Articles | Volume 19, issue 2
https://doi.org/10.5194/bg-19-491-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-19-491-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
28 Jan 2022
Research article |
| 28 Jan 2022
| 28 Jan 2022
Thirty-eight years of CO2 fertilization has outpaced growing aridity to drive greening of Australian woody ecosystems
Sami W. Rifai
CORRESPONDING AUTHOR
ARC Centre of Excellence for Climate Extremes, University of
New South Wales, Sydney, NSW 2052, Australia
Climate Change Research Centre, University of New South Wales,
Sydney, NSW 2052, Australia
Martin G. De Kauwe
ARC Centre of Excellence for Climate Extremes, University of
New South Wales, Sydney, NSW 2052, Australia
Climate Change Research Centre, University of New South Wales,
Sydney, NSW 2052, Australia
Evolution & Ecology Research Centre, University of New South
Wales, Sydney, NSW 2052, Australia
School of Biological Sciences, University of Bristol, Bristol,
BS8 1TQ, UK
Anna M. Ukkola
ARC Centre of Excellence for Climate Extremes, University of
New South Wales, Sydney, NSW 2052, Australia
Research School of Earth Sciences, Australian National
University, Canberra, ACT 0200, Australia
Lucas A. Cernusak
College of Science and Engineering, James Cook University,
Cairns, QLD 4188, Australia
Patrick Meir
Research School of Biology, The Australian National
University, Acton, ACT 2601, Australia
School of Geosciences, University of Edinburgh, Edinburgh
EH89XP, UK
Belinda E. Medlyn
Hawkesbury Institute for the Environment, Western Sydney
University, Penrith, NSW 2753, Australia
Andy J. Pitman
ARC Centre of Excellence for Climate Extremes, University of
New South Wales, Sydney, NSW 2052, Australia
Climate Change Research Centre, University of New South Wales,
Sydney, NSW 2052, Australia
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Xueyan Cheng, Chunhui Zhan, Martin G. De Kauwe, Anke Hildebrandt, and Rene Orth
Biogeosciences, 23, 5117–5131, https://doi.org/10.5194/bg-23-5117-2026, https://doi.org/10.5194/bg-23-5117-2026, 2026
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Using long-term satellite greenness data and a machine-learning attribution approach, we assessed vegetation responses to wet and dry soil moisture extremes and identified their dominant drivers. Dry extremes caused widespread negative greenness anomalies mainly controlled by pre-extreme conditions and seasonal timing, while wet extremes showed more heterogeneous responses additionally shaped by climate and topography, highlighting contrasting ecosystem sensitivity to hydrological extremes.
Kristian Schufft, Katrin Fleischer, Anja Rammig, Belinda E. Medlyn, Min Zhao, Lin Yu, Catriona A. Macdonald, and Sönke Zaehle
EGUsphere, https://doi.org/10.5194/egusphere-2026-3553, https://doi.org/10.5194/egusphere-2026-3553, 2026
This preprint is open for discussion and under review for Biogeosciences (BG).
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Soil respiration and its underlying drivers are heavily influenced by nutrient availability. We used a computer model that simulates ecosystem processes to investigate how phosphorus (P) addition influences soil respiration and its underlying drivers. Our model suggests that, reduced root exudation, the labile C flux from roots into soils, causes reduced soil respiration under P addition. Our results bring a new perspective in the dependence of soil respiration on nutrient availability.
Kristian Schufft, Katrin Fleischer, Anja Rammig, Lin Yu, Mingkai Jiang, Belinda E. Medlyn, and Sönke Zaehle
Biogeosciences, 23, 4529–4559, https://doi.org/10.5194/bg-23-4529-2026, https://doi.org/10.5194/bg-23-4529-2026, 2026
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Root exudation describes a process in which plants allocate labile carbon (C) through roots into soils, thereby influencing microbial C and nutrient cycling. We implemented root exudation in a computer model that simulates ecosystem processes. Increased atmospheric CO2 increased root exudation, but the additional input was partially offset by enhanced microbial respiration. Our research brings new perspectives in modelling soil C and nutrient cycling in forests under increasing CO2.
Simon P. Heselschwerdt, Thorsten Wagener, Lan Wang-Erlandsson, Anna M. Ukkola, Yannis Markonis, Yuting Yang, and Peter Greve
Earth Syst. Dynam., 17, 811–827, https://doi.org/10.5194/esd-17-811-2026, https://doi.org/10.5194/esd-17-811-2026, 2026
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Precipitation on land is split into different pathways, contributing to runoff (blue water) or to plant water use (green water). Climate change alters this balance and shapes how future precipitation is divided. We use Earth system models to study these changes and their controls. We find that stronger extreme five-day precipitation is the main control and routes more future precipitation into blue water, even where average precipitation decreases, with implications for freshwater availability.
Elisa Z. Stefaniak, David T. Tissue, Daniel S. Falster, and Belinda E. Medlyn
EGUsphere, https://doi.org/10.5194/egusphere-2026-1474, https://doi.org/10.5194/egusphere-2026-1474, 2026
This preprint is open for discussion and under review for Biogeosciences (BG).
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Tree growth-storage trade-off occurs as trees require storage for stress survival. We simulate a forest of idealised tree species to explore the competitive success of alternative growth vs storage strategies. Composition shifted from growth- to storage-prioritising strategies with increasing stress intensity and stochasticity largely due to higher mortality in growth-prioritising strategies. We show new insights into tree survival and adaptation of tree communities to increasing stress.
Anna M. Ukkola, Steven Thomas, Elisabeth Vogel, Ulrike Bende-Michl, Steven Siems, Vjekoslav Matic, and Wendy Sharples
Hydrol. Earth Syst. Sci., 30, 1463–1485, https://doi.org/10.5194/hess-30-1463-2026, https://doi.org/10.5194/hess-30-1463-2026, 2026
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Future drought changes in Australia –the driest inhabited continent on Earth– have remained stubbornly uncertain. We assess future drought changes in Australia using projections from climate and hydrological models. We show an increasing probability of drought in highly-populated and agricultural regions of Australia in coming decades, suggesting potential impacts on agricultural activities, ecosystems and urban water supply.
