Articles | Volume 14, issue 3
https://doi.org/10.5194/esd-14-549-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-14-549-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Opening Pandora's box: reducing global circulation model uncertainty in Australian simulations of the carbon cycle
Lina Teckentrup
CORRESPONDING AUTHOR
ARC Centre of Excellence for Climate Extremes, Sydney, NSW, Australia
Climate Change Research Centre, University of New South Wales, Sydney, NSW, Australia
Martin G. De Kauwe
School of Biological Sciences, University of Bristol, Bristol, UK
Gab Abramowitz
ARC Centre of Excellence for Climate Extremes, Sydney, NSW, Australia
Climate Change Research Centre, University of New South Wales, Sydney, NSW, Australia
Andrew J. Pitman
ARC Centre of Excellence for Climate Extremes, Sydney, NSW, Australia
Climate Change Research Centre, University of New South Wales, Sydney, NSW, Australia
Anna M. Ukkola
ARC Centre of Excellence for Climate Extremes, Sydney, NSW, Australia
Climate Change Research Centre, University of New South Wales, Sydney, NSW, Australia
Sanaa Hobeichi
ARC Centre of Excellence for Climate Extremes, Sydney, NSW, Australia
Climate Change Research Centre, University of New South Wales, Sydney, NSW, Australia
Bastien François
Laboratoire des Sciences du Climat et l'Environnement (LSCE-IPSL) CNRS/CEA/UVSQ, UMR8212, Université Paris-Saclay, Gif-sur-Yvette, France
Benjamin Smith
Hawkesbury Institute for the Environment, Western Sydney University, Penrith, NSW, Australia
Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
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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
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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 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.
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
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Nitrous oxide (N2O) is a powerful greenhouse gas mainly released from natural and agricultural soils. This study examines how global soil N2O emissions changed from 1961 to 2020 and identifies key factors driving these changes using an ecological model. The findings highlight croplands as the largest source, with factors like fertilizer use and climate change enhancing emissions. Rising CO2 levels, however, can partially mitigate N2O emissions through increased plant nitrogen uptake.
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EGUsphere, https://doi.org/10.5194/egusphere-2024-3966, https://doi.org/10.5194/egusphere-2024-3966, 2025
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Joséphine Schmutz, Mathieu Vrac, Bastien François, and Burak Bulut
EGUsphere, https://doi.org/10.5194/egusphere-2025-461, https://doi.org/10.5194/egusphere-2025-461, 2025
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In recent years, Europe has faced severe hot and dry events affecting biodiversity, agriculture, and health. Understanding past significant variation in their occurrence is key for adaptation. This paper identifies emerging hotspots in Europe and North Africa. Since the 1970s, the Iberian Peninsula, Maghreb, and Central Europe have seen more frequent events, driven by rising temperature maxima, while Eastern Europe has experienced a decline due to changes in drought.
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
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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.
Prashant Paudel, Stefan Olin, Mark Tjoelker, Mikael Pontarp, Daniel Metcalfe, and Benjamin Smith
EGUsphere, https://doi.org/10.5194/egusphere-2024-3977, https://doi.org/10.5194/egusphere-2024-3977, 2025
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Ecological processes respond to changes in rainfall conditions. Competition and stress created by water availability are two primary components at two ends of the rainfall gradient. In wetter areas, plants compete for resources, while in drier regions, stress limits growth. The complex interaction between plant characters and their response to growth conditions governs ecosystem processes. These findings can be used to understand how future rainfall changes could impact ecosystems.
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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Revised manuscript accepted for HESS
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Atmospheric variables from climate models often present biases relative to the past. In order to use these models to assess the impact of climate change on processes of interest, it is necessary to correct these biases. We tested several Multivariate Bias Correction Methods (MBCMs) for 5 physical variables that are input variables for 4 process models. We provide recommendations regarding the use of MBCMs when multivariate and time dependent processes are involved.
Jennifer A. Holm, David M. Medvigy, Benjamin Smith, Jeffrey S. Dukes, Claus Beier, Mikhail Mishurov, Xiangtao Xu, Jeremy W. Lichstein, Craig D. Allen, Klaus S. Larsen, Yiqi Luo, Cari Ficken, William T. Pockman, William R. L. Anderegg, and Anja Rammig
Biogeosciences, 20, 2117–2142, https://doi.org/10.5194/bg-20-2117-2023, https://doi.org/10.5194/bg-20-2117-2023, 2023
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Unprecedented climate extremes (UCEs) are expected to have dramatic impacts on ecosystems. We present a road map of how dynamic vegetation models can explore extreme drought and climate change and assess ecological processes to measure and reduce model uncertainties. The models predict strong nonlinear responses to UCEs. Due to different model representations, the models differ in magnitude and trajectory of forest loss. Therefore, we explore specific plant responses that reflect knowledge gaps.
H. E. Markus Meier, Marcus Reckermann, Joakim Langner, Ben Smith, and Ira Didenkulova
Earth Syst. Dynam., 14, 519–531, https://doi.org/10.5194/esd-14-519-2023, https://doi.org/10.5194/esd-14-519-2023, 2023
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The Baltic Earth Assessment Reports summarise the current state of knowledge on Earth system science in the Baltic Sea region. The 10 review articles focus on the regional water, biogeochemical and carbon cycles; extremes and natural hazards; sea-level dynamics and coastal erosion; marine ecosystems; coupled Earth system models; scenario simulations for the regional atmosphere and the Baltic Sea; and climate change and impacts of human use. Some highlights of the results are presented here.
Bastien François and Mathieu Vrac
Nat. Hazards Earth Syst. Sci., 23, 21–44, https://doi.org/10.5194/nhess-23-21-2023, https://doi.org/10.5194/nhess-23-21-2023, 2023
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Compound events (CEs) result from a combination of several climate phenomena. In this study, we propose a new methodology to assess the time of emergence of CE probabilities and to quantify the contribution of marginal and dependence properties of climate phenomena to the overall CE probability changes. By applying our methodology to two case studies, we show the importance of considering changes in both marginal and dependence properties for future risk assessments related to CEs.
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.
