Articles | Volume 19, issue 7
https://doi.org/10.5194/bg-19-1913-2022
© 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-19-1913-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
05 Apr 2022
Research article |
| 05 Apr 2022
| 05 Apr 2022
Examining the role of environmental memory in the predictability of carbon and water fluxes across Australian ecosystems
ARC Centre of Excellence for Climate Extremes, Sydney, NSW 2052, Australia
Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
Martin G. De Kauwe
School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, UK
ARC Centre of Excellence for Climate Extremes, Sydney, NSW 2052, Australia
Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
Gab Abramowitz
ARC Centre of Excellence for Climate Extremes, Sydney, NSW 2052, Australia
Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
Jamie Cleverly
Terrestrial Ecosystem Research Network, College of Science and Engineering, James Cook University, Cairns, QLD 4870, Australia
Nina Hinko-Najera
School of Ecosystem and Forest Sciences, The University of Melbourne, 4 Water Street, Creswick, VIC 3363, Australia
Mark J. Hovenden
Biological Sciences, School of Natural Sciences, University of Tasmania, Hobart, TAS 7005, Australia
Yao Liu
Department of Geography and Environmental Sciences, Northumbria University, Newcastle upon Tyne, NE1 8ST, UK
Andy J. Pitman
ARC Centre of Excellence for Climate Extremes, Sydney, NSW 2052, Australia
Climate Change Research Centre, University of New South Wales, Sydney, NSW 2052, Australia
Kiona Ogle
School of Informatics, Computing, and Cyber Systems, Northern Arizona University, Flagstaff, Arizona 86011, USA
Related authors
Jon Cranko Page, Martin G. De Kauwe, Andy J. Pitman, Isaac R. Towers, Gabriele Arduini, Martin J. Best, Craig Ferguson, Jürgen Knauer, Hyungjun Kim, David M. Lawrence, Tomoko Nitta, Keith W. Oleson, Catherine Ottlé, Anna Ukkola, Nicholas Vuichard, and Gab Abramowitz
EGUsphere, https://doi.org/10.5194/egusphere-2025-4149, https://doi.org/10.5194/egusphere-2025-4149, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
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.
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
Short summary
Short summary
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.
Jon Cranko Page, Martin G. De Kauwe, Andy J. Pitman, Isaac R. Towers, Gabriele Arduini, Martin J. Best, Craig Ferguson, Jürgen Knauer, Hyungjun Kim, David M. Lawrence, Tomoko Nitta, Keith W. Oleson, Catherine Ottlé, Anna Ukkola, Nicholas Vuichard, and Gab Abramowitz
EGUsphere, https://doi.org/10.5194/egusphere-2025-4149, https://doi.org/10.5194/egusphere-2025-4149, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
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.
Georgina Falster, Gab Abramowitz, Sanaa Hobeichi, Cath Hughes, Pauline Treble, Nerilie J. Abram, Michael I. Bird, Alexandre Cauquoin, Bronwyn Dixon, Russell Drysdale, Chenhui Jin, Niels Munksgaard, Bernadette Proemse, Jonathan J. Tyler, Martin Werner, and Carol Tadros
EGUsphere, https://doi.org/10.5194/egusphere-2025-2458, https://doi.org/10.5194/egusphere-2025-2458, 2025
This preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).
Short summary
Short summary
We used a random forest approach to produce estimates of monthly precipitation stable isotope variability from 1962–2023, at high resolution across the entire Australian continent. Comprehensive skill and sensitivity testing shows that our random forest models skilfully predict precipitation isotope values in places and times that observations are not available. We make all outputs publicly available, facilitating use in fields from ecology and hydrology to archaeology and forensic science.
Lingfei Wang, Gab Abramowitz, Ying-Ping Wang, Andy Pitman, Philippe Ciais, and Daniel S. Goll
EGUsphere, https://doi.org/10.5194/egusphere-2025-2545, https://doi.org/10.5194/egusphere-2025-2545, 2025
Short summary
Short summary
Accurate estimates of global soil organic carbon (SOC) content and its spatial pattern are critical for future climate change mitigation. However, the most advanced mechanistic 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
EGUsphere, https://doi.org/10.5194/egusphere-2024-4024, https://doi.org/10.5194/egusphere-2024-4024, 2025
Short summary
Short summary
Australia is regularly subjected to severe and widespread drought. By using multiple drought indicators, we show that while there have been widespread decreases in droughts since the beginning of the 20th century. However, 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.
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
Short summary
Short summary
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.
Anjana Devanand, Jason Evans, Andy Pitman, Sujan Pal, David Gochis, and Kevin Sampson
EGUsphere, https://doi.org/10.5194/egusphere-2024-3148, https://doi.org/10.5194/egusphere-2024-3148, 2024
Short summary
Short summary
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.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Bimal K. Bhattacharya, Kaniska Mallick, Devansh Desai, Ganapati S. Bhat, Ross Morrison, Jamie R. Clevery, William Woodgate, Jason Beringer, Kerry Cawse-Nicholson, Siyan Ma, Joseph Verfaillie, and Dennis Baldocchi
Biogeosciences, 19, 5521–5551, https://doi.org/10.5194/bg-19-5521-2022, https://doi.org/10.5194/bg-19-5521-2022, 2022
Short summary
Short summary
Evaporation retrieval in heterogeneous ecosystems is challenging due to empirical estimation of ground heat flux and complex parameterizations of conductances. We developed a parameter-sparse coupled ground heat flux-evaporation model and tested it across different limits of water stress and vegetation fraction in the Northern/Southern Hemisphere. The model performed particularly well in the savannas and showed good potential for evaporative stress monitoring from thermal infrared satellites.
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
Short summary
Short summary
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.
