</td>
  </tr>
  <tr>
    <td>Sue</td>
    <td>E.</td>
    <td><b>Trumbore</b></td>
    <td>0000-0003-3885-6202</td>
  </tr>
  <tr>
    <td>Phillip</td>
    <td>J.</td>
    <td><b>van Mantgem</b></td>
    <td>0000-0002-3068-9422</td>
  </tr>
  <tr>
    <td>Steven</td>
    <td>L.</td>
    <td><b>Voelker</b></td>
    <td>0000-0002-0110-3381</td>
  </tr>
  <tr>
    <td>Anthony</td>
    <td>P.</td>
    <td><b>Walker</b></td>
    <td>0000-0003-0557-5594</td>
  </tr>
  <tr>
    <td>Mary</td>
    <td>E.</td>
    <td><b>Whelan</b></td>
    <td>0000-0002-2067-1835</td>
  </tr>
  <tr>
    <td>Sönke</td>
    <td></td>
    <td><b>Zaehle</b></td>
    <td>0000-0001-5602-7956</td>
  </tr>
  <tr>
    <td>Pieter</td>
    <td>A.</td>
    <td><b>Zuidema</b></td>
    <td>0000-0001-8100-1168</td>
  </tr>
</table>
Josep

Penuelas

0000-0002-7215-0150

Julia

Pongratz

0000-0003-0372-3960

A.

Shafer

Powell

0000-0002-9622-0061

Manon

E. B.

Sabot

0000-0002-9440-4553

Juergen

Schleucher

0000-0002-4815-3466

Stephen

Sitch

0000-0003-1821-8561

Ben

Sulman

0000-0002-3265-6691

Benton

Taylor

0000-0002-9834-9192

César

Terrer

0000-0002-5479-3486

S.

Margaret

Torn

0000-0002-8174-0099

Kathleen

Treseder

0000-0003-2847-6935

Anna

T.

Trugman

0000-0002-7903-9711

Sue

E.

Trumbore

0000-0003-3885-6202

Phillip

J.

van Mantgem

0000-0002-3068-9422

Steven

L.

Voelker

0000-0002-0110-3381

Anthony

P.

Walker

0000-0003-0557-5594

Mary

E.

Whelan

0000-0002-2067-1835

Sönke

Zaehle

0000-0001-5602-7956

Pieter

A.

Zuidema

0000-0001-8100-1168
# I. Introduction

Photosynthesis uses the energy in sunlight to bind CO₂ to a five-carbon sugar, transferring CO₂ from the atmosphere to plants (Calvin & Benson, 1948; Farquhar *et al.*, 1980). Sugars produced by photosynthesis provide the building blocks and the primary fuel for much of life on Earth. Plant tissues, many microbes, animals, and dead organic matter are all composed of carbon-rich compounds formed from these photosynthetic sugars. In many environments, an increase in atmospheric CO₂ concentration [CO₂] increases photosynthesis. Thus an increase in [CO₂] leads to greater plant sugar availability with the potential to increase the total amount of carbon stored in the live and dead organic matter in an ecosystem. These observations have led to the CO₂-fertilisation hypothesis (Box 1): that plant responses to increasing atmospheric [CO₂] drive increases in terrestrial-ecosystem carbon storage creating negative feedback on atmospheric [CO₂] growth.

Since the industrial revolution human activities have increased [CO₂] by 48 % (1760-2019, 277-411 ppm), an increase in atmospheric CO₂-carbon of 277 Pg C (Friedlingstein *et al.*, 2019). However, global-scale carbon accounting quantifies anthropogenic emissions to the atmosphere at 645 Pg C and suggests a substantial 'natural' terrestrial carbon sink (a net flux of carbon from the atmosphere to intact terrestrial ecosystems) which currently removes the equivalent of 33±9 % of anthropogenic atmospheric CO₂ (2009-2018 (Friedlingstein *et al.*, 2019). Along with the ocean carbon sink, this terrestrial carbon sink is mitigating the rate of climate change. Process-based carbon-cycle models attribute increasing [CO₂] (iCO₂, Table 1) as the primary driver of the terrestrial carbon sink, albeit with substantial uncertainty (Huntzinger *et al.*, 2017; Arora *et al.*, 2019). However, iCO₂ is not the only global-change factor that can influence terrestrial carbon stocks. Anthropogenic land-use and land-cover change (hereafter land-use change) and recovery (Pugh et al. 2019), nitrogen cycle changes (Fowler *et al.*, 2013), and climate change all affect ecosystem carbon stocks (Keenan & Williams, 2018). A vast and overwhelming literature often disagrees on the size and duration of CO₂-driven increases in terrestrial carbon storage and predictive understanding of this process is a long-standing and unresolved scientific goal.

Predictive understanding of how terrestrial ecosystems respond to iCO2 requires knowledge of a range of processes, their interactions, and how these processes scale. For example, terrestrial ecosystem responses begin with photosynthesis inside the leaf, yet scale to have long-term global impacts. All the relevant processes must be understood across scales, and ultimately at the global scale because iCO2 and climate change are global-scale phenomena with decadal to centennial
# I. Introduction

Photosynthesis uses the energy in sunlight to bind CO₂ to a five-carbon sugar, transferring CO₂ from the atmosphere to plants (Calvin & Benson, 1948; Farquhar *et al.*, 1980). Sugars produced by photosynthesis provide the building blocks and the primary fuel for much of life on Earth. Plant tissues, many microbes, animals, and dead organic matter are all composed of carbon-rich compounds formed from these photosynthetic sugars. In many environments, an increase in atmospheric CO₂ concentration [CO₂] increases photosynthesis. Thus an increase in [CO₂] leads to greater plant sugar availability with the potential to increase the total amount of carbon stored in the live and dead organic matter in an ecosystem. These observations have led to the CO₂-fertilisation hypothesis (Box 1): that plant responses to increasing atmospheric [CO₂] drive increases in terrestrial-ecosystem carbon storage creating negative feedback on atmospheric [CO₂] growth.

Since the industrial revolution human activities have increased [CO₂] by 48 % (1760-2019, 277-411 ppm), an increase in atmospheric CO₂-carbon of 277 Pg C (Friedlingstein *et al.*, 2019). However, global-scale carbon accounting quantifies anthropogenic emissions to the atmosphere at 645 Pg C and suggests a substantial ‘natural’ terrestrial carbon sink (a net flux of carbon from the atmosphere to intact terrestrial ecosystems) which currently removes the equivalent of 33±9 % of anthropogenic atmospheric CO₂ (2009-2018 (Friedlingstein *et al.*, 2019). Along with the ocean carbon sink, this terrestrial carbon sink is mitigating the rate of climate change. Process-based carbon-cycle models attribute increasing [CO₂] (iCO₂, Table 1) as the primary driver of the terrestrial carbon sink, albeit with substantial uncertainty (Huntzinger *et al.*, 2017; Arora *et al.*, 2019). However, iCO₂ is not the only global-change factor that can influence terrestrial carbon stocks. Anthropogenic land-use and land-cover change (hereafter land-use change) and recovery (Pugh et al. 2019), nitrogen cycle changes (Fowler *et al.*, 2013), and climate change all affect ecosystem carbon stocks (Keenan & Williams, 2018). A vast and overwhelming literature often disagrees on the size and duration of CO₂-driven increases in terrestrial carbon storage and predictive understanding of this process is a long-standing and unresolved scientific goal.

Predictive understanding of how terrestrial ecosystems respond to iCO2 requires knowledge of a range of processes, their interactions, and how these processes scale. For example, terrestrial ecosystem responses begin with photosynthesis inside the leaf, yet scale to have long-term global impacts. All the relevant processes must be understood across scales, and ultimately at the global scale because iCO2 and climate change are global-scale phenomena with decadal to centennial
dynamics.

Given that around 50 % of plant biomass is carbon acquired via photosynthesis, it is reasonable to assume that increased photosynthesis increases plant biomass production (BP) and experimentally elevated [CO₂] (eCO₂) commonly increases BP (e.g. Baig *et al.*, 2015). However, in natural ecosystems iCO₂ may not always increase BP, primarily because plant tissues require nutrients, and BP responses to iCO₂ will interact with soil nutrient availability and other limiting factors (Strain & Bazzaz, 1983; Rastetter *et al.*, 1997). A related argument is that present day [CO₂] is likely to supply plants with unprecedented carbon availability that may be surplus to BP requirements (Körner, 2003a). This is because for at least one million years prior to the industrial revolution [CO₂] was much lower (170-300 ppm) (Bereiter *et al.*, 2015).

Ecosystem carbon stocks are the result of both inputs (BP for plants or litter production for soils) and outputs. Thus for the CO₂-fertilisation hypothesis to hold true, the residence time of carbon in an ecosystem must not be reduced by an amount that would negate effects of increased BP on terrestrial carbon pools. However, it has been suggested that both vegetation and soil carbon residence times may be reduced by iCO₂ (van Groenigen *et al.*, 2014; Körner, 2017).

Drawing from multiple disciplines, vast quantities of diverse data have been collected on the [CO₂]-responses of many processes. Often this evidence can appear conflicting. For example, many Free Air CO₂ Enrichment (FACE) experiments show BP gains (Walker *et al.*, 2019), while others show none (Bader *et al.*, 2013; Ellsworth *et al.*, 2017). Many tree-ring studies indicate historical increases in intrinsic water-use efficiency (iWUE) but no detectable change in BP (Peñuelas *et al.*, 2011; van der Sleen *et al.*, 2015), while the majority of forest-inventory analyses suggest biomass gains (Brienen *et al.*, 2015; Hubau *et al.*, 2020). Flux-tower data, global CO₂-flask networks, and remote-sensing data are now of sufficient timescales (decades) to study CO₂ responses against background variability, but have led to different inferences (Kolby Smith *et al.*, 2016; Fernández-Martínez *et al.*, 2017).

This literature represents a wealth of information and inference that can appear fragmented, posing an opportunity for integration. Thus our overall goal is to provide a synthetic review of key lines of evidence related to the CO₂-fertilisation hypothesis, specifically:

1. overview of theory and potential mechanisms within the CO₂-fertilisation hypothesis;

2. quantitative evaluation of the evidence, identifying agreement and major conflicts;

3. resolution of apparent conflicts and, where not possible, identification of key knowledge gaps
dynamics.

Given that around 50 % of plant biomass is carbon acquired via photosynthesis, it is reasonable to assume that increased photosynthesis increases plant biomass production (BP) and experimentally elevated [CO₂] (eCO₂) commonly increases BP (e.g. Baig *et al.*, 2015). However, in natural ecosystems iCO₂ may not always increase BP, primarily because plant tissues require nutrients, and BP responses to iCO₂ will interact with soil nutrient availability and other limiting factors (Strain & Bazzaz, 1983; Rastetter *et al.*, 1997). A related argument is that present day [CO₂] is likely to supply plants with unprecedented carbon availability that may be surplus to BP requirements (Körner, 2003a). This is because for at least one million years prior to the industrial revolution [CO₂] was much lower (170-300 ppm) (Bereiter *et al.*, 2015).

Ecosystem carbon stocks are the result of both inputs (BP for plants or litter production for soils) and outputs. Thus for the CO₂-fertilisation hypothesis to hold true, the residence time of carbon in an ecosystem must not be reduced by an amount that would negate effects of increased BP on terrestrial carbon pools. However, it has been suggested that both vegetation and soil carbon residence times may be reduced by iCO₂ (van Groenigen *et al.*, 2014; Körner, 2017).

Drawing from multiple disciplines, vast quantities of diverse data have been collected on the [CO₂]-responses of many processes. Often this evidence can appear conflicting. For example, many Free Air CO₂ Enrichment (FACE) experiments show BP gains (Walker *et al.*, 2019), while others show none (Bader *et al.*, 2013; Ellsworth *et al.*, 2017). Many tree-ring studies indicate historical increases in intrinsic water-use efficiency (iWUE) but no detectable change in BP (Peñuelas *et al.*, 2011; van der Sleen *et al.*, 2015), while the majority of forest-inventory analyses suggest biomass gains (Brienen *et al.*, 2015; Hubau *et al.*, 2020). Flux-tower data, global CO₂-flask networks, and remote-sensing data are now of sufficient timescales (decades) to study CO₂ responses against background variability, but have led to different inferences (Kolby Smith *et al.*, 2016; Fernández-Martínez *et al.*, 2017).

This literature represents a wealth of information and inference that can appear fragmented, posing an opportunity for integration. Thus our overall goal is to provide a synthetic review of key lines of evidence related to the CO₂-fertilisation hypothesis, specifically:

1. overview of theory and potential mechanisms within the CO₂-fertilisation hypothesis;

2. quantitative evaluation of the evidence, identifying agreement and major conflicts;

3. resolution of apparent conflicts and, where not possible, identification of key knowledge gaps
to guide future studies.

We structure this multi-disciplinary review within the mechanistic theory of the five broad processes that are key to the CO₂-fertilisation hypothesis (Box 1, Fig. 1a): gross primary production (GPP), plant biomass production (BP), vegetation mortality rate (k<sub>veg</sub>), soil organic matter decomposition rate (k<sub>soil</sub>), and terrestrial carbon storage (C<sub>eco</sub>). Within each of these high-level processes, numerous inter-related mechanisms and sub-processes shape terrestrial ecosystem CO₂ responses (Fig. 1b, Section II).

Within these processes we integrate four primary evidence themes (Box 2). eCO₂ studies in evidence theme 1 provide the only direct evidence for CO₂ responses but are restricted in space and time. Observation studies (evidence themes 2-4) span a broader range of evidence types covering larger spatial-scales and longer temporal-scales but provide only indirect evidence for the effect of iCO₂ on terrestrial ecosystems.

To quantify and standardise CO₂ effects across variables and varying ranges of [CO₂] we report data as a relativised β-factor:

$$ \beta = \ln(y_e/y_a) / \ln(CO_{2,e}/CO_{2,a}) \qquad (\text{Eq. 1}) $$

where y_a and y_e are the value of any response variable at lower [CO₂] (CO₂_a) and higher [CO₂] (CO₂_e). Other methods to calculate the β-factor have been proposed (e.g. Friedlingstein et al., 1995) but we use Eq. 1 for ease of interpretation that results from scale-independence (Supporting Information Notes S1, Fig. S1). A β of 1 represents direct proportionality between a variable's CO₂ response and the change in CO₂. Where possible (i.e. when reported at source) we report uncertainties as 95 % confidence intervals.

As described above, attributing iCO₂ as the cause of trends is confounded by covarying factors which also drive variability in the terrestrial carbon sink. We discuss these other global-change factors in the context of attribution, but do not cover them in depth. The difference between direct evidence from eCO₂ experiments and indirect evidence from historical data (concurrent with a suite of global-change factors) motivates our use of two abbreviations: eCO₂ and iCO₂. As with eCO₂ and iCO₂, we distinguish direct CO₂ responses (β<sub>dir</sub>) from indirect apparent CO₂ 'responses' (β<sub>app</sub>).
to guide future studies.

We structure this multi-disciplinary review within the mechanistic theory of the five broad processes that are key to the CO₂-fertilisation hypothesis (Box 1, Fig. 1a): gross primary production (GPP), plant biomass production (BP), vegetation mortality rate ($k_{veg}$), soil organic matter decomposition rate ($k_{soil}$), and terrestrial carbon storage ($C_{eco}$). Within each of these high-level processes, numerous inter-related mechanisms and sub-processes shape terrestrial ecosystem CO₂ responses (Fig. 1b, Section II).

Within these processes we integrate four primary evidence themes (Box 2). eCO₂ studies in evidence theme 1 provide the only direct evidence for CO₂ responses but are restricted in space and time. Observation studies (evidence themes 2-4) span a broader range of evidence types covering larger spatial-scales and longer temporal-scales but provide only indirect evidence for the effect of iCO₂ on terrestrial ecosystems.

To quantify and standardise CO₂ effects across variables and varying ranges of [CO₂] we report data as a relativised β-factor:

$$ \beta = \ln(y_e/y_a) / \ln(CO_{2,e}/CO_{2,a}) \qquad (\text{Eq. 1}) $$

where $y_a$ and $y_e$ are the value of any response variable at lower [CO₂] (CO₂,a) and higher [CO₂] (CO₂,e). Other methods to calculate the β-factor have been proposed (e.g. Friedlingstein et al., 1995) but we use Eq. 1 for ease of interpretation that results from scale-independence (Supporting Information Notes S1, Fig. S1). A β of 1 represents direct proportionality between a variable's CO₂ response and the change in CO₂. Where possible (i.e. when reported at source) we report uncertainties as 95 % confidence intervals.

As described above, attributing iCO₂ as the cause of trends is confounded by covarying factors which also drive variability in the terrestrial carbon sink. We discuss these other global-change factors in the context of attribution, but do not cover them in depth. The difference between direct evidence from eCO₂ experiments and indirect evidence from historical data (concurrent with a suite of global-change factors) motivates our use of two abbreviations: eCO₂ and iCO₂. As with eCO₂ and iCO₂, we distinguish direct CO₂ responses ($\beta_{dir}$) from indirect apparent CO₂ 'responses' ($\beta_{app}$).
## II. Theory – a hierarchy of mechanism

### 1 Direct plant physiological responses to CO₂

Photosynthesis is limited by CO₂ or light (Farquhar *et al.*, 1980). When CO₂ is limiting, theory predicts that eCO₂ increases leaf-scale net carbon assimilation ($A_{net}$) ($\beta_{dir,hist}$ = 0.86, Table S1). The enzyme that fixes CO₂ (RuBisCO) also catalyses an oxygenation reaction, which results in CO₂ loss (photorespiration; Farquhar *et al.*, 1980). eCO₂ also suppresses photorespiration (Fig. 2a). Given that photorespiration always occurs during C3 photosynthesis, the suppression of photorespiration by eCO₂ increases $A_{net}$ also when light is limiting, but with a lower response ($\beta_{dir,hist}$ = 0.31). Canopy-scale $A_{net}$ results from a mixture of CO₂ and light-limited photosynthesis, thus has an intermediate eCO₂ response that depends on the fraction of light-saturated leaves in the canopy ($\beta_{dir,hist}$ = 0.60±0.3, c. 280 to 400 ppm; Fig 2c). As [CO₂] increases, the fraction of light-saturated leaves in the canopy is expected to decrease and therefore historical eCO₂ response of GPP is expected to be higher than the future response ($\beta_{dir,fut}$ = 0.46±0.2, c. 400 to 550 ppm; Fig 2c).

C4 plants have evolved to concentrate carbon, thus saturating photosynthesis and suppressing photorespiration at low [CO₂] (Ehleringer & Björkman, 1977). Therefore $A_{net}$ in C4 plants is not directly influenced by [CO₂] above c. 200 ppm (Fig. 2a), though water savings from reduced $g_s$ may stimulate $A_{net}$ indirectly (Leakey *et al.*, 2004).

Photosynthesis requires the acquisition of other resources and eCO₂-stimulation of $A_{net}$ increases $A_{net}$ per unit resource consumption, i.e. increases resource use-efficiencies (UE) of: water (WUE), light (LUE), and leaf nitrogen (Cowan, 1982; Drake *et al.*, 1997). Increased use-efficiencies imply a shift in a plant's resource-use economy (Bloom *et al.*, 1985) which is commonly studied using optimisation theory.

Optimisation theory predicts that a change in the ratio of $A_{net}$ to $g_s$ (intrinsic WUE, iWUE) in proportion to the change in [CO₂] ($\beta_{dir}$ c. 1; Fig 2d) maximises the benefit of carbon gain while minimising the cost of water lost for C3 (Medlyn *et al.*, 2011) and C4 plants (Lin *et al.*, 2015). Canopy-scaling theory predicts that the increase in iWUE is preserved at the canopy scale (Fig 2e). Where the response of $A_{net}$ to eCO₂ is less than proportional ($\beta_{dir} < 1$) the increase in iWUE (i.e. $A_{net}/g_s$) implies a reduction in $g_s$ (canopy-scale iWUE $\beta_{dir,hist}$ = 1.1±0.1, $A_{net}$ $\beta_{dir,hist}$ = 0.60±0.3, thus $g_s$ $\beta_{dir,hist}$ = -0.53.±0.2; Fig. 2f,i). Due to the lower predicted $A_{net}$ in the future, the predicted decrease in $g_s$ is greater ($\beta_{dir,fut}$ = -0.62.±0.1).
## II. Theory – a hierarchy of mechanism

### 1 Direct plant physiological responses to CO₂

Photosynthesis is limited by CO₂ or light (Farquhar *et al.*, 1980). When CO₂ is limiting, theory predicts that eCO₂ increases leaf-scale net carbon assimilation ($A_{net}$) ($\beta_{dir,hist}$ = 0.86, Table S1). The enzyme that fixes CO₂ (RuBisCO) also catalyses an oxygenation reaction, which results in CO₂ loss (photorespiration; Farquhar *et al.*, 1980). eCO₂ also suppresses photorespiration (Fig. 2a). Given that photorespiration always occurs during C3 photosynthesis, the suppression of photorespiration by eCO₂ increases $A_{net}$ also when light is limiting, but with a lower response ($\beta_{dir,hist}$ = 0.31). Canopy-scale $A_{net}$ results from a mixture of CO₂ and light-limited photosynthesis, thus has an intermediate eCO₂ response that depends on the fraction of light-saturated leaves in the canopy ($\beta_{dir,hist}$ = 0.60±0.3, c. 280 to 400 ppm; Fig 2c). As [CO₂] increases, the fraction of light-saturated leaves in the canopy is expected to decrease and therefore historical eCO₂ response of GPP is expected to be higher than the future response ($\beta_{dir,fut}$ = 0.46±0.2, c. 400 to 550 ppm; Fig 2c).