David Martín Belda, Peter Anthoni, David Wårlind, Stefan Olin, Guy Schurgers, Jing Tang, Benjamin Smith, and Almut Arneth
Geosci. Model Dev., 15, 6709–6745, https://doi.org/10.5194/gmd-15-6709-2022, https://doi.org/10.5194/gmd-15-6709-2022, 2022
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We present a number of augmentations to the ecosystem model LPJ-GUESS, which will allow us to use it in studies of the interactions between the land biosphere and the climate. The new module enables calculation of fluxes of energy and water into the atmosphere that are consistent with the modelled vegetation processes. The modelled fluxes are in fair agreement with observations across 21 sites from the FLUXNET network.
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.
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
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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.
Adrian Gustafson, Paul A. Miller, Robert G. Björk, Stefan Olin, and Benjamin Smith
Biogeosciences, 18, 6329–6347, https://doi.org/10.5194/bg-18-6329-2021, https://doi.org/10.5194/bg-18-6329-2021, 2021
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We performed model simulations of vegetation change for a historic period and a range of climate change scenarios at a high spatial resolution. Projected treeline advance continued at the same or increased rates compared to our historic simulation. Temperature isotherms advanced faster than treelines, revealing a lag in potential vegetation shifts that was modulated by nitrogen availability. At the year 2100 projected treelines had advanced by 45–195 elevational metres depending on the scenario.
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.
Mats Lindeskog, Benjamin Smith, Fredrik Lagergren, Ekaterina Sycheva, Andrej Ficko, Hans Pretzsch, and Anja Rammig
Geosci. Model Dev., 14, 6071–6112, https://doi.org/10.5194/gmd-14-6071-2021, https://doi.org/10.5194/gmd-14-6071-2021, 2021
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Forests play an important role in the global carbon cycle and for carbon storage. In Europe, forests are intensively managed. To understand how management influences carbon storage in European forests, we implement detailed forest management into the dynamic vegetation model LPJ-GUESS. We test the model by comparing model output to typical forestry measures, such as growing stock and harvest data, for different countries in Europe.
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.
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
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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
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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
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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
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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.
Taraka Davies-Barnard, Johannes Meyerholt, Sönke Zaehle, Pierre Friedlingstein, Victor Brovkin, Yuanchao Fan, Rosie A. Fisher, Chris D. Jones, Hanna Lee, Daniele Peano, Benjamin Smith, David Wårlind, and Andy J. Wiltshire
Biogeosciences, 17, 5129–5148, https://doi.org/10.5194/bg-17-5129-2020, https://doi.org/10.5194/bg-17-5129-2020, 2020
Cited articles
Abramowitz, G., Herger, N., Gutmann, E., Hammerling, D., Knutti, R., Leduc, M., Lorenz, R., Pincus, R., and Schmidt, G. A.: ESD Reviews: Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing, Earth Syst. Dynam., 10, 91–105, https://doi.org/10.5194/esd-10-91-2019, 2019. a, b
Ahlström, A., Schurgers, G., Arneth, A., and Smith, B.: Robustness and
uncertainty in terrestrial ecosystem carbon response to CMIP5 climate
change projections, Environ. Res. Lett., 7, 044008,
https://doi.org/10.1088/1748-9326/7/4/044008, 2012. a, b, c
Ahlström, 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, b
Ahlström, A., Schurgers, G., and Smith, B.: The large influence of climate
model bias on terrestrial carbon cycle simulations, Environ. Res.
Lett., 12, 014004, https://doi.org/10.1088/1748-9326/12/1/014004, 2017. a, b, c
Annan, J. D. and Hargreaves, J. C.: On the meaning of independence in climate science, Earth Syst. Dynam., 8, 211–224, https://doi.org/10.5194/esd-8-211-2017, 2017. a, b
Bárdossy, A. and Pegram, G.: Multiscale spatial recorrelation of RCM
precipitation to produce unbiased climate change scenarios over large areas
and small, Water Resour. Res., 48, W09502,
https://doi.org/10.1029/2011WR011524, 2012. a, b
Berg, P., Feldmann, H., and Panitz, H.-J.: Bias correction of high resolution
regional climate model data, J. Hydrol., 448–449, 80–92,
https://doi.org/10.1016/j.jhydrol.2012.04.026, 2012. a
Bi, D., Dix, M. R., Marsland, S. J., Farrell, S. P. O., Rashid, H. A., Uotila,
P., Hirst, A. C., Kowalczyk, E. A., Golebiewski, M., Sullivan, A., Yan, H.,
Hannah, N., Franklin, C., Sun, Z., Vohralik, P. F., Watterson, I. G., Zhou,
X., Fiedler, R. A. S., Collier, M. A., Ma, Y., Noonan, J. A., Stevens, L.,
Uhe, P., Zhu, H., Griffies, S. M., Hill, R., Harris, C., and Puri, K.: The
ACCESS coupled model: description, control climate and evaluation,
Aust. Meteorol. Ocean., 63, 41–64, 2013. a