Sami W. Rifai, Martin G. De Kauwe, Anna M. Ukkola, Lucas A. Cernusak, Patrick Meir, Belinda E. Medlyn, and Andy J. Pitman
Biogeosciences, 19, 491–515, https://doi.org/10.5194/bg-19-491-2022, https://doi.org/10.5194/bg-19-491-2022, 2022
Short summary
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.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Sanaa Hobeichi, Gab Abramowitz, and Jason P. Evans
Hydrol. Earth Syst. Sci., 25, 3855–3874, https://doi.org/10.5194/hess-25-3855-2021, https://doi.org/10.5194/hess-25-3855-2021, 2021
Short summary
Short summary
Evapotranspiration (ET) links the water, energy and carbon cycle on land. Reliable ET estimates are key to understand droughts and flooding. We develop a new ET dataset, DOLCE V3, by merging multiple global ET datasets, and we show that it matches ET observations better and hence is more reliable than its parent datasets. Next, we use DOLCE V3 to examine recent changes in ET and find that ET has increased over most of the land, decreased in some regions, and has not changed in some other regions
Anna B. Harper, Karina E. Williams, Patrick C. McGuire, Maria Carolina Duran Rojas, Debbie Hemming, Anne Verhoef, Chris Huntingford, Lucy Rowland, Toby Marthews, Cleiton Breder Eller, Camilla Mathison, Rodolfo L. B. Nobrega, Nicola Gedney, Pier Luigi Vidale, Fred Otu-Larbi, Divya Pandey, Sebastien Garrigues, Azin Wright, Darren Slevin, Martin G. De Kauwe, Eleanor Blyth, Jonas Ardö, Andrew Black, Damien Bonal, Nina Buchmann, Benoit Burban, Kathrin Fuchs, Agnès de Grandcourt, Ivan Mammarella, Lutz Merbold, Leonardo Montagnani, Yann Nouvellon, Natalia Restrepo-Coupe, and Georg Wohlfahrt
Geosci. Model Dev., 14, 3269–3294, https://doi.org/10.5194/gmd-14-3269-2021, https://doi.org/10.5194/gmd-14-3269-2021, 2021
Short summary
Short summary
We evaluated 10 representations of soil moisture stress in the JULES land surface model against site observations of GPP and latent heat flux. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES. In addition, using soil matric potential presents the opportunity to include parameters specific to plant functional type to further improve modeled fluxes.
Lina Teckentrup, Martin G. De Kauwe, Andrew J. Pitman, and Benjamin Smith
Biogeosciences, 18, 2181–2203, https://doi.org/10.5194/bg-18-2181-2021, https://doi.org/10.5194/bg-18-2181-2021, 2021
Short summary
Short summary
The El Niño–Southern Oscillation (ENSO) describes changes in the sea surface temperature patterns of the Pacific Ocean. This influences the global weather, impacting vegetation on land. There are two types of El Niño: central Pacific (CP) and eastern Pacific (EP). In this study, we explored the long-term impacts on the carbon balance on land linked to the two El Niño types. Using a dynamic vegetation model, we simulated what would happen if only either CP or EP El Niño events had occurred.
Mengyuan Mu, Martin G. De Kauwe, Anna M. Ukkola, Andy J. Pitman, Teresa E. Gimeno, Belinda E. Medlyn, Dani Or, Jinyan Yang, and David S. Ellsworth
Hydrol. Earth Syst. Sci., 25, 447–471, https://doi.org/10.5194/hess-25-447-2021, https://doi.org/10.5194/hess-25-447-2021, 2021
Short summary
Short summary
Land surface model (LSM) is a critical tool to study land responses to droughts and heatwaves, but lacking comprehensive observations limited past model evaluations. Here we use a novel dataset at a water-limited site, evaluate a typical LSM with a range of competing model hypotheses widely used in LSMs and identify marked uncertainty due to the differing process assumptions. We show the extensive observations constrain model processes and allow better simulated land responses to these extremes.
Cited articles
Abramowitz, G.: Towards a public, standardized, diagnostic benchmarking system for land surface models, Geosci. Model Dev., 5, 819–827, https://doi.org/10.5194/gmd-5-819-2012, 2012. a, b
Abramowitz, G., Pitman, A., Gupta, H., Kowalczyk, E., and Wang, Y.: Systematic
Bias in Land Surface Models, J. Hydrometeorol., 8,
989–1001, https://doi.org/10.1175/JHM628.1, 2007. a
Abramowitz, G., Leuning, R., Clark, M., and Pitman, A.: Evaluating the
Performance of Land Surface Models, J. Clim., 21,
5468–5481, https://doi.org/10.1175/2008JCLI2378.1, 2008. a
Anderegg, W. R. L., Schwalm, C., Biondi, F., Camarero, J. J., Koch, G., Litvak,
M., Ogle, K., Shaw, J. D., Shevliakova, E., Williams, A. P., Wolf, A., Ziaco,
E., and Pacala, S.: Pervasive Drought Legacies in Forest Ecosystems and Their
Implications for Carbon Cycle Models, Science, 349, 528–532,
https://doi.org/10.1126/science.aab1833, 2015. a, b, c, d
Arndt, S., Hinko-Najera, N., and Griebel, A.: Wombat Wombat State Forest Flux Data Collection Level 6, Terrestrial Ecosystem Research Network (TERN) [data set], https://hdl.handle.net/102.100.100/14237 (last access: 21 September 2021), 2013. a
Barraza, V., Restrepo-Coupe, N., Huete, A., Grings, F., Beringer, J.,
Cleverly, J., and Eamus, D.: Estimation of Latent Heat Flux over Savannah
Vegetation across the North Australian Tropical Transect from Multiple
