Articles | Volume 13, issue 4
https://doi.org/10.5194/esd-13-1505-2022
© 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/esd-13-1505-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Nov 2022
Research article |
| 07 Nov 2022
| 07 Nov 2022
Interannual global carbon cycle variations linked to atmospheric circulation variability
Na Li
CORRESPONDING AUTHOR
Max Planck Institute for Biogeochemistry, Jena, Germany
Sebastian Sippel
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Alexander J. Winkler
Max Planck Institute for Biogeochemistry, Jena, Germany
Miguel D. Mahecha
Remote Sensing Center for Earth System Research, Leipzig University, Leipzig, Germany
Markus Reichstein
Max Planck Institute for Biogeochemistry, Jena, Germany
Ana Bastos
Max Planck Institute for Biogeochemistry, Jena, Germany
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Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., L-4-W1-2026, 167–174, https://doi.org/10.5194/isprs-archives-L-4-W1-2026-167-2026, https://doi.org/10.5194/isprs-archives-L-4-W1-2026-167-2026, 2026
Wenli Zhao, Alexander J. Winkler, Markus Reichstein, Rene Orth, and Pierre Gentine
Hydrol. Earth Syst. Sci., 30, 5373–5394, https://doi.org/10.5194/hess-30-5373-2026, https://doi.org/10.5194/hess-30-5373-2026, 2026
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We used explainable machine learning that incorporates memory effects to study how plants respond to weather and drought. Using data from 90 sites worldwide, we show that memory plays a key role in regulating plant water stress. Forests and savannas rely on longer past conditions than grasslands, reflecting differences in rooting depth and water use. These insights improve our ability to anticipate ecosystem vulnerability as droughts intensify.
Marina Friedel, Johanna Beikert, Ana Bastos, Nora Linscheid, and Marlene Kretschmer
EGUsphere, https://doi.org/10.5194/egusphere-2026-4689, https://doi.org/10.5194/egusphere-2026-4689, 2026
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
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We show that the winter Northern Hemisphere stratospheric polar vortex (SPV) – strong stratospheric winds shaping European winter weather – impacts vegetation across Europe from winter into summer. We establish the mechanisms linking winter SPV to summertime vegetation, and show that in France's breadbasket region, SPV anomalies translate into measurable crop yield anomalies. Winter SPV thus emerges as an underappreciated long-lead precursor of European vegetation and crop yield.
Gergana Gyuleva, Reto Knutti, Robin Noyelle, Urs Beyerle, Erich Fischer, and Sebastian Sippel
EGUsphere, https://doi.org/10.48550/arXiv.2608.13219, https://doi.org/10.48550/arXiv.2608.13219, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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How much Earth warms depends on climate feedbacks, which change with sea surface temperatures. This effect is usually modelled by prescribing observed sea-surface temperatures to atmospheric models. We test this standard approach in idealised runs and find it fails to reproduce the true feedback. We also find that feedback variations can be fully explained via statistical noise. Our results suggest historical "pattern effect" estimates are not robust, increasing uncertainty in future warming.
Siyuan Wang, Hui Yang, Sujan Koirala, Maurizio Santoro, Anna Candotti, Ulrich Weber, Ranit De, Claire Robin, Felix Cremer, Matthias Forkel, Markus Reichstein, and Nuno Carvalhais
Earth Syst. Sci. Data, 18, 5895–5913, https://doi.org/10.5194/essd-18-5895-2026, https://doi.org/10.5194/essd-18-5895-2026, 2026
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Forest disturbances are difficult to predict in models because they occur randomly. We discovered that the long-term rules of disturbance known as
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Tim Stripp, Zhixuan Guo, Pierre Friedlingstein, Michael O’Sullivan, Stephen Sitch, Ana Bastos, Philippe Ciais, Jefferson Goncalves de Souza, Akihiko Ito, Wei Li, and Lei Ma
EGUsphere, https://doi.org/10.5194/egusphere-2026-3584, https://doi.org/10.5194/egusphere-2026-3584, 2026
This preprint is open for discussion and under review for Biogeosciences (BG).
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Fire is a key process within the Earth system, yet it is often poorly represented in land carbon cycle models used to estimate how much carbon land ecosystems absorb from the atmosphere. We improved these models by applying long-term burned area data reconstructed from satellite observations. This reduced biases and uncertainty in fire carbon emission estimates and improved estimates of land carbon uptake, implying this method could enhance global and regional carbon budget estimates.
Tobias Braun, Sara M. Vallejo-Bernal, Norbert Marwan, Juergen Kurths, Johannes Quaas, Albert Díaz-Guilera, Luis Gimeno, and Miguel D. Mahecha
Earth Syst. Dynam., 17, 695–716, https://doi.org/10.5194/esd-17-695-2026, https://doi.org/10.5194/esd-17-695-2026, 2026
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Atmospheric rivers (ARs) move vast amounts of water through the atmosphere and often cause weather extremes, yet they are usually studied as regional events. Using 84 years of mapped AR trajectories, we reveal the global "roadmap" of ARs, a transport network of high-activity hubs, sparse atmospheric highways & hierarchical basins. Our approach shows how water vapor is systematically channelled through an atmospheric transport network, offering new ways to study changes in the global water cycle.
Franziska Müller, Laura Eifler, Felix Cremer, Pieter Beck, Gustau Camps-Valls, and Ana Bastos
Nat. Hazards Earth Syst. Sci., 26, 2785–2815, https://doi.org/10.5194/nhess-26-2785-2026, https://doi.org/10.5194/nhess-26-2785-2026, 2026
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Forest health is increasingly threatened, but disturbances like wind damage and insect outbreaks are hard to track. Our Sentinel-1 Disturbance Mapping (S1DM) approach combines satellite radar with survey data, improving detection for wind and bark beetle impacts and often spotting them earlier. Defoliators remain difficult to capture, but this method strengthens monitoring and supports better forest management.
Maximilian Söchting and Miguel D. Mahecha
Earth Syst. Dynam., 17, 687–693, https://doi.org/10.5194/esd-17-687-2026, https://doi.org/10.5194/esd-17-687-2026, 2026
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As the amount of data collected by satellites and generated by climate models to monitor Earth's climate and environment continues to expand in size and complexity, it becomes increasingly difficult for non-experts to explore these type of data sets. We present an interactive physical exhibit in the shape of a cube that enables anyone to explore these large environmental data sets across space, time, and variables, independent of their technical knowledge, through direct physical interaction.
Zavud Baghirov, Markus Reichstein, Basil Kraft, Bernhard Ahrens, Marco Körner, and Martin Jung
Geosci. Model Dev., 19, 4467–4496, https://doi.org/10.5194/gmd-19-4467-2026, https://doi.org/10.5194/gmd-19-4467-2026, 2026
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We introduce a new global model that links how water and carbon move through land ecosystems. By combining process knowledge with artificial intelligence that learns from observations, we model daily changes in vegetation, water and carbon cycle processes. This model outperforms both purely data-driven and traditional process models, especially in dry and tropical regions. This advance could improve current understanding of water–carbon cycle relationships.
Anna T. Schackow, Susan C. Steele-Dunne, David T. Milodowski, Jean-Marc Limousin, and Ana Bastos
Biogeosciences, 23, 3365–3386, https://doi.org/10.5194/bg-23-3365-2026, https://doi.org/10.5194/bg-23-3365-2026, 2026
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Plants regulate how much water they lose and how much carbon they take in, but rising heat and dryness make this balance harder. We studied how water movement inside plant stems changes during the day and relates to dryness in the air and soil. By analyzing these daily patterns, we identified signals of stress that could be tracked not only with sensors in plants but also from satellites, offering new ways to monitor global vegetation health.
Guohua Liu, Philippe Ciais, Shengli Tao, Hui Yang, Chenwei Xiao, and Ana Bastos
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-246, https://doi.org/10.5194/essd-2026-246, 2026
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We developed a long-term, moderate-resolution global dataset of forest above ground biomass, from satellite radar data and machine learning. The significance of this dataset lies in its ability to provide a continuous, moderate-resolution view of biomass carbon dynamics, offering a valuable resource for supporting studies of climate change, disturbance, and land use, and will help improve Earth System models and future carbon cycle.
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The Coupled Model Intercomparison Project (CMIP) is an international consortium of climate modeling groups that produce coordinated experiments in order to evaluate human influence on the climate and test knowledge of Earth systems. This paper describes the data requested for Earth systems research in CMIP7. We detail the request for model output of the carbon cycle, the flows of energy among the atmosphere, land and the oceans, and interactions between these and the global climate.
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Shifts in spring and autumn growth due to climate warming change how plants reflect sunlight and release heat and moisture into the air, modulating surface warming. The strength of these effects and their regional variability remain poorly understood. Using satellite and climate data, we show that earlier spring growth increases moisture release, especially in forests, while autumn changes are smaller and less consistent. Impacts on land-atmosphere interactions vary by ecosystem and data source.
Samuel Upton, Markus Reichstein, Wouter Peters, Santiago Botía, Jacob A. Nelson, Sophia Walther, Martin Jung, Fabian Gans, László Haszpra, and Ana Bastos
Atmos. Chem. Phys., 26, 2561–2595, https://doi.org/10.5194/acp-26-2561-2026, https://doi.org/10.5194/acp-26-2561-2026, 2026
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Laura Eifler, Franziska Müller, and Ana Bastos
Biogeosciences, 23, 1291–1325, https://doi.org/10.5194/bg-23-1291-2026, https://doi.org/10.5194/bg-23-1291-2026, 2026
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Forests provide ecosystem services and biodiversity, but they are increasingly affected by disturbances. We evaluate five forest disturbance datasets across the Unites States to assess their consistency in space, timing, and disturbance agents. While datasets show good agreement in disturbance timing, spatial overlap and agent attribution differ substantially. This emphasizes the need for enhanced data quality assessment, integration, and accuracy to better understand forest disturbances.
Na Li, Sebastian Sippel, Nora Linscheid, Miguel D. Mahecha, Markus Reichstein, and Ana Bastos
Biogeosciences, 23, 767–792, https://doi.org/10.5194/bg-23-767-2026, https://doi.org/10.5194/bg-23-767-2026, 2026
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We study when anthropogenic signal becomes detectable in the global land carbon sink, which has risen since the 1950s due to CO₂ fertilization and mid- to high-latitude warming. The signal emerges earlier at the global than at regional scales. Future scenarios (2016–2100) take longer to detect than the historical period (1851–2014) because the signal is weaker relative to larger natural variability. Removing circulation-induced variability with dynamical adjustment shortens the detection time.
