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51. Is the climate change mitigation effect of enhanced silicate weathering governed by biological processes?

Author

Vicca, Sara, primary, Goll, Daniel S., additional, Hagens, Mathilde, additional, Hartmann, Jens, additional, Janssens, Ivan A., additional, Neubeck, Anna, additional, Peñuelas, Josep, additional, Poblador, Sílvia, additional, Rijnders, Jet, additional, Sardans, Jordi, additional, Struyf, Eric, additional, Swoboda, Philipp, additional, van Groenigen, Jan Willem, additional, Vienne, Arthur, additional, and Verbruggen, Erik, additional

Published
2021
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52. Assessing the representation of the Australian carbon cycle in global vegetation models

Author

Teckentrup, Lina, primary, De Kauwe, Martin G., additional, Pitman, Andrew J., additional, Goll, Daniel S., additional, Haverd, Vanessa, additional, Jain, Atul K., additional, Joetzjer, Emilie, additional, Kato, Etsushi, additional, Lienert, Sebastian, additional, Lombardozzi, Danica, additional, McGuire, Patrick C., additional, Melton, Joe R., additional, Nabel, Julia E. M. S., additional, Pongratz, Julia, additional, Sitch, Stephen, additional, Walker, Anthony P., additional, and Zaehle, Sönke, additional

Published
2021
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53. Global maps and factors driving forest foliar elemental composition: the importance of evolutionary history

Author

Vallicrosa, Helena, primary, Sardans, Jordi, additional, Maspons, Joan, additional, Zuccarini, Paolo, additional, Fernández‐Martínez, Marcos, additional, Bauters, Marijn, additional, Goll, Daniel S., additional, Ciais, Philippe, additional, Obersteiner, Michael, additional, Janssens, Ivan A., additional, and Peñuelas, Josep, additional

Published
2021
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54. Response to Comments on “Recent global decline of CO 2 fertilization effects on vegetation photosynthesis”

Author

Wang, Songhan, primary, Zhang, Yongguang, additional, Ju, Weimin, additional, Chen, Jing M., additional, Cescatti, Alessandro, additional, Sardans, Jordi, additional, Janssens, Ivan A., additional, Wu, Mousong, additional, Berry, Joseph A., additional, Campbell, J. Elliott, additional, Fernández-Martínez, Marcos, additional, Alkama, Ramdane, additional, Sitch, Stephen, additional, Smith, William K., additional, Yuan, Wenping, additional, He, Wei, additional, Lombardozzi, Danica, additional, Kautz, Markus, additional, Zhu, Dan, additional, Lienert, Sebastian, additional, Kato, Etsushi, additional, Poulter, Benjamin, additional, Sanders, Tanja G. M., additional, Krüger, Inken, additional, Wang, Rong, additional, Zeng, Ning, additional, Tian, Hanqin, additional, Vuichard, Nicolas, additional, Jain, Atul K., additional, Wiltshire, Andy, additional, Goll, Daniel S., additional, and Peñuelas, Josep, additional

Published
2021
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55. Slowdown of the greening trend in natural vegetation with further rise in atmospheric CO<sub>2</sub>

Author

Winkler, Alexander J., primary, Myneni, Ranga B., additional, Hannart, Alexis, additional, Sitch, Stephen, additional, Haverd, Vanessa, additional, Lombardozzi, Danica, additional, Arora, Vivek K., additional, Pongratz, Julia, additional, Nabel, Julia E. M. S., additional, Goll, Daniel S., additional, Kato, Etsushi, additional, Tian, Hanqin, additional, Arneth, Almut, additional, Friedlingstein, Pierre, additional, Jain, Atul K., additional, Zaehle, Sönke, additional, and Brovkin, Victor, additional

Published
2021
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56. A global map of root biomass across the world's forests

Author

Huang, Yuanyuan, primary, Ciais, Phillipe, additional, Santoro, Maurizio, additional, Makowski, David, additional, Chave, Jerome, additional, Schepaschenko, Dmitry, additional, Abramoff, Rose Z., additional, Goll, Daniel S., additional, Yang, Hui, additional, Chen, Ye, additional, Wei, Wei, additional, and Piao, Shilong, additional

Published
2021
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60. Insights on Nitrogen and Phosphorus Co‐Limitation in Global Croplands From Theoretical and Modeling Fertilization Experiments

Author

Ringeval, Bruno, primary, Kvakić, Marko, additional, Augusto, Laurent, additional, Ciais, Philippe, additional, Goll, Daniel S., additional, Mueller, Nathaniel D., additional, Müller, Christoph, additional, Nesme, Thomas, additional, Vuichard, Nicolas, additional, Wang, Xuhui, additional, and Pellerin, Sylvain, additional

Published
2021
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61. Response to comments on 'Recent global decline of CO₂ fertilization effects on vegetation photosynthesis'

Author

Wang, Songhan, Zhang, Yongguang, Ju, Weimin, Chen, Jing M., Cescatti, Alessandro, Sardans, Jordi, Janssens, Ivan, Wu, Mousong, Berry, Joseph A., Campbell, J. Elliott, Fernandez-Martinez, Marcos, Alkama, Ramdane, Sitch, Stephen, Smith, William K., Yuan, Wenping, He, Wei, Lombardozzi, Danica, Kautz, Markus, Zhu, Dan, Lienert, Sebastian, Kato, Etsushi, Poulter, Benjamin, Sanders, Tanja G. M., Krueger, Inken, Wang, Rong, Zeng, Ning, Tian, Hanqin, Vuichard, Nicolas, Jain, Atul K., Wiltshire, Andy, Goll, Daniel S., and Penuelas, Josep

Subjects
Biology, Engineering sciences. Technology
Abstract

Our study suggests that the global CO2 fertilization effect (CFE) on vegetation photosynthesis has declined during the past four decades. The Comments suggest that the temporal inconsistency in AVHRR data and the attribution method undermine the results' robustness. Here, we provide additional evidence that these arguments did not affect our finding and that the global decline in CFE is robust.

Published
2021

62. Global patterns and drivers of soil total phosphorus concentration

Author

He, Xianjin, Augusto, Laurent, Goll, Daniel S., Ringeval, Bruno, Wang, Yingping, Helfenstein, Julian, Huang, Yuanyuan, Yu, Kailiang, Wang, Zhiqiang, Yang, Yongchuan, Hou, Enqing, He, Xianjin, Augusto, Laurent, Goll, Daniel S., Ringeval, Bruno, Wang, Yingping, Helfenstein, Julian, Huang, Yuanyuan, Yu, Kailiang, Wang, Zhiqiang, Yang, Yongchuan, and Hou, Enqing

Abstract

Soil represents the largest phosphorus (P) stock in terrestrial ecosystems. Determining the amount of soil P is a critical first step in identifying sites where ecosystem functioning is potentially limited by soil P availability. However, global patterns and predictors of soil total P concentration remain poorly understood. To address this knowledge gap, we constructed a database of total P concentration of 5275 globally distributed (semi-)natural soils from 761 published studies. We quantified the relative importance of 13 soil-forming variables in predicting soil total P concentration and then made further predictions at the global scale using a random forest approach. Soil total P concentration varied significantly among parent material types, soil orders, biomes, and continents and ranged widely from 1.4 to 9630.0 (median 430.0 and mean 570.0) kg-1 across the globe. About two-thirds (65%) of the global variation was accounted for by the 13 variables that we selected, among which soil organic carbon concentration, parent material, mean annual temperature, and soil sand content were the most important ones. While predicted soil total P concentrations increased significantly with latitude, they varied largely among regions with similar latitudes due to regional differences in parent material, topography, and/or climate conditions. Soil P stocks (excluding Antarctica) were estimated to be 26.8±3.1 (mean±standard deviation)Pg and 62.2±8.9Pg (1PgCombining double low line1×1015g) in the topsoil (0-30cm) and subsoil (30-100cm), respectively. Our global map of soil total P concentration as well as the underlying drivers of soil total P concentration can be used to constraint Earth system models that represent the P cycle and to inform quantification of global soil P availability. Raw datasets and global maps generated in this study are available at 10.6084/m9.figshare.14583375 (He et al., 2021).