C4 plants have evolved to concentrate carbon, thus saturating photosynthesis and suppressing photorespiration at low [CO₂] (Ehleringer & Björkman, 1977). Therefore $A_{net}$ in C4 plants is not directly influenced by [CO₂] above c. 200 ppm (Fig. 2a), though water savings from reduced $g_s$ may stimulate $A_{net}$ indirectly (Leakey *et al.*, 2004).

Photosynthesis requires the acquisition of other resources and eCO₂-stimulation of $A_{net}$ increases $A_{net}$ per unit resource consumption, i.e. increases resource use-efficiencies (UE) of: water (WUE), light (LUE), and leaf nitrogen (Cowan, 1982; Drake *et al.*, 1997). Increased use-efficiencies imply a shift in a plant's resource-use economy (Bloom *et al.*, 1985) which is commonly studied using optimisation theory.

Optimisation theory predicts that a change in the ratio of $A_{net}$ to $g_s$ (intrinsic WUE, iWUE) in proportion to the change in [CO₂] ($\beta_{dir}$ c. 1; Fig 2d) maximises the benefit of carbon gain while minimising the cost of water lost for C3 (Medlyn *et al.*, 2011) and C4 plants (Lin *et al.*, 2015). Canopy-scaling theory predicts that the increase in iWUE is preserved at the canopy scale (Fig 2e). Where the response of $A_{net}$ to eCO₂ is less than proportional ($\beta_{dir} < 1$) the increase in iWUE (i.e. $A_{net}/g_s$) implies a reduction in $g_s$ (canopy-scale iWUE $\beta_{dir,hist}$ = 1.1±0.1, $A_{net}$ $\beta_{dir,hist}$ = 0.60±0.3, thus $g_s$ $\beta_{dir,hist}$ = -0.53.±0.2; Fig. 2f,i). Due to the lower predicted $A_{net}$ in the future, the predicted decrease in $g_s$ is greater ($\beta_{dir,fut}$ = -0.62.±0.1).
Optimisation theory also predicts reduction in photosynthetic carboxylation capacity ($V_{cmax}$), reducing nitrogen demand (Bowes, 1991; Drake et al., 1997). A reduction in leaf nitrogen may also occur due to limited plant-available soil nitrogen (Section II.2) or physiological competition for the products of electron transport (Bloom et al., 2012).

## 2 Plant biomass production

BP of leaf, wood, and root tissues is controlled by the interplay of source (resource acquisition), sink (metabolic tissue production) (Muller et al., 2011; Fatichi et al., 2019), and regulatory processes (phenology, hormones) (Schwartz, 2013; Bahuguna & Jagadish, 2015). Within this framework eCO2 can increase BP when BP is either carbon-source limited or when eCO2 can alleviate other limitations. Plant BP is carbon-source limited when in competition with respiration for available carbon and when light limits BP (Lloyd & Farquhar, 2008). Sustained periods of high growth may also reduce carbon stores (Würth et al., 2005), potentially leading to carbon-source limitation.

BP is also carbon-sink limited by stoichiometric nutrient requirements (Elser et al., 2010). Thus increased BP requires either increased nutrient acquisition or increased stoichiometric carbon-to-nutrient ratios. Increased plant-available carbon may be able to "pay" for increased nutrient acquisition via a number of mechanisms (e.g. increased fine-root BP, mycorrhizal investment, exudation, atmospheric N fixation) (Luxmoore, 1981; Hungate et al., 1999; Fleischer et al., 2019). Changing stoichiometry may result in feedbacks that compound nutrient limitations by reducing decomposition rates and nutrient availability (Comins & McMurtrie, 1993), known as progressive nitrogen limitation (Luo et al., 2004).

In environments where BP is primarily sink-limited, e.g. tree-lines (temperature-limitation), arid and semi-arid (water-limitation), increased carbon availability may have little effect on BP (Kramer, 1981; Körner, 2003b). However, in water-limited environments, increased iWUE could increase BP (Mooney et al., 1991; Wullschleger et al., 2002). Leaf area index (LAI) may also be limited by water availability (Woodward, 1987; Yang et al., 2018) and increased WUE may increase LAI and light absorption leading to indirect positive feedback on GPP and transpiration (Fatichi et al., 2016; Trancoso et al., 2017).

If BP is restricted by sink limitation, biomass production efficiency (BP per unit GPP) would decrease and the labile products of photosynthesis would accumulate. If BP is stimulated this may be as short-lived, primary tissues (leaves and fine-roots) or long-lived, secondary tissues (wood) (De Kauwe et
Optimisation theory also predicts reduction in photosynthetic carboxylation capacity ($V_{cmax}$), reducing nitrogen demand (Bowes, 1991; Drake et al., 1997). A reduction in leaf nitrogen may also occur due to limited plant-available soil nitrogen (Section II.2) or physiological competition for the products of electron transport (Bloom et al., 2012).

## 2 Plant biomass production

BP of leaf, wood, and root tissues is controlled by the interplay of source (resource acquisition), sink (metabolic tissue production) (Muller et al., 2011; Fatichi et al., 2019), and regulatory processes (phenology, hormones) (Schwartz, 2013; Bahuguna & Jagadish, 2015). Within this framework eCO2 can increase BP when BP is either carbon-source limited or when eCO2 can alleviate other limitations. Plant BP is carbon-source limited when in competition with respiration for available carbon and when light limits BP (Lloyd & Farquhar, 2008). Sustained periods of high growth may also reduce carbon stores (Würth et al., 2005), potentially leading to carbon-source limitation.

BP is also carbon-sink limited by stoichiometric nutrient requirements (Elser et al., 2010). Thus increased BP requires either increased nutrient acquisition or increased stoichiometric carbon-to-nutrient ratios. Increased plant-available carbon may be able to “pay” for increased nutrient acquisition via a number of mechanisms (e.g. increased fine-root BP, mycorrhizal investment, exudation, atmospheric N fixation) (Luxmoore, 1981; Hungate et al., 1999; Fleischer et al., 2019). Changing stoichiometry may result in feedbacks that compound nutrient limitations by reducing decomposition rates and nutrient availability (Comins & McMurtrie, 1993), known as progressive nitrogen limitation (Luo et al., 2004).

In environments where BP is primarily sink-limited, e.g. tree-lines (temperature-limitation), arid and semi-arid (water-limitation), increased carbon availability may have little effect on BP (Kramer, 1981; Körner, 2003b). However, in water-limited environments, increased iWUE could increase BP (Mooney et al., 1991; Wullschleger et al., 2002). Leaf area index (LAI) may also be limited by water availability (Woodward, 1987; Yang et al., 2018) and increased WUE may increase LAI and light absorption leading to indirect positive feedback on GPP and transpiration (Fatichi et al., 2016; Trancoso et al., 2017).

If BP is restricted by sink limitation, biomass production efficiency (BP per unit GPP) would decrease and the labile products of photosynthesis would accumulate. If BP is stimulated this may be as short-lived, primary tissues (leaves and fine-roots) or long-lived, secondary tissues (wood) (De Kauwe et
al., 2014). Division of carbon among these tissues determines residence time of carbon in plant biomass. Wood has greater residence time and thus greater potential to increase C<sub>veg</sub> accumulation over multiple years. Greater production of short-lived tissues (i.e. leaves and fine-roots) may increase resource capture and will increase litter carbon inputs to the soil.

## 3 Plant mortality

Increases in mortality rates reduce vegetation residence times and have the potential to offset any biomass gains resulting from increased BP (Eq. B3) (Bugmann & Bigler, 2011; Körner, 2017). Hydraulic failure, and less-so, carbon starvation are thought to be interrelated mechanisms of plant death (McDowell et al., 2008). By easing the carbon and hydraulic impacts of abiotic and biotic stressors such as drought, or pest and pathogen attack, eCO2 could potentially decrease mortality. Greater carbon resources could supply greater maintenance respiration, stored carbon reserves, or synthesis of defense compounds (McDowell et al., 2008). More efficient water use (Section II.1) could delay the onset or the intensity of drought, which could reduce the risk of xylem-conductivity losses.

Indirect influences on mortality may emerge from the acceleration of individual size-growth. Increased growth could reduce small-size related mortality by speeding individuals out of the hazards of early life (e.g., browsing) and increasing their ability to acquire resources (Metcalfe et al., 2014; Hülsmann et al., 2018). Conversely, increased growth could increase large-size mortality risk, with tall trees being more susceptible to hydraulic stress, windthrow, lightning, and certain pests or pathogens (Bugmann & Bigler, 2011; Bennett et al., 2015; Körner, 2017; Trugman et al., 2018).

At the stand-scale, increased growth may accelerate post-disturbance successional dynamics (McDowell et al., 2020). Intensified competition for light, water, and nutrients could lead to early reorganisation and transition (self-thinning) phases of development (Bormann & Likens, 1979), but also an earlier switch from transition to steady-state phases (Miller et al., 2016). Acceleration of stand-development by eCO2 may or may not change self-thinning relationships (tree-size to stem-density) of a forest stand, with no change leading to no change in biomass. However, acceleration of stand-development could increase biomass at the landscape scale by closing forest gaps more quickly. Differential mortality effects on different plant species could alter competitive dynamics, community composition, and associated stand properties, e.g. among fast-growing, ruderal/pioneer species and more conservative, slow-growing species (Ruiz-Benito et al. 2017).
al., 2014). Division of carbon among these tissues determines residence time of carbon in plant biomass. Wood has greater residence time and thus greater potential to increase C<sub>veg</sub> accumulation over multiple years. Greater production of short-lived tissues (i.e. leaves and fine-roots) may increase resource capture and will increase litter carbon inputs to the soil.

## 3 Plant mortality

Increases in mortality rates reduce vegetation residence times and have the potential to offset any biomass gains resulting from increased BP (Eq. B3) (Bugmann & Bigler, 2011; Körner, 2017). Hydraulic failure, and less-so, carbon starvation are thought to be interrelated mechanisms of plant death (McDowell et al., 2008). By easing the carbon and hydraulic impacts of abiotic and biotic stressors such as drought, or pest and pathogen attack, eCO2 could potentially *decrease* mortality. Greater carbon resources could supply greater maintenance respiration, stored carbon reserves, or synthesis of defense compounds (McDowell et al., 2008). More efficient water use (Section II.1) could delay the onset or the intensity of drought, which could reduce the risk of xylem-conductivity losses.

Indirect influences on mortality may emerge from the acceleration of individual size-growth. Increased growth could reduce small-size related mortality by speeding individuals out of the hazards of early life (e.g., browsing) and increasing their ability to acquire resources (Metcalfe et al., 2014; Hülsmann et al., 2018). Conversely, increased growth could increase large-size mortality risk, with tall trees being more susceptible to hydraulic stress, windthrow, lightning, and certain pests or pathogens (Bugmann & Bigler, 2011; Bennett et al., 2015; Körner, 2017; Trugman et al., 2018).

At the stand-scale, increased growth may accelerate post-disturbance successional dynamics (McDowell et al., 2020). Intensified competition for light, water, and nutrients could lead to early reorganisation and transition (self-thinning) phases of development (Bormann & Likens, 1979), but also an earlier switch from transition to steady-state phases (Miller et al., 2016). Acceleration of stand-development by eCO2 may or may not change self-thinning relationships (tree-size to stem-density) of a forest stand, with no change leading to no change in biomass. However, acceleration of stand-development could increase biomass at the landscape scale by closing forest gaps more quickly. Differential mortality effects on different plant species could alter competitive dynamics, community composition, and associated stand properties, e.g. among fast-growing, ruderal/pioneer species and more conservative, slow-growing species (Ruiz-Benito et al. 2017).
## 4 Organic matter decomposition

Residence times of litter and soil organic matter (SOM) vary from minutes to millenia and can respond rapidly to environmental perturbation (Trumbore, 2009; Schmidt et al., 2011; Dwivedi et al., 2019). Increases in SOM decomposition rates reduces SOM residence times and has the potential to offset any eCO2-related increases in litter inputs. Accelerated decomposition of litter and particulate SOM (i.e. priming) can result from microbial responses to increased labile-carbon availability (Kuzyakov et al., 2000; Blagodatskaya et al., 2014), including at depth (Fontaine et al., 2007). Organic acids produced by roots can destabilise mineral-associated SOM (Keiluweit et al., 2015). eCO2 effects on environmental conditions could also affect SOM decomposition. CO2-related increases in soil water (Section II.1) would likely stimulate decomposition in water-limited ecosystems (Castanha et al., 2018), but could reduce oxygen availability (slowing decomposition) in energy-limited ecosystems.

Microbial activity has also been linked to the formation of mineral-associated SOM (Cotrufo et al., 2013; Liang et al., 2017), and potentially soil aggregates (Ge et al., 2018), which might slow decomposition by restricting microbial access to SOM (Kögel-Knabner et al., 2008). Changing stoichiometry might slow decomposition (Section II.2). Roots can distribute carbon deeper in the soil where decomposition is slower and capacity for mineral stabilization is higher (Jackson et al., 2017; Hicks Pries et al., 2018).

Greater decomposition rates might also increase soil nutrient availability, potentially reducing plant nutrient limitation (Treseder, 2004; Dijkstra, 2008) or increasing microbial immobilisation. Over longer timescales, nutrient immobilisation can reduce nutrient losses, leading to accumulation of ecosystem nutrient stocks which may enhance mineralisation and progressively release plants from nutrient limitation (Rastetter et al., 1997; Walker et al., 2015).

## 5 Terrestrial ecosystem carbon responses to CO2

The response of terrestrial carbon storage to eCO2 (ΔC<sub>eco</sub>) is the net result of the above discussed processes. Potential increases in BP and litter production are balanced by potential increases in loss rates (Eqs. B3 and B4). Increased BP of short-lived primary tissues such as leaves and fine-roots could lead to greater biomass of these transient C<sub>veg</sub> pools and to increased litter inputs to the soil. If wood BP is stimulated by eCO2, over medium timescales (annual to several decades) ecosystem biomass could increase due to the longer residence time of wood. However, wood BP is tied to tree
## 4 Organic matter decomposition

Residence times of litter and soil organic matter (SOM) vary from minutes to millenia and can respond rapidly to environmental perturbation (Trumbore, 2009; Schmidt et al., 2011; Dwivedi et al., 2019). Increases in SOM decomposition rates reduces SOM residence times and has the potential to offset any eCO2-related increases in litter inputs. Accelerated decomposition of litter and particulate SOM (i.e. priming) can result from microbial responses to increased labile-carbon availability (Kuzyakov et al., 2000; Blagodatskaya et al., 2014), including at depth (Fontaine et al., 2007). Organic acids produced by roots can destabilise mineral-associated SOM (Keiluweit et al., 2015). eCO2 effects on environmental conditions could also affect SOM decomposition. CO2-related increases in soil water (Section II.1) would likely stimulate decomposition in water-limited ecosystems (Castanha et al., 2018), but could reduce oxygen availability (slowing decomposition) in energy-limited ecosystems.

Microbial activity has also been linked to the formation of mineral-associated SOM (Cotrufo et al., 2013; Liang et al., 2017), and potentially soil aggregates (Ge et al., 2018), which might slow decomposition by restricting microbial access to SOM (Kögel-Knabner et al., 2008). Changing stoichiometry might slow decomposition (Section II.2). Roots can distribute carbon deeper in the soil where decomposition is slower and capacity for mineral stabilization is higher (Jackson et al., 2017; Hicks Pries et al., 2018).

Greater decomposition rates might also increase soil nutrient availability, potentially reducing plant nutrient limitation (Treseder, 2004; Dijkstra, 2008) or increasing microbial immobilisation. Over longer timescales, nutrient immobilisation can reduce nutrient losses, leading to accumulation of ecosystem nutrient stocks which may enhance mineralisation and progressively release plants from nutrient limitation (Rastetter et al., 1997; Walker et al., 2015).

## 5 Terrestrial ecosystem carbon responses to CO2

The response of terrestrial carbon storage to eCO2 (ΔC<sub>eco</sub>) is the net result of the above discussed processes. Potential increases in BP and litter production are balanced by potential increases in loss rates (Eqs. B3 and B4). Increased BP of short-lived primary tissues such as leaves and fine-roots could lead to greater biomass of these transient C<sub>veg</sub> pools and to increased litter inputs to the soil. If wood BP is stimulated by eCO2, over medium timescales (annual to several decades) ecosystem biomass could increase due to the longer residence time of wood. However, wood BP is tied to tree
size-growth rates and the effects of tree size on mortality rates may be either positive or negative (Section II.3). Greater wood BP or greater wood mortality rates would result in greater coarse woody debris, which may immobilise nutrients (e.g. Zimmerman et al., 1995).

Increased plant inputs to litter and soil (e.g., wood, leaf and root litter, root exudates, and mycorrhizal subsidies) could increase $C_{soil}$. However, the complex processes that drive the formation and decomposition of SOM make the response of $C_{soil}$ to eCO2 difficult to predict (Schmidt et al., 2011; Dwivedi et al., 2019; Section II.4). Increased soil mineralisation rates could lead to greater $C_{eco}$ if nutrients are redistributed from soils to plants, which have higher carbon:nutrient ratios and hence can store more carbon per unit nutrient (Rastetter et al., 1992; Zaehle et al., 2014).

A one-pool ecosystem carbon model (Box 1) with simplifying assumptions (BP $\beta_{dir}$ = GPP $\beta_{dir}$; residence time $\beta_{dir}$ = 0) provides baseline expected $\beta_{dir}$ for carbon storage (Fig. 2j-l). The model indicates that when starting carbon storage is non-zero, $\beta_{dir}$ depends on the time of measurement (Fig. 2j,k). Based on the observed [CO₂] trend (Le Quéré et al., 2018), the model indicates that $\beta_{dir}$ calculations over a 30 year period (typical of forest-inventory analysis) are generally a little smaller ($\beta_{dir,hist}$ c. 0.5) than steady-state ($\beta_{dir,hist}$ = 0.6; Fig 2. l). Departures from these expected $\beta$'s derived from GPP responses alone provide a guide to the magnitude of positive and negative feedbacks in eCO2 studies and can help to guide iCO2 attribution in historical studies.

# III. The evidence

## 1 Physiology

### Carbon assimilation and GPP

Evidence across FACE experiments (11 sites, 45 species) showed that eCO2 increased leaf-level, light-saturated photosynthesis ($\beta_{dir}$ = 0.73±0.2, see Supporting Information Notes S3 for methods), and supports differences between $C_3$ ($\beta_{dir}$ = 0.79±0.2) and $C_4$ species ($\beta_{dir}$ = 0.27±0.2) (Ainsworth & Long, 2005; all reported $\beta$'s are in Table 2). Evidence suggests that maximum photosynthetic capacity acclimated (reduced) to eCO2, primarily maximum carboxylation capacity ($\beta_{dir}$ = -0.38±0.1) (Ainsworth & Long, 2005; Ainsworth & Rogers, 2007). Nevertheless, in many forest eCO2 experiments photosynthetic stimulation (>5 years) was only minimally affected by acclimation (Crous et al., 2008; Bader et al., 2010; Ellsworth et al., 2017).

Indirect evidence also suggests increased photosynthesis with iCO2. Deuterium isotopomers of
size-growth rates and the effects of tree size on mortality rates may be either positive or negative (Section II.3). Greater wood BP or greater wood mortality rates would result in greater coarse woody debris, which may immobilise nutrients (e.g. Zimmerman et al., 1995).