Peter Pfleiderer, Anna Merrifield, István Dunkl, Homer Durand, Enora Cariou, Julien Cattiaux, Gustau Camps-Valls, and Sebastian Sippel
Weather Clim. Dynam., 7, 89–108, https://doi.org/10.5194/wcd-7-89-2026, https://doi.org/10.5194/wcd-7-89-2026, 2026
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Due to changes in atmospheric circulation some regions are warming quicker than others. Statistical methods are used to estimate how much of the local summer temperature changes are due to circulation changes. We evaluate these methods by comparing their estimates to special simulations representing only temperature changes related to circulation changes. By applying the methods to observations of 1979–2023 we find that half of the warming over parts of Europe is related to circulation changes.
Mélanie Weynants, Chaonan Ji, Nora Linscheid, Ulrich Weber, Miguel D. Mahecha, and Fabian Gans
Earth Syst. Sci. Data, 17, 6621–6645, https://doi.org/10.5194/essd-17-6621-2025, https://doi.org/10.5194/essd-17-6621-2025, 2025
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The impacts of climate extremes such as heatwaves and droughts can be made worse when they happen at the same time. Dheed is a global database of dry and hot compound extreme events from 1950 to 2023. It can be combined with other data to study the impacts of those events on terrestrial ecosystems, specific species or human societies. Dheed's analysis confirms that extremely dry and hot days have become more common on all continents in recent decades, especially in Europe and Africa.
Victor Brovkin, Benjamin M. Sanderson, Noel G. Brizuela, Tomohiro Hajima, Tatiana Ilyina, Chris D. Jones, Charles Koven, David Lawrence, Peter Lawrence, Hongmei Li, Spencer Liddcoat, Anastasia Romanou, Roland Séférian, Lori T. Sentman, Abigail L. S. Swann, Jerry Tjiputra, Tilo Ziehn, and Alexander J. Winkler
Earth Syst. Dynam., 16, 2021–2034, https://doi.org/10.5194/esd-16-2021-2025, https://doi.org/10.5194/esd-16-2021-2025, 2025
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Idealized experiments with Earth system models provide a basis for understanding the response of the carbon cycle to emissions. We show that most models exhibit a quasi-linear relationship between cumulative carbon uptake on land and in the ocean and hypothesize that this relationship does not depend on emission pathways. We reduce the coupled system to only one differential equation, which represents a powerful simplification of the Earth system dynamics as a function of fossil fuel emissions.
Friedrich J. Bohn, Giles B. Sioen, Ana Bastos, Yolandi Ernst, Marcin P. Jarzebski, Niak S. Koh, Romina Martin, Anja Rammig, Alex Godoy-Faúndez, Alexandros Gasparatos, Alvaro G. Gutiérrez, Amanda J. Aceituno, Andra-Ioana Horcea-Milcu, Andrea Marais-Potgieter, Ayyoob Sharifi, Caroline Howe, Cornelia B. Krug, Eduardo E. Acosta, Emmanuel F. Nzunda, Erik Andersson, Hans-Otto Pörtner, Helen Sooväli-Sepping, Ishihara Hiroe, Ivan Palmegiani, Kaera Coetzer, Kirsten Thonike, Krizler Tanalgo, Lisa Biber-Freudenberger, Nicholas O. Oguge, Mi S. Park, Milena Gross, Pablo De La Cruz, Paula R. Prist, Peng Bi, Rivera Diego, Roman Isaac, Rosemary McFarlane, Sinikka J. Paulus, Stefanie Burkhart, Sung-Ching Lee, Susanne Müller, Uchi D. Terhile, Wan-Yu Shih, William K. Smith, Viola Hakkarainen, Virginia Murray, Yuki Yoshida, Yohannes T. Damtew, and Zeenat Niazi
EGUsphere, https://doi.org/10.5194/egusphere-2025-3619, https://doi.org/10.5194/egusphere-2025-3619, 2025
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The aim of this series is to provide decision-makers with valuable insights into the current state of biosphere research. Firstly, it is intended to ensure the flow of information between the comprehensive assessment reports of the IPCC and IPBES. On the other hand, it is intended to support economic and political decisions closely related to the biosphere with scientifically sound findings – including uncertainties – and comprehensive polysolutions, helping to solve the earth system polycrisis.
Basil Kraft, Jacob A. Nelson, Sophia Walther, Fabian Gans, Ulrich Weber, Gregory Duveiller, Markus Reichstein, Weijie Zhang, Marc Rußwurm, Devis Tuia, Marco Körner, Zayd Hamdi, and Martin Jung
Biogeosciences, 22, 3965–3987, https://doi.org/10.5194/bg-22-3965-2025, https://doi.org/10.5194/bg-22-3965-2025, 2025
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This study evaluates machine learning approaches for upscaling evapotranspiration from the site to the global scale. Sequential models capture temporal dynamics better, especially with precipitation data, but all models show biases in data-scarce regions. Improved upscaling requires richer training data, informed covariate selection, and physical constraints to enhance robustness and reduce extrapolation errors.
Zhixuan Guo, Wei Li, Philippe Ciais, Stephen Sitch, Guido R. van der Werf, Simon P. K. Bowring, Ana Bastos, Florent Mouillot, Jiaying He, Minxuan Sun, Lei Zhu, Xiaomeng Du, Nan Wang, and Xiaomeng Huang
Earth Syst. Sci. Data, 17, 3599–3618, https://doi.org/10.5194/essd-17-3599-2025, https://doi.org/10.5194/essd-17-3599-2025, 2025
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To address the limitations of short time spans in satellite data and spatiotemporal discontinuity in site records, we reconstructed global monthly burned area maps at a 0.5° resolution for 1901–2020 using machine learning models. The global burned area is predicted at 3.46 × 106–4.58 × 106 km² per year, showing a decline from 1901 to 1978, an increase from 1978 to 2008 and a sharper decrease from 2008 to 2020. This dataset provides a benchmark for studies on fire ecology and the carbon cycle.
Guohua Liu, Philippe Ciais, Shengli Tao, Hui Yang, and Ana Bastos
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-330, https://doi.org/10.5194/essd-2025-330, 2025
Manuscript not accepted for further review
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We have developed a long-term and high-resolution global map of above-ground biomass changes from 1993 to 2020 using radar data and machine learning approach. This dataset can help understand the effects of disturbances, land-use changes, and extreme events on global carbon cycle, and enhance the representation of these processes in Earth System Models.
Theertha Kariyathan, Ana Bastos, Markus Reichstein, Wouter Peters, and Julia Marshall
Atmos. Chem. Phys., 25, 7863–7878, https://doi.org/10.5194/acp-25-7863-2025, https://doi.org/10.5194/acp-25-7863-2025, 2025
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The carbon uptake period (CUP) is the time period when land absorbs more CO2 than it emits. While atmospheric CO2 mole fraction measurements can be used to assess CUP changes, atmospheric transport and asynchronous timing across regions reduce the accuracy of the estimates. Forward model experiments show that only ~ 50 % of prescribed shifts in CUP timing applied to surface fluxes (ΔCUPNEE) are captured in simulated CO2 mole fraction data at monitoring sites like the Barrow Atmospheric Baseline Observatory.
Laura Nadolski, Tarek S. El-Madany, Jacob Nelson, Arnaud Carrara, Gerardo Moreno, Richard Nair, Yunpeng Luo, Anke Hildebrandt, Victor Rolo, Markus Reichstein, and Sung-Ching Lee
Biogeosciences, 22, 2935–2958, https://doi.org/10.5194/bg-22-2935-2025, https://doi.org/10.5194/bg-22-2935-2025, 2025
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Semi-arid ecosystems are crucial for Earth's carbon balance and are sensitive to changes in nitrogen (N) and phosphorus (P) levels. Their carbon dynamics are complex and not fully understood. We studied how long-term nutrient changes affect carbon exchange. In summer, the addition of N+P changed plant composition and productivity. In transitional seasons, carbon exchange was less weather-dependent with N. The addition of N and N+P increases carbon-exchange variability, driven by grass greenness.
Friedrich J. Bohn, Ana Bastos, Romina Martin, Anja Rammig, Niak Sian Koh, Giles B. Sioen, Bram Buscher, Louise Carver, Fabrice DeClerck, Moritz Drupp, Robert Fletcher, Matthew Forrest, Alexandros Gasparatos, Alex Godoy-Faúndez, Gregor Hagedorn, Martin C. Hänsel, Jessica Hetzer, Thomas Hickler, Cornelia B. Krug, Stasja Koot, Xiuzhen Li, Amy Luers, Shelby Matevich, H. Damon Matthews, Ina C. Meier, Mirco Migliavacca, Awaz Mohamed, Sungmin O, David Obura, Ben Orlove, Rene Orth, Laura Pereira, Markus Reichstein, Lerato Thakholi, Peter H. Verburg, and Yuki Yoshida
Biogeosciences, 22, 2425–2460, https://doi.org/10.5194/bg-22-2425-2025, https://doi.org/10.5194/bg-22-2425-2025, 2025
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An interdisciplinary collaboration of 36 international researchers from 35 institutions highlights recent findings in biosphere research. Within eight themes, they discuss issues arising from climate change and other anthropogenic stressors and highlight the co-benefits of nature-based solutions and ecosystem services. Based on an analysis of these eight topics, we have synthesized four overarching insights.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
Geosci. Model Dev., 18, 2921–2943, https://doi.org/10.5194/gmd-18-2921-2025, https://doi.org/10.5194/gmd-18-2921-2025, 2025
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We use an innovative approach to studying the Earth's water cycle by integrating advanced machine learning techniques with a traditional water cycle model. Our model is designed to learn from observational data, with a particular emphasis on understanding the influence of vegetation on water movement. By closely aligning with real-world observations, our model offers new possibilities for enhancing our understanding of the water cycle and its interactions with vegetation.
Marleen Pallandt, Marion Schrumpf, Holger Lange, Markus Reichstein, Lin Yu, and Bernhard Ahrens
Biogeosciences, 22, 1907–1928, https://doi.org/10.5194/bg-22-1907-2025, https://doi.org/10.5194/bg-22-1907-2025, 2025
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As soils warm due to climate change, soil organic carbon (SOC) decomposes faster due to increased microbial activity, given sufficient available moisture. We modelled the microbial decomposition of plant litter and residue at different depths and found that deep soil layers are more sensitive than topsoils. Warming causes SOC loss, but its extent depends on the litter type and its temperature sensitivity, which can either counteract or amplify losses. Droughts may also counteract warming-induced SOC losses.