Sensors and Global Meteorological Data, Agr. Forest Meteorol.,
232, 689–703, https://doi.org/10.1016/j.agrformet.2016.10.013, 2017. a, b
Barron-Gafford, G. A., Cable, J. M., Bentley, L. P., Scott, R. L., Huxman,
T. E., Jenerette, G. D., and Ogle, K.: Quantifying the Timescales over Which
Exogenous and Endogenous Conditions Affect Soil Respiration, New Phytol.,
202, 442–454, https://doi.org/10.1111/nph.12675, 2014. a
Bastos, A., Ciais, P., Friedlingstein, P., Sitch, S., Pongratz, J., Fan, L.,
Wigneron, J. P., Weber, U., Reichstein, M., Fu, Z., Anthoni, P., Arneth, A.,
Haverd, V., Jain, A. K., Joetzjer, E., Knauer, J., Lienert, S., Loughran, T.,
McGuire, P. C., Tian, H., Viovy, N., and Zaehle, S.: Direct and Seasonal
Legacy Effects of the 2018 Heat Wave and Drought on European Ecosystem
Productivity, Sci. Adv., 6, eaba2724, https://doi.org/10.1126/sciadv.aba2724,
2020. a
Beringer, J., Hutley, L., and Northwood, M.: Daly Daly Uncleared Flux Data Collection Level 6, Terrestrial Ecosystem Research Network (TERN) [data set], https://hdl.handle.net/102.100.100/14239
(last access: 21 September 2021), 2015a. a
Beringer, J., Hutley, L., and Northwood, M.: Dry River Flux Data Collection Level 6, Terrestrial Ecosystem Research Network (TERN) [data set], https://hdl.handle.net/102.100.100/14229
(last access: 21 September 2021), 2015b. a
Beringer, J., Hutley, L., and Northwood, M.: Howard Springs Flux Data Collection Level 6, Terrestrial Ecosystem Research Network (TERN) [data set], https://hdl.handle.net/102.100.100/14234
(last access: 21 September 2021), 2015c. a
Beringer, J., Hutley, L., and Northwood, M.: Sturt Plains Flux Data Collection Level 6, Terrestrial Ecosystem Research Network (TERN) [data set], https://hdl.handle.net/102.100.100/14230 (last access: 21 September 2021), 2015d. a
Beringer, J., Hutley, L., Hinko-Najera, N., and McHugh, I.: Whroo Flux Data Collection Level 6, Terrestrial Ecosystem Research Network (TERN) [data set],
https://hdl.handle.net/102.100.100/14232 (last access: 21 September 2021),
2017. a
Beringer, J., Hutley, L. B., McHugh, I., Arndt, S. K., Campbell, D., Cleugh, H. A., Cleverly, J., Resco de Dios, V., Eamus, D., Evans, B., Ewenz, C., Grace, P., Griebel, A., Haverd, V., Hinko-Najera, N., Huete, A., Isaac, P., Kanniah, K., Leuning, R., Liddell, M. J., Macfarlane, C., Meyer, W., Moore, C., Pendall, E., Phillips, A., Phillips, R. L., Prober, S. M., Restrepo-Coupe, N., Rutledge, S., Schroder, I., Silberstein, R., Southall, P., Yee, M. S., Tapper, N. J., van Gorsel, E., Vote, C., Walker, J., and Wardlaw, T.: An introduction to the Australian and New Zealand flux tower network – OzFlux, Biogeosciences, 13, 5895–5916, https://doi.org/10.5194/bg-13-5895-2016, 2016. a
Best, M. J., Abramowitz, G., Johnson, H. R., Pitman, A. J., Balsamo, G., Boone,
A., Cuntz, M., Decharme, B., Dirmeyer, P. A., Dong, J., Ek, M., Guo, Z.,
Haverd, V., van den Hurk, B. J. J., Nearing, G. S., Pak, B.,
Peters-Lidard, C., Santanello, J. A., Stevens, L., and Vuichard, N.: The
Plumbing of Land Surface Models: Benchmarking Model Performance,
J. Hydrometeorol., 16, 1425–1442, https://doi.org/10.1175/JHM-D-14-0158.1,
2015. a, b, c
Cable, J. M., Ogle, K., Barron-Gafford, G. A., Bentley, L. P., Cable, W. L.,
Scott, R. L., Williams, D. G., and Huxman, T. E.: Antecedent Conditions
Influence Soil Respiration Differences in Shrub and Grass Patches,
Ecosystems, 16, 1230–1247, https://doi.org/10.1007/s10021-013-9679-7, 2013. a, b, c
Charrad, M., Ghazzali, N., Boiteau, V., and Niknafs, A.: NbClust: An R Package for Determining the Relevant
Number of Clusters in a Data Set, J. Stat. Softw.,
61, 1–36, https://doi.org/10.18637/jss.v061.i06, 2014. a
Ciais, P., Reichstein, M., Viovy, N., Granier, A., Ogée, J., Allard, V.,
Aubinet, M., Buchmann, N., Bernhofer, C., Carrara, A., Chevallier, F.,
De Noblet, N., Friend, A. D., Friedlingstein, P., Grünwald, T., Heinesch,
B., Keronen, P., Knohl, A., Krinner, G., Loustau, D., Manca, G., Matteucci,
G., Miglietta, F., Ourcival, J. M., Papale, D., Pilegaard, K., Rambal, S.,
Seufert, G., Soussana, J. F., Sanz, M. J., Schulze, E. D., Vesala, T., and
Valentini, R.: Europe-Wide Reduction in Primary Productivity Caused by the
Heat and Drought in 2003, Nature, 437, 529–533, https://doi.org/10.1038/nature03972,
2005. a, b
Cleverly, J., Eamus, D., Faux, R., Grant, N., and Li, Z.: Alice Springs Mulga Flux Data Collection Level 5, Terrestrial Ecosystem Research Network (TERN) [data set], https://hdl.handle.net/102.100.100/14217 (last access: 21 September 2021), 2015. a
Cleverly, J., Boulain, N., Villalobos-Vega, R., Grant, N., Faux, R., Wood,
C., Cook, P. G., Yu, Q., Leigh, A., and Eamus, D.: Dynamics of Component
Carbon Fluxes in a Semi-Arid Acacia Woodland, Central Australia,
J. Geophys. Res.-Biogeosci., 118, 1168–1185,
https://doi.org/10.1002/jgrg.20101, 2013. a
Cleverly, J., Eamus, D., Restrepo Coupe, N., Chen, C., Maes, W., Li, L., Faux,
R., Santini, N. S., Rumman, R., Yu, Q., and Huete, A.: Soil Moisture Controls
on Phenology and Productivity in a Semi-Arid Critical Zone, Sci.