Jon Cranko Page, Martin G. De Kauwe, Andy J. Pitman, Isaac R. Towers, Gabriele Arduini, Martin J. Best, Craig R. Ferguson, Jürgen Knauer, Hyungjun Kim, David M. Lawrence, Tomoko Nitta, Keith W. Oleson, Catherine Ottlé, Anna Ukkola, Nicholas Vuichard, Xiaoni Wang-Faivre, and Gab Abramowitz
Biogeosciences, 23, 263–282, https://doi.org/10.5194/bg-23-263-2026, https://doi.org/10.5194/bg-23-263-2026, 2026
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This paper used a large dataset of observations, machine learning predictions, and computer model simulations to test how well land surface models represent the water, energy, and carbon cycles. We found that the models work well under "normal" weather but do not meet performance expectations during coinciding extreme conditions. Since these extremes are relatively rare, targeted model improvements could deliver major performance gains.
Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, Andy Pitman, Philippe Ciais, and Daniel S. Goll
Biogeosciences, 22, 7845–7863, https://doi.org/10.5194/bg-22-7845-2025, https://doi.org/10.5194/bg-22-7845-2025, 2025
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Accurate estimates of global soil organic carbon (SOC) content and its spatial pattern are critical for future climate change mitigation. However, the most advanced process-based SOC models struggle to do this task. Here we apply multiple explainable machine learning methods to identify missing variables and misrepresented relationships between environmental factors and SOC in these models, offering new insights to guide model development for more reliable SOC predictions.
Matthew O. Grant, Anna M. Ukkola, Elisabeth Vogel, Sanaa Hobeichi, Andy J. Pitman, Alex Raymond Borowiak, and Keirnan Fowler
Hydrol. Earth Syst. Sci., 29, 5555–5573, https://doi.org/10.5194/hess-29-5555-2025, https://doi.org/10.5194/hess-29-5555-2025, 2025
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Australia is regularly subjected to severe and widespread drought. By using multiple drought indicators, we show that although there have been widespread decreases in droughts since the beginning of the 20th century, many regions have seen an increase in droughts in more recent decades. Despite these changes, our analysis shows that they remain within the range of observed variability and are not unprecedented in the context of past droughts.
Anjana Devanand, Jason P. Evans, Andy J. Pitman, Sujan Pal, David Gochis, and Kevin Sampson
Hydrol. Earth Syst. Sci., 29, 4491–4513, https://doi.org/10.5194/hess-29-4491-2025, https://doi.org/10.5194/hess-29-4491-2025, 2025
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Including lateral flow increases evapotranspiration near major river channels in high-resolution land surface simulations in southeast Australia, consistent with observations. The 1-km resolution model shows a widespread pattern of dry ridges that does not exist at coarser resolutions. Our results have implications for improved simulations of droughts and future water availability.
Gab Abramowitz, Anna Ukkola, Sanaa Hobeichi, Jon Cranko Page, Mathew Lipson, Martin G. De Kauwe, Samuel Green, Claire Brenner, Jonathan Frame, Grey Nearing, Martyn Clark, Martin Best, Peter Anthoni, Gabriele Arduini, Souhail Boussetta, Silvia Caldararu, Kyeungwoo Cho, Matthias Cuntz, David Fairbairn, Craig R. Ferguson, Hyungjun Kim, Yeonjoo Kim, Jürgen Knauer, David Lawrence, Xiangzhong Luo, Sergey Malyshev, Tomoko Nitta, Jerome Ogee, Keith Oleson, Catherine Ottlé, Phillipe Peylin, Patricia de Rosnay, Heather Rumbold, Bob Su, Nicolas Vuichard, Anthony P. Walker, Xiaoni Wang-Faivre, Yunfei Wang, and Yijian Zeng
Biogeosciences, 21, 5517–5538, https://doi.org/10.5194/bg-21-5517-2024, https://doi.org/10.5194/bg-21-5517-2024, 2024
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This paper evaluates land models – computer-based models that simulate ecosystem dynamics; land carbon, water, and energy cycles; and the role of land in the climate system. It uses machine learning and AI approaches to show that, despite the complexity of land models, they do not perform nearly as well as they could given the amount of information they are provided with about the prediction problem.
Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, Andy Pitman, and Raphael A. Viscarra Rossel
SOIL, 10, 619–636, https://doi.org/10.5194/soil-10-619-2024, https://doi.org/10.5194/soil-10-619-2024, 2024
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Effective management of soil organic carbon (SOC) requires accurate knowledge of its distribution and factors influencing its dynamics. We identify the importance of variables in spatial SOC variation and estimate SOC stocks in Australia using various models. We find there are significant disparities in SOC estimates when different models are used, highlighting the need for a critical re-evaluation of land management strategies that rely on the SOC distribution derived from a single approach.
Elizabeth S. Duan, Luciana Chavez Rodriguez, Nicole Hemming-Schroeder, Baptiste Wijas, Habacuc Flores-Moreno, Alexander W. Cheesman, Lucas A. Cernusak, Michael J. Liddell, Paul Eggleton, Amy E. Zanne, and Steven D. Allison
Biogeosciences, 21, 3321–3338, https://doi.org/10.5194/bg-21-3321-2024, https://doi.org/10.5194/bg-21-3321-2024, 2024
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Understanding the link between climate and carbon fluxes is crucial for predicting how climate change will impact carbon sinks. We estimated carbon dioxide (CO2) fluxes from deadwood in tropical Australia using wood moisture content and temperature. Our model predicted that the majority of deadwood carbon is released as CO2, except when termite activity is detected. Future models should also incorporate wood traits, like species and chemical composition, to better predict fluxes.
Georgina M. Falster, Nicky M. Wright, Nerilie J. Abram, Anna M. Ukkola, and Benjamin J. Henley
Hydrol. Earth Syst. Sci., 28, 1383–1401, https://doi.org/10.5194/hess-28-1383-2024, https://doi.org/10.5194/hess-28-1383-2024, 2024
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Multi-year droughts have severe environmental and economic impacts, but the instrumental record is too short to characterise multi-year drought variability. We assessed the nature of Australian multi-year droughts using simulations of the past millennium from 11 climate models. We show that multi-decadal
megadroughtsare a natural feature of the Australian hydroclimate. Human-caused climate change is also driving a tendency towards longer droughts in eastern and southwestern Australia.
