Articles | Volume 11, issue 3
https://doi.org/10.5194/esd-11-641-2020
© Author(s) 2020. 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-11-641-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impact of environmental changes and land management practices on wheat production in India
Shilpa Gahlot
Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New Delhi, 110016, India
Tzu-Shun Lin
Department of Atmospheric Sciences, University of Illinois, Urbana, IL 61801, USA
Atul K. Jain
Department of Atmospheric Sciences, University of Illinois, Urbana, IL 61801, USA
Centre for Atmospheric Sciences, Indian Institute of Technology Delhi, New Delhi, 110016, India
Vinay K. Sehgal
Department of Agricultural Physics, Indian Agricultural Research
Institute, New Delhi, 110012, India
Rajkumar Dhakar
Department of Agricultural Physics, Indian Agricultural Research
Institute, New Delhi, 110012, India
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Carbon fluxes from agroecosystems change the carbon cycle and the amount of CO2 in the air. Using the Integrated Science Assessment Model (ISAM), we looked at the carbon cycle in areas where spring wheat is grown. The results showed that fluxes vary a lot between regions, mostly because planting times are different. According to our investigation into which variables have the greatest impact on the carbon cycle, nitrogen fertilizers added to crops have the greatest impact on carbon uptake.
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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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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.
Kangari Narender Reddy, Shilpa Gahlot, Somnath Baidya Roy, Gudimetla Venkateswara Varma, Vinay Kumar Sehgal, and Gayatri Vangala
Earth Syst. Dynam., 14, 915–930, https://doi.org/10.5194/esd-14-915-2023, https://doi.org/10.5194/esd-14-915-2023, 2023
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Carbon fluxes from agroecosystems change the carbon cycle and the amount of CO2 in the air. Using the Integrated Science Assessment Model (ISAM), we looked at the carbon cycle in areas where spring wheat is grown. The results showed that fluxes vary a lot between regions, mostly because planting times are different. According to our investigation into which variables have the greatest impact on the carbon cycle, nitrogen fertilizers added to crops have the greatest impact on carbon uptake.
Sian Kou-Giesbrecht, Vivek K. Arora, Christian Seiler, Almut Arneth, Stefanie Falk, Atul K. Jain, Fortunat Joos, Daniel Kennedy, Jürgen Knauer, Stephen Sitch, Michael O'Sullivan, Naiqing Pan, Qing Sun, Hanqin Tian, Nicolas Vuichard, and Sönke Zaehle
Earth Syst. Dynam., 14, 767–795, https://doi.org/10.5194/esd-14-767-2023, https://doi.org/10.5194/esd-14-767-2023, 2023
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Nitrogen (N) is an essential limiting nutrient to terrestrial carbon (C) sequestration. We evaluate N cycling in an ensemble of terrestrial biosphere models. We find that variability in N processes across models is large. Models tended to overestimate C storage per unit N in vegetation and soil, which could have consequences for projecting the future terrestrial C sink. However, N cycling measurements are highly uncertain, and more are necessary to guide the development of N cycling in models.