Zhu Deng, Philippe Ciais, Liting Hu, Adrien Martinez, Marielle Saunois, Rona L. Thompson, Kushal Tibrewal, Wouter Peters, Brendan Byrne, Giacomo Grassi, Paul I. Palmer, Ingrid T. Luijkx, Zhu Liu, Junjie Liu, Xuekun Fang, Tengjiao Wang, Hanqin Tian, Katsumasa Tanaka, Ana Bastos, Stephen Sitch, Benjamin Poulter, Clément Albergel, Aki Tsuruta, Shamil Maksyutov, Rajesh Janardanan, Yosuke Niwa, Bo Zheng, Joël Thanwerdas, Dmitry Belikov, Arjo Segers, and Frédéric Chevallier
Earth Syst. Sci. Data, 17, 1121–1152, https://doi.org/10.5194/essd-17-1121-2025, https://doi.org/10.5194/essd-17-1121-2025, 2025
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This study reconciles national greenhouse gas (GHG) inventories with updated atmospheric inversion results to evaluate discrepancies for three principal GHG fluxes at the national level. Compared to our previous study, new satellite-based CO2 inversions were included and an updated mask of managed lands was used, improving agreement for Brazil and Canada. The proposed methodology can be regularly applied as a check to assess the gap between top-down inversions and bottom-up inventories.
Wenli Zhao, Alexander J. Winkler, Markus Reichstein, Rene Orth, and Pierre Gentine
EGUsphere, https://doi.org/10.5194/egusphere-2025-365, https://doi.org/10.5194/egusphere-2025-365, 2025
Preprint archived
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We developed a machine learning model that accounts for the memory effects of soil moisture and vegetation to predict Evaporative Fraction (EF) without relying on soil moisture as a direct input. The model accurately predicts EF during dry periods for the unseen sites, highlighting the key of meteorological memory effects. The learned memory effect related to rooting depth and soil water holding capacity could potentially serve as proxies for assessing the plant water stress.
István Dunkl, Ana Bastos, and Tatiana Ilyina
Earth Syst. Dynam., 16, 151–167, https://doi.org/10.5194/esd-16-151-2025, https://doi.org/10.5194/esd-16-151-2025, 2025
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While the El Niño–Southern Oscillation, a climate mode, has a similar impact on CO2 growth rates across Earth system models, there is significant uncertainty in the processes behind this relationship. We found a compensatory effect that masks differences in the sensitivity of carbon fluxes to climate anomalies and observed that the carbon fluxes contributing to global CO2 anomalies originate from different regions and are caused by different drivers.
Jacob A. Nelson, Sophia Walther, Fabian Gans, Basil Kraft, Ulrich Weber, Kimberly Novick, Nina Buchmann, Mirco Migliavacca, Georg Wohlfahrt, Ladislav Šigut, Andreas Ibrom, Dario Papale, Mathias Göckede, Gregory Duveiller, Alexander Knohl, Lukas Hörtnagl, Russell L. Scott, Jiří Dušek, Weijie Zhang, Zayd Mahmoud Hamdi, Markus Reichstein, Sergio Aranda-Barranco, Jonas Ardö, Maarten Op de Beeck, Dave Billesbach, David Bowling, Rosvel Bracho, Christian Brümmer, Gustau Camps-Valls, Shiping Chen, Jamie Rose Cleverly, Ankur Desai, Gang Dong, Tarek S. El-Madany, Eugenie Susanne Euskirchen, Iris Feigenwinter, Marta Galvagno, Giacomo A. Gerosa, Bert Gielen, Ignacio Goded, Sarah Goslee, Christopher Michael Gough, Bernard Heinesch, Kazuhito Ichii, Marcin Antoni Jackowicz-Korczynski, Anne Klosterhalfen, Sara Knox, Hideki Kobayashi, Kukka-Maaria Kohonen, Mika Korkiakoski, Ivan Mammarella, Mana Gharun, Riccardo Marzuoli, Roser Matamala, Stefan Metzger, Leonardo Montagnani, Giacomo Nicolini, Thomas O'Halloran, Jean-Marc Ourcival, Matthias Peichl, Elise Pendall, Borja Ruiz Reverter, Marilyn Roland, Simone Sabbatini, Torsten Sachs, Marius Schmidt, Christopher R. Schwalm, Ankit Shekhar, Richard Silberstein, Maria Lucia Silveira, Donatella Spano, Torbern Tagesson, Gianluca Tramontana, Carlo Trotta, Fabio Turco, Timo Vesala, Caroline Vincke, Domenico Vitale, Enrique R. Vivoni, Yi Wang, William Woodgate, Enrico A. Yepez, Junhui Zhang, Donatella Zona, and Martin Jung
Biogeosciences, 21, 5079–5115, https://doi.org/10.5194/bg-21-5079-2024, https://doi.org/10.5194/bg-21-5079-2024, 2024
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The movement of water, carbon, and energy from the Earth's surface to the atmosphere, or flux, is an important process to understand because it impacts our lives. Here, we outline a method called FLUXCOM-X to estimate global water and CO2 fluxes based on direct measurements from sites around the world. We go on to demonstrate how these new estimates of net CO2 uptake/loss, gross CO2 uptake, total water evaporation, and transpiration from plants compare to previous and independent estimates.
Francesco Martinuzzi, Miguel D. Mahecha, Gustau Camps-Valls, David Montero, Tristan Williams, and Karin Mora
Nonlin. Processes Geophys., 31, 535–557, https://doi.org/10.5194/npg-31-535-2024, https://doi.org/10.5194/npg-31-535-2024, 2024
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We investigated how machine learning can forecast extreme vegetation responses to weather. Examining four models, no single one stood out as the best, though "echo state networks" showed minor advantages. Our results indicate that while these tools are able to generally model vegetation states, they face challenges under extreme conditions. This underlines the potential of artificial intelligence in ecosystem modeling, also pinpointing areas that need further research.
Anca Anghelea, Ewelina Dobrowolska, Gunnar Brandt, Martin Reinhardt, Miguel Mahecha, Tejas Morbagal Harish, and Stephan Meissl
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-2024, 13–18, https://doi.org/10.5194/isprs-archives-XLVIII-4-2024-13-2024, https://doi.org/10.5194/isprs-archives-XLVIII-4-2024-13-2024, 2024
Anne F. Van Loon, Sarra Kchouk, Alessia Matanó, Faranak Tootoonchi, Camila Alvarez-Garreton, Khalid E. A. Hassaballah, Minchao Wu, Marthe L. K. Wens, Anastasiya Shyrokaya, Elena Ridolfi, Riccardo Biella, Viorica Nagavciuc, Marlies H. Barendrecht, Ana Bastos, Louise Cavalcante, Franciska T. de Vries, Margaret Garcia, Johanna Mård, Ileen N. Streefkerk, Claudia Teutschbein, Roshanak Tootoonchi, Ruben Weesie, Valentin Aich, Juan P. Boisier, Giuliano Di Baldassarre, Yiheng Du, Mauricio Galleguillos, René Garreaud, Monica Ionita, Sina Khatami, Johanna K. L. Koehler, Charles H. Luce, Shreedhar Maskey, Heidi D. Mendoza, Moses N. Mwangi, Ilias G. Pechlivanidis, Germano G. Ribeiro Neto, Tirthankar Roy, Robert Stefanski, Patricia Trambauer, Elizabeth A. Koebele, Giulia Vico, and Micha Werner
Nat. Hazards Earth Syst. Sci., 24, 3173–3205, https://doi.org/10.5194/nhess-24-3173-2024, https://doi.org/10.5194/nhess-24-3173-2024, 2024
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Drought is a creeping phenomenon but is often still analysed and managed like an isolated event, without taking into account what happened before and after. Here, we review the literature and analyse five cases to discuss how droughts and their impacts develop over time. We find that the responses of hydrological, ecological, and social systems can be classified into four types and that the systems interact. We provide suggestions for further research and monitoring, modelling, and management.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
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Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Miguel D. Mahecha, Guido Kraemer, and Fabio Crameri
Earth Syst. Dynam., 15, 1153–1159, https://doi.org/10.5194/esd-15-1153-2024, https://doi.org/10.5194/esd-15-1153-2024, 2024
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Our paper examines the visual representation of the planetary boundary concept, which helps convey Earth's capacity to sustain human life. We identify three issues: exaggerated impact sizes, confusing color patterns, and inaccessibility for colour-vision deficiency. These flaws can lead to overstating risks. We suggest improving these visual elements for more accurate and accessible information for decision-makers.
Sebastian Sippel, Clair Barnes, Camille Cadiou, Erich Fischer, Sarah Kew, Marlene Kretschmer, Sjoukje Philip, Theodore G. Shepherd, Jitendra Singh, Robert Vautard, and Pascal Yiou
Weather Clim. Dynam., 5, 943–957, https://doi.org/10.5194/wcd-5-943-2024, https://doi.org/10.5194/wcd-5-943-2024, 2024
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Winter temperatures in central Europe have increased. But cold winters can still cause problems for energy systems, infrastructure, or human health. Here we tested whether a record-cold winter, such as the one observed in 1963 over central Europe, could still occur despite climate change. The answer is yes: it is possible, but it is very unlikely. Our results rely on climate model simulations and statistical rare event analysis. In conclusion, society must be prepared for such cold winters.
Francesco Martinuzzi, Miguel D. Mahecha, David Montero, Lazaro Alonso, and Karin Mora
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W12-2024, 89–95, https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-89-2024, https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-89-2024, 2024
David Montero, Miguel D. Mahecha, César Aybar, Clemens Mosig, and Sebastian Wieneke
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W12-2024, 105–112, https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-105-2024, https://doi.org/10.5194/isprs-archives-XLVIII-4-W12-2024-105-2024, 2024
Jasper M. C. Denissen, Adriaan J. Teuling, Sujan Koirala, Markus Reichstein, Gianpaolo Balsamo, Martha M. Vogel, Xin Yu, and René Orth
Earth Syst. Dynam., 15, 717–734, https://doi.org/10.5194/esd-15-717-2024, https://doi.org/10.5194/esd-15-717-2024, 2024
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Heat extremes have severe implications for human health and ecosystems. Heat extremes are mostly introduced by large-scale atmospheric circulation but can be modulated by vegetation. Vegetation with access to water uses solar energy to evaporate water into the atmosphere. Under dry conditions, water may not be available, suppressing evaporation and heating the atmosphere. Using climate projections, we show that regionally less water is available for vegetation, intensifying future heat extremes.
Jan Sodoge, Christian Kuhlicke, Miguel D. Mahecha, and Mariana Madruga de Brito
Nat. Hazards Earth Syst. Sci., 24, 1757–1777, https://doi.org/10.5194/nhess-24-1757-2024, https://doi.org/10.5194/nhess-24-1757-2024, 2024
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We delved into the socio-economic impacts of the 2018–2022 drought in Germany. We derived a dataset covering the impacts of droughts in Germany between 2000 and 2022 on sectors such as agriculture and forestry based on newspaper articles. Notably, our study illustrated that the longer drought had a wider reach and more varied effects. We show that dealing with longer droughts requires different plans compared to shorter ones, and it is crucial to be ready for the challenges they bring.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
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To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Sinikka J. Paulus, Rene Orth, Sung-Ching Lee, Anke Hildebrandt, Martin Jung, Jacob A. Nelson, Tarek Sebastian El-Madany, Arnaud Carrara, Gerardo Moreno, Matthias Mauder, Jannis Groh, Alexander Graf, Markus Reichstein, and Mirco Migliavacca
Biogeosciences, 21, 2051–2085, https://doi.org/10.5194/bg-21-2051-2024, https://doi.org/10.5194/bg-21-2051-2024, 2024
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Porous materials are known to reversibly trap water from the air, even at low humidity. However, this behavior is poorly understood for soils. In this analysis, we test whether eddy covariance is able to measure the so-called adsorption of atmospheric water vapor by soils. We find that this flux occurs frequently during dry nights in a Mediterranean ecosystem, while EC detects downwardly directed vapor fluxes. These results can help to map moisture uptake globally.
