Articles | Volume 21, issue 22
https://doi.org/10.5194/bg-21-5027-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-21-5027-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Nov 2024
Research article |
| 15 Nov 2024
| 15 Nov 2024
Distinct impacts of the El Niño–Southern Oscillation and Indian Ocean Dipole on China's gross primary production
Ran Yan
Frontiers Science Center for Critical Earth Material Cycling, International Institute for Earth System Science, Nanjing University, Nanjing, Jiangsu 210023, China
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China
Frontiers Science Center for Critical Earth Material Cycling, International Institute for Earth System Science, Nanjing University, Nanjing, Jiangsu 210023, China
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China
Weimin Ju
CORRESPONDING AUTHOR
Frontiers Science Center for Critical Earth Material Cycling, International Institute for Earth System Science, Nanjing University, Nanjing, Jiangsu 210023, China
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China
Xiuli Xing
Department of Environmental Science and Engineering, Fudan University, No. 2005, Songhu Road, Yangpu District, Shanghai 200438, China
Joint Center for Data Assimilation Research and Applications/Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center ON Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China
Meirong Wang
Joint Center for Data Assimilation Research and Applications/Key Laboratory of Meteorological Disaster, Ministry of Education/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center ON Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044, China
Tibet field station for scientific observation and research on atmospheric water cycle in Mêdog/Xigazê and Mêdog National Climate Observatory, Tibet Meteorological Service, Lhasa 850000, China
Jingye Tan
Frontiers Science Center for Critical Earth Material Cycling, International Institute for Earth System Science, Nanjing University, Nanjing, Jiangsu 210023, China
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China
Xunmei Wang
Frontiers Science Center for Critical Earth Material Cycling, International Institute for Earth System Science, Nanjing University, Nanjing, Jiangsu 210023, China
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China
Hengmao Wang
Frontiers Science Center for Critical Earth Material Cycling, International Institute for Earth System Science, Nanjing University, Nanjing, Jiangsu 210023, China
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China
Fei Jiang
Frontiers Science Center for Critical Earth Material Cycling, International Institute for Earth System Science, Nanjing University, Nanjing, Jiangsu 210023, China
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing, Jiangsu 210023, China
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Ji Li, Xuguang Chi, Aijun Ding, Weimin Ju, Yongguang Zhang, Jing M. Chen, and Huilin Chen
EGUsphere, https://doi.org/10.5194/egusphere-2025-5569, https://doi.org/10.5194/egusphere-2025-5569, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
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Accurate measurement of methane's isotopic fingerprint is crucial for identifying its sources. However, water vapor interference and instrument drift can cause significant errors, especially in humid air. This study evaluated two calibration methods and found that calibrating for individual methane isotopes with a water vapor correction provided accurate and stable results for both dry and humid air. This highlights the need for robust calibration to ensure reliable methane source attribution.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Kjetil Aas, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Nicolas Bellouin, Alice Benoit-Cattin, Carla F. Berghoff, Raffaele Bernardello, Laurent Bopp, Ida B. M. Brasika, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Nathan O. Collier, Thomas H. Colligan, Margot Cronin, Laique Djeutchouang, Xinyu Dou, Matt P. Enright, Kazutaka Enyo, Michael Erb, Wiley Evans, Richard A. Feely, Liang Feng, Daniel J. Ford, Adrianna Foster, Filippa Fransner, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Jefferson Goncalves De Souza, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Bertrand Guenet, Özgür Gürses, Kirsty Harrington, Ian Harris, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Akihiko Ito, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Steve D. Jones, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Yawen Kong, Jan Ivar Korsbakken, Charles Koven, Taro Kunimitsu, Xin Lan, Junjie Liu, Zhiqiang Liu, Zhu Liu, Claire Lo Monaco, Lei Ma, Gregg Marland, Patrick C. McGuire, Galen A. McKinley, Joe Melton, Natalie Monacci, Erwan Monier, Eric J. Morgan, David R. Munro, Jens D. Müller, Shin-Ichiro Nakaoka, Lorna R. Nayagam, Yosuke Niwa, Tobias Nutzel, Are Olsen, Abdirahman M. Omar, Naiqing Pan, Sudhanshu Pandey, Denis Pierrot, Zhangcai Qin, Pierre A. G. Regnier, Gregor Rehder, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, Ingunn Skjelvan, T. Luke Smallman, Victoria Spada, Mohanan G. Sreeush, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Didier Swingedouw, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Xiangjun Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Erik van Ooijen, Guido van der Werf, Sebastiaan J. van de Velde, Anthony Walker, Rik Wanninkhof, Xiaojuan Yang, Wenping Yuan, Xu Yue, and Jiye Zeng
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-659, https://doi.org/10.5194/essd-2025-659, 2025
Preprint under review for ESSD
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The Global Carbon Budget 2025 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2025). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Shuzhuang Feng, Fei Jiang, Yongguang Zhang, Huilin Chen, Honglin Zhuang, Shumin Wang, Shengxi Bai, Hengmao Wang, and Weimin Ju
Atmos. Chem. Phys., 25, 15121–15143, https://doi.org/10.5194/acp-25-15121-2025, https://doi.org/10.5194/acp-25-15121-2025, 2025
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Using satellite data and advanced modeling, this study inverted daily high-resolution anthropogenic CH4 emissions across China and Shanxi Province. We found that China's 2022 CH4 emissions were 45.1 TgCH4·yr⁻¹, significantly lower than previous estimates, especially in coal mining and waste sectors. The inversion substantially reduced emission uncertainties and improved CH4 concentration simulations. These results suggest China’s climate mitigation burden may have been overestimated.