Lina Teckentrup, Martin G. De Kauwe, Gab Abramowitz, Andrew J. Pitman, Anna M. Ukkola, Sanaa Hobeichi, Bastien François, and Benjamin Smith
Earth Syst. Dynam., 14, 549–576, https://doi.org/10.5194/esd-14-549-2023, https://doi.org/10.5194/esd-14-549-2023, 2023
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Studies analyzing the impact of the future climate on ecosystems employ climate projections simulated by global circulation models. These climate projections display biases that translate into significant uncertainty in projections of the future carbon cycle. Here, we test different methods to constrain the uncertainty in simulations of the carbon cycle over Australia. We find that all methods reduce the bias in the steady-state carbon variables but that temporal properties do not improve.
Yuan Zhang, Devaraju Narayanappa, Philippe Ciais, Wei Li, Daniel Goll, Nicolas Vuichard, Martin G. De Kauwe, Laurent Li, and Fabienne Maignan
Geosci. Model Dev., 15, 9111–9125, https://doi.org/10.5194/gmd-15-9111-2022, https://doi.org/10.5194/gmd-15-9111-2022, 2022
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There are a few studies to examine if current models correctly represented the complex processes of transpiration. Here, we use a coefficient Ω, which indicates if transpiration is mainly controlled by vegetation processes or by turbulence, to evaluate the ORCHIDEE model. We found a good performance of ORCHIDEE, but due to compensation of biases in different processes, we also identified how different factors control Ω and where the model is wrong. Our method is generic to evaluate other models.
Keirnan Fowler, Murray Peel, Margarita Saft, Tim J. Peterson, Andrew Western, Lawrence Band, Cuan Petheram, Sandra Dharmadi, Kim Seong Tan, Lu Zhang, Patrick Lane, Anthony Kiem, Lucy Marshall, Anne Griebel, Belinda E. Medlyn, Dongryeol Ryu, Giancarlo Bonotto, Conrad Wasko, Anna Ukkola, Clare Stephens, Andrew Frost, Hansini Gardiya Weligamage, Patricia Saco, Hongxing Zheng, Francis Chiew, Edoardo Daly, Glen Walker, R. Willem Vervoort, Justin Hughes, Luca Trotter, Brad Neal, Ian Cartwright, and Rory Nathan
Hydrol. Earth Syst. Sci., 26, 6073–6120, https://doi.org/10.5194/hess-26-6073-2022, https://doi.org/10.5194/hess-26-6073-2022, 2022
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Recently, we have seen multi-year droughts tending to cause shifts in the relationship between rainfall and streamflow. In shifted catchments that have not recovered, an average rainfall year produces less streamflow today than it did pre-drought. We take a multi-disciplinary approach to understand why these shifts occur, focusing on Australia's over-10-year Millennium Drought. We evaluate multiple hypotheses against evidence, with particular focus on the key role of groundwater processes.
Yitong Yao, Emilie Joetzjer, Philippe Ciais, Nicolas Viovy, Fabio Cresto Aleina, Jerome Chave, Lawren Sack, Megan Bartlett, Patrick Meir, Rosie Fisher, and Sebastiaan Luyssaert
Geosci. Model Dev., 15, 7809–7833, https://doi.org/10.5194/gmd-15-7809-2022, https://doi.org/10.5194/gmd-15-7809-2022, 2022
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To facilitate more mechanistic modeling of drought effects on forest dynamics, our study implements a hydraulic module to simulate the vertical water flow, change in water storage and percentage loss of stem conductance (PLC). With the relationship between PLC and tree mortality, our model can successfully reproduce the large biomass drop observed under throughfall exclusion. Our hydraulic module provides promising avenues benefiting the prediction for mortality under future drought events.
Rebecca J. Oliver, Lina M. Mercado, Doug B. Clark, Chris Huntingford, Christopher M. Taylor, Pier Luigi Vidale, Patrick C. McGuire, Markus Todt, Sonja Folwell, Valiyaveetil Shamsudheen Semeena, and Belinda E. Medlyn
Geosci. Model Dev., 15, 5567–5592, https://doi.org/10.5194/gmd-15-5567-2022, https://doi.org/10.5194/gmd-15-5567-2022, 2022
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We introduce new representations of plant physiological processes into a land surface model. Including new biological understanding improves modelled carbon and water fluxes for the present in tropical and northern-latitude forests. Future climate simulations demonstrate the sensitivity of photosynthesis to temperature is important for modelling carbon cycle dynamics in a warming world. Accurate representation of these processes in models is necessary for robust predictions of climate change.
Jon Cranko Page, Martin G. De Kauwe, Gab Abramowitz, Jamie Cleverly, Nina Hinko-Najera, Mark J. Hovenden, Yao Liu, Andy J. Pitman, and Kiona Ogle
Biogeosciences, 19, 1913–1932, https://doi.org/10.5194/bg-19-1913-2022, https://doi.org/10.5194/bg-19-1913-2022, 2022
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Although vegetation responds to climate at a wide range of timescales, models of the land carbon sink often ignore responses that do not occur instantly. In this study, we explore the timescales at which Australian ecosystems respond to climate. We identified that carbon and water fluxes can be modelled more accurately if we include environmental drivers from up to a year in the past. The importance of antecedent conditions is related to ecosystem aridity but is also influenced by other factors.
Anna M. Ukkola, Gab Abramowitz, and Martin G. De Kauwe
Earth Syst. Sci. Data, 14, 449–461, https://doi.org/10.5194/essd-14-449-2022, https://doi.org/10.5194/essd-14-449-2022, 2022
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Flux towers provide measurements of water, energy, and carbon fluxes. Flux tower data are invaluable in improving and evaluating land models but are not suited to modelling applications as published. Here we present flux tower data tailored for land modelling, encompassing 170 sites globally. Our dataset resolves several key limitations hindering the use of flux tower data in land modelling, including incomplete forcing variable, data format, and low data quality.
Lina Teckentrup, Martin G. De Kauwe, Andrew J. Pitman, Daniel S. Goll, Vanessa Haverd, Atul K. Jain, Emilie Joetzjer, Etsushi Kato, Sebastian Lienert, Danica Lombardozzi, Patrick C. McGuire, Joe R. Melton, Julia E. M. S. Nabel, Julia Pongratz, Stephen Sitch, Anthony P. Walker, and Sönke Zaehle
Biogeosciences, 18, 5639–5668, https://doi.org/10.5194/bg-18-5639-2021, https://doi.org/10.5194/bg-18-5639-2021, 2021
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The Australian continent is included in global assessments of the carbon cycle such as the global carbon budget, yet the performance of dynamic global vegetation models (DGVMs) over Australia has rarely been evaluated. We assessed simulations by an ensemble of dynamic global vegetation models over Australia and highlighted a number of key areas that lead to model divergence on both short (inter-annual) and long (decadal) timescales.