Axel Kleidon, Gabriele Messori, Somnath Baidya Roy, Ira Didenkulova, and Ning Zeng
Earth Syst. Dynam., 14, 241–242, https://doi.org/10.5194/esd-14-241-2023, https://doi.org/10.5194/esd-14-241-2023, 2023
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Luke Gregor, Judith Hauck, Corinne Le Quéré, 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, Ramdane Alkama, Almut Arneth, Vivek K. Arora, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Henry C. Bittig, Laurent Bopp, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Wiley Evans, Stefanie Falk, Richard A. Feely, Thomas Gasser, Marion Gehlen, Thanos Gkritzalis, Lucas Gloege, Giacomo Grassi, Nicolas Gruber, Özgür Gürses, Ian Harris, Matthew Hefner, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Atul K. Jain, Annika Jersild, Koji Kadono, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Keith Lindsay, Junjie Liu, Zhu Liu, Gregg Marland, Nicolas Mayot, Matthew J. McGrath, Nicolas Metzl, Natalie M. Monacci, David R. Munro, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Naiqing Pan, Denis Pierrot, Katie Pocock, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Carmen Rodriguez, Thais M. Rosan, Jörg Schwinger, Roland Séférian, Jamie D. Shutler, Ingunn Skjelvan, Tobias Steinhoff, Qing Sun, Adrienne J. Sutton, Colm Sweeney, Shintaro Takao, Toste Tanhua, Pieter P. Tans, Xiangjun Tian, Hanqin Tian, Bronte Tilbrook, Hiroyuki Tsujino, Francesco Tubiello, Guido R. van der Werf, Anthony P. Walker, Rik Wanninkhof, Chris Whitehead, Anna Willstrand Wranne, Rebecca Wright, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, Jiye Zeng, and Bo Zheng
Earth Syst. Sci. Data, 14, 4811–4900, https://doi.org/10.5194/essd-14-4811-2022, https://doi.org/10.5194/essd-14-4811-2022, 2022
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The Global Carbon Budget 2022 describes the datasets and methodology used to quantify the anthropogenic emissions of carbon dioxide (CO2) and their partitioning among the atmosphere, the land ecosystems, and the ocean. 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.
Pierre Friedlingstein, Matthew W. Jones, Michael O'Sullivan, Robbie M. Andrew, Dorothee C. E. Bakker, Judith Hauck, Corinne Le Quéré, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Josep G. Canadell, Philippe Ciais, Rob B. Jackson, Simone R. Alin, Peter Anthoni, Nicholas R. Bates, Meike Becker, Nicolas Bellouin, Laurent Bopp, Thi Tuyet Trang Chau, Frédéric Chevallier, Louise P. Chini, Margot Cronin, Kim I. Currie, Bertrand Decharme, Laique M. Djeutchouang, Xinyu Dou, Wiley Evans, Richard A. Feely, Liang Feng, Thomas Gasser, Dennis Gilfillan, Thanos Gkritzalis, Giacomo Grassi, Luke Gregor, Nicolas Gruber, Özgür Gürses, Ian Harris, Richard A. Houghton, George C. Hurtt, Yosuke Iida, Tatiana Ilyina, Ingrid T. Luijkx, Atul Jain, Steve D. Jones, Etsushi Kato, Daniel Kennedy, Kees Klein Goldewijk, Jürgen Knauer, Jan Ivar Korsbakken, Arne Körtzinger, Peter Landschützer, Siv K. Lauvset, Nathalie Lefèvre, Sebastian Lienert, Junjie Liu, Gregg Marland, Patrick C. McGuire, Joe R. Melton, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Tsuneo Ono, Denis Pierrot, Benjamin Poulter, Gregor Rehder, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Thais M. Rosan, Jörg Schwinger, Clemens Schwingshackl, Roland Séférian, Adrienne J. Sutton, Colm Sweeney, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Francesco Tubiello, Guido R. van der Werf, Nicolas Vuichard, Chisato Wada, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Chao Yue, Xu Yue, Sönke Zaehle, and Jiye Zeng
Earth Syst. Sci. Data, 14, 1917–2005, https://doi.org/10.5194/essd-14-1917-2022, https://doi.org/10.5194/essd-14-1917-2022, 2022
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The Global Carbon Budget 2021 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
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.
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).
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.
Tanvi Gupta and Somnath Baidya Roy
Adv. Geosci., 56, 129–139, https://doi.org/10.5194/adgeo-56-129-2021, https://doi.org/10.5194/adgeo-56-129-2021, 2021
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In this paper we study how the momentum extracted by wind turbines get replenished so that the wind farm can continue to function. We use a numerical model to simulate the dynamics of a hypothetical coastal wind farm in the Arabian Sea under sea breeze conditions. Results show that vertical turbulent eddies can replenish more than half of the lost momentum, but horizontal advection also plays a role near the wind farm edges especially in sparsely packed wind farms.