Yu Mao, Weimin Ju, Hengmao Wang, Liangyun Liu, Haikun Wang, Shuzhuang Feng, Mengwei Jia, and Fei Jiang
Atmos. Chem. Phys., 25, 14187–14204, https://doi.org/10.5194/acp-25-14187-2025, https://doi.org/10.5194/acp-25-14187-2025, 2025
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The Russia-Ukraine war caused significant NOx emission reductions in Ukraine, exceeding COVID-19 impacts. Using TROPOMI data, we found a 15% drop in 2022 and 8% in 2023, with Eastern Ukraine (frontline) seeing declines of 29% and 17%. Industry emissions fell most (34% in 2022), while war-related NOx partially offset reductions (8-10%). Emissions closely tracked military actions, revealing conflict’s disruptive effects on socio-economic activity and air pollution.
Rong Shang, Xudong Lin, Jing M. Chen, Yunjian Liang, Keyan Fang, Mingzhu Xu, Yulin Yan, Weimin Ju, Guirui Yu, Nianpeng He, Li Xu, Liangyun Liu, Jing Li, Wang Li, Jun Zhai, and Zhongmin Hu
Earth Syst. Sci. Data, 17, 3219–3241, https://doi.org/10.5194/essd-17-3219-2025, https://doi.org/10.5194/essd-17-3219-2025, 2025
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Forest age is critical for carbon cycle modeling and effective forest management. Existing datasets, however, have low spatial resolutions or limited temporal coverage. This study introduces China's annual forest age dataset (CAFA), spanning 1986–2022 at a 30 m resolution. By tracking forest disturbances, we annually update ages. Validation shows small errors for disturbed forests and larger errors for undisturbed forests. CAFA can enhance carbon cycle modeling and forest management in China.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Hongmei Li, Ingrid T. Luijkx, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Carla F. Berghoff, Henry C. Bittig, Laurent Bopp, Patricia Cadule, Katie Campbell, Matthew A. Chamberlain, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Thomas Colligan, Jeanne Decayeux, Laique M. Djeutchouang, Xinyu Dou, Carolina Duran Rojas, Kazutaka Enyo, Wiley Evans, Amanda R. Fay, Richard A. Feely, Daniel J. Ford, Adrianna Foster, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul K. Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Etsushi Kato, Ralph F. Keeling, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Xin Lan, Siv K. Lauvset, Nathalie Lefèvre, Zhu Liu, Junjie Liu, Lei Ma, Shamil Maksyutov, Gregg Marland, Nicolas Mayot, Patrick C. McGuire, Nicolas Metzl, Natalie M. Monacci, Eric J. Morgan, Shin-Ichiro Nakaoka, Craig Neill, Yosuke Niwa, Tobias Nützel, Lea Olivier, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Zhangcai Qin, Laure Resplandy, Alizée Roobaert, Thais M. Rosan, Christian Rödenbeck, Jörg Schwinger, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Roland Séférian, Shintaro Takao, Hiroaki Tatebe, Hanqin Tian, Bronte Tilbrook, Olivier Torres, Etienne Tourigny, Hiroyuki Tsujino, Francesco Tubiello, Guido van der Werf, Rik Wanninkhof, Xuhui Wang, Dongxu Yang, Xiaojuan Yang, Zhen Yu, Wenping Yuan, Xu Yue, Sönke Zaehle, Ning Zeng, and Jiye Zeng
Earth Syst. Sci. Data, 17, 965–1039, https://doi.org/10.5194/essd-17-965-2025, https://doi.org/10.5194/essd-17-965-2025, 2025
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The Global Carbon Budget 2024 describes the methodology, main results, and datasets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2024). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Xingyu Wang, Fei Jiang, Hengmao Wang, Zhengqi Zhang, Mousong Wu, Jun Wang, Wei He, Weimin Ju, and Jing M. Chen
Atmos. Chem. Phys., 25, 867–880, https://doi.org/10.5194/acp-25-867-2025, https://doi.org/10.5194/acp-25-867-2025, 2025
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The role of OCO-3 XCO2 retrievals in estimating global terrestrial carbon fluxes is unclear. We investigate this by assimilating OCO-3 XCO2 retrievals alone and in combination with OCO-2 XCO2. The assimilation of OCO-3 XCO2 alone underestimates global land sinks, mainly at high latitudes, due to the lack of observations beyond 52° S and 52° N, large variations in the number of data, and varying observation times, while the joint assimilation of OCO-2 and OCO-3 XCO2 has the best performance.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
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In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Huajie Zhu, Xiuli Xing, Mousong Wu, Weimin Ju, and Fei Jiang
Biogeosciences, 21, 3735–3760, https://doi.org/10.5194/bg-21-3735-2024, https://doi.org/10.5194/bg-21-3735-2024, 2024
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Ecosystem carbonyl sulfide (COS) fluxes were employed to optimize GPP estimation across ecosystems with the Biosphere-atmosphere Exchange Process Simulator (BEPS), which was developed for simulating the canopy COS uptake under its state-of-the-art two-leaf modeling framework. Our results showcased the efficacy of COS in improving model prediction and reducing prediction uncertainty of GPP and enhanced insights into the sensitivity, identifiability, and interactions of parameters related to COS.