Published
2021

63. Global evaluation of the nutrient-enabled version of the land surface model ORCHIDEE-CNP v1.2 (r5986)

Author

Sun, Yan, Goll, Daniel S., Chang, Jinfeng, Ciais, Philippe, Guenet, Betrand, Helfenstein, Julian, Huang, Yuanyuan, Lauerwald, Ronny, Maignan, Fabienne, Naipal, Victoria, Wang, Yilong, Yang, Hui, Zhang, Haicheng, Sun, Yan, Goll, Daniel S., Chang, Jinfeng, Ciais, Philippe, Guenet, Betrand, Helfenstein, Julian, Huang, Yuanyuan, Lauerwald, Ronny, Maignan, Fabienne, Naipal, Victoria, Wang, Yilong, Yang, Hui, and Zhang, Haicheng

Abstract

The availability of phosphorus (P) and nitrogen (N) constrains the ability of ecosystems to use resources such as light, water and carbon. In turn, nutrients impact the distribution of productivity, ecosystem carbon turnovers and their net exchange of CO2 with the atmosphere in response to variation of environmental conditions in both space and time. In this study, we evaluated the performance of the global version of the land surface model ORCHIDEE-CNP (v1.2), which explicitly simulates N and P biogeochemistry in terrestrial ecosystems coupled with carbon, water and energy transfers. We used data from remote sensing, ground-based measurement networks and ecological databases. Components of the N and P cycle at different levels of aggregation (from local to global) are in good agreement with datadriven estimates. When integrated for the period 1850 to 2017 forced with variable climate, rising CO2 and land use change, we show that ORCHIDEE-CNP underestimates the land carbon sink in the Northern Hemisphere (NH) during recent decades despite an a priori realistic gross primary productivity (GPP) response to rising CO2. This result suggests either that processes other than CO2 fertilization, which are omitted in ORCHIDEE-CNP such as changes in biomass turnover, are predominant drivers of the northern land sink and/or that the model parameterizations produce emerging nutrient limitations on biomass growth that are too strict in northern areas. In line with the latter, we identified biases in the simulated large-scale patterns of leaf and soil stoichiometry as well as plant P use efficiency, pointing towards P limitations that are too severe towards the poles. Based on our analysis of ecosystem resource use efficiencies and nutrient cycling, we propose ways to address the model biases by giving priority to better representing processes of soil organic P mineralization and soil inorganic P transformation, followed by refining the biomass production efficiency under increas

Published
2021

64. Modelled land use and land cover change emissions – a spatio-temporal comparison of different approaches

Author

Obermeier, Wolfgang A., primary, Nabel, Julia E. M. S., additional, Loughran, Tammas, additional, Hartung, Kerstin, additional, Bastos, Ana, additional, Havermann, Felix, additional, Anthoni, Peter, additional, Arneth, Almut, additional, Goll, Daniel S., additional, Lienert, Sebastian, additional, Lombardozzi, Danica, additional, Luyssaert, Sebastiaan, additional, McGuire, Patrick C., additional, Melton, Joe R., additional, Poulter, Benjamin, additional, Sitch, Stephen, additional, Sullivan, Michael O., additional, Tian, Hanqin, additional, Walker, Anthony P., additional, Wiltshire, Andrew J., additional, Zaehle, Soenke, additional, and Pongratz, Julia, additional

Published
2021
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65. Greening drylands despite warming consistent with carbon dioxide fertilization effect

Author

Gonsamo, Alemu, primary, Ciais, Philippe, additional, Miralles, Diego G., additional, Sitch, Stephen, additional, Dorigo, Wouter, additional, Lombardozzi, Danica, additional, Friedlingstein, Pierre, additional, Nabel, Julia E. M. S., additional, Goll, Daniel S., additional, O'Sullivan, Michael, additional, Arneth, Almut, additional, Anthoni, Peter, additional, Jain, Atul K., additional, Wiltshire, Andy, additional, Peylin, Philippe, additional, and Cescatti, Alessandro, additional

Published
2021
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66. Five years of variability in the global carbon cycle: comparing an estimate from the Orbiting Carbon Observatory-2 and process-based models

Author

Chen, Zichong, primary, Huntzinger, Deborah N, additional, Liu, Junjie, additional, Piao, Shilong, additional, Wang, Xuhui, additional, Sitch, Stephen, additional, Friedlingstein, Pierre, additional, Anthoni, Peter, additional, Arneth, Almut, additional, Bastrikov, Vladislav, additional, Goll, Daniel S, additional, Haverd, Vanessa, additional, Jain, Atul K, additional, Joetzjer, Emilie, additional, Kato, Etsushi, additional, Lienert, Sebastian, additional, Lombardozzi, Danica L, additional, McGuire, Patrick C, additional, Melton, Joe R, additional, Nabel, Julia E M S, additional, Pongratz, Julia, additional, Poulter, Benjamin, additional, Tian, Hanqin, additional, Wiltshire, Andrew J, additional, Zaehle, Sönke, additional, and Miller, Scot M, additional

Published
2021
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67. Global evaluation of the nutrient-enabled version of the land surface model ORCHIDEE-CNP v1.2 (r5986)

Author

Sun, Yan, primary, Goll, Daniel S., additional, Chang, Jinfeng, additional, Ciais, Philippe, additional, Guenet, Betrand, additional, Helfenstein, Julian, additional, Huang, Yuanyuan, additional, Lauerwald, Ronny, additional, Maignan, Fabienne, additional, Naipal, Victoria, additional, Wang, Yilong, additional, Yang, Hui, additional, and Zhang, Haicheng, additional

Published
2021
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69. Estimates of mean residence times of phosphorus in commonly considered inorganic soil phosphorus pools

Author

Helfenstein, Julian, Pistocchi, Chiara, Oberson, Astrid, Tamburini, Federica, Goll, Daniel S., Frossard, Emmanuel, Institute of Agricultural Sciences [Zürich], Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Ecologie fonctionnelle et biogéochimie des sols et des agro-écosystèmes (UMR Eco&Sols), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, ddc:570, Life Science, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment
Abstract

Quantification of turnover of inorganic soil phosphorus (P) pools is essential to improve our understanding of P cycling in soil–plant systems and improve representations of the P cycle in land surface models. Turnover can be quantified using mean residence time (MRT); however, to date there is little information on MRT of P in soil P pools. We introduce an approach to quantify MRT of P in sequentially extracted inorganic soil P pools using data from isotope exchange kinetic experiments. Our analyses of 53 soil samples from the literature showed that MRT of labile P (resin- and bicarbonate-extractable P) was on the order of minutes to hours for most soils, MRT in NaOH-extractable P (NaOH-P) was in the range of days to months, and MRT in HCl-extractable P (HCl-P) was on the order of years to millennia. Multiple-regression models were able to capture 54 %–63 % of the variability in MRT among samples and showed that land use was the most important predictor of MRT of P in labile and NaOH pools. MRT of P in HCl-P was strongly dependent on pH, as high-pH soils tended to have longer MRTs. This was interpreted to be related to the composition of HCl-P. Under high pH, HCl-P contains mostly apatite, with a low solubility, whereas under low-pH conditions, HCl-P may contain more exchangeable P forms. These results suggest that current land surface models underestimate the dynamics of inorganic soil P pools and could be improved by reducing model MRTs of the labile and NaOH-P pools, considering soil-type-dependent MRTs rather than universal exchange rates and allowing for two-way exchange between HCl-P and the soil solution. ISSN:1726-4170 ISSN:1726-4170

Published
2020

70. Recent global decline of CO₂ fertilization effects on vegetation photosynthesis

Author

Wang, Songhan, Zhang, Yongguang, Ju, Weimin, Chen, Jing M., Ciais, Philippe, Cescatti, Alessandro, Sardans, Jordi, Janssens, Ivan, Wu, Mousong, Berry, Joseph A., Campbell, Elliott, Fernandez-Martinez, Marcos, Alkama, Ramdane, Sitch, Stephen, Friedlingstein, Pierre, Smith, William K., Yuan, Wenping, He, Wei, Lombardozzi, Danica, Kautz, Markus, Zhu, Dan, Lienert, Sebastian, Kato, Etsushi, Poulter, Benjamin, Sanders, Tanja G. M., Krüger, Inken, Wang, Rong, Zeng, Ning, Tian, Hanqin, Vuichard, Nicolas, Jain, Atul K., Wiltshire, Andy, Haverd, Vanessa, Goll, Daniel S., and Peñuelas, Josep

Subjects
Biology, Engineering sciences. Technology
Abstract

The enhanced vegetation productivity driven by increased concentrations of carbon dioxide (CO2) [i.e., the CO2 fertilization effect (CFE)] sustains an important negative feedback on climate warming, but the temporal dynamics of CFE remain unclear. Using multiple long-term satellite- and ground-based datasets, we showed that global CFE has declined across most terrestrial regions of the globe from 1982 to 2015, correlating well with changing nutrient concentrations and availability of soil water. Current carbon cycle models also demonstrate a declining CFE trend, albeit one substantially weaker than that from the global observations. This declining trend in the forcing of terrestrial carbon sinks by increasing amounts of atmospheric CO2 implies a weakening negative feedback on the climatic system and increased societal dependence on future strategies to mitigate climate warming.