Boe, J.: Interdependency in multimodel climate projections: Component
replication and result similarity, Geophys. Res. Lett., 45,
2771–2779, 2018. a
Boucher, O., Servonnat, J., Albright, A. L., Aumont, O., Balkanski, Y.,
Bastrikov, V., Bekki, S., Bonnet, R., Bony, S., Bopp, L., Braconnot, P.,
Brockmann, P.and Cadule, P., Caubel, A., Cheruy, F., Codron, F., Cozic, A.,
Cugnet, D., D'Andrea, F., Davini, P., de Lavergne, C., Denvil, S., Deshayes,
J., Devilliers, M., Ducharne, A., Dufresne, J.-L., Dupont, E., Éthé, C.,
Fairhead, L., Falletti, L., Flavoni, S., Foujols, M.-A., Gardoll, S.,
Gastineau, G., Ghattas, J., Grandpeix, J.-Y., Guenet, B., Guez, L., E.,
Guilyardi, E., Guimberteau, M., Hauglustaine, D., Hourdin, F., Idelkadi, A.,
Joussaume, S., Kageyama, M., Khodri, M., Krinner, G., Lebas, N., Levavasseur,
G., Lévy, C., Li, L., Lott, F., Lurton, T., Luyssaert, S., Madec, G.,
Madeleine, J.-B., Maignan, F., Marchand, M., Marti, O., Mellul, L.,
Meurdesoif, Y., Mignot, J., Musat, I., Ottlé, C., Peylin, P., Planton, Y.,
Polcher, J., Rio, C., Rochetin, N., Rousset, C., Sepulchre, P., Sima, A.,
Swingedouw, D., Thiéblemont, R., Traore, A. K., Vancoppenolle, M., Vial, J.,
Vialard, J., Viovy, N., and Vuichard, N.: Presentation and Evaluation of the
IPSL-CM6A-LR Climate Model, J. Adv. Model. Earth Sy.,
12, e2019MS002010, https://doi.org/10.1029/2019MS002010,
2020. a
Brunner, L., Lorenz, R., Zumwald, M., and Knutti, R.: Quantifying uncertainty
in European climate projections using combined performance-independence
weighting, Environ. Res. Lett., 14, 124010,
https://doi.org/10.1088/1748-9326/ab492f, 2019. a
Brunner, L., Pendergrass, A. G., Lehner, F., Merrifield, A. L., Lorenz, R., and Knutti, R.: Reduced global warming from CMIP6 projections when weighting models by performance and independence, Earth Syst. Dynam., 11, 995–1012, https://doi.org/10.5194/esd-11-995-2020, 2020. a
Cannon, A. J.: Selecting GCM Scenarios that Span the Range of Changes in a
Multimodel Ensemble: Application to CMIP5 Climate Extremes Indices, J. Climate, 28, 1260–1267, https://doi.org/10.1175/JCLI-D-14-00636.1, 2015. a
Cannon, A. J., Sobie, S. R., and Murdock, T. Q.: Bias Correction of GCM
Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in
Quantiles and Extremes?, J. Climate, 28, 6938–6959,
https://doi.org/10.1175/JCLI-D-14-00754.1, 2015. a, b
Cao, J., Wang, B., Yang, Y.-M., Ma, L., Li, J., Sun, B., Bao, Y., He, J., Zhou, X., and Wu, L.: The NUIST Earth System Model (NESM) version 3: description and preliminary evaluation, Geosci. Model Dev., 11, 2975–2993, https://doi.org/10.5194/gmd-11-2975-2018, 2018. a
Casanueva, A., Bedia, J., Herrera García, S., Fernández, J., and Gutiérrez,
J.: Direct and component-wise bias correction of multi-variate climate
indices: the percentile adjustment function diagnostic tool, Climatic
Change, 147, 411–425, https://doi.org/10.1007/s10584-018-2167-5, 2018. a
Casanueva, A., Herrera, S., Iturbide, M., Lange, S., Jury, M., Dosio, A.,
Maraun, D., and Gutiérrez, J. M.: Testing bias adjustment methods for
regional climate change applications under observational uncertainty and
resolution mismatch, Atmos. Sci. Lett., 21, e978,
https://doi.org/10.1002/asl.978, 2020. a, b, c
Cheaib, A., Badeau, V., Boe, J., Chuine, I., Delire, C., Dufrêne, E.,
François, C., Gritti, E. S., Legay, M., Pagé, C., Thuiller, W., Viovy,
N., and Leadley, P.: Climate change impacts on tree ranges: model
intercomparison facilitates understanding and quantification of uncertainty,
Ecol. Lett., 15, 533–544,
https://doi.org/10.1111/j.1461-0248.2012.01764.x, 2012. a
Chen, J., Brissette, F. P., and Leconte, R.: Uncertainty of downscaling method
in quantifying the impact of climate change on hydrology, J.
Hydrol., 401, 190–202,
https://doi.org/10.1016/j.jhydrol.2011.02.020, 2011. a
Cherchi, A., Fogli, P. G., Lovato, T., Peano, D., Iovino, D., Gualdi, S.,
Masina, S., Scoccimarro, E., Materia, S., Bellucci, A., and Navarra, A.:
Global Mean Climate and Main Patterns of Variability in the CMCC-CM2 Coupled
Model, J. Adv. Model. Earth Sy., 11, 185–209,
https://doi.org/10.1029/2018MS001369, 2019. a
Cleverly, J., Eamus, D., Luo, Q., Restrepo-Coupe, N., Kljun, N., Ma, X., Ewenz,
C., Li, L., Yu, Q., and Huete, A.: The importance of interacting climate
modes on Australia's contribution to global carbon cycle extremes,
Sci. Rep., 6, 23113, https://doi.org/10.1038/srep23113, 2016. a
Deo, R. and Şahin, M.: Application of the extreme learning machine algorithm
for the prediction of monthly Effective Drought Index in eastern Australia,
Atmos. Res., 153, 512–525, https://doi.org/10.1016/j.atmosres.2014.10.016, 2015. a
Déqué, M.: Frequency of precipitation and temperature extremes over France
in an anthropogenic scenario: Model results and statistical correction
according to observed values, Global Planet. Change, 57, 16–26,
https://doi.org/10.1016/j.gloplacha.2006.11.030, 2007. a, b
Déqué, M. and Somot, S.: Weighted frequency distributions express
modelling uncertainties in the ENSEMBLES regional climate experiments,
Clim. Res., 44, 195–209, https://doi.org/10.3354/cr00866, 2010. a
