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6. Global critical soil moisture thresholds of plant water stress

Author

Fu, Zheng, Ciais, Philippe, Wigneron, Jean-Pierre, Gentine, Pierre, Feldman, Andrew F., Makowski, David, Viovy, Nicolas, Kemanian, Armen R., Goll, Daniel S., Stoy, Paul C., Prentice, Iain Colin, Yakir, Dan, Liu, Liyang, Ma, Hongliang, Li, Xiaojun, Huang, Yuanyuan, Yu, Kailiang, Zhu, Peng, Li, Xing, Zhu, Zaichun, Lian, Jinghui, and Smith, William K.

Published
2024
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11. Sensitivity of gross primary productivity to climatic drivers during the summer drought of 2018 in Europe

Author

Fu, Zheng, Ciais, Philippe, Bastos, Ana, Stoy, Paul C., Yang, Hui, Green, Julia K., Wang, Bingxue, Yu, Kailiang, Huang, Yuanyuan, Knohl, Alexander, Šigut, Ladislav, Gharun, Mana, Cuntz, Matthias, Arriga, Nicola, Roland, Marilyn, Peichl, Matthias, Migliavacca, Mirco, Cremonese, Edoardo, Varlagin, Andrej, Brümmer, Christian, de la Motte, Louis Gourlez, Fares, Silvano, Buchmann, Nina, El-Madany, Tarek S., Pitacco, Andrea, Vendrame, Nadia, Li, Zhaolei, Vincke, Caroline, Magliulo, Enzo, and Koebsch, Franziska

Published
2020

16. Magnitude of urban heat islands largely explained by climate and population

Author

Manoli, Gabriele, Fatichi, Simone, Schläpfer, Markus, Yu, Kailiang, Crowther, Thomas W., Meili, Naika, and Burlando, Paolo

Subjects
City planning -- Environmental aspects, Atmospheric temperature -- Measurement, Climatic changes -- Environmental aspects, Urban heat islands -- Environmental aspects, Environmental issues, Science and technology, Zoology and wildlife conservation
Abstract

Urban heat islands (UHIs) exacerbate the risk of heat-related mortality associated with global climate change. The intensity of UHIs varies with population size and mean annual precipitation, but a unifying explanation for this variation is lacking, and there are no geographically targeted guidelines for heat mitigation. Here we analyse summertime differences between urban and rural surface temperatures ([DELTA]T.sub.s) worldwide and find a nonlinear increase in [DELTA]T.sub.s with precipitation that is controlled by water or energy limitations on evapotranspiration and that modulates the scaling of [DELTA]T.sub.s with city size. We introduce a coarse-grained model that links population, background climate, and UHI intensity, and show that urban-rural differences in evapotranspiration and convection efficiency are the main determinants of warming. The direct implication of these nonlinearities is that mitigation strategies aimed at increasing green cover and albedo are more efficient in dry regions, whereas the challenge of cooling tropical cities will require innovative solutions. The effect of cities on urban climate (often warmer but sometimes cooler than their surroundings) is largely explained by local hydroclimate and patterns of city development., Author(s): Gabriele Manoli [sup.1] [sup.6] , Simone Fatichi [sup.1] , Markus Schläpfer [sup.2] , Kailiang Yu [sup.3] , Thomas W. Crowther [sup.3] , Naika Meili [sup.1] [sup.2] , Paolo Burlando [...]

Published
2019
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21. Nitrogen addition delays the emergence of an aridity-induced threshold for plant biomass.

Author

Li, Hailing, Terrer, César, Berdugo, Miguel, Maestre, Fernando T, Zhu, Zaichun, Peñuelas, Josep, Yu, Kailiang, Luo, Lin, Gong, Jie-Yu, and Ye, Jian-Sheng

Subjects
PLANT biomass, DESERTIFICATION, LEAF area index, GLOBAL environmental change, LAND degradation, WATER efficiency
Abstract

Crossing certain aridity thresholds in global drylands can lead to abrupt decays of ecosystem attributes such as plant productivity, potentially causing land degradation and desertification. It is largely unknown, however, whether these thresholds can be altered by other key global change drivers known to affect the water-use efficiency and productivity of vegetation, such as elevated CO2 and nitrogen (N). Using >5000 empirical measurements of plant biomass, we showed that crossing an aridity (1–precipitation/potential evapotranspiration) threshold of ∼0.50, which marks the transition from dry sub-humid to semi-arid climates, led to abrupt declines in aboveground biomass (AGB) and progressive increases in root:shoot ratios, thus importantly affecting carbon stocks and their distribution. N addition significantly increased AGB and delayed the emergence of its aridity threshold from 0.49 to 0.55 (P  < 0.05). By coupling remote sensing estimates of leaf area index with simulations from multiple models, we found that CO2 enrichment did not alter the observed aridity threshold. By 2100, and under the RCP 8.5 scenario, we forecast a 0.3% net increase in the global land area exceeding the aridity threshold detected under a scenario that includes N deposition, in comparison to a 2.9% net increase if the N effect is not considered. Our study thus indicates that N addition could mitigate to a great extent the negative impact of increasing aridity on plant biomass in drylands. These findings are critical for improving forecasts of abrupt vegetation changes in response to ongoing global environmental change. [ABSTRACT FROM AUTHOR]

Published
2023
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22. Biogeographic pattern of living vegetation carbon turnover time in mature forests across continents.

Author

Yu, Kailiang, Ciais, Philippe, Bloom, Anthony A., Wang, Jinsong, Liu, Zhihua, Chen, Han Y. H., Wang, Yilong, Chen, Yizhao, and Ballantyne, Ashley P.

Subjects
*VEGETATION patterns, *FOREST surveys, *FOREST density, *CONTINENTS, *TEMPERATE forests, *WOODY plants
Abstract

Aim: Theoretically, woody biomass turnover time (τ) quantified using outflux (i.e. tree mortality) predicts biomass dynamics better than using influx (i.e. productivity). This study aims at using forest inventory data to empirically test the outflux approach and generate a spatially explicit understanding of woody τ in mature forests. We further compared woody τ estimates with dynamic global vegetation models (DGVMs) and with a data assimilation product of C stocks and fluxes—CARDAMOM. Location: Continents. Time Period: Historic from 1951 to 2018. Major Taxa Studied: Trees and forests. Methods: We compared the approaches of using outflux versus influx for estimating woody τ and predicting biomass accumulation rates. We investigated abiotic and biotic drivers of spatial woody τ and generated a spatially explicit map of woody τ at a 0.25‐degree resolution across continents using machine learning. We further examined whether six DGVMs and CARDAMOM generally captured the observational pattern of woody τ. Results: Woody τ quantified by the outflux approach better (with R2 0.4–0.5) predicted the biomass accumulation rates than the influx approach (with R2 0.1–0.4) across continents. We found large spatial variations of woody τ for mature forests, with highest values in temperate forests (98.8 ± 2.6 y) followed by boreal forests (73.9 ± 3.6 y) and tropical forests. The map of woody τ extrapolated from plot data showed higher values in wetter eastern and pacific coast USA, Africa and eastern Amazon. Climate (temperature and aridity index) and vegetation structure (tree density and forest age) were the dominant drivers of woody τ across continents. The highest woody τ in temperate forests was not captured by either DGVMs or CARDAMOM. Main Conclusions: Our study empirically demonstrated the preference of using outflux over influx to estimate woody τ for predicting biomass accumulation rates. The spatially explicit map of woody τ and the underlying drivers provide valuable information to improve the representation of forest demography and carbon turnover processes in DGVMs. [ABSTRACT FROM AUTHOR]

Published
2023
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24. Global depth distribution of belowground net primary productivity and its drivers.

Author

Xiao, Liujun, Wang, Guocheng, Chang, Jinfeng, Chen, Yaoyao, Guo, Xiaowei, Mao, Xiali, Wang, Mingming, Zhang, Shuai, Shi, Zhou, Luo, Yiqi, Cheng, Lei, Yu, Kailiang, Mo, Fei, and Luo, Zhongkui

Subjects
NUCLEAR power plants, SOIL depth, CARBON in soils, SOIL dynamics, SOIL profiles
Abstract

Aim: This study aimed to infer the allocation of belowground net primary productivity (BNPP) to sequential soil depths down to 2 m across the globe at a 1 km resolution and assess underlying environmental drivers. Location: Global. Time Period: Contemporary (1932–2017). Major Taxa Studied: Terrestrial plants. Methods: Global datasets including field net primary production (NPP, i.e., the difference between plant assimilated and respired carbon) from 725 soil profiles, root biomass and its depth distribution from 559 soil profiles were compiled and used to infer the depth distribution of BNPP across the globe and digitally map depth‐resolved BNPP globally at 1 km resolution. Drivers of the depth distribution of BNPP were evaluated using machine learning‐based models. Results: Global average BNPP allocated to the 0–20 cm soil layer is estimated to be 1.1 Mg C ha−1 yr−1, accounting for ~60% of total BNPP. Across the globe, the depth distribution of BNPP varies largely, and more BNPP is allocated to deeper layers in hotter and drier regions. Edaphic, climatic and topographic properties (in order of importance) explain >80% of such variability; and the direction and magnitude of the influence of individual properties are soil depth‐ and biome‐dependent. Main Conclusions: The findings suggest that mean annual temperature and precipitation are the two most important factors regulating BNPP across the globe. Soil properties such as soil actual evaporation and total nitrogen also play a vital role in regulating the depth distribution of BNPP. The maps of BNPP provide global benchmarks of depth‐resolved BNPP for the prediction of whole‐profile soil carbon dynamics across biomes. [ABSTRACT FROM AUTHOR]

Published
2023
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25. Global maps of soil temperature