Published
2020

71. Spatial Pattern and Environmental Drivers of Acid Phosphatase Activity in Europe

Author

Sun, Yan, Goll, Daniel S., Ciais, Philippe, Peng, Shushi, Margalef, Olga, Asensio, Dolores, Sardans i Galobart, Jordi, and Peñuelas, Josep

Subjects
Big Data, soil acid phosphatase, Soil acid phosphatase, Phosphorus cycling, Partial correlation analysis, partial correlation analysis, Back-propagation artificial network, Regression tree, back-propagation artificial network, Europe, Artificial Intelligence, Computer Science (miscellaneous), ddc:550, regression tree, Information Systems, Original Research, phosphorus cycling
Abstract

Acid phosphatase produced by plants and microbes plays a fundamental role in the recycling of soil phosphorus (P). A quantification of the spatial variation in potential acid phosphatase activity (AP) on large spatial scales and its drivers can help to reduce the uncertainty in our understanding of bio-availability of soil P. We applied two machine-learning methods (Random forests and back-propagation artificial networks) to simulate the spatial patterns of AP across Europe by scaling up 126 site observations of potential AP activity from field samples measured in the laboratory, using 12 environmental drivers as predictors. The back-propagation artificial network (BPN) method explained 58% of AP variability, more than the regression tree model (49%). In addition, BPN was able to identify the gradients in AP along three transects in Europe. Partial correlation analysis revealed that soil nutrients (total nitrogen, total P, and labile organic P) and climatic controls (annual precipitation, mean annual temperature, and temperature amplitude) were the dominant factors influencing AP variations in space. Higher AP occurred in regions with higher mean annual temperature, precipitation and higher soil total nitrogen. Soil TP and Po were non-monotonically correlated with modeled AP for Europe, indicating diffident strategies of P utilization by biomes in arid and humid area. This study helps to separate the influences of each factor on AP production and to reduce the uncertainty in estimating soil P availability. The BPN model trained with European data, however, could not produce a robust global map of AP due to the lack of representative measurements of AP for tropical regions. Filling this data gap will help us to understand the physiological basis of P-use strategies in natural soils.

Published
2020

72. Scaling carbon fluxes from eddy covariance sites to globe : Synthesis and evaluation of the FLUXCOM approach

Author

Jung, Martin, Schwalm, Christopher, Migliavacca, Mirco, Walther, Sophia, Camps-Valls, Gustau, Koirala, Sujan, Anthoni, Peter, Besnard, Simon, Bodesheim, Paul, Carvalhais, Nuno, Chevallier, Frederic, Gans, Fabian, Goll, Daniel S., Haverd, Vanessa, Köhler, Philipp, Ichii, Kazuhito, Jain, Atul K., Liu, Junzhi, Lombardozzi, Danica, Nabel, Julia E.M.S., Nelson, Jacob A., O'Sullivan, Michael, Pallandt, Martijn, Papale, Dario, Peters, Wouter, Pongratz, Julia, Rödenbeck, Christian, Sitch, Stephen, Tramontana, Gianluca, Walker, Anthony, Weber, Ulrich, Reichstein, Markus, Jung, Martin, Schwalm, Christopher, Migliavacca, Mirco, Walther, Sophia, Camps-Valls, Gustau, Koirala, Sujan, Anthoni, Peter, Besnard, Simon, Bodesheim, Paul, Carvalhais, Nuno, Chevallier, Frederic, Gans, Fabian, Goll, Daniel S., Haverd, Vanessa, Köhler, Philipp, Ichii, Kazuhito, Jain, Atul K., Liu, Junzhi, Lombardozzi, Danica, Nabel, Julia E.M.S., Nelson, Jacob A., O'Sullivan, Michael, Pallandt, Martijn, Papale, Dario, Peters, Wouter, Pongratz, Julia, Rödenbeck, Christian, Sitch, Stephen, Tramontana, Gianluca, Walker, Anthony, Weber, Ulrich, and Reichstein, Markus

Abstract

FLUXNET comprises globally distributed eddy-covariance-based estimates of carbon fluxes between the biosphere and the atmosphere. Since eddy covariance flux towers have a relatively small footprint and are distributed unevenly across the world, upscaling the observations is necessary to obtain global-scale estimates of biosphere-atmosphere exchange. Based on cross-consistency checks with atmospheric inversions, sun-induced fluorescence (SIF) and dynamic global vegetation models (DGVMs), here we provide a systematic assessment of the latest upscaling efforts for gross primary production (GPP) and net ecosystem exchange (NEE) of the FLUXCOM initiative, where different machine learning methods, forcing data sets and sets of predictor variables were employed. Spatial patterns of mean GPP are consistent across FLUXCOM and DGVM ensembles ( at 1 spatial resolution) while the majority of DGVMs show, for 70 of the land surface, values outside the FLUXCOM range. Global mean GPP magnitudes for 2008-2010 from FLUXCOM members vary within 106 and 130 PgC class with the largest uncertainty in the tropics. Seasonal variations in independent SIF estimates agree better with FLUXCOM GPP (mean global pixel-wise) than with GPP from DGVMs (mean global pixel-wise). Seasonal variations in FLUXCOM NEE show good consistency with atmospheric inversion-based net land carbon fluxes, particularly for temperate and boreal regions. Interannual variability of global NEE in FLUXCOM is underestimated compared to inversions and DGVMs. The FLUXCOM version which also uses meteorological inputs shows a strong co-variation in interannual patterns with inversions (for 2001-2010). Mean regional NEE from FLUXCOM shows larger uptake than inversion and DGVM-based estimates, particularly in the tropics with discrepancies of up to several hundred grammes of carbon per square metre per year. These discrepancies can only partly be reconciled by carbon loss pathways that are implicit in inversions but not captured

Published
2020

76. Slow-down of the greening trend in natural vegetation with further rise in atmospheric CO2

Author

Winkler, Alexander J., primary, Myneni, Ranga B., additional, Hannart, Alexis, additional, Sitch, Stephen, additional, Haverd, Vanessa, additional, Lombardozzi, Danica, additional, Arora, Vivek K., additional, Pongratz, Julia, additional, Nabel, Julia E. M. S., additional, Goll, Daniel S., additional, Kato, Etsushi, additional, Tian, Hanqin, additional, Arneth, Almut, additional, Friedlingstein, Pierre, additional, Jain, Atul K., additional, Zaehle, Sönke, additional, and Brovkin, Victor, additional

Published
2021
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77. Supplementary material to "Slow-down of the greening trend in natural vegetation with further rise in atmospheric CO2"

Author

Winkler, Alexander J., primary, Myneni, Ranga B., additional, Hannart, Alexis, additional, Sitch, Stephen, additional, Haverd, Vanessa, additional, Lombardozzi, Danica, additional, Arora, Vivek K., additional, Pongratz, Julia, additional, Nabel, Julia E. M. S., additional, Goll, Daniel S., additional, Kato, Etsushi, additional, Tian, Hanqin, additional, Arneth, Almut, additional, Friedlingstein, Pierre, additional, Jain, Atul K., additional, Zaehle, Sönke, additional, and Brovkin, Victor, additional

Published
2021
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78. Recent global decline of CO 2 fertilization effects on vegetation photosynthesis

Author

Wang, Songhan, primary, Zhang, Yongguang, additional, Ju, Weimin, additional, Chen, Jing M., additional, Ciais, Philippe, additional, Cescatti, Alessandro, additional, Sardans, Jordi, additional, Janssens, Ivan A., additional, Wu, Mousong, additional, Berry, Joseph A., additional, Campbell, Elliott, additional, Fernández-Martínez, Marcos, additional, Alkama, Ramdane, additional, Sitch, Stephen, additional, Friedlingstein, Pierre, additional, Smith, William K., additional, Yuan, Wenping, additional, He, Wei, additional, Lombardozzi, Danica, additional, Kautz, Markus, additional, Zhu, Dan, additional, Lienert, Sebastian, additional, Kato, Etsushi, additional, Poulter, Benjamin, additional, Sanders, Tanja G. M., additional, Krüger, Inken, additional, Wang, Rong, additional, Zeng, Ning, additional, Tian, Hanqin, additional, Vuichard, Nicolas, additional, Jain, Atul K., additional, Wiltshire, Andy, additional, Haverd, Vanessa, additional, Goll, Daniel S., additional, and Peñuelas, Josep, additional

Published
2020
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79. Global soil organic carbon changes and economic revenues with biochar application.

Author

Han, Mengjie, Zhao, Qing, Li, Wei, Ciais, Philippe, Wang, Ying‐Ping, Goll, Daniel S., Zhu, Lei, Zhao, Zhe, Wang, Jingmeng, Wei, Yuan, and Wu, Fengchang

Subjects
BIOCHAR, CARBON in soils, ECONOMIC change, CROP residues, PLANT capacity, CARBON pricing, CARBON emissions, FERTILITY clinics
Abstract

Biochar has been proposed as a promising negative CO2 emission technology to mitigate future climate change with the additional benefit of increasing agricultural production. However, the spatial responses of soil organic carbon (SOC) to biochar addition in cropland are still uncertain, and the economic feasibility of large‐scale biochar implementation remains unclear. Here, we analyzed the response of SOC to biochar addition using 389 paired field measurements. The results show that biochar addition significantly increased SOC by 45.8% on average with large regional variations. Using a random forest model trained with soil, climate, biotic, biochar, and management factors, we found that the response of SOC to biochar addition was mainly dependent on biochar application rates, initial SOC, edaphic (e.g., pH), and climatic (e.g., mean annual precipitation) variables. Combined with the predicted SOC changes to biochar addition on the global cropland, we assessed the revenue of the biochar system based on the current and potential pyrolysis plants in the world using the life‐cycle analysis. Net revenue of the currently existing 144 pyrolysis plants increases with larger plant capacity and higher carbon price. Potential revenue of building new plants is high in regions like America and Europe but low in regions with infertile soil, low crop residues availability, and inconvenient transportation. The global CO2 removal of biochar application is 6.6 Tg CO2e (CO2 equivalent) year−1 with a net revenue of $ 177 million dollars at a carbon price of $ 50 t−1 CO2 for current pyrolysis plants with a biomass‐processing capacity of 20,000 t year−1. Our study provides a full economic assessment of idealized biochar addition scenarios and identifies the locations with maximal potential revenues with new pyrolysis plants. [ABSTRACT FROM AUTHOR]

Published
2022
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80. Toward a Global Model for Soil Inorganic Phosphorus Dynamics: Dependence of Exchange Kinetics and Soil Bioavailability on Soil Physicochemical Properties.