Shuzhuang Feng, Fei Jiang, Tianlu Qian, Nan Wang, Mengwei Jia, Songci Zheng, Jiansong Chen, Fang Ying, and Weimin Ju
Atmos. Chem. Phys., 24, 7481–7498, https://doi.org/10.5194/acp-24-7481-2024, https://doi.org/10.5194/acp-24-7481-2024, 2024
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We developed a multi-air-pollutant inversion system to estimate non-methane volatile organic compound (NMVOC) emissions using TROPOMI formaldehyde retrievals. We found that the inversion significantly improved formaldehyde simulations and reduced NMVOC emission uncertainties. The optimized NMVOC emissions effectively corrected the overestimation of O3 levels, mainly by decreasing the rate of the RO2 + NO reaction and increasing the rate of the NO2 + OH reaction.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Peter Landschützer, Corinne Le Quéré, Ingrid T. Luijkx, Glen P. Peters, Wouter Peters, Julia Pongratz, Clemens Schwingshackl, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone R. Alin, Peter Anthoni, Leticia Barbero, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Bertrand Decharme, Laurent Bopp, Ida Bagus Mandhara Brasika, Patricia Cadule, Matthew A. Chamberlain, Naveen Chandra, Thi-Tuyet-Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Xinyu Dou, Kazutaka Enyo, Wiley Evans, Stefanie Falk, Richard A. Feely, Liang Feng, Daniel J. Ford, Thomas Gasser, Josefine Ghattas, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Jens Heinke, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Andrew R. Jacobson, Atul Jain, Tereza Jarníková, Annika Jersild, Fei Jiang, Zhe Jin, Fortunat Joos, Etsushi Kato, Ralph F. Keeling, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Xin Lan, Nathalie Lefèvre, Hongmei Li, Junjie Liu, Zhiqiang Liu, Lei Ma, Greg Marland, Nicolas Mayot, Patrick C. McGuire, Galen A. McKinley, Gesa Meyer, Eric J. Morgan, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin M. O'Brien, Are Olsen, Abdirahman M. Omar, Tsuneo Ono, Melf Paulsen, Denis Pierrot, Katie Pocock, Benjamin Poulter, Carter M. Powis, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Roland Séférian, T. Luke Smallman, Stephen M. Smith, Reinel Sospedra-Alfonso, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Erik van Ooijen, Rik Wanninkhof, Michio Watanabe, Cathy Wimart-Rousseau, Dongxu Yang, Xiaojuan Yang, Wenping Yuan, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 15, 5301–5369, https://doi.org/10.5194/essd-15-5301-2023, https://doi.org/10.5194/essd-15-5301-2023, 2023
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The Global Carbon Budget 2023 describes the methodology, main results, and data sets used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, land ecosystems, and the ocean over the historical period (1750–2023). These living datasets are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Shanlei Sun, Zaoying Bi, Jingfeng Xiao, Yi Liu, Ge Sun, Weimin Ju, Chunwei Liu, Mengyuan Mu, Jinjian Li, Yang Zhou, Xiaoyuan Li, Yibo Liu, and Haishan Chen
Earth Syst. Sci. Data, 15, 4849–4876, https://doi.org/10.5194/essd-15-4849-2023, https://doi.org/10.5194/essd-15-4849-2023, 2023
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Based on various existing datasets, we comprehensively considered spatiotemporal differences in land surfaces and CO2 effects on plant stomatal resistance to parameterize the Shuttleworth–Wallace model, and we generated a global 5 km ensemble mean monthly potential evapotranspiration (PET) dataset (including potential transpiration PT and soil evaporation PE) during 1982–2015. The new dataset may be used by academic communities and various agencies to conduct various studies.