Aheli Das and Somnath Baidya Roy
Adv. Geosci., 56, 89–96, https://doi.org/10.5194/adgeo-56-89-2021, https://doi.org/10.5194/adgeo-56-89-2021, 2021
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In this study we evaluated subseasonal-seasonal scale forecasts of solar radiation, wind speed, temperature and relative humidity over India from 6 global models by comparing against observations. Results show that the overall quality of the forecasts are not good. However, they demonstrate enough skill suggesting that further improvement through calibration may make then useful for the renewable energy sector.
Lina Teckentrup, Martin G. De Kauwe, Andrew J. Pitman, Daniel S. Goll, Vanessa Haverd, Atul K. Jain, Emilie Joetzjer, Etsushi Kato, Sebastian Lienert, Danica Lombardozzi, Patrick C. McGuire, Joe R. Melton, Julia E. M. S. Nabel, Julia Pongratz, Stephen Sitch, Anthony P. Walker, and Sönke Zaehle
Biogeosciences, 18, 5639–5668, https://doi.org/10.5194/bg-18-5639-2021, https://doi.org/10.5194/bg-18-5639-2021, 2021
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The Australian continent is included in global assessments of the carbon cycle such as the global carbon budget, yet the performance of dynamic global vegetation models (DGVMs) over Australia has rarely been evaluated. We assessed simulations by an ensemble of dynamic global vegetation models over Australia and highlighted a number of key areas that lead to model divergence on both short (inter-annual) and long (decadal) timescales.
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.
Tanvi Gupta and Somnath Baidya Roy
Wind Energ. Sci., 6, 1089–1106, https://doi.org/10.5194/wes-6-1089-2021, https://doi.org/10.5194/wes-6-1089-2021, 2021
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Wind turbines extract momentum from atmospheric flow and convert that to electricity. This study explores recovery processes in wind farms that replenish the momentum so that wind farms can continue to function. Experiments with a numerical model show that momentum transport by turbulent eddies from above the wind turbines is the major contributor to recovery except for strong wind conditions and low wind turbine density, where horizontal advection can also play a major role.
Zichong Chen, Junjie Liu, Daven K. Henze, Deborah N. Huntzinger, Kelley C. Wells, Stephen Sitch, Pierre Friedlingstein, Emilie Joetzjer, Vladislav Bastrikov, Daniel S. Goll, Vanessa Haverd, Atul K. Jain, Etsushi Kato, Sebastian Lienert, Danica L. Lombardozzi, Patrick C. McGuire, Joe R. Melton, Julia E. M. S. Nabel, Benjamin Poulter, Hanqin Tian, Andrew J. Wiltshire, Sönke Zaehle, and Scot M. Miller
Atmos. Chem. Phys., 21, 6663–6680, https://doi.org/10.5194/acp-21-6663-2021, https://doi.org/10.5194/acp-21-6663-2021, 2021
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NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite observes atmospheric CO2 globally. We use a multiple regression and inverse model to quantify the relationships between OCO-2 and environmental drivers within individual years for 2015–2018 and within seven global biomes. Our results point to limitations of current space-based observations for inferring environmental relationships but also indicate the potential to inform key relationships that are very uncertain in process-based models.