Author

Ying-Ping Wang, Yuanyuan Huang, Augusto, Laurent, Goll, Daniel S., Helfenstein, Julian, and Enqing Hou

Subjects
PHOSPHORUS in soils, SOILS, BIOAVAILABILITY, SOIL dynamics, SOIL sampling, SOIL solutions
Abstract

The representation of phosphorus (P) cycling in global land models remains quite simplistic, particularly on soil inorganic phosphorus. For example, sorption and desorption remain unresolved and their dependence on soil physical and chemical properties is ignored. Empirical parameter values are usually based on expert knowledge or data from few sites with debatable global representativeness in most global land models. To overcome these issues, we compiled from data of inorganic soil P fractions and calculated the fraction of added P remaining in soil solution over time of 147 soil samples to optimize three parameters in a model of soil inorganic P dynamics. The calibrated model performed well (r 2 > 0.7 for 122 soil samples). Model parameters vary by several orders of magnitude, and correlate with soil P fractions of different inorganic pools, soil organic carbon and oxalate extractable metal oxide concentrations among the soil samples. The modeled bioavailability of soil P depends on, not only, the desorption rates of labile and sorbed pool, inorganic phosphorus fractions, the slope of P sorbed against solution P concentration, but also on the ability of biological uptake to deplete solution P concentration and the time scale. The model together with the empirical relationships of model parameters on soil properties can be used to quantify bioavailability of soil inorganic P on various timescale especially when coupled within global land models. [ABSTRACT FROM AUTHOR]

Published
2022
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81. Global evaluation of nutrient enabled version land surface model ORCHIDEE-CNP v1.2 (r5986)

Author

Sun, Yan, primary, Goll, Daniel S., additional, Chang, Jinfeng, additional, Ciais, Philippe, additional, Guenet, Betrand, additional, Helfenstein, Julian, additional, Huang, Yuanyuan, additional, Lauerwald, Ronny, additional, Maignan, Fabienne, additional, Naipal, Victoria, additional, Wang, Yilong, additional, Yang, Hui, additional, and Zhang, Haicheng, additional

Published
2020
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82. Supplementary material to "Global evaluation of nutrient enabled version land surface model ORCHIDEE-CNP v1.2 (r5986)"

Author

Sun, Yan, primary, Goll, Daniel S., additional, Chang, Jinfeng, additional, Ciais, Philippe, additional, Guenet, Betrand, additional, Helfenstein, Julian, additional, Huang, Yuanyuan, additional, Lauerwald, Ronny, additional, Maignan, Fabienne, additional, Naipal, Victoria, additional, Wang, Yilong, additional, Yang, Hui, additional, and Zhang, Haicheng, additional

Published
2020
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83. Empirical estimates of regional carbon budgets imply reduced global soil heterotrophic respiration

Author

Ciais, Philippe, primary, Yao, Yitong, additional, Gasser, Thomas, additional, Baccini, Alessandro, additional, Wang, Yilong, additional, Lauerwald, Ronny, additional, Peng, Shushi, additional, Bastos, Ana, additional, Li, Wei, additional, Raymond, Peter A, additional, Canadell, Josep G, additional, Peters, Glen P, additional, Andres, Rob J, additional, Chang, Jinfeng, additional, Yue, Chao, additional, Dolman, A Johannes, additional, Haverd, Vanessa, additional, Hartmann, Jens, additional, Laruelle, Goulven, additional, Konings, Alexandra G, additional, King, Anthony W, additional, Liu, Yi, additional, Luyssaert, Sebastiaan, additional, Maignan, Fabienne, additional, Patra, Prabir K, additional, Peregon, Anna, additional, Regnier, Pierre, additional, Pongratz, Julia, additional, Poulter, Benjamin, additional, Shvidenko, Anatoly, additional, Valentini, Riccardo, additional, Wang, Rong, additional, Broquet, Grégoire, additional, Yin, Yi, additional, Zscheischler, Jakob, additional, Guenet, Bertrand, additional, Goll, Daniel S, additional, Ballantyne, Ashley-P, additional, Yang, Hui, additional, Qiu, Chunjing, additional, and Zhu, Dan, additional

Published
2020
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84. Scaling carbon fluxes from eddy covariance sites to globe: synthesis and evaluation of the FLUXCOM approach

Author

Jung, Martin, primary, Schwalm, Christopher, additional, Migliavacca, Mirco, additional, Walther, Sophia, additional, Camps-Valls, Gustau, additional, Koirala, Sujan, additional, Anthoni, Peter, additional, Besnard, Simon, additional, Bodesheim, Paul, additional, Carvalhais, Nuno, additional, Chevallier, Frédéric, additional, Gans, Fabian, additional, Goll, Daniel S., additional, Haverd, Vanessa, additional, Köhler, Philipp, additional, Ichii, Kazuhito, additional, Jain, Atul K., additional, Liu, Junzhi, additional, Lombardozzi, Danica, additional, Nabel, Julia E. M. S., additional, Nelson, Jacob A., additional, O'Sullivan, Michael, additional, Pallandt, Martijn, additional, Papale, Dario, additional, Peters, Wouter, additional, Pongratz, Julia, additional, Rödenbeck, Christian, additional, Sitch, Stephen, additional, Tramontana, Gianluca, additional, Walker, Anthony, additional, Weber, Ulrich, additional, and Reichstein, Markus, additional

Published
2020
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85. Microbial dynamics and soil physicochemical properties explain large‐scale variations in soil organic carbon

Author

Zhang, Haicheng, primary, Goll, Daniel S., additional, Wang, Ying‐Ping, additional, Ciais, Philippe, additional, Wieder, William R., additional, Abramoff, Rose, additional, Huang, Yuanyuan, additional, Guenet, Bertrand, additional, Prescher, Anne‐Katrin, additional, Viscarra Rossel, Raphael A., additional, Barré, Pierre, additional, Chenu, Claire, additional, Zhou, Guoyi, additional, and Tang, Xuli, additional

Published
2020
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86. A global map of root biomass across the world’s forests

Author

Huang, Yuanyuan, primary, Ciais, Phillipe, additional, Santoro, Maurizio, additional, Makowski, David, additional, Chave, Jerome, additional, Schepaschenko, Dmitry, additional, Abramoff, Rose, additional, Goll, Daniel S., additional, Yang, Hui, additional, Chen, Ye, additional, Wei, Wei, additional, and Piao, Shilong, additional

Published
2020
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87. Global Carbon Budget 2019

Author

Friedlingstein, Pierre, primary, Jones, Matthew W., additional, O'Sullivan, Michael, additional, Andrew, Robbie M., additional, Hauck, Judith, additional, Peters, Glen P., additional, Peters, Wouter, additional, Pongratz, Julia, additional, Sitch, Stephen, additional, Le Quéré, Corinne, additional, Bakker, Dorothee C. E., additional, Canadell, Josep G., additional, Ciais, Philippe, additional, Jackson, Robert B., additional, Anthoni, Peter, additional, Barbero, Leticia, additional, Bastos, Ana, additional, Bastrikov, Vladislav, additional, Becker, Meike, additional, Bopp, Laurent, additional, Buitenhuis, Erik, additional, Chandra, Naveen, additional, Chevallier, Frédéric, additional, Chini, Louise P., additional, Currie, Kim I., additional, Feely, Richard A., additional, Gehlen, Marion, additional, Gilfillan, Dennis, additional, Gkritzalis, Thanos, additional, Goll, Daniel S., additional, Gruber, Nicolas, additional, Gutekunst, Sören, additional, Harris, Ian, additional, Haverd, Vanessa, additional, Houghton, Richard A., additional, Hurtt, George, additional, Ilyina, Tatiana, additional, Jain, Atul K., additional, Joetzjer, Emilie, additional, Kaplan, Jed O., additional, Kato, Etsushi, additional, Klein Goldewijk, Kees, additional, Korsbakken, Jan Ivar, additional, Landschützer, Peter, additional, Lauvset, Siv K., additional, Lefèvre, Nathalie, additional, Lenton, Andrew, additional, Lienert, Sebastian, additional, Lombardozzi, Danica, additional, Marland, Gregg, additional, McGuire, Patrick C., additional, Melton, Joe R., additional, Metzl, Nicolas, additional, Munro, David R., additional, Nabel, Julia E. M. S., additional, Nakaoka, Shin-Ichiro, additional, Neill, Craig, additional, Omar, Abdirahman M., additional, Ono, Tsuneo, additional, Peregon, Anna, additional, Pierrot, Denis, additional, Poulter, Benjamin, additional, Rehder, Gregor, additional, Resplandy, Laure, additional, Robertson, Eddy, additional, Rödenbeck, Christian, additional, Séférian, Roland, additional, Schwinger, Jörg, additional, Smith, Naomi, additional, Tans, Pieter P., additional, Tian, Hanqin, additional, Tilbrook, Bronte, additional, Tubiello, Francesco N., additional, van der Werf, Guido R., additional, Wiltshire, Andrew J., additional, and Zaehle, Sönke, additional

Published
2019
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88. Global maps and factors driving forest foliar elemental composition: the importance of evolutionary history.