Total Environ., 568, 1227–1237, https://doi.org/10.1016/j.scitotenv.2016.05.142,
2016. a
Cranko Page, J., De Kauwe, M. G., Abramowitz, G., Liu, Y., and Ogle, K.: OzFlux_SAM, Zenodo [code], https://doi.org/10.5281/zenodo.6361060, 2022. a
De Kauwe, M. G., Kala, J., Lin, Y.-S., Pitman, A. J., Medlyn, B. E., Duursma, R. A., Abramowitz, G., Wang, Y.-P., and Miralles, D. G.: A test of an optimal stomatal conductance scheme within the CABLE land surface model, Geosci. Model Dev., 8, 431–452, https://doi.org/10.5194/gmd-8-431-2015, 2015a. a
De Kauwe, M. G., Zhou, S.-X., Medlyn, B. E., Pitman, A. J., Wang, Y.-P., Duursma, R. A., and Prentice, I. C.: Do land surface models need to include differential plant species responses to drought? Examining model predictions across a mesic-xeric gradient in Europe, Biogeosciences, 12, 7503–7518, https://doi.org/10.5194/bg-12-7503-2015, 2015b. a
Decker, M., Or, D., Pitman, A., and Ukkola, A.: New Turbulent Resistance
Parameterization for Soil Evaporation Based on a Pore-Scale Model: Impact
on Surface Fluxes in CABLE, J. Adv. Model. Earth
Syst., 9, 220–238, https://doi.org/10.1002/2016MS000832, 2017. a
Fanjul, L. and Jones, H. G.: Rapid Stomatal Responses to Humidity, Planta, 154,
135–138, https://doi.org/10.1007/BF00387906, 1982. a
Fatichi, S., Leuzinger, S., and Körner, C.: Moving beyond Photosynthesis:
From Carbon Source to Sink-Driven Vegetation Modeling, New Phytol., 201,
1086–1095, https://doi.org/10.1111/nph.12614, 2014. a
Feldman, A. F., Short Gianotti, D. J., Konings, A. G., Gentine, P., and Entekhabi, D.: Patterns of plant rehydration and growth following pulses of soil moisture availability, Biogeosciences, 18, 831–847, https://doi.org/10.5194/bg-18-831-2021, 2021. a, b
Fick, S. E. and Hijmans, R. J.: WorldClim 2: New 1-Km Spatial Resolution
Climate Surfaces for Global Land Areas, Int. J. Climatol.,
37, 4302–4315, https://doi.org/10.1002/joc.5086, 2017. a, b
Flach, M., Sippel, S., Gans, F., Bastos, A., Brenning, A., Reichstein, M., and Mahecha, M. D.: Contrasting biosphere responses to hydrometeorological extremes: revisiting the 2010 western Russian heatwave, Biogeosciences, 15, 6067–6085, https://doi.org/10.5194/bg-15-6067-2018, 2018. a
Frank, D., Reichstein, M., Bahn, M., Thonicke, K., Frank, D., Mahecha, M. D.,
Smith, P., van der Velde, M., Vicca, S., Babst, F., Beer, C., Buchmann, N.,
Canadell, J. G., Ciais, P., Cramer, W., Ibrom, A., Miglietta, F., Poulter,
B., Rammig, A., Seneviratne, S. I., Walz, A., Wattenbach, M., Zavala, M. A.,
and Zscheischler, J.: Effects of Climate Extremes on the Terrestrial Carbon
Cycle: Concepts, Processes and Potential Future Impacts, Glob. Change
Biol., 21, 2861–2880, https://doi.org/10.1111/gcb.12916, 2015. 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
Griebel, A., Bennett, L. T., Metzen, D., Cleverly, J., Burba, G., and Arndt,
S. K.: Effects of Inhomogeneities within the Flux Footprint on the
Interpretation of Seasonal, Annual, and Interannual Ecosystem Carbon
Exchange, Agr. Forest Meteorol., 221, 50–60,
https://doi.org/10.1016/j.agrformet.2016.02.002, 2016. a
Griebel, A., Bennett, L. T., Metzen, D., Pendall, E., Lane, P. N. J., and
Arndt, S. K.: Trading Water for Carbon: Maintaining
Photosynthesis at the Cost of Increased Water Loss During High
Temperatures in a Temperate Forest, J. Geophys. Res.-Biogeosci., 125, e2019JG005239, https://doi.org/10.1029/2019JG005239, 2020. a
Harms, R. L. and Roebroeck, A.: Robust and Fast Markov Chain Monte Carlo
Sampling of Diffusion MRI Microstructure Models, Front.