Martin Jung, Jacob Nelson, Mirco Migliavacca, Tarek El-Madany, Dario Papale, Markus Reichstein, Sophia Walther, and Thomas Wutzler
Biogeosciences, 21, 1827–1846, https://doi.org/10.5194/bg-21-1827-2024, https://doi.org/10.5194/bg-21-1827-2024, 2024
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We present a methodology to detect inconsistencies in perhaps the most important data source for measurements of ecosystem–atmosphere carbon, water, and energy fluxes. We expect that the derived consistency flags will be relevant for data users and will help in improving our understanding of and our ability to model ecosystem–climate interactions.
Samuel Upton, Markus Reichstein, Fabian Gans, Wouter Peters, Basil Kraft, and Ana Bastos
Atmos. Chem. Phys., 24, 2555–2582, https://doi.org/10.5194/acp-24-2555-2024, https://doi.org/10.5194/acp-24-2555-2024, 2024
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Data-driven eddy-covariance upscaled estimates of the global land–atmosphere net CO2 exchange (NEE) show important mismatches with regional and global estimates based on atmospheric information. To address this, we create a model with a dual constraint based on bottom-up eddy-covariance data and top-down atmospheric inversion data. Our model overcomes shortcomings of each approach, producing improved NEE estimates from local to global scale, helping to reduce uncertainty in the carbon budget.
Wolfgang Alexander Obermeier, Clemens Schwingshackl, Ana Bastos, Giulia Conchedda, Thomas Gasser, Giacomo Grassi, Richard A. Houghton, Francesco Nicola Tubiello, Stephen Sitch, and Julia Pongratz
Earth Syst. Sci. Data, 16, 605–645, https://doi.org/10.5194/essd-16-605-2024, https://doi.org/10.5194/essd-16-605-2024, 2024
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We provide and compare country-level estimates of land-use CO2 fluxes from a variety and large number of models, bottom-up estimates, and country reports for the period 1950–2021. Although net fluxes are small in many countries, they are often composed of large compensating emissions and removals. In many countries, the estimates agree well once their individual characteristics are accounted for, but in other countries, including some of the largest emitters, substantial uncertainties exist.
Jan De Pue, Sebastian Wieneke, Ana Bastos, José Miguel Barrios, Liyang Liu, Philippe Ciais, Alirio Arboleda, Rafiq Hamdi, Maral Maleki, Fabienne Maignan, Françoise Gellens-Meulenberghs, Ivan Janssens, and Manuela Balzarolo
Biogeosciences, 20, 4795–4818, https://doi.org/10.5194/bg-20-4795-2023, https://doi.org/10.5194/bg-20-4795-2023, 2023
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The gross primary production (GPP) of the terrestrial biosphere is a key source of variability in the global carbon cycle. To estimate this flux, models can rely on remote sensing data (RS-driven), meteorological data (meteo-driven) or a combination of both (hybrid). An intercomparison of 11 models demonstrated that RS-driven models lack the sensitivity to short-term anomalies. Conversely, the simulation of soil moisture dynamics and stress response remains a challenge in meteo-driven models.
Chenwei Xiao, Sönke Zaehle, Hui Yang, Jean-Pierre Wigneron, Christiane Schmullius, and Ana Bastos
Earth Syst. Dynam., 14, 1211–1237, https://doi.org/10.5194/esd-14-1211-2023, https://doi.org/10.5194/esd-14-1211-2023, 2023
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Ecosystem resistance reflects their susceptibility during adverse conditions and can be changed by land management. We estimate ecosystem resistance to drought and temperature globally. We find a higher resistance to drought in forests compared to croplands and an evident loss of resistance to drought when primary forests are converted to secondary forests or they are harvested. Old-growth trees tend to be more resistant in some forests and crops benefit from irrigation during drought periods.
Richard Nair, Yunpeng Luo, Tarek El-Madany, Victor Rolo, Javier Pacheco-Labrador, Silvia Caldararu, Kendalynn A. Morris, Marion Schrumpf, Arnaud Carrara, Gerardo Moreno, Markus Reichstein, and Mirco Migliavacca
EGUsphere, https://doi.org/10.5194/egusphere-2023-2434, https://doi.org/10.5194/egusphere-2023-2434, 2023
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We studied a Mediterranean ecosystem to understand carbon uptake efficiency and its controls. These ecosystems face potential nitrogen-phosphorus imbalances due to pollution. Analysing six years of carbon data, we assessed controls at different timeframes. This is crucial for predicting such vulnerable regions. Our findings revealed N limitation on C uptake, not N:P imbalance, and strong influence of water availability. whether drought or wetness promoted net C uptake depended on timescale.
Theertha Kariyathan, Ana Bastos, Julia Marshall, Wouter Peters, Pieter Tans, and Markus Reichstein
Atmos. Meas. Tech., 16, 3299–3312, https://doi.org/10.5194/amt-16-3299-2023, https://doi.org/10.5194/amt-16-3299-2023, 2023
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The timing and duration of the carbon uptake period (CUP) are sensitive to the occurrence of major phenological events, which are influenced by recent climate change. This study presents an ensemble-based approach for quantifying the timing and duration of the CUP and their uncertainty when derived from atmospheric CO2 measurements with noise and gaps. The CUP metrics derived with the approach are more robust and have less uncertainty than when estimated with the conventional methods.
Hoontaek Lee, Martin Jung, Nuno Carvalhais, Tina Trautmann, Basil Kraft, Markus Reichstein, Matthias Forkel, and Sujan Koirala
Hydrol. Earth Syst. Sci., 27, 1531–1563, https://doi.org/10.5194/hess-27-1531-2023, https://doi.org/10.5194/hess-27-1531-2023, 2023
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We spatially attribute the variance in global terrestrial water storage (TWS) interannual variability (IAV) and its modeling error with two data-driven hydrological models. We find error hotspot regions that show a disproportionately large significance in the global mismatch and the association of the error regions with a smaller-scale lateral convergence of water. Our findings imply that TWS IAV modeling can be efficiently improved by focusing on model representations for the error hotspots.
Robert Vautard, Geert Jan van Oldenborgh, Rémy Bonnet, Sihan Li, Yoann Robin, Sarah Kew, Sjoukje Philip, Jean-Michel Soubeyroux, Brigitte Dubuisson, Nicolas Viovy, Markus Reichstein, Friederike Otto, and Iñaki Garcia de Cortazar-Atauri
Nat. Hazards Earth Syst. Sci., 23, 1045–1058, https://doi.org/10.5194/nhess-23-1045-2023, https://doi.org/10.5194/nhess-23-1045-2023, 2023
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A deep frost occurred in early April 2021, inducing severe damages in grapevine and fruit trees in France. We found that such extreme frosts occurring after the start of the growing season such as those of April 2021 are currently about 2°C colder [0.5 °C to 3.3 °C] in observations than in preindustrial climate. This observed intensification of growing-period frosts is attributable, at least in part, to human-caused climate change, making the 2021 event 50 % more likely [10 %–110 %].
Iris Elisabeth de Vries, Sebastian Sippel, Angeline Greene Pendergrass, and Reto Knutti
Earth Syst. Dynam., 14, 81–100, https://doi.org/10.5194/esd-14-81-2023, https://doi.org/10.5194/esd-14-81-2023, 2023
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Precipitation change is an important consequence of climate change, but it is hard to detect and quantify. Our intuitive method yields robust and interpretable detection of forced precipitation change in three observational datasets for global mean and extreme precipitation, but the different observational datasets show different magnitudes of forced change. Assessment and reduction of uncertainties surrounding forced precipitation change are important for future projections and adaptation.
Sinikka Jasmin Paulus, Tarek Sebastian El-Madany, René Orth, Anke Hildebrandt, Thomas Wutzler, Arnaud Carrara, Gerardo Moreno, Oscar Perez-Priego, Olaf Kolle, Markus Reichstein, and Mirco Migliavacca
Hydrol. Earth Syst. Sci., 26, 6263–6287, https://doi.org/10.5194/hess-26-6263-2022, https://doi.org/10.5194/hess-26-6263-2022, 2022
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In this study, we analyze small inputs of water to ecosystems such as fog, dew, and adsorption of vapor. To measure them, we use a scaling system and later test our attribution of different water fluxes to weight changes. We found that they occur frequently during 1 year in a dry summer ecosystem. In each season, a different flux seems dominant, but they all mainly occur during the night. Therefore, they could be important for the biosphere because rain is unevenly distributed over the year.
Melissa Ruiz-Vásquez, Sungmin O, Alexander Brenning, Randal D. Koster, Gianpaolo Balsamo, Ulrich Weber, Gabriele Arduini, Ana Bastos, Markus Reichstein, and René Orth
Earth Syst. Dynam., 13, 1451–1471, https://doi.org/10.5194/esd-13-1451-2022, https://doi.org/10.5194/esd-13-1451-2022, 2022
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Subseasonal forecasts facilitate early warning of extreme events; however their predictability sources are not fully explored. We find that global temperature forecast errors in many regions are related to climate variables such as solar radiation and precipitation, as well as land surface variables such as soil moisture and evaporative fraction. A better representation of these variables in the forecasting and data assimilation systems can support the accuracy of temperature forecasts.
Xin Yu, René Orth, Markus Reichstein, Michael Bahn, Anne Klosterhalfen, Alexander Knohl, Franziska Koebsch, Mirco Migliavacca, Martina Mund, Jacob A. Nelson, Benjamin D. Stocker, Sophia Walther, and Ana Bastos
Biogeosciences, 19, 4315–4329, https://doi.org/10.5194/bg-19-4315-2022, https://doi.org/10.5194/bg-19-4315-2022, 2022
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Identifying drought legacy effects is challenging because they are superimposed on variability driven by climate conditions in the recovery period. We develop a residual-based approach to quantify legacies on gross primary productivity (GPP) from eddy covariance data. The GPP reduction due to legacy effects is comparable to the concurrent effects at two sites in Germany, which reveals the importance of legacy effects. Our novel methodology can be used to quantify drought legacies elsewhere.