Author

Vallicrosa, Helena, Sardans, Jordi, Maspons, Joan, Zuccarini, Paolo, Fernández‐Martínez, Marcos, Bauters, Marijn, Goll, Daniel S., Ciais, Philippe, Obersteiner, Michael, Janssens, Ivan A., and Peñuelas, Josep

Subjects
ATMOSPHERIC deposition, WOODY plants, PLANTS, EVOLUTIONARY models
Abstract

Summary: Consistent information on the current elemental composition of vegetation at global scale and the variables that determine it is lacking.To fill this gap, we gathered a total of 30 912 georeferenced records on woody plants foliar concentrations of nitrogen (N), phosphorus (P) and potassium (K) from published databases, and produced global maps of foliar N, P and K concentrations for woody plants using neural networks at a resolution of 1 km2. We used data for climate, atmospheric deposition, soil and morphoclimatic groups to train the neural networks.Foliar N, P and K do not follow clear global latitudinal patterns but are consistent with the hypothesis of soil substrate age. We additionally built generalized linear mixed models to investigate the evolutionary history effect together with the effects of environmental effects. In this comparison, evolutionary history effects explained most of the variability in all cases (mostly > 60%).These results emphasize the determinant role of evolutionary history in foliar elemental composition, which should be incorporated in upcoming dynamic global vegetation models. [ABSTRACT FROM AUTHOR]

Published
2022
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89. Global carbon budget 2019

Author

Friedlingstein, Pierre, Jones, Matthew W., O'Sullivan, Michael, Andrew, Robbie, Hauck, Judith, Peters, Glen Philip, Peters, Wouter, Pongratz, Julia, Sitch, Stephen, Le Quéré, Corinne, Bakker, Dorothée C.E., Canadell, Josep G., Ciais, Philippe, Jackson, Robert B., Anthoni, Peter, Barbero, Leticia, Bastos, Ana, Bastrikov, Vladislav, Becker, Meike, Bopp, Laurent, Buitenhuis, Erik, Chandra, Naveen, Chevallier, Frédéric, Chini, Louise P., Currie, Kim I., Feely, Richard A., Gehlen, Marion, Gilfillan, Dennis, Gkritzalis, Thanos, Goll, Daniel S., Gruber, Nicolas, Gutekunst, Sören, Harris, Ian, Haverd, Vanessa, Houghton, Richard A., Hurtt, George, Ilyina, Tatiana, Jain, Atul K., Joetzjer, Emilie, Kaplan, Jed O., Kato, Etsushi, Goldewijk, Kees Klein, Korsbakken, Jan Ivar, Landschutzer, Peter, Lauvset, Siv Kari, Lefevre, Nathalie, Lenton, Andrew, Lienert, Sebastian, Lombardozzi, Danica, Marland, Gregg, McGuire, Patrick C., Melton, Joe R., Metzl, Nicolas, Munro, David R., Nabel, Julia E.M.S., Nakaoka, Shin-Ichiro, Neill, Craig, Omar, Abdirahman, Ono, Tsuneo, Peregon, Anna, Pierrot, Denis, Poulter, Benjamin, Rehder, Gregor, Resplandy, Laure, Robertson, Eddy, Rödenbeck, Christian, Séférian, Roland, Schwinger, Jörg, Smith, Naomi, Tans, Pieter P., Tian, Hanqin, Tilbrook, Bronte, Tubiello, Francesco N., van der Werf, Guido R., Wiltshire, Andrew J., and Zaehle, Sönke

Abstract

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the “global carbon budget” – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (EFF) are based on energy statistics and cement production data, while emissions from land use change (ELUC), mainly deforestation, are based on land use and land use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2009–2018), EFF was 9.5±0.5 GtC yr−1, ELUC 1.5±0.7 GtC yr−1, GATM 4.9±0.02 GtC yr−1 (2.3±0.01 ppm yr−1), SOCEAN 2.5±0.6 GtC yr−1, and SLAND 3.2±0.6 GtC yr−1, with a budget imbalance BIM of 0.4 GtC yr−1 indicating overestimated emissions and/or underestimated sinks. For the year 2018 alone, the growth in EFF was about 2.1 % and fossil emissions increased to 10.0±0.5 GtC yr−1, reaching 10 GtC yr−1 for the first time in history, ELUC was 1.5±0.7 GtC yr−1, for total anthropogenic CO2 emissions of 11.5±0.9 GtC yr−1 (42.5±3.3 GtCO2). Also for 2018, GATM was 5.1±0.2 GtC yr−1 (2.4±0.1 ppm yr−1), SOCEAN was 2.6±0.6 GtC yr−1, and SLAND was 3.5±0.7 GtC yr−1, with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 407.38±0.1 ppm averaged over 2018. For 2019, preliminary data for the first 6–10 months indicate a reduced growth in EFF of +0.6 % (range of −0.2 % to 1.5 %) based on national emissions projections for China, the USA, the EU, and India and projections of gross domestic product corrected for recent changes in the carbon intensity of the economy for the rest of the world. Overall, the mean and trend in the five components of the global carbon budget are consistently estimated over the period 1959–2018, but discrepancies of up to 1 GtC yr−1 persist for the representation of semi-decadal variability in CO2 fluxes. A detailed comparison among individual estimates and the introduction of a broad range of observations shows (1) no consensus in the mean and trend in land use change emissions over the last decade, (2) a persistent low agreement between the different methods on the magnitude of the land CO2 flux in the northern extra-tropics, and (3) an apparent underestimation of the CO2 variability by ocean models outside the tropics. This living data update documents changes in the methods and data sets used in this new global carbon budget and the progress in understanding of the global carbon cycle compared with previous publications of this data set (Le Quéré et al., 2018a, b, 2016, 2015a, b, 2014, 2013). The data generated by this work are available at https://doi.org/10.18160/gcp-2019 (Friedlingstein et al., 2019).

Published
2019

90. Developments in the MPI‐M Earth System Model version 1.2 (MPI‐ESM1.2) and Its Response to Increasing CO

Author

Mauritsen, Thorsten, Bader, Jürgen, Becker, Tobias, Behrens, Jörg, Bittner, Matthias, Brokopf, Renate, Brovkin, Victor, Claussen, Martin, Crueger, Traute, Esch, Monika, Fast, Irina, Fiedler, Stephanie, Fläschner, Dagmar, Gayler, Veronika, Giorgetta, Marco, Goll, Daniel S., Haak, Helmuth, Hagemann, Stefan, Hedemann, Christopher, Hohenegger, Cathy, Ilyina, Tatiana, Jahns, Thomas, Jimenéz‐De‐La‐Cuesta, Diego, Jungclaus, Johann, Kleinen, Thomas, Kloster, Silvia, Kracher, Daniela, Kinne, Stefan, Kleberg, Deike, Lasslop, Gitta, Kornblueh, Luis, Marotzke, Jochem, Matei, Daniela, Meraner, Katharina, Mikolajewicz, Uwe, Modali, Kameswarrao, Möbis, Benjamin, Müller, Wolfgang A., Nabel, Julia E. M. S., Nam, Christine C. W., Notz, Dirk, Nyawira, Sarah‐Sylvia, Paulsen, Hanna, Peters, Karsten, Pincus, Robert, Pohlmann, Holger, Pongratz, Julia, Popp, Max, Raddatz, Thomas Jürgen, Rast, Sebastian, Redler, Rene, Reick, Christian H., Rohrschneider, Tim, Schemann, Vera, Schmidt, Hauke, Schnur, Reiner, Schulzweida, Uwe, Six, Katharina D., Stein, Lukas, Stemmler, Irene, Stevens, Bjorn, Storch, Jin‐Song, Tian, Fangxing, Voigt, Aiko, Vrese, Philipp, Wieners, Karl‐Hermann, Wilkenskjeld, Stiig, Winkler, Alexander, and Roeckner, Erich