Mengyuan Mu, Martin G. De Kauwe, Anna M. Ukkola, Andy J. Pitman, Weidong Guo, Sanaa Hobeichi, and Peter R. Briggs
Earth Syst. Dynam., 12, 919–938, https://doi.org/10.5194/esd-12-919-2021, https://doi.org/10.5194/esd-12-919-2021, 2021
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Groundwater can buffer the impacts of drought and heatwaves on ecosystems, which is often neglected in model studies. Using a land surface model with groundwater, we explained how groundwater sustains transpiration and eases heat pressure on plants in heatwaves during multi-year droughts. Our results showed the groundwater’s influences diminish as drought extends and are regulated by plant physiology. We suggest neglecting groundwater in models may overstate projected future heatwave intensity.
Cited articles
Ackerman, D., Millet, D. B., and Chen, X.: Global Estimates of Inorganic
Nitrogen Deposition Across Four Decades, Global Biogeochem. Cy., 33, 100–107,
https://doi.org/10.1029/2018GB005990, 2019. a
Adzhar, R., Kelley, D. I., Dong, N., Torello Raventos, M., Veenendaal, E., Feldpausch, T. R., Philips, O. L., Lewis, S., Sonké, B., Taedoumg, H., Schwantes Marimon, B., Domingues, T., Arroyo, L., Djagbletey, G., Saiz, G., and Gerard, F.: Assessing MODIS Vegetation Continuous Fields tree cover product (collection 6): performance and applicability in tropical forests and savannas, Biogeosciences Discuss. [preprint], https://doi.org/10.5194/bg-2020-460, in review, 2021. a
Ahlstrom, A., Raupach, M. R., Schurgers, G., Smith, B., Arneth, A., Jung, M.,
Reichstein, M., Canadell, J. G., Friedlingstein, P., Jain, A. K., Kato, E.,
Poulter, B., Sitch, S., Stocker, B. D., Viovy, N., Wang, Y. P., Wiltshire,
A., Zaehle, S., and Zeng, N.: The Dominant Role of Semi-Arid Ecosystems in
the Trend and Variability of the Land CO2 Sink, Science, 348, 895–899,
https://doi.org/10.1126/science.aaa1668, 2015. a
Ainsworth, E. A. and Rogers, A.: The Response of Photosynthesis and Stomatal
Conductance to Rising [CO2]: Mechanisms and
Environmental Interactions: Photosynthesis and Stomatal Conductance
Responses to Rising [CO2], Plant Cell. Environ., 30, 258–270,
https://doi.org/10.1111/j.1365-3040.2007.01641.x, 2007. a
Atlas of Living Australia: The Dead Tree Detective| Project|BioCollect, available at:
https://biocollect.ala.org.au/acsa/project/index/77285a13-e231-49e8-b212-660c66c74bac (last access: 28 November 2020),
2020. a
Australian Department of Agriculture, Water and the Environment: Australian
Department of Agriculture, Water and the Environment, available at:
http://www.environment.gov.au/ (last access: 31 March 2021), 2020. a
Bastos, A., Running, S. W., Gouveia, C., and Trigo, R. M.: The global NPP dependence on ENSO: La Niña and the extraordinary year of 2011, J. Geophys. Res.-Biogeo., 118, 1247–1255,
https://doi.org/10.1002/jgrg.20100, 2013. a
Bronaugh, D. and Werner, A. (for the Pacific Climate Impacts Consortium): Zyp: Zhang + Yue-Pilon Trends Package, available at:
https://CRAN.R-project.org/package=zyp (last access: 24 January 2022), 2019. a
Bureau of Meteorology: Annual Australian Climate Statement 2019, available at:
http://www.bom.gov.au/climate/current/annual/aus/2019/ (last access: 24 October 2020),
2019. a
Carlson, T. N. and Ripley, D. A.: On the Relation between NDVI, Fractional
Vegetation Cover, and Leaf Area Index, Remote Sens. Environ., 62, 241–252,
https://doi.org/10.1016/S0034-4257(97)00104-1, 1997. a, b
Cortés, J., Mahecha, M. D., Reichstein, M., Myneni, R. B., Chen, C., and
Brenning, A.: Where Are Global Vegetation Greening and Browning Trends
Significant?, Geophys. Res. Lett., 48, e2020GL091496, https://doi.org/10.1029/2020GL091496, 2021. a
CSIRO and Bureau of Meteorology: State of the Climate 2020, available at:
https://www.csiro.au/-/media/OnA/Files/State-of-the-Climate-2020.pdf (last access: 12 November 2020), 2020. a
Davis, T. W., Prentice, I. C., Stocker, B. D., Thomas, R. T., Whitley, R. J., Wang, H., Evans, B. J., Gallego-Sala, A. V., Sykes, M. T., and Cramer, W.: Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture, Geosci. Model Dev., 10, 689–708, https://doi.org/10.5194/gmd-10-689-2017, 2017. a
De Kauwe, M. G., Medlyn, B. E., Zaehle, S., Walker, A. P., Dietze, M. C., Wang,
Y.-P., Luo, Y., Jain, A. K., El-Masri, B., Hickler, T., Wå rlind, D., Weng,
E., Parton, W. J., Thornton, P. E., Wang, S., Prentice, I. C., Asao, S.,
Smith, B., McCarthy, H. R., Iversen, C. M., Hanson, P. J., Warren, J. M.,
Oren, R., and Norby, R. J.: Where Does the Carbon Go? A Model-Data
Intercomparison of Vegetation Carbon Allocation and Turnover Processes at Two
Temperate Forest Free-Air CO2 Enrichment Sites, New Phytol., 203,
883–899, https://doi.org/10.1111/nph.12847, 2014. a
De Kauwe, M. G., Medlyn, B. E., Ukkola, A. M., Mu, M., Sabot, M. E. B., Pitman,
A. J., Meir, P., Cernusak, L. A., Rifai, S. W., Choat, B., Tissue, D. T.,
Blackman, C. J., Li, X., Roderick, M., and Briggs, P. R.: Identifying Areas
at Risk of Drought-induced Tree Mortality across South-Eastern
