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182 results on '"Nishina, Kazuya"'

9. Dynamics of radiocaesium within forests in Fukushima—results and analysis of a model inter-comparison

Author

Hashimoto, Shoji, Tanaka, Taku, Komatsu, Masabumi, Gonze, Marc-André, Sakashita, Wataru, Kurikami, Hiroshi, Nishina, Kazuya, Ota, Masakazu, Ohashi, Shinta, Calmon, Philippe, Coppin, Frederic, Imamura, Naohiro, Hayashi, Seiji, Hirai, Keizo, Hurtevent, Pierre, Koarashi, Jun, Manaka, Takuya, Miura, Satoru, Shinomiya, Yoshiki, Shaw, George, and Thiry, Yves

Published
2021
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12. Quantification of global and national nitrogen budgets for crop production

Author

Zhang, Xin, Zou, Tan, Lassaletta, Luis, Mueller, Nathaniel D., Tubiello, Francesco N., Lisk, Matthew D., Lu, Chaoqun, Conant, Richard T., Dorich, Christopher D., Gerber, James, Tian, Hanqin, Bruulsema, Tom, Maaz, Tai McClellan, Nishina, Kazuya, Bodirsky, Benjamin Leon, Popp, Alexander, Bouwman, Lex, Beusen, Arthur, Chang, Jinfeng, Havlík, Petr, Leclère, David, Canadell, Josep G., Jackson, Robert B., Heffer, Patrick, Wanner, Nathan, Zhang, Weifeng, and Davidson, Eric A.

Published
2021
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13. Monsoon Asia Rice Calendar (MARC): a gridded rice calendar in monsoon Asia based on Sentinel-1 and Sentinel-2 images.

Author

Zhao, Xin, Nishina, Kazuya, Izumisawa, Haruka, Masutomi, Yuji, Osako, Seima, and Yamamoto, Shuhei

Subjects
*HARVESTING time, *REMOTE-sensing images, *AGRICULTURE, *RICE, *CLIMATE change
Abstract

An accurate and spatially explicit large-scale rice calendar can enhance the understanding of agricultural practices and their ecological services, particularly in monsoon Asia. However, currently available global- or continental-scale rice calendars suffer from coarse resolution, poor recording, and outdated information; thus, they do not provide detailed and consistent information on rice phenology. To address this limitation, this study mapped a new (2019–2020) gridded (0.5°×0.5° resolution) rice calendar for monsoon Asia based on Sentinel-1 and Sentinel-2 satellite images. The novelty of this rice calendar lies in the development of a consistent optimal methodological framework that allows the spatially explicit characterization of the rice transplanting date, harvesting date, and number of rice cropping seasons. The methodological framework incorporates two steps: (1) detection of rice phenological dates and number of rice cropping seasons via the combination of a feature-based algorithm and the fitted Weibull function; (2) spatiotemporal integration of the detected transplanting and harvesting dates derived from Step 1 using von Mises maximum likelihood estimates. Results revealed that the proposed rice calendar can accurately identify the rice phenological dates for three croppings in monsoon Asia. When compared with single rice data from the census-based RiceAtlas calendar, the proposed calendar exhibited better results than the MODIS-based Rice Crop Calendar for Asia (RICA). It exhibited biases of 4 and - 6 d for the transplanting and harvesting dates, respectively, with 10 and 15 d lower MAE values and 6 and 15 d lower RMSE values for the transplanting and harvesting dates, respectively. In total, the proposed rice calendar can detect single-, double-, and triple-rice-cropping seasons with an area of 0.53×106 , 0.45×106 , and 0.09×106 km 2 , respectively. This novel gridded rice calendar fills the gaps in 0.5° rice calendars across major global rice production areas, facilitating research on rice phenology that is relevant to climate change. The developed gridded Monsoon Asia Rice Calendar (MARC) is available at 10.17595/20230728.001 (Zhao and Nishina, 2023). [ABSTRACT FROM AUTHOR]

Published
2024
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23. Carbon residence time dominates uncertainty in terrestrial vegetation responses to future climate and atmospheric CO 2

Author

Friend, Andrew D., Lucht, Wolfgang, Rademacher, Tim T., Keribin, Rozenn, Betts, Richard, Cadule, Patricia, Ciais, Philippe, Clark, Douglas B., Dankers, Rutger, Falloon, Pete D., Ito, Akihiko, Kahana, Ron, Kleidon, Axel, Lomas, Mark R., Nishina, Kazuya, Ostberg, Sebastian, Pavlick, Ryan, Peylin, Philippe, Schaphoff, Sibyll, Vuichard, Nicolas, Warszawski, Lila, Wiltshire, Andy, and Woodward, F. Ian