Subjects
Earth sciences, ddc:550
Published
2019

91. Global carbon budget 2019

Author

Environmental Sciences, Friedlingstein, Pierre, Jones, Matthew W., O'Sullivan, Michael, Andrew, Robbie M., Hauck, Judith, Peters, Glen P., Peters, Wouter, Pongratz, Julia, Sitch, Stephen, Le Quéré, Corinne, DBakker, Orothee C.E., Canadell1, Josep G., Ciais1, Philippe, Jackson, Robert B., Anthoni1, Peter, Barbero, Leticia, Bastos, Ana, Bastrikov, Vladislav, Becker, Meike, Bopp, Laurent, Buitenhuis, Erik, Chandra, Naveen, Chevallier, Frédéric, Chini, Louise P., Currie, Kim I., Feely, Richard A., Gehlen, Marion, Gilfillan, Dennis, Gkritzalis, Thanos, Goll, Daniel S., Gruber, Nicolas, Gutekunst, Sören, Harris, Ian, Haverd, Vanessa, Houghton, Richard A., Hurtt, George, Ilyina, Tatiana, Jain, Atul K., Joetzjer, Emilie, Kaplan, Jed O., Kato, Etsushi, Goldewijk, Kees Klein, Korsbakken, Jan Ivar, Landschützer, Peter, Lauvset, Siv K., Lefèvre, Nathalie, Lenton, Andrew, Lienert, Sebastian, Lombardozzi, Danica, Marland, Gregg, McGuire, Patrick C., Melton, Joe R., Metzl, Nicolas, Munro, David R., Nabel, Julia E.M.S., Nakaoka, Shin Ichiro, Neill, Craig, Omar, Abdirahman M., Ono, Tsuneo, Peregon, Anna, Pierrot, Denis, Poulter, Benjamin, Rehder, Gregor, Resplandy, Laure, Robertson, Eddy, Rödenbeck, Christian, Séférian, Roland, Schwinger, Jörg, Smith, Naomi, Tans, Pieter P., Tian, Hanqin, Tilbrook, Bronte, Tubiello, Francesco N., Van Der Werf, Guido R., Wiltshire, Andrew J., Zaehle, Sönke, Environmental Sciences, Friedlingstein, Pierre, Jones, Matthew W., O'Sullivan, Michael, Andrew, Robbie M., Hauck, Judith, Peters, Glen P., Peters, Wouter, Pongratz, Julia, Sitch, Stephen, Le Quéré, Corinne, DBakker, Orothee C.E., Canadell1, Josep G., Ciais1, Philippe, Jackson, Robert B., Anthoni1, Peter, Barbero, Leticia, Bastos, Ana, Bastrikov, Vladislav, Becker, Meike, Bopp, Laurent, Buitenhuis, Erik, Chandra, Naveen, Chevallier, Frédéric, Chini, Louise P., Currie, Kim I., Feely, Richard A., Gehlen, Marion, Gilfillan, Dennis, Gkritzalis, Thanos, Goll, Daniel S., Gruber, Nicolas, Gutekunst, Sören, Harris, Ian, Haverd, Vanessa, Houghton, Richard A., Hurtt, George, Ilyina, Tatiana, Jain, Atul K., Joetzjer, Emilie, Kaplan, Jed O., Kato, Etsushi, Goldewijk, Kees Klein, Korsbakken, Jan Ivar, Landschützer, Peter, Lauvset, Siv K., Lefèvre, Nathalie, Lenton, Andrew, Lienert, Sebastian, Lombardozzi, Danica, Marland, Gregg, McGuire, Patrick C., Melton, Joe R., Metzl, Nicolas, Munro, David R., Nabel, Julia E.M.S., Nakaoka, Shin Ichiro, Neill, Craig, Omar, Abdirahman M., Ono, Tsuneo, Peregon, Anna, Pierrot, Denis, Poulter, Benjamin, Rehder, Gregor, Resplandy, Laure, Robertson, Eddy, Rödenbeck, Christian, Séférian, Roland, Schwinger, Jörg, Smith, Naomi, Tans, Pieter P., Tian, Hanqin, Tilbrook, Bronte, Tubiello, Francesco N., Van Der Werf, Guido R., Wiltshire, Andrew J., and Zaehle, Sönke

Published
2019

92. Global patterns and drivers of soil total phosphorus concentration.

Author

Xianjin He, Augusto, Laurent, Goll, Daniel S., Ringeval, Bruno, Yingping Wang, Helfenstein, Julian, Yuanyuan Huang, Kailiang Yu, Zhiqiang Wang, Yongchuan Yang, and Enqing Hou

Subjects
PHOSPHORUS in soils, RANDOM forest algorithms, KNOWLEDGE gap theory, TOPSOIL, SOIL mapping
Abstract

Soils represent the largest phosphorus (P) reserves on land and determining the amount is a critical first step for identifying sites where ecosystem functioning is potentially limited by soil P availability. However, global patterns and predictors of soil total P concentration remain poorly understood. To address this knowledge gap, we constructed a database of total P concentration of 5,275 globally distributed (semi-)natural soils from 761 published studies. We quantified the relative importance of 13 soil-forming variables in predicting soil total P concentration and then made further predictions at the global scale using a random forest approach. Soil total P concentration varied significantly among parent material types, soil orders, biomes, and continents, and ranged widely from 1.4 to 9,630.0 (median 430.0 and mean 570.0) mg kg-1 across the globe. About two-thirds (65%) of the global variation was accounted for by the 13 variables that we selected, among which soil organic carbon concentration, parent material, mean annual temperature, and soil sand content were the most important. While global predictions of soil total P concentration increased significantly with latitude, they varied largely among regions with similar latitudes due to regional differences in parent material, topography, and/or climate conditions. Global soil P stocks (excluding Antarctica) were estimated to be 26.8 ± 3.1 (mean ± standard deviation) Pg and 62.2 ± 8.9 Pg (1 Pg=1×1015 g) in the topsoil (0-30 cm) and subsoil (30-100 cm), respectively. Our global map of soil total P concentration as well as the underlying drivers of soil total P concentration can be used to constraint Earth system models that represent the P cycle and to inform quantification of global soil P availability. Raw datasets and global maps generated in this study are available at https://doi.org/10.6084/m9.figshare.14583375 (He et al., 2021). [ABSTRACT FROM AUTHOR]

Published
2021
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93. ORCHIDEE-MICT (v8.4.1), a land surface model for the high latitudes: model description and validation

Author

Guimberteau, Matthieu, Zhu, Dan, Maignan, Fabienne, Huang, Ye, Yue, Chao, Dantec-Nédélec, Sarah, Ottlé, Catherine, Jornet-Puig, Albert, Bastos, Ana, Laurent, Pierre, Goll, Daniel S., Bowring, Simon, Chang, Jinfeng, Guenet, Bertrand, Tifafi, Marwa, Peng, Shushi, Krinner, Gerhard, Ducharne, Agnès, Wang, Fuxing, Wang, Tao, Wang, Xuhui, Wang, Yilong, Yin, Zun, Lauerwald, Ronny, Joetzjer, Emilie, Qiu, Chunjing, Kim, Hyungjun, Ciais, Philippe, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] ( LSCE ), Université de Versailles Saint-Quentin-en-Yvelines ( UVSQ ) -Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Centre National de la Recherche Scientifique ( CNRS ), Huazhong University of Science & Technology [Wuhan] ( HUST ), Joint Unit, LSCE, Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ), Instituto de Geociências, Universidade Federal do Rio Grande do Sul [Porto Alegre] ( UFRGS ), Département de Physique des Particules (ex SPP) ( DPP ), Institut de Recherches sur les lois Fondamentales de l'Univers ( IRFU ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay, Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques ( LOCEAN ), Muséum National d'Histoire Naturelle ( MNHN ) -Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ), Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University [Beijing], Laboratoire de glaciologie et géophysique de l'environnement ( LGGE ), Observatoire des Sciences de l'Univers de Grenoble ( OSUG ), Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Université Joseph Fourier - Grenoble 1 ( UJF ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Grenoble Alpes ( UGA ) -Centre National de la Recherche Scientifique ( CNRS ), Milieux Environnementaux, Transferts et Interactions dans les hydrosystèmes et les Sols ( METIS ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -École pratique des hautes études ( EPHE ) -Centre National de la Recherche Scientifique ( CNRS ), Laboratoire de Météorologie Dynamique (UMR 8539) ( LMD ), Université Pierre et Marie Curie - Paris 6 ( UPMC ) -Institut national des sciences de l'Univers ( INSU - CNRS ) -École polytechnique ( X ) -École des Ponts ParisTech ( ENPC ) -Centre National de la Recherche Scientifique ( CNRS ) -Département des Géosciences - ENS Paris, École normale supérieure - Paris ( ENS Paris ) -École normale supérieure - Paris ( ENS Paris ), Laboratoire d'Informatique pour l'Entreprise et les Systèmes de Production ( LIESP ), Université Lumière - Lyon 2 ( UL2 ) -École Centrale de Lyon ( ECL ), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Institut National des Sciences Appliquées de Lyon ( INSA Lyon ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ), Université Libre de Bruxelles [Bruxelles] ( ULB ), Montana State University ( MSU ), Korea Advanced Institute of Science and Technology ( KAIST ), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Modélisation des Surfaces et Interfaces Continentales (MOSAIC), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS), Universidade Federal do Rio Grande do Sul [Porto Alegre] (UFRGS), Ingénierie des Matériaux Polymères - Laboratoire des Matériaux Macromoléculaires (IMP-LMM), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet [Saint-Étienne] (UJM)-Centre National de la Recherche Scientifique (CNRS), Institut des Géosciences de l’Environnement [2017-2019] (IGE [2017-2019]), Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019] (Grenoble INP [2007-2019])-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Milieux Environnementaux, Transferts et Interactions dans les hydrosystèmes et les Sols (METIS), École pratique des hautes études (EPHE)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de Météorologie Dynamique (UMR 8539) (LMD), Département des Géosciences - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École des Ponts ParisTech (ENPC)-École polytechnique (X)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC), Université libre de Bruxelles (ULB), Montana State University (MSU), Korea Advanced Institute of Science and Technology (KAIST), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Huazhong University of Science and Technology [Wuhan] (HUST), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Département de Physique des Particules (ex SPP) (DPhP), Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Institut des Géosciences de l’Environnement (IGE), Institut de Recherche pour le Développement (IRD)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Université Pierre et Marie Curie - Paris 6 (UPMC)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-École des Ponts ParisTech (ENPC)-Centre National de la Recherche Scientifique (CNRS)-Département des Géosciences - ENS Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Key Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research - Chinese Academy of Sciences, CAS Center for Excellence in Tibetan Plateau Earth Sciences, Modélisation INVerse pour les mesures atmosphériques et SATellitaires (SATINV), ICOS-ATC (ICOS-ATC), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Département de Physique des Particules (ex SPP) (DPP), Institut Pierre-Simon-Laplace (IPSL (FR_636)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Institut de Recherche pour le Développement (IRD)-Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU), Université Pierre et Marie Curie - Paris 6 (UPMC)-École pratique des hautes études (EPHE), and Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut de Recherche pour le Développement (IRD)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, [ SDU.OCEAN ] Sciences of the Universe [physics]/Ocean, Atmosphere, [SDE.MCG]Environmental Sciences/Global Changes, ddc:550, [ SDU.ENVI ] Sciences of the Universe [physics]/Continental interfaces, environment, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, ComputingMilieux_MISCELLANEOUS
Abstract