Shuzhuang Feng, Fei Jiang, Zheng Wu, Hengmao Wang, Wei He, Yang Shen, Lingyu Zhang, Yanhua Zheng, Chenxi Lou, Ziqiang Jiang, and Weimin Ju
Geosci. Model Dev., 16, 5949–5977, https://doi.org/10.5194/gmd-16-5949-2023, https://doi.org/10.5194/gmd-16-5949-2023, 2023
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We document the system development and application of a Regional multi-Air Pollutant Assimilation System (RAPAS v1.0). This system is developed to optimize gridded source emissions of CO, SO2, NOx, primary PM2.5, and coarse PM10 on a regional scale via simultaneously assimilating surface measurements of CO, SO2, NO2, PM2.5, and PM10. A series of sensitivity experiments demonstrates the advantage of the “two-step” inversion strategy and the robustness of the system in estimating the emissions.
Jing M. Chen, Rong Wang, Yihong Liu, Liming He, Holly Croft, Xiangzhong Luo, Han Wang, Nicholas G. Smith, Trevor F. Keenan, I. Colin Prentice, Yongguang Zhang, Weimin Ju, and Ning Dong
Earth Syst. Sci. Data, 14, 4077–4093, https://doi.org/10.5194/essd-14-4077-2022, https://doi.org/10.5194/essd-14-4077-2022, 2022
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Green leaves contain chlorophyll pigments that harvest light for photosynthesis and also emit chlorophyll fluorescence as a byproduct. Both chlorophyll pigments and fluorescence can be measured by Earth-orbiting satellite sensors. Here we demonstrate that leaf photosynthetic capacity can be reliably derived globally using these measurements. This new satellite-based information overcomes a bottleneck in global ecological research where such spatially explicit information is currently lacking.
Fei Jiang, Weimin Ju, Wei He, Mousong Wu, Hengmao Wang, Jun Wang, Mengwei Jia, Shuzhuang Feng, Lingyu Zhang, and Jing M. Chen
Earth Syst. Sci. Data, 14, 3013–3037, https://doi.org/10.5194/essd-14-3013-2022, https://doi.org/10.5194/essd-14-3013-2022, 2022
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A 10-year (2010–2019) global monthly terrestrial NEE dataset (GCAS2021) was inferred from the GOSAT ACOS v9 XCO2 product. It shows strong carbon sinks over eastern N. America, the Amazon, the Congo Basin, Europe, boreal forests, southern China, and Southeast Asia. It has good quality and can reflect the impacts of extreme climates and large-scale climate anomalies on carbon fluxes well. We believe that this dataset can contribute to regional carbon budget assessment and carbon dynamics research.
Ruqi Yang, Jun Wang, Ning Zeng, Stephen Sitch, Wenhan Tang, Matthew Joseph McGrath, Qixiang Cai, Di Liu, Danica Lombardozzi, Hanqin Tian, Atul K. Jain, and Pengfei Han
Earth Syst. Dynam., 13, 833–849, https://doi.org/10.5194/esd-13-833-2022, https://doi.org/10.5194/esd-13-833-2022, 2022
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We comprehensively investigate historical GPP trends based on five kinds of GPP datasets and analyze the causes for any discrepancies among them. Results show contrasting behaviors between modeled and satellite-based GPP trends, and their inconsistencies are likely caused by the contrasting performance between satellite-derived and modeled leaf area index (LAI). Thus, the uncertainty in satellite-based GPP induced by LAI undermines its role in assessing the performance of DGVM simulations.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
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The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Fei Jiang, Hengmao Wang, Jing M. Chen, Weimin Ju, Xiangjun Tian, Shuzhuang Feng, Guicai Li, Zhuoqi Chen, Shupeng Zhang, Xuehe Lu, Jane Liu, Haikun Wang, Jun Wang, Wei He, and Mousong Wu
Atmos. Chem. Phys., 21, 1963–1985, https://doi.org/10.5194/acp-21-1963-2021, https://doi.org/10.5194/acp-21-1963-2021, 2021
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We present a 6-year inversion from 2010 to 2015 for the global and regional carbon fluxes using only the GOSAT XCO2 retrievals. We find that the XCO2 retrievals could significantly improve the modeling of atmospheric CO2 concentrations and that the inferred interannual variations in the terrestrial carbon fluxes in most land regions have a better relationship with the changes in severe drought area or leaf area index, or are more consistent with the previous estimates about drought impact.
Cited articles
Ahlstrom, A., Raupach, M. R., Schurgers, G., Smith, B., Arneth, A., Jung, M., Reichstein, M., Canadell, J. G., Friedlingstein, P., Jain, A. K., Kato, E., Poulter, B., Sitch, S., Stocker, B. D., Viovy, N., Wang, Y. P., Wiltshire, A., Zaehle, S., 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.
Bastos, A., Ciais, P., Friedlingstein, P., Sitch, S., and Zaehle, S.: Direct and seasonal legacy effects of the 2018 heat wave and drought on European ecosystem productivity, Sci. Adv., 6, eaba2724, https://doi.org/10.1126/sciadv.aba2724, 2020.