Published
2014

24. Multisectoral climate impact hotspots in a warming world

Author

Piontek, Franziska, Müller, Christoph, Pugh, Thomas A. M., Clark, Douglas B., Deryng, Delphine, Elliott, Joshua, de Jesus Colón González, Felipe, Flörke, Martina, Folberth, Christian, Franssen, Wietse, Frieler, Katja, Friend, Andrew D., Gosling, Simon N., Hemming, Deborah, Khabarov, Nikolay, Kim, Hyungjun, Lomas, Mark R., Masaki, Yoshimitsu, Mengel, Matthias, Morse, Andrew, Neumann, Kathleen, Nishina, Kazuya, Ostberg, Sebastian, Pavlick, Ryan, Ruane, Alex C., Schewe, Jacob, Schmid, Erwin, Stacke, Tobias, Tang, Qiuhong, Tessler, Zachary D., Tompkins, Adrian M., Warszawski, Lila, Wisser, Dominik, and Schellnhuber, Hans Joachim

Published
2014

25. 地球温暖化による影響連鎖の全体像の可視化と市民対話

Author

Yokohata, Tokuta, Takahashi, Kiyoshi, Emori, Seita, Nishina, Kazuya, Tanaka, Katsumasa, ISERI, Yoshihiko, Honda, Yasushi, Kiguchi, Masashi, Kanae, Shinjiro, Okamoto, Akiko, Iwasaki, Akane, Maeda, Nodoka, and Oki, Taikan

Subjects
地球温暖化, 気候変動, リスク連鎖, 可視化, 市民対話
Abstract

パリ協定における目標を達成するために脱炭素社会を実現し，今後も変化する気候に社会が適応するためには，多くの人々が気候変動のリスクに関して理解を深めることが重要な課題である。このため我々の研究グループは，これまでに気候変動リスク連鎖を包括的に分かりやすく可視化する手法の開発を行った。本論文では，我々が開発した手法によって得られた，水資源・食料・エネルギー・産業とインフラ・自然生態系・災害と安全保障・健康の7つの分野に関連するリスク連鎖の可視化結果（ネットワーク図・フローチャート）について議論することにより，気候リスク連鎖の全体像を明らかにする。また，可視化結果を利用して行った市民対話イベントの実例を紹介することにより，我々の開発したネットワーク図・フローチャートの有用性や，気候リスクに関する市民対話の重要性について論じる。さらに，日本における気候変動リスク評価の概要について紹介し，気候変動リスク連鎖を評価するための今後の課題について議論する。

Published
2021

28. History of anthropogenic Nitrogen inputs (HaNi) to the terrestrial biosphere: a 5 arcmin resolution annual dataset from 1860 to 2019

Author

Tian, Hanqin, primary, Bian, Zihao, additional, Shi, Hao, additional, Qin, Xiaoyu, additional, Pan, Naiqing, additional, Lu, Chaoqun, additional, Pan, Shufen, additional, Tubiello, Francesco N., additional, Chang, Jinfeng, additional, Conchedda, Giulia, additional, Liu, Junguo, additional, Mueller, Nathaniel, additional, Nishina, Kazuya, additional, Xu, Rongting, additional, Yang, Jia, additional, You, Liangzhi, additional, and Zhang, Bowen, additional

Published
2022
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36. Projecting Exposure to Extreme Climate Impact Events Across Six Event Categories and Three Spatial Scales

Author

Lange, Stefan, Volkholz, Jan, Geiger, Tobias, Zhao, Fang, Vega, Iliusi, Veldkamp, Ted, Reyer, Christopher P. O., Warszawski, Lila, Huber, Veronika, Schewe, Jacob, Bresch, David N., Chang, Jinfeng, Ciais, Philippe, Dury, Marie, Emanuel, Kerry, Folberth, Christian, Gerten, Dieter, Gosling, Simon N., Grillakis, Manolis, Hanasaki, Naota, Henrot, Alexandra?Jane, Hickler, Thomas, Honda, Yasushi, Ito, Akihiko, Khabarov, Nikolay, Koutroulis, Aristeidis, Liu, Wenfeng, Nishina, Kazuya, Ostberg, Sebastian, Seneviratne, Sonia I., Stacke, Tobias, Steinkamp, J?rg, Thiery, Wim, Wada, Yoshihide, Willner, Sven, Yang, Hong, Yoshikawa, Minoru, Yue, Chao, and Frieler, Katja