Pierre Friedlingstein, Michael O'Sullivan, Matthew W. Jones, Robbie M. Andrew, Judith Hauck, Are Olsen, Glen P. Peters, Wouter Peters, Julia Pongratz, Stephen Sitch, Corinne Le Quéré, Josep G. Canadell, Philippe Ciais, Robert B. Jackson, Simone Alin, Luiz E. O. C. Aragão, Almut Arneth, Vivek Arora, Nicholas R. Bates, Meike Becker, Alice Benoit-Cattin, Henry C. Bittig, Laurent Bopp, Selma Bultan, Naveen Chandra, Frédéric Chevallier, Louise P. Chini, Wiley Evans, Liesbeth Florentie, Piers M. Forster, Thomas Gasser, Marion Gehlen, Dennis Gilfillan, Thanos Gkritzalis, Luke Gregor, Nicolas Gruber, Ian Harris, Kerstin Hartung, Vanessa Haverd, Richard A. Houghton, Tatiana Ilyina, Atul K. Jain, Emilie Joetzjer, Koji Kadono, Etsushi Kato, Vassilis Kitidis, Jan Ivar Korsbakken, Peter Landschützer, Nathalie Lefèvre, Andrew Lenton, Sebastian Lienert, Zhu Liu, Danica Lombardozzi, Gregg Marland, Nicolas Metzl, David R. Munro, Julia E. M. S. Nabel, Shin-Ichiro Nakaoka, Yosuke Niwa, Kevin O'Brien, Tsuneo Ono, Paul I. Palmer, Denis Pierrot, Benjamin Poulter, Laure Resplandy, Eddy Robertson, Christian Rödenbeck, Jörg Schwinger, Roland Séférian, Ingunn Skjelvan, Adam J. P. Smith, Adrienne J. Sutton, Toste Tanhua, Pieter P. Tans, Hanqin Tian, Bronte Tilbrook, Guido van der Werf, Nicolas Vuichard, Anthony P. Walker, Rik Wanninkhof, Andrew J. Watson, David Willis, Andrew J. Wiltshire, Wenping Yuan, Xu Yue, and Sönke Zaehle
Earth Syst. Sci. Data, 12, 3269–3340, https://doi.org/10.5194/essd-12-3269-2020, https://doi.org/10.5194/essd-12-3269-2020, 2020
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The Global Carbon Budget 2020 describes the data sets and methodology used to quantify the emissions of carbon dioxide and their partitioning among the atmosphere, land, and ocean. These living data are updated every year to provide the highest transparency and traceability in the reporting of CO2, the key driver of climate change.
Narender Kangari Reddy and Somnath Baidya Roy
Adv. Geosci., 54, 79–87, https://doi.org/10.5194/adgeo-54-79-2020, https://doi.org/10.5194/adgeo-54-79-2020, 2020
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In this study, we apply the Genetic Algorithm technique that mimics the natural selection process observed in nature to design optimal layouts for massive wind farms off the southeastern coast of India using real wind data. Our results show that layout optimization leads to large improvements in power generation (up to 28 %), efficiency (up to 34 %), and cost (up to 25 %) due to the reduction in wake losses.
Cited articles
Allen Jr., L. H., Baker, J. T., and Boote, K. J.: The CO2 fertilization
effect: higher carbohydrate production and retention as biomass and seed
yield, in: Global climate change and agricultural production, Direct and
indirect effects of changing hydrological, pedological and plant
physiological processes, edited by: Bazzaz, F. and Sombroek, W., John Wiley
and Sons Ltd., Chichester, UK, available at: http://www.fao.org/docrep/w5183e/w5183e06.htm (last access: 18 April 2019), 1996.
Asseng, S., Ewert, F., Martre, P., Rötter, R. P., Lobell, D. B., Cammarano, D., Kimball, B. A., Ottman, M., Wall, G., White, J., Reynolds, M., Alderman, P., Prasad, P., Aggarwal, P., Anothai, J., Basso, B., Biernath, C., Challinor, A., De Sanctis, G., Doltra, J., Fereres, E., Garcia-Vila, M., Gayler, S., Hoogenboom, G., Hunt, L., Izaurralde, R., Jabloun, M., Jones, C., Kersebaum, K., Koehler, A.-K., Müller, C., Naresh Kumar, S., Nendel, C., O’Leary, G., Olesen, J., Palosuo, T., Priesack, E., Eyshi Rezaei, E., Ruane, A., Semenov, M., Shcherbak, I., Stöckle, C., Stratonovitch, P., Streck, T., Supit, I., Tao, F., Thorburn, P., Waha, K., Wang, E., Wallach, D., Wolf, J., Zhao, Z., and Zhu, Y.: Rising temperatures reduce global wheat production, Nat. Clim. Change, 5, 143–147,
143, https://doi.org/10.1038/nclimate2470, 2015.