Neuroinformatics, 12, 97, https://doi.org/10.3389/fninf.2018.00097, 2018. a
Haughton, N., Abramowitz, G., Pitman, A. J., Or, D., Best, M. J., Johnson,
H. R., Balsamo, G., Boone, A., Cuntz, M., Decharme, B., Dirmeyer, P. A.,
Dong, J., Ek, M., Guo, Z., Haverd, V., van den Hurk, B. J. J., Nearing,
G. S., Pak, B., Santanello, J. A., Stevens, L. E., and Vuichard, N.: The
Plumbing of Land Surface Models: Is Poor Performance a Result of Methodology
or Data Quality?, J. Hydrometeorol., 17, 1705–1723,
https://doi.org/10.1175/JHM-D-15-0171.1, 2016. a, b, c
Haughton, N., Abramowitz, G., De Kauwe, M. G., and Pitman, A. J.: Does predictability of fluxes vary between FLUXNET sites?, Biogeosciences, 15, 4495–4513, https://doi.org/10.5194/bg-15-4495-2018, 2018a. a
Haughton, N., Abramowitz, G., and Pitman, A. J.: On the predictability of land surface fluxes from meteorological variables, Geosci. Model Dev., 11, 195–212, https://doi.org/10.5194/gmd-11-195-2018, 2018b. a
Haverd, V., Smith, B., Nieradzik, L. P., and Briggs, P. R.: A stand-alone tree demography and landscape structure module for Earth system models: integration with inventory data from temperate and boreal forests, Biogeosciences, 11, 4039–4055, https://doi.org/10.5194/bg-11-4039-2014, 2014. a
Haverd, V., Smith, B., Nieradzik, L., Briggs, P. R., Woodgate, W., Trudinger, C. M., Canadell, J. G., and Cuntz, M.: A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis, Geosci. Model Dev., 11, 2995–3026, https://doi.org/10.5194/gmd-11-2995-2018, 2018. a
Hovenden, M. J., Newton, P. C. D., and Wills, K. E.: Seasonal Not Annual
Rainfall Determines Grassland Biomass Response to Carbon Dioxide, Nature,
511, 583–586, https://doi.org/10.1038/nature13281, 2014. a
Hovenden, M. J., Newton, P. C. D., and Newton, P. C. D.: Variability in
Precipitation Seasonality Limits Grassland Biomass Responses to Rising
CO2: Historical and Projected Climate Analyses, Climatic Change;
Dordrecht, Climatic Change, 149, 219–231, https://doi.org/10.1007/s10584-018-2227-x,
2018. a
Huang, Y., Gerber, S., Huang, T., and Lichstein, J. W.: Evaluating the Drought
Response of CMIP5 Models Using Global Gross Primary Productivity, Leaf
Area, Precipitation, and Soil Moisture Data, Global Biogeochem. Cy.,
30, 1827–1846, https://doi.org/10.1002/2016GB005480, 2016. a
Humphrey, V., Zscheischler, J., Ciais, P., Gudmundsson, L., Sitch, S., and
Seneviratne, S. I.: Sensitivity of Atmospheric CO 2 Growth Rate to
Observed Changes in Terrestrial Water Storage, Nature, 560, 628–631,
https://doi.org/10.1038/s41586-018-0424-4, 2018. a
Hutley, L. B., Beringer, J., Isaac, P. R., Hacker, J. M., and Cernusak, L. A.:
A Sub-Continental Scale Living Laboratory: Spatial Patterns of Savanna
Vegetation over a Rainfall Gradient in Northern Australia, Agr. Forest Meteorol., 151, 1417–1428,
https://doi.org/10.1016/j.agrformet.2011.03.002, 2011. a
Huxman, T. E., Snyder, K. A., Tissue, D., Leffler, A. J., Ogle, K., Pockman,
W. T., Sandquist, D. R., Potts, D. L., and Schwinning, S.: Precipitation
Pulses and Carbon Fluxes in Semiarid and Arid Ecosystems, Oecologia, 141,
254–268, https://doi.org/10.1007/s00442-004-1682-4, 2004. a
Isaac, P., Cleverly, J., McHugh, I., van Gorsel, E., Ewenz, C., and Beringer, J.: OzFlux data: network integration from collection to curation, Biogeosciences, 14, 2903–2928, https://doi.org/10.5194/bg-14-2903-2017, 2017. a
Jones, S., Rowland, L., Cox, P., Hemming, D., Wiltshire, A., Williams, K., Parazoo, N. C., Liu, J., da Costa, A. C. L., Meir, P., Mencuccini, M., and Harper, A. B.: The impact of a simple representation of non-structural carbohydrates on the simulated response of tropical forests to drought, Biogeosciences, 17, 3589–3612, https://doi.org/10.5194/bg-17-3589-2020, 2020. a
Kannenberg, S. A., Schwalm, C. R., and Anderegg, W. R. L.: Ghosts of the Past:
How Drought Legacy Effects Shape Forest Functioning and Carbon Cycling,
Ecol. Lett., 23, 891–901, https://doi.org/10.1111/ele.13485, 2020. a, b
Katul, G., Lai, C.-T., Schäfer, K., Vidakovic, B., Albertson, J.,
Ellsworth, D., and Oren, R.: Multiscale Analysis of Vegetation Surface
Fluxes: From Seconds to Years, Adv. Water Resour., 24, 1119–1132,
https://doi.org/10.1016/S0309-1708(01)00029-X, 2001. a
Keenan, T., Baker, I., Barr, A., Ciais, P., Davis, K., Dietze, M., Dragoni, D.,
Gough, C. M., Grant, R., Hollinger, D., Hufkens, K., Poulter, B., McCaughey,
H., Raczka, B., Ryu, Y., Schaefer, K., Tian, H., Verbeeck, H., Zhao, M., and
Richardson, A. D.: Terrestrial Biosphere Model Performance for Inter-Annual
Variability of Land-Atmosphere CO2 Exchange, Glob. Change Biol., 18,
1971–1987, https://doi.org/10.1111/j.1365-2486.2012.02678.x, 2012. a
Knapp, A. K. and Smith, M. D.: Variation among Biomes in Temporal
Dynamics of Aboveground Primary Production, Science, 291, 481–484,
2001. a
Knapp, A. K., Ciais, P., and Smith, M. D.: Reconciling Inconsistencies in
Precipitation – Productivity Relationships: Implications for Climate
Change, New Phytol., 214, 41–47, https://doi.org/10.1111/nph.14381, 2017. a
Knight, J.: Root Distributions and Water Uptake Patterns in Eucalypts and
Other Species, The ways trees use water, Rural Industries Research and Development Corporation, pp. 66–93, ISBN 0-642-57811-7, 1999. a
Kolus, H. R., Huntzinger, D. N., Schwalm, C. R., Fisher, J. B., McKay, N.,
Fang, Y., Michalak, A. M., Schaefer, K., Wei, Y., Poulter, B., Mao, J.,
Parazoo, N. C., and Shi, X.: Land Carbon Models Underestimate the Severity
and Duration of Drought's Impact on Plant Productivity, Sci. Rep.,
9, 2758, https://doi.org/10.1038/s41598-019-39373-1, 2019. a
Kowalczyk, E., Wang, Y., Law, R., Davies, H., Mcgregor, J., and Abramowitz, G.:
The CSIRO Atmosphere Biosphere Land Exchange (CABLE) Model for Use in
Climate Models and as an Offline Model, CSIRO Mar. Atmos.