Bastos, A., Orth, R., Reichstein, M., Ciais, P., Viovy, N., Zaehle, S., Anthoni, P., Arneth, A., Gentine, P., Joetzjer, E., Lienert, S., Loughran, T., McGuire, P. C., O, S., Pongratz, J., and Sitch, S.: Vulnerability of European ecosystems to two compound dry and hot summers in 2018 and 2019, Earth Syst. Dynam., 12, 1015–1035, https://doi.org/10.5194/esd-12-1015-2021, 2021.
Bauch, M.: Chapter 15 – Impacts of extreme events on medieval societies: Insights from climate history, in: Climate Extremes and Their Implications for Impact and Risk Assessment, edited by: Sillmann, J., Sippel, S., and Russo, S., Elsevier, https://doi.org/10.1016/B978-0-12-814895-2.00015-X, 279–291, 2020.
Capotondi, A., Wittenberg, A., Newman, M., Di, L., Yu, J., Braconnot, P., Cole, J., Dewitte, B., Giese, B., Guilyardi, E., Jin, F., Karnauskas, K., Kirtman, B., Lee, T., Schneider, N., Xue, Y., and Yeh, S.: Understanding ENSO Diversity, B. Am. Meteorol. Soc., 96, 921–938, https://doi.org/10.1175/BAMS-D-13-00117.1, 2015.
Chen, J., Liu, J., Cihlar, J., and Goulden, M.: Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications, Ecol. Model., 124, 99–119, https://doi.org/10.1016/S0304-3800(99)00156-8, 1999.
Chen, J. M., Mo, G., Pisek, J., Liu, J., Deng, F., Ishizawa, M., and Chan, D.: Effects of foliage clumping on the estimation of global terrestrial gross primary productivity, Global Biogeochem. Cy., 26, GB1019, https://doi.org/10.1029/2010GB003996, 2012.
Chen, J., Ju, W., Ciais, P., Viovy, N., Liu, R., Liu, Y., and Lu X.: Vegetation structural change since 1981 significantly enhanced the terrestrial carbon sink, Nat. Commun., 10, 4259, https://doi.org/10.1038/s41467-019-12257-8, 2019.
Chen, Z., Chen, J., Zhang, S., Zheng, X., Ju, W., Mo, G., Lu, X.: Optimization of Terrestrial Ecosystem Model Parameters Using Atmospheric CO2 Concentration Data With the Global Carbon Assimilation System (GCAS), J. Geophys. Res.-Biogeo., 122, 3218–3237, https://doi.org/10.1002/2016JG003716, 2017.
Fischer, G., Nachtergaele, F., Prieler, S., van Velthuizen, H. T., Verelst, L., Wiberg, D.: Global Agro-ecological Zones Assessment for Agriculture (GAEZ 2008) [data set], IIASA, Laxenburg, Austria and FAO, Rome, Italy, https://data.tpdc.ac.cn/zh-hans/data/611f7d50-b419-4d14-b4dd-4a944b141175 (last access: 5 November 2024), 2008.
Friedl, M., Sulla-Menashe, D.: MCD12Q1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid V006, NASA EOSDIS Land Processes Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MCD12Q1.006, 2019.
Gough, C.: Terrestrial Primary Production: Fuel for Life, Nature Education Knowledge, 3, 28, 2011.
Ham, Y., Choi, J., and Kug, J.: The weakening of the ENSO–Indian Ocean Dipole (IOD) coupling strength in recent decades, Clim. Dynam., 49, 249–261, https://doi.org/10.1007/s00382-016-3339-5, 2017.
He, L., Chen, J., Liu, J., Bélair, S., and Luo, X.: Assessment of SMAP soil moisture for global simulation of gross primary production, J. Geophys. Res.-Biogeo., 122, 1549–1563, https://doi.org/10.1002/2016jg003603, 2017.
He, L., Chen, J., Liu, J., Zheng, T., Wang, R., Joiner, J., Chou, S., Cheng, B., Liu, Y., and Liu, R.: Diverse photosynthetic capacity of global ecosystems mapped by satellite chlorophyll fluorescence measurements, Remote Sens. Environ., 232, 111344, https://doi.org/10.1016/j.rse.2019.111344, 2019.
He, L., Chen J., Mostovoy, G., and Gonsamo, A.: Soil Moisture Active Passive Improves Global Soil Moisture Simulation in a Land Surface Scheme and Reveals Strong Irrigation Signals Over Farmlands, Geophys. Res. Lett., 48, e2021GL092658, https://doi.org/10.1029/2021gl092658, 2021a.
He, L., Wang, R., Mostovoy, G., Liu, J., Chen, J., Shang, J., Liu, J., McNairn, H., and Powers, J.: Crop Biomass Mapping Based on Ecosystem Modeling at Regional Scale Using High Resolution Sentinel-2 Data, Remote Sens.-Basel, 13, 806, https://doi.org/10.3390/rs13040806, 2021b.