Barman, R., Jain, A. K., and Liang, M.: Climate-driven uncertainties in
modeling terrestrial gross primary production: A site level to global-scale
analysis, Glob. Change Biol., 20, 1394–1411, 2014a.
Barman, R., Jain, A. K., and Liang, M.: Climate-driven uncertainties in
modeling terrestrial energy and water fluxes: A site-level to globalscale
analysis, Glob. Change Biol., 20, 1885–1900, 2014b.
Bondeau, A., Smith, P. C., Zaehle, S., Schaphoff, S., Lucht, W., Cramer, W.,
Gerten, D., Lotze-Campen, H., Müller, C., Reichstein, M., and Smith, B :
Modelling the role of agriculture for the 20th century global terrestrial
carbon balance, Glob. Change Biol., 13, 679–706, 2007.
Chowdhury, D., Bharadwaj, A., and Sehgal, V. K.: Mega–Environment Concept
in Agriculture: A Review, International Journal of Current Microbiology and
Applied Sciences, 8, 2147–2152, 2019.
Dentener, F. J.: Global Maps of Atmospheric Nitrogen Deposition, 1860, 1993,
and 2050, ORNL DAAC, Oak Ridge, Tennessee, USA, https://doi.org/10.3334/ORNLDAAC/830, 2006.
Deryng, D., Conway, D., Ramankutty, N., Price, J., and Warren, R.: Global
crop yield response to extreme heat stress under multiple climate change
futures, Environ. Res. Lett., 9, 034011, https://doi.org/10.1088/1748-9326/9/3/034011, 2014.
Drewniak, B., Song, J., Prell, J., Kotamarthi, V. R., and Jacob, R.: Modeling agriculture in the Community Land Model, Geosci. Model Dev., 6, 495–515, https://doi.org/10.5194/gmd-6-495-2013, 2013.
Dubey, S. K., Tripathi, S. K., and Pranuthi, G.: Effect of Elevated CO2
on Wheat Crop: Mechanism and Impact, Crit. Rev. Env. Sci. Tec., 45,
2283–2304, 2015.
FAO Crop Information:
http://www.fao.org/land-water/databases-and-software/crop-information/wheat/en/,
last access: 15 November 2018.
FAOSTAT online database: http://www.fao.org/faostat/en/#data/QC, last
access: 15 March 2019.
Farooq, M., Bramley, H., Palta, J. A., and Siddique, K. H.: Heat stress in
wheat during reproductive and grain-filling phases, Crit. Rev. Plant
Sci., 30, 491–507, 2011.
Gahlot, S., Shu, S., Jain, A. K., and Baidya Roy, S.: Estimating trends and
variation of net biome productivity in India for 1980–2012 using a land
surface model, Geophys. Res. Lett., 44, 11573–11579, https://doi.org/10.1002/2017GL075777, 2017.
Kimball, B. A.: Crop responses to elevated CO2 and interactions with H2O, N,
and temperature, Curr. Opin. Plant Biol., 31, 36–43, 2016.
Koehler, A. K., Challinor, A. J., Hawkins, E., and Asseng, S.: Influences of
increasing temperature on Indian wheat: quantifying limits to
predictability, Environ. Res. Lett., 8, 034016, https://doi.org/10.1088/1748-9326/8/3/034016, 2013.
Leakey, A. D., Ainsworth, E. A., Bernacchi, C. J., Rogers, A., Long, S. P.,
and Ort, D. R.: Elevated CO2 effects on plant carbon, nitrogen, and
water relations: six important lessons from FACE, J. Exp. Bot., 60,
2859–2876, 2009.
Lobell, D. B., Sibley, A., and Ortiz-Monasterio, J. I.: Extreme heat effects
on wheat senescence in India, Nat. Clim. Change, 2, 186–189, 2012.
Lokupitiya, E., Denning, S., Paustian, K., Baker, I., Schaefer, K., Verma, S., Meyers, T., Bernacchi, C. J., Suyker, A., and Fischer, M.: Incorporation of crop phenology in Simple Biosphere Model (SiBcrop) to improve land-atmosphere carbon exchanges from croplands, Biogeosciences, 6, 969–986, https://doi.org/10.5194/bg-6-969-2009, 2009.