Australia, Glob. Change Biol., 26, 5716–5733, https://doi.org/10.1111/gcb.15215, 2020. a
DiMiceli, C., Carroll, R., Sohlberg, D., Kim, M., and Townshend, J.: MOD44B
MODIS/Terra Vegetation Continuous Fields Yearly L3 Global 250 m SIN
Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MOD44B.006, 2017. a, b
Donohue, R. J., McVICAR, T. R., and Roderick, M. L.: Climate-Related Trends in
Australian Vegetation Cover as Inferred from Satellite Observations,
1981–2006, Glob. Change Biol., 15, 1025–1039, https://doi.org/10.1111/j.1365-2486.2008.01746.x, 2009. a, b, c
Donohue, R. J., Roderick, M. L., McVicar, T. R., and Farquhar, G. D.: Impact of CO2 fertilization on maximum foliage cover across the globe's warm, arid environments, Geophys. Res. Lett., 40, 3031–3035, https://doi.org/10.1002/grl.50563, 2013. a, b, c, d
Donohue, R. J., Roderick, M. L., McVicar, T. R., and Yang, Y.: A simple hypothesis of how leaf and canopy-level transpiration and assimilation respond to elevated CO2 reveals distinct response patterns between disturbed and undisturbed vegetation, J. Geophys. Res.-Biogeo., 122, 168–184,
https://doi.org/10.1002/2016JG003505, 2017. a, b, c
Dowle, M. and Srinivasan, A.: Data.Table: Extension of “data.Frame”, R package version 1.14.0, available at: https://CRAN.R-project.org/package=data.table (last access: 24 January 2022), 2019. a
Drake, B. G., Gonzàlez-Meler, M. A., and Long, S. P.: MORE EFFICIENT
PLANTS: A Consequence of Rising Atmospheric CO2?, Annu. Rev. Plant Phys., 48, 609–639,
https://doi.org/10.1146/annurev.arplant.48.1.609, 1997. a
Drake, J. E., Macdonald, C. A., Tjoelker, M. G., Crous, K. Y., Gimeno, T. E.,
Singh, B. K., Reich, P. B., Anderson, I. C., and Ellsworth, D. S.: Short-term carbon cycling responses of a mature eucalypt woodland to gradual stepwise enrichment of atmospheric CO2 concentration, Glob. Change Biol., 22,
380–390, https://doi.org/10.1111/gcb.13109, 2016. a
Evans, M. C.: Deforestation in Australia: drivers, trends and policy responses, Pacific Conservation Biology, 22, 130–150, https://doi.org/10.1071/PC15052, 2016. a
Frankenberg, C., Yin, Y., Byrne, B., He, L., and Gentine, P.: Comment on
“Recent Global Decline of CO2 Fertilization Effects on Vegetation
Photosynthesis”, Science, 373, eabg2947, https://doi.org/10.1126/science.abg2947,
2021. a, b
Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L., and Justice, C. O.: The
Collection 6 MODIS Burned Area Mapping Algorithm and Product, Remote Sens. Environ., 217,
72–85, https://doi.org/10.1016/j.rse.2018.08.005, 2018. a, b
Gill, T., Johansen, K., Phinn, S., Trevithick, R., Scarth, P., and Armston, J.:
A Method for Mapping Australian Woody Vegetation Cover by Linking
Continental-Scale Field Data and Long-Term Landsat Time Series, Int. J. Remote Sens., 38,
679–705, https://doi.org/10.1080/01431161.2016.1266112, 2017. a
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., and Moore,
R.: Google Earth Engine: Planetary-Scale Geospatial Analysis for
Everyone, Remote Sens. Environ., 202, 18–27, https://doi.org/10.1016/j.rse.2017.06.031, 2017. a
Greve, P., Roderick, M. L., Ukkola, A. M., and Wada, Y.: The Aridity Index
under Global Warming, Environ. Res. Lett., 14, 124006, https://doi.org/10.1088/1748-9326/ab5046, 2019. a
Hansen, M. C., Potapov, P. V., Moore, R., Hancher, M., Turubanova, S. A.,
Tyukavina, A., Thau, D., Stehman, S. V., Goetz, S. J., Loveland, T. R.,
Kommareddy, A., Egorov, A., Chini, L., Justice, C. O., and Townshend, J.
R. G.: High-Resolution Global Maps of 21st-Century Forest Cover
Change, Science, 342, 850–853, https://doi.org/10.1126/science.1244693, 2013. a
Harris, I., Jones, P., Osborn, T., and Lister, D.: Updated High-Resolution
Grids of Monthly Climatic Observations – the CRU TS3.10 Dataset:
UPDATED HIGH-RESOLUTION GRIDS OF MONTHLY CLIMATIC OBSERVATIONS, Int. J. Climatol., 34,
623–642, https://doi.org/10.1002/joc.3711, 2014. a
Harris, R. M. B., Beaumont, L. J., Vance, T. R., Tozer, C. R., Remenyi, T. A.,
Perkins-Kirkpatrick, S. E., Mitchell, P. J., Nicotra, A. B., McGregor, S.,
Andrew, N. R., Letnic, M., Kearney, M. R., Wernberg, T., Hutley, L. B.,
Chambers, L. E., Fletcher, M.-S., Keatley, M. R., Woodward, C. A.,
Williamson, G., Duke, N. C., and Bowman, D. M. J. S.: Biological Responses to
the Press and Pulse of Climate Trends and Extreme Events, Nat. Clim. Change, 8, 579–587,
https://doi.org/10.1038/s41558-018-0187-9, 2018. a
Hasegawa, S., Macdonald, C. A., and Power, S. A.: Elevated Carbon Dioxide
Increases Soil Nitrogen and Phosphorus Availability in a Phosphorus-Limited
Eucalyptus Woodland, Glob. Change Biol., 22, 1628–1643, https://doi.org/10.1111/gcb.13147,
2016. a
Hattersley, P. W.: The Distribution of C3 and C4 Grasses in
Australia in Relation to Climate, Oecologia, 57, 113–128, https://doi.org/10.1007/BF00379569,
1983. a
Ji, L. and Brown, J. F.: Effect of NOAA Satellite Orbital Drift on
AVHRR-Derived Phenological Metrics, Int. J. Appl. Earth Obs., 62, 215–223,