He, Q., Ju, W., Dai, S., He, W., Song, L., Wang, S., Li, X., and Mao, G.: Drought Risk of Global Terrestrial Gross Primary Productivity Over the Last 40 Years Detected by a Remote Sensing-Driven Process Model. J. Geophys. Res.-Biogeo., 126, e2020JG005944, https://doi.org/10.1029/2020JG005944, 2021.
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., Thépaut, J-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.adbb2d47, 2023.
Houghton, R. A.: Balancing the global carbon budget, Annu. Rev. Earth Pl. Sc., 35, 313–347, https://doi.org/10.1146/annurev.earth.35.031306.140057, 2007.
Huang, B., Thorne, P., Banzon, V., Boyer, T., Chepurin, G., Lawrimore, J., Menne, M., Smith, T., Vose, R., and Zhang, H.: Extended reconstructed Sea surface temperature, Version 5 (ERSSTv5): Upgrades, validations, and intercomparisons, J. Climate, 30, 8179–8205, https://doi.org/10.1175/JCLI-D-16-0836.1, 2017.
Humphrey, V., Zscheischler, J., Ciais, P., Gudmundsson, L., Sitch, S., and Seneviratne, SI.: 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.
Humphrey, V., Berg, A., Ciais, P., Gentine, P., Jung, M., Reichstein, M., Seneviratne, SI., 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.
Joiner, J., Yoshida, Y., Zhang, Y., Duveiller, G., Jung, M., Lyapustin, A., Wang, Y., and Tucker, C. J.: Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data, Remote Sens.-Basel, 10, 1346, https://doi.org/10.3390/rs10091346, 2018.
Ju, W., Chen J., Black T., Barr, A., Liu, J., and Chen, B.: Modelling multi-year coupled carbon and water fluxes in a boreal aspen forest, Agr. Forest Meteorol., 140, 136–151, https://doi.org/10.1016/j.agrformet.2006.08.008, 2006.
Jung, M., Reichstein, M., Schwalm, C. R., Huntingford, C., Sitch, S., Ahlstrom, A., Arneth, A., Camps-Valls, G., Ciais, P., Friedlingstein, P., Gans, F., Ichii, K., Ain, A., 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.
Kim, J., Kug J., and Jeong S.: Intensification of terrestrial carbon cycle related to El Niño-Southern Oscillation under greenhouse warming, Nat. Commun., 8, 1674, https://doi.org/10.1038/s41467-017-01831-7, 2017.
Lan, X., Tans, P., and Thoning, K. W.: Trends in globally-averaged CO2 determined from NOAA Global Monitoring Laboratory measurements, Global Monitoring Laboratory [data set], https://doi.org/10.15138/9N0H-ZH07, 2022.
Li, X., Cheng, G., Liu, S,, Xiao, Q., Ma, M., Jin, R., Che, T., Liu, Q., Wang, W., Qi, Y., Wen, J., Li, H., Zhu, G., Guo, J., Ran, Y., Wang, S., Zhu, Z., Zhou, J., Hu, X., Xu, Z.: Heihe watershed allied telemetry experimental research (HiWATER): scientific objectives and experimental design, B. Am. Meteorol. Soc., 94, 1145–1160, https://doi.org/10.1175/BAMS-D-12-00154.1, 2013.
Li, Y., Dan, L., Peng, J., Wang, J., Yang, F., Gao, D., Yang, X., and Yu, Q.: Response of Growing Season Gross Primary Production to El Niño in Different Phases of the Pacific Decadal Oscillation over Eastern China Based on Bayesian Model Averaging, Adv. Atmos. Sci., 38, 1580–1595, https://doi.org/10.1007/s00376-021-0265-1, 2021.
Liu, R., Liu, Y., and Chen, J.: GLOBMAP global Leaf Area Index since 1981 (Version 3.0), Zenodo [data set], https://doi.org/10.5281/zenodo.4700264, 2021.
Liu, J., Chen J., Cihlar, J., and Park W.: A process-based boreal ecosystem productivity simulator using remote sensing inputs, Remote Sens. Environ., 62, 158–175, https://doi.org/10.1016/S0034-4257(97)00089-8, 1997.
Liu, Y., Liu, R., and Chen, J.: Retrospective retrieval of long-term consistent global leaf area index (1981-2011) from combined AVHRR and MODIS data, J. Geophys. Res.-Biogeo.,, 117, G04003, https://doi.org/10.1029/2012JG002084, 2012.
Liu, Y., Yang X., Wang, E., and Xue, C.: Climate and crop yields impacted by ENSO episodes on the North China Plain: 1956–2006, Reg. Environ. Change., 14, 49–59, https://doi.org/10.1007/s10113-013-0455-1, 2014.