Increased plant inputs to litter and soil (e.g., wood, leaf and root litter, root exudates, and mycorrhizal subsidies) could increase $C_{soil}$. However, the complex processes that drive the formation and decomposition of SOM make the response of $C_{soil}$ to eCO2 difficult to predict (Schmidt et al., 2011; Dwivedi et al., 2019; Section II.4). Increased soil mineralisation rates could lead to greater $C_{eco}$ if nutrients are redistributed from soils to plants, which have higher carbon:nutrient ratios and hence can store more carbon per unit nutrient (Rastetter et al., 1992; Zaehle et al., 2014).

A one-pool ecosystem carbon model (Box 1) with simplifying assumptions (BP $\beta_{dir}$ = GPP $\beta_{dir}$; residence time $\beta_{dir}$ = 0) provides baseline expected $\beta_{dir}$ for carbon storage (Fig. 2j-l). The model indicates that when starting carbon storage is non-zero, $\beta_{dir}$ depends on the time of measurement (Fig. 2j,k). Based on the observed [CO$_2$] trend (Le Quéré et al., 2018), the model indicates that $\beta_{dir}$ calculations over a 30 year period (typical of forest-inventory analysis) are generally a little smaller ($\beta_{dir,hist}$ c. 0.5) than steady-state ($\beta_{dir,hist}$ = 0.6; Fig 2. l). Departures from these expected $\beta$'s derived from GPP responses alone provide a guide to the magnitude of positive and negative feedbacks in eCO2 studies and can help to guide iCO2 attribution in historical studies.

# III. The evidence

## 1 Physiology

### Carbon assimilation and GPP

Evidence across FACE experiments (11 sites, 45 species) showed that eCO2 increased leaf-level, light-saturated photosynthesis ($\beta_{dir}$ = 0.73±0.2, see Supporting Information Notes S3 for methods), and supports differences between $C_3$ ($\beta_{dir}$ = 0.79±0.2) and $C_4$ species ($\beta_{dir}$ = 0.27±0.2) (Ainsworth & Long, 2005; all reported $\beta$'s are in Table 2). Evidence suggests that maximum photosynthetic capacity acclimated (reduced) to eCO2, primarily maximum carboxylation capacity ($\beta_{dir}$ = -0.38±0.1) (Ainsworth & Long, 2005; Ainsworth & Rogers, 2007). Nevertheless, in many forest eCO2 experiments photosynthetic stimulation (>5 years) was only minimally affected by acclimation (Crous et al., 2008; Bader et al., 2010; Ellsworth et al., 2017).

Indirect evidence also suggests increased photosynthesis with iCO2. Deuterium isotopomers of
glucose in plant archives indicate that the leaf-level photorespiration:assimilation ratio decreased since pre-industry ($\beta_{app}$ = -0.99) (Ehlers et al., 2015), which translates to an increase in photosynthesis ($\beta_{app}$ = 1.0) (Ehlers et al., 2015). GPP estimates from eddy-covariance (23 sites, c. 20 years) suggest a recent increase ($\beta_{app}$ = 1.6±0.9), implicating a substantial iCO2 contribution ($\beta_{dir,hist}$ = 1.2±0.6) (Fernández-Martínez et al., 2017). Eddy-covariance data used to calibrate a model suggests a lower iCO2 response ($\beta_{dir,hist}$ = 0.5±0.2) (Ueyama et al., 2020).

Ice-core measurements of atmospheric carbonyl sulfide (OCS) combined with mass-balance analysis suggests that global GPP has increased since pre-industry ($\beta_{app}$ = 0.95±0.2) (Campbell et al., 2017). As do ice-core measurements of atmospheric O₂ isotopes combined with models ($\beta_{app}$ = 1.3±2.3) (Ciais et al., 2012). Satellite-based evapotranspiration combined with an ecosystem WUE model estimated increased GPP during recent decades ($\beta_{app}$ = 1.1±0.5) (Cheng et al., 2017). 14 methods to estimate GPP from satellite-based fAPAR resulted in wide-ranging iCO2 sensitivities ($\beta_{dir}$ range = -0.39±0.34 to 1.6±1, mean = 0.52±0.3; 2000-2014) (Sun et al., 2019).

## Water use efficiency, stomatal conductance, and transpiration

Experimental evidence also supports increased iWUE in response to eCO₂ ($\beta_{dir}$ = 1.2±0.4; 4 sites, 7 species) (Ainsworth & Long, 2005). At two FACE experiments (Duke University and Oak Ridge National Laboratory, ORNL), tree-ring $\delta^{13}$C implies increased iWUE ($\beta_{dir}$ = 1.4 and 1.3, respectively) (Battipaglia et al., 2013). Tree-ring $\delta^{13}$C samples from across the globe suggest increased iWUE in many biomes since pre-industry in: northern boreal gymnosperms ($\beta_{app}$ = 1.2±2 to 1.5±1.6) (Saurer et al., 2004), tropical forests ($\beta_{app}$ = 1.0) (van der Sleen et al., 2015), and a wide range of forest biomes ($\beta_{app}$ = 1.19; Keller et al., 2017). Attribution to iCO₂ also suggests increases in iWUE in European Pinus and Quercus ($\beta_{dir}$ = 1.0±0.6 and 0.67±0.9; 9-14 sites) (Frank et al., 2015). Additional environmental factors have contributed to observed iWUE trends, e.g. drying trends have increased iWUE (Saurer et al., 2014).

$\delta^{13}$C in atmospheric CO₂ combined with mass-balance modelling suggests a global increase in iWUE since pre-industry ($\beta_{app}$ = 0.94±0.2) (Keeling et al., 2017).

Evidence from Duke and ORNL FACE experiments supports increases in ecosystem-scale plant WUE (annual BP/T; $\beta_{dir,hist}$ = 0.76 and 1.1, respectively) (De Kauwe et al., 2013). Inferred from eddy-covariance, 'inherent' WUE (VPD.GPP/ET) increased in temperate and boreal forests with notably higher magnitude ($\beta_{app}$ = 4.72; 21 sites) (Keenan et al., 2013). A follow up study reduced this estimate ($\beta_{app}$ = 2.5) (Mastrotheodoros et al., 2017). An eddy-covariance calibrated, canopy-scale
glucose in plant archives indicate that the leaf-level photorespiration:assimilation ratio decreased since pre-industry ($\beta_{app}$ = -0.99) (Ehlers et al., 2015), which translates to an increase in photosynthesis ($\beta_{app}$ = 1.0) (Ehlers et al., 2015). GPP estimates from eddy-covariance (23 sites, c. 20 years) suggest a recent increase ($\beta_{app}$ = 1.6±0.9), implicating a substantial iCO2 contribution ($\beta_{dir,hist}$ = 1.2±0.6) (Fernández-Martínez et al., 2017). Eddy-covariance data used to calibrate a model suggests a lower iCO2 response ($\beta_{dir,hist}$ = 0.5±0.2) (Ueyama et al., 2020).

Ice-core measurements of atmospheric carbonyl sulfide (OCS) combined with mass-balance analysis suggests that global GPP has increased since pre-industry ($\beta_{app}$ = 0.95±0.2) (Campbell et al., 2017). As do ice-core measurements of atmospheric O₂ isotopes combined with models ($\beta_{app}$ = 1.3±2.3) (Ciais et al., 2012). Satellite-based evapotranspiration combined with an ecosystem WUE model estimated increased GPP during recent decades ($\beta_{app}$ = 1.1±0.5) (Cheng et al., 2017). 14 methods to estimate GPP from satellite-based fAPAR resulted in wide-ranging iCO2 sensitivities ($\beta_{dir}$ range = -0.39±0.34 to 1.6±1, mean = 0.52±0.3; 2000-2014) (Sun et al., 2019).

## Water use efficiency, stomatal conductance, and transpiration

Experimental evidence also supports increased iWUE in response to eCO₂ ($\beta_{dir}$ = 1.2±0.4; 4 sites, 7 species) (Ainsworth & Long, 2005). At two FACE experiments (Duke University and Oak Ridge National Laboratory, ORNL), tree-ring $\delta^{13}$C implies increased iWUE ($\beta_{dir}$ = 1.4 and 1.3, respectively) (Battipaglia et al., 2013). Tree-ring $\delta^{13}$C samples from across the globe suggest increased iWUE in many biomes since pre-industry in: northern boreal gymnosperms ($\beta_{app}$ = 1.2±2 to 1.5±1.6) (Saurer et al., 2004), tropical forests ($\beta_{app}$ = 1.0) (van der Sleen et al., 2015), and a wide range of forest biomes ($\beta_{app}$ = 1.19; Keller et al., 2017). Attribution to iCO₂ also suggests increases in iWUE in European Pinus and Quercus ($\beta_{dir}$ = 1.0±0.6 and 0.67±0.9; 9-14 sites) (Frank et al., 2015). Additional environmental factors have contributed to observed iWUE trends, e.g. drying trends have increased iWUE (Saurer et al., 2014).

$\delta^{13}$C in atmospheric CO₂ combined with mass-balance modelling suggests a global increase in iWUE since pre-industry ($\beta_{app}$ = 0.94±0.2) (Keeling et al., 2017).

Evidence from Duke and ORNL FACE experiments supports increases in ecosystem-scale plant WUE (annual BP/T; $\beta_{dir,hist}$ = 0.76 and 1.1, respectively) (De Kauwe et al., 2013). Inferred from eddy-covariance, 'inherent' WUE (VPD.GPP/ET) increased in temperate and boreal forests with notably higher magnitude ($\beta_{app}$ = 4.72; 21 sites) (Keenan et al., 2013). A follow up study reduced this estimate ($\beta_{app}$ = 2.5) (Mastrotheodoros et al., 2017). An eddy-covariance calibrated, canopy-scale
model suggested iCO2 reduced $g_s$ ($\beta_{dir,hist} = -0.28\pm0.09$) and increased iWUE ($\beta_{dir,hist} = 0.73\pm0.2$) (Ueyama et al., 2020). Satellite-based models (2000-2013) of GPP and ET suggest smaller or decreased WUE (GPP/ET) ($\beta_{app} = -0.49$ and $0.28$) (Tang et al., 2014; Xue et al., 2015).

Experimental evidence has thoroughly demonstrated reduced leaf-scale $g_s$ in response to eCO2 ($\beta_{dir,fut} = -0.22\pm0.15$) (Medlyn et al., 2001). Averaged across FACE experiments (12 sites, 40 species), eCO2 reduced $g_s$ ($\beta_{dir,fut} = -0.60\pm0.2$) but with substantial variability across functional groups (Ainsworth & Long, 2005) and disturbance history (Donohue et al., 2017). Notably for *Eucalyptus aligna* in whole-tree chambers, canopy-scale iWUE was very tightly constrained ($\beta_{dir,fut} = 0.98\pm0.2$), and variability in the $A_{net}$ response controlled the $g_s$ response (Barton et al., 2012).

Across four FACE experiments (Duke, EucFACE, ORNL, Swiss Canopy Crane), transpiration responses were only reduced by eCO2 at ORNL ($\beta_{dir,fut} = -0.54$), an ecosystem that is rarely water limited (Leuzinger & Körner 2010) (De Kauwe et al., 2013; Gimeno et al., 2018). Airborne remote sensing suggested decreased evapo-transpiration with long-term volcanically-derived eCO2 in California (Cawse-Nicholson et al., 2018). Stream-gauge networks indicate global increases in runoff (Gedney et al. 2006), in agreement with reduced $g_s$ over the northern hemisphere extratropics (Knauer et al., 2017). However, decreases in runoff have also been observed (Ukkola et al., 2016; Trancoso et al., 2017) and modest run-off increases across the tropics have been driven by precipitation increases (Yang et al., 2016).

## 2 Biomass production

eCO2 increased BP in four temperate-forest, stand-scale (25-30 m diameter) FACE experiments in the early years ($\beta_{dir,fut} = 0.56\pm0.2$) (Norby et al., 2005) and over a full decade ($\beta_{dir,fut} = 0.49\pm0.3$) (Walker et al., 2019). These forest ecosystems were in the early phases of secondary succession (initiated 1-13 years following a major disturbance). In three later-succession forests (c. 100 years old), BP did not respond to eCO2 (note fine-root BP was often not measured): deciduous broadleaved trees ($\beta_{dir,fut} = -0.097\pm1.0$ to $0.55\pm1.7$) (8 years; Bader et al., 2013), *Picea abies* ($\beta_{dir,fut} = -0.30\pm0.7$, 5 years eCO2; Klein et al., 2016), and a low productivity *Eucalyptus* woodland ($\beta_{dir,fut} = -0.26\pm0.6$, 4 years eCO2; Ellsworth et al., 2017; Jiang et al., 2020).

eCO2 consistently decreased specific leaf area ($\beta_{dir,fut} = -0.16\pm0.07$) (Ainsworth & Long, 2005), which requires increased leaf BP at a given LAI (De Kauwe et al., 2014). Synthesis of experiments (19 sites) suggests that eCO2 increased grassland leaf and stem BP ($\beta_{dir,fut} = 0.17\pm0.07$) (Hovenden et
model suggested iCO2 reduced $g_s$ ($\beta_{dir,hist} = -0.28\pm0.09$) and increased iWUE ($\beta_{dir,hist} = 0.73\pm0.2$) (Ueyama et al., 2020). Satellite-based models (2000-2013) of GPP and ET suggest smaller or decreased WUE (GPP/ET) ($\beta_{app} = -0.49$ and $0.28$) (Tang et al., 2014; Xue et al., 2015).

Experimental evidence has thoroughly demonstrated reduced leaf-scale $g_s$ in response to eCO2 ($\beta_{dir,fut} = -0.22\pm0.15$) (Medlyn et al., 2001). Averaged across FACE experiments (12 sites, 40 species), eCO₂ reduced $g_s$ ($\beta_{dir,fut} = -0.60\pm0.2$) but with substantial variability across functional groups (Ainsworth & Long, 2005) and disturbance history (Donohue et al., 2017). Notably for *Eucalyptusaligna* in whole-tree chambers, canopy-scale iWUE was very tightly constrained ($\beta_{dir,fut} = 0.98\pm0.2$), and variability in the $A_{net}$ response controlled the $g_s$ response (Barton et al., 2012).

Across four FACE experiments (Duke, EucFACE, ORNL, Swiss Canopy Crane), transpiration responses were only reduced by eCO2 at ORNL ($\beta_{dir,fut} = -0.54$), an ecosystem that is rarely water limited (Leuzinger & Körner 2010) (De Kauwe et al., 2013; Gimeno et al., 2018). Airborne remote sensing suggested decreased evapo-transpiration with long-term volcanically-derived eCO2 in California (Cawse-Nicholson et al., 2018). Stream-gauge networks indicate global increases in runoff (Gedney et al. 2006), in agreement with reduced $g_s$ over the northern hemisphere extratropics (Knauer et al., 2017). However, decreases in runoff have also been observed (Ukkola et al., 2016; Trancoso et al., 2017) and modest run-off increases across the tropics have been driven by precipitation increases (Yang et al., 2016).

## 2 Biomass production

eCO2 increased BP in four temperate-forest, stand-scale (25-30 m diameter) FACE experiments in the early years ($\beta_{dir,fut} = 0.56\pm0.2$) (Norby et al., 2005) and over a full decade ($\beta_{dir,fut} = 0.49\pm0.3$) (Walker et al., 2019). These forest ecosystems were in the early phases of secondary succession (initiated 1-13 years following a major disturbance). In three later-succession forests (c. 100 years old), BP did not respond to eCO2 (note fine-root BP was often not measured): deciduous broadleaved trees ($\beta_{dir,fut} = -0.097\pm1.0$ to $0.55\pm1.7$) (8 years; Bader et al., 2013), *Picea abies* ($\beta_{dir,fut} = -0.30\pm0.7$, 5 years eCO2; Klein et al., 2016), and a low productivity *Eucalyptus* woodland ($\beta_{dir,fut} = -0.26\pm0.6$, 4 years eCO2; Ellsworth et al., 2017; Jiang et al., 2020).

eCO2 consistently decreased specific leaf area ($\beta_{dir,fut} = -0.16\pm0.07$) (Ainsworth & Long, 2005), which requires increased leaf BP at a given LAI (De Kauwe et al., 2014). Synthesis of experiments (19 sites) suggests that eCO2 increased grassland leaf and stem BP ($\beta_{dir,fut} = 0.17\pm0.07$) (Hovenden et
al., 2019), related to summer water savings and spring water availability (Morgan et al., 2004; Hovenden et al., 2019). Meta-analysis found eCO2 increased fine-root BP across experiments ($\beta_{dir,fut}$ = 0.56) ($\beta_{dir,fut}$ = 0.56), in forests ($\beta_{dir,fut}$ = 0.92), and to a lesser degree in grasslands ($\beta_{dir,fut}$ = 0.18) (Nowak et al., 2004).

Tree-ring analysis at CO₂-springs in Italy (two sites) suggests that eCO2 increased Quercus ilex tree ring-width (a proxy for wood BP) initially ($\beta_{app}$ = 0.49-0.81), and the increase diminished as trees aged (Hättenschwiler et al., 1997). Basal-area increment (BAI) analysis showed the eCO2 response stabilised at around 10 years ($\beta_{app}$ = 0.27) (Norby et al., 1999).

A large number of tree-ring studies have found little evidence for increases in wood BP. No detectable trends in BAI were found across tropical forests (3 sites, 12 species) (van der Sleen et al., 2015), and both increasing and decreasing trends were found across North American boreal forests (598 sites, 19 species) (Girardin et al., 2016). Syntheses across biomes found no significant increase in tree ring-width since 1950 ($\beta_{app}$ = 0.23±0.8; 40 sites) (Peñuelas et al., 2011) and variable responses of BAI ($\beta_{app}$ = 0.45±0.7; 37 sites, 22 species) (Silva & Anand, 2013). Conversely, Pinus and Quercus tree-rings from Missouri showed a positive response to iCO2 that diminished with tree age ($\beta_{app}$ = 3.3, at age one; $\beta_{app}$ = 1.1, at age 50) (Voelker et al., 2006).

Evidence from multi-plot inventory data consistently show increasing wood biomass (Section III.5), but few of these studies quantify wood BP. A single census interval of eastern-US Forest Inventory Analysis plots (20,000) suggested very little change in wood BP (Caspersen, 2000), but with high uncertainty (Joos et al., 2002). Two large tropical-forest plots showed no change in aboveground wood BP (Clark et al., 2010; Rutishauser et al., 2019). In contrast, tropical forest-plot networks (321 and 244) suggest that aboveground wood BP increased in Amazonia ($\beta_{app}$ = 1.2±0.6) (Brienen et al., 2015) and Africa ($\beta_{app}$ = 0.69±0.63) with a regression-attributed iCO2 response ($\beta_{app}$ = 0.54±1) (Hubau et al., 2020). Analysis of worldwide forest plots (695) suggested that wood BP increased ($\beta_{app}$ = 0.94±1.1) in recent decades (Yu et al., 2019).

## BP-nutrient interactions and progressive nitrogen limitation

At Duke FACE, nitrogen availability influenced the magnitude of BP responses (McCarthy et al., 2010) and experiments in later succession systems with no BP response were limited by nitrogen (Flakaliden; Sigurdsson et al., 2013) and phosphorus (EucFACE; Ellsworth et al., 2017). Limiting factors were not examined for a number of the other later succession experiments (Bader et al., 2013; Klein et al., 2016).
al., 2019), related to summer water savings and spring water availability (Morgan et al., 2004; Hovenden et al., 2019). Meta-analysis found eCO2 increased fine-root BP across experiments ($\beta_{dir,fut}$ = 0.56) ($\beta_{dir,fut}$ = 0.56), in forests ($\beta_{dir,fut}$ = 0.92), and to a lesser degree in grasslands ($\beta_{dir,fut}$ = 0.18) (Nowak et al., 2004).

Tree-ring analysis at CO₂-springs in Italy (two sites) suggests that eCO2 increased *Quercus ilex* tree ring-width (a proxy for wood BP) initially ($\beta_{app}$ = 0.49-0.81), and the increase diminished as trees aged (Hättenschwiler et al., 1997). Basal-area increment (BAI) analysis showed the eCO2 response stabilised at around 10 years ($\beta_{app}$ = 0.27) (Norby et al., 1999).