Res., 13, 1615, https://doi.org/10.4225/08/58615c6a9a51d, 2006. a
Kowalczyk, E., Stevens, L., Law, R., Dix, M., Wang, Y., Harman, I., Haynes, K.,
Srbinovsky, J., Pak, B., and Ziehn, T.: The Land Surface Model Component of
ACCESS: Description and Impact on the Simulated Surface Climatology,
Aust. Meteorol. Oceanogr. J., 63, 65–82,
https://doi.org/10.22499/2.6301.005, 2013. a
Kruschke, J. K.: Doing Bayesian Data Analysis: A Tutorial with R,
JAGS, and Stan, Academic Press, Boston, edition 2 edn., 143–191,
ISBN 978-0-12-405888-0, 2015. a
Lasslop, G., Reichstein, M., Kattge, J., and Papale, D.: Influences of
Observation Errors in Eddy Flux Data on Inverse Model Parameter Estimation,
Biogeosciences, 5, 1311–1324, https://doi.org/10.5194/bg-5-1311-2008, 2008. a
Lauenroth, W. K. and Sala, O. E.: Long-Term Forage Production of North
American Shortgrass Steppe, Ecol. Appl., 2, 397–403,
https://doi.org/10.2307/1941874, 1992. a, b, c
Lemoine, N. P., Griffin-Nolan, R. J., Lock, A. D., and Knapp, A. K.: Drought
Timing, Not Previous Drought Exposure, Determines Sensitivity of Two
Shortgrass Species to Water Stress, Oecologia, 188, 965–975,
https://doi.org/10.1007/s00442-018-4265-5, 2018. a
Liu, L., Zhang, Y., Wu, S., Li, S., and Qin, D.: Water Memory Effects and Their
Impacts on Global Vegetation Productivity and Resilience, Sci. Rep.,
8, 2962, https://doi.org/10.1038/s41598-018-21339-4, 2018. a, b
Lorenz, R., Pitman, A. J., Donat, M. G., Hirsch, A. L., Kala, J., Kowalczyk, E. A., Law, R. M., and Srbinovsky, J.: Representation of climate extreme indices in the ACCESS1.3b coupled atmosphere–land surface model, Geosci. Model Dev., 7, 545–567, https://doi.org/10.5194/gmd-7-545-2014, 2014. a
Macfarlane, C., Prober, S., and Wiehl, G.: Great Western Woodlands Flux Data Collection Level 6, Terrestrial Ecosystem Research Network (TERN) [data set], https://hdl.handle.net/102.100.100/14226 (last access: 21 September 2021), 2013. a
Mahecha, M. D., Reichstein, M., Jung, M., Seneviratne, S. I., Zaehle, S., Beer,
C., Braakhekke, M. C., Carvalhais, N., Lange, H., Le Maire, G., and Moors,
E.: Comparing Observations and Process-Based Simulations of
Biosphere-Atmosphere Exchanges on Multiple Timescales, J. Geophys.
Res.-Biogeosci., 115, G02003, https://doi.org/10.1029/2009JG001016, 2010. a
Mencuccini, M. and Hölttä, T.: The Significance of Phloem Transport for
the Speed with Which Canopy Photosynthesis and Belowground Respiration Are
Linked, New Phytol., 185, 189–203,
https://doi.org/10.1111/j.1469-8137.2009.03050.x, 2010. a
Meyer, W., Ewenz, C., Koerber, G., and Lubcke, T.: Calperum Flux Data Collection Level 6, Terrestrial Ecosystem Research Network (TERN) [data set], https://hdl.handle.net/102.100.100/14236 (last access: 21 September 2021), 2013. a
Mottl, O., Flantua, S. G. A., Bhatta, K. P., Felde, V. A., Giesecke, T.,
Goring, S., Grimm, E. C., Haberle, S., Hooghiemstra, H., Ivory, S., Kuneš, P., Wolters, S., Seddon, A. W. R., and Williams, J. W.: Global
Acceleration in Rates of Vegetation Change over the Past 18,000 Years,
Science, 372, 860–864, https://doi.org/10.1126/science.abg1685, 2021. a
Nearing, G. S., Ruddell, B. L., Clark, M. P., Nijssen, B., and Peters-Lidard,
C.: Benchmarking and Process Diagnostics of Land Models, J.
Hydrometeorol., 19, 1835–1852, https://doi.org/10.1175/JHM-D-17-0209.1, 2018. a
Ogle, K. and Barber, J. J.: Plant and Ecosystem Memory, CHANCE, 29, 16–22,
https://doi.org/10.1080/09332480.2016.1181961, 2016. a
Ogle, K., Barber, J. J., Barron-Gafford, G. A., Bentley, L. P., Young, J. M.,
Huxman, T. E., Loik, M. E., and Tissue, D. T.: Quantifying Ecological Memory
in Plant and Ecosystem Processes, Ecol. Lett., 18, 221–235,
https://doi.org/10.1111/ele.12399, 2015. a, b, c
Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G.