Author

Winkler, Manuela, Plichta, Roman, Buysse, Pauline, Lohila, Annalea, Spicher, Fabien, Boeckx, Pascal, Wild, Jan, Feigenwinter, Iris, Olejnik, Janusz, Risch, Anita, Khuroo, Anzar, Lynn, Joshua, di Cella, Umberto, Schmidt, Marius, Urbaniak, Marek, Marchesini, Luca, Govaert, Sanne, Uogintas, Domas, Assis, Rafael, Medinets, Volodymyr, Abdalaze, Otar, Varlagin, Andrej, Dolezal, Jiri, Myers, Jonathan, Randall, Krystal, Bauters, Marijn, Jimenez, Juan, Stoll, Stefan, Petraglia, Alessandro, Mazzolari, Ana, Ogaya, Romà, Tyystjärvi, Vilna, Hammerle, Albin, Wipf, Sonja, Lorite, Juan, Fanin, Nicolas, Benavides, Juan, Scholten, Thomas, Yu, Zicheng, Veen, G., Treier, Urs, Candan, Onur, Bell, Michael, Hörtnagl, Lukas, Siebicke, Lukas, Vives-Ingla, Maria, Eugster, Werner, Grelle, Achim, Stemkovski, Michael, Theurillat, Jean-Paul, Matula, Radim, Dorrepaal, Ellen, Steinbrecher, Rainer, Alatalo, Juha, Fenu, Giuseppe, Arzac, Alberto, Homeier, Jürgen, Porro, Francesco, Robinson, Sharon, Ghosn, Dany, Haugum, Siri, Ziemblińska, Klaudia, Camargo, José, Zhao, Peng, Niittynen, Pekka, Liljebladh, Bengt, Normand, Signe, Dias, Arildo, Larson, Christian, Peichl, Matthias, Collier, Laura, Myers-Smith, Isla, Zong, Shengwei, Kašpar, Vít, Cooper, Elisabeth, Haider, Sylvia, von Oppen, Jonathan, Cutini, Maurizio, Benito-Alonso, José-Luis, Luoto, Miska, Klemedtsson, Leif, Higgens, Rebecca, Zhang, Jian, Speed, James, Nijs, Ivan, Macek, Martin, Steinwandter, Michael, Poyatos, Rafael, Niedrist, Georg, Curasi, Salvatore, Yang, Yan, Dengler, Jürgen, Géron, Charly, de Pablo, Miguel, Xenakis, Georgios, Kreyling, Juergen, Forte, Tai, Bailey, Joseph, Knohl, Alexander, Goulding, Keith, Wilkinson, Matthew, Kljun, Natascha, Roupsard, Olivier, Stiegler, Christian, Verbruggen, Erik, Wingate, Lisa, Lamprecht, Andrea, Hamid, Maroof, Rossi, Graziano, Descombes, Patrice, Hrbacek, Filip, Bjornsdottir, Katrin, Poulenard, Jérôme, Meeussen, Camille, Guénard, Benoit, Venn, Susanna, Dimarco, Romina, Man, Matěj, Scharnweber, Tobias, Chown, Steven, Pio, Casimiro, Way, Robert, Erickson, Todd, Fernández-Pascual, Eduardo, Pușcaș, Mihai, Orsenigo, Simone, Di Musciano, Michele, Enquist, Brian, Newling, Emily, Tagesson, Torbern, Kemppinen, Julia, Serra-Diaz, Josep, Gottschall, Felix, Schuchardt, Max, Pitacco, Andrea, Jump, Alistair, Exton, Dan, Carnicer, Jofre, Aschero, Valeria, Urban, Anastasiya, Daskalova, Gergana, Santos, Cinthya, Goeckede, Mathias, Bruna, Josef, Andrews, Christopher, Jónsdóttir, Ingibjörg, Casanova-Katny, Angélica, Moriana-Armendariz, Mikel, Ewers, Robert, Pärtel, Meelis, Sagot, Clotilde, Herbst, Mathias, De Frenne, Pieter, Milbau, Ann, Gobin, Anne, Alexander, Jake, Kopecký, Martin, Buchmann, Nina, Kotowska, Martyna, Puchalka, Radoslaw, Penuelas, Josep, Gigauri, Khatuna, Prokushkin, Anatoly, Moiseev, Pavel, Jentsch, Anke, Klisz, Marcin, Barrio, Isabel, Ammann, Christof, Panov, Alexey, Van Geel, Maarten, Finckh, Manfred, Vaccari, Francesco, Erschbamer, Brigitta, Backes, Amanda, Robroek, Bjorn, Campoe, Otávio, Ahmadian, Negar, Boike, Julia, Thomas, Haydn, Pastor, Ada, Smith, Stuart, Pauli, Harald, Kollár, Jozef, de Cássia Guimarães Mesquita, Rita, Michaletz, Sean, Fuentes-Lillo, Eduardo, Urban, Josef, Greenwood, Sarah, Lens, Luc, Van de Vondel, Stijn, Vitale, Luca, Remmele, Sabine, Naujokaitis-Lewis, Ilona, Meusburger, Katrin, Cremonese, Edoardo, Barros, Agustina, Bokhorst, Stef, Svátek, Martin, Allonsius, Camille, Høye, Toke, Smiljanic, Marko, Hik, David, Canessa, Rafaella, van den Hoogen, Johan, Altman, Jan, Björkman, Mats, Cesarz, Simone, Blonder, Benjamin, Kazakis, George, Opedal, Øystein, Assmann, Jakob, Tanentzap, Andrew, Sidenko, Nikita, le Maire, Guerric, Ursu, Tudor-Mihai, Montagnani, Leonardo, Muffler, Lena, Hederová, Lucia, Rubtsov, Alexey, Pauchard, Aníbal, Tielbörger, Katja, Sørensen, Mia, Crowther, Thomas, Remmers, Wolfram, Pitteloud, Camille, Zyryanov, Viacheslav, Nilsson, Matts, Bazzichetto, Manuele, Sallo-Bravo, Jhonatan, Moiseev, Dmitry, Spasojevic, Marko, Haase, Peter, Pearse, William, Tutton, Rosamond, Fazlioglu, Fatih, Siqueira, David, Ardö, Jonas, Nardino, Marianna, Tomaselli, Marcello, Pavelka, Marian, García, Rafael, Nosetto, Marcelo, Bon, Matteo, Semenchuk, Philipp, Choler, Philippe, Scott, Tony, Halbritter, Aud, Dušek, Jiří, Mackenzie, Roy, Stanisci, Angela, Nouvellon, Yann, Kovács, Bence, Haesen, Stef, Veenendaal, Elmar, Juszczak, Radoslaw, Verheijen, Frank, de Andrade, Ana, Verbeeck, Hans, Bader, Maaike, RENAULT, David, Zimmermann, Reiner, Ferlian, Olga, Medinets, Sergiy, Walz, Josefine, Rossi, Christian, Rocha, Adrian, Lembrechts, Jonas, Jactel, Hervé, Brum, Barbara, Aartsma, Peter, Kobler, Johannes, Eisenhauer, Nico, Bjerke, Jarle, Pellissier, Loïc, Ueyama, Masahito, Manca, Giovanni, Bahalkeh, Khadijeh, Meysman, Filip, Niessner, Armin, Curtis, Robin, Six, Johan, Saccone, Patrick, Wang, Runxi, Ahrends, Antje, Okello, Joseph, Kolle, Olaf, Portillo-Estrada, Miguel, Laska, Kamil, Freeman, Erika, Di Cecco, Valter, Ashcroft, Michael, Steinbauer, Klaus, Della Chiesa, Stefano, van den Brink, Liesbeth, Herberich, Maximiliane, Loubet, Benjamin, Barančok, Peter, Hermanutz, Luise, Souza, Bartolomeu, Contador, Tamara, Zhang, Zhaochen, Aerts, Rien, Stephan, Jörg, Chojnicki, Bogdan, Manco, Antonio, Larson, Keith, Mondoni, Andrea, Palaj, Andrej, Schmeddes, Jonas, Hepenstrick, Daniel, Järveoja, Järvi, Manise, Tanguy, Barthel, Matti, Marciniak, Felipe, Weigel, Robert, Rixen, Christian, Turtureanu, Pavel, Hoffrén, Raúl, Iwata, Hiroki, Vittoz, Pascal, Wedegärtner, Ronja, Penczykowski, Rachel, Phartyal, Shyam, Sitková, Zuzana, Nagy, Laszlo, Ujházy, Karol, Heinesch, Bernard, Berauer, Bernd, Ogée, Jérôme, Malfasi, Francesco, Greise, Caroline, Helfter, Carole, Mosedale, Jonathan, Senior, Rebecca, Magliulo, Enzo, Nuñez, Martin, García, María, Wohlfahrt, Georg, Carbognani, Michele, Thomas, Andrew, Eklundh, Lars, Erfanian, Mohammad, Villar, Luis, Maier, Regine, Dahlberg, C., Guglielmin, Mauro, Jucker, Tommaso, Kelly, Julia, Olesen, Jørgen, Lang, Simone, Tanneberger, Franziska, Gharun, Mana, Jackowicz-Korczynski, Marcin, Convey, Peter, Aalto, Juha, Scheffers, Brett, Ujházyová, Mariana, Andres, Christian, Arriga, Nicola, Smith-Tripp, Sarah, Kanka, Róbert, Dick, Jan, Leihy, Rachel, Van Meerbeek, Koenraad, Maclean, Ilya, Vangansbeke, Pieter, Pampuch, Timo, Čiliak, Marek, Guillemot, Joannès, Sarneel, Judith, Souza, José, Svoboda, Miroslav, Björk, Robert, Merinero, Sonia, Zellweger, Florian, Simpson, Elizabeth, Cannone, Nicoletta, Abedi, Mehdi, Seipel, Tim, Klinges, David, Máliš, František, Basham, Edmund, Sewerniak, Piotr, Schwartz, Naomi, Trouillier, Mario, Vandvik, Vigdis, Shekhar, Ankit, Munoz-Rojas, Miriam, Nicklas, Lena, Goded, Ignacio, Manolaki, Paraskevi, Radujković, Dajana, Yu, Kailiang, Phoenix, Gareth, Cifuentes, Edgar, Seeber, Julia, Deronde, Bart, Lenoir, Jonathan, Frei, Esther, Wilmking, Martin, Hylander, Kristoffer, Graae, Bente, Calzado, M., Wang, Yifeng, Hampe, Arndt, Somers, Ben, Mörsdorf, Martin, Jastrzebowski, Szymon, Ejtehadi, Hamid, Terrestrial Ecology (TE), Universidad de Alcalá. Departamento de Geología, Geografía y Medio Ambiente, BioGeoClimate Modelling Lab, Department of Geosciences and Geography, Helsinki Institute of Sustainability Science (HELSUS), Institute for Atmospheric and Earth System Research (INAR), Universiteit Antwerpen = University of Antwerpen [Antwerpen], Ecosystèmes, biodiversité, évolution [Rennes] (ECOBIO), Université de Rennes (UR)-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Rennes (OSUR), Université de Rennes (UR)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Centre National de la Recherche Scientifique (CNRS), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire d'Ecologie Alpine (LECA ), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), LTSER Zone Atelier Alpes, Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), 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)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, 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), Département Performances des systèmes de production et de transformation tropicaux (Cirad-PERSYST), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad), Senckenberg Research Institute and Natural History Museum [Frankfurt], Senckenberg – Leibniz Institution for Biodiversity and Earth System Research - Senckenberg Gesellschaft für Naturforschung, Leibniz Association-Leibniz Association, Biodiversité, Gènes & Communautés (BioGeCo), Université de Bordeaux (UB)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Environnements, Dynamiques et Territoires de Montagne (EDYTEM), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), SILVA (SILVA), AgroParisTech-Université de Lorraine (UL)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Ecologie et Dynamique des Systèmes Anthropisés - UMR CNRS 7058 (EDYSAN), Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), 12P1819N, Fonds Wetenschappelijk Onderzoek, ANR-10-LABX-0045,COTE,COntinental To coastal Ecosystems: evolution, adaptability and governance(2010), ANR-13-ISV7-0004,ODYSSEE,De nouvelles voies pour la modélisation des dynamiques d'assemblages d'espèces intégrant l'écologie et l'évolution: le cas des écosystèmes de montagne des Alpes et des Carpates(2013), ANR-20-EBI5-0004,ASICS,ASsessing and mitigating the effects of climate change and biological Invasions on the spatial redistribution of biodiversity in Cold environmentS(2020), ANR-19-CE32-0005,IMPRINT,IMpacts des PRocessus mIcroclimatiques sur la redistributioN de la biodiversiTé forestière en contexte de réchauffement du macroclimat(2019), European Project: 774124 , H2020,H2020-SFS-2017-2,SUPER-G (2018), European Project: 282910,EC:FP7:ENV,FP7-ENV-2011,ECLAIRE(2011), European Project: 641918,H2020,H2020-SC5-2014-two-stage,AfricanBioServices(2015), European Project: 678841,H2020,ERC-2015-STG,NICH(2016), European Project: 871128,eLTER PLUS (2020), European Project: 861974, H2020,SOCIETAL CHALLENGES - Food security, sustainable agriculture and forestry, marine, maritime and inland water research, and the bioeconomy,SustainSahel(2020), Lembrechts, Jonas J [0000-0002-1933-0750], van den Hoogen, Johan [0000-0001-6624-8461], Aalto, Juha [0000-0001-6819-4911], De Frenne, Pieter [0000-0002-8613-0943], Kemppinen, Julia [0000-0001-7521-7229], Kopecký, Martin [0000-0002-1018-9316], Luoto, Miska [0000-0001-6203-5143], Maclean, Ilya MD [0000-0001-8030-9136], Crowther, Thomas W [0000-0001-5674-8913], Bailey, Joseph J [0000-0002-9526-7095], Haesen, Stef [0000-0002-4491-4213], Klinges, David H [0000-0002-7900-9379], Niittynen, Pekka [0000-0002-7290-029X], Scheffers, Brett R [0000-0003-2423-3821], Van Meerbeek, Koenraad [0000-0002-9260-3815], Aartsma, Peter [0000-0001-5086-856X], Abdalaze, Otar [0000-0001-8140-0900], Abedi, Mehdi [0000-0002-1499-0119], Aerts, Rien [0000-0001-6694-0669], Ahmadian, Negar [0000-0002-7427-7198], Ahrends, Antje [0000-0002-5083-7760], Alatalo, Juha M [0000-0001-5084-850X], Alexander, Jake M [0000-0003-2226-7913], Allonsius, Camille Nina [0000-0003-2599-9941], Altman, Jan [0000-0003-4879-5773], Ammann, Christof [0000-0002-0783-5444], Andres, Christian [0000-0003-0576-6446], Andrews, Christopher [0000-0003-2428-272X], Ardö, Jonas [0000-0002-9318-0973], Arriga, Nicola [0000-0001-5321-3497], Arzac, Alberto [0000-0002-3361-5349], Aschero, Valeria [0000-0003-3865-4133], Assis, Rafael L [0000-0001-8468-6414], Assmann, Jakob Johann [0000-0002-3492-8419], Bader, Maaike Y [0000-0003-4300-7598], Bahalkeh, Khadijeh [0000-0003-1485-0316], Barančok, Peter [0000-0003-1171-2524], Barrio, Isabel C [0000-0002-8120-5248], Barros, Agustina [0000-0002-6810-2391], Basham, Edmund W [0000-0002-0167-7908], Bauters, Marijn [0000-0003-0978-6639], Bazzichetto, Manuele [0000-0002-9874-5064], Marchesini, Luca Belelli [0000-0001-8408-4675], Bell, Michael C [0000-0002-3401-7746], Benavides, Juan C [0000-0002-9694-2195], Benito Alonso, José Luis [0000-0003-1086-8834], Berauer, Bernd J [0000-0002-9472-1532], Bjerke, Jarle W [0000-0003-2721-1492], Björk, Robert G [0000-0001-7346-666X], Björkman, Mats P [0000-0001-5768-1976], Björnsdóttir, Katrin [0000-0001-7421-9441], Blonder, Benjamin [0000-0002-5061-2385], Boeckx, Pascal [0000-0003-3998-0010], Boike, Julia [0000-0002-5875-2112], Bokhorst, Stef [0000-0003-0184-1162], Brum, Bárbara NS [0000-0002-8421-3200], Brůna, Josef [0000-0002-4839-4593], Buchmann, Nina [0000-0003-0826-2980], Camargo, José Luís [0000-0003-0370-9878], Campoe, Otávio C [0000-0001-9810-8834], Candan, Onur [0000-0002-9254-4122], Canessa, Rafaella [0000-0002-6979-9880], Cannone, Nicoletta [0000-0002-3390-3965], Carbognani, Michele [0000-0001-7701-9859], Carnicer, Jofre [0000-0001-7454-8296], Casanova-Katny, Angélica [0000-0003-3860-1445], Cesarz, Simone [0000-0003-2334-5119], Chojnicki, Bogdan [0000-0002-9012-4060], Choler, Philippe [0000-0002-9062-2721], Chown, Steven L [0000-0001-6069-5105], Cifuentes, Edgar F [0000-0001-5918-5861], Čiliak, Marek [0000-0002-6720-9365], Contador, Tamara [0000-0002-0250-9877], Convey, Peter [0000-0001-8497-9903], Cooper, Elisabeth J [0000-0002-0634-1282], Cremonese, Edoardo [0000-0002-6708-8532], Curasi, Salvatore R [0000-0002-4534-3344], Cutini, Maurizio 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Subjects
0106 biological sciences, Zoology and botany: 480 [VDP], Q1, 01 natural sciences, Global map, SDG 13 - Climate Action, Soil temperature, Zone climatique, bepress|Physical Sciences and Mathematics|Environmental Sciences, bioclimatic variables, global maps, microclimate, near-surface temperatures, soil temperature, soil-dwelling organisms, temperature offset, weather stations, ComputingMilieux_MISCELLANEOUS, General Environmental Science, Global and Planetary Change, GB, Geology, PE&RC, 6. Clean water, Near-surface soil temperature, international, [SDE]Environmental Sciences, 551: Geologie und Hydrologie, Plantenecologie en Natuurbeheer, Température du sol, Near-surface temperature, Near-surface temperatures, Biologie, P40 - Météorologie et climatologie, bepress|Physical Sciences and Mathematics|Earth Sciences, MITIGATION, bepress|Life Sciences|Ecology and Evolutionary Biology, bepress|Physical Sciences and Mathematics|Oceanography and Atmospheric Sciences and Meteorology|Climate, Bioclimatic variables, Settore BIO/07 - ECOLOGIA, 577: Ökologie, Biology, Ecosystem, Ekologi, Changement climatique, Cartographie, Biology and Life Sciences, Microclimate, 15. Life on land, bepress|Physical Sciences and Mathematics|Environmental Sciences|Environmental Monitoring, Agriculture and Soil Science, 0401 agriculture, forestry, and fisheries, Temperature offset, Weather stations, Plan_S-Compliant-OA, Soil, bepress|Life Sciences, ddc:550, Geología, Ecology, Temperature, 04 agricultural and veterinary sciences, Biological Sciences, FOREST, Weather station, Variation saisonnière, Chemistry, Bioclimatologie, bepress|Physical Sciences and Mathematics, 1171 Geosciences, Technology and Engineering, Climate Change, Plant Ecology and Nature Conservation, MOISTURE, LITTER DECOMPOSITION, PERMAFROST, ddc:570, SUITABILITY, G1, bepress|Physical Sciences and Mathematics|Oceanography and Atmospheric Sciences and Meteorology, Global maps, VDP::Mathematics and natural scienses: 400::Zoology and botany: 480, Environmental Chemistry, Zoologiske og botaniske fag: 480 [VDP], Soil-dwelling organisms, Aquatic Ecology, P30 - Sciences et aménagement du sol, Bioclimatic variable, SNOW-COVER, bepress|Physical Sciences and Mathematics|Earth Sciences|Soil Science, Earth sciences, PLANT-RESPONSES, CLIMATIC CONTROLS, Soil-dwelling organism, 13. Climate action, Earth and Environmental Sciences, VDP::Matematikk og naturvitenskap: 400::Zoologiske og botaniske fag: 480, 040103 agronomy & agriculture, Réchauffement global, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, Environmental Sciences, 010606 plant biology & botany
Abstract