Döscher, R., Acosta, M., Alessandri, A., Anthoni, P., Arsouze, T., Bergman, T., Bernardello, R., Boussetta, S., Caron, L.-P., Carver, G., Castrillo, M., Catalano, F., Cvijanovic, I., Davini, P., Dekker, E., Doblas-Reyes, F. J., Docquier, D., Echevarria, P., Fladrich, U., Fuentes-Franco, R., Gröger, M., v. Hardenberg, J., Hieronymus, J., Karami, M. P., Keskinen, J.-P., Koenigk, T., Makkonen, R., Massonnet, F., Ménégoz, M., Miller, P. A., Moreno-Chamarro, E., Nieradzik, L., van Noije, T., Nolan, P., O'Donnell, D., Ollinaho, P., van den Oord, G., Ortega, P., Prims, O. T., Ramos, A., Reerink, T., Rousset, C., Ruprich-Robert, Y., Le Sager, P., Schmith, T., Schrödner, R., Serva, F., Sicardi, V., Sloth Madsen, M., Smith, B., Tian, T., Tourigny, E., Uotila, P., Vancoppenolle, M., Wang, S., Wårlind, D., Willén, U., Wyser, K., Yang, S., Yepes-Arbós, X., and Zhang, Q.: The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6, Geosci. Model Dev., 15, 2973–3020, https://doi.org/10.5194/gmd-15-2973-2022, 2022. a, b
Dunne, J. P., Horowitz, L. W., Adcroft, A. J., Ginoux, P., Held, I. M., John,
J. G., Krasting, J. P., Malyshev, S., Naik, V., Paulot, F., Shevliakova, E.,
Stock, C. A., Zadeh, N., Balaji, V., Blanton, C., Dunne, K. A., Dupuis, C.,
Durachta, J., Dussin, R., Gauthier, P. P. G., Griffies, S. M., Guo, H.,
Hallberg, R. W., Harrison, M., He, J., Hurlin, W., McHugh, C., Menzel, R.,
Milly, P. C. D., Nikonov, S., Paynter, D. J., Ploshay, J., Radhakrishnan, A.,
Rand, K., Reichl, B. G., Robinson, T., Schwarzkopf, D. M., Sentman, L. T.,
Underwood, S., Vahlenkamp, H., Winton, M., Wittenberg, A. T., Wyman, B.,
Zeng, Y., and Zhao, M.: The GFDL Earth System Model Version 4.1 (GFDL-ESM
4.1): Overall Coupled Model Description and Simulation Characteristics,
J. Adv. Model. Earth Sy., 12, e2019MS002015,
https://doi.org/10.1029/2019MS002015, 2020. a
ESGF: CMIP6 output, https://esgf-node.llnl.gov/search/cmip6/, last access: 1 May 2023. a
Evans, J. P., Ji, F., Lee, C., Smith, P., Argüeso, D., and Fita, L.: Design of a regional climate modelling projection ensemble experiment – NARCliM, Geosci. Model Dev., 7, 621–629, https://doi.org/10.5194/gmd-7-621-2014, 2014. a, b
Famien, A. M., Janicot, S., Ochou, A. D., Vrac, M., Defrance, D., Sultan, B., and Noël, T.: A bias-corrected CMIP5 dataset for Africa using the CDF-t method – a contribution to agricultural impact studies, Earth Syst. Dynam., 9, 313–338, https://doi.org/10.5194/esd-9-313-2018, 2018. a
Fisher, R., McDowell, N., Purves, D., Moorcroft, P., Sitch, S., Cox, P.,
Huntingford, C., Meir, P., and Woodward, I. F.: Assessing uncertainties in a
second-generation dynamic vegetation model caused by ecological scale
limitations, New Phytol., 187, 666–681,
https://doi.org/10.1111/j.1469-8137.2010.03340.x, 2010. a
Fisher, R. A., Koven, C. D., Anderegg, W. R. L., Christoffersen, B. O., Dietze,
M. C., Farrior, C. E., Holm, J. A., Hurtt, G. C., Knox, R. G., Lawrence,
P. J., Lichstein, J. W., Longo, M., Matheny, A. M., Medvigy, D.,
Muller-Landau, H. C., Powell, T. L., Serbin, S., Sato, H., Shuman, J. K.,
Smith, B., Trugman, A. T., Viskari, T., Verbeeck, H., Weng, E., Xu, C., Xu,
X., Zhang, T., and Moorcroft, P. R.: Vegetation demographics in Earth System
Models: A review of progress and priorities, Glob. Change Biol., 24,
35–54, https://doi.org/10.1111/gcb.13910, 2018. a, b
Flato, G., Marotzke, J., Abiodun, B., Braconnot, P., Chou, S. C., Collins, W.,
Cox, P., Driouech, F., Emori, S., Eyring, V., Forest, C., Gleckler, P.,
Guilyardi, E., Jakob, C., Kattsov, V., Reason, C., and Rummukainen, M.:
Evaluation of climate models, in: Climate Change 2013: The Physical Science
Basis. Contribution of Working Group I to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin,
D., Plattner, G.-K., Tignor, M., Allen, S. K., Doschung, J., Nauels, A., Xia,
Y., Bex, V., and Midgley, P. M., 741–882, Cambridge University Press,
Cambridge, UK, https://doi.org/10.1017/CBO9781107415324.020, 2013. a
Freedman, D. and Diaconis, P.: On the histogram as a density estimator: L2
theory, Z. Wahrscheinlichkeit.,
57, 453–476, 1981. a
Gallagher, R. V., Butt, N., Carthey, A. J. R., Tulloch, A., Bland, L., Clulow,
S., Newsome, T., Dudaniec, R. Y., and Adams, V. M.: A guide to using species
trait data in conservation, One Earth, 4, 927–936,
https://doi.org/10.1016/j.oneear.2021.06.013, 2021. a
Gershunov, A., Shulgina, T., Clemesha, R., Guirguis, K., Pierce, D., Dettinger,
M., Lavers, D., Cayan, D., Polade, S., Kalansky, J., and Ralph, F.:
Precipitation regime change in Western North America: The role of
Atmospheric Rivers, Sci. Rep., 9, 9944,
https://doi.org/10.1038/s41598-019-46169-w, 2019. a, b
Gohar, L. K., Lowe, J. A., and Bernie, D.: The Impact of Bias Correction and
Model Selection on Passing Temperature Thresholds, J. Geophys.
Res.-Atmos., 122, 12045–12061,
https://doi.org/10.1002/2017JD026797, 2017. a
Grose, M. R., Narsey, S., Delage, F. P., Dowdy, A. J., Bador, M., Boschat, G.,
Chung, C., Kajtar, J. B., Rauniyar, S., Freund, M. B., Lyu, K., Rashid, H.,
Zhang, X., Wales, S., Trenham, C., Holbrook, N. J., Cowan, T., Alexander, L.,
Arblaster, J. M., and Power, S.: Insights From CMIP6 for Australia's Future
Climate, Earth's Future, 8, e2019EF001469,
https://doi.org/10.1029/2019EF001469, 2020. a
Hagemann, S., Chen, C., Haerter, J. O., Heinke, J., Gerten, D., and Piani, C.:
Impact of a Statistical Bias Correction on the Projected Hydrological
Changes Obtained from Three GCMs and Two Hydrology Models, J.