A large number of tree-ring studies have found little evidence for increases in wood BP. No detectable trends in BAI were found across tropical forests (3 sites, 12 species) (van der Sleen et al., 2015), and both increasing and decreasing trends were found across North American boreal forests (598 sites, 19 species) (Girardin et al., 2016). Syntheses across biomes found no significant increase in tree ring-width since 1950 ($\beta_{app}$ = 0.23±0.8; 40 sites) (Peñuelas et al., 2011) and variable responses of BAI ($\beta_{app}$ = 0.45±0.7; 37 sites, 22 species) (Silva & Anand, 2013). Conversely, *Pinus* and *Quercus* tree-rings from Missouri showed a positive response to iCO2 that diminished with tree age ($\beta_{app}$ = 3.3, at age one; $\beta_{app}$ = 1.1, at age 50) (Voelker et al., 2006).

Evidence from multi-plot inventory data consistently show increasing wood biomass (Section III.5), but few of these studies quantify wood BP. A single census interval of eastern-US Forest Inventory Analysis plots (20,000) suggested very little change in wood BP (Caspersen, 2000), but with high uncertainty (Joos et al., 2002). Two large tropical-forest plots showed no change in aboveground wood BP (Clark et al., 2010; Rutishauser et al., 2019). In contrast, tropical forest-plot networks (321 and 244) suggest that aboveground wood BP increased in Amazonia ($\beta_{app}$ = 1.2±0.6) (Brienen et al., 2015) and Africa ($\beta_{app}$ = 0.69±0.63) with a regression-attributed iCO2 response ($\beta_{app}$ = 0.54±1) (Hubau et al., 2020). Analysis of worldwide forest plots (695) suggested that wood BP increased ($\beta_{app}$ = 0.94±1.1) in recent decades (Yu et al., 2019).

## BP-nutrient interactions and progressive nitrogen limitation

At Duke FACE, nitrogen availability influenced the magnitude of BP responses (McCarthy et al., 2010) and experiments in later succession systems with no BP response were limited by nitrogen (Flakaliden; Sigurdsson et al., 2013) and phosphorus (EucFACE; Ellsworth et al., 2017). Limiting factors were not examined for a number of the other later succession experiments (Bader et al., 2013; Klein et al., 2016).
eCO2 experiments in early-succession ecosystems suggest that BP gains were supported by increased nitrogen acquisition rather than changes in stoichiometry (Finzi et al., 2007; Zaehle et al., 2014). Nitrogen acquisition was increased through increased fine-root BP (see above), changing root traits (Iversen, 2010; Nie et al., 2013; Beidler et al., 2015), and below-ground carbon flux to mycorrhizal symbionts and rhizosphere microbial associations (Section III.4; (Drake et al., 2011; Phillips et al., 2011; Terrer et al., 2018). Meta-analysis suggests that eCO2 increased nitrogen fixation in more intensively manipulated experiments but not in more natural settings (total 441 studies, rates were scaled to plant or ground-area units; B.A. Hungate, unpublished).

Experimental evidence for progressive nitrogen limitation is limited to a single forest (ORNL; Norby et al., 2010) and a single grassland (Biocon; Reich et al., 2006). Paleo-climatic evidence suggests that despite increasing carbon storage the nitrogen cycle became more open between the last glacial maximum and the industrial revolution (Fischer et al., 2019; Jeltsch-Thömmes et al., 2019).

## Leaf area, water, and land cover interactions

In some low LAI ecosystems, eCO2 increased LAI, but did not in higher LAI (c. 5) ecosystems (Norby & Zak, 2011; Bader et al., 2013). However, low LAI (c. 1) at EucFACE did not respond to eCO2 (Duursma et al., 2016). The LAI response to eCO2 in low LAI systems has been interpreted as CO2 accelerating open canopies towards closure (Körner, 2006). However, evidence from two FACE sites (Duke and Rhinelander) suggests that LAI can be higher at canopy closure (Walker et al., 2019). Higher above-ground biomass in some grasslands (Hovenden et al., 2019) indicates potential LAI increases, though increases in leaf mass per unit area would reduce the LAI response relative to the biomass response. High grassland biomass responses have been linked to low soil matric potential (Morgan et al., 2004), though more complex interactions with precipitation seasonality have also been indicated (Hovenden et al., 2019).

Satellite data show “greening” trends over much of the planet, inferred as increasing LAI (Zhu et al., 2016; Mao et al., 2016) and with model-based attribution primarily to iCO2 (Zhu et al., 2016).

Consistent with theory, satellite greenness data suggests increased foliage cover in warm and semiarid regions, likely an iCO2 effect via increased WUE (Donohue et al., 2013). Tree-rings have indicated decreasing sensitivity to rainfall or drought in the Eastern US, possibly indicating WUE-mediated iCO2 response (Wyckoff & Bowers, 2010; Helcoski et al., 2019). However, less severe droughts, noted in the Eastern US, likely appear as reduced growth sensitivity (Maxwell et al., 2016). At the Florida scrub oak experiment, eCO2 alleviated drought-related declines in NEP (Li et al.,
eCO2 experiments in early-succession ecosystems suggest that BP gains were supported by increased nitrogen acquisition rather than changes in stoichiometry (Finzi et al., 2007; Zaehle et al., 2014). Nitrogen acquisition was increased through increased fine-root BP (see above), changing root traits (Iversen, 2010; Nie et al., 2013; Beidler et al., 2015), and below-ground carbon flux to mycorrhizal symbionts and rhizosphere microbial associations (Section III.4; (Drake et al., 2011; Phillips et al., 2011; Terrer et al., 2018). Meta-analysis suggests that eCO2 increased nitrogen fixation in more intensively manipulated experiments but not in more natural settings (total 441 studies, rates were scaled to plant or ground-area units; B.A. Hungate, unpublished).

Experimental evidence for progressive nitrogen limitation is limited to a single forest (ORNL; Norby et al., 2010) and a single grassland (Biocon; Reich et al., 2006). Paleo-climatic evidence suggests that despite increasing carbon storage the nitrogen cycle became more open between the last glacial maximum and the industrial revolution (Fischer et al., 2019; Jeltsch-Thömmes et al., 2019).

## Leaf area, water, and land cover interactions

In some low LAI ecosystems, eCO2 increased LAI, but did not in higher LAI (c. 5) ecosystems (Norby & Zak, 2011; Bader et al., 2013). However, low LAI (c. 1) at EucFACE did not respond to eCO2 (Duursma et al., 2016). The LAI response to eCO2 in low LAI systems has been interpreted as CO₂ accelerating open canopies towards closure (Körner, 2006). However, evidence from two FACE sites (Duke and Rhinelander) suggests that LAI can be higher at canopy closure (Walker et al., 2019). Higher above-ground biomass in some grasslands (Hovenden et al., 2019) indicates potential LAI increases, though increases in leaf mass per unit area would reduce the LAI response relative to the biomass response. High grassland biomass responses have been linked to low soil matric potential (Morgan et al., 2004), though more complex interactions with precipitation seasonality have also been indicated (Hovenden et al., 2019).

Satellite data show “greening” trends over much of the planet, inferred as increasing LAI (Zhu et al., 2016; Mao et al., 2016) and with model-based attribution primarily to iCO2 (Zhu et al., 2016).

Consistent with theory, satellite greenness data suggests increased foliage cover in warm and semi-arid regions, likely an iCO2 effect via increased WUE (Donohue et al., 2013). Tree-rings have indicated decreasing sensitivity to rainfall or drought in the Eastern US, possibly indicating WUE-mediated iCO2 response (Wyckoff & Bowers, 2010; Helcoski et al., 2019). However, less severe droughts, noted in the Eastern US, likely appear as reduced growth sensitivity (Maxwell et al., 2016). At the Florida scrub oak experiment, eCO2 alleviated drought-related declines in NEP (Li et al.,
2007) but the opposite was observed at the Nevada desert FACE (Jasoni et al., 2005).

## 3 Plant mortality

Greenhouse experiments with potted plants have found little benefit of eCO2 on survival during drought or high temperature (e.g. Duan et al., 2014; Bachofen et al., 2018). However, remote sensing evidence shows increased vegetation cover in drylands (Donohue et al., 2013; Section III.2) which possibly suggests a reduction in mortality in those regions.

We are unaware of direct or indirect evidence for CO₂-related increases in individual-scale mortality, but growth-mortality relationships provide insight. Evidence supports both an inter-specific growth-survival tradeoff (Wright et al., 2010; Bugmann & Bigler, 2011) and an intra-specific tradeoff (Bigler & Veblen, 2009; Di Filippo et al., 2012, 2015; Büntgen et al., 2019). However, there are common exceptions with some high-growth-rate species with long lifespans (Rüger et al., 2020), and other species that show no, or even negative, growth-mortality relationships (Ireland et al., 2014; Cailleret et al., 2017).

Experimental evidence for stand-scale mortality responses to eCO2 is rare. In the young, regenerating stand at Rhinelander FACE, over 11 years of eCO2 lowered rates of self-thinning (i.e. higher stand basal area for any given stem density) (Kubiske et al., 2019).

At broader scales, most inventory networks have shown increases in stand-scale mortality rates. Increases in biomass mortality have been observed in Amazon forests ($\beta_{app} = 2.4$) (Brienen et al., 2015) and across continents ($\beta_{app} = 1.6-3.9$) (Yu et al., 2019). Tree stem mortality rates have increased, across species, elevation, and tree size, in the western US ($\beta_{app} = 6.2\pm3$; van Mantgem et al., 2009) and in Canada ($\beta_{app} = 6.1$) (Peng et al., 2011). However, none of these studies conclusively attribute trends to iCO2 and other global change (e.g. temperature) and biotic (e.g. pest and pathogens) agents have often been attributed drivers of mortality trends (Peng et al., 2011; Luo & Chen, 2015). Finally, several networks observed decreases or non-significant changes, e.g. in stem mortality rates in Germany (Pretzsch et al., 2014) and biomass mortality in tropical Africa ($\beta_{app} = -0.88\pm2$), although multiple-regression estimated that CO₂ increased mortality ($\beta_{dir,hist} = 1.8\pm4$) (Hubau et al., 2020).

## 4 Organic matter decomposition

Evidence for changes in SOM-decomposition rates comes primarily from experiments. Many eCO2
2007) but the opposite was observed at the Nevada desert FACE (Jasoni et al., 2005).

## 3 Plant mortality

Greenhouse experiments with potted plants have found little benefit of eCO2 on survival during drought or high temperature (e.g. Duan et al., 2014; Bachofen et al., 2018). However, remote sensing evidence shows increased vegetation cover in drylands (Donohue et al., 2013; Section III.2) which possibly suggests a reduction in mortality in those regions.

We are unaware of direct or indirect evidence for CO₂-related increases in individual-scale mortality, but growth-mortality relationships provide insight. Evidence supports both an inter-specific growth-survival tradeoff (Wright et al., 2010; Bugmann & Bigler, 2011) and an intra-specific tradeoff (Bigler & Veblen, 2009; Di Filippo et al., 2012, 2015; Büntgen et al., 2019). However, there are common exceptions with some high-growth-rate species with long lifespans (Rüger et al., 2020), and other species that show no, or even negative, growth-mortality relationships (Ireland et al., 2014; Cailleret et al., 2017).

Experimental evidence for stand-scale mortality responses to eCO2 is rare. In the young, regenerating stand at Rhinelander FACE, over 11 years of eCO2 lowered rates of self-thinning (i.e. higher stand basal area for any given stem density) (Kubiske et al., 2019).

At broader scales, most inventory networks have shown increases in stand-scale mortality rates. Increases in biomass mortality have been observed in Amazon forests ($\beta_{app} = 2.4$) (Brienen et al., 2015) and across continents ($\beta_{app} = 1.6-3.9$) (Yu et al., 2019). Tree stem mortality rates have increased, across species, elevation, and tree size, in the western US ($\beta_{app} = 6.2 \pm 3$; van Mantgem et al., 2009) and in Canada ($\beta_{app} = 6.1$) (Peng et al., 2011). However, none of these studies conclusively attribute trends to iCO2 and other global change (e.g. temperature) and biotic (e.g. pest and pathogens) agents have often been attributed drivers of mortality trends (Peng et al., 2011; Luo & Chen, 2015). Finally, several networks observed decreases or non-significant changes, e.g. in stem mortality rates in Germany (Pretzsch et al., 2014) and biomass mortality in tropical Africa ($\beta_{app} = -0.88 \pm 2$), although multiple-regression estimated that CO₂ increased mortality ($\beta_{dir,hist} = 1.8 \pm 4$) (Hubau et al., 2020).

## 4 Organic matter decomposition

Evidence for changes in SOM-decomposition rates comes primarily from experiments. Many eCO2
experiments have demonstrated increased plant litter production and allocation of carbon
belowground (e.g. Drake *et al.*, 2011; Iversen *et al.*, 2012). Meta-analysis (53 experiments, primarily
FACE and OTC) showed that eCO2 increased litter production ($\beta_{dir,fut} = 0.4 \pm 0.1$) and SOM-
decomposition rates ($\beta_{dir,fut} = 0.34 \pm 0.2$) (van Groenigen *et al.*, 2014), yet priming effects are difficult to
detect in field studies (van Groenigen *et al.*, 2014; Georgiou *et al.*, 2015).

Results from ecosystem-scale experiments indicate some heterogeneity and nuance in these
responses. For example, in a scrub oak ecosystem, 6 years of eCO2 increased SOM decay despite
unchanged microbial biomass (Carney *et al.*, 2007), and at ORNL FACE a decade of eCO2 resulted
in a small but non-significant increase in surface-soil SOM decomposition along with a reduction in
microbial nitrogen (Iversen *et al.*, 2012). In a later-succession forest, eCO2 increased microbial
biomass ($\beta_{dir,fut} = 0.40 \pm 0.4$) but with no change in soil respiration ($\beta_{dir,fut} = -0.18 \pm 0.7$) (Bader & Körner,
2010). At EucFACE +30 ppm eCO2 increased soil respiration ($\beta_{dir,fut} = 1.3$), but a further increase of
120 ppm produced no additional effect after 3 months ($\beta_{dir,fut} = 0.3$) or 3 years ($\beta_{dir,fut} = 0.21$) (Drake *et
al.*, 2016, 2018). This 3-year response was non-significant but accounted for about half of the
additional carbon acquired under eCO2 (Jiang *et al.*, 2020).

Data on long-term changes in SOM decomposition in response to iCO2 remains limited. Synthesis of
23 flux-towers with increased GPP (Section III.1) suggested a non-significant increase in ecosystem
respiration ($R_e$) ($\beta_{app} = 0.58 \pm 1$) (Fernández-Martínez *et al.*, 2017). Synthesis and statistical upscaling
of chamber measurements suggested that global soil respiration has increased ($\beta_{app} = 0.22$) (Bond-
Lamberty & Thomson, 2010). Statistical predictors of this trend include temperature anomaly and
year (possibly an iCO2 effect). Notably, heterotrophic respiration would be expected to increase if
$C_{soil}$ increased, even with no change in decomposition rates.

Accelerated SOM decomposition may release nutrients and feed back onto the activity of plant
processes. For example, at Duke FACE increased root exudation ($\beta_{dir,fut} = 1.1 \pm 0.6$) was coupled with
a non-significant but substantial increase in microbial biomass ($\beta_{dir,fut} = 1.1 \pm 1.3$) and production of
nitrogen-acquiring extracellular enzymes (Phillips *et al.*, 2011). Exoenzyme activity was increased at
Duke and Rhinelander FACE (Larson *et al.*, 2002; Finzi *et al.*, 2006), although no change in nitrogen
mineralisation was observed in lab incubations (Zak *et al.*, 2003), perhaps suggesting that
stimulation of microbial activity required plant inputs. Conversely, leaf $\delta^{15}$N suggests that eCO2 may
have increased nitrogen mineralisation but not ring-width in mature trees in a European forest (Bader
*et al.*, 2013). eCO2 increased nitrogen and phosphorus mineralisation for a limited period at
experiments have demonstrated increased plant litter production and allocation of carbon
belowground (e.g. Drake *et al.*, 2011; Iversen *et al.*, 2012). Meta-analysis (53 experiments, primarily
FACE and OTC) showed that eCO2 increased litter production ($\beta_{dir,fut}$ = 0.4±0.1) and SOM-
decomposition rates ($\beta_{dir,fut}$ = 0.34±0.2) (van Groenigen *et al.*, 2014), yet priming effects are difficult to
detect in field studies (van Groenigen *et al.*, 2014; Georgiou *et al.*, 2015).

Results from ecosystem-scale experiments indicate some heterogeneity and nuance in these
responses. For example, in a scrub oak ecosystem, 6 years of eCO2 increased SOM decay despite
unchanged microbial biomass (Carney *et al.*, 2007), and at ORNL FACE a decade of eCO2 resulted
in a small but non-significant increase in surface-soil SOM decomposition along with a reduction in
microbial nitrogen (Iversen *et al.*, 2012). In a later-succession forest, eCO2 increased microbial
biomass ($\beta_{dir,fut}$ = 0.40±0.4) but with no change in soil respiration ($\beta_{dir,fut}$ = -0.18±0.7) (Bader & Körner,
2010). At EucFACE +30 ppm eCO2 increased soil respiration ($\beta_{dir,fut}$ = 1.3), but a further increase of
120 ppm produced no additional effect after 3 months ($\beta_{dir,fut}$ = 0.3) or 3 years ($\beta_{dir,fut}$ = 0.21) (Drake *et
al.*, 2016, 2018). This 3-year response was non-significant but accounted for about half of the
additional carbon acquired under eCO2 (Jiang *et al.*, 2020).

Data on long-term changes in SOM decomposition in response to iCO2 remains limited. Synthesis of
23 flux-towers with increased GPP (Section III.1) suggested a non-significant increase in ecosystem
respiration ($R_e$) ($\beta_{app}$ =0.58±1) (Fernández-Martínez *et al.*, 2017). Synthesis and statistical upscaling
of chamber measurements suggested that global soil respiration has increased ($\beta_{app}$ =0.22) (Bond-
Lamberty & Thomson, 2010). Statistical predictors of this trend include temperature anomaly and
year (possibly an iCO2 effect). Notably, heterotrophic respiration would be expected to increase if
$C_{soil}$ increased, even with no change in decomposition rates.

Accelerated SOM decomposition may release nutrients and feed back onto the activity of plant
processes. For example, at Duke FACE increased root exudation ($\beta_{dir,fut}$ = 1.1±0.6) was coupled with
a non-significant but substantial increase in microbial biomass ($\beta_{dir,fut}$ = 1.1±1.3) and production of
nitrogen-acquiring extracellular enzymes (Phillips *et al.*, 2011). Exoenzyme activity was increased at
Duke and Rhinelander FACE (Larson *et al.*, 2002; Finzi *et al.*, 2006), although no change in nitrogen
mineralisation was observed in lab incubations (Zak *et al.*, 2003), perhaps suggesting that
stimulation of microbial activity required plant inputs. Conversely, leaf $\delta^{15}$N suggests that eCO2 may
have increased nitrogen mineralisation but not ring-width in mature trees in a European forest (Bader
*et al.*, 2013). eCO2 increased nitrogen and phosphorus mineralisation for a limited period at
EucFACE (Hasegawa *et al.*, 2016) and enzyme activity in an alpine forest (Souza *et al.*, 2017).

Conversely, meta-analysis suggests eCO2 increased fine root C:N ratios ($\beta_{dir,fut}$ = 0.13) (Nie *et al.*, 2013), which are associated with lower decomposability.

Contrasting mycorrhizal associations have been linked to biomass responses under low soil nitrogen conditions (Phillips *et al.*, 2013; Terrer *et al.*, 2016). Ectomycorrhizal (ECM) fungi are assumed capable of stimulating SOM decomposition, while arbuscular mycorrhizal (AM) fungi are not, resulting in increased nitrogen in aboveground BP in ECM trees but not in AM plants, primarily grasses (Terrer *et al.*, 2018). Conversely, AM association with *Avena fatua* in a lab and field setting increased SOM-decomposition rates under eCO2 ($\beta_{dir,fut}$ = 1.4) (Cheng *et al.*, 2012).