Abstract

©2020. The Authors. The extent and impact of climate-related extreme events depend on the underlying meteorological, hydrological, or climatological drivers as well as on human factors such as land use or population density. Here we quantify the pure effect of historical and future climate change on the exposure of land and population to extreme climate impact events using an unprecedentedly large ensemble of harmonized climate impact simulations from the Inter-Sectoral Impact Model Intercomparison Project phase 2b. Our results indicate that global warming has already more than doubled both the global land area and the global population annually exposed to all six categories of extreme events considered: river floods, tropical cyclones, crop failure, wildfires, droughts, and heatwaves. Global warming of 2°C relative to preindustrial conditions is projected to lead to a more than fivefold increase in cross-category aggregate exposure globally. Changes in exposure are unevenly distributed, with tropical and subtropical regions facing larger increases than higher latitudes. The largest increases in overall exposure are projected for the population of South Asia.

Published
2020

37. Environmental transfer parameters for radionuclides released from the accident at the Fukushima Daiichi Nuclear Power Plant. Chapter 5 Forest ecosystems

Author

Hashimoto, Shoji, Komatsu, Masabumi, Imamura, Naohiro, Ohashi, Shinta, Kato, Hiroaki, Howard, B., Tagami, Keiko, Nishina, Kazuya, and al., et

Abstract

Approximately 70% of the territory affected by the Fukushima Daiichi accident is covered by forest (Fig. 5.1) [5.1]. Forests in Fukushima prefecture consist of about 0.34 million ha of forest plantations and 0.58 million ha of natural/semi natural forest. In this section, radiocaesium interception by tree canopy, transfer to tree and forest products (sansai, mushrooms and game animals), and mobility in tree and soil are described.

Published
2020

39. Understanding the uncertainty in global forest carbon turnover

Author

Pugh, Thomas A. M., primary, Rademacher, Tim, additional, Shafer, Sarah L., additional, Steinkamp, Jörg, additional, Barichivich, Jonathan, additional, Beckage, Brian, additional, Haverd, Vanessa, additional, Harper, Anna, additional, Heinke, Jens, additional, Nishina, Kazuya, additional, Rammig, Anja, additional, Sato, Hisashi, additional, Arneth, Almut, additional, Hantson, Stijn, additional, Hickler, Thomas, additional, Kautz, Markus, additional, Quesada, Benjamin, additional, Smith, Benjamin, additional, and Thonicke, Kirsten, additional

Published
2020
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40. Climate Extreme Versus Carbon Extreme: Responses of Terrestrial Carbon Fluxes to Temperature and Precipitation

Author

Pan, Shufen, primary, Yang, Jia, additional, Tian, Hanqin, additional, Shi, Hao, additional, Chang, Jinfeng, additional, Ciais, Philippe, additional, Francois, Louis, additional, Frieler, Katja, additional, Fu, Bojie, additional, Hickler, Thomas, additional, Ito, Akihiko, additional, Nishina, Kazuya, additional, Ostberg, Sebastian, additional, Reyer, Christopher P.O., additional, Schaphoff, Sibyll, additional, Steinkamp, Jörg, additional, and Zhao, Fang, additional

Published
2020
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42. Supplementary material to "Understanding the uncertainty in global forest carbon turnover"

Author

Pugh, Thomas A. M., primary, Rademacher, Tim T., additional, Shafer, Sarah L., additional, Steinkamp, Jörg, additional, Barichivich, Jonathan, additional, Beckage, Brian, additional, Haverd, Vanessa, additional, Harper, Anna, additional, Heinke, Jens, additional, Nishina, Kazuya, additional, Rammig, Anja, additional, Sato, Hisashi, additional, Arneth, Almut, additional, Hantson, Stijn, additional, Hickler, Thomas, additional, Kautz, Markus, additional, Quesada, Benjamin, additional, Smith, Benjamin, additional, and Thonicke, Kirsten, additional

Published
2020
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43. Forest ecosystems