JJL received funding from the Research Foundation Flanders (grant nr. 12P1819N). The project received funding from the Research Foundation Flanders (grants nrs, G018919N, W001919N). JVDH and TWC received funding from DOB Ecology. JA received funding from the University of Helsinki, Faculty of Science (MICROCLIM, grant nr. 7510145) and Academy of Finland Flagship (grant no. 337552). PDF, CM and PV received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (ERC Starting Grant FORMICA 757833). JK received funding from the Arctic Interactions at the University of Oulu and Academy of Finland (318930, Profi 4), Maaja vesitekniikan tuki ry., Tiina and Antti Herlin Foundation, Nordenskiold Samfundet and Societas pro Fauna et Flora Fennica. MK received funding from the Czech Science Foundation (grant nr. 20-28119S) and the Czech Academy of Sciences (grant nr. RVO 67985939). TWC received funding from National Geographic Society grant no. 9480-14 and WW-240R-17. MA received funding from CISSC (program ICRP (grant nr:2397) and INSF (grant nr: 96005914). The Royal Botanic Garden Edinburgh is supported by the Scottish Government's Rural and Environment Science and Analytical Services Division. JMA received funding from the Funding Org. Qatar Petroleum (grant nr. QUEX-CAS-QP-RD-18/19). JMA received funding from the European Union's Horizon 2020 research and innovation program (grant no. 678841) and from the Swiss National Science Foundation (grant no. 31003A_176044). JA was supported by research grants LTAUSA19137 (program INTER-EXCELLENCE, subprogram INTER-ACTION) provided by Czech Ministry of Education, Youth and Sports and 20-05840Y of the Czech Science Foundation. AA was supported by the Ministry of Science and Higher Education of the Russian Federation (grant FSRZ-2020-0014). SN, UAT, JJA, and JvO received funding from the Independent Research Fund Denmark (7027-00133B). LvdB, KT, MYB and RC acknowledge funding from the German Research Foundation within the Priority Program SPP-1803 'EarthShape: Earth Surface Shaping by Biota' (grant TI 338/14-1&2 and BA 3843/6-1). PB was supported by grant project VEGA of the Ministry of Education of the Slovak Republic and the Slovak Academy of Sciences No. 2/0132/18. Forest Research received funding from the Forestry Commission (climate change research programme). JCB acknowledges the support of Universidad Javeriana. JLBA received funding from the Direccion General de Cambio Climatico del Gobierno de Aragon; JLBA acknowledges fieldwork assistance by Ana Acin, the Ordesa y Monte Perdido National Park, and the Servicio de Medio Ambiente de Soria de la Junta de Castilla y Leon. RGB and MPB received funding from BECC - Biodiversity and Ecosystem services in a Changing Climate. MPB received funding from The European Union's Horizon 2020 research and innovation program under the Marie Skodowska-Curie Grant Agreement No. 657627 and The Swedish Research Council FORMAS - future research leaders No. 2016-01187. JB received funding from the Czech Academy of Sciences (grant nr. RVO 67985939). NB received funding from the SNF (grant numbers 40FA40_154245, 20FI21_148992, 20FI20_173691, 407340_172433) and from the EU (contract no. 774124). ICOS EU research infrastructure. EU FP7 NitroEurope. EU FP7 ECLAIRE. The authors from Biological Dynamics of Forest Fragments Project, PDBFF, Instituto Nacional de Pesquisas da Amazonia, Brazil were supported by the MCTI/CNPq/FNDCT - AcAo Transversal no68/2013 - Programa de Grande Escala da Biosfera-Atmosfera na Amazonia - LBA; Project 'Como as florestas da Amazonia Central respondem as variacoes climaticas? Efeitos sobre dinamica florestal e sinergia com a fragmentacAo florestal'. This is the study 829 of the BDFFP Technical Series. to The EUCFLUX Cooperative Research Program and Forest Science and Research Institute-IPEF. NC acknowledges funding by Stelvio National Park. JC was funded by the Spanish government grant CGL2016-78093-R. ANID-FONDECYT 1181745 AND INSTITUTO ANTARTICO CHILENO (INACH FR-0418). SC received funding from the German Research Foundation (grant no. DFG- FZT 118, 202548816). The National Science Foundation, Poland (grant no. UMO-2017/27/B/ST10/02228), within the framework of the 'Carbon dioxide uptake potential of sphagnum peatlands in the context of atmospheric optical parameters and climate changes' (KUSCO2) project. SLC received funding from the South African National Research Foundation and the Australian Research Council. FM, M, KU and MU received funding from Slovak Research and Development Agency (no. APVV-19-0319). Instituto Antartico Chileno (INACH_RT-48_16), Iniciativa Cientifica Milenio Nucleo Milenio de Salmonidos Invasores INVASAL, Institute of Ecology and Biodiversity (IEB), CONICYT PIA APOYO CCTE AFB170008. PC is supported by NERC core funding to the BAS 'Biodiversity, Evolution and Adaptation Team. EJC received funding from the Norwegian Research Council (grant number 230970). GND was supported by NERC E3 doctoral training partnership grant (NE/L002558/1) at the University of Edinburgh and the Carnegie Trust for the Universities of Scotland. Monitoring stations on Livingston Island, Antarctica, were funded by different research projects of the Gobern of Spain (PERMAPLANET CTM2009-10165-E; ANTARPERMA CTM2011-15565-E; PERMASNOW CTM2014-52021-R), and the PERMATHERMAL arrangement between the University of Alcala and the Spanish Polar Committee. GN received funding from the Autonomous Province of Bolzano (ITA). The infrastructure, part of the UK Environmental Change Network, was funded historically in part by ScotNature and NERC National Capability LTS-S: UK-SCAPE; NE/R016429/1). JD was supported by the Czech Science Foundation (GA17-19376S) and MSMT (LTAUSA18007). ED received funding from the Kempe Foundation (JCK-1112 and JCK-1822). The infrastructure was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme I (NPU I), grant number LO1415 and by the project for national infrastructure support CzeCOS/ICOS Reg. No. LM2015061. NE received funding from the German Research Foundation (DFG- FZT 118, 202548816). BE received funding from the GLORIA-EU project no EVK2-CT2000-00056, the Autonomous Province of Bolzano (ITA), from the Tiroler Wissenschaftsfonds and from the University of Innsbruck. RME was supported by funding to the SAFE Project from the Sime Darby Foundation. OF received funding from the German Research Foundation (DFG- FZT 118, 202548816). EFP was supported by the Jardin Botanico Atlantico (SV-20-GIJON-JBA). MF was funded by the German Federal Ministry of Education and Research (BMBF) in the context of The Future Okavango (Grant No. 01LL0912) and SASSCAL (01LG1201M; 01LG1201N) projects. EFL received funding from ANID PIA / BASAL FB210006. RAG received funding from Fondecyt 11170516, CONICYT PIA AFB170008 and ANID PIA / BASAL FB210006. MBG received funding from National Parks (DYNBIO, #1656/2015) and The Spanish Research Agency (VULBIMON, #CGL2017-90040-R). MG received funding from the Swiss National Science Foundation (ICOS-CH Phase 2 20FI20_173691). FG received funding from the German Research Foundation (DFG- FZT 118, 202548816). KG and TS received funding from the UK Biotechnology and Biological Research Council (grant = 206/D16053). SG was supported by the Research Foundation Flanders (FWO) (project G0H1517N). KJ and PH received funding from the EU Horizon2020 INFRAIA project eLTER-PLUS (871128), the project LTER-CWN (FFG, F&E Infrastrukturforderung, project number 858024) and the Austrian Climate Research Program (ACRP7 - CentForCSink - KR14AC7K11960). SH and ARB received funding through iDiv funded by the German Research Foundation (DFG- FZT 118, 202548816). LH received funding from the Czech Science Foundation (grant nr. 20-28119S) and the Czech Academy of Sciences (grant nr. RVO 67985939). MH received funding from the Baden-Wurttemberg Ministry of Science, Research and Arts via the project DRIeR (Drought impacts, processes and resilience: making the in-visible visible). LH received funding from International Polar Year, Weston Foundation, and ArcticNet. DH received funding from Natural Sciences and Engineering Council (Canada) (RGPIN-06691). TTH received funding from Independent Research Fund Denmark (grant no. 8021-00423B) and Villum Foundation (grant no. 17523). Ministry of Education, Youth and Sports of the Czech Republic (projects LM2015078, VAN2020/01 and CZ.02.1.01/0.0/0.0/16_013/0001708). KH, CG and CJD received funding from Bolin Centre for Climate Research, Stockholm University and from the Swedish research council Formas [grant n:o 2014-00530 to KH]. JJ received funding from the Funding Org. Swedish Forest Society Foundation (grant nr. 2018-485-Steg 2 2017) and Swedish Research Council FORMAS (grant nr. 2018-00792). AJ received funding from the German Federal Ministry of Education and Research BMBF (Grant Nr. FKZ 031B0516C SUSALPS) and the Oberfrankenstiftung (Grant Nr. OFS FP00237). ISJ received funding from the Energy Research Fund (NYR-11 - 2019, NYR-18 - 2020). TJ was supported by a UK NERC Independent Research Fellowship (grant number: NE/S01537X/1). RJ received funding from National Science Centre of Poland (grant number: 2016/21/B/ST10/02271) and Polish National Centre for Research and Development (grant number: Pol-Nor/203258/31/2013). VK received funding from the Czech Academy of Sciences (grant nr. RVO 67985939). AAK received funding from MoEFCC, Govt of India (AICOPTAX project F. No. 22018/12/2015/RE/Tax). NK received funding from FORMAS (grants nr. 2018-01781, 2018-02700, 2019-00836), VR, support from the research infrastructure ICOS-SE. BK received funding from the National Research, Development and Innovation Fund of Hungary (grant nr. K128441). Ministry of Education, Youth and Sports of the Czech Republic (projects LM2015078 and CZ.02.1.01/0.0/0.0/16_013/0001708). Project B1-RNM-163-UGR-18-Programa Operativo FEDER 2018, partially funded data collection. Norwegian Research Council (NORKLIMA grants #184912 and #244525) awarded to Vigdis Vandvik. MM received funding from the Czech Science Foundation (grant nr. 20-28119S) and the Czech Academy of Sciences (grant nr. RVO 67985939). Project CONICYT-PAI 79170119 and ANID-MPG 190029 awarded to Roy Mackenzie. This work was partly funded by project MIUR PON Cluster OT4CLIMA. RM received funding from the SNF project number 407340_172433. FM received funding from the Stelvio National Park. PM received funding from AIAS-COFUND fellowship programme supported by the Marie Skodowska- Curie actions under the European Union's Seventh Framework Pro-gramme for Research, Technological development and Demonstration (grant agreement no 609033) and the Aarhus University Research Foundation, Denmark. RM received funding from the Ministry of Education, Youth and Sports of the Czech Republic (project LTT17033). SM and VM received funding from EU FP6 NitroEurope (grant nr. 17841), EU FP7 ECLAIRE (grant nr. 282910), the Ministry of Education and Science of Ukraine (projects nr. 505, 550, 574, 602), GEF-UNEP funded "Toward INMS" project (grant nr. NEC05348) and ENI CBC BSB PONTOS (grant nr. BSB 889). The authors from Biological Dynamics of Forest Fragments Project, PDBFF, Instituto Nacional de Pesquisas da Amazonia, Brazil were supported by the MCTI/CNPq/FNDCT - AcAo Transversal no68/2013 - Programa de Grande Escala da Biosfera-Atmosfera na Amazonia - LBA; Project 'Como as florestas da Amazonia Central respondem as variacoes climaticas? Efeitos sobre dinamica florestal e sinergia com a fragmentacAo florestal'. FJRM was financially supported by the Netherlands Organization for Scientific Research (VICI grant 016.VICI.170.072) and Research Foundation Flanders (FWO-SBO grant S000619N). STM received funding from New Frontiers in Research Fund-Exploration (grant nr. NFRF-2018-02043) and NSERC Discovery. MMR received funding from the Australian Research Council Discovery Early Career Research Award (grant nr. DE180100570). JAM received funding from the National Science Foundation (DEB 1557094), International Center for Advanced Renewable Energy and Sustainability (I-CARES) at Washington University in St. Louis, ForestGEO, and Tyson Research Center. IM-S was funded by the UK Natural Environment Research Council through the ShrubTundra Project (NE/M016323/1). MBN received funding from FORMAS, VR, Kempe Foundations support from the research infrastructures ICOS and SITES. MDN received funding from CONICET (grant nr. PIP 112-201501-00609). Spanish Ministry of Science grant PID2019-110521GB-I00 and Catalan government grant 2017-1005. French National Research Agency (ANR) in the frame of the Cluster of Excellence COTE (project HydroBeech, ANR-10-LABX-45). VLIR-OUS, under the Institutional University Coorperation programme (IUC) with Mountains of the Moon University. Project LAS III 77/2017/B entitled: \"Estimation of net carbon dioxide fluxes exchanged between the forest ecosystem on post-agricultural land and between the tornado-damaged forest area and the atmosphere using spectroscopic and numerical methods\", source of funding: General Directorate of State Forests, Warsaw, Poland. Max Planck Society (Germany), RFBR, Krasnoyarsk Territory and Krasnoyarsk Regional Fund of Science, project number 20-45-242908. Estonian Research Council (PRG609), and the European Regional Development Fund (Centre of Excellence EcolChange). Canada-Denmark Arctic Research Station Early Career Scientist Exchange Program, from Polar knowledge Canada (POLAR) and the Danish Agency for Science and Higher Education. AP received funding from Fondecyt 1180205, CONICYT PIA AFB170008 and ANID PIA / BASAL FB210006. MP received funding from the Funding Org. Knut and Alice Wallenberg Foundation (grant nr. 2015.0047), and acknowledges funding from the Swedish Research Council (VR) with contributing research institutes to both the SITES and ICOS Sweden infrastructures. JP and RO were funded by the Spanish Ministry of Science grant PID2019-110521GB-I00, the fundacion Ramon Areces grant ELEMENTAL-CLIMATE, and the Catalan government grant 2017-1005. MPB received funding from the Svalbard Environmental Protection Fund (grant project number 15/128) and the Research Council of Norway (Arctic Field Grant, project number 269957). RP received funding from the Ministry of Education, Youth and Sports of the Czech Republic (grant INTER-TRANSFER nr. LTT20017). LTSER Zone Atelier Alpes; Federation FREE-Alpes. RP received funding from a Humboldt Fellowship for Experienced Researchers. Prokushkin AS and Zyryanov VI contribution has been supported by the RFBR grant #18-05-60203-Arktika. RPu received founding from the Polish National Science Centre (grant project number 2017/27/B/NZ8/00316). ODYSSEE project (ANR-13-ISV7-0004, PN-II-ID-JRP-RO-FR-2012). KR was supported through an Australian Government Research Training Program Scholarship. Fieldwork was supported by the Global Challenges program at the University of Wollongong, the ARC the Australian Antarctic Division and INACH. DR was funded by the project SUBANTECO IPEV 136 (French Polar Institute Paul-Emile Victor), Zone Atelier CNRS Antarctique et Terres Australes, SAD Region Bretagne (Project INFLICT), BiodivERsa 2019-2020 BioDivClim call 'ASICS' (ANR-20-EBI5-0004). SAR received funding from the Australian Research Council. NSF grant #1556772 to the University of Notre Dame. Pavia University (Italy). OR received funding from EU-LEAP-Agri (RAMSES II), EU-DESIRA (CASSECS), EU-H2020 (SustainSahel), AGROPOLIS and TOTAL Foundations (DSCATT), CGIAR (GLDC). AR was supported by the Russian Science Foundation (Grant 18-74-10048). Parc national des Ecrins. JS received funding from Vetenskapsradet grant nr (No: 2014-04270), ALTER-net multi-site grant, River LIFE project (LIFE08 NAT/S/000266), Flexpeil. Helmholtz Association long-term research program TERENO (Terrestrial Environmental Observatories). PS received funding from the Polish Ministry of Science and Higher Education (grant nr. N N305 304840). AS acknowledges funding by ETH Zurich project FEVER ETH-27 19-1. LSC received funding from NSERC Canada Graduate Scholarship (Doctoral) Program; LSC was also supported by ArcticNet-NCE (insert grant #). Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (141513/2017-9); FundacAo Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro (E26/200.84/2019). ZS received funding from the SRDA (grants nos. APVV-16-0325 and APVV-20-0365) and from the ERDF (grant no. ITMS 313011S735, CE LignoSilva). JS, MB and CA received funding from core budget of ETH Zurich. State excellence Program M-V \"WETSCAPES\". AfricanBioServices project funded by the EU Horizon 2020 grant number 641918. The authors from KIT/IMK-IFU acknowledge the funding received within the German Terrestrial Environmental Observatories (TERENO) research program of the Helmholtz Association and from the Bavarian Ministry of the Environment and Public Health (UGV06080204000). Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), project number 192626868, in the framework of the collaborative German-Indonesian research project CRC 990 (SFB): 'EFForTS, Ecological and Socioeconomic Functions of Tropical Lowland Rainforest Transformation Systems (Sumatra, Indonesia)'. MS received funding from the Ministry of Education, Youth and Sports of the Czech Republic (grant nr. INTER-TRANSFER LTT19018). TT received funding from the Swedish National Space Board (SNSB Dnr 95/16) and the CASSECS project supported by the European Union. HJDT received funding from the UK Natural Environment Research Council (NERC doctoral training partnership grant NE/L002558/1). German Science Foundation (DFG) GraKo 2010 \"Response\". PDT received funding from the MEMOIRE project (PN-III-P1-1.1-PD2016-0925). Arctic Challenge for Sustainability II (ArCS II; JPMXD1420318865). JU received funding from Czech Science Foundation (grant nr. 21-11487S). TU received funding from the Romanian Ministry of Education and Research (CCCDI - UEFISCDI -project PN-III-P2-2.1-PED-2019-4924 and PN2019-2022/19270201-Ctr. 25N BIODIVERS 3-BIOSERV). AV acknowledge funding from RSF, project 21-14-00209. GFV received funding from the Dutch Research Council NWO (Veni grant, no. 863.14.013). Australian Research Council Discovery Early Career Research Award DE140101611. FGAV received funding from the Portuguese Science Foundation (FCT) under CEECIND/02509/2018, CESAM (UIDP/50017/2020+UIDB/50017/2020), FCT/MCTES through national funds, and the co-funding by the FEDER, within the PT2020 Partnership Agreement and Compete 2020. Ordesa y Monte Perdido National Park. MVI received funding from the Spanish Ministry of Science and Innovation through a doctoral grant (FPU17/05869). JW received funding from the Czech Science Foundation (grant nr. 20-28119S) and the Czech Academy of Sciences (grant nr. RVO 67985939). CR and SW received funding from the Swiss Federal Office for the Environment (FOEN) and the de Giacomi foundation. YY received funding from the National Natural Science Foundation of China (Grant no. 41861134039 and 41941015). ZY received funding from the National Natural Science Foundation of China (grant nr. 41877458). FZ received funding from the Swiss National Science Foundation (grant nr. 172198 and 193645). PZ received funding from the Funding Org. Knut and Alice Wallenberg Foundation (grant no. 2015.0047). JL received funding from (i) the Agence Nationale de la Recherche (ANR), under the framework of the young investigators (JCJC) funding instrument (ANR JCJC Grant project NoANR-19-CE32-0005-01: IMPRINT) (ii) the Centre National de la Recherche Scientifique (CNRS) (Defi INFINITI 2018: MORFO); and the Structure Federative de Recherche (SFR) Condorcet (FR CNRS 3417: CREUSE). Fieldwork in the Arctic got facilitated by funding from the EU INTERACT program. SN, UAT, JJA and JvO would like to thank the field team of the Vegetation Dynamics group for their efforts and hard work. We acknowledge Dominique Tristan for letting access to the field. For the logistic support the crew of INACH and Gabriel de Castilla Station team on Deception Island. We thank the Inuvialuit and Kluane First Nations for the opportunity to work on their land. MAdP acknowledges fieldwork assistance and logistics support to Unidad de Tecnologia Marina CSIC, and the crew of Juan Carlos I and Gabriel de Castilla Spanish Antarctic Stations, as well as to the different colleagues from UAH that helped on the instrument maintenance. ERF acknowledges fieldwork assistance by Martin Heggli. MBG acknowledges fieldwork and technical assistance by P Abadia, C Benede, P Bravo, J Gomez, M Grasa, R Jimenez, H Miranda, B Ponz, J Revilla and P Tejero and the Ordesa and Monte Perdido National Park staff. LH acknowledges field assistance by John Jacobs, Andrew Trant, Robert Way, Darroch Whitaker; we acknowledge the Inuit of Nunatsiavut, and the Co-management Board of Torngat Mountains National Park for their support of this project and acknowledge that the field research was conducted on their traditional lands. We thank our many bear guides, especially Boonie, Eli, Herman, John and Maria Merkuratsuk. AAK acknowledges field support of Akhtar Malik, Rameez Ahmad. Part of microclimatic records from Saxony was funded by the Saxon Switzerland National Park Administration. Tyson Research Center. JP acknowledges field support of Emmanuel Malet (Edytem) and Rangers of Reserves Naturelles de Haute-Savoie (ASTERS). Practical help: Roel H. Janssen, N. Huig, E. Bakker, Schools in the tepaseforsoket, Forskar fredag, Erik Herberg. The support by the Bavarian Forest National Park administration is highly appreciated. LvdB acknowledges CONAF and onsite support from the park rangers from PN Pan de Azucar, PN La Campana, PN Nahuelbuta and from communidad agricola Quebrada de Talca. JL and FS acknowledge Manuel Nicolas and all forest officers from the Office National des Forets (ONF) who are in charge of the RENECOFOR network and who provided help and local support for the installation and maintenance of temperature loggers in the field., Research in global change ecology relies heavily on global climatic grids derived from estimates of air temperature in open areas at around 2 m above the ground. These climatic grids do not reflect conditions below vegetation canopies and near the ground surface, where critical ecosystem functions occur and most terrestrial species reside. Here, we provide global maps of soil temperature and bioclimatic variables at a 1-km2 resolution for 0–5 and 5–15 cm soil depth. These maps were created by calculating the difference (i.e. offset) between in situ soil temperature measurements, based on