Hydrometeorol., 12, 556–578, https://doi.org/10.1175/2011JHM1336.1, 2011. a, b
Harris, I.: CRU JRA v2.0: A forcings dataset of gridded land surface blend of
Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data; Jan.1901–Dec.2018, Centre for Environmental Data Analysis (CEDA) [data set],
https://catalogue.ceda.ac.uk/uuid/7f785c0e80aa4df2b39d068ce7351bbb (last access: March 2021),
2019. a, b, c
Harris, I., Jones, P., Osborn, T., and Lister, D.: Updated high-resolution
grids of monthly climatic observations – the CRU TS3.10 Dataset,
Int. J. Climatol., 34, 623–642,
https://doi.org/10.1002/joc.3711, 2014. 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, 2018. a, b
Haverd, V., Raupach, M. R., Briggs, P. R., Canadell, J. G., Davis, S. J., Law, R. M., Meyer, C. P., Peters, G. P., Pickett-Heaps, C., and Sherman, B.: The Australian terrestrial carbon budget, Biogeosciences, 10, 851–869, https://doi.org/10.5194/bg-10-851-2013, 2013. a
Held, I. M., Guo, H., Adcroft, A., Dunne, J. P., Horowitz, L. W., Krasting, J.,
Shevliakova, E., Winton, M., Zhao, M., Bushuk, M., Wittenberg, A. T., Wyman,
B., Xiang, B., Zhang, R., Anderson, W., Balaji, V., Donner, L., Dunne, K.,
Durachta, J., Gauthier, P. P. G., Ginoux, P., Golaz, J.-C., Griffies, S. M.,
Hallberg, R., Harris, L., Harrison, M., Hurlin, W., John, J., Lin, P., Lin,
S.-J., Malyshev, S., Menzel, R., Milly, P. C. D., Ming, Y., Naik, V.,
Paynter, D., Paulot, F., Ramaswamy, V., Reichl, B., Robinson, T., Rosati, A.,
Seman, C., Silvers, L. G., Underwood, S., and Zadeh, N.: Structure and
Performance of GFDL's CM4.0 Climate Model, J. Adv. Model.
Earth Sy., 11, 3691–3727, https://doi.org/10.1029/2019MS001829,
2019. a
Herger, N., Abramowitz, G., Knutti, R., Angélil, O., Lehmann, K., and Sanderson, B. M.: Selecting a climate model subset to optimise key ensemble properties, Earth Syst. Dynam., 9, 135–151, https://doi.org/10.5194/esd-9-135-2018, 2018. a, b
Herger, N., Abramowitz, G., Sherwood, S., Knutti, R., Angélil, O., and Sisson,
S.: Ensemble optimisation, multiple constraints and overconfidence: a case
study with future Australian precipitation change, Clim. Dynam., 53, 1581–1596,
https://doi.org/10.1007/s00382-019-04690-8, 2019. a, b
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons,
A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati,
G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M.,
Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P.,
Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global
reanalysis, Q. J. Roy. Meteor. Soc., 146,
1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Huntingford, C., Jeffers, E. S., Bonsall, M. B., Christensen, H. M., Lees, T.,
and Yang, H.: Machine learning and artificial intelligence to aid climate
change research and preparedness, Environ. Res. Lett., 14,
124007, https://doi.org/10.1088/1748-9326/ab4e55, 2019. a
Iizumi, T., Takikawa, H., Hirabayashi, Y., Hanasaki, N., and Nishimori, M.:
Contributions of different bias-correction methods and reference
meteorological forcing data sets to uncertainty in projected temperature and
precipitation extremes, J. Geophys. Res.-Atmos., 122,
7800–7819, https://doi.org/10.1002/2017JD026613, 2017. a
IPCC: Climate Change 2013: The Physical Science Basis. Contribution of
Working Group I to the Fifth Assessment Report of the Intergovernmental Panel
on Climate Change, edited by: Stocker, T., Qin, D., Plattner, G.-K., Tignor,
M., Allen, S., Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley,
P. M., Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA, https://doi.org/10.1017/CBO9781107415324, 2013. a
IPCC: Regional fact sheet – Australasia, in: Climate Change 2021: The
Physical Science Basis. Contribution of Working Group I to the Sixth
Assessment Report of the Intergovernmental Panel on Climate Change, edited by:
Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L., Péan, C., Berger,
S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I., Huang, M., Leitzell, K.,
Lonnoy, E., Matthews, J., Maycock, T., Waterfield, T., Yelekçi, O., Yu, R.,
and Zhou, B., Cambridge University Press, Cambridge, United Kingdom and New
York, NY, USA, 2021. a
Johnson, F. and Sharma, A.: What are the impacts of bias correction on future
drought projections?, J. Hydrol., 525, 472–485,
https://doi.org/10.1016/j.jhydrol.2015.04.002, 2015. a, b
Jun, M., Knutti, R., and Nychka, D. W.: Spatial Analysis to Quantify Numerical
Model Bias and Dependence, J. Am. Stat.
Assoc., 103, 934–947, https://doi.org/10.1198/016214507000001265, 2008. a
Jung, M., Reichstein, M., Ciais, P., Seneviratne, S., Sheffield, J., Goulden,
M., Bonan, G., Cescatti, A., Chen, J., de Jeu, R., Dolman, H. A., Eugster,
W., Gerten, D., Gianelle, D., Gobron, N., Heinke, J., Kimball, J., Law, B.,
and Montagnani, L.: Recent Decline in the Global Land Evapotranspiration
Trend Due to Limited Moisture Supply, Nature, 467, 951–954,
https://doi.org/10.1038/nature09396, 2010. a
Knutti, R., Abramowitz, G., Collins, M., Eyring, V., Gleckler, P., Hewitson,
B., and Mearns, L.: Good Practice Guidance Paper on Assessing and Combining Multi Model Climate Projections, Meeting Report of the Intergovernmental Panel on Climate Change Expert Meeting on Assessing and Combining Multi Model Climate Projections, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., and Midgley, P. M., IPCC Working Group I Technical Support Unit, University of Bern, 582 pp., 2010a. a, b, c
Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., and Meehl, G. A.: Challenges
in Combining Projections from Multiple Climate Models, J. Climate,
23, 2739–2758, https://doi.org/10.1175/2009JCLI3361.1, 2010b. a, b, c
Knutti, R., Sedláček, J., Sanderson, B. M., Lorenz, R., Fischer, E. M., and
Eyring, V.: A climate model projection weighting scheme accounting for
performance and interdependence, Geophys. Res. Lett., 44,
1909–1918, https://doi.org/10.1002/2016GL072012, 2017. a
Kobayashi, S., Ota, Y., Harada, Y., Ebita, A., Moriya, M., Onoda, H., Onogi,