Author

Hashimoto, Shoji, Komatsu, Masabumi, Imamura, Naohiro, Ohashi, Shinta, Kato, Hiroaki, Nishina, Kazuya, Tagami, Keiko, Uchida, Shigeo, Shaw, George, Wood, Mike, Beresford, Nick, Howard, Brenda, Fesenko, Sergey, Thiry, Yves, Hashimoto, Shoji, Komatsu, Masabumi, Imamura, Naohiro, Ohashi, Shinta, Kato, Hiroaki, Nishina, Kazuya, Tagami, Keiko, Uchida, Shigeo, Shaw, George, Wood, Mike, Beresford, Nick, Howard, Brenda, Fesenko, Sergey, and Thiry, Yves

Abstract

Chapter 5 of the report of Working Group 4 Transfer Processes and Data for Radiological Impact Assessment, Subgroup 2 on Fukushima Data within the IAEA Programme on Modelling and Data for Radiological Impact Assessments (MODARIA II).

Published
2020

44. Nitrogen and Oxygen Isotope Signatures of Nitrogen Compounds during Anammox in the Laboratory and a Wastewater Treatment Plant

Author

Kotajima, Shotoku, primary, Koba, Keisuke, additional, Ikeda, Daisuke, additional, Terada, Akihiko, additional, Isaka, Kazuichi, additional, Nishina, Kazuya, additional, Kimura, Yuuya, additional, Makabe, Akiko, additional, Yano, Midori, additional, Fujitani, Hirotsugu, additional, Ushiki, Norisuke, additional, Tsuneda, Satoshi, additional, and Yoh, Muneoki, additional

Published
2020
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45. Additional file 1: of Emissions of nitrous oxide (N2O) from soil surfaces and their historical changes in East Asia: a model-based assessment

Author

Ito, Akihiko, Nishina, Kazuya, Ishijima, Kentaro, Hashimoto, Shoji, and Inatomi, Motoko

Abstract

Figure S1. Cropland fraction in East Asia derived from Hurtt et al. (2011). Figure S2. Comparison between the simulated monthly N2O emissions and observed atmospheric N2O concentrations in East Asia. The observational data were obtained from the World Data Center of Greenhouse Gases ( https://gaw.kishou.go.jp/ ). Figure S3. Comparison of the VISIT estimations (EX0) with the estimates of Hashimoto (2012, updated). (a) Annual N2O emissions and (b) grid-based scatter diagram. Orange line shows linear regression (y [N2O emissions by VISIT] = 1.25 x [N2O emissions by Hashimoto (2012)] + 0.0596, in kg N2O ha− 1 yr.− 1; R2 = 0.44). (DOCX 1792 kb)

Published
2018
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46. Evaluating changes of biomass in global vegetation models: the role of turnover fluctuations and ENSO events

Author

Cantú, Anselmo García, Frieler, Katja, Reyer, Christopher, Ciais, Philippe, Chang, Jinfeng, Ito, Akihiko, Nishina, Kazuya, François, Louis, Henrot, Alexandra-Jane, Hickler, Thomas, Steinkamp, Jörg, Rafique, Rashid, Zhao, Fang, Ostberg, Sebastian, SCHAPHOFF, SIBYLL, Tian, Hanqin, Pan, Shufen, Yang, Jia, Morfopoulos, Catherine, Betts, Richard, Potsdam Institute for Climate Impact Research (PIK), 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)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), ICOS-ATC (ICOS-ATC), 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), National Institute for Environmental Studies (NIES), Global Centre for Environmental Research (GCER), University of Newcastle (UoN), Unité de Modélisation du Climat et des Cycles Biogéochimiques (UMCCB), Université de Liège, Department of Physical Geography and Ecosystem Science [Lund], Lund University [Lund], Joint Global Change Research Institute, Pacific Northwest National Laboratory (PNNL)-University of Maryland [College Park], University of Maryland System-University of Maryland System, Auburn University (AU), 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), and 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)

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, vegetation optical depth, ENSO, terrestrial ecosystems, ISIMIP2a, interannual variability, biomass, global vegetation models, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, ComputingMilieux_MISCELLANEOUS
Abstract