Liu, Y., Xiao, J., Ju, W., Zhu, G., Wu, X., Fan,W., Li, D., and Zhou, Y.: Satellite-derived LAI products exhibit large discrepancies and can lead to substantial uncertainty in simulated carbon and water fluxes, Remote Sens. Environ., 206, 174–188, https://doi.org/10.1016/j.rse.2017.12.024, 2018.
Mercado, L., Bellouin, N., Sitch, S., Boucher, O., Huntingford, C., Wild, M., Cox, P.: Impact of changes in diffuse radiation on the global land carbon sink, Nature, 458, 1014–1017, https://doi.org/10.1038/nature07949, 2009.
Muñoz, S. J.: ERA5-Land monthly averaged data from 1950 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.68d2bb30, 2019.
Norman, J. M.: Simulation of microclimates, in: Biometeorology in Integrated Pest Management, edited by: Hatfield, J. and Thomason, I., Academic Press, New York, CA, 65–99, https://doi.org/10.1016/B978-0-12-332850-2.50009-8, 1982.
Piao, S., Sitch, S., Ciais, P., Friedlingstein, P., Peylin, P., Wang, X., Ahlstrom, A., Anav, A., Canadell, J., Cong, N., Huntingford, C., Jung, M., Levis, S., Levy, PE., Li, J., Lin, X., Lomas, M., Lu, M., Luo, Y., Ma, Y., Myneni, R., Poulter, B., Sun, Z., Wang, T., Viovy, N., Zaehle, S., and Zeng, N.: Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends, Glob. Change Biol., 2117–2132, https://doi.org/10.1111/gcb.12187, 2013.
Piao, S., Wang, X., Wang, K., Li, X., Bastos, A., Canadell, J., Ciais, P., Friedlingstein, P., and Sitch, S.: Interannual variation of terrestrial carbon cycle: Issues and perspectives, Glob. Change Biol., 26, 300–318, https://doi.org/10.1111/gcb.14884, 2020.
Ryu, Y., Berry J., and Baldocchi, D.: What is global photosynthesis? History, uncertainties and opportunities, Remote Sens. Environ., 223, 95–114, https://doi.org/10.1016/j.rse.2019.01.016, 2019.
Saji, N. and Yamagata, T.: Possible impacts of Indian Ocean Dipole mode events on global climate, Clim. Res., 25, 151–169, https://doi.org/10.3354/cr025151, 2003.
Saji, N., Goswami, B., Vinayachandran, P., and Yamagata, T.: A dipole mode in the tropical Indian Ocean, Nature, 401, 360–363, https://doi.org/10.1038/43855, 1999.
Sanders-DeMott, R., Ouimette, A., Lepine, L., Fogarty, S., Burakowski, E., Contosta, A., Ollinger, S.: Divergent carbon cycle response of forest and grass-dominated northern temperate ecosystems to record winter warming, Glob. Change Biol., 26, 1519–1531, https://doi.org/10.1111/gcb.14850, 2020.
Schimel, D., Stephens, B., and Fisher, J.: Effect of increasing CO2 on the terrestrial carbon cycle, P. Natl. Acad. Sci. USA, 112, 436–441, https://doi.org/10.1073/pnas.1407302112/-/DCSupplemental, 2015.
Wang, J., Zeng, N., and Wang, M.: Interannual variability of the atmospheric CO2 growth rate: roles of precipitation and temperature, Biogeosciences, 13, 2339–2352, https://doi.org/10.5194/bg-13-2339-2016, 2016.
Wang, J., Zeng, N., Wang, M., Jiang, F., Chen, J., Friedlingstein, P., Jain, A. K., Jiang, Z., Ju, W., Lienert, S., Nabel, J., Sitch, S., Viovy, N., Wang, H., and Wiltshire, A. J.: Contrasting interannual atmospheric CO2 variabilities and their terrestrial mechanisms for two types of El Niños, Atmos. Chem. Phys., 18, 10333–10345, https://doi.org/10.5194/acp-18-10333-2018, 2018.
Wang, J., Liu, Z., Zeng, N., Jiang, F., Wang, H., and Ju, W.: Spaceborne detection of XCO2 enhancement induced by Australian mega-bushfires, Environ. Res. Lett., 15, 124069, https://doi.org/10.1088/1748-9326/abc846, 2020.
Wang, J., Jiang, F., Wang, H., Qiu, B., Wu, M., He, W., Ju, W., Zhang, Y., Chen, J., and Zhou, Y.: Constraining global terrestrial gross primary productivity in a global carbon assimilation system with OCO-2 chlorophyll fluorescence data, Agr. Forest Meteorol., 304–305, 108424, https://doi.org/10.1016/j.agrformet.2021.108424, 2021a.
Wang, J., Wang, M., Kim, J., Joiner, J., Zeng, N., Jiang, F., Wang, H., He, W., Wu, M., Chen, T., Ju, W., and Chen, J.: Modulation of Land Photosynthesis by the Indian Ocean Dipole: Satellite-Based Observations and CMIP6 Future Projections, Earths Future, 9, e2020EF001942, https://doi.org/10.1029/2020ef001942, 2021b.