D. Montero, C. Aybar, M. D. Mahecha, and S. Wieneke
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-4-W1-2022, 301–306, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-301-2022, https://doi.org/10.5194/isprs-archives-XLVIII-4-W1-2022-301-2022, 2022
Philip J. Ward, James Daniell, Melanie Duncan, Anna Dunne, Cédric Hananel, Stefan Hochrainer-Stigler, Annegien Tijssen, Silvia Torresan, Roxana Ciurean, Joel C. Gill, Jana Sillmann, Anaïs Couasnon, Elco Koks, Noemi Padrón-Fumero, Sharon Tatman, Marianne Tronstad Lund, Adewole Adesiyun, Jeroen C. J. H. Aerts, Alexander Alabaster, Bernard Bulder, Carlos Campillo Torres, Andrea Critto, Raúl Hernández-Martín, Marta Machado, Jaroslav Mysiak, Rene Orth, Irene Palomino Antolín, Eva-Cristina Petrescu, Markus Reichstein, Timothy Tiggeloven, Anne F. Van Loon, Hung Vuong Pham, and Marleen C. de Ruiter
Nat. Hazards Earth Syst. Sci., 22, 1487–1497, https://doi.org/10.5194/nhess-22-1487-2022, https://doi.org/10.5194/nhess-22-1487-2022, 2022
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The majority of natural-hazard risk research focuses on single hazards (a flood, a drought, a volcanic eruption, an earthquake, etc.). In the international research and policy community it is recognised that risk management could benefit from a more systemic approach. In this perspective paper, we argue for an approach that addresses multi-hazard, multi-risk management through the lens of sustainability challenges that cut across sectors, regions, and hazards.
Zhu Deng, Philippe Ciais, Zitely A. Tzompa-Sosa, Marielle Saunois, Chunjing Qiu, Chang Tan, Taochun Sun, Piyu Ke, Yanan Cui, Katsumasa Tanaka, Xin Lin, Rona L. Thompson, Hanqin Tian, Yuanzhi Yao, Yuanyuan Huang, Ronny Lauerwald, Atul K. Jain, Xiaoming Xu, Ana Bastos, Stephen Sitch, Paul I. Palmer, Thomas Lauvaux, Alexandre d'Aspremont, Clément Giron, Antoine Benoit, Benjamin Poulter, Jinfeng Chang, Ana Maria Roxana Petrescu, Steven J. Davis, Zhu Liu, Giacomo Grassi, Clément Albergel, Francesco N. Tubiello, Lucia Perugini, Wouter Peters, and Frédéric Chevallier
Earth Syst. Sci. Data, 14, 1639–1675, https://doi.org/10.5194/essd-14-1639-2022, https://doi.org/10.5194/essd-14-1639-2022, 2022
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In support of the global stocktake of the Paris Agreement on climate change, we proposed a method for reconciling the results of global atmospheric inversions with data from UNFCCC national greenhouse gas inventories (NGHGIs). Here, based on a new global harmonized database that we compiled from the UNFCCC NGHGIs and a comprehensive framework presented in this study to process the results of inversions, we compared their results of carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O).
Basil Kraft, Martin Jung, Marco Körner, Sujan Koirala, and Markus Reichstein
Hydrol. Earth Syst. Sci., 26, 1579–1614, https://doi.org/10.5194/hess-26-1579-2022, https://doi.org/10.5194/hess-26-1579-2022, 2022
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We present a physics-aware machine learning model of the global hydrological cycle. As the model uses neural networks under the hood, the simulations of the water cycle are learned from data, and yet they are informed and constrained by physical knowledge. The simulated patterns lie within the range of existing hydrological models and are plausible. The hybrid modeling approach has the potential to tackle key environmental questions from a novel perspective.
Philippe Ciais, Ana Bastos, Frédéric Chevallier, Ronny Lauerwald, Ben Poulter, Josep G. Canadell, Gustaf Hugelius, Robert B. Jackson, Atul Jain, Matthew Jones, Masayuki Kondo, Ingrid T. Luijkx, Prabir K. Patra, Wouter Peters, Julia Pongratz, Ana Maria Roxana Petrescu, Shilong Piao, Chunjing Qiu, Celso Von Randow, Pierre Regnier, Marielle Saunois, Robert Scholes, Anatoly Shvidenko, Hanqin Tian, Hui Yang, Xuhui Wang, and Bo Zheng
Geosci. Model Dev., 15, 1289–1316, https://doi.org/10.5194/gmd-15-1289-2022, https://doi.org/10.5194/gmd-15-1289-2022, 2022
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The second phase of the Regional Carbon Cycle Assessment and Processes (RECCAP) will provide updated quantification and process understanding of CO2, CH4, and N2O emissions and sinks for ten regions of the globe. In this paper, we give definitions, review different methods, and make recommendations for estimating different components of the total land–atmosphere carbon exchange for each region in a consistent and complete approach.
J. Pacheco-Labrador, U. Weber, X. Ma, M. D. Mahecha, N. Carvalhais, C. Wirth, A. Huth, F. J. Bohn, G. Kraemer, U. Heiden, FunDivEUROPE members, and M. Migliavacca
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-1-W1-2021, 49–55, https://doi.org/10.5194/isprs-archives-XLVI-1-W1-2021-49-2022, https://doi.org/10.5194/isprs-archives-XLVI-1-W1-2021-49-2022, 2022
Ana Bastos, René Orth, Markus Reichstein, Philippe Ciais, Nicolas Viovy, Sönke Zaehle, Peter Anthoni, Almut Arneth, Pierre Gentine, Emilie Joetzjer, Sebastian Lienert, Tammas Loughran, Patrick C. McGuire, Sungmin O, Julia Pongratz, and Stephen Sitch
Earth Syst. Dynam., 12, 1015–1035, https://doi.org/10.5194/esd-12-1015-2021, https://doi.org/10.5194/esd-12-1015-2021, 2021
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Temperate biomes in Europe are not prone to recurrent dry and hot conditions in summer. However, these conditions may become more frequent in the coming decades. Because stress conditions can leave legacies for many years, this may result in reduced ecosystem resilience under recurrent stress. We assess vegetation vulnerability to the hot and dry summers in 2018 and 2019 in Europe and find the important role of inter-annual legacy effects from 2018 in modulating the impacts of the 2019 event.
Alexander J. Winkler, Ranga B. Myneni, Alexis Hannart, Stephen Sitch, Vanessa Haverd, Danica Lombardozzi, Vivek K. Arora, Julia Pongratz, Julia E. M. S. Nabel, Daniel S. Goll, Etsushi Kato, Hanqin Tian, Almut Arneth, Pierre Friedlingstein, Atul K. Jain, Sönke Zaehle, and Victor Brovkin
Biogeosciences, 18, 4985–5010, https://doi.org/10.5194/bg-18-4985-2021, https://doi.org/10.5194/bg-18-4985-2021, 2021
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Satellite observations since the early 1980s show that Earth's greening trend is slowing down and that browning clusters have been emerging, especially in the last 2 decades. A collection of model simulations in conjunction with causal theory points at climatic changes as a key driver of vegetation changes in natural ecosystems. Most models underestimate the observed vegetation browning, especially in tropical rainforests, which could be due to an excessive CO2 fertilization effect in models.
Cited articles
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
Bacastow, R. B.: Modulation of atmospheric carbon dioxide by the Southern
Oscillation, Nature, 261, 116–118, https://doi.org/10.1038/261116a0, 1976. a, b
Ballantyne, A. P., Alden, C. B., Miller, J. B., Tans, P. ., and White, J. W. C.: Increase in observed net carbon dioxide uptake by land and oceans during the past 50 years, Nature, 488, 70–72, https://doi.org/10.1038/nature11299, 2012. a
Basile, S. J., Lin, X., Wieder, W. R., Hartman, M. D., and Keppel-Aleks, G.:
Leveraging the signature of heterotrophic respiration on atmospheric CO2 for model benchmarking, Biogeosciences, 17, 1293–1308,
https://doi.org/10.5194/bg-17-1293-2020, 2020.