Lu, Y., Williams, I. N., Bagley, J. E., Torn, M. S., and Kueppers, L. M.: Representing winter wheat in the Community Land Model (version 4.5), Geosci. Model Dev., 10, 1873–1888, https://doi.org/10.5194/gmd-10-1873-2017, 2017.
Luo, Q., Bellotti, W., Williams, M., and Wang, E.: Adaptation to climate
change of wheat growing in South Australia: analysis of management and
breeding strategies, Agr. Ecosyst. Environ., 129, 261–267, 2009.
MAFW: Agricultural Statistics at a Glance 2016, Directorate of Economics and
Statistics, Ministry of Agriculture, Government of India, PDES-256 (E),
500-2017 – (DSK-III), available at: https://eands.dacnet.nic.in/PDF/Glance-2016.pdf (last access: 17 November 2019), 2017.
Maiorano, A., Martre, P., Asseng, S., Ewert, F., Müller, C., Rötter,
R. P., Ruane, A. C., Semenov, M. A., Wallach, D., Wang, E., Alderman, P. D.,
Kassie, B. T., Biernath, C., Basso, B., Cammarano, D., Challinor, A. J.,
Doltra, J., Dumont, B., Rezaei, E. E., Gayler, S., Kersebaum, K. C.,
Kimball, B. A., Koehler, A. K., Liu, B., O'Leary, G. J., Olesen, J. E.,
Ottman, M. J., Priesack, E., Reynolds, M., Stratonovich, P., Streck, T.,
Thorburn, P. J., Waha, K., Wall, G. W., White, J. W., Zhao, Z., and Zhu, Y.:
Crop model improvement reduces the uncertainty of the response to
temperature of multi-model ensembles, Field Crop Res., 202, 5–20, 2017.
MOA: Status Paper on Wheat, Directorate of Wheat Development, Ministry of
Agriculture, Govt. of India, 180 pp., available at: https://www.nfsm.gov.in/StatusPaper/Wheat2016.pdf (last access: 14 April 2019), 2016.
Monfreda, C., Ramankutty, N., and Foley, J. A.: Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net
primary production in the year 2000, Global Biogeochem. Cy., 22, GB1022,
https://doi.org/10.1029/2007GB002947, 2008.
Mueller, N. D., Gerber, J. S., Johnston, M., Ray, D. K., Ramankutty, N., and
Foley, J. A.: Closing yield gaps through nutrient and water management,
Nature, 490, 254–257, 2012.
Myers, S. S., Smith, M. R., Guth, S., Golden, C. D., Vaitla, B., Mueller, N.
D., Dangour, A. D., and Huybers, P.: Climate change and global food systems:
potential impacts on food security and undernutrition, Annu. Rev. Publ.
Health, 38, 259–277, 2017.
NFSM: Crop Calendar by National Food Security Mission (NFSM), Ministry of
Agriculture and Farmers Welfare, Government of India, available at:
https://nfsm.gov.in/nfmis/rpt/calenderreport.aspx, last access: 5 January 2018.
Ortiz, R., Sayre, K. D., Govaerts, B., Gupta, R., Subbarao, G. V., Ban, T.,
Hodson, D., Dixon, J. M., Ortiz-Monasterio, J. I., and Reynolds, M.: Climate
change: Can wheat beat the heat?, Agr. Ecosyst. Environ., 126, 46–58, 2008.
Ren, X., Weitzel, M., O'Neill, B. C., Lawrence, P., Meiyappan, P., Levis, S., Balistreri, E. J., and Dalton, M.: Avoided economic impacts of climate change on agriculture: integrating a land surface model (CLM) with a global economic model (iPETS), Climatic Change, 146, 517–531, 2018.