# 5 Terrestrial ecosystem carbon

## Direct evidence from site-scale studies

In the four longest-running FACE experiments eCO2 over a decade increased $C_{veg}$ increment ($\beta_{dir,fut}$ = 0.60±0.4) in these early succession temperate forests (Walker *et al.*, 2019). eCO2 of geological origin increased tree basal area in 30 year old trees ($\beta_{dir,fut}$ = 0.23-0.39) (Hättenschwiler *et al.*, 1997). Conversely, in the later succession forest at EucFACE, four years of eCO2 did not increase $C_{veg}$ increment (Jiang *et al.*, 2020), likely because of phosphorus limitation (Ellsworth *et al.*, 2017). Other experiments in later succession forests did not quantify $C_{veg}$. Meta-analysis and extrapolation (138 experiments) predicted a global increase in $C_{veg}$ ($\beta_{dir,fut}$ = 0.22±0.1) related to soil C:N ratio in AM-associated ecosystems and soil phosphorus in ECM-associated ecosystems (Terrer *et al.*, 2019). Biomass responses were generally higher in ECM systems than in AM systems (Terrer *et al.*, 2016), while another meta-analysis showed analogous biomass responses in trees compared with grasses (Song *et al.*, 2019).

Synthesis of meta-analyses found that eCO2 increased $C_{soil}$ across all (>200) experiments analysed ($\beta_{dir,fut}$ = 0.039±0.03) but not in field experiments lasting ≥2 years without nitrogen addition (25) ($\beta_{dir,fut}$ = 0.0054±0.03) (Hungate *et al.*, 2009). However, $C_{soil}$ responses to eCO2 at individual sites are mixed. For example, a decade of eCO2 increased $C_{soil}$ at ORNL FACE ($\beta_{dir,fut}$ = 0.51±0.6, 0-90 cm) (Iversen *et al.*, 2012) and in a desert ecosystem ($\beta_{dir,fut}$ = 0.59±0.62) (Evans *et al.*, 2014), but not in a scrub oak ecosystem ($\beta_{dir,fut}$ = -0.15±0.5) (Hungate *et al.*, 2013). In the desert ecosystem, inorganic carbonate pools may have contributed to increases in $C_{soil}$ through nocturnal CO₂ uptake (Hamerlynk *et al.* 2013) though net effects are likely small (Soper *et al.*, 2016).
EucFACE (Hasegawa *et al.*, 2016) and enzyme activity in an alpine forest (Souza *et al.*, 2017).

Conversely, meta-analysis suggests eCO2 increased fine root C:N ratios ($\beta_{dir,fut}$ = 0.13) (Nie *et al.*, 2013), which are associated with lower decomposability.

Contrasting mycorrhizal associations have been linked to biomass responses under low soil nitrogen conditions (Phillips *et al.*, 2013; Terrer *et al.*, 2016). Ectomycorrhizal (ECM) fungi are assumed capable of stimulating SOM decomposition, while arbuscular mycorrhizal (AM) fungi are not, resulting in increased nitrogen in aboveground BP in ECM trees but not in AM plants, primarily grasses (Terrer *et al.*, 2018). Conversely, AM association with *Avena fatua* in a lab and field setting increased SOM-decomposition rates under eCO2 ($\beta_{dir,fut}$ = 1.4) (Cheng *et al.*, 2012).

# 5 Terrestrial ecosystem carbon

## Direct evidence from site-scale studies

In the four longest-running FACE experiments eCO2 over a decade increased $C_{veg}$ increment ($\beta_{dir,fut}$ = 0.60±0.4) in these early succession temperate forests (Walker *et al.*, 2019). eCO2 of geological origin increased tree basal area in 30 year old trees ($\beta_{dir,fut}$ = 0.23-0.39) (Hättenschwiler *et al.*, 1997).

Conversely, in the later succession forest at EucFACE, four years of eCO2 did not increase $C_{veg}$ increment (Jiang *et al.*, 2020), likely because of phosphorus limitation (Ellsworth *et al.*, 2017). Other experiments in later succession forests did not quantify $C_{veg}$. Meta-analysis and extrapolation (138 experiments) predicted a global increase in $C_{veg}$ ($\beta_{dir,fut}$ = 0.22±0.1) related to soil C:N ratio in AM-associated ecosystems and soil phosphorus in ECM-associated ecosystems (Terrer *et al.*, 2019).

Biomass responses were generally higher in ECM systems than in AM systems (Terrer *et al.*, 2016), while another meta-analysis showed analogous biomass responses in trees compared with grasses (Song *et al.*, 2019).

Synthesis of meta-analyses found that eCO2 increased $C_{soil}$ across all (>200) experiments analysed ($\beta_{dir,fut}$ = 0.039±0.03) but not in field experiments lasting ≥2 years without nitrogen addition (25) ($\beta_{dir,fut}$ = 0.0054±0.03) (Hungate *et al.*, 2009). However, $C_{soil}$ responses to eCO2 at individual sites are mixed. For example, a decade of eCO2 increased $C_{soil}$ at ORNL FACE ($\beta_{dir,fut}$ = 0.51±0.6, 0-90 cm) (Iversen *et al.*, 2012) and in a desert ecosystem ($\beta_{dir,fut}$ = 0.59±0.62) (Evans *et al.*, 2014), but not in a scrub oak ecosystem ($\beta_{dir,fut}$ = -0.15±0.5) (Hungate *et al.*, 2013). In the desert ecosystem, inorganic carbonate pools may have contributed to increases in $C_{soil}$ through nocturnal CO₂ uptake (Hamerlynk *et al.* 2013) though net effects are likely small (Soper *et al.*, 2016).
Given limited data, litter addition experiments can also provide insight. Synthesis of priming
responses to litter addition (26 studies) suggested that 32 % of litter inputs accumulate as C<sub>soil</sub> (Liang
et al., 2018). 10-30 years of doubled aboveground-litter inputs in temperate forests increased C<sub>soil</sub> at
two sites (29±13 % and 33±28 %) but had no effect at three sites (Lajtha et al., 2018), nor in one
tropical forest (Sayer et al., 2019). Based on these responses and assuming doubled CO<sub>2</sub> doubles
litter production (which is unlikely), β<sub>dir,hist</sub> would range from 0 to 0.41±0.3.

Measurement of NEP requires whole-ecosystem enclosure, thus data are few. In a US salt-marsh
higher rates of NEP were sustained over 19 years in both C3 and C4 communities (Drake 2014). A
data-assimilation approach provided a comprehensive carbon budget at EucFACE showing no
change in C<sub>eco</sub> (Jiang et al., 2020).

Indirect evidence from global and regional studies

Spatially-explicit atmospheric [CO₂] measurements, fossil-fuel emissions, and other data are
integrated using atmospheric transport models to infer terrestrial net biome production (NBP). These
“inversions” suggest a global NBP of 2.3±0.9 (MACC-II), 2.3±1.5 (Jena-CarboScope) (1995-2014;
Fernández-Martínez *et al.*, 2019), and 1.9±0.5 PgC y⁻¹ (2010-2014) (Li *et al.*, 2018) and all estimated
positive trends in global NBP (βₐpp = 19±7, 11±4, 9.8±5). These estimates of NBP include both
‘natural’ NBP and land-use change-related (instantaneous and legacy) NBP.

Global land-use change-related NBP was estimated from bookkeeping models at -1.4±1.4 PgC y⁻¹ (2000-2009; Friedlingstein et al., 2019), and are predominantly in the tropics (-1.4±0.3 PgC y⁻¹) with fluxes outside the tropics balancing to a net flux of near zero (Houghton & Nassikas, 2017). Regional analysis of NBP show a strong sink in Northern Hemisphere extra-tropics 2.3±0.6 PgC y⁻¹ (1992-1996), 2.2±0.5 PgC y⁻¹ (2001-2004) but a substantial source in the tropics -1.1±1.5 (1992-1996) and -0.9±0.9 PgC y⁻¹ (2001-2004) (Gurney et al., 2004; Peylin et al., 2013). Combined with land-use change-related NBP, these inversion results suggest small ‘natural’ NBP in the tropics (c. 0.3-0.5). However, analysis of the vertical atmospheric [CO₂] gradient suggested close-to-neutral tropical NBP (Stephens et al., 2007), implying ‘natural’ NBP of similar magnitude and opposite sign to land-use change-related NBP, attributed primarily to iCO₂ (Schimel et al., 2015).

Flask, aircraft, and satellite-based measurements show trends in the seasonal-cycle amplitude of
[CO₂] since c. 1960 (Keeling et al., 1996; Graven et al., 2013; Yin et al., 2018), implying seasonal
intensification of northern NBP (β<sub>app</sub> = 2.2±0.6) (Graven et al., 2013). iCO₂ has been implicated as a
major driver of these trends (Forkel et al., 2016; Bastos et al., 2019), though increasing crop
Given limited data, litter addition experiments can also provide insight. Synthesis of priming responses to litter addition (26 studies) suggested that 32 % of litter inputs accumulate as C<sub>soil</sub> (Liang et al., 2018). 10-30 years of doubled aboveground-litter inputs in temperate forests increased C<sub>soil</sub> at two sites (29±13 % and 33±28 %) but had no effect at three sites (Lajtha et al., 2018), nor in one tropical forest (Sayer et al., 2019). Based on these responses and assuming doubled CO<sub>2</sub> doubles litter production (which is unlikely), β<sub>dir,hist</sub> would range from 0 to 0.41±0.3.

Measurement of NEP requires whole-ecosystem enclosure, thus data are few. In a US salt-marsh higher rates of NEP were sustained over 19 years in both C3 and C4 communities (Drake 2014). A data-assimilation approach provided a comprehensive carbon budget at EucFACE showing no change in C<sub>eco</sub> (Jiang et al., 2020).

## Indirect evidence from global and regional studies

Spatially-explicit atmospheric [CO₂] measurements, fossil-fuel emissions, and other data are integrated using atmospheric transport models to infer terrestrial net biome production (NBP). These “inversions” suggest a global NBP of 2.3±0.9 (MACC-II), 2.3±1.5 (Jena-CarboScope) (1995-2014; Fernández-Martínez et al., 2019), and 1.9±0.5 PgC y⁻¹ (2010-2014) (Li et al., 2018) and all estimated positive trends in global NBP (β<sub>app</sub> = 19±7, 11±4, 9.8±5). These estimates of NBP include both ‘natural’ NBP and land-use change-related (instantaneous and legacy) NBP.

Global land-use change-related NBP was estimated from bookkeeping models at -1.4±1.4 PgC y⁻¹ (2000-2009; Friedlingstein et al., 2019), and are predominantly in the tropics (-1.4±0.3 PgC y⁻¹) with fluxes outside the tropics balancing to a net flux of near zero (Houghton & Nassikas, 2017). Regional analysis of NBP show a strong sink in Northern Hemisphere extra-tropics 2.3±0.6 PgC y⁻¹ (1992-1996), 2.2±0.5 PgC y⁻¹ (2001-2004) but a substantial source in the tropics -1.1±1.5 (1992-1996) and -0.9±0.9 PgC y⁻¹ (2001-2004) (Gurney et al., 2004; Peylin et al., 2013). Combined with land-use change-related NBP, these inversion results suggest small ‘natural’ NBP in the tropics (c. 0.3-0.5). However, analysis of the vertical atmospheric [CO₂] gradient suggested close-to-neutral tropical NBP (Stephens et al., 2007), implying ‘natural’ NBP of similar magnitude and opposite sign to land-use change-related NBP, attributed primarily to iCO₂ (Schimel et al., 2015).

Flask, aircraft, and satellite-based measurements show trends in the seasonal-cycle amplitude of [CO₂] since c. 1960 (Keeling et al., 1996; Graven et al., 2013; Yin et al., 2018), implying seasonal intensification of northern NBP (β<sub>app</sub> = 2.2±0.6) (Graven et al., 2013). iCO₂ has been implicated as a major driver of these trends (Forkel et al., 2016; Bastos et al., 2019), though increasing crop
production (Gray et al., 2014; Zeng et al., 2014) and warming-induced increasing vegetation cover (Keenan & Riley, 2018) are also likely candidates.

Carbon budgeting estimated global ‘natural’ NBP at 3.6±1.0 PgC y⁻¹ (2009-2018) and 141 PgC since 1959 from the budget residual, and 3.2±1.2 PgC y⁻¹ and 130 PgC from process-based models (Friedlingstein et al., 2019). Based on the residual estimate of ‘natural’ NBP and the lower and upper bounds of either global vegetation or global ecosystem carbon stocks results in βₐpp = 0.93-1.4 (assuming all the sink is in vegetation) or βₐpp = 0.18-0.29 for ecosystem carbon (global vegetation and non-permafrost soils).

Synthesis and extrapolation of global inventory data suggested increased C<sub>eco</sub> (β<sub>app</sub> = 1.0±0.6), C<sub>veg</sub> (β<sub>app</sub> = 1.9), C<sub>soil</sub> (β<sub>app</sub> = 0.31), litter carbon (β<sub>app</sub> = 0.92), and dead wood carbon (β<sub>app</sub> = 0.64) (Pan et al., 2011). Little additional data on C<sub>soil</sub> changes over the historical period are available. Evidence from multi-plot forest-inventory data consistently shows net gains in wood C<sub>veg</sub> in recent decades in tropical Africa (β<sub>app</sub> = 0.77) (Hubau et al., 2020), the Amazon (β<sub>app</sub> = 0.69) (Brienen et al., 2015), Borneo (β<sub>app</sub> = 0.48±0.3) (Qie et al., 2017), and in large 50 ha plots across the tropics (β<sub>app</sub> = 0.30±0.24) (Chave et al., 2008). Wood C<sub>veg</sub> also increased in plots across the Eastern US (β<sub>app</sub> = 2.9±1.5) (McMahon et al., 2010) and globally (β<sub>app</sub> = 0.82±0.5) (Yu et al., 2019). Long-term geological CO₂ release was associated with reduced lidar-estimated aboveground C<sub>veg</sub> (Cawse-Nicholson et al., 2018).

Flux-towers measure NEP directly, yet have been running for a relatively short time. Synthesis of 23 flux-towers indicate increased NEP (β<sub>app</sub> = 4.3±2), with high CO₂ sensitivity (β<sub>dir,hist</sub> = 4.6±2) (Fernández-Martínez et al., 2017).

# IV. Synthesis

## 1 Evidence for the CO₂-fertilisation hypothesis

In this section we integrate and interpret the evidence for change in the components of the carbon cycle during the historical record concurrent with increasing [CO₂] (iCO₂; c. 280-400 ppm), in response to elevated [CO₂] (eCO₂; c. 390-500 ppm), and the probability and magnitude of iCO₂ as a driving factor in the historical change. In doing so we acknowledge that we are mixing evidence across scales, measurements, methods of analysis, and in some cases different variables that may not be perfectly comparable. However, this is required for a broad synthesis and a formal meta-
production (Gray et al., 2014; Zeng et al., 2014) and warming-induced increasing vegetation cover (Keenan & Riley, 2018) are also likely candidates.

Carbon budgeting estimated global ‘natural’ NBP at 3.6±1.0 PgC y⁻¹ (2009-2018) and 141 PgC since 1959 from the budget residual, and 3.2±1.2 PgC y⁻¹ and 130 PgC from process-based models (Friedlingstein et al., 2019). Based on the residual estimate of ‘natural’ NBP and the lower and upper bounds of either global vegetation or global ecosystem carbon stocks results in βₐpp = 0.93-1.4 (assuming all the sink is in vegetation) or βₐpp = 0.18-0.29 for ecosystem carbon (global vegetation and non-permafrost soils).

Synthesis and extrapolation of global inventory data suggested increased C<sub>eco</sub> (β<sub>app</sub> = 1.0±0.6), C<sub>veg</sub> (β<sub>app</sub> = 1.9), C<sub>soil</sub> (β<sub>app</sub> = 0.31), litter carbon (β<sub>app</sub> = 0.92), and dead wood carbon (β<sub>app</sub> = 0.64) (Pan et al., 2011). Little additional data on C<sub>soil</sub> changes over the historical period are available. Evidence from multi-plot forest-inventory data consistently shows net gains in wood C<sub>veg</sub> in recent decades in tropical Africa (β<sub>app</sub> = 0.77) (Hubau et al., 2020), the Amazon (β<sub>app</sub> = 0.69) (Brienen et al., 2015), Borneo (β<sub>app</sub> = 0.48±0.3) (Qie et al., 2017), and in large 50 ha plots across the tropics (β<sub>app</sub> = 0.30±0.24) (Chave et al., 2008). Wood C<sub>veg</sub> also increased in plots across the Eastern US (β<sub>app</sub> = 2.9±1.5) (McMahon et al., 2010) and globally (β<sub>app</sub> = 0.82±0.5) (Yu et al., 2019). Long-term geological CO₂ release was associated with reduced lidar-estimated aboveground C<sub>veg</sub> (Cawse-Nicholson et al., 2018).

Flux-towers measure NEP directly, yet have been running for a relatively short time. Synthesis of 23 flux-towers indicate increased NEP (β<sub>app</sub> = 4.3±2), with high CO₂ sensitivity (β<sub>dir,hist</sub> = 4.6±2) (Fernández-Martínez et al., 2017).

# IV. Synthesis

## 1 Evidence for the CO₂-fertilisation hypothesis

In this section we integrate and interpret the evidence for change in the components of the carbon cycle during the historical record concurrent with increasing [CO₂] (iCO₂; c. 280-400 ppm), in response to elevated [CO₂] (eCO₂; c. 390-500 ppm), and the probability and magnitude of iCO₂ as a driving factor in the historical change. In doing so we acknowledge that we are mixing evidence across scales, measurements, methods of analysis, and in some cases different variables that may not be perfectly comparable. However, this is required for a broad synthesis and a formal meta-
analysis is not our intention. We assign confidence as either “high” (all estimates agree), “medium” (estimate means disagree, substantial overlap in confidence intervals), or “low” (estimate means disagree, little overlap in confidence intervals).

Physiology

A number of independent lines of indirect evidence—ice-core OCS (Campbell et al., 2017) and O¹⁸ (Ciais et al., 2012), glucose isotopomers (Ehlers et al., 2015), satellite ET (Cheng et al., 2017), and flux-partitioned eddy-covariance (Fernández-Martínez et al., 2017)—provide **high confidence that terrestrial GPP has increased concurrently with iCO2**. Estimates of the GPP increase disagree by a factor of 1.7 (βₐpp = 0.95-1.6, mean = 1.2; Table 2), but overlap in confidence intervals (Fig. 3, Fig. S2) indicates that these estimates are consistent and suggests **medium confidence in the magnitude of the increase in GPP concurrent with iCO2**. Above the canopy-scale GPP can be measured only indirectly, and most of these estimates are a function of the [CO₂] trend (Box 3; isotopomers, satellite, OCS) which introduces a circularity. However, we place less confidence in estimates (usually satellite-based) that omit a CO₂ effect from the theory used in their GPP estimation (Box 3; De Kauwe et al., 2016). Flux-partitioned eddy-covariance provides the only estimate of GPP that does not require [CO₂] in its calculation and provides the highest βₐpp of 1.6±0.9 (Fernández-Martínez et al., 2017). A smaller proportion of this change was attributed to iCO2 (βₐdir,hist = 1.2±0.6).

Synthesis of direct evidence from experiments provides **high confidence that ecosystem-scale eCO2 increases diurnal photosynthesis in leaves** (βₐdir,fut = 0.68±0.2). This increase is very similar to the theoretical value for a light-saturated leaf (βₐdir,fut = 0.70±0.2, Table S1). The theoretical value for the canopy-scale photosynthesis response to iCO2 (280-410 ppm, βₐdir,fut = 0.60±0.3, Table S1) is about half the observed mean increase in GPP concurrent with iCO2 (βₐdir,hist = 1.2). For iCO2 to be the sole driver of the observed responses would require all leaves to be operating at the light-saturated rate of increase *and* would require additional positive feedbacks of equivalent magnitude.

The majority of global models tend to follow the theoretical response to iCO2 (Keenan et al., 2016). A carbon cycle model was able to replicate the OCS increase in GPP (βₐpp = 0.95±0.2) and change in northern seasonal [CO₂] amplitude by hypothesising leaf optimisation and predicting a substantial increase in LAI (note the phosphorus cycle was disabled) (Haverd et al., 2020). However, it is not clear that leaves optimise as hypothesised (Smith and Keenan, 2020), and models consistently represent allocation and LAI simplistically. For example, LAI trends are inferred in high-LAI tropical
analysis is not our intention. We assign confidence as either “high” (all estimates agree), “medium” (estimate means disagree, substantial overlap in confidence intervals), or “low” (estimate means disagree, little overlap in confidence intervals).