V. N., Underwood, E. C., D'amico, J. A., Itoua, I., Strand, H. E., Morrison,
J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H., Kura, Y., Lamoreux,
J. F., Wettengel, W. W., Hedao, P., and Kassem, K. R.: Terrestrial
Ecoregions of the World: A New Map of Life on Earth:
A New Global Map of Terrestrial Ecoregions Provides an Innovative Tool
for Conserving Biodiversity, BioScience, 51, 933–938,
https://doi.org/10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2, 2001. a
OzFlux Australian and New Zealand Flux Research and Monitoring: https://www.ozflux.org.au/ (last access: 21 September 2021), 2021. a
Parton, W. J., Stewart, J. W. B., and Cole, C. V.: Dynamics of C, N,
P and S in Grassland Soils: A Model, Biogeochemistry, 5, 109–131,
https://doi.org/10.1007/BF02180320, 1988. a
Pendall, E., Griebel, A., and Barton, C., and Metzen, D.: Cumberland Plain Flux Data Collection Level 6, Terrestrial Ecosystem Research Network (TERN) [data set], https://hdl.handle.net/102.100.100/25164 (last access: 21 September 2021), 2019. 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
Pitman, A. J., Avila, F. B., Abramowitz, G., Wang, Y. P., Phipps, S. J., and
de Noblet-Ducoudré, N.: Importance of Background Climate in Determining
Impact of Land-Cover Change on Regional Climate, Nat. Clim. Change, 1,
472–475, https://doi.org/10.1038/nclimate1294, 2011. a
Plummer, M.: JAGS: A Program for Analysis of Bayesian Graphical
Models Using Gibbs Sampling, 3rd International Workshop on Distributed
Statistical Computing (DSC 2003); Vienna, Austria, 124, 2003. a
Plummer, M., Best, N., Cowles, K., and Vines, K.: CODA: Convergence
Diagnosis and Output Analysis for MCMC, R News, 6, 7–11, 2006. a
R Core Team: R: A Language and Environment for Statistical Computing, R
Foundation for Statistical Computing, 2020. a
Raupach, M. R.: Simplified Expressions for Vegetation Roughness Length and
Zero-Plane Displacement as Functions of Canopy Height and Area Index,
Bound.-Lay. Meteorol., 71, 211–216, https://doi.org/10.1007/BF00709229, 1994. a
Raupach, M. R., Finkele, K., and Zhang, L.: SCAM: A Soil-Canopy-Atmosphere
Model: Description and Comparisons with Field Data, Technical Report 132,
CSIRO Centre for Environmental Mechanics, Canberra, ACT, Australia, 1997. a
Renchon, A. A., Drake, J. E., Macdonald, C. A., Sihi, D., Hinko-Najera, N.,
Tjoelker, M. G., Arndt, S. K., Noh, N. J., Davidson, E., and Pendall, E.:
Concurrent Measurements of Soil and Ecosystem Respiration in a
Mature Eucalypt Woodland: Advantages, Lessons, and Questions,
J. Geophys. Res.-Biogeosci., 126, e2020JG006221,
https://doi.org/10.1029/2020JG006221, 2021. a
Richardson, A. D., Hollinger, D. Y., Burba, G. G., Davis, K. J., Flanagan,
L. B., Katul, G. G., William Munger, J., Ricciuto, D. M., Stoy, P. C.,
Suyker, A. E., Verma, S. B., and Wofsy, S. C.: A Multi-Site Analysis of
Random Error in Tower-Based Measurements of Carbon and Energy Fluxes,
Agr. Forest Meteorol., 136, 1–18,
https://doi.org/10.1016/j.agrformet.2006.01.007, 2006. a
Ryan, E. M., Ogle, K., Zelikova, T. J., LeCain, D. R., Williams, D. G., Morgan,
J. A., and Pendall, E.: Antecedent Moisture and Temperature Conditions
Modulate the Response of Ecosystem Respiration to Elevated CO2 and
Warming, Glob. Change Biol., 21, 2588–2602, https://doi.org/10.1111/gcb.12910,
2015. a
Ryan, E. M., Ogle, K., Peltier, D., Walker, A. P., De Kauwe, M. G., Medlyn,
B. E., Williams, D. G., Parton, W., Asao, S., Guenet, B., Harper, A. B., Lu,
X., Luus, K. A., Zaehle, S., Shu, S., Werner, C., Xia, J., and Pendall, E.:
Gross Primary Production Responses to Warming, Elevated CO2, and Irrigation: Quantifying the Drivers of Ecosystem
Physiology in a Semiarid Grassland, Glob. Change Biol., 23, 3092–3106,
https://doi.org/10.1111/gcb.13602, 2017. a, b
Sala, O. E., Gherardi, L. A., Reichmann, L., Jobbágy, E., and Peters, D.:
Legacies of Precipitation Fluctuations on Primary Production: Theory and Data
Synthesis, Philos. T. Roy. Soc. B, 367, 3135–3144, https://doi.org/10.1098/rstb.2011.0347, 2012. a, b
Samuels-Crow, K. E., Ogle, K., and Litvak, M. E.: Atmosphere-Soil
Interactions Govern Ecosystem Flux Sensitivity to Environmental
Conditions in Semiarid Woody Ecosystems Over Varying Timescales,
J. Geophys. Res.-Biogeosci., 125, e2019JG005554,
https://doi.org/10.1029/2019JG005554, 2020. a
Schaaf, C. and Wang, Z.: MCD43A3 MODIS/Terra+Aqua BRDF/Albedo
Daily L3 Global – 500m V006, NASA EOSDIS Land Processes DAAC,
https://doi.org/10.5067/MODIS/MCD43A3.006, 2015. a
Seabloom, E. W., Borer, E. T., and Tilman, D.: Grassland Ecosystem Recovery
after Soil Disturbance Depends on Nutrient Supply Rate, Ecol. Lett., 23,
ele.13591, https://doi.org/10.1111/ele.13591, 2020. a
Silberstein, R., Lambert, P., Lardner, T., and Macfarlane, C.: Gingin Flux Data Collection Level 6, Terrestrial Ecosystem Research Network (TERN) [data set], https://hdl.handle.net/102.100.100/22677 (last access: 21 September 2021), 2015. a
Smith, N. G. and Dukes, J. S.: Plant Respiration and Photosynthesis in
Global-Scale Models: Incorporating Acclimation to Temperature and CO2,
Glob. Change Biol., 19, 45–63, https://doi.org/10.1111/j.1365-2486.2012.02797.x,
2013. a
Stoll, J. and Kitchen, M.: Tumbarumba Flux Data Collection Level 6, Terrestrial Ecosystem Research Network (TERN) [data set], https://hdl.handle.net/102.100.100/14241 (last access: 21 September 2021), 2013. a
Su, Y.-S. and Yajima, M.: R2jags: Using R to Run 'JAGS', R package