International audience; The high-latitude regions of the Northern Hemisphere are a nexus for the interaction between land surface physical properties and their exchange of carbon and energy with the atmosphere. At these latitudes, two carbon pools of planetary significance-those of the permanently frozen soils (permafrost), and of the great expanse of boreal forest-are vulnerable to destabilization in the face of currently observed climatic warming, the speed and intensity of which are expected to increase with time. Improved projections of future Arctic and boreal ecosystem transformation require improved land surface models that integrate processes specific to these cold biomes. To this end, this study lays out relevant new parameterizations in the ORCHIDEE-MICT land surface model. These describe the interactions between soil carbon, soil temperature and hydrology, and their resulting feedbacks on water and CO 2 fluxes, in addition to a recently developed fire module. Outputs from ORCHIDEE-MICT, when forced by two climate input datasets, are extensively evaluated against (i) temperature gradients between the atmosphere and deep soils, (ii) the hydrological components comprising the water balance of the largest high-latitude basins, and (iii) CO 2 flux and carbon stock observations. The model performance is good with respect to empirical data, despite a simulated excessive plant water stress and Published by Copernicus Publications on behalf of the European Geosciences Union. 122 M. Guimberteau et al.: ORCHIDEE-MICT, a LSM for the high latitudes a positive land surface temperature bias. In addition, acute model sensitivity to the choice of input forcing data suggests that the calibration of model parameters is strongly forcing-dependent. Overall, we suggest that this new model design is at the forefront of current efforts to reliably estimate future perturbations to the high-latitude terrestrial environment.

Published
2018

96. Global Carbon Budget 2018

Author

Le Quéré, Corinne, primary, Andrew, Robbie M., additional, Friedlingstein, Pierre, additional, Sitch, Stephen, additional, Hauck, Judith, additional, Pongratz, Julia, additional, Pickers, Penelope A., additional, Korsbakken, Jan Ivar, additional, Peters, Glen P., additional, Canadell, Josep G., additional, Arneth, Almut, additional, Arora, Vivek K., additional, Barbero, Leticia, additional, Bastos, Ana, additional, Bopp, Laurent, additional, Chevallier, Frédéric, additional, Chini, Louise P., additional, Ciais, Philippe, additional, Doney, Scott C., additional, Gkritzalis, Thanos, additional, Goll, Daniel S., additional, Harris, Ian, additional, Haverd, Vanessa, additional, Hoffman, Forrest M., additional, Hoppema, Mario, additional, Houghton, Richard A., additional, Hurtt, George, additional, Ilyina, Tatiana, additional, Jain, Atul K., additional, Johannessen, Truls, additional, Jones, Chris D., additional, Kato, Etsushi, additional, Keeling, Ralph F., additional, Goldewijk, Kees Klein, additional, Landschützer, Peter, additional, Lefèvre, Nathalie, additional, Lienert, Sebastian, additional, Liu, Zhu, additional, Lombardozzi, Danica, additional, Metzl, Nicolas, additional, Munro, David R., additional, Nabel, Julia E. M. S., additional, Nakaoka, Shin-ichiro, additional, Neill, Craig, additional, Olsen, Are, additional, Ono, Tsueno, additional, Patra, Prabir, additional, Peregon, Anna, additional, Peters, Wouter, additional, Peylin, Philippe, additional, Pfeil, Benjamin, additional, Pierrot, Denis, additional, Poulter, Benjamin, additional, Rehder, Gregor, additional, Resplandy, Laure, additional, Robertson, Eddy, additional, Rocher, Matthias, additional, Rödenbeck, Christian, additional, Schuster, Ute, additional, Schwinger, Jörg, additional, Séférian, Roland, additional, Skjelvan, Ingunn, additional, Steinhoff, Tobias, additional, Sutton, Adrienne, additional, Tans, Pieter P., additional, Tian, Hanqin, additional, Tilbrook, Bronte, additional, Tubiello, Francesco N., additional, van der Laan-Luijkx, Ingrid T., additional, van der Werf, Guido R., additional, Viovy, Nicolas, additional, Walker, Anthony P., additional, Wiltshire, Andrew J., additional, Wright, Rebecca, additional, Zaehle, Sönke, additional, and Zheng, Bo, additional

Published
2018
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97. A global map of root biomass across the world's forests.

Author

Yuanyuan Huang, Ciais, Phillipe, Santoro, Maurizio, Makowski, David, Chave, Jerome, Schepaschenko, Dmitry, Abramoff, Rose Z., Goll, Daniel S., Hui Yang, Ye Chen, Wei Wei, and Shilong Piao

Subjects
FOREST biomass, BIOMASS, BIOMASS estimation, FOREST measurement, TEMPERATE forests, TAIGAS
Abstract

As a key component of the Earth system, root plays the key role in linking Earth's lithosphere, hydrosphere, biosphere, and atmosphere. Here we combine 10307 field measurements of forest root biomass worldwide with global observations of forest structure, climatic conditions, topography, land management and soil characteristics to derive a spatially explicit global high-resolution (~ 1km) root biomass dataset, including fine and coarse roots. In total, 142 ± 25 (95% CI) Pg of live dry matter biomass is stored below-ground, representing a global average root:shoot biomass ratio of 0.25 ± 0.10. Our estimations of total root biomass in tropical, temperate and boreal forests are 44-226% smaller than earlier studies(Jackson et al., 1997; Robinson, 2007; Saugier et al., 2001). The smaller estimation is attributable to the updated forest area, spatially explicit above-ground biomass density used to predict the patterns of root biomass, new root measurements and upscaling methodology. We show specifically that the root shoot allometry is one underlying driver that leads to methodological overestimation of root biomass in previous estimations. Raw datasets and global maps generated in this study are deposited at the open access repository Figshare (https://figshare.com/articles/Supporting%5Fdata%5Fand%5Fcode%5Ffor%5FA%5Fglobal%5Fmap%5Fof%5Froot%5Fbiomass%5Facross%5Fthe%5Fworld%5Fs%5Fforests/12199637) [ABSTRACT FROM AUTHOR]

Published
2021
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98. Global Carbon Budget 2018