a
Bastos, A., Running, S. W., Gouveia, C., and Trigo, R. M.: The global NPP
dependence on ENSO: La Niña and the extraordinary year of 2011, J. Geophys. Res.-Biogeo., 118, 1247–1255, https://doi.org/10.1002/jgrg.20100, 2013. a
Bastos, A., Friedlingstein, P., Sitch, S., Chen, C., Mialon, A., Wigneron,
J.-P., Arora, V. K., Briggs, P. R., Canadell, J. G., Ciais, P., Chevallier,
F., Cheng, L., Delire, C., Haverd, V., Jain, A. K., Joos, F., Kato, E.,
Lienert, S., Lombardozzi, D., Melton, J. R., Myneni, R., Nabel, J. E. M. S.,
Pongratz, J., Poulter, B., Rödenbeck, C., Séférian, R., Tian, H., van Eck, C., Viovy, N., Vuichard, N., Walker, A. P., Wiltshire, A., Yang, J., Zaehle, S., Zeng, N., and Zhu, D.: Impact of the 2015/2016 El Niño on the
terrestrial carbon cycle constrained by bottom-up and top-down approaches,
Philos. T. Roy. Soc. B, 373, 20170304, https://doi.org/10.1098/rstb.2017.0304,
2018. a
Bastos, A., Sullivan, M., Ciais, P., Makowski, D., Sitch, S., Friedlingstein,
P., Chevalier, F., Rödenbeck, C., Pongratz, J., Luijkx, I., Patra, P.,
Peylin, P., Canadell, J., Lauerwald, R., Li, W., Smith, N., Peters, W., Goll, D., Jain, A., Kato, E., Lienert, S., Lombardozzi, D., Haverd, V., Nabel, J.,
Tian, H., Walker, A., and Zaehle, S.: Aggregated regional estimates of net
atmosphere-land CO2 fluxes from the five atmospheric inversions and 16 Dynamic Global Vegetation Models, supplemental data to Bastos et al. (2019), ICOS ERIC – Carbon Portal [data set], https://doi.org/10.18160/1SVH-3DNB, 2019. a
Bastos, A., O'Sullivan, M., Ciais, P., Makowski, D., Sitch, S., Friedlingstein, P., Chevallier, F., Rödenbeck, C., Pongratz, J., Luijkx, I. T., Patra, P. K., Peylin, P., Canadell, J. G., Lauerwald, R., Li, W., Smith, N. E., Peters, W., Goll, D. S., Jain, A., Kato, E., Lienert, S., Lombardozzi, D. L., Haverd, V., Nabel, J. E. M. S., Poulter, B., Tian, H., Walker, A. P., and Zaehle, S.: Sources of uncertainty in regional and global terrestrial CO2 exchange estimates, Global Biogeochem. Cy., 34,
e2019GB006393, https://doi.org/10.1029/2019GB006393, 2020. a, b, c, d
Bell, B., Hersbach, H., Berrisford, P., Dahlgren, P., Horányi, A.,
Muñoz Sabater, J., Nicolas, J., Radu, R., Schepers, D., Simmons, A., Soci, C., and Thépaut, J.-N.: ERA5 monthly averaged data on pressure levels from 1950 to 1978 (preliminary version), Copernicus Climate Change Service (C3S) Climate Data Store (CDS),
https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels-monthly-means-preliminary,
last access: 17 December 2020. a, b, c
Boden, T. A., Marland, G., and Andres, R. J.: Global, Regional, and National
Fossil-Fuel CO2 Emissions, Tech. rep., Oak Ridge National Laboratory, US Department of Energy, Oak Ridge, Tenn., USA,
http://cdiac.ornl.gov/trends/emis/overview_2014.html, last access: July 2017. a
Bonan, G. B.: Ecological climatology, concepts and applications, Cambridge
University Press, ISBN 978-1-107-04377-0, 2016. a
Chen, W. Y. and Van den Dool, H.: Sensitivity of Teleconnection Patterns to the Sign of Their Primary Action Center, Mon.Weather Rev., 131, 2885–2899, https://doi.org/10.1175/1520-0493(2003)131<2885:SOTPTT>2.0.CO;2, 2003. a
Chevallier, F., Fisher, M., Peylin, P., Serrar, S., Bousquet, P., Bréon,
F.-M., Chédin, A., and Ciais, P.: Inferring CO2 sources and sinks from satellite observations: Method and application to TOVS data, J. Geophys. Res., 110, D24309, https://doi.org/10.1029/2005JD006390, 2005. a, b, c
Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J., Chhabra, A., DeFries, R., Galloway, J., Heimann, M., Jones, C., Le Quéré, C., Myneni, B. R., Piao, S., and Thornton, P.: Carbon and Other Biogeochemical Cycles, 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, Cambridge University Press, Cambridge, UK and New York, NY, USA,
https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_all_final.pdf (last access: 20 March 2022), 2013. a
Cleveland, W., Grosse, E., and Shyu, W.: Local regression models, in: Statistical Models in S, Chapman and Hall, 309–376, ISBN 9780412830402, 1991. a
Cleverly, J., Eamus, D., Luo, Q., Coupe, N. R., 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, Scient. Rep., 6, 23113, https://doi.org/10.1038/srep23113, 2016. a, b, c
Cox, P. M., Pearson, D., Booth, B. B., Friedlingstein, P., Huntingford, C.,
Jones, C. D., and Luke, C. M.: Sensitivity of tropical carbon to climate
change constrained by carbon dioxide variability, Nature, 494, 341–344,
https://doi.org/10.1038/nature11882, 2013. a
Deser, C., Phillips, A., Bourdette, V., and Teng, H.: Uncertainty in climate
change projections: the role of internal variability, Clim. Dynam., 38, 527–546, https://doi.org/10.1007/s00382-010-0977-x, 2012. a
Deser, C., Lehner, F., Rodgers, K. B., Ault, T., Delworth, T. L., DiNezio,
P. N., Fiore, A., Frankignoul, C., Fyfe, J. C., Horton, D. E., Kay, J. E.,
Knutti, R., Lovenduski, N. S., Marotzke, J., McKinnon, K. A., Minobe, S.,
Randerson, J., Screen, J. A., Simpson, I. R., and Ting, M.: Insights from
Earth system model initial-condition large ensembles and future prospects,
Nat. Clim. Change, 10, 277–286, https://doi.org/10.1038/s41558-020-0731-2, 2020. a
DeVries, T., Holzer, M., and Primeau, F.: Recent increase in oceanic carbon
uptake driven by weaker upper-ocean overturning, Nature, 542, 215–218,
https://doi.org/10.1038/nature21068, 2017. a
Dlugokencky, E. and Tans, P.: Trends in atmospheric carbon dioxide, NOAA/ESRL – National Oceanic and Atmospheric Administration, Earth Syem Research Laboratory, http://www.esrl.noaa.gov/gmd/ccgg/trends/global.html, last
access: 3 November 2019. a
Dufour, C. O., Le Sommer, J., Gehlen, M., Orr, J. C., Molines, J. M., Simeon,
J., and Barnier, B.: Eddy compensation and controls of the enhanced
sea-to-air CO2 flux during positive phases of the Southern Annular Mode, Global Biogeochem. Cy., 27, 950–961, https://doi.org/10.1002/gbc.20090, 2013. a
Enfield, D. B., Mestas-Nuñez, A. M., Mayer, D. A., and Cid-Serrano, L.: How ubiquitous is the dipole relationship in tropical Atlantic sea surface
temperatures?, J. Geophys. Res., 104, 7841–7848, https://doi.org/10.1029/1998JC900109, 1999. a
Enfield, D. B., Mestas-Nuñez, A. M., and Trimble, P. J.: The Atlantic
Multidecadal Oscillation and its relation to rainfall and river flows in the
continental U.S., Geophys. Res. Lett., 28, 2077–2080, https://doi.org/10.1029/2000GL012745, 2001. a, b, c
Friedlingstein, P., Meinshausen, M., Arora, V. K., Jones, C. D., Anav, A.,
Liddicoat, S. K., and Knutti, R.: Uncertainties in CMIP5 climate projections
due to carbon cycle feedbacks, J. Climate, 27, 511–526,
https://doi.org/10.1175/JCLI-D-12-00579.1, 2014. a
Friedlingstein, P., Jones, M. W., O'Sullivan, M., Andrew, R. M., Hauck, J.,
Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Le Quéré, C., Bakker, D. C. E., Canadell, J. G., Ciais, P., Jackson, R. B., Anthoni, P., Barbero, L., Bastos, A., Bastrikov, V., Becker, M., Bopp, L., Buitenhuis, E., Chandra, N., Chevallier, F., Chini, L. P., Currie, K. I., Feely, R. A., Gehlen, M., Gilfillan, D., Gkritzalis, T., Goll, D. S., Gruber, N., Gutekunst, S., Harris, I., Haverd, V., Houghton, R. A., Hurtt, G., Ilyina, T., Jain, A. K., Joetzjer, E., Kaplan, J. O., Kato, E., Klein Goldewijk, K., Korsbakken, J. I., Landschützer, P., Lauvset, S. K., Lefèvre, N., Lenton, A., Lienert, S., Lombardozzi, D., Marland, G., McGuire, P. C., Melton, J. R., Metzl, N., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S.-I., Neill, C., Omar, A. M., Ono, T., Peregon, A., Pierrot, D., Poulter, B., Rehder, G., Resplandy, L., Robertson, E., Rödenbeck, C., Séférian, R., Schwinger, J., Smith, N., Tans, P. P., Tian, H., Tilbrook, B., Tubiello, F. N., van der Werf, G. R., Wiltshire, A. J., and Zaehle, S.: Global Carbon Budget 2019, Earth Syst. Sci. Data, 11, 1783–1838, https://doi.org/10.5194/essd-11-1783-2019, 2019. a, b
Friedlingstein, P., Jones, M. W., O'Sullivan, M., Andrew, R. M., Bakker, D.
C. E., Hauck, J., Le Quéré, C., Peters, G. P., Peters, W., Pongratz, J., Sitch, S., Canadell, J. G., Ciais, P., Jackson, R. B., Alin, S. R., Anthoni, P., Bates, N. R., Becker, M., Bellouin, N., Bopp, L., Chau, T. T. T., Chevallier, F., Chini, L. P., Cronin, M., Currie, K. I., Decharme, B.,
Djeutchouang, L. M., Dou, X., Evans, W., Feely, R. A., Feng, L., Gasser, T.,
Gilfillan, D., Gkritzalis, T., Grassi, G., Gregor, L., Gruber, N., Gürses, O., Harris, I., Houghton, R. A., Hurtt, G. C., Iida, Y., Ilyina, T., Luijkx, I. T., Jain, A., Jones, S. D., Kato, E., Kennedy, D., Klein Goldewijk, K., Knauer, J., Korsbakken, J. I., Körtzinger, A., Landschützer, P., Lauvset, S. K., Lefèvre, N., Lienert, S., Liu, J., Marland, G., McGuire, P. C., Melton, J. R., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S.-I., Niwa, Y., Ono, T., Pierrot, D., Poulter, B., Rehder, G., Resplandy, L., Robertson, E., Rödenbeck, C., Rosan, T. M., Schwinger, J., Schwingshackl, C., Séférian, R., Sutton, A. J., Sweeney, C., Tanhua, T., Tans, P. P., Tian, H., Tilbrook, B., Tubiello, F., van der Werf, G. R., Vuichard, N., Wada, C., Wanninkhof, R., Watson, A. J., Willis, D., Wiltshire, A. J., Yuan, W., Yue, C., Yue, X., Zaehle, S., and Zeng, J.: Global Carbon Budget 2021, Earth Syst. Sci. Data, 14, 1917–2005, https://doi.org/10.5194/essd-14-1917-2022, 2022. a
Friedman, J., Hastie, T., and Tibshirani, R.: Regularization paths for
generalized linear models via coordinate descent, J. Stat. Softw., 33, 1–22, https://doi.org/10.18637/jss.v033.i01, 2010. a, b