Wang, J., Jiang, F., Ju, W., Wang, M., Sitch, S., Arora, V., Chen, J., Goll, D., He, W., Jain, A., Li, X., Joiner, J., Poulter, B., Seferian, R., Wang, H.,Wu, M., Xiao, J., Yuan, W., Yue, X., Zaehle, S.: Enhanced India-Africa Carbon Uptake and Asia-Pacific Carbon Release Associated With the 2019 Extreme Positive Indian Ocean Dipole, Geophys. Res. Lett., 49, e2022GL100950, https://doi.org/10.1029/2022gl100950, 2022.
Wang, M., Wang, J., Cai, Q., Zeng, N., Lu, X., Yang, R., Jiang, F., Wang, H., and Ju, W.: Considerable Uncertainties in Simulating Land Carbon Sinks Induced by Different Precipitation Products, J. Geophys. Res.-Biogeo., 126, e2021JG006524, https://doi.org/10.1029/2021JG006524, 2021.
Williams, C. A. and Hanan, N. P.: ENSO and IOD teleconnections for African ecosystems: evidence of destructive interference between climate oscillations, Biogeosciences, 8, 27–40, https://doi.org/10.5194/bg-8-27-2011, 2011.
Yan, R., Wang, J., Ju, W., Goll, D., Jain, A., Sitch, S., Tian, H., Benjamin, P., Jiang, F., and Wang, H.: Interactive effects of the El Niño-Southern Oscillation and Indian Ocean Dipole on the tropical net ecosystem productivity, Agr. Forest Meteorol., 336, 109472, https://doi.org/10.1016/j.agrformet.2023.109472, 2023.
Yang, R., Wang, J., Zeng, N., Sitch, S., Tang, W., McGrath, M. J., Cai, Q., Liu, D., Lombardozzi, D., Tian, H., Jain, A. K., and Han, P.: Divergent historical GPP trends among state-of-the-art multi-model simulations and satellite-based products, Earth Syst. Dynam., 13, 833–849, https://doi.org/10.5194/esd-13-833-2022, 2022.
Yang, Y., Xie, S.-P., Wu, L., Kosaka, Y., Lau, N.-C., and Vecchi, G. A.: Seasonality and Predictability of the Indian Ocean Dipole Mode: ENSO Forcing and Internal Variability, J. Climate, 28, 8021–8036, https://doi.org/10.1175/JCLI-D-15-0078.1, 2015.
Ying, K., Peng, J., Dan, L., and Zheng, X.: Ocean–atmosphere Teleconnections Play a Key Role in the Interannual Variability of Seasonal Gross Primary Production in China, Ad v. Atmos. Sci., 39, 1329–1342, https://doi.org/10.1007/s00376-021-1226-4, 2022.
Yu, G., Ren, W., Chen, Z., Zhang, L., Wang, Q., Wen, X., He, N., Zhang, L., Fang, H., Zhu, X., Gao, Y., and Sun, X.: Construction and progress of Chinese terrestrial ecosystem carbon, nitrogen and water fluxes coordinated observation, J. Geogr. Sci. 26, 803–826, https://doi.org/10.1007/s11442-016-1300-5, 2016 (data available at: https://chinaflux.org/, last access: October 2022).
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.
Zhang, X., Wang, Y., Peng, S., Rayner, P., Ciais, P., Silver, J., Piao, S., Zhu, Z., Lu, X., Zheng, X.: Dominant regions and drivers of the variability of the global land carbon sink across timescales, Glob. Change Biol., 24, 3954–3968, https://doi.org/10.1111/gcb.14275, 2018.
Zhang, Y., Dannenberg, M., Hwang, T., and Song, C.: El Niño-Southern Oscillation-Induced Variability of Terrestrial Gross Primary Production During the Satellite Era, J. Geophys. Res.-Biogeo., 124, 2419–2431, https://doi.org/10.1029/2019jg005117, 2019.
Zhang, Y., Zhou, W., Wang, X., Chen, S., Chen, J., and Li, S.: Indian Ocean Dipole and ENSO's mechanistic importance in modulating the ensuing-summer precipitation over Eastern China, npj Clim. Atmos. Sci., 5, 48, https://doi.org/10.1038/s41612-022-00271-5, 2022.
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.
Short summary
Our study reveals that the effects of the El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) on China's gross primary production (GPP) are basically opposite, with obvious seasonal changes. Soil moisture primarily influences GPP during ENSO events (except spring) and temperature during IOD events (except fall). Quantitatively, China's annual GPP displays modest positive anomalies during La Niña and negative anomalies in El Niño years, driven by significant seasonal variations.
Our study reveals that the effects of the El Niño–Southern Oscillation (ENSO) and Indian Ocean...
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