K., Kamahori, H., Kobayashi, C., Endo, H., Miyaoka, K., and Takahashi, K.:
The JRA-55 Reanalysis: General Specifications and Basic Characteristics,
J. Meteorol. Soc. Jpn. Ser. II, 93, 5–48,
https://doi.org/10.2151/jmsj.2015-001, 2015. a
Kolusu, S. R., Siderius, C., Todd, M. C., Bhave, A., Conway, D., James, R.,
Washington, R., Geressu, R., Harou, J. J., and Kashaigili, J. J.:
Sensitivity of projected climate impacts to climate model weighting:
multi-sector analysis in eastern Africa, Climatic Change, 164, 36,
https://doi.org/10.1007/s10584-021-02991-8, 2021. a, b
Lamarque, J.-F., Dentener, F., McConnell, J., Ro, C.-U., Shaw, M., Vet, R., Bergmann, D., Cameron-Smith, P., Dalsoren, S., Doherty, R., Faluvegi, G., Ghan, S. J., Josse, B., Lee, Y. H., MacKenzie, I. A., Plummer, D., Shindell, D. T., Skeie, R. B., Stevenson, D. S., Strode, S., Zeng, G., Curran, M., Dahl-Jensen, D., Das, S., Fritzsche, D., and Nolan, M.: Multi-model mean nitrogen and sulfur deposition from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): evaluation of historical and projected future changes, Atmos. Chem. Phys., 13, 7997–8018, https://doi.org/10.5194/acp-13-7997-2013, 2013. a
Law, R. M., Ziehn, T., Matear, R. J., Lenton, A., Chamberlain, M. A., Stevens, L. E., Wang, Y.-P., Srbinovsky, J., Bi, D., Yan, H., and Vohralik, P. F.: The carbon cycle in the Australian Community Climate and Earth System Simulator (ACCESS-ESM1) – Part 1: Model description and pre-industrial simulation, Geosci. Model Dev., 10, 2567–2590, https://doi.org/10.5194/gmd-10-2567-2017, 2017. a
Liu, S.-M., Chen, Y.-H., Rao, J., Cao, C., Li, S.-Y., Ma, M.-H., and Wang,
Y.-B.: Parallel Comparison of Major Sudden Stratospheric Warming Events in
CESM1-WACCM and CESM2-WACCM, Atmosphere, 10, 679, https://doi.org/10.3390/atmos10110679,
2019. a
Liu, Y., Racah, E., Prabhat, Correa, J., Khosrowshahi, A., Lavers, D.,
Kunkel, K., Wehner, M., and Collins, W.: Application of Deep
Convolutional Neural Networks for Detecting Extreme Weather in Climate
Datasets, arXiv [preprint], https://doi.org/10.48550/arXiv.1605.01156, 4 May 2016. a
Maraun, D., Shepherd, T., Widmann, M., Zappa, G., Walton, D., Gutiérrez, J.,
Hagemann, S., Richter, I., Soares, P., Hall, A., and Mearns, L.: Towards
process-informed bias correction of climate change simulations, Nat.
Clim. Change, 7, 3418, https://doi.org/10.1038/nclimate3418, 2017. a
Martyn Clark, M., Gangopadhyay, S., Hay, L., Rajagopalan, B., and Wilby, R.:
The Schaake Shuffle: A Method for Reconstructing Space–Time Variability in
Forecasted Precipitation and Temperature Fields, J.
Hydrometeorol., 5, 243–262,
https://doi.org/10.1175/1525-7541(2004)005<0243:TSSAMF>2.0.CO;2, 2004. a
Massoud, E., Espinoza, V., Guan, B., and Waliser, D.: Global Climate Model
Ensemble Approaches for Future Projections of Atmospheric Rivers, Earth's
Future, 7, 1136–1151, https://doi.org/10.1029/2019EF001249, 2019. a
Massoud, E. C., Lee, H., Gibson, P. B., Loikith, P., and Waliser, D. E.:
Bayesian Model Averaging of Climate Model Projections Constrained by
Precipitation Observations over the Contiguous United States, J.
Hydrometeorol., 21, 2401–2418, https://doi.org/10.1175/JHM-D-19-0258.1, 2020. a
Maurer, E. P. and Pierce, D. W.: Bias correction can modify climate model simulated precipitation changes without adverse effect on the ensemble mean, Hydrol. Earth Syst. Sci., 18, 915–925, https://doi.org/10.5194/hess-18-915-2014, 2014. a, b
Mauritsen, T., Bader, J., Becker, T., Behrens, J., Bittner, M., Brokopf, R.,
Brovkin, V., Claussen, M., Crueger, T., Esch, M., Fast, I., Fiedler, S.,
Fläschner, D., Gayler, V., Giorgetta, M., Goll, D. S., Haak, H., Hagemann,
S., Hedemann, C., Hohenegger, C., Ilyina, T., Jahns, T., Jimenéz-de-la
Cuesta, D., Jungclaus, J., Kleinen, T., Kloster, S., Kracher, D., Kinne, S.,
Kleberg, D., Lasslop, G., Kornblueh, L., Marotzke, J., Matei, D., Meraner,
K., Mikolajewicz, U., Modali, K., Möbis, B., Müller, W. A., Nabel, J. E.
M. S., Nam, C. C. W., Notz, D., Nyawira, S.-S., Paulsen, H., Peters, K.,
Pincus, R., Pohlmann, H., Pongratz, J., Popp, M., Raddatz, T. J., Rast, S.,
Redler, R., Reick, C. H., Rohrschneider, T., Schemann, V., Schmidt, H.,
Schnur, R., Schulzweida, U., Six, K. D., Stein, L., Stemmler, I., Stevens,
B., von Storch, J.-S., Tian, F., Voigt, A., Vrese, P., Wieners, K.-H.,
Wilkenskjeld, S., Winkler, A., and Roeckner, E.: Developments in the MPI-M
Earth System Model version 1.2 (MPI-ESM1.2) and Its Response to Increasing
CO2, J. Adv. Model. Earth Sy., 11, 998–1038,
https://doi.org/10.1029/2018MS001400, 2019. a, b
Meinshausen, M., Nicholls, Z. R. J., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., Beyerle, U., Gessner, C., Nauels, A., Bauer, N., Canadell, J. G., Daniel, J. S., John, A., Krummel, P. B., Luderer, G., Meinshausen, N., Montzka, S. A., Rayner, P. J., Reimann, S., Smith, S. J., van den Berg, M., Velders, G. J. M., Vollmer, M. K., and Wang, R. H. J.: The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500, Geosci. Model Dev., 13, 3571–3605, https://doi.org/10.5194/gmd-13-3571-2020, 2020. a
Merrifield, A. L., Brunner, L., Lorenz, R., Medhaug, I., and Knutti, R.: An investigation of weighting schemes suitable for incorporating large ensembles into multi-model ensembles, Earth Syst. Dynam., 11, 807–834, https://doi.org/10.5194/esd-11-807-2020, 2020. a, b
Michelangeli, P.-A., Vrac, M., and Loukos, H.: Probabilistic downscaling
approaches: Application to wind cumulative distribution functions,
Geophys. Res. Lett., 36, L11708, https://doi.org/10.1029/2009GL038401,
2009. a, b, c
Müller, W. A., Jungclaus, J. H., Mauritsen, T., Baehr, J., Bittner, M.,
Budich, R., Bunzel, F., Esch, M., Ghosh, R., Haak, H., Ilyina, T., Kleine,
T., Kornblueh, L., Li, H., Modali, K., Notz, D., Pohlmann, H., Roeckner, E.,
Stemmler, I., Tian, F., and Marotzke, J.: A Higher-resolution Version of the
Max Planck Institute Earth System Model (MPI-ESM1.2-HR), J.