time series from over 1200 1-km2 p ixels ( summarized f rom 8 519 u nique t emperature sensors) across all the world's major terrestrial biomes, and coarse-grained air temperature estimates from ERA5-Land (an atmospheric reanalysis by the European Centre for Medium-Range Weather Forecasts). We show that mean annual soil temperature differs markedly from the corresponding gridded air temperature, by up to 10°C (mean = 3.0 ± 2.1°C), with substantial variation across biomes and seasons. Over the year, soils in cold and/or dry biomes are substantially warmer (+3.6 ± 2.3°C) than gridded air temperature, whereas soils in warm and humid environments are on average slightly cooler (−0.7 ± 2.3°C). The observed substantial and biome-specific offsets emphasize that the projected impacts of climate and climate change on near-surface biodiversity and ecosystem functioning are inaccurately assessed when air rather than soil temperature is used, especially in cold environments. The global soil-related bioclimatic variables provided here are an important step forward for any application in ecology and related disciplines. Nevertheless, we highlight the need to fill remaining geographic gaps by collecting more in situ measurements of microclimate conditions to further enhance the spatiotemporal resolution of global soil temperature products for ecological applications., FWO G018919N W001919N 12P1819N, DOB Ecology, University of Helsinki, Faculty of Science (MICROCLIM) 7510145, European Research Council (ERC) FORMICA 757833, Arctic Interactions at the University of Oulu, Academy of Finland 318930 337552, Maaja vesitekniikan tuki ry., Tiina and Antti Herlin Foundation, Nordenskiold Samfundet, Societas pro Fauna et Flora Fennica, Grant Agency of the Czech Republic 20-28119S 20-05840Y GA17-19376S 21-11487S, Czech Academy of Sciences RVO 67985939, National Geographic Society 9480-14 WW-240R-17, CISSC (program ICRP) 2397, Iran National Science Foundation (INSF) 96005914, Scottish Government's Rural and Environment Science and Analytical Services Division, Qatar Petroleum QUEX-CAS-QP-RD-18/19, European Union's Horizon 2020 research and innovation program 678841, Swiss National Science Foundation (SNSF), European Commission 172198 193645 31003A_176044, Ministry of Education, Youth & Sports - Czech Republic LTAUSA19137, Ministry of Science and Higher Education of the Russian Federation FSRZ-2020-0014, Independent Research Fund Denmark 8021-00423B 7027-00133B, German Research Foundation (DFG) DFG- FZT 118 202548816 TI 338/14-1 TI 338/14-2 BA 3843/6-1, grant project VEGA of the Ministry of Education of the Slovak Republic Slovak Academy of Sciences 2/0132/18, Forestry Commission, Universidad Javeriana, Direccion General de Cambio Climatico del Gobierno de Aragon, European Union's Horizon 2020 research and innovation program under the Marie Skodowska-Curie Grant 657627 SNF 407340_172433 40FA40_154245 20FI21_148992 20FI20_173691, European Commission 17841 774124, MCTI/CNPq/FNDCT 68/2013, Project 'Como as florestas da Amazonia Central respondem as variacoes climaticas? Efeitos sobre dinamica florestal e sinergia com a fragmentacAo florestal', Spanish Government, European Commission CGL2016-78093-R, ANID-FONDECYT 1181745, National Science Foundation, Poland UMO-2017/27/B/ST10/02228, National Research Foundation - South Africa, Australian Research Council, Slovak Research and Development Agency APVV-19-0319, Instituto Antartico Chileno INACH_RT-48_16 INACH FR-0418, Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) PIA APOYO CCTE AFB170008 PIA AFB170008, UK Research & Innovation (UKRI), Natural Environment Research Council (NERC), Research Council of Norway, European Commission 230970, NERC E3 doctoral training partnership grant at the University of Edinburgh NE/L002558/1, Carnegie Trust for the Universities of Scotland, Gobern of Spain PERMAPLANET CTM2009-10165-E ANTARPERMA CTM2011-15565-E PERMASNOW CTM2014-52021-R, University of Alcala, Spanish Polar Committee, Autonomous Province of Bolzano (ITA), ScotNature, NERC National Capability LTS-S: UK-SCAPE NE/R016429/1, Ministry of Education, Youth & Sports - Czech Republic LTAUSA18007, Kempe Foundation JCK-1112 JCK-1822, Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme I (NPU I) LO1415, project for national infrastructure support CzeCOS/ICOS LM2015061 GLORIA-EU EVK2-CT2000-00056, Tiroler Wissenschaftsfonds, University of Innsbruck, Sime Darby Foundation, Jardin Botanico Atlantico SV-20-GIJON-JBA, Federal Ministry of Education & Research (BMBF) 01LL0912 01LG1201M 01LG1201N, Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT FONDECYT 11170516 1180205, ANID PIA / BASAL FB210006, National Parks (DYNBIO) 1656/2015, Spanish Research Agency (VULBIMON) CGL2017-90040-R, Swiss National Science Foundation (SNSF) 20FI20_173691, Biotechnology and Biological Sciences Research Council (BBSRC) 206/D16053 FWO G0H1517N, EU Horizon2020 INFRAIA project eLTER-PLUS 871128, project LTER-CWN (FFG, F&E Infrastrukturforderung) 858024, Austrian Climate Research Program ACRP7 - CentForCSink - KR14AC7K11960, iDiv by the German Research Foundation DFG- FZT 118 202548816, Baden-Wurttemberg Ministry of Science, Research and Arts, Weston Foundation, ArcticNet, Natural Sciences and Engineering Research Council of Canada (NSERC) RGPIN-06691, Villum Foundation 17523, Ministry of Education, Youth & Sports - Czech Republic LM2015078 VAN2020/01 CZ.02.1.01/0.0/0.0/16_013/0001708 LTT17033 LTT20017 INTER-TRANSFER LTT19018, Bolin Centre for Climate Research, Stockholm University, Swedish Research Council Swedish Research Council Formas 2014-00530 2018-00792 2016-01187, Swedish Forest Society Foundation 2018-485-Steg 2 2017, Federal Ministry of Education & Research (BMBF) FKZ 031B0516C SUSALPS, Oberfrankenstiftung OFS FP00237, Energy Research Fund NYR-11 - 2019 NYR-18 - 2020, UK NERC Independent Research Fellowship NE/S01537X/1, National Science Centre, Poland 2016/21/B/ST10/02271, Polish National Centre for Research and Development Pol-Nor/203258/31/2013, MoEFCC, Govt of India (AICOPTAX project) 22018/12/2015/RE/Tax, Swedish Research Council Formas 2018-01781 2018-02700 2019-00836, research infrastructure ICOS-SE, National Research, Development and Innovation Fund of Hungary K128441, Programa Operativo FEDER 2018 B1-RNM-163-UGR-18, Norwegian Research Council (NORKLIMA grants) 184912 244525, CONICYT-PAI 79170119, ANID-MPG 190029, project MIUR PON Cluster OT4CLIMA, Stelvio National Park, AIAS-COFUND fellowship programme - Marie Skodowska- Curie actions under the European Union's Seventh Framework Pro-gramme for Research, Technological development and Demonstration 609033, Aarhus University Research Foundation, Denmark, EU FP6 NitroEurope 17841, EU FP7 ECLAIRE 282910, Ministry of Education and Science of Ukraine 505 550 574 602, GEF-UNEP NEC05348, ENI CBC BSB PONTOS BSB 889, Netherlands Organization for Scientific Research (NWO) 016.VICI.170.072, New Frontiers in Research Fund-Exploration NFRF-2018-02043, Natural Sciences and Engineering Research Council of Canada (NSERC), Australian Research Council DE180100570, National Science Foundation (NSF) DEB 1557094, International Center for Advanced Renewable Energy and Sustainability (I-CARES) at Washington University in St. Louis, Smithsonian Institution Smithsonian Tropical Research Institute, Tyson Research Center, UK Natural Environment Research Council through the ShrubTundra Project NE/M016323/1, Swedish Research Council Formas Swedish Research Council, Kempe Foundations - research infrastructure ICOS Kempe Foundations - research infrastructure SITES, Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET) PIP 112-201501-00609, Spanish Government PID2019-110521GB-I00, Catalan government 2017-1005, French National Research Agency (ANR) ANR-10-LABX-45, General Directorate of State Forests, Warsaw, Poland, Max Planck Society, Russian Foundation for Basic Research (RFBR), Krasnoyarsk Territory Krasnoyarsk Regional Fund of Science 20-45-242908, Estonian Research Council PRG609, Knut & Alice Wallenberg Foundation 2015.0047, Swedish Research Council, fundacion Ramon Areces grant ELEMENTAL-CLIMATE, Svalbard Environmental Protection Fund 15/128, Research Council of Norway 269957, Humboldt Fellowship for Experienced Researchers, Russian Foundation for Basic Research (RFBR) 18-05-60203-Arktika, Polish National Science Centre 2017/27/B/NZ8/00316, ODYSSEE project (PN-II-ID-JRP-RO-FR-2012) ANR-13-ISV7-0004, Australian Government, Department of Industry, Innovation and Science, Global Challenges program at the University of Wollongong, ARC the Australian Antarctic Division, INACH, project SUBANTECO IPEV 136 (French Polar Institute Paul-Emile Victor), Zone Atelier CNRS Antarctique et Terres Australes, SAD Region Bretagne (Project INFLICT), BiodivERsa 2019-2020 BioDivClim call 'ASICS' ANR-20-EBI5-0004, National Science Foundation (NSF) 1556772, EU-LEAP-Agri (RAMSES II) EU-DESIRA (CASSECS) EU-H2020 (SustainSahel), AGROPOLIS, Total SA, CGIAR, Russian Science Foundation (RSF) 18-74-10048, Swedish Research Council 2014-04270, ALTER-net multi-site grant, River LIFE project LIFE08 NAT/S/000266, Flexpeil, Ministry of Science and Higher Education, Poland N N305 304840, ETH Zurich FEVER ETH-27 19-1, NSERC Canada Graduate Scholarship (Doctoral) Program, ArcticNet-NCE, Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ) 141513/2017-9, Fundacao Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio De Janeiro (FAPERJ) E26/200.84/2019, SRDA APVV-16-0325 APVV-20-0365, ERDF (CE LignoSilva) ITMS 313011S735, ETH Zurich, EU Horizon 2020 641918, German Terrestrial Environmental Observatories (TERENO) research program of the Helmholtz Association, Bavarian Ministry of the Environment and Public Health UGV06080204000 German Research Foundation (DFG) 192626868, Swedish National Space Board (SNSB) 95/16, CASSECS project by the European Union, Natural Environment Research Council (NERC) NE/L002558/1, MEMOIRE project PN-III-P1-1.1-PD2016-0925, Arctic Challenge for Sustainability II (ArCS II) JPMXD1420318865, Consiliul National al Cercetarii Stiintifice (CNCS), Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii (UEFISCDI) PN-III-P2-2.1-PED-2019-4924 PN2019-2022/19270201, 25N BIODIVERS 3-BIOSERV, Russian Science Foundation (RSF) 21-14-00209., Netherlands Organization for Scientific Research (NWO) 863.14.013, Australian Research Council DE140101611, Portuguese Foundation for Science and Technology CEECIND/02509/2018 CESAM UIDP/50017/2020+UIDB/50017/2020, Portuguese Foundation for Science and Technology European Commission, FEDER, within the PT2020 Partnership Agreement, Compete 2020, Spanish Government FPU17/05869, Swiss Federal Office for the Environment (FOEN), Giacomi foundation, National Natural Science Foundation of China (NSFC) 41861134039 41941015 41877458, French National Research Agency (ANR) ANR-19-CE32-0005-01 Centre National de la Recherche Scientifique (CNRS), Structure Federative de Recherche (SFR) Condorcet (FR CNRS 3417: CREUSE), EU INTERACT program, Inuit of Nunatsiavut, Co-management Board of Torngat Mountains National Park, Saxon Switzerland National Park Administration, Bavarian Forest National Park administration, BECC - Biodiversity and Ecosystem services in a Changing Climate, Research Foundation Flanders (FWO-SBO) S000619N