Physiology

A number of independent lines of indirect evidence—ice-core OCS (Campbell et al., 2017) and O¹⁸ (Ciais et al., 2012), glucose isotopomers (Ehlers et al., 2015), satellite ET (Cheng et al., 2017), and flux-partitioned eddy-covariance (Fernández-Martínez et al., 2017)—provide **high confidence that terrestrial GPP has increased concurrently with iCO2**. Estimates of the GPP increase disagree by a factor of 1.7 (βₐpp = 0.95-1.6, mean = 1.2; Table 2), but overlap in confidence intervals (Fig. 3, Fig. S2) indicates that these estimates are consistent and suggests **medium confidence in the magnitude of the increase in GPP concurrent with iCO2**. Above the canopy-scale GPP can be measured only indirectly, and most of these estimates are a function of the [CO₂] trend (Box 3; isotopomers, satellite, OCS) which introduces a circularity. However, we place less confidence in estimates (usually satellite-based) that omit a CO₂ effect from the theory used in their GPP estimation (Box 3; De Kauwe et al., 2016). Flux-partitioned eddy-covariance provides the only estimate of GPP that does not require [CO₂] in its calculation and provides the highest βₐpp of 1.6±0.9 (Fernández-Martínez et al., 2017). A smaller proportion of this change was attributed to iCO2 (βₐdir,hist = 1.2±0.6).

Synthesis of direct evidence from experiments provides **high confidence that ecosystem-scale eCO2 increases diurnal photosynthesis in leaves** (βₐdir,fut = 0.68±0.2). This increase is very similar to the theoretical value for a light-saturated leaf (βₐdir,fut = 0.70±0.2, Table S1). The theoretical value for the canopy-scale photosynthesis response to iCO2 (280-410 ppm, βₐdir,fut = 0.60±0.3, Table S1) is about half the observed mean increase in GPP concurrent with iCO2 (βₐdir,hist = 1.2). For iCO2 to be the sole driver of the observed responses would require all leaves to be operating at the light-saturated rate of increase *and* would require additional positive feedbacks of equivalent magnitude.

The majority of global models tend to follow the theoretical response to iCO2 (Keenan et al., 2016). A carbon cycle model was able to replicate the OCS increase in GPP (βₐpp = 0.95±0.2) and change in northern seasonal [CO₂] amplitude by hypothesising leaf optimisation and predicting a substantial increase in LAI (note the phosphorus cycle was disabled) (Haverd et al., 2020). However, it is not clear that leaves optimise as hypothesised (Smith and Keenan, 2020), and models consistently represent allocation and LAI simplistically. For example, LAI trends are inferred in high-LAI tropical
rainforests (Zhu *et al.*, 2016). In these regions models are likely predicting an increase in maximum LAI, which conflicts with experimental evidence and resource investment theory. An alternative hypothesis is that iCO2 accelerates the recovery of forest-gaps such that landscape-scale LAI is greater—a hypothesis not represented by any of the models used for attribution. Outside of tropical forests, changes in LAI are related to both iCO2 (Donohue *et al.*, 2013) and temperature-stimulated increases in growing season length (Keenan and Riley, 2018). An additional consideration is that models tend to under-estimate GPP relative to solar-induced fluorescence (a GPP proxy) in agricultural regions (Guanter *et al.*, 2014; Walker *et al.*, 2017), agriculture being another major factor of global change. Taken together, we place **high confidence that the historical GPP increase was primarily driven by iCO2 and also that iCO2 was not the sole driving factor**. However, it is unclear which factors might be driving the additional change in GPP.

A number of independent lines of indirect evidence—tree-ring $\delta^{13}$C (e.g. Saurer *et al.*, 2004; Peñuelas *et al.*, 2011; Frank *et al.*, 2015), flux-partitioned eddy-covariance (Keenan *et al.*, 2013; Mastrotheodorus *et al.*, 2017), and atmospheric $\delta^{13}$C (Keeling *et al.*, 2017)—provide **high confidence that iWUE (across leaf to global scales) and WUE (across leaf to ecosystem scales) have increased over the historical period** ($\beta_{app} = 0.85-3.9$, mean = 1.5). There remain large differences (factor of 5) between these estimates of the increase, primarily due to the eddy-covariance estimates ($\beta_{app} = 2.4\pm2.0$ and $3.9\pm2.5$). The causes for these differences are not fully understood, though scale (Medlyn *et al.*, 2017), plasticity (Mastrotheodorus *et al.*, 2017), high variability and short time scales (indicated by the high uncertainty), and GPP trends that are higher than expected from iCO2 alone (see above) all play a role. Eddy covariance estimates skew the mean and the modal change is around $\beta_{app} = 1$ (Fig. 3), similar to the mean for iCO2-attribution studies ($\beta_{dir,hist} = 0.80$) and the theoretical value for iWUE ($\beta_{dir,hist} = 1.1$). As with GPP, other than eddy-covariance these indirect methods use [CO$_2$] in their calculation (Box 3). Satellite estimates of WUE suffer from very short time-periods (13 years) with low signal-to-noise ratio, leaving little confidence in these trend estimates. Direct evidence from multiple experiments support iWUE and WUE increases ($\beta_{dir,fut} = 0.65-1.6$, mean=1.1) in agreement with predictions from theory (Figure 2). Taken together this evidence provides **high confidence that iCO2 has increased iWUE, medium confidence that the magnitude is in accordance with theory, and low confidence in the magnitude of the historical change in WUE**.

How do these changes in iWUE translate to changes in water use? Theory predicts that iWUE ($A_{net}/g_s$) responses are very tightly constrained ($\beta_{dir}$ c. 1), so if the change in $A_{net}$ is below 1, $g_s$ will
rainforests (Zhu *et al.*, 2016). In these regions models are likely predicting an increase in maximum LAI, which conflicts with experimental evidence and resource investment theory. An alternative hypothesis is that iCO2 accelerates the recovery of forest-gaps such that landscape-scale LAI is greater—a hypothesis not represented by any of the models used for attribution. Outside of tropical forests, changes in LAI are related to both iCO2 (Donohue *et al.*, 2013) and temperature-stimulated increases in growing season length (Keenan and Riley, 2018). An additional consideration is that models tend to under-estimate GPP relative to solar-induced fluorescence (a GPP proxy) in agricultural regions (Guanter *et al.*, 2014; Walker *et al.*, 2017), agriculture being another major factor of global change. Taken together, we place **high confidence that the historical GPP increase was primarily driven by iCO2 and also that iCO2 was not the sole driving factor**. However, it is unclear which factors might be driving the additional change in GPP.

A number of independent lines of indirect evidence—tree-ring $\delta^{13}$C (e.g. Saurer *et al.*, 2004; Peñuelas *et al.*, 2011; Frank *et al.*, 2015), flux-partitioned eddy-covariance (Keenan *et al.*, 2013; Mastrotheodorus *et al.*, 2017), and atmospheric $\delta^{13}$C (Keeling *et al.*, 2017)—provide **high confidence that iWUE (across leaf to global scales) and WUE (across leaf to ecosystem scales) have increased over the historical period** ($\beta_{app} = 0.85-3.9$, mean = 1.5). There remain large differences (factor of 5) between these estimates of the increase, primarily due to the eddy-covariance estimates ($\beta_{app} = 2.4\pm2.0$ and $3.9\pm2.5$). The causes for these differences are not fully understood, though scale (Medlyn *et al.*, 2017), plasticity (Mastrotheodorus *et al.*, 2017), high variability and short time scales (indicated by the high uncertainty), and GPP trends that are higher than expected from iCO2 alone (see above) all play a role. Eddy covariance estimates skew the mean and the modal change is around $\beta_{app} = 1$ (Fig. 3), similar to the mean for iCO2-attribution studies ($\beta_{dir,hist} = 0.80$) and the theoretical value for iWUE ($\beta_{dir,hist} = 1.1$). As with GPP, other than eddy-covariance these indirect methods use [CO$_2$] in their calculation (Box 3). Satellite estimates of WUE suffer from very short time-periods (13 years) with low signal-to-noise ratio, leaving little confidence in these trend estimates. Direct evidence from multiple experiments support iWUE and WUE increases ($\beta_{dir,fut} = 0.65-1.6$, mean=1.1) in agreement with predictions from theory (Figure 2). Taken together this evidence provides **high confidence that iCO2 has increased iWUE, medium confidence that the magnitude is in accordance with theory, and low confidence in the magnitude of the historical change in WUE**.

How do these changes in iWUE translate to changes in water use? Theory predicts that iWUE ($A_{net}/g_s$) responses are very tightly constrained ($\beta_{dir}$ c. 1), so if the change in $A_{net}$ is below 1, $g_s$ will
decrease (Barton et al., 2012). The observed changes in GPP ($\beta_{app}$ c. 1) suggest that widespread and broad-scale reductions in $g_s$ may not have occurred. Reductions in stomatal conductance may occur at points in time or space but as spatial and temporal scale increases, iCO2-induced decreases in stomatal conductance likely translate into smaller decreases in transpiration (Field et al. 1995; Körner et al., 2007).

Increased vegetation cover in semi-arid regions (Donohue et al., 2013; Ukkola et al. 2016), increased rooting depth (Y. Yang et al., unpublished; Iversen 2010), soil-water feedback on $g_s$, competition, and atmospheric coupling (Jarvis & McNaughton, 1986; Buckley et al., 2017; Sperry et al., 2019; Sabot et al., 2020) are all mechanisms that may lead to no change in water use at larger scales. This is especially likely to be the case in water-limited regions where long-term transpiration is primarily precipitation driven (Fatichi et al. 2016) i.e. plants use the water that is available.

## Biomass production

Ecosystem-scale forest-inventory networks suggest increases in wood BP concurrent with iCO2 (mean $\beta_{app}$ c. 1; Brienen et al., 2015; Yu et al., 2019, Hubau et al., 2020). Conversely, evidence from tree-rings is mixed (e.g. Peñuelas et al., 2011; Silva & Anand et al., 2013). Both of these methods are subject to potential sampling biases (Box 3). However, the tree-ring biases are potentially larger and can be either positive (Nehbas-Ahles et al., 2014) or negative (Brienen et al., 2016). The inventory evidence provides **medium confidence in an increase in wood BP over the historical period, with low confidence in the magnitude ($\beta_{app}$ c. 1)**. However, this is an area of disagreement among several in our authorship group.

Many studies show increased BP in response to eCO2 (e.g. Baig et al., 2015), but these studies are often short-lived and under artificial conditions. Evidence from long-term, large-scale FACE experiments (<10 experiments) is mixed, with both increases (e.g. Norby et al., 2005) and no change in BP observed (e.g. Jiang et al., 2020; Bader et al., 2013) ($\beta_{dir,fut}$ = -0.3-0.56, mean = 0.19). Many studies show a BP response to eCO2 that is higher at sites with higher nutrient availability (e.g. Terrer et al., 2018), that is greater when nutrients were added (e.g. Sigurdsson et al., 2013; Reich et al., 2006), or no response when nutrients are low (e.g. Sigurdsson et al., 2013; Ellsworth et al., 2017). However, strong evidence for the widely held progressive nitrogen limitation hypothesis is restricted to two experiments (Biocon, ORNL) (Reich et al., 2006; Norby et al., 2010). At both of these experiments nutrient dynamics also caused declining BP in the ambient treatments, indicating that eCO2 responses can be tied, via nutrient availability, to underlying ecosystem dynamics.
decrease (Barton et al., 2012). The observed changes in GPP ($\beta_{app}$ c. 1) suggest that widespread and broad-scale reductions in $g_s$ may not have occurred. Reductions in stomatal conductance may occur at points in time or space but as spatial and temporal scale increases, iCO2-induced decreases in stomatal conductance likely translate into smaller decreases in transpiration (Field et al. 1995; Körner et al., 2007).

Increased vegetation cover in semi-arid regions (Donohue et al., 2013; Ukkola et al. 2016), increased rooting depth (Y. Yang et al., unpublished; Iversen 2010), soil-water feedback on $g_s$, competition, and atmospheric coupling (Jarvis & McNaughton, 1986; Buckley et al., 2017; Sperry et al., 2019; Sabot et al., 2020) are all mechanisms that may lead to no change in water use at larger scales. This is especially likely to be the case in water-limited regions where long-term transpiration is primarily precipitation driven (Fatichi et al. 2016) i.e. plants use the water that is available.

## Biomass production

Ecosystem-scale forest-inventory networks suggest increases in wood BP concurrent with iCO2 (mean $\beta_{app}$ c. 1; Brienen et al., 2015; Yu et al., 2019, Hubau et al., 2020). Conversely, evidence from tree-rings is mixed (e.g. Peñuelas et al., 2011; Silva & Anand et al., 2013). Both of these methods are subject to potential sampling biases (Box 3). However, the tree-ring biases are potentially larger and can be either positive (Nehbas-Ahles et al., 2014) or negative (Brienen et al., 2016). The inventory evidence provides **medium confidence in an increase in wood BP over the historical period, with low confidence in the magnitude ($\beta_{app}$ c. 1)**. However, this is an area of disagreement among several in our authorship group.

Many studies show increased BP in response to eCO2 (e.g. Baig et al., 2015), but these studies are often short-lived and under artificial conditions. Evidence from long-term, large-scale FACE experiments (<10 experiments) is mixed, with both increases (e.g. Norby et al., 2005) and no change in BP observed (e.g. Jiang et al., 2020; Bader et al., 2013) ($\beta_{dir,fut}$ = -0.3-0.56, mean = 0.19). Many studies show a BP response to eCO2 that is higher at sites with higher nutrient availability (e.g. Terrer et al., 2018), that is greater when nutrients were added (e.g. Sigurdsson et al., 2013; Reich et al., 2006), or no response when nutrients are low (e.g. Sigurdsson et al., 2013; Ellsworth et al., 2017). However, strong evidence for the widely held progressive nitrogen limitation hypothesis is restricted to two experiments (Biocon, ORNL) (Reich et al., 2006; Norby et al., 2010). At both of these experiments nutrient dynamics also caused declining BP in the ambient treatments, indicating that eCO2 responses can be tied, via nutrient availability, to underlying ecosystem dynamics.
BP responses were observed in earlier-succession more-disturbed ecosystems, which also tend to have higher nutrient availability (Körner 2006). The experiments with no response were often situated in later-succession forests, some of which were also severely limited by nutrients. The forest inventories in which BP increases concurrently with iCO2 were observed in later-succession, primarily tropical, forests that are assumed to be strongly nutrient limited. These inventory responses are high (β<sub>app</sub> c. 1) compared with the results from experiments even in earlier-succession forests (β<sub>dir,fut</sub> = 0.49±0.3). However, the evidence is insufficient to robustly evaluate how eCO2 affects late-successional and tropical forests. Taken together, this evidence suggests **high confidence that eCO2 can stimulate BP (β<sub>dir,fut</sub> c. 0.5)**, that the response is diminished by nutrient limitations, and that the observed inventory response is likely due to iCO2 and additional factors.

Vegetation mortality

A number of independent plot networks provide **high confidence that tree mortality has increased over the historical period but low confidence in the magnitude (β<sub>app</sub> = -1.2-7.4, mean = 2.8; Fig. 3, Fig. S3)**. The greatest changes are primarily attributed to drought. Causes of mortality are often stochastic, multi-factorial, and play out over long time periods, making trend identification and attribution at ecosystem and landscape scales uncertain (McMahon *et al.*, 2019). For individual scale mortality, an intra-specific growth-survival tradeoff is apparent for some species (e.g. Di Fillipo *et al.*, 2015), which would reduce lifespans if iCO2 increases wood BP. However an intra-specific growth-survival tradeoff is not ubiquitous among species (e.g. Cailleret *et al.*, 2017).

Greenhouse eCO2 experiments suggest that eCO2 does not reduce drought-related mortality (e.g. Duan *et al.*, 2014; Bachofen *et al.*, 2018). However, eCO2 commonly increased leaf area in these experiments, increasing transpiration that likely exacerbated mortality risk (Duan *et al.*, 2018). What does this mean for eCO2 responses in ecosystems? Due to the juvenile growth stage of these plants, leaf area increases were much higher than expected in closed canopy systems (see Box 2), and increased root BP from eCO2 would exacerbate pot-volume constraints on root proliferation. Inference from these experiments is limited. At the stand scale there is very limited evidence that eCO2 might change self-thinning relationships allowing higher basal area for a given stem density (Kubiske *et al.*, 2019). Evidence for changes in mortality caused by iCO2 is weak and mostly indirect with limited support for both increases and decreases in individual and stand-scale mortality rates. Taken together (CS11) the response of mortality to iCO2 and eCO2 is unknown, even the direction of change is unclear.
BP responses were observed in earlier-succession more-disturbed ecosystems, which also tend to have higher nutrient availability (Körner 2006). The experiments with no response were often situated in later-succession forests, some of which were also severely limited by nutrients. The forest inventories in which BP increases concurrently with iCO2 were observed in later-succession, primarily tropical, forests that are assumed to be strongly nutrient limited. These inventory responses are high (β<sub>app</sub> c. 1) compared with the results from experiments even in earlier-succession forests (β<sub>dir,fut</sub> = 0.49±0.3). However, the evidence is insufficient to robustly evaluate how eCO2 affects late-successional and tropical forests. Taken together, this evidence suggests **high confidence that eCO2 can stimulate BP (β<sub>dir,fut</sub> c. 0.5)**, that the response is diminished by nutrient limitations, and that the observed inventory response is likely due to iCO2 and additional factors.

Vegetation mortality

A number of independent plot networks provide **high confidence that tree mortality has increased over the historical period but low confidence in the magnitude (β<sub>app</sub> = -1.2-7.4, mean = 2.8; Fig. 3, Fig. S3)**. The greatest changes are primarily attributed to drought. Causes of mortality are often stochastic, multi-factorial, and play out over long time periods, making trend identification and attribution at ecosystem and landscape scales uncertain (McMahon *et al.*, 2019). For individual scale mortality, an intra-specific growth-survival tradeoff is apparent for some species (e.g. Di Fillipo *et al.*, 2015), which would reduce lifespans if iCO2 increases wood BP. However an intra-specific growth-survival tradeoff is not ubiquitous among species (e.g. Cailleret *et al.*, 2017).

Greenhouse eCO2 experiments suggest that eCO2 does not reduce drought-related mortality (e.g. Duan *et al.*, 2014; Bachofen *et al.*, 2018). However, eCO2 commonly increased leaf area in these experiments, increasing transpiration that likely exacerbated mortality risk (Duan *et al.*, 2018). What does this mean for eCO2 responses in ecosystems? Due to the juvenile growth stage of these plants, leaf area increases were much higher than expected in closed canopy systems (see Box 2), and increased root BP from eCO2 would exacerbate pot-volume constraints on root proliferation. Inference from these experiments is limited. At the stand scale there is very limited evidence that eCO2 might change self-thinning relationships allowing higher basal area for a given stem density (Kubiske *et al.*, 2019). Evidence for changes in mortality caused by iCO2 is weak and mostly indirect with limited support for both increases and decreases in individual and stand-scale mortality rates. Taken together (CS11) the response of mortality to iCO2 and eCO2 is unknown, even the direction of change is unclear.
## Organic matter decomposition

The few studies of soil or ecosystem respiration show small ($\beta_{app} = 0.22$; Bond-Lamberty & Thompson 2010) or non-significant increases ($\beta_{app} = 0.58 \pm 1$; Fernández-Martínez et al., 2017). These trends could possibly be related to increasing heterotrophic respiration and decomposition, but increasing temperature is inferred as the cause and not iCO2 (e.g. Bond-Lamberty et al., 2018). Due to the low number of studies there is **low confidence that SOM decomposition has increased over the historical period, but it is unknown whether SOM decomposition rates have increased**.

Evidence from eCO2 experiments generally supports the theory that rising [CO₂] increases SOM-decomposition rates (e.g. van Greonigen et al., 2015) due to increases in microbial biomass, rhizosphere priming, mycorrhizal association, and increases in soil water content (see refs in Section III.4). Smaller changes in decomposition rates have been associated with lower microbial biomass and higher soil water (Bader & Körner, 2010; Iversen et al., 2012). Taken together, the evidence suggests **medium confidence that eCO₂ increases rates of SOM decomposition but with low confidence in the magnitude**. Increasing SOM decomposition will also release nutrients that may be available for plant growth and BP. Plant nutrient acquisition through mycorrhizal and other root-microbe interactions are likely mediators of this process (Terrer et al., 2018). Notably, the large step-change in eCO2 experiments compared with the more gradual iCO2, could lead to a greater imbalance of available resources resulting in a carbon surplus (Box 3) that could fuel greater microbial activity. It is worth noting that increased SOM-decomposition rates do not necessarily imply lower Cₐ₉₆ᵢₐ₉₆ᵢₐ₉₆ᵢ litter inputs are also increasing (Liang et al., 2018).