This paper evaluates the ability of eight global vegetation models to reproduce recent trends and inter-annual variability of biomass in natural terrestrial ecosystems. For the purpose of this evaluation, the simulated trajectories of biomass are expressed in terms of the relative rate of change in biomass (RRB), defined as the deviation of the actual rate of biomass turnover from its equilibrium counterpart. Cumulative changes in RRB explain long-term changes in biomass pools. RRB simulated by the global vegetation models is compared with its observational equivalent, derived from vegetation optical depth reconstructions of above-ground biomass (AGB) over the period 1993–2010. According to the RRB analysis, the rate of global biomass growth described by the ensemble of simulations substantially exceeds the observation. The observed fluctuations of global RRB are significantly correlated with El Niño Southern Oscillation events (ENSO), but only some of the simulations reproduce this correlation. However, the ENSO sensitivity of RRB in the tropics is not significant in the observation, while it is in some of the simulations. This mismatch points to an important limitation of the observed AGB reconstruction to capture biomass variations in tropical forests. Important discrepancies in RRB were also identified at the regional scale, in the tropical forests of Amazonia and Central Africa, as well as in the boreal forests of north-western America, western and central Siberia. In each of these regions, the RRBs derived from the simulations were analyzed in connection with underlying differences in net primary productivity and biomass turnover rate ̶as a basis for exploring in how far differences in simulated changes in biomass are attributed to the response of the carbon uptake to CO2 increments, as well as to the model representation of factors affecting the rates of mortality and turnover of foliage and roots. Overall, our findings stress the usefulness of using RRB to evaluate complex vegetation models and highlight the importance of conducting further evaluations of both the actual rate of biomass turnover and its equilibrium counterpart, with special focus on their background values and sources of variation. In turn, this task would require the availability of more accurate multi-year observational data of biomass and net primary productivity for natural ecosystems, as well as detailed and updated information on land-cover classification.

Published
2018
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47. Benchmarking carbon fluxes of the ISIMIP2a biome models

Author

Chang, Jinfeng, Ciais, Philippe, Wang, Xuhui, Piao, Shilong, Asrar, Ghassem, Betts, Richard, Chevallier, Frédéric, Dury, Marie, François, Louis, Frieler, Katja, Ros, Anselmo García Cantú, Henrot, Alexandra-Jane, Hickler, Thomas, Ito, Akihiko, Morfopoulos, Catherine, Munhoven, Guy, Nishina, Kazuya, Ostberg, Sebastian, Pan, Shufen, Peng, Shushi, Rafique, Rashid, Reyer, Christopher, Rödenbeck, Christian, Schaphoff, Sibyll, Steinkamp, Jörg, Tian, Hanqin, Viovy, Nicolas, Yang, Jia, Zeng, Ning, Zhao, Fang, 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)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-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)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-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)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), ICOS-ATC (ICOS-ATC), 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), Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University [Beijing], Modélisation INVerse pour les mesures atmosphériques et SATellitaires (SATINV), Université de Liège, Unité de Modélisation du Climat et des Cycles Biogéochimiques (UMCCB), Department of Physical Geography and Ecosystem Science [Lund], Lund University [Lund], Potsdam Institute for Climate Impact Research (PIK), Max-Planck-Institut für Biogeochemie (MPI-BGC), Shandong Agricultural University (SDAU), Modélisation des Surfaces et Interfaces Continentales (MOSAIC), 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 de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Muséum national d'Histoire naturelle (MNHN)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), 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)-Université Paris Diderot - Paris 7 (UPD7)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris), and 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)

Subjects
[PHYS.PHYS.PHYS-GEO-PH]Physics [physics]/Physics [physics]/Geophysics [physics.geo-ph], ComputingMilieux_MISCELLANEOUS
Abstract