https://doi.org/10.1016/j.jag.2017.06.013, 2017. a, b
Jiang, M., Caldararu, S., Zhang, H., Fleischer, K., Crous, K. Y., Yang, J.,
De Kauwe, M. G., Ellsworth, D. S., Reich, P. B., Tissue, D. T., Zaehle, S.,
and Medlyn, B. E.: Low Phosphorus Supply Constrains Plant Responses to
Elevated CO2: A Meta-analysis, Glob. Change Biol., 26, 5856–5873,
https://doi.org/10.1111/gcb.15277, 2020a. a
Jiang, M., Medlyn, B. E., Drake, J. E., Duursma, R. A., Anderson, I. C.,
Barton, C. V. M., Boer, M. M., Carrillo, Y., Castañeda Gómez, L.,
Collins, L., Crous, K. Y., De Kauwe, M. G., dos Santos, B. M., Emmerson,
K. M., Facey, S. L., Gherlenda, A. N., Gimeno, T. E., Hasegawa, S., Johnson,
S. N., Kännaste, A., Macdonald, C. A., Mahmud, K., Moore, B. D., Nazaries,
L., Neilson, E. H. J., Nielsen, U. N., Niinemets, U., Noh, N. J.,
Ochoa-Hueso, R., Pathare, V. S., Pendall, E., Pihlblad, J., Piñeiro, J.,
Powell, J. R., Power, S. A., Reich, P. B., Renchon, A. A., Riegler, M.,
Rinnan, R., Rymer, P. D., Salomón, R. L., Singh, B. K., Smith, B.,
Tjoelker, M. G., Walker, J. K. M., Wujeska-Klause, A., Yang, J., Zaehle, S.,
and Ellsworth, D. S.: The Fate of Carbon in a Mature Forest under Carbon
Dioxide Enrichment, Nature, 580, 227–231, https://doi.org/10.1038/s41586-020-2128-9,
2020b. a, b, c, d
King, A. D., Pitman, A. J., Henley, B. J., Ukkola, A. M., and Brown, J. R.: The
Role of Climate Variability in Australian Drought, Nat. Clim. Change, 10, 177–179,
https://doi.org/10.1038/s41558-020-0718-z, 2020. a
Knapp, A. K., Chen, A., Griffin-Nolan, R. J., Baur, L. E., Carroll, C. J.,
Gray, J. E., Hoffman, A. M., Li, X., Post, A. K., Slette, I. J., Collins,
S. L., Luo, Y., and Smith, M. D.: Resolving the Dust Bowl Paradox of
Grassland Responses to Extreme Drought, P. Natl. Acad. Sci. USA, 117, 22249–22255,
https://doi.org/10.1073/pnas.1922030117, 2020. a
Liao, Z., Van Dijk, A. I. J. M., He, B., Larraondo, P. R., and Scarth, P. F.:
Woody Vegetation Cover, Height and Biomass at 25-m Resolution across
Australia Derived from Multiple Site, Airborne and Satellite
Observations, Int. J. Appl. Earth Obs., 93, 102209, https://doi.org/10.1016/j.jag.2020.102209, 2020. a
McAlpine, C. A., Etter, A., Fearnside, P. M., Seabrook, L., and Laurance,
W. F.: Increasing World Consumption of Beef as a Driver of Regional and
Global Change: A Call for Policy Action Based on Evidence from
Queensland (Australia), Colombia and Brazil, Global Environ. Chang., 19, 21–33,
https://doi.org/10.1016/j.gloenvcha.2008.10.008, 2009. a
McMurtrie, R. E., Norby, R. J., Medlyn, B. E., Dewar, R. C., Pepper, D. A.,
Reich, P. B., Barton, C. V. M., McMurtrie, R. E., Norby, R. J., Medlyn,
B. E., Dewar, R. C., Pepper, D. A., Reich, P. B., and Barton, C. V. M.: Why
Is Plant-Growth Response to Elevated CO2 Amplified When Water Is
Limiting, but Reduced When Nitrogen Is Limiting? A Growth-Optimisation
Hypothesis, Funct. Plant Biol., 35, 521–534, https://doi.org/10.1071/FP08128, 2008. a
Medlyn, B. E., Barton, C. V. M., Broadmeadow, M. S. J., Ceulemans, R., Angelis,
P. D., Forstreuter, M., Freeman, M., Jackson, S. B., Kellomäki, S., Laitat,
E., Rey, A., Roberntz, P., Sigurdsson, B. D., Strassemeyer, J., Wang, K.,
Curtis, P. S., and Jarvis, P. G.: Stomatal Conductance of Forest Species
after Long-Term Exposure to Elevated CO2 Concentration: A Synthesis, New Phytol., 149,
247–264, https://doi.org/10.1046/j.1469-8137.2001.00028.x, 2001. a
Medlyn, B. E., Duursma, R. A., Eamus, D., Ellsworth, D. S., Prentice, I. C.,
Barton, C. V. M., Crous, K. Y., De Angelis, P., Freeman, M., and Wingate, L.:
Reconciling the optimal and empirical approaches to modelling stomatal conductance, Glob. Change Biol., 17,
2134–2144, https://doi.org/10.1111/j.1365-2486.2010.02375.x, 2011. a
Medlyn, B. E., Kauwe, M. G. D., Zaehle, S., Walker, A. P., Duursma, R. A.,
Luus, K., Mishurov, M., Pak, B., Smith, B., Wang, Y.-P., Yang, X., Crous,
K. Y., Drake, J. E., Gimeno, T. E., Macdonald, C. A., Norby, R. J., Power,
S. A., Tjoelker, M. G., and Ellsworth, D. S.: Using Models to Guide Field
Experiments: A Priori Predictions for the CO2 Response of a Nutrient- and
Water-Limited Native Eucalypt Woodland, Glob. Change Biol., 22, 2834–2851,
https://doi.org/10.1111/gcb.13268, 2016. a, b
Milly, P. C. D. and Dunne, K. A.: Potential Evapotranspiration and Continental
Drying, Nat. Clim. Change, 6, 946–949, https://doi.org/10.1038/nclimate3046, 2016. a
Moore, C. E., Brown, T., Keenan, T. F., Duursma, R. A., van Dijk, A. I. J. M., Beringer, J., Culvenor, D., Evans, B., Huete, A., Hutley, L. B., Maier, S., Restrepo-Coupe, N., Sonnentag, O., Specht, A., Taylor, J. R., van Gorsel, E., and Liddell, M. J.: Reviews and syntheses: Australian vegetation phenology: new insights from satellite remote sensing and digital repeat photography, Biogeosciences, 13, 5085–5102, https://doi.org/10.5194/bg-13-5085-2016, 2016. a
Morison, J. I. L.: Sensitivity of Stomata and Water Use Efficiency to High
CO2, Plant Cell Environ., 8, 467–474, https://doi.org/10.1111/j.1365-3040.1985.tb01682.x, 1985. a