Rosenzweig, C., Elliott, J., Deryng, D., Ruane, A. C., Müller, C.,
Arneth, A., Boote, K. J., Folberth, C., Glotter, M., Khabarov, N., Neumann,
K., Piontek, F., Pugh, T. A. M., Schmid, E., Stehfest, E., Yang, H., and
Jones, J. W.: Assessing agricultural risks of climate change in the 21st
century in a global gridded crop model intercomparison, P. Natl. Acad. Sci.
USA, 111, 3268–3273, 2014.
Sacks, W. J., Deryng, D., Foley, J. A., and Ramankutty, N.: Crop planting
dates: an analysis of global patterns, Global Ecol. Biogeogr., 19, 607–620,
2010.
Sen, P. K.: Estimates of the regression coefficient based on Kendall's
tau, J. Am. Stat. Assoc. 63, 1379–1389, 1968.
Siebert, S., Kummu, M., Porkka, M., Döll, P., Ramankutty, N., and Scanlon, B. R.: A global data set of the extent of irrigated land from 1900 to 2005, Hydrol. Earth Syst. Sci., 19, 1521–1545, https://doi.org/10.5194/hess-19-1521-2015, 2015.
Song, Y., Jain, A. K., and McIsaac, G. F.: Implementation of dynamic crop growth processes into a land surface model: evaluation of energy, water and carbon fluxes under corn and soybean rotation, Biogeosciences, 10, 8039–8066, https://doi.org/10.5194/bg-10-8039-2013, 2013.
Song, Y., Jain, A. K., Landuyt, W., Kheshgi, H. S., and Khanna, M.:
Estimates of biomass yield for perennial bioenergy grasses in the USA,
BioEnerg. Res., 8, 688–715, 2015.
Song, Y., Cervarich, M., Jain, A. K., Kheshgi, H. S., Landuyt, W., and Cai,
X.: The interplay between bioenergy grass production and water resources in
the United States of America, Environ. Sci. Technol., 50, 3010–3019, 2016.
Stratonovitch, P. and Semenov, M. A.: Heat tolerance around flowering in
wheat identified as a key trait for increased yield potential in Europe
under climate change, J. Exp. Bot., 66, 3599–3609, 2015.
Tack, J., Barkley, A., and Hendricks, N.: Irrigation offsets wheat yield
reductions from warming temperatures, Environ. Res. Lett., 12, 114027, https://doi.org/10.1088/1748-9326/aa8d27, 2017.
USDA: India Grain and Feed Annual 2018, Global Agriculture Information
Network Report Number IN8027, USDA Foreign Agriculture Service, available at: https://gain.fas.usda.gov/Recent GAIN Publications/Grain and Feed Annual_New Delhi_India_3-16-2018.pdf (last access: 20 April 2019), 2018.
Viovy, N.: CRUNCEP Version 7 – Atmospheric Forcing Data for the Community
Land Model, Research Data Archive at the National Center for Atmospheric
Research, Computational and Information Systems Laboratory, https://doi.org/10.5065/PZ8F-F017, 2018.
Zhao, G., Bryan, B. A., and Song, X.: Sensitivity and uncertainty analysis
of the APSIM-wheat model: Interactions between cultivar, environmental, and
management parameters, Ecol. Model., 279, 1–11, 2014.
Zhao, H., Dai, T., Jing, Q., Jiang, D., and Cao, W.: Leaf senescence and grain filling affected by post-anthesis high temperatures in two different wheat cultivars, Plant Growth Regul., 51, 149–158, 2007.
Zohaib, M., Kim, H., and Choi, M.: Detecting global irrigated areas using
satellite and reanalysis products, Sci. Total Environ., 677, 679–691, 2019.
Short summary
Spring wheat, a staple for millions of people in India and the world, is vulnerable to changing environmental and management factors. Using a new spring wheat model, we find that over the 1980–2016 period elevated CO2 levels, irrigation, and nitrogen fertilizers led to an increase of 30 %, 12 %, and 15 % in countrywide production, respectively. In contrast, rising temperatures have reduced production by 18 %. These effects vary across the country, thereby affecting production at regional scales.
Spring wheat, a staple for millions of people in India and the world, is vulnerable to changing...
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