The purpose of this study is to evaluate the eight ISIMIP2a biome models against independent estimates of long-term net carbon fluxes (i.e. Net Biome Productivity, NBP) over terrestrial ecosystems for the recent four decades (1971–2010). We evaluate modeled global NBP against 1) the updated global residual land sink (RLS) plus land use emissions (E LUC) from the Global Carbon Project (GCP), presented as R + L in this study by Le Quéré et al (2015), and 2) the land CO2 fluxes from two atmospheric inversion systems: Jena CarboScope s81_v3.8 and CAMS v15r2, referred to as F Jena and F CAMS respectively. The model ensemble-mean NBP (that includes seven models with land-use change) is higher than but within the uncertainty of R + L, while the simulated positive NBP trend over the last 30 yr is lower than that from R + L and from the two inversion systems. ISIMIP2a biome models well capture the interannual variation of global net terrestrial ecosystem carbon fluxes. Tropical NBP represents 31 ± 17% of global total NBP during the past decades, and the year-to-year variation of tropical NBP contributes most of the interannual variation of global NBP. According to the models, increasing Net Primary Productivity (NPP) was the main cause for the generally increasing NBP. Significant global NBP anomalies from the long-term mean between the two phases of El Niño Southern Oscillation (ENSO) events are simulated by all models (p < 0.05), which is consistent with the R + L estimate (p = 0.06), also mainly attributed to NPP anomalies, rather than to changes in heterotrophic respiration (Rh). The global NPP and NBP anomalies during ENSO events are dominated by their anomalies in tropical regions impacted by tropical climate variability. Multiple regressions between R + L, F Jena and F CAMS interannual variations and tropical climate variations reveal a significant negative response of global net terrestrial ecosystem carbon fluxes to tropical mean annual temperature variation, and a non-significant response to tropical annual precipitation variation. According to the models, tropical precipitation is a more important driver, suggesting that some models do not capture the roles of precipitation and temperature changes adequately.

Published
2017
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48. Influence of δ18O of water on measurements of δ18O of nitrite and nitrate.

Author

Kobayashi, Kanae, Fukushima, Keitaro, Onishi, Yuji, Nishina, Kazuya, Makabe, Akiko, Yano, Midori, Wankel, Scott D., Koba, Keisuke, and Okabe, Satoshi

Subjects
ISOTOPE exchange reactions, NITROUS oxide, WATER sampling, NITRITES, MASS spectrometry
Abstract

Rationale: Oxygen isotope ratio measurements of NO2− and NO3− by the azide method and denitrifier method are sensitive to the δ18O value of the sample water. However, the influence of δ18OH2O on those measurements has not been quantitatively evaluated and documented so far. Therefore, we investigated the influence of δ18OH2O of a sample on the δ18O analysis of NO2− and NO3−. Methods: We prepared NO2− and NO3− standards (with known δ18ONO2‐ and δ18ONO3‐ values) dissolved in waters having different δ18OH2O values (δ18OH2O = −12.6, 25.9, 56.7, and 110.1‰). Nitrite and nitrate were converted into N2O using the azide method and the denitrifier method, respectively. The isotope ratios of the generated N2O were measured with a Sercon purge‐and‐trap gas chromatography/isotope ratio mass spectrometry (PT‐GC/IRMS) system. The measured δ18O values of the produced N2O were plotted against known δ18ONO2‐ and δ18ONO3‐ values to evaluate the influence of exchange of an oxygen atom with H2O during the conversion of NO2− into N2O and NO3− into N2O, respectively. Results: The degree of oxygen isotope exchange was 10.8 ± 0.3% in the azide method and 5.5 ± 1.0% in the denitrifier method, indicating that the azide method is more susceptible to artifacts arising from differences in the δ18OH2O value of water than the denitrifier method. Thus, the intercept of the standard calibration curve must be corrected to account for differences in δ18OH2O. Abiotic NO2–H2O equilibrium isotope effect experiments yielded a rate constant of (1.13 ± 007) × 10−2 (h−1) and an equilibrium isotope effect of 11.9 ± 0.1‰ under the condition of pH = 7.5, 30°C, and 2.5% salinity. Conclusions: Oxygen isotope ratio measurements of NO2− by the azide method are highly sensitive to δ18OH2O as a result of significant oxygen isotope exchange between NO2− and H2O. Therefore, to obtain the most accurate measurements water with the same δ18OH2O value as that of the sample must be used to make the NO2− and NO3− standards. [ABSTRACT FROM AUTHOR]

Published
2021
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49. Evapotranspiration simulations in ISIMIP2a—Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets

Author

Wartenburger, Richard, primary, Seneviratne, Sonia I, additional, Hirschi, Martin, additional, Chang, Jinfeng, additional, Ciais, Philippe, additional, Deryng, Delphine, additional, Elliott, Joshua, additional, Folberth, Christian, additional, Gosling, Simon N, additional, Gudmundsson, Lukas, additional, Henrot, Alexandra-Jane, additional, Hickler, Thomas, additional, Ito, Akihiko, additional, Khabarov, Nikolay, additional, Kim, Hyungjun, additional, Leng, Guoyong, additional, Liu, Junguo, additional, Liu, Xingcai, additional, Masaki, Yoshimitsu, additional, Morfopoulos, Catherine, additional, Müller, Christoph, additional, Schmied, Hannes Müller, additional, Nishina, Kazuya, additional, Orth, Rene, additional, Pokhrel, Yadu, additional, Pugh, Thomas A M, additional, Satoh, Yusuke, additional, Schaphoff, Sibyll, additional, Schmid, Erwin, additional, Sheffield, Justin, additional, Stacke, Tobias, additional, Steinkamp, Joerg, additional, Tang, Qiuhong, additional, Thiery, Wim, additional, Wada, Yoshihide, additional, Wang, Xuhui, additional, Weedon, Graham P, additional, Yang, Hong, additional, and Zhou, Tian, additional