Published
2021

28. Robust Cascaded Deadbeat Predictive Control for Dual Three-Phase Variable-Flux PMSM Considering Intrinsic Delay in Speed Loop.

Author

Yu, Kailiang, Wang, Zheng, Hua, Wei, and Cheng, Ming

Subjects
*SYNCHRONOUS electric motors, *GENERALIZED integrals, *PREDICTION models, *TORQUE, *CASCADE control, *SPEED
Abstract

In this article, a robust cascaded deadbeat predictive speed control method (PSC) is proposed with speed and disturbance observer for the dual three-phase variable-flux permanent-magnet synchronous motor (VF-PMSM) drives. Considering the intrinsic delay in speed loop, an improved predictive speed model has been derived in discrete domain. In turn, a speed observer is proposed for the cascaded PSC to mitigate the issue of this intrinsic delay. In order to improve the system robustness, the torque disturbance observer including the generalized proportional integral observer and the sliding-mode observer has been proposed for parameter variation and load torque perturbation. Furthermore, the stability analysis indicates that the margin of parameter mismatch is determined by the coefficient in the speed observer, which can be expressed as the equivalent gain in discrete domain. The performance comparison between the conventional PSC method and the proposed PSC method has been investigated for parameter robustness. The experimental results are presented to verify the improvements of the proposed PSC method for the dual three-phase VF-PMSM drives under model uncertainty and magnetization manipulation conditions. [ABSTRACT FROM AUTHOR]

Published
2022
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29. Universal Control Scheme of Dual Three-Phase PMSM Drives With Single Open-Phase Fault.

Author

Yu, Kailiang, Wang, Zheng, Gu, Minrui, and Wang, Xueqing

Subjects
*TORQUE control, *SYNCHRONOUS electric motors, *FAULT-tolerant control systems, *NOTCH filters, *FAULT diagnosis, *WIND power
Abstract

The fault-tolerant control method of dual three-phase permanent-magnet synchronous motor (PMSM) drives has gained more and more attention in some applications, such as aircraft system and offshore wind energy system. In this letter, the reason for the torque ripple caused by the standard control method has been investigated in view of the frequency domain after the occurrence of a single open-phase fault. Based on the theoretical analysis, a universal control scheme has been proposed for the dual three-phase PMSM drives with two isolated neutral points, which can be implemented under either healthy condition or fault condition. The key is to modify with a closed-loop controller on harmonic subspace with the notch filter. The proposed method not only realizes the seamless transition between two operation conditions without fault diagnosis but also avoids the reconfiguration of the control structure. Moreover, the system optimization of the minimum copper loss under postfault condition has been integrated into the proposed method. The experimental results are presented to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Published
2022
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30. How do functional traits influence tree demographic properties in a subtropical monsoon forest?

Author

He, Pengcheng, Lian, Juyu, Ye, Qing, Liu, Hui, Zheng, Yi, Yu, Kailiang, Zhu, Shidan, Li, Ronghua, Yin, Deyi, Ye, Wanhui, and Wright, Ian J.

Subjects
TREE growth, TROPICAL dry forests, TREE mortality, HYDRAULIC conductivity, WOOD density, FOREST dynamics
Abstract

Functional traits are good predictors of plant responses and adaptations to ever‐changing environments. However, forecasting forest community dynamics is challenging because the relationships among different tree demographic properties (growth, mortality and recruitment) and how functional traits are associated with tree demography remain largely unknown.Here, in a 20‐ha subtropical forest permanent plot, we quantified the rates of tree growth, mortality and recruitment across 53 dominant tree species (diameter at breast height; DBH ≥ 1 cm) from 2005 to 2020. Functional traits that are closely related to plant photosynthesis, nutrients, hydraulics and drought tolerance were measured.We found that tree growth rate (GR) varied independently from rates of tree mortality and recruitment. Hydraulic conductivity was positively correlated with GR (explaining 27% variation—the strongest relationship observed) whereas wood density was negatively correlated with GR. Leaf life span was negatively related to tree mortality. Species with high carbon assimilation rate, nutrient concentration and hydraulic conductivity had high recruitment rates. Leaf turgor loss point was unrelated to plant demography. Principal component analysis revealed that species with quick resource acquisition rates had high rates of growth and recruitment.Our results illustrate that the correlations among tree demographic properties were weak in this subtropical forest with monsoonal climate. Most notably, against expectations, there was no observed trade‐off between growth and mortality. Individual functional traits explained up to 27% of each demographic rate. Variation in recruitment rate was aligned with traits indexing the leaf economic spectrum and also plant hydraulic variation. A better understanding of the role of disturbances on trait–demography relationships would help build a deeper and more nuanced understanding of the ecology of subtropical monsoon forests. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Published
2022
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32. Position Sensorless Control of IPMSM Using Adjustable Frequency Setting Square-Wave Voltage Injection.

Author

Yu, Kailiang and Wang, Zheng

Subjects
*PERMANENT magnet motors, *SQUARE waves, *VOLTAGE, *COST control
Abstract

Position sensorless control of interior permanent magnet synchronous motors (IPMSMs) is of great importance for the sake of the cost reduction and robustness improvement. An improved position sensorless method based on adjustable high-frequency (HF) square-wave voltage injection has been proposed for the IPMSM drive under zero- and low-speed regions. The general detection method of position from positive- and negative-sequence HF current response has been developed by the simple algebraic operations of data at some adjacent sampling instants. In addition, the proposed method not only can retain the advantage of the removal of low-pass filters in current feedback but also can increase the voltage margin of inverter with adjustable frequency setting of HF signal compared with existing square-wave voltage injection methods. Comprehensive experimental results have been given to verify the proposed theoretical analysis. [ABSTRACT FROM AUTHOR]

Published
2022
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33. Global pattern of soil priming effect intensity and its environmental drivers.

Author

Mo, Fei, Ren, Chengjie, Yu, Kailiang, Zhou, Zhenghu, Phillips, Richard P., Luo, Zhongkui, Zhang, Yeye, Dang, Yuteng, Han, Juan, Ye, Jian‐Sheng, Vinay, Nangia, Liao, Yuncheng, Xiong, Youcai, and Wen, Xiaoxia

Subjects
PLANT growing media, PLANT litter, CLIMATE feedbacks, SANDY soils, SOILS, SOIL amendments, FOREST litter
Abstract

The microbial priming effect—the decomposition of soil organic carbon (SOC) induced by plant inputs—has long been considered an important driver of SOC dynamics, yet we have limited understanding about the direction, intensity, and drivers of priming across ecosystem types and biomes. This gap hinders our ability to predict how shifts in litter inputs under global change can affect climate feedbacks. Here, we synthesized 18,919 observations of CO2 effluxes in 802 soils across the globe to test the relative effects (i.e., log response ratio [RR]) of litter additions on native SOC decomposition and identified the dominant environmental drivers in natural ecosystems and agricultural lands. Globally, litter additions enhanced native SOC decomposition (RR = 0.35, 95% CI: 0.32–0.38), with greater priming effects occurring with decreasing latitude and more in agricultural soils (RR = 0.43) than in uncultivated soils (RR = 0.28). In natural ecosystems, soil pH and microbial community composition (e.g., bacteria: fungi ratio) were the best predictors of priming, with greater effects occurring in acidic, bacteria‐dominated sandy soils. In contrast, the substrate properties of plant litter and soils were the most important drivers of priming in agricultural systems since soils with high C:N ratios and those receiving large inputs of low‐quality litter had the highest priming effects. Collectively, our results suggest that, though different factors may control priming effects, the ubiquitous nature of priming means that alterations of litter quality and quantity owing to global changes will likely have consequences for global C cycling and climate forcing. [ABSTRACT FROM AUTHOR]

Published
2022
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34. The biogeography of relative abundance of soil fungi versus bacteria in surface topsoil.