Adv. Model. Earth Sy., 10, 1383–1413,
https://doi.org/10.1029/2017MS001217, 2018. a
Pak, G., Noh, Y., Lee, M.-I., Yeh, S.-W., Kim, D., Kim, S.-Y., Lee, J.-L., Lee,
H., Hyun, S.-H., Lee, K.-Y., Lee, J.-H., Park, Y.-G., Jin, H., Park, H., and
Kim, Y.: Korea Institute of Ocean Science and Technology Earth System Model
and Its Simulation Characteristics, Ocean Sci. J., 56, 18–45,
https://doi.org/10.1007/s12601-021-00001-7, 2021. a
Pennell, C. and Reichler, T.: On the Effective Number of Climate Models,
J. Climate, 24, 2358–2367, https://doi.org/10.1175/2010JCLI3814.1, 2011. a, b
Pierce, D., Barnett, T., Santer, B., and Gleckler, P.: Selecting Global
Climate Models for Regional Climate Change Studies, P.
Natl. Acad. Sci. USA, 106, 8441–8446,
https://doi.org/10.1073/pnas.0900094106, 2009. a
Poulter, B., Frank, D., Ciais, P., Myneni, R., Andela, N., Bi, J., Broquet, G.,
Canadell, J., Chevallier, F., Liu, Y., Running, S., Sitch, S., and van der
Werf, G.: 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
Randall, D. A., Wood, R. A., Bony, S., Colman, R., Fichefet, T., Fyfe, J.,
Kattsov, V., Pitman, A., Shukla, J., Srinivasan, J., Stouffer, R. J., Sumi,
A., and Taylor, K. E.: Climate models and their evaluation, Chap. 8, in:
Climate Change 2007: the physical science basis. Contribution of Working
Group I to the Fourth Assessment Report of the Intergovernmental Panel on
Climate Change, edited by: Solomon, S., Qin, D., Manning, M., Chen, Z.,
Marquis, M., Averyt, K. B., Tignor, M., and Miller, H. L., 589–662,
Cambridge University Press, Cambridge, UK, 2007. a
Robin, Y.: SBCK (Statistical Bias Correction Kit), GitHub [code], https://github.com/yrobink/SBCK, last access: 1 July 2022. a
Robin, Y. and Vrac, M.: Is time a variable like the others in multivariate statistical downscaling and bias correction?, Earth Syst. Dynam., 12, 1253–1273, https://doi.org/10.5194/esd-12-1253-2021, 2021. a
Robin, Y., Vrac, M., Naveau, P., and Yiou, P.: Multivariate stochastic bias corrections with optimal transport, Hydrol. Earth Syst. Sci., 23, 773–786, https://doi.org/10.5194/hess-23-773-2019, 2019. a, b, c
Sanderson, B. M., Wehner, M., and Knutti, R.: Skill and independence weighting for multi-model assessments, Geosci. Model Dev., 10, 2379–2395, https://doi.org/10.5194/gmd-10-2379-2017, 2017. a
Seland, Ø., Bentsen, M., Olivié, D., Toniazzo, T., Gjermundsen, A., Graff, L. S., Debernard, J. B., Gupta, A. K., He, Y.-C., Kirkevåg, A., Schwinger, J., Tjiputra, J., Aas, K. S., Bethke, I., Fan, Y., Griesfeller, J., Grini, A., Guo, C., Ilicak, M., Karset, I. H. H., Landgren, O., Liakka, J., Moseid, K. O., Nummelin, A., Spensberger, C., Tang, H., Zhang, Z., Heinze, C., Iversen, T., and Schulz, M.: Overview of the Norwegian Earth System Model (NorESM2) and key climate response of CMIP6 DECK, historical, and scenario simulations, Geosci. Model Dev., 13, 6165–6200, https://doi.org/10.5194/gmd-13-6165-2020, 2020. a, b
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W.,
Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., Thonicke, K., and
Venevsky, S.: Evaluation of ecosystem dynamics, plant geography and
terrestrial carbon cycling in the LPJ dynamic global vegetation model,
Glob. Change Biol., 9, 161–185, https://doi.org/10.1046/j.1365-2486.2003.00569.x,
2003. a
Smith, B., Wårlind, D., Arneth, A., Hickler, T., Leadley, P., Siltberg, J., and Zaehle, S.: Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model, Biogeosciences, 11, 2027–2054, https://doi.org/10.5194/bg-11-2027-2014, 2014. a, b, c, d, e
Sperry, J. S., Venturas, M. D., Todd, H. N., Trugman, A. T., Anderegg, W.
R. L., Wang, Y., and Tai, X.: The impact of rising CO2 and acclimation on
the response of US forests to global warming, P. Natl.