Published
2018
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50. Evapotranspiration simulations in ISIMIP2a-Evaluation of spatio-temporal characteristics with a comprehensive ensemble of independent datasets

Author

Wartenburger, Richard, Seneviratne, Sonia, Hirschi, Martin, Chang, Jinfeng, Ciais, Philippe, Deryng, Delphine, Elliott, Joshua, Folberth, Christian, Gosling, Simon N., Gudmundsson, Lukas, Henrot, Alexandra-Jane, Hickler, Thomas, Ito, Akihiko, Khabarov, Nikolay, Kim, Hyungjun, Leng, Guoyong, Liu, Junguo, Liu, Xingcai, Masaki, Yoshimitsu, Morfopoulos, Catherine, Müller, Christoph, Müller Schmied, Hannes, Nishina, Kazuya, Orth, Rene, Pokhrel, Yadu, Pugh, Thomas A. M., Satoh, Yusuke, Schaphoff, Sibyll, Schmid, Erwin, Sheffield, Justin, Stacke, Tobias, Steinkamp, Joerg, Tang, Qiuhong, Thiery, Wim, Wada, Yoshihide, Wang, Xuhui, Weedon, Graham P., Yang, Hong, Zhou, Tian, Wartenburger, Richard, Seneviratne, Sonia, Hirschi, Martin, Chang, Jinfeng, Ciais, Philippe, Deryng, Delphine, Elliott, Joshua, Folberth, Christian, Gosling, Simon N., Gudmundsson, Lukas, Henrot, Alexandra-Jane, Hickler, Thomas, Ito, Akihiko, Khabarov, Nikolay, Kim, Hyungjun, Leng, Guoyong, Liu, Junguo, Liu, Xingcai, Masaki, Yoshimitsu, Morfopoulos, Catherine, Müller, Christoph, Müller Schmied, Hannes, Nishina, Kazuya, Orth, Rene, Pokhrel, Yadu, Pugh, Thomas A. M., Satoh, Yusuke, Schaphoff, Sibyll, Schmid, Erwin, Sheffield, Justin, Stacke, Tobias, Steinkamp, Joerg, Tang, Qiuhong, Thiery, Wim, Wada, Yoshihide, Wang, Xuhui, Weedon, Graham P., Yang, Hong, and Zhou, Tian

Abstract

Actual land evapotranspiration (ET) is a key component of the global hydrological cycle and an essential variable determining the evolution of hydrological extreme events under different climate change scenarios. However, recently available ET products show persistent uncertainties that are impeding a precise attribution of human-induced climate change. Here, we aim at comparing a range of independent global monthly land ET estimates with historical model simulations from the global water, agriculture, and biomes sectors participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). Among the independent estimates, we use the EartH2Observe Tier-1 dataset (E2O), two commonly used reanalyses, a pre-compiled ensemble product (LandFlux-EVAL), and an updated collection of recently published datasets that algorithmically derive ET from observations or observations-based estimates (diagnostic datasets). A cluster analysis is applied in order to identify spatio-temporal differences among all datasets and to thus identify factors that dominate overall uncertainties. The clustering is controlled by several factors including the model choice, the meteorological forcing used to drive the assessed models, the data category (models participating in the different sectors of ISIMIP2a, E2O models, diagnostic estimates, reanalysis-based estimates or composite products), the ET scheme, and the number of soil layers in the models. By using these factors to explain spatial and spatio-temporal variabilities in ET, we find that the model choice mostly dominates (24%-40% of variance explained), except for spatio-temporal patterns of total ET, where the forcing explains the largest fraction of the variance (29%). The most dominant clusters of datasets are further compared with individual diagnostic and reanalysis-based estimates to assess their representation of selected heat waves and droughts in the Great Plains, Central Europe and western Russia.

Published
2018
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