## Terrestrial ecosystem carbon

Multiple independent lines of evidence—global-scale carbon budgeting (Friedlingstein et al., 2019), atmospheric inversions (e.g. Peylin et al., 2016; Fernández-Martínez et al., 2019), seasonal [CO₂] amplitude trends (Graven et al., 2013), and forest inventories (e.g. Pan et al., 2011; Hubau et al., 2020)—imply a CO₂ sink in terrestrial ecosystems (Fig. 3, Fig. S4). This evidence provides **high confidence that terrestrial ecosystem carbon has increased over the historical period, with substantial changes in the ‘natural’ carbon sink almost balanced by a net carbon source from land-use change**. Global carbon budgeting and global forest analysis suggest responses concurrent with iCO₂ in the range, $\beta_{app} = 0.18-1.0$. The ‘natural’ carbon store response estimated for global intact forests ($\beta_{app} = 0.66 \pm 0.4$; Pan et al., 2011) is higher than estimated for the ‘natural’ land surface
## Organic matter decomposition

The few studies of soil or ecosystem respiration show small ($\beta_{app} = 0.22$; Bond-Lamberty & Thompson 2010) or non-significant increases ($\beta_{app} = 0.58 \pm 1$; Fernández-Martínez et al., 2017). These trends could possibly be related to increasing heterotrophic respiration and decomposition, but increasing temperature is inferred as the cause and not iCO2 (e.g. Bond-Lamberty et al., 2018). Due to the low number of studies there is **low confidence that SOM decomposition has increased over the historical period, but it is unknown whether SOM decomposition rates have increased**.

Evidence from eCO2 experiments generally supports the theory that rising [CO₂] increases SOM-decomposition rates (e.g. van Greonigen et al., 2015) due to increases in microbial biomass, rhizosphere priming, mycorrhizal association, and increases in soil water content (see refs in Section III.4). Smaller changes in decomposition rates have been associated with lower microbial biomass and higher soil water (Bader & Körner, 2010; Iversen et al., 2012). Taken together, the evidence suggests **medium confidence that eCO₂ increases rates of SOM decomposition but with low confidence in the magnitude**. Increasing SOM decomposition will also release nutrients that may be available for plant growth and BP. Plant nutrient acquisition through mycorrhizal and other root-microbe interactions are likely mediators of this process (Terrer et al., 2018). Notably, the large step-change in eCO2 experiments compared with the more gradual iCO2, could lead to a greater imbalance of available resources resulting in a carbon surplus (Box 3) that could fuel greater microbial activity. It is worth noting that increased SOM-decomposition rates do not necessarily imply lower Cₜₐᵦᵢ₏ litter inputs are also increasing (Liang et al., 2018).

## Terrestrial ecosystem carbon

Multiple independent lines of evidence—global-scale carbon budgeting (Friedlingstein et al., 2019), atmospheric inversions (e.g. Peylin et al., 2016; Fernández-Martínez et al., 2019), seasonal [CO₂] amplitude trends (Graven et al., 2013), and forest inventories (e.g. Pan et al., 2011; Hubau et al., 2020)—imply a CO₂ sink in terrestrial ecosystems (Fig. 3, Fig. S4). This evidence provides **high confidence that terrestrial ecosystem carbon has increased over the historical period, with substantial changes in the ‘natural’ carbon sink almost balanced by a net carbon source from land-use change**. Global carbon budgeting and global forest analysis suggest responses concurrent with iCO2 in the range, $\beta_{app} = 0.18-1.0$. The ‘natural’ carbon store response estimated for global intact forests ($\beta_{app} = 0.66 \pm 0.4$; Pan et al., 2011) is higher than estimated for the ‘natural’ land surface
($\beta_{app}$ =0.18-0.29; Friedlingstein *et al.*, 2019). Trends observed in eddy-covariance NEP (site-scale 'natural' sink) and inversion NBP (global-scale combined 'natural' and land-use sink) are extremely high ($\beta_{app}$ = 4.3-19, mean 11). The extremely high $\beta_{app}$ for global NBP (and to a lesser degree NEP) results from global NBP being near zero as the 'natural' sink is almost balanced by the net source from land-use change, thus small absolute changes can be relatively high (Box 3).

CO2 effects on terrestrial carbon are convolved with the effects of concurrent anthropogenic changes in climate, nitrogen deposition, and land-use change, including agricultural intensification and fire management. Attribution analyses indicate a primary role for iCO2 (e.g., Schimel et al., 2015; Keenan et al., 2016; Bastos et al., 2019; Fernández-Martínez et al., 2019; Haverd et al., 2020). These analyses depend on the inclusion of accurate explanatory-variable datasets and accurate process representation in models, which may not be the case. Quantification of the effect of iCO2 on carbon storage in terrestrial ecosystems remains elusive.

As with BP responses, studies of forest inventories show higher $C_{veg}$ responses ($\beta_{app}$ = 0.3-2, mean = 0.85) than studies of eCO2 experiments ($\beta_{app}$ = 0.22-0.39) (Fig. 3). However, the highest values come from two analyses: one that includes global forest regrowth ($\beta_{app}$ = 1.9; Pan et al., 2011) and younger (c. 50-100 years old) temperate forests ($\beta_{app}$ = 2±1; McMahon et al., 2011). Exclusion of these higher change studies results in a narrower range ($\beta_{app}$ = 0.3-0.85, mean = 0.57). This exclusion narrows the difference between responses inferred from iCO2 and eCO2 studies, which is consistent with theory as relative stock changes are under-estimated more in short-term experiments than in inventory-type studies (Fig. S2). Responses of vegetation carbon increment may give a more accurate estimate of responses in systems that are far from equilibrium when initially exposed to eCO2 (Fig. S2). Vegetation carbon increment responses estimated from FACE experiments ($\beta_{app}$ = 0.60±0.4; Walker et al., 2019) are consistent with the reduced range from inventory studies.

However, the theoretical under-estimation of undisturbed forest-inventory responses (Fig. S2) yet similarity of these responses with those from disturbed forests subjected to eCO2 and not the lower values from undisturbed forests (e.g. Jiang et al., 2020), requires further consideration. Either eCO2 experiments are under-estimating responses or other factors have affected the inventory evidence.

Both of these evidence types are likely missing the full extent of mortality (e.g. Chambers et al., 2013), and evidence from larger-scale 50 ha plots suggests a lower response for intact tropical forests ($\beta_{app}$ = 0.30±0.2; Chave et al., 2008).

Evidence for changes in $C_{soil}$ is mixed and context dependent. On average there is no detectable
($\beta_{app}$ =0.18-0.29; Friedlingstein *et al.*, 2019). Trends observed in eddy-covariance NEP (site-scale 'natural' sink) and inversion NBP (global-scale combined 'natural' and land-use sink) are extremely high ($\beta_{app}$ = 4.3-19, mean 11). The extremely high $\beta_{app}$ for global NBP (and to a lesser degree NEP) results from global NBP being near zero as the 'natural' sink is almost balanced by the net source from land-use change, thus small absolute changes can be relatively high (Box 3).

CO2 effects on terrestrial carbon are convolved with the effects of concurrent anthropogenic changes in climate, nitrogen deposition, and land-use change, including agricultural intensification and fire management. Attribution analyses indicate a primary role for iCO2 (e.g., Schimel et al., 2015; Keenan et al., 2016; Bastos et al., 2019; Fernández-Martínez et al., 2019; Haverd et al., 2020). These analyses depend on the inclusion of accurate explanatory-variable datasets and accurate process representation in models, which may not be the case. Quantification of the effect of iCO2 on carbon storage in terrestrial ecosystems remains elusive.

As with BP responses, studies of forest inventories show higher $C_{veg}$ responses ($\beta_{app}$ = 0.3-2, mean = 0.85) than studies of eCO2 experiments ($\beta_{app}$ = 0.22-0.39) (Fig. 3). However, the highest values come from two analyses: one that includes global forest regrowth ($\beta_{app}$ = 1.9; Pan et al., 2011) and younger (c. 50-100 years old) temperate forests ($\beta_{app}$ = 2±1; McMahon et al., 2011). Exclusion of these higher change studies results in a narrower range ($\beta_{app}$ = 0.3-0.85, mean = 0.57). This exclusion narrows the difference between responses inferred from iCO2 and eCO2 studies, which is consistent with theory as relative stock changes are under-estimated more in short-term experiments than in inventory-type studies (Fig. S2). Responses of vegetation carbon increment may give a more accurate estimate of responses in systems that are far from equilibrium when initially exposed to eCO2 (Fig. S2). Vegetation carbon increment responses estimated from FACE experiments ($\beta_{app}$ = 0.60±0.4; Walker et al., 2019) are consistent with the reduced range from inventory studies.

However, the theoretical under-estimation of undisturbed forest-inventory responses (Fig. S2) yet similarity of these responses with those from disturbed forests subjected to eCO2 and not the lower values from undisturbed forests (e.g. Jiang et al., 2020), requires further consideration. Either eCO2 experiments are under-estimating responses or other factors have affected the inventory evidence.

Both of these evidence types are likely missing the full extent of mortality (e.g. Chambers et al., 2013), and evidence from larger-scale 50 ha plots suggests a lower response for intact tropical forests ($\beta_{app}$ = 0.30±0.2; Chave et al., 2008).

Evidence for changes in $C_{soil}$ is mixed and context dependent. On average there is no detectable
response across experiments (Hungate et al., 2009), though at some individual sites, C<sub>soil</sub> did
accumulate (e.g. Iversen et al., 2012; Evans et al., 2014). The only study (to our knowledge) of soil
carbon changes concurrent with iCO2 suggests a relative response in global forests (β<sub>app</sub> = 0.31; Pan
et al., 2011), that would be substantial if extrapolated to mineral soils globally. As with vegetation
carbon stocks, the long-term, relative responses of soil carbon stocks are likely underestimated by
short-term eCO2 experiments (Fig. S2). Taken together, evidence suggests **medium confidence**
**that eCO2 increases ecosystem carbon stocks over short to medium timescales and iCO2 has**
**contributed to the change over the historical period, but with low confidence in the**
**magnitude.**

## 2 What we need to know

Confidence in the magnitude of CO₂ effects is generally low. In particular, iCO₂-attribution is a major
challenge in testing the CO₂-fertilisation hypothesis over the historical period. Attribution often relies
on empirical regression which simply indicates correlation, anything with a trend over the historical
period will correlate with iCO₂. We advocate using log-log β as a stable (Notes S1; Fig. S1),
relativised metric for comparison with theoretical expectations and other studies.

Process-based models are also used to deconvolve causation from multiple global-change factors.
Models often represent key mechanisms over-simplistically and yet are also equifinal, while model
ensembles represent a non-random sample of non-independent models (Beven, 2006; Fatichi et al.,
2019; Sanderson & Fisher, 2020). Thus, models need always to be interpreted in the context of the
mechanisms they represent, those they do not, how representations might bias results, and how well
they reproduce observations (e.g. Medlyn et al., 2015). Mechanistic models (or modules) of BP,
resource acquisition and allocation, how soil and plant water status affect g_s, plant-microbe effects on
soil decomposition, vegetation structure and demography (e.g. competition, mortality), and land-use
need to be applied more extensively to the CO₂-fertilisation hypothesis. Alternative hypotheses to
explain observed phenomena should be evaluated within model ensembles, and calibrated to allow
the hypotheses to compete on an equal footing (e.g. Zhang et al., 2015). Agile and extensible
models (e.g. Clark et al., 2015; Walker et al., 2018) will be needed to rapidly incorporate this
understanding, including uncertainty, into the internally-consistent and quantitative systems-level
theory that models represent.

It is crucial that future eCO2 experiments are designed and resourced to understand the mechanistic
basis for responses (or lack thereof) and do not simply report significance or effect-sizes. Integration
response across experiments (Hungate et al., 2009), though at some individual sites, C<sub>soil</sub> did
accumulate (e.g. Iversen et al., 2012; Evans et al., 2014). The only study (to our knowledge) of soil
carbon changes concurrent with iCO2 suggests a relative response in global forests (β<sub>app</sub> = 0.31; Pan
et al., 2011), that would be substantial if extrapolated to mineral soils globally. As with vegetation
carbon stocks, the long-term, relative responses of soil carbon stocks are likely underestimated by
short-term eCO2 experiments (Fig. S2). Taken together, evidence suggests **medium confidence**
**that eCO2 increases ecosystem carbon stocks over short to medium timescales and iCO2 has**
**contributed to the change over the historical period, but with low confidence in the**
**magnitude.**

## 2 What we need to know

Confidence in the magnitude of CO₂ effects is generally low. In particular, iCO₂-attribution is a major
challenge in testing the CO₂-fertilisation hypothesis over the historical period. Attribution often relies
on empirical regression which simply indicates correlation, anything with a trend over the historical
period will correlate with iCO₂. We advocate using log-log β as a stable (Notes S1; Fig. S1),
relativised metric for comparison with theoretical expectations and other studies.

Process-based models are also used to deconvolve causation from multiple global-change factors.
Models often represent key mechanisms over-simplistically and yet are also equifinal, while model
ensembles represent a non-random sample of non-independent models (Beven, 2006; Fatichi et al.,
2019; Sanderson & Fisher, 2020). Thus, models need always to be interpreted in the context of the
mechanisms they represent, those they do not, how representations might bias results, and how well
they reproduce observations (e.g. Medlyn et al., 2015). Mechanistic models (or modules) of BP,
resource acquisition and allocation, how soil and plant water status affect g_s, plant-microbe effects on
soil decomposition, vegetation structure and demography (e.g. competition, mortality), and land-use
need to be applied more extensively to the CO₂-fertilisation hypothesis. Alternative hypotheses to
explain observed phenomena should be evaluated within model ensembles, and calibrated to allow
the hypotheses to compete on an equal footing (e.g. Zhang et al., 2015). Agile and extensible
models (e.g. Clark et al., 2015; Walker et al., 2018) will be needed to rapidly incorporate this
understanding, including uncertainty, into the internally-consistent and quantitative systems-level
theory that models represent.

It is crucial that future eCO2 experiments are designed and resourced to understand the mechanistic
basis for responses (or lack thereof) and do not simply report significance or effect-sizes. Integration
with extensible, process-based models will help evaluate and explore the mechanistic basis for
observed responses (Medlyn et al., 2015). During the lifetime of long-term experiments, new
hypotheses will arise to explain unexpected or key observations that may help to provide context and
mechanisms underlying the observed responses. These long-term experiments represent very large
investments and for relatively small additional investment, related studies can test mechanistic
hypotheses as they arise during an experiment's lifetime.

Suggestions for high-priority future studies:

**Understanding the mechanistic basis for GPP increases observed over the historical period and how this relates to water use.** GPP, iWUE, and water use are intimately tied. The mechanisms by which plants might adjust to iCO2 (photosynthetic acclimation/optimisation, more and deeper roots, gs responses to water status) are not fully understood and thus not well explored within models. A quantitative synthesis canopy or stand-scale photosynthetic responses in eCO2 experiments would be informative.

**Biomass production** inferred from tree-rings and forest inventories reach very different conclusions. Where possible, studies that can integrate these two types of evidence, such as tree-ring sampling at inventory sites (e.g. Dye *et al.*, 2016; Evans *et al.*, 2017), acknowledging respective biases will be fruitful. The mechanisms underlying how increased GPP leads to increased BP and increased nutrient acquisition through plant-microbe associations are key areas for future study, especially over successional gradients. eCO2 studies in mid and late-succession ecosystems, and tropical, boreal, semi-arid, and savannah ecosystems will help to address the young, temperate ecosystem bias in eCO2 studies.

**How iCO2 affects mortality** is key for understanding C<sub>veg</sub> and community responses to iCO2. As mortality is a relatively rare event in established vegetation, change detection and attribution of causation require large-scale, long-term monitoring, and ideally, experiments (Hartmann *et al.*, 2018). Understanding the mechanics of observed growth-mortality tradeoffs and whether iCO2 may be alleviating mortality in semi-arid regions are high-priority.

**Studies of the C<sub>soil</sub> decomposition rate** over the historical period are practically non-existent, additional studies are required. As with BP, efforts to fully understand plant-microbe-soil (and likely invertebrate), carbon-nutrient interactions continue to be a high-priority. Furthermore, investigation of responses in deep soil layers are few or non-existent. Understanding how the opposing processes of increased litter production, root-microbe interactions, increased decomposition rates, and rates of
with extensible, process-based models will help evaluate and explore the mechanistic basis for
observed responses (Medlyn et al., 2015). During the lifetime of long-term experiments, new
hypotheses will arise to explain unexpected or key observations that may help to provide context and
mechanisms underlying the observed responses. These long-term experiments represent very large
investments and for relatively small additional investment, related studies can test mechanistic
hypotheses as they arise during an experiment's lifetime.

Suggestions for high-priority future studies:

**Understanding the mechanistic basis for GPP increases observed over the historical period and how this relates to water use.** GPP, iWUE, and water use are intimately tied. The mechanisms by which plants might adjust to iCO2 (photosynthetic acclimation/optimisation, more and deeper roots, gs responses to water status) are not fully understood and thus not well explored within models. A quantitative synthesis canopy or stand-scale photosynthetic responses in eCO2 experiments would be informative.

**Biomass production** inferred from tree-rings and forest inventories reach very different conclusions. Where possible, studies that can integrate these two types of evidence, such as tree-ring sampling at inventory sites (e.g. Dye *et al.*, 2016; Evans *et al.*, 2017), acknowledging respective biases will be fruitful. The mechanisms underlying how increased GPP leads to increased BP and increased nutrient acquisition through plant-microbe associations are key areas for future study, especially over successional gradients. eCO2 studies in mid and late-succession ecosystems, and tropical, boreal, semi-arid, and savannah ecosystems will help to address the young, temperate ecosystem bias in eCO2 studies.

**How iCO2 affects mortality** is key for understanding C<sub>veg</sub> and community responses to iCO2. As mortality is a relatively rare event in established vegetation, change detection and attribution of causation require large-scale, long-term monitoring, and ideally, experiments (Hartmann *et al.*, 2018). Understanding the mechanics of observed growth-mortality tradeoffs and whether iCO2 may be alleviating mortality in semi-arid regions are high-priority.

**Studies of the C<sub>soil</sub> decomposition rate** over the historical period are practically non-existent, additional studies are required. As with BP, efforts to fully understand plant-microbe-soil (and likely invertebrate), carbon-nutrient interactions continue to be a high-priority. Furthermore, investigation of responses in deep soil layers are few or non-existent. Understanding how the opposing processes of increased litter production, root-microbe interactions, increased decomposition rates, and rates of
mineral-associated SOM formation balance to affect C<sub>soil</sub> throughout the soil profile will be key to predictive understanding. This may be especially relevant in non-forest ecosystems, where the largest potential change in carbon storage is belowground.

iCO2 affects ecosystem carbon primarily through effects on NEP, thus understanding of C<sub>eco</sub> responses to iCO2 will emerge from the above research priorities. Further, NBP is what the atmosphere 'sees', which includes additional non-respiratory carbon losses caused by fire (anthropogenic and wild), hydrological export, and export of consumer goods. iCO2 may interact with some non-NEP fluxes, e.g. greater grassland BP leading to higher fuel loads, greater BP in regrowing forests following land-use change. Land-use change NBP is often calculated without considering iCO2 and separately from 'natural' NBP caused by iCO2, climate change, nitrogen deposition, and other factors (e.g. Friedlingstein et al., 2019). Though the boundary between these fluxes is blurred (Pongratz et al., 2014). Integrated studies that consider all of these factors, especially land-use change (including iCO2-acceleration of regrowth following disturbance, e.g. Pugh et al., 2019), agriculture, and 'natural' fluxes will yield insight.

V. Conclusions

To evaluate the CO₂-fertilisation hypothesis, we synthesised evidence from wide-ranging disciplines within an integrated theoretical framework. We have medium or high confidence that GPP, iWUE, BP, and mortality have all increased over the historical period. However, we often have low or medium confidence in the magnitude, and low confidence in how much of the change is attributable to iCO2.

The complex nature of the problem demands integrated studies, and further integration is required to fully combine the broad evidence in a way that is scale, bias, and uncertainty aware (Box 3). Inference regarding trends and responses (or lack thereof) should always be grounded in the context-dependence and biases associated with a particular study. Further experiments and observations are needed to help reconcile differences among evidence streams. For example, tree-ring sampling at flux sites or forest-inventory plots, proximal remote sensing at flux and experiment sites, and model-data integration to reconcile diverse data streams would all help to provide an integrated understanding of this complex problem. A holistic, community-based approach will enable the greatest advances and provide the most robust information to decision makers.