Author

Yu, Kailiang, van den Hoogen, Johan, Wang, Zhiqiang, Averill, Colin, Routh, Devin, Smith, Gabriel Reuben, Drenovsky, Rebecca E., Scow, Kate M., Mo, Fei, Waldrop, Mark P., Yang, Yuanhe, Tang, Weize, De Vries, Franciska T., Bardgett, Richard D., Manning, Peter, Bastida, Felipe, Baer, Sara G., Bach, Elizabeth M., García, Carlos, and Wang, Qingkui

Subjects
*SOIL fungi, *SOIL air, *SOIL ecology, *BIOGEOGRAPHY, *FUNCTIONAL groups, *TOPSOIL
Abstract

Fungi and bacteria are the two dominant groups of soil microbial communities worldwide. By controlling the turnover of soil organic matter, these organisms directly regulate the cycling of carbon between the soil and the atmosphere. Fundamental differences in the physiology and life history of bacteria and fungi suggest that variation in the biogeography of relative abundance of soil fungi versus bacteria could drive striking differences in carbon decomposition and soil organic matter formation between different biomes. However, a lack of global and predictive information on the distribution of these organisms in terrestrial ecosystems has prevented the inclusion of relative abundance of soil fungi versus bacteria and the associated processes in global biogeochemical models. Here, we used a global-scale dataset of >3000 distinct observations of abundance of soil fungi versus bacteria in the surface topsoil (up to 15 cm) to generate the first quantitative and high-spatial-resolution (1 km 2) explicit map of soil fungal proportion, defined as fungi/fungi + bacteria, across terrestrial ecosystems. We reveal striking latitudinal trends where fungal dominance increases in cold and high-latitude environments with large soil carbon stocks. There was a strong nonlinear response of fungal dominance to the environmental gradient, i.e., mean annual temperature (MAT) and net primary productivity (NPP). Fungi dominated in regions with low MAT and NPP and bacteria dominated in regions with high MAT and NPP, thus representing slow vs. fast soil energy channels, respectively, a concept with a long history in soil ecology. These high-resolution models provide the first steps towards representing the major soil microbial groups and their functional differences in global biogeochemical models to improve predictions of soil organic matter turnover under current and future climate scenarios. Raw datasets and global maps generated in this study are available at 10.6084/m9.figshare.19556419 (Yu, 2022). [ABSTRACT FROM AUTHOR]

Published
2022
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36. The global biogeography of soil priming effect intensity.

Author

Ren, Chengjie, Mo, Fei, Zhou, Zhenghu, Bastida, Felipe, Delgado‐Baquerizo, Manuel, Wang, Jieying, Zhang, Xinyi, Luo, Yiqi, Griffis, Timothy J., Han, Xinhui, Wei, Gehong, Wang, Jun, Zhong, Zekun, Feng, Yongzhong, Ren, Guangxin, Wang, Xiaojiao, Yu, Kailiang, Zhao, Fazhu, Yang, Gaihe, and Yuan, Fenghui

Subjects
SOIL texture, SANDY soils, SOILS, SOIL dynamics, BIOGEOGRAPHY, SOIL mapping, CLIMATE change
Abstract

Aim: Fresh carbon (C) inputs to the soil can have important consequences for the decomposition rates of soil organic matter (priming effect), thereby impacting the delicate global C balance at the soil–atmosphere interface. Yet, the environmental factors that control soil priming effect intensity remain poorly understood at a global scale. Location: Global. Time period: 1980–2020. Major taxa studied: Soil priming effect intensity. Methods: We conducted a global dataset of CO2 effluxes in 711 pairwise soils with 13C or 14C simple C sources inputs and without C inputs from incubation experiments in which isotope‐labelled C was used to quantify fresh C‐induced rather than exudate‐induced priming. Results: Soil priming effect intensity is predominantly positive. Soil texture and C content were identified as the most important factors associated with priming effects, with sandy soils from tropical and mid‐latitudes supporting the highest soil priming effect intensity, and soils with greater C content and fine textures from high latitudes maintaining the lowest soil priming effects. The negative association between C content and soil priming effect intensity was also indirectly driven by changing mean annual temperature, net primary productivity, and fungi : bacteria ratio. Using this information, we generated a global map of soil priming effect intensity, and found that the priming was lower at high latitudes and higher at lower latitudes. Main conclusions: Global patterns of soil priming effect intensity can be predicted using environmental data, with soil texture and C content playing a predominant role in explaining in priming effects. These effects were also indirectly driven by climate, vegetation and soil microbial properties. We present the first global atlas of soil priming effect intensity and advance our knowledge on the potential mechanisms underlying soil priming effect intensity, which are integral to improving the climate change and soil C dynamics components of Earth System models. [ABSTRACT FROM AUTHOR]

Published
2022
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37. 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, and Hou, Enqing

Subjects
VOLCANIC soils, PHOSPHORUS in soils, TUNDRAS
Abstract

3 Results 3.1 Characteristics of soil total P concentration across the world Our soil total P concentration database included 5275 measurements from 1894 geographically distinct sites and covered 6 continents, all major biomes, and all 12 USDA soil orders in terrestrial ecosystems (Fig. Besides soil total P concentration and site coordinates, we also included climate variables (i.e., MAT and MAP), vegetation type, and soil physiochemical properties (e.g., SOC, soil clay and sand contents, soil pH) in our database whenever available. Soil chronosequences provide a unique opportunity to isolate the effect of soil age from other soil-forming factors on soil P dynamics and have shown that soil age negatively impacts soil total P concentration (Wardle et al., 2004; Delgado-Baquerizo et al., 2020; Vitousek et al., 2010; Walker and Syers, 1976). In terrestrial ecosystems, to a depth of 1 m from the land surface, most of the P is found in the soil (Zhang et al., 2021). [Extracted from the article]

Published
2021
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38. An Online Flux Estimation for Dual Three-Phase SPMSM Drives Using Position-Offset Injection.

Author

Yu, Kailiang, Wang, Zheng, Wang, Xueqing, and Zou, Zhixiang

Subjects
*SWITCHED reluctance motors, *FLUX (Energy), *SYNCHRONOUS electric motors, *SIGNAL-to-noise ratio
Abstract

The rotor flux linkage of permanent-magnet synchronous motor (PMSM) is of great importance for monitoring and high-performance control. However, most of the existing online estimation methods need prior knowledge of other PMSM parameters such as stator resistance and dq axis inductance, which are difficult to realize in the rank-deficient models of electrical machines. This article proposes a simple online estimation method of rotor flux linkage for the dual three-phase surface-mounted PMSM (SPMSM) drives with the assistance of injected position offset. The independent estimation model has been developed for the rotor flux linkage by appropriate setting of positon offset between two sets of three-phase windings of dual three-phase SPMSM drives. The proposed method not only has little influence on the output torque and high signal-to-noise ratio for observation, but also offers the superior performance in estimation accuracy irrespective of the influence of VSI nonlinearity. In addition, the proposed position-offset-based method is suitable for the full-speed region including the field-weakening operation. Finally, comprehensive experimental results are presented to verify validity of the proposed method under different working conditions. [ABSTRACT FROM AUTHOR]

Published
2021
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39. Interleaved Model Predictive Control for Three-Level Neutral-Point-Clamped Dual Three-Phase PMSM Drives With Low Switching Frequencies.

Author

Gu, Minrui, Wang, Zheng, Yu, Kailiang, Wang, Xueqing, and Cheng, Ming

Subjects
PREDICTION models, SYNCHRONOUS electric motors, PREDICTIVE control systems, TORQUE control
Abstract

In this article, the control strategy is studied for the neutral-point-clamped three-level inverter-fed dual three-phase permanent-magnet synchronous motor (PMSM) drive with low switching frequencies. An interleaved finite-control-set model predictive control (MPC) scheme is proposed, where a two-layer MPC is designed to solve the multiobjective optimization problem. The two sets of windings in PMSM are sampled and controlled in an interleaved way so that the control delay and the prediction horizon of the drive system are reduced by half. Moreover, the proposed interleaved control scheme increases the equivalent sampling and control frequency from the perspective of the whole drive system, and thus provides better steady-state performance and dynamic performance. With the switching states of one inverter remaining unchanged, the cross traversal of vector candidate sets between two sets of windings is avoided, and the computational burden can be reduced effectively. Experimental results are given to verify the validity and effectiveness of the proposed interleaved control scheme. [ABSTRACT FROM AUTHOR]

Published
2021
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40. Understanding climate change from a global analysis of city analogues

Author

Bastin, Jean-Francois, Clark, Emily, Elliott, Thomas, Hart, Simon, van den Hoogen, Johan, Hordijk, Iris, Ma, Haozhi, Majumder, Sabiha, Manoli, Gabriele, Maschler, Julia, Mo, Lidong, Routh, Devin, Yu, Kailiang, Zohner, Constantin M., and Crowther, Thomas W.

Subjects
Cartography, Atmospheric Science, Climate Change, Science, Research and Analysis Methods, Geographical locations, Mathematical and Statistical Techniques, Statistical Methods, Geographic Areas, Northern Hemisphere, Climatology, Principal Component Analysis, Latitude, Geography, Statistics, Europe, Earth sciences, Multivariate Analysis, Physical Sciences, People and Places, North America, Medicine, Southern Hemisphere, Mathematics, Research Article, Urban Areas
Abstract

Combating climate change requires unified action across all sectors of society. However, this collective action is precluded by the ‘consensus gap’ between scientific knowledge and public opinion. Here, we test the extent to which the iconic cities around the world are likely to shift in response to climate change. By analyzing city pairs for 520 major cities of the world, we test if their climate in 2050 will resemble more closely to their own current climate conditions or to the current conditions of other cities in different bioclimatic regions. Even under an optimistic climate scenario (RCP 4.5), we found that 77% of future cities are very likely to experience a climate that is closer to that of another existing city than to its own current climate. In addition, 22% of cities will experience climate conditions that are not currently experienced by any existing major cities. As a general trend, we found that all the cities tend to shift towards the sub-tropics, with cities from the Northern hemisphere shifting to warmer conditions, on average ~1000 km south (velocity ~20 km.year-1), and cities from the tropics shifting to drier conditions. We notably predict that Madrid’s climate in 2050 will resemble Marrakech’s climate today, Stockholm will resemble Budapest, London to Barcelona, Moscow to Sofia, Seattle to San Francisco, Tokyo to Changsha. Our approach illustrates how complex climate data can be packaged to provide tangible information. The global assessment of city analogues can facilitate the understanding of climate change at a global level but also help land managers and city planners to visualize the climate futures of their respective cities, which can facilitate effective decision-making in response to on-going climate change., PLoS ONE, 14 (7), ISSN:1932-6203

Published
2019

41. Variations of carbon allocation and turnover time across tropical forests.

Author

Yang, Hui, Ciais, Philippe, Wang, Yilong, Huang, Yuanyuan, Wigneron, Jean‐Pierre, Bastos, Ana, Chave, Jérôme, Chang, Jinfeng, Doughty, Christopher, Fan, Lei, Goll, Daniel, Joetzjer, Emilie, Li, Wei, Lucas, Richard, Quegan, Shaun, Le Toan, Thuy, Yu, Kailiang, and Gillespie, Thomas

Subjects
TROPICAL forests, TIME management, FOREST surveys, CARBON cycle, CARBON
Abstract

Aim: The carbon sink in tropical forests is a highly uncertain component of the global carbon budget. An understanding of the processes controlling this sink requires better quantification of carbon allocation, stocks and turnover times. Location: Tropical forests. Time period: 2010–2017. Major taxa studied: Tropical forest ecosystem. Methods: We develop a novel data assimilation system using satellite‐based annual above‐ground biomass derived from L‐band vegetation optical depth with 25 km × 25 km grid spacing, together with leaf area, to constrain the 25 km × 25 km carbon allocation patterns of net primary productivity (NPP) into the wood, leaf and root pools, and their turnover times. Our average data‐driven estimates of these variables are broadly consistent with independent ground‐based estimates of NPP allocation and wood turnover from forest inventory plots. Results: In tropical forest, on average, the NPP allocation into wood (0.30 ± 0.04) is significantly higher than that into leaves (0.24 ± 0.07). From the wet to dry tropics, forest NPP allocation into both wood and leaves declines slightly. The turnover times of forest leaf pools exhibit little spatial variation, whereas the turnover times of wood pools in Africa (median and interquartile range: 50.1‐4.0+5.5 years) are slightly longer than those in South America (48.2‐3.3+4.0 years) and Southeast Asia (48.3‐3.1+5.4 years). Our datasets reveal emergent trade‐offs across climatic and vegetation gradients between growth and life span/turnover for both wood and leaves. The spatial gradients of NPP allocation to wood/leaves are associated with canopy height, adjusted by climate condition and nutrient acquisition. The spatial gradients of wood and leaf turnover times are influenced mainly by climate and leaf characteristics. Main conclusions: Our data‐driven estimates of carbon allocation and turnover times provide a basis for more detailed exploration of these mechanisms in field studies. This highlights that improved model representation of carbon allocation and turnover is necessary for more accurate prediction of future carbon dynamics. [ABSTRACT FROM AUTHOR]

Published
2021
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42. Optimization theory explains nighttime stomatal responses.