Frölicher, T. L., Joos, F., Raible, C. C., and Sarmiento, J. L.: Atmospheric CO2 response to volcanic eruptions: The role of ENSO, season, and variability, Global Biogeochem. Cy., 27, 239–251,
https://doi.org/10.1002/gbc.20028, 2013. a
Ghil, M.: Natural climate variability, in: Volume 1, The Earth system: physical and chemical dimensions of global environmental change, from Encyclopedia of Global Environmental Change, John Wiley and Sons, Ltd, Chichester, 544–549,
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.15.3765&rep=rep1&type=pdf
(last access: 20 February 2022), 2002. a
Gu, G. and Adler, R. F.: Precipitation and temperature variations on the
interannual time scale: Assessing the impact of ENSO and volcanic eruptions,
J. Climate, 24, 2258–2270, https://doi.org/10.1175/2010JCLI3727.1, 2011. a
Hansis, E., Davis, S. J., and Pongratz, J.: Relevance of methodological choices for accounting of land use change carbon fluxes, Global Biogeochem. Cy., 29, 1230–1246, https://doi.org/10.1002/2014GB004997, 2015. a
Harris, I., Osborn, T. J., Jones, P., and Lister, D.: Version 4 of the CRU TS
monthly high-resolution gridded multivariate climate dataset, Sci. Data, 7, 109, https://doi.org/10.1038/s41597-020-0453-3, 2020. a
Hauck, J., Zeising, M., Le Quéré, C., Gruber, N., Bakker, D. C. E., Bopp, L., Chau, T. T. T., Gürses, O., Ilyina, T., Landschützer, P., Lenton, A., Resplandy, L., Rödenbeck, C., Schwinger, J., and Séférian, R.: Consistency and challenges in the ocean carbon sink estimate for the Global Carbon Budget, Front. Mar. Sci., 7, 852,
https://doi.org/10.3389/fmars.2020.571720, 2020. a
Henley, B. J., Gergis, J., Karoly, D. J., Power, S. B., Kennedy, J., and
Folland, C. K.: A Tripole Index for the Interdecadal Pacific Oscillation,
Clim. Dynam., 45, 3077–3090, https://doi.org/10.1007/s00382-015-2525-1, 2015. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A.,
Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 monthly averaged data on pressure levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), https://doi.org/10.24381/cds.6860a573, 2019. a, b
Higgins, R. W., Leetmaa, A., Xue, Y., and Barnston, A.: Dominant factors
influencing the seasonal predictability of U.S. precipitation and surface air
temperature, J. Climate, 13, 3994–4017,
https://doi.org/10.1175/1520-0442(2000)013<3994:DFITSP>2.0.CO;2, 2000. a
Higgins, R. W., Leetmaa, A., and Kousky, V. E.: Relationships between climate
variability and winter temperature extremes in the United States, J. Climate, 15, 1555–1572, https://doi.org/10.1175/1520-0442(2002)015<1555:RBCVAW>2.0.CO;2, 2002. a
Houghton, R. A. and Nassikas, A. A.: Global and re- gional fluxes of carbon
from land use and land cover change 1850–2015, Global Biogeochem. Cy., 31,
456–472, https://doi.org/10.1002/2016GB005546, 2017. a
Hsieh, W. W.: Nonlinear multivariate and time series analysis by neural network methods, Rev. Geophys., 42, RG1003, https://doi.org/10.1029/2002RG000112, 2004. a
Humphrey, V., Zscheischler, J., Ciais, P., Gudmundsson, L., Sitch, S., and
Seneviratne, S. I.: Sensitivity of atmospheric CO2 growth rate to
observed changes in terrestrial water storage, Nature, 560, 628–631,
https://doi.org/10.1038/s41586-018-0424-4, 2018. a
Humphrey, V., Berg, A., Ciais, P., Gentine, P., Jung, M., Reichstein, M.,
Seneviratne, S. I., and Frankenberg, C.: Soil moisture–atmosphere feedback
dominates land carbon uptake variability, Nature, 592, 65–69,
https://doi.org/10.1038/s41586-021-03325-5, 2021. a
Hurrell, J. W., Holland, M. M., Gent, P. R., Ghan, S., Kay, J. E., Kushner,
P. J., Lamarque, J.-F., Large, W. G., Lawrence, D., Lindsay, K., Lipscomb,
W. H., Long, M. C., Mahowald, N., Marsh, D. R., Neale, R. B., Rasch, P.,
Vavrus, S., Vertenstein, M., Bader, D., Collins, W. D., Hack, J. J., Kiehl,
J., and Marshall, S.: The community earth system model: a framework for
collaborative research, B. Am. Meteorol. Soc., 94, 1339–1360, https://doi.org/10.1175/Bams-D-12-00121.1, 2013. a
IPCC: Climate Change 2013: The Physical Science Basis, in: 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., Boschung, J., Nauels, A., Xia, Y., Bex, V., and
Midgley, P. M., Cambridge University Press, Cambridge, UK and New York, NY, USA, p. 223, 232, 233, 470, 473, 489, 502, 504, 745, 749, 1535,
https://www.ipcc.ch/site/assets/uploads/2018/02/WG1AR5_all_final.pdf
(last access: 20 March 2022), 2013. a, b, c, d, e, f, g
Jones, P. D., Salinger, M. J., and Mullan, A. B.: Extratropical circulation
indices in the Southern Hemisphere based on station data, Int. J. Climatol., 19, 1301–1317,
https://doi.org/10.1002/(SICI)1097-0088(199910)19:12<1301::AID-JOC425>3.0.CO;2-P, 1999. a
Jung, M., Reichstein, M., Schwalm, C. R., Huntingford, C., Sitch, S.,
Ahlström, A., Arneth, A., Camps-Valls, G., Ciais, P., Friedlingstein, P.,
Gans, F., Ichii, K., Jain, A. K., Kato, E., Papale, D., Poulter, B., Raduly,
B., Rödenbeck, C., Tramontana, G., Viovy, N., Wang, Y.-P., Weber, U.,
Zaehle, S., and Zeng, N.: Compensatory water effects link yearly global land
CO2 sink changes to temperature, Nature, 541, 516–520,
https://doi.org/10.1038/nature20780, 2017. a, b, c, d, e
Keeling, C. D., Whorf, T. P., Wahlen, M., and van der Plichtt, J.: Interannual extremes in the rate of rise of atmospheric carbon dioxide since 1980, Nature, 375, 666–670, https://doi.org/10.1038/375666a0, 1995. a, b
King, M. P., Yu, E., and Sillmann, J.: Impact of strong and extreme El Niños
on European hydroclimate, Tellus A, 72, 1–10, https://doi.org/10.1080/16000870.2019.1704342, 2020. a
Kumar, A. and Hoerling, M. P.: Interpretation and implications of the observed inter-El Niño variability, J. Climate, 10, 83–91,
https://doi.org/10.1175/1520-0442(1997)010<0083:IAIOTO>2.0.CO;2, 1997. a
Le Quéré, C., Andrew, R. M., Friedlingstein, P., Sitch, S., Hauck, J., Pongratz, J., Pickers, P. A., Korsbakken, J. I., Peters, G. P., Canadell,
J. G., Arneth, A., Arora, V. K., Barbero, L., Bastos, A., Bopp, L.,
Chevallier, F., Chini, L. P., Ciais, P., Doney, S. C., Gkritzalis, T., Goll,
D. S., Harris, I., Haverd, V., Hoffman, F. M., Hoppema, M., Houghton, R. A.,
Hurtt, G., Ilyina, T., Jain, A. K., Johannessen, T., Jones, C. D., Kato, E.,
Keeling, R. F., Goldewijk, K. K., Landschützer, P., Lefèvre, N., Lienert, S., Liu, Z., Lombardozzi, D., Metzl, N., Munro, D. R., Nabel, J. E. M. S., Nakaoka, S., Neill, C., Olsen, A., Ono, T., Patra, P., Peregon, A., Peters, W., Peylin, P., Pfeil, B., Pierrot, D., Poulter, B., Rehder, G., Resplandy, L., Robertson, E., Rocher, M., Rödenbeck, C., Schuster, U., Schwinger, J., Séférian, R., Skjelvan, I., Steinhoff, T., Sutton, A., Tans, P. P., Tian, H., Tilbrook, B., Tubiello, F. N., van der Laan-Luijkx, I. T., van der Werf, G. R., Viovy, N., Walker, A. P., Wiltshire, A. J., Wright, R., Zaehle, S., and Zheng, B.: Global Carbon Budget 2018, Earth Syst. Sci. Data, 10, 2141–2194, https://doi.org/10.5194/essd-10-2141-2018, 2018. a, b, c, d, e, f, g, h, i, j
Li, N.: Inter-annual global carbon cycle variations linked to atmospheric circulation variability_script_NaLi, Edmond [code], https://doi.org/10.17617/3.DMUQZY, 2022. a
Madden, R. A.: Estimates of the natural variability of time-averaged sea-level pressure, Mon. Weather Rev., 104, 942–952,
https://doi.org/10.1175/1520-0493(1976)104<0942:EOTNVO>2.0.CO;2, 1976. a
Mantua, N. J. and Hare, S. R.: The Pacific Decadal Oscillation, J. Oceanogr., 58, 35–44, https://doi.org/10.1023/A:1015820616384, 2002. a
Mantua, N. J., Hare, S. R., Zhang, Y., Wallace, J. M., and Francis, R. C.: A
Pacific interdecadal climate oscillation with impacts on salmon production, B. Am. Meteorol. Soc., 78, 1069–1080, https://doi.org/10.1175/1520-0477(1997)078<1069:APICOW>2.0.CO;2, 1997. a, b
McBride, J. L. and Nicholls, N.: Seasonal relationships between Australian
rainfall and the Southern Oscillation, Mon. Weather Rev., 111, 1998–2004, https://doi.org/10.1175/1520-0493(1983)111<1998:SRBARA>2.0.CO;2, 1983. a
Meehl, G. A., Washington, W. M., Arblaster, J. M., Hu, A., Teng, H., Kay, J. E., Gettelman, A., Lawrence, D. M., Sanderson, B. M., and Strand, W. G.:
Climate change projections in CESM1 (CAM5) compared to CCSM4, J. Climate, 26,
6287–6308, https://doi.org/10.1175/jcli-d-12-00572.1, 2013b. a
Met Office: Cartopy: a cartographic python library with a Matplotlib
interface, Met Office, Exeter, Devon, https://scitools.org.uk/cartopy (last access: 22 August 2022), 2010–2015. a
Mo, K. C.: Relationships between low-frequency variability in the Southern
Hemisphere and sea surface temperature anomalies, J. Climate, 13, 3599–3610, https://doi.org/10.1175/1520-0442(2000)013<3599:RBLFVI>2.0.CO;2, 2000. a
Monahan, A. H.: Nonlinear principal component analysis: Tropical Indo-Pacific
sea surface temperature and sea level pressure, J. Climate, 14, 219–233,
https://doi.org/10.1175/1520-0442(2001)013<0219:NPCATI>2.0.CO;2, 2001. a
Neale, R. B., Gettelman, A., Park, S., Chen, C. C., Lauritzen, P. H.,
Williamson, D. L., Conley, A. J., Kinnison, D., Marsh, D., Smith, A. K., Vitt, F. M., Garcia, R., Lamarque, J.-F., Mills, M. J., Tilmes, S., Morrison, H., Cameron, P., Collins, W. D., Lacono, M. J., Easter, R. C., Liu, X., Ghan, S. J., Rasch, P. J., and Taylor, M. A.: Description of the NCAR community atmosphere model (CAM 5.0), NCAR Technical Note No. NCAR/TN-486+STR, NCAR, https://doi.org/10.5065/wgtk-4g06, 2012. a
Newman, M., Alexander, M. A., Ault, T. R., Cobb, K. M., Deser, C., Lorenzo, E. D., Mantua, N. J., Miller, A. J., Minobe, S., Nakamura, H., Schneider, N.,
Vimont, D. J., Phillips, A. S., Scott, J. D., and Smith, C. A.: The Pacific
Decadal Oscillation, Revisited, J. Climate, 29, 4399–4427,