Acad. Sci. USA, 116, 25734–25744, https://doi.org/10.1073/pnas.1913072116,
2019. a
Swart, N. C., Cole, J. N. S., Kharin, V. V., Lazare, M., Scinocca, J. F., Gillett, N. P., Anstey, J., Arora, V., Christian, J. R., Hanna, S., Jiao, Y., Lee, W. G., Majaess, F., Saenko, O. A., Seiler, C., Seinen, C., Shao, A., Sigmond, M., Solheim, L., von Salzen, K., Yang, D., and Winter, B.: The Canadian Earth System Model version 5 (CanESM5.0.3), Geosci. Model Dev., 12, 4823–4873, https://doi.org/10.5194/gmd-12-4823-2019, 2019. a
Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., Sudo, K., Sekiguchi, M., Abe, M., Saito, F., Chikira, M., Watanabe, S., Mori, M., Hirota, N., Kawatani, Y., Mochizuki, T., Yoshimura, K., Takata, K., O'ishi, R., Yamazaki, D., Suzuki, T., Kurogi, M., Kataoka, T., Watanabe, M., and Kimoto, M.: Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6, Geosci. Model Dev., 12, 2727–2765, https://doi.org/10.5194/gmd-12-2727-2019, 2019. a
Teckentrup, L.: lteckentrup/CMIP6_australia: Analysis code submission (v1.0.0), Zenodo [code], https://doi.org/10.5281/zenodo.7882380, 2023. a
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
Thao, S., Garvik, M., Mariethoz, G., and Vrac, M.: Combining global climate
models using graph cuts, Clim. Dynam., 59, 2345–2361,
https://doi.org/10.1007/s00382-022-06213-4, 2022. a
Thonicke, K., Venevsky, S., Sitch, S., and Cramer, W.: The role of fire
disturbance for global vegetation dynamics: Coupling fire into a Dynamic
Global Vegetation Model, Global Ecol. Biogeogr., 10, 661–677,
https://doi.org/10.1046/j.1466-822X.2001.00175.x, 2001. a
Ukkola, A. M., Keenan, T. F., Kelley, D. I., and Prentice, I. C.: Vegetation
plays an important role in mediating future water resources, Environ.
Res. Lett., 11, 094022, https://doi.org/10.1088/1748-9326/11/9/094022, 2016. a
Ukkola, A. M., De Kauwe, M. G., Roderick, M. L., Abramowitz, G., and Pitman,
A. J.: Robust Future Changes in Meteorological Drought in CMIP6 Projections
Despite Uncertainty in Precipitation, Geophys. Res. Lett., 47,
e2020GL087820, https://doi.org/10.1029/2020GL087820, 2020. a, b
Volodin, E. M., Mortikov, E. V., Kostrykin, S. V., Galin, V. Y., Lykossov,
V. N., Gritsun, A. S., Nikolay A. Diansky, N. A., Gusev, A. V., Iakovlev,
N. G., Shestakova, A. A., and Emelina, S. V.: Simulation of the modern
climate using the INM-CM48 climate model, Russ J. Numer. Anal. M., 33, 367–374,
https://doi.org/10.1515/rnam-2018-0032, 2018. a, b
Vrac, M.: Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R2D2) bias correction, Hydrol. Earth Syst. Sci., 22, 3175–3196, https://doi.org/10.5194/hess-22-3175-2018, 2018. a
Vrac, M., Drobinski, P., Merlo, A., Herrmann, M., Lavaysse, C., Li, L., and Somot, S.: Dynamical and statistical downscaling of the French Mediterranean climate: uncertainty assessment, Nat. Hazards Earth Syst. Sci., 12, 2769–2784, https://doi.org/10.5194/nhess-12-2769-2012, 2012. a
Wang, B., Zheng, L., Liu, D. L., Ji, F., Clark, A., and Yu, Q.: Using
multi-model ensembles of CMIP5 global climate models to reproduce observed
monthly rainfall and temperature with machine learning methods in Australia,
Int. J. Climatol., 38, 4891–4902,
https://doi.org/10.1002/joc.5705, 2018. a
Wood, A., Leung, L., Sridhar, V., and Lettenmaier, D.: Hydrologic Implications
of Dynamical and Statistical Approaches to Downscaling Climate Model
Outputs, Climatic Change, 62, 189–216,
https://doi.org/10.1023/B:CLIM.0000013685.99609.9e, 2004. a, b
Wu, C., Chen, Y., Peng, C., Li, Z., and Hong, X.: Modeling and estimating
aboveground biomass of Dacrydium pierrei in China using machine learning with
climate change, J. Environ. Manage., 234, 167–179,
https://doi.org/10.1016/j.jenvman.2018.12.090, 2019. a
Wu, T., Lu, Y., Fang, Y., Xin, X., Li, L., Li, W., Jie, W., Zhang, J., Liu, Y., Zhang, L., Zhang, F., Zhang, Y., Wu, F., Li, J., Chu, M., Wang, Z., Shi, X., Liu, X., Wei, M., Huang, A., Zhang, Y., and Liu, X.: The Beijing Climate Center Climate System Model (BCC-CSM): the main progress from CMIP5 to CMIP6, Geosci. Model Dev., 12, 1573–1600, https://doi.org/10.5194/gmd-12-1573-2019, 2019. a
Wu, Z., Ahlström, A., Smith, B., Ardö, J., Eklundh, L., Fensholt, R., and
Lehsten, V.: Climate data induced uncertainty in model-based estimations of
terrestrial primary productivity, Environ. Res. Lett., 12,
064013, https://doi.org/10.1088/1748-9326/aa6fd8, 2017. a
Yang, W., Gardelin, M., Olsson, J., and Bosshard, T.: Multi-variable bias correction: application of forest fire risk in present and future climate in Sweden, Nat. Hazards Earth Syst. Sci., 15, 2037–2057, https://doi.org/10.5194/nhess-15-2037-2015, 2015.
a
Yang, Y., Guan, H., Batelaan, O., McVicar, T., Long, D., Piao, S., Liang, W.,
Liu, B., Jin, Z., and Simmons, C.: Contrasting response of water use
efficiency to drought across global terrestrial ecosystems, Sci.
Rep., 6, 23284, https://doi.org/10.1038/srep23284, 2016. a
Yukimoto, S., Kawai, H., Koshiro, T., Oshima, N., Yoshida, K., Urakawa, S.,
Tsujino, H., Deushi, M., Tanaka, T., Hosaka, M., Yabu, S., Yoshimura, H.,
Shindo, E., Mizuta, R., Obata, A., Adachi, Y., and Ishii, M.: The
Meteorological Research Institute Earth System Model Version 2.0, MRI-ESM2.0:
Description and Basic Evaluation of the Physical Component, J.
Meteorol. Soc. Jpn. Ser. II, 97, 931–965,
https://doi.org/10.2151/jmsj.2019-051, 2019. a
Zscheischler, J., Fischer, E. M., and Lange, S.: The effect of univariate bias adjustment on multivariate hazard estimates, Earth Syst. Dynam., 10, 31–43, https://doi.org/10.5194/esd-10-31-2019, 2019. a, b, c
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.
Studies analyzing the impact of the future climate on ecosystems employ climate projections...
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