Author

Wang, Yujie, Anderegg, William R. L., Venturas, Martin D., Trugman, Anna T., Yu, Kailiang, and Frankenberg, Christian

Subjects
MATHEMATICAL optimization, EVAPORATIVE cooling, LEAF temperature, WATER use, PLANT-water relationships, AQUATIC plants
Abstract

Summary: Nocturnal transpiration is widely observed across species and biomes, and may significantly impact global water, carbon, and energy budgets. However, it remains elusive why plants lose water at night and how to model it at large scales.We hypothesized that plants optimize nighttime leaf diffusive conductance (gwn) to balance potential daytime photosynthetic benefits and nocturnal transpiration benefits. We quantified nighttime benefits from respiratory reductions due to evaporative leaf cooling. We described nighttime costs in terms of a reduced carbon gain during the day because of water use at night. We measured nighttime stomatal responses and tested our model with water birch (Betula occidentalis) saplings grown in a glasshouse.The gwn of water birch decreased with drier soil, higher atmospheric CO2, wetter air, lower leaf temperature, and lower leaf respiration rate. Our model predicted all these responses correctly, except for the response of gwn to air humidity. Our results also suggested that the slow decrease in gwn after sunset could be associated with decreasing leaf respiration.The optimality‐based nocturnal transpiration model smoothly integrates with daytime stomatal optimization approaches, and thus has the potential to quantitatively predict nocturnal transpiration across space and time. [ABSTRACT FROM AUTHOR]

Published
2021
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45. Improved Collaborative Control of Standalone Brushless Doubly Fed Induction Generator Under Unbalanced and Nonlinear Loads Considering Voltage Rating of Converters.

Author

Xu, Wei, Yu, Kailiang, Liu, Yi, and Chen, Junjie

Subjects
*INDUCTION generators, *VOLTAGE-frequency converters, *VOLTAGE control, *HIGH voltages, *HARMONIC suppression filters, *TRACKING control systems, *CASCADE converters
Abstract

In standalone brushless doubly-fed induction generator (BDFIG) power generation systems, unbalanced and nonlinear loads can produce distorted power winding (PW) voltage, and consequently affects the normal operation of other loads and degrades the performance of the power generation system. Although the negative-sequence and harmonic components of the PW voltage can be eliminated by the conventional control methods implemented in either machine-side converter (MSC) or load-side converter (LSC), it causes higher voltage rating of converters and needs more current sensors. In this article, the detailed analysis for the impact of conventional control methods on the voltage rating of the two converters is presented. And then, an improved collaborative control method considering the power converter voltage rating is proposed, in which the MSC can be employed to eliminate the seventh-harmonic voltage of PW, and the LSC can be used to reject the negative-sequence and fifth-harmonic components of PW voltage without extra current sensors. The proportional-integral-resonant controller is applied to the current control loops in both MSC and LSC so as to obtain good current track ability. Comprehensive experimental results based on a 30-kVA BDFIG demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Published
2020
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46. Improved Coordinated Control of Standalone Brushless Doubly Fed Induction Generator Supplying Nonlinear Loads.

Author

Xu, Wei, Yu, Kailiang, Liu, Yi, and Gao, Jianping

Subjects
*INDUCTION generators, *HARMONIC suppression filters, *LOADERS (Machines), *ELECTRIC power filters, *VOLTAGE control
Abstract

This paper proposes an improved coordinated control of standalone brushless doubly fed induction generator (BDFIG) supplying nonlinear loads. Due to the existence of nonlinear loads, the voltage at the point of common coupling becomes distorted with a large number of low-order harmonics, which severely degrades the performance of power generation systems. In order to eliminate the included low-order harmonics, a coordinated control method of harmonic mitigation has been presented in this paper for the control of both machine side and load side converters. Then, a weight factor is introduced to the proposed control method for distributing the contribution of harmonic mitigation between the two converters. Comprehensive experimental results on a 30 kVA BDFIG are presented to verify the proposed control method. [ABSTRACT FROM AUTHOR]

Published
2019
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47. The scaling of fine root nitrogen versus phosphorus in terrestrial plants: A global synthesis.

Author

Wang, Zhiqiang, Yu, Kailiang, Lv, Shiqi, Niklas, Karl J., Mipam, Tserang Donko, Crowther, Thomas W., Umaña, María N., Zhao, Qi, Huang, Heng, Reich, Peter B., and Niu, Shuli

Subjects
*PHOSPHORUS, *NITROGEN, *PLANT growth, *NITROGEN in soils, *PLANT species, *RESOURCE allocation, *ATMOSPHERIC nitrogen
Abstract

Leaves and roots may differ in nitrogen (N), phosphorus (P) and N:P stoichiometry, which can influence plant growth and ecosystem functioning. As compared to leaves, however, relatively little is known about the N versus P scaling relationship and N:P stoichiometry in root systems, particularly in fine roots.We used a global dataset comprising 1,890 observations for a total of 763 terrestrial plant species in 123 families (spanning 433 sites world‐wide) to examine live fine root N and P concentrations and stoichiometry, and to determine the scaling of N versus P within and across different plant groups and ecosystems.The global geometric mean values of fine root N and P concentrations and N:P ratios were 10.84 mg/g, 0.94 mg/g and 11.55, respectively. Fine root N and P concentrations and N:P ratios varied both within and across plant groups and ecosystems. A 0.82‐power law described the scaling of fine root N with respect to P across the entire dataset and for major plant phylogenetic and functional groups; however, the numerical value of the N versus P scaling exponent declined from the tropics to higher latitudes and varied significantly at different local sites. Soil nutrient account for much of the variation observed in the scaling of fine root N versus P concentration at different local sites.This study advances our knowledge about limiting resource allocation strategies in below‐ground organs and has important implications for modelling plant growth at local, regional and global levels. A free Plain Language Summary can be found within the Supporting Information of this article. [ABSTRACT FROM AUTHOR]

Published
2019
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48. Improved Sensorless Phase Control of Stand-Alone Brushless Doubly-Fed Machine Under Unbalanced Loads for Ship Shaft Power Generation.

Author

Xu, Wei, Dong, Dinghao, Liu, Yi, Yu, Kailiang, and Gao, Jianping

Subjects
MARINE machinery, SENSORLESS control systems, BEARING currents in electric machinery
Abstract

The brushless doubly-fed machine (BDFM) without a brush and slip ring has some advantages of high operation reliability and less maintenance cost, which is very suitable for the ship shaft power generation. In this application, the BDFM mainly works on the stand-alone state, in which the power winding (PW) voltage should be controlled directly. The unbalanced load is a typical working state, which mostly results in the unbalance of PW voltage and affects the normal operation of loads severely. The sensorless control strategy can enhance the operation safety under extreme environment and cut down the cost effectively, which is highly desired to be applied in the BDFM-based ship shaft power generation. In this paper, an improved sensorless phase control strategy is proposed, which can work effectively under the unbalanced load condition. First, the sensorless operation is achieved by controlling the $q$ -component of PW voltage, which can realize the phase control simultaneously. Then, the unbalanced compensation is designed in details. Finally, both simulation and experiments have demonstrated that, by the proposed method, the PW voltage phase can be controlled precisely and the voltage unbalance factor is lower than the permissible level. [ABSTRACT FROM AUTHOR]

Published
2018
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49. Flexible Power Distribution Control in an Asymmetrical-Cascaded-Multilevel-Converter-Based Hybrid Energy Storage System.

Author

Jiang, Wei, Xue, Shuai, Zhang, Lizong, Xu, Wei, Yu, Kailiang, Chen, Wu, and Zhang, Lei

Subjects
ELECTRIC power distribution, ENERGY storage, ROTARY converters, ELECTRIC power, ELECTRIC power systems
Abstract

This paper proposes a novel control method for an asymmetrical-cascaded-multilevel-converter-based hybrid energy storage system (HESS), which includes one battery and several electrical double-layer capacitors (EDLCs). Traditionally, it is challenging to control the power distribution between different cascaded H-bridges by using different energy storage components as dc sources. In this paper, a feedforward space-vector-modulation (FFSVM) technique is used to distribute the active power between the battery and EDLCs. With the proposed voltage feedforward mechanism, the HESS can flexibly operate in the normal operation mode, the high-power output mode, and the reverse power absorption mode. The principle and effectiveness of the improved FFSVM technique are analyzed. Meanwhile, the state-of-charge-balancing mechanisms are introduced. The simulation results obtained in MATLAB/Simulink as well as the experimental results of a hybrid energy storage test bed based on the proposed system are presented to verify its performance. [ABSTRACT FROM PUBLISHER]

Published
2018
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50. The effect of nitrogen availability and water conditions on competition between a facultative CAM plant and an invasive grass.

Author

Yu, Kailiang, D'Odorico, Paolo, Carr, David E., Personius, Ashden, and Collins, Scott L.

Subjects
*CRASSULACEAN acid metabolism, *GRASSES, *NITROGEN content of plants, *CARBOXYLATION, *DROUGHT tolerance
Abstract

Abstract Plants with crassulacean acid metabolism ( CAM) are increasing their abundance in drylands worldwide. The drivers and mechanisms underlying the increased dominance of CAM plants and CAM expression (i.e., nocturnal carboxylation) in facultative CAM plants, however, remain poorly understood. We investigated how nutrient and water availability affected competition between Mesembryanthemum crystallinum (a model facultative CAM species) and the invasive C3 grass Bromus mollis that co-occur in California's coastal grasslands. Specifically we investigated the extent to which water stress, nutrients, and competition affect nocturnal carboxylation in M. crystallinum. High nutrient and low water conditions favored M. crystallinum over B. mollis, in contrast to high water conditions. While low water conditions induced nocturnal carboxylation in 9-week-old individuals of M. crystallinum, in these low water treatments, a 66% reduction in nutrient applied over the entire experiment did not further enhance nocturnal carboxylation. In high water conditions M. crystallinum both alone and in association with B. mollis did not perform nocturnal carboxylation, regardless of the nutrient levels. Thus, nocturnal carboxylation in M. crystallinum was restricted by strong competition with B. mollis in high water conditions. This study provides empirical evidence of the competitive advantage of facultative CAM plants over grasses in drought conditions and of the restricted ability of M. crystallinum to use their photosynthetic plasticity (i.e., ability to switch to CAM behavior) to compete with grasses in well-watered conditions. We suggest that a high drought tolerance could explain the increased dominance of facultative CAM plants in a future environment with increased drought and nitrogen deposition, while the potential of facultative CAM plants such as M. crystallinum to expand to wet environments is expected to be limited. [ABSTRACT FROM AUTHOR]

Published
2017
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