Muñoz Sabater, J.: ERA5-Land hourly data from 1981 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.e2161bac, 2019. a
Murphy, B. P. and Bowman, D. M. J. S.: Seasonal Water Availability Predicts the
Relative Abundance of C3 and C4
Grasses in Australia, Global Ecol. Biogeogr., 16, 160–169,
https://doi.org/10.1111/j.1466-8238.2006.00285.x, 2007. a, b
Myneni, R., Knyazikhin, Y., and Park, T.: MCD15A3H MODIS/Terra+Aqua Leaf Area Index/FPAR 4-day L4 Global 500 m SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD15A3H.006, 2015. a
Nolan, R. H., Boer, M. M., Collins, L., de Dios, V. R., Clarke, H., Jenkins,
M., Kenny, B., and Bradstock, R. A.: Causes and Consequences of Eastern
Australia's 2019–20 Season of Mega-Fires, Glob. Change Biol., 26, 1039–1041,
https://doi.org/10.1111/gcb.14987, 2020. a
Padfield, D. and Matheson, G.: Nls.Multstart: Robust Non-Linear
Regression Using AIC Scores, R package version 1.2.0, available at:
https://CRAN.R-project.org/package=nls.multstart (last access: 24 January 2022),
2020. a
Pebesma, E.: Stars: Spatiotemporal Arrays, Raster and Vector Data
Cubes, GitHub [software], available at: https://r-spatial.github.io/stars/ https://github.com/r-spatial/stars/ (last access: 24 January 2022),
2020. a
Perkins, S. E., Alexander, L. V., and Nairn, J. R.: Increasing Frequency,
Intensity and Duration of Observed Global Heatwaves and Warm Spells, Geophys. Res. Lett., 39, L20714,
https://doi.org/10.1029/2012GL053361, 2012. a
Peters, J. M. R., López, R., Nolf, M., Hutley, L. B., Wardlaw, T., Cernusak,
L. A., and Choat, B.: Living on the Edge: A Continental-Scale Assessment
of Forest Vulnerability to Drought, Glob. Change Biol., 27, 3620–3641, https://doi.org/10.1111/gcb.15641,
2021. a
Poulter, B., Frank, D., Ciais, P., Myneni, R. B., Andela, N., Bi, J., Broquet,
G., Canadell, J. G., Chevallier, F., Liu, Y. Y., Running, S. W., Sitch, S.,
and van der Werf, G. R.: Contribution of Semi-Arid Ecosystems to Interannual
Variability of the Global Carbon Cycle, Nature, 509, 600–603,
https://doi.org/10.1038/nature13376, 2014. a
Raupach, M.: Equilibrium Evaporation and the Convective Boundary Layer, Bound.-Lay. Meteorol., 96,
107–142, 2000. a
Rifai, S. W.: sw-rifai/eastern-Australia-CO2-NDVI-change: revision_1 (revision_1), Zenodo [code, data set], https://doi.org/10.5281/zenodo.5711964, 2021a. a
Rifai, S. W.: Thirty-eight years of CO2 fertilization have
outpaced growing aridity to drive greening of
Australian woody ecosystems, version 0.1, Zenodo [data set], https://doi.org/10.5281/zenodo.4340064, 2021b. a
Schaaf, C. and Wang, Z.: MCD43A4 MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF Adjusted RefDaily L3 Global – 500 m V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD43A4.006, 2015. a, b
Seabrook, L., McAlpine, C., and Fensham, R.: Cattle, Crops and Clearing:
Regional Drivers of Landscape Change in the Brigalow Belt,
Queensland, Australia, 1840–2004, Landscape Urban Plan., 78, 373–385,
https://doi.org/10.1016/j.landurbplan.2005.11.007, 2006. a
Sexton, J. O., Song, X.-P., Feng, M., Noojipady, P., Anand, A., Huang, C., Kim,
D.-H., Collins, K. M., Channan, S., DiMiceli, C., and Townshend, J. R.:
Global, 30-m Resolution Continuous Fields of Tree Cover: Landsat-Based
Rescaling of MODIS Vegetation Continuous Fields with Lidar-Based
Estimates of Error, Int. J. Digit. Earth, 6, 427–448, https://doi.org/10.1080/17538947.2013.786146, 2013. a
Smith, S. D., Charlet, T. N., Zitzer, S. F., Abella, S. R., Vanier, C. H., and
Huxman, T. E.: Long-Term Response of a Mojave Desert Winter Annual Plant
Community to a Whole-Ecosystem Atmospheric CO2
Manipulation (FACE), Glob. Change Biol., 20, 879–892, https://doi.org/10.1111/gcb.12411, 2014. a
Specht, R.: Water Use by Perennial Evergreen Plant Communities in
Australia and Papua New Guinea, Aust. J. Bot., 20, 273–299, https://doi.org/10.1071/BT9720273,
1972. a, b
Teckentrup, L., De Kauwe, M. G., Pitman, A. J., Goll, D. S., Haverd, V., Jain, A. K., Joetzjer, E., Kato, E., Lienert, S., Lombardozzi, D., McGuire, P. C., Melton, J. R., Nabel, J. E. M. S., Pongratz, J., Sitch, S., Walker, A. P., and Zaehle, S.: Assessing the representation of the Australian carbon cycle in global vegetation models, Biogeosciences, 18, 5639–5668, https://doi.org/10.5194/bg-18-5639-2021, 2021. a, b
Trancoso, R., Larsen, J. R., McVicar, T. R., Phinn, S. R., and McAlpine, C. A.:
CO2-vegetation Feedbacks and Other Climate Changes
Implicated in Reducing Base Flow, Geophys. Res. Lett., 44, 2310–2318, https://doi.org/10.1002/2017GL072759,
2017. a
Ukkola, A. M., Roderick, M. L., Barker, A., and Pitman, A. J.: Exploring the
Stationarity of Australian Temperature, Precipitation and Pan Evaporation
Records over the Last Century, Environ. Res. Lett., 14, 124035, https://doi.org/10.1088/1748-9326/ab545c,
2019. a, b
van Dijk, A. I. J. M., Beck, H. E., Crosbie, R. S., de Jeu, R. A. M., Liu,