The required size of climate-change mitigation efforts depends directly on how future terrestrial
mineral-associated SOM formation balance to affect C<sub>soil</sub> throughout the soil profile will be key to predictive understanding. This may be especially relevant in non-forest ecosystems, where the largest potential change in carbon storage is belowground.

iCO2 affects ecosystem carbon primarily through effects on NEP, thus understanding of C<sub>eco</sub> responses to iCO2 will emerge from the above research priorities. Further, NBP is what the atmosphere 'sees', which includes additional non-respiratory carbon losses caused by fire (anthropogenic and wild), hydrological export, and export of consumer goods. iCO2 may interact with some non-NEP fluxes, e.g. greater grassland BP leading to higher fuel loads, greater BP in regrowing forests following land-use change. Land-use change NBP is often calculated without considering iCO2 and separately from 'natural' NBP caused by iCO2, climate change, nitrogen deposition, and other factors (e.g. Friedlingstein et al., 2019). Though the boundary between these fluxes is blurred (Pongratz et al., 2014). Integrated studies that consider all of these factors, especially land-use change (including iCO2-acceleration of regrowth following disturbance, e.g. Pugh et al., 2019), agriculture, and 'natural' fluxes will yield insight.

V. Conclusions

To evaluate the CO2-fertilisation hypothesis, we synthesised evidence from wide-ranging disciplines within an integrated theoretical framework. We have medium or high confidence that GPP, iWUE, BP, and mortality have all increased over the historical period. However, we often have low or medium confidence in the magnitude, and low confidence in how much of the change is attributable to iCO2.

The complex nature of the problem demands integrated studies, and further integration is required to fully combine the broad evidence in a way that is scale, bias, and uncertainty aware (Box 3).

Inference regarding trends and responses (or lack thereof) should always be grounded in the context-dependence and biases associated with a particular study. Further experiments and observations are needed to help reconcile differences among evidence streams. For example, tree-ring sampling at flux sites or forest-inventory plots, proximal remote sensing at flux and experiment sites, and model-data integration to reconcile diverse data streams would all help to provide an integrated understanding of this complex problem. A holistic, community-based approach will enable the greatest advances and provide the most robust information to decision makers.

The required size of climate-change mitigation efforts depends directly on how future terrestrial
carbon storage evolves. Evidence for the CO₂-fertilisation hypothesis suggests a highly valuable ecosystem service that is buying us time in the fight against climate change, though the size of this subsidy remains unclear. Based on diminishing theoretical GPP responses, likely increasing nutrient limitations, increasing mortality, and other negative temperature-related effects (Peñuelas et al., 2017) it is highly likely that increases in terrestrial carbon storage due to iCO₂ will decline into the future. A decline in this subsidy will result in accelerated climate change on the current trajectory of anthropogenic CO₂ emissions.
carbon storage evolves. Evidence for the CO₂-fertilisation hypothesis suggests a highly valuable ecosystem service that is buying us time in the fight against climate change, though the size of this subsidy remains unclear. Based on diminishing theoretical GPP responses, likely increasing nutrient limitations, increasing mortality, and other negative temperature-related effects (Peñuelas et al., 2017) it is highly likely that increases in terrestrial carbon storage due to iCO₂ will decline into the future. A decline in this subsidy will result in accelerated climate change on the current trajectory of anthropogenic CO₂ emissions.
**Data availability statement**

Data and analysis scripts used in this publication can be found at ESS-DIVE https://data.ess-dive.lbl.gov/view/doi:10.15485/1644687

**Acknowledgements**

This paper was outlined and informed by the "Integrating CO₂-fertilisation evidence streams and theory (ICOFEST)" meeting held at Biosphere II in September 2018. The meeting was supported by the U.S. Department of Energy Office of Science, Biological and Environmental Research through the Free Air CO₂ Enrichment Model Data Synthesis (FACE-MDS) project. ORNL is managed by UT-Battelle, LLC, for the DOE under contract DE-AC05-00OR22725. MGDK acknowledges support from Australian Research Council (ARC) Discovery Grant (DP190101823). DJPM and WKS acknowledge support by NASA Terrestrial Ecosystems Grant 80NSSC19M0103. SZ received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 647204). KJAT acknowledges support from Smithsonian's Forest Global Earth Observatory (ForestGEO). JBF contributed to this research at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration with support from the NASA IDS program. VH acknowledges support from the Earth Systems and Climate Change Hub funded by the Australian. FJ acknowledges support by the Swiss NSF (#200020_172476). TFK was supported by the NASA Terrestrial Ecology Program IDS Award NNH17AE86I. PC and JP were supported by the European Research Council Synergy grant ERC-2013-SyG-2013-610028 IMBALANCE-P. J. Pongratz was supported by the German Research Foundation's Emmy Noether Program. MEBS acknowledges support from the Australian Research Council Centre of Excellence for Climate Extremes (CE170100023). CT was supported by a Lawrence Fellow award through Lawrence Livermore National Laboratory (LLNL) under contract DE-AC52-07NA27344 with the U.S. Department of Energy. MST was supported by the U.S. Department of Energy, Office of Science under contract number DE-AC02-05CH11231. ATT acknowledges funding from the USDA National Institute of Food and Agriculture, Agricultural and Food Research Initiative Competitive Programme Grant No. 2018-67012-31496. SLV was supported by the US National Science Foundation Paleo Perspectives on Climate Change Program. PvM was supported by the U.S. Geological Survey Ecosystems Mission
**Data availability statement**

Data and analysis scripts used in this publication can be found at ESS-DIVE https://data.ess-dive.lbl.gov/view/doi:10.15485/1644687

**Acknowledgements**

This paper was outlined and informed by the "Integrating CO₂-fertilisation evidence streams and theory (ICOFEST)" meeting held at Biosphere II in September 2018. The meeting was supported by the U.S. Department of Energy Office of Science, Biological and Environmental Research through the Free Air CO₂ Enrichment Model Data Synthesis (FACE-MDS) project. ORNL is managed by UT-Battelle, LLC, for the DOE under contract DE-AC05-00OR22725. MGDK acknowledges support from Australian Research Council (ARC) Discovery Grant (DP190101823). DJPM and WKS acknowledge support by NASA Terrestrial Ecosystems Grant 80NSSC19M0103. SZ received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 647204). KJAT acknowledges support from Smithsonian's Forest Global Earth Observatory (ForestGEO). JBF contributed to this research at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration with support from the NASA IDS program. VH acknowledges support from the Earth Systems and Climate Change Hub funded by the Australian. FJ acknowledges support by the Swiss NSF (#200020_172476). TFK was supported by the NASA Terrestrial Ecology Program IDS Award NNH17AE86I. PC and JP were supported by the European Research Council Synergy grant ERC-2013-SyG-2013-610028 IMBALANCE-P. J. Pongratz was supported by the German Research Foundation's Emmy Noether Program. MEBS acknowledges support from the Australian Research Council Centre of Excellence for Climate Extremes (CE170100023). CT was supported by a Lawrence Fellow award through Lawrence Livermore National Laboratory (LLNL) under contract DE-AC52-07NA27344 with the U.S. Department of Energy. MST was supported by the U.S. Department of Energy, Office of Science under contract number DE-AC02-05CH11231. ATT acknowledges funding from the USDA National Institute of Food and Agriculture, Agricultural and Food Research Initiative Competitive Programme Grant No. 2018-67012-31496. SLV was supported by the US National Science Foundation Paleo Perspectives on Climate Change Program. PvM was supported by the U.S. Geological Survey Ecosystems Mission
Area. Government sponsorship acknowledged. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

**Author contributions**

APW conceived and wrote the paper, with major contributions from MGDK, AB, KG, SM. APW, ASP, and BT collated the data. The ICOFEST meeting was organised by APW, SB, KGC, MGDK, RK, BM, DJPM, RJN, and SZ. All authors attended or contributed to the meeting and commented and provided feedback on the manuscript.
Area. Government sponsorship acknowledged. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.

**Author contributions**

APW conceived and wrote the paper, with major contributions from MGDK, AB, KG, SM. APW, ASP, and BT collated the data. The ICOFEST meeting was organised by APW, SB, KGC, MGDK, RK, BM, DJPM, RJN, and SZ. All authors attended or contributed to the meeting and commented and provided feedback on the manuscript.
Table 1. Acronyms and abbreviations

Table 2. CO₂ responses across studies.

Figure 1. Conceptual diagrams of the terrestrial carbon cycle and the action of CO₂

Figure 2. Modelled leaf-scale physiological responses to CO₂

Figure 3. Comparison of β values

Box 1. The CO₂-fertilisation hypothesis

Box 2. The evidence themes

Box 3. Consideration of methods and bias
Table 1. Acronyms and abbreviations

Table 2. CO₂ responses across studies.

Figure 1. Conceptual diagrams of the terrestrial carbon cycle and the action of CO₂

Figure 2. Modelled leaf-scale physiological responses to CO₂

Figure 3. Comparison of β values

Box 1. The CO₂-fertilisation hypothesis

Box 2. The evidence themes

Box 3. Consideration of methods and bias
## Figure Captions

Figure 1. Conceptual diagrams of the terrestrial carbon cycle and the action of elevated atmospheric [CO₂] (eCO₂). a) Simple pool and flux (3D shapes) diagram of the terrestrial carbon cycle showing key pools, fluxes, and processes relevant to the CO₂-fertilisation hypothesis as described in Box 1. 2D arrows represent direct (solid) or indirect (dashed) positive (triangular arrow heads), or the possibility of both positive and negative (circular) potential influences of eCO₂. b) Rich conceptual diagram of a landscape-scale carbon cycle and the influence of eCO₂ showing more processes (see Section II) and their inter-connected, multi-scale nature. Solid arrows (3D and 2D) represent carbon flows, dotted arrows represent influence. Abbreviations not in Table 1: Cᵢ/c—internal or chloroplastic [CO₂], A_c—carboxylation limited photosynthesis, Γ—photorespiration, C:Nleaf—leaf carbon to nitrogen ratio, T—transpiration, LULCC—land use and land cover change, CWD—coarse woody debris.

Figure 2. Modelled theoretical responses to atmospheric CO₂ concentration ([CO₂], ppm) of (a-c) photosynthesis, Aₘet (µmol m⁻² s⁻¹, black) and photorespiration (µmol m⁻² s⁻¹, dark grey, a only); (d-f) iWUE (µmol mol⁻¹), (g-i) stomatal conductance (mol m⁻² s⁻¹), and (j-l) carbon storage (kg C m⁻²) under ambient (blue) and elevated (red) [CO₂]. Leaf (a, d, g) and canopy (b, e, h) scale for C3 (solid line) and C4 (dashed, leaf-scale only) plants. Variation in b, e, h from a 1000 member ensemble (mean, sd, and 95 percentile shown)—a factorial combination of 100 top-of-canopy Vcmax values (mean = 60, sd = 10) and 10 values of the Jmax to Vcmax slope (mean = 1.63, sd = 0.2), the iWUE response does not vary in this ensemble. Distributions of βdir for historical (purple, 280 to 400 ppm) and future (yellow, 400 to 550 ppm) [CO₂] changes (c, f, i) of diurnally-integrated, canopy-scale variables that includes the same leaf physiology variation as in b, e, h, plus three levels of temperature (10, 15, 25 °C) and relative humidity (50, 70, 90 %) combined in factorial. Tri-modality in the GPP β distributions from the temperature variation. β distributions weighted by the variables' absolute response to CO₂. Carbon storage (j-l) calculated using a simple one-pool model with the mean βdir,fut GPP response applied to BP for [CO₂] at 400 and 550 ppm when (j) initial carbon stores are in equilibrium or (k) 10 % of equilibrium. (l) βdir,hist response when initial carbon stores are assumed in equilibrium at 280 ppm and using the observed historical CO₂ record. Instantaneous βdir for absolute carbon storage are shown (j-l, right y-axis, yellow or purple), as well as βdir,hist calculated using carbon
## Figure Captions

Figure 1. Conceptual diagrams of the terrestrial carbon cycle and the action of elevated atmospheric [CO₂] (eCO₂). a) Simple pool and flux (3D shapes) diagram of the terrestrial carbon cycle showing key pools, fluxes, and processes relevant to the CO₂-fertilisation hypothesis as described in Box 1. 2D arrows represent direct (solid) or indirect (dashed) positive (triangular arrow heads), or the possibility of both positive and negative (circular) potential influences of eCO₂. b) Rich conceptual diagram of a landscape-scale carbon cycle and the influence of eCO₂ showing more processes (see Section II) and their inter-connected, multi-scale nature. Solid arrows (3D and 2D) represent carbon flows, dotted arrows represent influence. Abbreviations not in Table 1: Cᵢ/c—internal or chloroplastic [CO₂], A_c—carboxylation limited photosynthesis, Γ—photorespiration, C:Nleaf—leaf carbon to nitrogen ratio, T—transpiration, LULCC—land use and land cover change, CWD—coarse woody debris.

Figure 2. Modelled theoretical responses to atmospheric CO₂ concentration ([CO₂], ppm) of (a-c) photosynthesis, Aₘet (µmol m⁻² s⁻¹, black) and photorespiration (µmol m⁻² s⁻¹, dark grey, a only); (d-f) iWUE (µmol mol⁻¹), (g-i) stomatal conductance (mol m⁻² s⁻¹), and (j-l) carbon storage (kg C m⁻²) under ambient (blue) and elevated (red) [CO₂]. Leaf (a, d, g) and canopy (b, e, h) scale for C3 (solid line) and C4 (dashed, leaf-scale only) plants. Variation in b, e, h from a 1000 member ensemble (mean, sd, and 95 percentile shown)—a factorial combination of 100 top-of-canopy Vₚmax values (mean = 60, sd = 10) and 10 values of the Jₘax to Vₚmax slope (mean = 1.63, sd = 0.2), the iWUE response does not vary in this ensemble. Distributions of βₐdir for historical (purple, 280 to 400 ppm) and future (yellow, 400 to 550 ppm) [CO₂] changes (c, f, i) of diurnally-integrated, canopy-scale variables that includes the same leaf physiology variation as in b, e, h, plus three levels of temperature (10, 15, 25 °C) and relative humidity (50, 70, 90 %) combined in factorial. Tri-modality in the GPP β distributions from the temperature variation. β distributions weighted by the variables' absolute response to CO₂. Carbon storage (j-l) calculated using a simple one-pool model with the mean βₐdir,fut GPP response applied to BP for [CO₂] at 400 and 550 ppm when (j) initial carbon stores are in equilibrium or (k) 10 % of equilibrium. (l) βₐdir,hist response when initial carbon stores are assumed in equilibrium at 280 ppm and using the observed historical CO₂ record. Instantaneous βₐdir for absolute carbon storage are shown (j-l, right y-axis, yellow or purple), as well as βₐdir,hist calculated using carbon
storage increment (green dashed), and βdir on 30-year change in carbon storage in elevated CO2 scenario (blue points). Further modelling details in Supporting Information Notes S3. Grey vertical lines (a, b, d, e, g, h) are at pre-industrial, 2010s, and projected end-of-century [CO2] (280, 400, 550 ppm). Grey vertical lines (c, f, i) are at βdir of 0 (solid) and 1 or -1 (dashed). Grey horizontal lines (j-l) are βdir when both ambient and elevated CO2 carbon pools are at steady state.

Figure 3. β distributions based on data from Table 2 for GPP, WUE, BP, kveg, ksoil, Cveg, and Csoil. Data are organised by CO2 response category—iCO2 (blue), attribution to iCO2 (green), and eCO2 (purple). See Supporting Information Figs. S2-S4 for further details.
storage increment (green dashed), and βdir on 30-year change in carbon storage in elevated CO2 scenario (blue points). Further modelling details in Supporting Information Notes S3. Grey vertical lines (a, b, d, e, g, h) are at pre-industrial, 2010s, and projected end-of-century [CO2] (280, 400, 550 ppm). Grey vertical lines (c, f, i) are at βdir of 0 (solid) and 1 or -1 (dashed). Grey horizontal lines (j-l) are βdir when both ambient and elevated CO2 carbon pools are at steady state.

Figure 3. β distributions based on data from Table 2 for GPP, WUE, BP, kveg, ksoil, Cveg, and Csoil. Data are organised by CO2 response category—iCO2 (blue), attribution to iCO2 (green), and eCO2 (purple). See Supporting Information Figs. S2-S4 for further details.
<table><caption>Table 1. Acronyms and abbreviations.</caption><tr><td>A<sub>net</sub></td><td>Net photosynthetic carbon assimilation</td></tr><tr><td>fAPAR</td><td>Fraction absorbed photosynthetically active radiation</td></tr><tr><td>BAI</td><td>Basal area increment</td></tr><tr><td>BP</td><td>Biomass production, the sum of all tissue production over a given time, typically a year</td></tr><tr><td>C<sub>x</sub></td><td>Carbon in pool x (where x is either: veg – vegetation, soil, eco – ecosystem)</td></tr><tr><td>CO<sub>2</sub></td><td>Carbon dioxide</td></tr><tr><td>[CO<sub>2</sub>]</td><td>Atmospheric CO<sub>2</sub> concentration</td></tr><tr><td>eCO<sub>2</sub></td><td>Elevated CO<sub>2</sub> from experiments and CO<sub>2</sub> springs</td></tr><tr><td>FACE</td><td>Free air CO<sub>2</sub> enrichment</td></tr><tr><td>GPP</td><td>Gross primary production</td></tr><tr><td>g<sub>s</sub></td><td>Stomatal conductance</td></tr><tr><td>iCO<sub>2</sub></td><td>Increasing CO<sub>2</sub> from fossil fuel emissions and land-use change</td></tr><tr><td>iWUE</td><td>Intrinsic WUE (A<sub>net</sub>/g<sub>s</sub>)</td></tr><tr><td>k<sub>x</sub></td><td>Turnover rate of carbon in pool x (see C<sub>x</sub>)</td></tr><tr><td>LAI</td><td>Leaf area index</td></tr><tr><td>NBP</td><td>Net biome production, net land atmosphere exchange</td></tr><tr><td>NEP</td><td>Net ecosystem production</td></tr><tr><td>OCS</td><td>Carbonyl sulphide</td></tr><tr><td>UE</td><td>Use efficiency</td></tr><tr><td>VPD</td><td>Vapour pressure deficit</td></tr><tr><td>WUE</td><td>Water use efficiency (transpiration/BP)</td></tr></table>
<table><caption>Table 1. Acronyms and abbreviations.</caption><tr><td>A<sub>net</sub></td><td>Net photosynthetic carbon assimilation</td></tr><tr><td>fAPAR</td><td>Fraction absorbed photosynthetically active radiation</td></tr><tr><td>BAI</td><td>Basal area increment</td></tr><tr><td>BP</td><td>Biomass production, the sum of all tissue production over a given time, typically a year</td></tr><tr><td>C<sub>x</sub></td><td>Carbon in pool x (where x is either: veg – vegetation, soil, eco – ecosystem)</td></tr><tr><td>CO<sub>2</sub></td><td>Carbon dioxide</td></tr><tr><td>[CO<sub>2</sub>]</td><td>Atmospheric CO<sub>2</sub> concentration</td></tr><tr><td>eCO<sub>2</sub></td><td>Elevated CO<sub>2</sub> from experiments and CO<sub>2</sub> springs</td></tr><tr><td>FACE</td><td>Free air CO<sub>2</sub> enrichment</td></tr><tr><td>GPP</td><td>Gross primary production</td></tr><tr><td>g<sub>s</sub></td><td>Stomatal conductance</td></tr><tr><td>iCO<sub>2</sub></td><td>Increasing CO<sub>2</sub> from fossil fuel emissions and land-use change</td></tr><tr><td>iWUE</td><td>Intrinsic WUE (A<sub>net</sub>/g<sub>s</sub>)</td></tr><tr><td>k<sub>x</sub></td><td>Turnover rate of carbon in pool x (see C<sub>x</sub>)</td></tr><tr><td>LAI</td><td>Leaf area index</td></tr><tr><td>NBP</td><td>Net biome production, net land atmosphere exchange</td></tr><tr><td>NEP</td><td>Net ecosystem production</td></tr><tr><td>OCS</td><td>Carbonyl sulphide</td></tr><tr><td>UE</td><td>Use efficiency</td></tr><tr><td>VPD</td><td>Vapour pressure deficit</td></tr><tr><td>WUE</td><td>Water use efficiency (transpiration/BP)</td></tr></table>