version 0.6-1, 2020. a
Sun, Q., Meyer, W. S., Koerber, G. R., and Marschner, P.: Rapid Recovery of Net
Ecosystem Production in a Semi-Arid Woodland after a Wildfire, Agr. Forest Meteorol., 291, 108099, https://doi.org/10.1016/j.agrformet.2020.108099,
2020. a
Teuling, A. J., Seneviratne, S. I., Stöckli, R., Reichstein, M., Moors, E.,
Ciais, P., Luyssaert, S., van den Hurk, B., Ammann, C., Bernhofer, C.,
Dellwik, E., Gianelle, D., Gielen, B., Grünwald, T., Klumpp, K.,
Montagnani, L., Moureaux, C., Sottocornola, M., and Wohlfahrt, G.:
Contrasting Response of European Forest and Grassland Energy Exchange to
Heatwaves, Nat. Geosci., 3, 722–727, https://doi.org/10.1038/ngeo950, 2010. a
Trabucco, A. and Zomer, R.: Global Aridity Index and Potential Evapotranspiration (ET0) Climate Database v2, figshare [data set], https://doi.org/10.6084/m9.figshare.7504448.v3, 2019. a, b, c
Ukkola, A. M., De Kauwe, M. G., Pitman, A. J., Best, M. J., Abramowitz, G.,
Haverd, V., Decker, M., and Haughton, N.: Land Surface Models Systematically
Overestimate the Intensity, Duration and Magnitude of Seasonal-Scale
Evaporative Droughts, Environ. Res. Lett., 11, 104012,
https://doi.org/10.1088/1748-9326/11/10/104012, 2016a. a
Ukkola, A. M., Pitman, A. J., Decker, M., De Kauwe, M. G., Abramowitz, G., Kala, J., and Wang, Y.-P.: Modelling evapotranspiration during precipitation deficits: identifying critical processes in a land surface model, Hydrol. Earth Syst. Sci., 20, 2403–2419, https://doi.org/10.5194/hess-20-2403-2016, 2016b. a
Ukkola, A. M., De Kauwe, M. G., Roderick, M. L., Burrell, A., Lehmann, P., and
Pitman, A. J.: Annual Precipitation Explains Variability in Dryland
Vegetation Greenness Globally but Not Locally, Glob. Change Biol., 27,
gcb.15729, https://doi.org/10.1111/gcb.15729, 2021. a
Vanoni, M., Bugmann, H., Nötzli, M., and Bigler, C.: Quantifying the
Effects of Drought on Abrupt Growth Decreases of Major Tree Species in
Switzerland, Ecol. Evol., 6, 3555–3570,
https://doi.org/10.1002/ece3.2146, 2016. a
von Buttlar, J., Zscheischler, J., Rammig, A., Sippel, S., Reichstein, M., Knohl, A., Jung, M., Menzer, O., Arain, M. A., Buchmann, N., Cescatti, A., Gianelle, D., Kiely, G., Law, B. E., Magliulo, V., Margolis, H., McCaughey, H., Merbold, L., Migliavacca, M., Montagnani, L., Oechel, W., Pavelka, M., Peichl, M., Rambal, S., Raschi, A., Scott, R. L., Vaccari, F. P., van Gorsel, E., Varlagin, A., Wohlfahrt, G., and Mahecha, M. D.: Impacts of droughts and extreme-temperature events on gross primary production and ecosystem respiration: a systematic assessment across ecosystems and climate zones, Biogeosciences, 15, 1293–1318, https://doi.org/10.5194/bg-15-1293-2018, 2018. a
Wang, J., Rich, P. M., and Price, K. P.: Temporal Responses of NDVI to
Precipitation and Temperature in the Central Great Plains, USA,
Int. J. Remote Sens., 24, 2345–2364,
https://doi.org/10.1080/01431160210154812, 2003.
a
Wang, Y. P. and Leuning, R.: A Two-Leaf Model for Canopy Conductance,
Photosynthesis and Partitioning of Available Energy I:: Model
Description and Comparison with a Multi-Layered Model, Agr.
Forest Meteorol., 91, 89–111, https://doi.org/10.1016/S0168-1923(98)00061-6, 1998. a
Wang, Y. P., Kowalczyk, E., Leuning, R., Abramowitz, G., Raupach, M. R., Pak,
B., van Gorsel, E., and Luhar, A.: Diagnosing Errors in a Land Surface Model
(CABLE) in the Time and Frequency Domains, J. Geophys.
Res.-Biogeosci., 116, G01034, https://doi.org/10.1029/2010JG001385, 2011. a, b
Weber, T. K. D., Gerling, L., Reineke, D., Weber, S., Durner, W., and Iden,
S. C.: Robust Inverse Modeling of Growing Season Net Ecosystem
Exchange in a Mountainous Peatland: Influence of Distributional
Assumptions on Estimated Parameters and Total Carbon Fluxes,
J. Adv. Model. Earth Syst., 10, 1319–1336,
https://doi.org/10.1029/2017MS001044, 2018. a
Whitley, R., Beringer, J., Hutley, L. B., Abramowitz, G., De Kauwe, M. G., Duursma, R., Evans, B., Haverd, V., Li, L., Ryu, Y., Smith, B., Wang, Y.-P., Williams, M., and Yu, Q.: A model inter-comparison study to examine limiting factors in modelling Australian tropical savannas, Biogeosciences, 13, 3245–3265, https://doi.org/10.5194/bg-13-3245-2016, 2016. a, b
Wilcox, K. R., Blair, J. M., Smith, M. D., and Knapp, A. K.: Does Ecosystem
Sensitivity to Precipitation at the Site-Level Conform to Regional-Scale
Predictions?, Ecology, 97, 561–568, https://doi.org/10.1890/15-1437.1, 2016. a
Zhang, T., Xu, M., Xi, Y., Zhu, J., Tian, L., Zhang, X., Wang, Y., Li, Y., Shi,
P., Yu, G., Sun, X., and Zhang, Y.: Lagged Climatic Effects on Carbon Fluxes
over Three Grassland Ecosystems in China, J. Plant Ecol., 8,
291–302, https://doi.org/10.1093/jpe/rtu026, 2015. a
Zomer, R., Bossio, D., Trabucco, A., Yuanjie, L., Gupta, D., and Singh, V.:
Trees and Water: Smallholder Agroforestry on Irrigated Lands in Northern
India, IWMI Research Report, Colombo, Sri Lanka, 2007. a
Zomer, R., Trabucco, A., Bossio, D., and Verchot, L.: Climate Change
Mitigation: A Spatial Analysis of Global Land Suitability for
Clean Development Mechanism Afforestation and Reforestation,
Agr. Ecosyst. Environ., 126, 67–80,
https://doi.org/10.1016/j.agee.2008.01.014, 2008. a
Short summary
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.
Although vegetation responds to climate at a wide range of timescales, models of the land carbon...
Altmetrics
Final-revised paper
Preprint