Author

Le Quere, Corinne, Andrew, Robbie M., Friedlingstein, Pierre, Sitch, Stephen, Hauck, Judith, Pongratz, Julia, Pickers, Penelope A., Korsbakken, Jan Ivar, Peters, Glen P., Canadell, Josep G., Arneth, Almut, Arora, Vivek K., Barbero, Leticia, Bastos, Ana, Bopp, Laurent, Chevallier, Frederic, Chini, Louise P., Ciais, Philippe, Doney, Scott C., Gkritzalis, Thanos, Goll, Daniel S., Harris, Ian, Haverd, Vanessa, Hoffman, Forrest M., Hoppema, Mario, Houghton, Richard A., Hurtt, George, Ilyina, Tatiana, Jain, Atul K., Johannessen, Truls, Jones, Chris D., Kato, Etsushi, Keeling, Ralph F., Goldewijk, Kees Klein, Landschuetzer, Peter, Lefevre, Nathalie, Lienert, Sebastian, Liu, Zhu, Lombardozzi, Danica, Metzl, Nicolas, Munro, David R., Nabel, Julia E. M. S., Nakaoka, Shin-ichiro, Neill, Craig, Olsen, Are, Ono, Tsueno, Patra, Prabir, Peregon, Anna, Peters, Wouter, Peylin, Philippe, Pfeil, Benjamin, Pierrot, Denis, Poulter, Benjamin, Rehder, Gregor, Resplandy, Laure, Robertson, Eddy, Rocher, Matthias, Roedenbeck, Christian, Schuster, Ute, Schwinger, Jorg, Seferian, Roland, Skjelvan, Ingunn, Steinhoff, Tobias, Sutton, Adrienne, Tans, Pieter P., Tian, Hanqin, Tilbrook, Bronte, Tubiello, Francesco N., Van Der Laan-luijkx, Ingrid T., Van Der Werf, Guido R., Viovy, Nicolas, Walker, Anthony P., Wiltshire, Andrew J., Wright, Rebecca, Zaehle, Soenke, Zheng, Bo, Le Quere, Corinne, Andrew, Robbie M., Friedlingstein, Pierre, Sitch, Stephen, Hauck, Judith, Pongratz, Julia, Pickers, Penelope A., Korsbakken, Jan Ivar, Peters, Glen P., Canadell, Josep G., Arneth, Almut, Arora, Vivek K., Barbero, Leticia, Bastos, Ana, Bopp, Laurent, Chevallier, Frederic, Chini, Louise P., Ciais, Philippe, Doney, Scott C., Gkritzalis, Thanos, Goll, Daniel S., Harris, Ian, Haverd, Vanessa, Hoffman, Forrest M., Hoppema, Mario, Houghton, Richard A., Hurtt, George, Ilyina, Tatiana, Jain, Atul K., Johannessen, Truls, Jones, Chris D., Kato, Etsushi, Keeling, Ralph F., Goldewijk, Kees Klein, Landschuetzer, Peter, Lefevre, Nathalie, Lienert, Sebastian, Liu, Zhu, Lombardozzi, Danica, Metzl, Nicolas, Munro, David R., Nabel, Julia E. M. S., Nakaoka, Shin-ichiro, Neill, Craig, Olsen, Are, Ono, Tsueno, Patra, Prabir, Peregon, Anna, Peters, Wouter, Peylin, Philippe, Pfeil, Benjamin, Pierrot, Denis, Poulter, Benjamin, Rehder, Gregor, Resplandy, Laure, Robertson, Eddy, Rocher, Matthias, Roedenbeck, Christian, Schuster, Ute, Schwinger, Jorg, Seferian, Roland, Skjelvan, Ingunn, Steinhoff, Tobias, Sutton, Adrienne, Tans, Pieter P., Tian, Hanqin, Tilbrook, Bronte, Tubiello, Francesco N., Van Der Laan-luijkx, Ingrid T., Van Der Werf, Guido R., Viovy, Nicolas, Walker, Anthony P., Wiltshire, Andrew J., Wright, Rebecca, Zaehle, Soenke, and Zheng, Bo

Abstract

Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere - the "global carbon budget" - is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions (E-FF) are based on energy statistics and cement production data, while emissions from land use and land-use change (E-LUC), mainly deforestation, are based on land use and land -use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly and its growth rate (G(ATM)) is computed from the annual changes in concentration. The ocean CO2 sink (S-OCEAN) and terrestrial CO2 sink (S-LAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (B-IM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as +/- 1 sigma. For the last decade available (2008-2017), E-FF was 9.4 +/- 0.5 GtC yr(-1), E-LUC 1.5 +/- 0.7 GtC yr(-1), G(ATM) 4.7 +/- 0.02 GtC yr(-1), S-OCEAN 2.4 +/- 0.5 GtC yr(-1), and S-LAND 3.2 +/- 0.8 GtC yr(-1), with a budget imbalance B-IM of 0.5 GtC yr(-1) indicating overestimated emissions and/or underestimated sinks. For the year 2017 alone, the growth in E-FF was about 1.6 % and emissions increased to 9.9 +/- 0.5 GtC yr(-1). Also for 2017, E-LUC was 1.4 +/- 0.7 GtC yr(-1), G(ATM) was 4.6 +/- 0.2 GtC yr(-1), S-OCEAN was 2.5 +/- 0.5 GtC yr(-1), and S-LAND was 3.8 +/- 0.8 GtC yr(-1), with a B-IM of 0.3 GtC. The global atmospheric CO2 concentration reached 405.0 +/- 0.1 ppm averaged over 2017. For 2018, preliminary data for the f

Published
2018
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99. Modeling the effects of litter stoichiometry and soil mineral N availability on soil organic matter formation using CENTURY-CUE (v1.0)

Author

Zhang, Haicheng, Goll, Daniel S., Manzoni, Stefano, Ciais, Philippe, Guenet, Bertrand, Huang, Yuanyuan, Zhang, Haicheng, Goll, Daniel S., Manzoni, Stefano, Ciais, Philippe, Guenet, Bertrand, and Huang, Yuanyuan

Abstract

Microbial decomposition of plant litter is a crucial process for the land carbon (C) cycle, as it directly controls the partitioning of litter C between CO2 released to the atmosphere versus the formation of new soil organic matter (SOM). Land surface models used to study the C cycle rarely considered flexibility in the decomposer C use efficiency (CUEd) defined by the fraction of decomposed litter C that is retained as SOM (as opposed to be respired). In this study, we adapted a conceptual formulation of CUEd based on assumption that litter decomposers optimally adjust their CUEd as a function of litter substrate C to nitrogen (N) stoichiometry to maximize their growth rates. This formulation was incorporated into the widely used CENTURY soil biogeochemical model and evaluated based on data from laboratory litter incubation experiments. Results indicated that the CENTURY model with new CUEd formulation was able to reproduce differences in respiration rate of litter with contrasting C: N ratios and under different levels of mineral N availability, whereas the default model with fixed CUEd could not. Using the model with flexible CUEd, we also illustrated that litter quality affected the long-term SOM formation. Litter with a small C: N ratio tended to form a larger SOM pool than litter with larger C: N ratios, as it could be more efficiently incorporated into SOM by microorganisms. This study provided a simple but effective formulation to quantify the effect of varying litter quality (N content) on SOM formation across temporal scales. Optimality theory appears to be suitable to predict complex processes of litter decomposition into soil C and to quantify how plant residues and manure can be harnessed to improve soil C sequestration for climate mitigation.

Published
2018
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100. Slow-down of the greening trend in natural vegetation with further rise in atmospheric CO2.

Author

Winkler, Alexander J., Myneni, Ranga B., Hannart, Alexis, Sitch, Stephen, Haverd, Vanessa, Lombardozzi, Danica, Arora, Vivek K., Pongratz, Julia, Nabel, Julia E. M. S., Goll, Daniel S., Kato, Etsushi, Hanqin Tian, Arneth, Almut, Friedlingstein, Pierre, Jain, Atul K., Zaehle, Sönke, and Brovkin, Victor

Subjects
LEAF area index, RAIN forests, RAINFALL anomalies, VEGETATION dynamics, CARBON emissions
Abstract

Satellite data reveal widespread changes of Earth's vegetation cover. Regions intensively attended to by humans are mostly greening due to land management. Natural vegetation, on the other hand, is exhibiting patterns of both greening and browning in all continents. Factors linked to anthropogenic carbon emissions, such as CO2 fertilization, climate change and consequent disturbances, such as fires and droughts, are hypothesized to be key drivers of changes in natural vegetation. A rigorous regional attribution at biome-level that can be scaled into a global picture of what is behind the observed changes is currently lacking. Here we analyze different datasets of decades-long satellite observations of global leaf area index (LAI, 1981-2017) as well as other proxies of vegetation changes, and identify several clusters of significant long-term changes. Using process-based model simulations (Earth system and land surface models), we disentangle the effects of anthropogenic carbon emissions on LAI in a probabilistic setting applying Causal Counterfactual Theory. The analysis prominently indicates the effects of climate change on many biomes - warming in northern ecosystems (greening) and rainfall anomalies in tropical biomes (browning). Our results do not support previously published accounts of dominant global-scale effects of CO2 fertilization. Altogether, our analysis reveals a slowing down of greening and strengthening of browning trends, particularly in the last two decades. Most models substantially underestimate the emerging vegetation browning, especially in the tropical rainforests. Leaf area loss in these productive ecosystems could be an early indicator of a slow-down in the terrestrial carbon sink. Models need to account for this effect to realize plausible climate projections of the 21st century. [ABSTRACT FROM AUTHOR]

Published
2021
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