Y. Y., Podger, G. M., Timbal, B., and Viney, N. R.: The Millennium Drought in southeast Australia (2001–2009): Natural and human causes and implications for water resources, ecosystems, economy, and society, Water Resour. Res., 49,
1040–1057, https://doi.org/10.1002/wrcr.20123, 2013. a
Venables, W. N. and Ripley, B. D.: Modern Applied Statistics with S,
Springer, 4th Edn., available at: http://www.stats.ox.ac.uk/pub/MASS4 (last access: 24 January 2022),
2002. a
Vermote, E. and NOAA CDR Program: NOAA Climate Data Record (CDR) of
AVHRR Surface Reflectance, Version 5, NOAA National Centers for Environmental Information [data set], https://doi.org/10.7289/V53776Z4, 2018. a, b
Walker, A. P., De Kauwe, M. G., Bastos, A., Belmecheri, S., Georgiou, K.,
Keeling, R. F., McMahon, S. M., Medlyn, B. E., Moore, D. J. P., Norby, R. J.,
Zaehle, S., Anderson-Teixeira, K. J., Battipaglia, G., Brienen, R. J. W.,
Cabugao, K. G., Cailleret, M., Campbell, E., Canadell, J. G., Ciais, P.,
Craig, M. E., Ellsworth, D. S., Farquhar, G. D., Fatichi, S., Fisher, J. B.,
Frank, D. C., Graven, H., Gu, L., Haverd, V., Heilman, K., Heimann, M.,
Hungate, B. A., Iversen, C. M., Joos, F., Jiang, M., Keenan, T. F., Knauer,
J., Körner, C., Leshyk, V. O., Leuzinger, S., Liu, Y., MacBean, N., Malhi,
Y., McVicar, T. R., Penuelas, J., Pongratz, J., Powell, A. S., Riutta, T.,
Sabot, M. E. B., Schleucher, J., Sitch, S., Smith, W. K., Sulman, B., Taylor,
B., Terrer, C., Torn, M. S., Treseder, K. K., Trugman, A. T., Trumbore,
S. E., Mantgem, P. J., Voelker, S. L., Whelan, M. E., and Zuidema, P. A.:
Integrating the Evidence for a Terrestrial Carbon Sink Caused by Increasing
Atmospheric CO2, New Phytol., 229, 2413–2445,
https://doi.org/10.1111/nph.16866, 2020. a
Wang, S., Zhang, Y., Ju, W., Chen, J. M., Ciais, P., Cescatti, A., Sardans, J.,
Janssens, I. A., Wu, M., Berry, J. A., Campbell, E., Fernández-Martínez,
M., Alkama, R., Sitch, S., Friedlingstein, P., Smith, W. K., Yuan, W., He,
W., Lombardozzi, D., Kautz, M., Zhu, D., Lienert, S., Kato, E., Poulter, B.,
Sanders, T. G. M., Krüger, I., Wang, R., Zeng, N., Tian, H., Vuichard, N.,
Jain, A. K., Wiltshire, A., Haverd, V., Goll, D. S., and Peñuelas, J.:
Recent Global Decline of CO2 Fertilization Effects on Vegetation
Photosynthesis, Science, 370, 1295–1300, https://doi.org/10.1126/science.abb7772, 2020. a, b, c, d
Winkler, A. J., Myneni, R. B., Hannart, A., Sitch, S., Haverd, V., Lombardozzi, D., Arora, V. K., Pongratz, J., Nabel, J. E. M. S., Goll, D. S., Kato, E., Tian, H., Arneth, A., Friedlingstein, P., Jain, A. K., Zaehle, S., and Brovkin, V.: Slowdown of the greening trend in natural vegetation with further rise in atmospheric CO2, Biogeosciences, 18, 4985–5010, https://doi.org/10.5194/bg-18-4985-2021, 2021. a
Wong, S.-C., Cowan, I. R., and Farquhar, G. D.: Leaf Conductance in
Relation to Rate of CO2 Assimilation: I. Influence of
Nitrogen Nutrition, Phosphorus Nutrition, Photon Flux Density,
and Ambient Partial Pressure of CO2 during Ontogeny, Plant Physiol., 78,
821–825, https://doi.org/10.1104/pp.78.4.821, 1985. a
Wood, S. N.: Generalized Additive Models: An Introduction with R,
Chapman and Hall/CRC, 2nd Edn., ISBN 978-1-49872-834-8, 2017. a
World Meteorological Organization: WMO Guidelines on the Calculation of
Climate Normals, available at:
https://library.wmo.int/doc_num.php?explnum_id=4166 (last access: 31 March 2020), 2017. a
Yang, J., Medlyn, B. E., De Kauwe, M. G., and Duursma, R. A.: Applying the
Concept of Ecohydrological Equilibrium to Predict Steady State Leaf
Area Index, J. Adv. Model. Earth Sy., 10, 1740–1758, https://doi.org/10.1029/2017MS001169, 2018. a
Zhu, Z., Piao, S., Myneni, R. B., Huang, M., Zeng, Z., Canadell, J. G., Ciais,
P., Sitch, S., Friedlingstein, P., Arneth, A., Cao, C., Cheng, L., Kato, E.,
Koven, C., Li, Y., Lian, X., Liu, Y., Liu, R., Mao, J., Pan, Y., Peng, S.,
Peñuelas, J., Poulter, B., Pugh, T. A. M., Stocker, B. D., Viovy, N., Wang,
X., Wang, Y., Xiao, Z., Yang, H., Zaehle, S., and Zeng, N.: Greening of the
Earth and Its Drivers, Nat. Clim. Change, 6, 791–795, https://doi.org/10.1038/nclimate3004, 2016. a, b, c, d
Zhu, Z., Zeng, H., Myneni, R. B., Chen, C., Zhao, Q., Zha, J., Zhan, S., and
MacLachlan, I.: Comment on “Recent Global Decline of CO2
Fertilization Effects on Vegetation Photosynthesis”, Science, 373, eabg5673,
https://doi.org/10.1126/science.abg5673, 2021. a
Short summary
Australia's woody ecosystems have experienced widespread greening despite a warming climate and repeated record-breaking droughts and heat waves. Increasing atmospheric CO2 increases plant water use efficiency, yet quantifying the CO2 effect is complicated due to co-occurring effects of global change. Here we harmonized a 38-year satellite record to separate the effects of climate change, land use change, and disturbance to quantify the CO2 fertilization effect on the greening phenomenon.
Australia's woody ecosystems have experienced widespread greening despite a warming climate and...
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