https://doi.org/10.1175/JCLI-D-15-0508.1, 2016. a
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel,
O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J.,
Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E.:
Scikit-learn: Machine Learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011. a
Peters, G. P., Minx, J. C., Weber, C. L., and Edenhofer, O.: Growth in emission transfers via international trade from 1990 to 2008, P. Natl. Acad. Sci. USA, 108, 8903–8908, https://doi.org/10.1073/pnas.1006388108, 2011. a
Piao, S., Wang, X., Wang, K., Li, X., Bastos, A., Canadell, J. G., Ciais, P.,
Friedlingstein, P., and Sitch, S.: Interannual variation of terrestrial
carbon cycle: Issues and perspectives, Global Change Biol., 26, 300–318,
https://doi.org/10.1111/gcb.14884, 2020. a, b, c, d
Pittock, A. B.: Patterns of climatic variation in Argentina and Chile, I: Precipitation, 1931–60, Mon. Weather Rev., 108, 1347–1361,
https://doi.org/10.1175/1520-0493(1980)108<1347:POCVIA>2.0.CO;2, 1980. a, b
Poulter, B., Frank, D., Ciais, P., Myneni, R. B., Andela, N., Bi, J., Broquet, G., Canadell, J. G., Chevallier, F., Liu, Y. Y., Running, S. W., Sitch, S., and van der Werf, G. R.: Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle, Nature, 509, 600–603,
https://doi.org/10.1038/nature13376, 2014. a, b
Randall, D. A., Wood, R. A., Bony, S., Colman, T., Fichefet, T., Fyfe, J.,
Kattsov, V., Pitman, A., Shukla, J., Srinivasan, J., Stouffer, R. J., Sumiand, A., and Taylor, K. E.: Climate models and their evaluation, 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., Cambridge University Press, Cambridge, UK and New York, NY, USA, 589–662,
https://www.ipcc.ch/site/assets/uploads/2018/02/ar4-wg1-chapter8-1.pdf
(last access: 23 October 2021), 2007. a
Rayner, N. A., Parker, D. E., Horton, E. B., Folland, C. K., Alexander, L. V., Rowell, D. P., Kent, E. C., and Kaplan, A.: Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late
nineteenth century, J. Geophys. Res.-Atmos., 108, 4407, https://doi.org/10.1029/2002JD002670, 2003. a, b
Reboita, M. S., Ambrizzi, T., Crespo, N. M., Dutra, L. M. M., de S. Ferreira,
G. W., Rehbein, A., Drumond, A., da Rocha, R. P., and de Souza, C. A.:
Impacts of teleconnection patterns on South America climate, Ann. NY Acad. Sci., 504, 116–153, https://doi.org/10.1111/nyas.14592, 2021. a, b
Rödenbeck, C.: Estimating CO2 sources and sinks from atmospheric mixing ratio measurements using a global inversion of atmospheric transport, Technical Report 6, Max Planck Institute for Biogeochemistry, Jena,
http://www.bgc-jena.mpg.de/CarboScope/s/tech_report6.pdf (last access: 1 November 2019), 2005. a
Rödenbeck, C., Houweling, S., Gloor, M., and Heimann, M.: CO2 flux history 1982–2001 inferred from atmospheric data using a global inversion of atmospheric transport, Atmos. Chem. Phys., 3, 1919–1964,
https://doi.org/10.5194/acp-3-1919-2003, 2003. a, b
Rödenbeck, C., Zaehle, S., Keeling, R., and Heimann, M.: How does the
terrestrial carbon exchange respond to inter-annual climatic variations? A
quantification based on atmospheric CO2 data, Biogeosciences, 15,
2481–2498, https://doi.org/10.5194/bg-15-2481-2018, 2018. a
Rodgers, K. B., Friederichs, P., and Latif, M.: Tropical Pacific decadal
variability and tts relation to decadal modulations of ENSO, J. Climate, 17, 3761–3774, https://doi.org/10.1175/1520-0442(2004)017<3761:TPDVAI>2.0.CO;2, 2004. a
Ropelewski, C. F. and Jones, P. D.: An extension of the Tahiti–Darwin
Southern Oscillation Index, Mon. Weather Rev., 115, 2161–2165,
https://doi.org/10.1175/1520-0493(1987)115<2161:AEOTTS>2.0.CO;2, 1987. a
Roxy, M. K., Dasgupta, P., McPhaden, M. J., Suematsu, T., Zhang, C., and Kim, D.: Twofold expansion of the Indo-Pacific warm pool warps the MJO life cycle, Nature, 575, 647–651, https://doi.org/10.1038/s41586-019-1764-4, 2019. a
Saji, N. H. and Yamagata, T.: Possible impacts of Indian Ocean Dipole mode
events on global climate, Clim. Res., 25, 151–169, 2003. a
Schimel, D., Stephens, B. B., and Fisher, J. B.: Effect of increasing CO2 on the terrestrial carbon cycle, P. Natl. Acad. Sci. USA, 112, 436–441, https://doi.org/10.1073/pnas.1407302112, 2015. a
Schneider, D. P., Okumura, Y., and Deser, C.: Observed Antarctic interannual
climate variability and tropical linkages, J. Climate, 25, 4048–4066, https://doi.org/10.1175/JCLI-D-11-00273.1, 2012. a
Schneider, E. K. and Kinter, J. L.: An examination of internally generated
variability in long climate simulations, Clim. Dynam., 10, 181–204,
https://doi.org/10.1007/BF00208987, 1994. a
Schopf, P. S. and Burgman, R. J.: A simple mechanism for ENSO residuals and
asymmetry, J. Climate, 19, 3167–3179, https://doi.org/10.1175/JCLI3765.1, 2006. a
Sheffield, J., Camargo, S. J., Fu, R., Hu, Q., Jiang, X., Johnson, N.,
Karnauskas, K. B., Kim, S. T., Kinter, J., Kumar, S., Langenbrunner, B.,
Maloney, E., Mariotti, A., Meyerson, J. E., Neelin, J. D., Nigam, S., Pan, Z., Ruiz-Barradas, A., Seager, R., Serra, Y. L., Sun, D.-Z., Wang, C., Xie,
S.-P., Yu, J.-Y., Zhang, T., and Zhao, M.: North American climate in CMIP5
experiments. Part II: evaluation of historical simulations of intraseasonal
to decadal variability, J. Climate, 26, 9247–9290,
https://doi.org/10.1175/JCLI-D-12-00593.1, 2013. a
Sippel, S., Meinshausen, N., Merrifield, A., Lehner, F., Pendergrass, A. G.,
Fischer, E., and Knutti, R.: Uncovering the Forced Climate Response from a
Single Ensemble Member Using Statistical Learning, J. Climate, 32, 5677–5699, https://doi.org/10.1175/JCLI-D-18-0882.1, 2019. a, b, c
Stenseth, N. C., Ottersen, G., Hurrell, J. W., Mysterud, A., Lima, M., Chan,
K.-S., Yoccoz, N. G., and Ådlandsvik, B.: Review article. Studying climate effects on ecology through the use of climate indices: the North Atlantic Oscillation, El Niño Southern Oscillation and beyond, P. Roy. Soc. Lond. B, 270, 2087–2096, https://doi.org/10.1098/rspb.2003.2415, 2003. a
Stolpe, M. B., Medhaug, I., Beyerle, U., and Knutti, R.: Weak dependence of
future global mean warming on the background climate state, Clim. Dynam., 53, 5079–5099, https://doi.org/10.1007/s00382-019-04849-3, 2019. a, b
Sun, F. and Yu, J.-Y.: A 10–15-yr modulation cycle of ENSO intensity, J. Climate, 22, 1718–1735, https://doi.org/10.1175/2008JCLI2285.1, 2009. a
UNFCCC: National Inventory Submissions, Tech. rep., UNFCCC,
https://unfccc.int/process/transparency-and-reporting/reporting-and-review-under-the-convention/greenhouse-gas-inventories-annex-i-parties/national-inventory-submissions-2018, last access: June 2018. a
van der Werf, G. R., Randerson, J. T., Collatz, G. J., Giglio, L., Kasibhatla, P. S., Arellano Jr., A. F., Olsen, S. C., and Kasischke, E. S.:
Continental-scale partitioning of fire emissions during the 1997 to 2001 El Niño/La Niña period, Science, 303, 73–76, https://doi.org/10.1126/science.1090753, 2004. a
van Wieringen, W. N.: Lecture notes on ridge regression, arXiv [preprint], arXiv:1509.09169v7, https://arxiv.org/pdf/1509.09169.pdf (last access: 23 June 2022), 2021. a
von Storch, H.: Analysis of climate variability applications of statistical
techniques, Springer-Verlag, Berlin, Heidelberg, p. 10, https://doi.org/10.1007/978-3-662-03167-4, 1995. a
Wang, K., Bastos, A., Ciais, P., Wang, X., Rödenbeck, C., Gentine, P.,
Chevallier, F., Humphrey, V. W., Huntingford, C., O'Sullivan, M., Seneviratne, S. I., Sitch, S., and Piao, S.: Regional and seasonal partitioning of water and temperature controls on global land carbon uptake
variability, Nat. Commun., 13, 3469, https://doi.org/10.1038/s41467-022-31175-w, 2022.
a, b
Wang, X., Piao, S., Ciais, P., Friedlingstein, P., Myneni, R. B., Cox, P.,
Heimann, M., Miller, J., Peng, S., Wang, T., Yang, H., and Chen, A.: A two-fold increase of carbon cycle sensitivity to tropical temperature variations, Nature, 506, 212–215, https://doi.org/10.1038/nature12915, 2014. a
Wills, R. C., Battisti, D. S., Hartmann, D. L., and Schneider, T.: Extracting
modes of variability and change from climate model ensembles, in: Proceedings
of the 7th International Workshop on Climate Informatics: CI 2017, NCAR
Technical Note NCAR/TN-536+PROC, NCAR, Boulder, USA,
https://climate-dynamics.org/wp-content/uploads/2017/12/ci2017_Wills_et_al.pdf (last access: 30 September 2021), 2017. a
Yu, J.-Y. and Kim, S. T.: Reversed spatial asymmetries between El Niño and La Niña and their linkage to decadal ENSO modulation in CMIP3 models, J. Climate, 24, 5423–5434, https://doi.org/10.1175/JCLI-D-11-00024.1, 2011. a
Zeng, N., Mariotti, A., and Wetzel, P.: Terrestrial mechanisms of interannual
CO2 variability, Global Biogeochem. Cy., 19, GB1016,
https://doi.org/10.1029/2004GB002273, 2005. a
Zhu, Z., Piao, S., Xu, Y., Bastos, A., Ciais, P., and Peng, S.: The effects of teleconnections on carbon fluxes of global terrestrial ecosystems, Geophys. Res. Lett., 44, 3209–3218, https://doi.org/10.1002/2016gl071743, 2017. a, b, c, d
Short summary
Quantifying the imprint of large-scale atmospheric circulation dynamics and associated carbon cycle responses is key to improving our understanding of carbon cycle dynamics. Using a statistical model that relies on spatiotemporal sea level pressure as a proxy for large-scale atmospheric circulation, we quantify the fraction of interannual variability in atmospheric CO2 growth rate and the land CO2 sink that are driven by atmospheric circulation variability.
Quantifying the imprint of large-scale atmospheric circulation dynamics and associated carbon...
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