Global maps of soil temperature

3110 | Glob Change Biol. 2022;28:3110–3144.wileyonlinelibrary.com/journal/gcb Received: 25 August 2021 | Accepted: 27 October 2021 DOI: 10.1111/gcb.16060 RESEARCH ARTICLE Global maps of soil temperature Jonas J. Lembrechts1 | Johan van den Hoogen2 | Juha Aalto3,4 | Michael B. Ashcroft5,6 | Pieter De Frenne7 | Julia Kemppinen8 | Martin Kopecký9,10 | Miska Luoto4 | Ilya M. D. Maclean11 | Thomas W. Crowther2 | Joseph J. Bailey12 | Stef Haesen13 | David H. Klinges14,15 | Pekka Niittynen4 | Brett R. Scheffers16 | Koenraad Van Meerbeek13 | Peter Aartsma17 | Otar Abdalaze18 | Mehdi Abedi19 | Rien Aerts20 | Negar Ahmadian19 | Antje Ahrends21 | Juha M. Alatalo22 | Jake M. Alexander23 | Camille Nina Allonsius24 | Jan Altman9,10 | Christof Ammann25 | Christian Andres26 | Christopher Andrews27 | Jonas Ardö28 | Nicola Arriga29 | Alberto Arzac30 | Valeria Aschero31,32 | Rafael L. Assis33 | Jakob Johann Assmann34,35 | Maaike Y. Bader36 | Khadijeh Bahalkeh19 | Peter Barančok37 | Isabel C. Barrio38 | Agustina Barros32 | Matti Barthel26 | Edmund W. Basham14 | Marijn Bauters39 | Manuele Bazzichetto40 | Luca Belelli Marchesini41 | Michael C. Bell42 | Juan C. Benavides43 | José Luis Benito Alonso44 | Bernd J. Berauer45,46 | Jarle W. Bjerke47 | Robert G. Björk48,49 | Mats P. Björkman48,49 | Katrin Björnsdóttir50 | Benjamin Blonder51 | Pascal Boeckx39 | Julia Boike52,53 | Stef Bokhorst20 | Bárbara N. S. Brum54 | Josef Brůna9 | Nina Buchmann26 | Pauline Buysse55 | José Luís Camargo56 | Otávio C. Campoe57 | Onur Candan58 | Rafaella Canessa36,59 | Nicoletta Cannone60 | Michele Carbognani61 | Jofre Carnicer62,63 | Angélica Casanova- Katny64 | Simone Cesarz65,66 | Bogdan Chojnicki67 | Philippe Choler68,69 | Steven L. Chown70 | Edgar F. Cifuentes71 | Marek Čiliak72 | Tamara Contador73,74 | Peter Convey75 | Elisabeth J. Cooper76 | Edoardo Cremonese77 | Salvatore R. Curasi78 | Robin Curtis11 | Maurizio Cutini79 | C. Johan Dahlberg80,81 | Gergana N. Daskalova82 | Miguel Angel de Pablo83 | Stefano Della Chiesa84 | Jürgen Dengler65,85,86 | Bart Deronde87 | Patrice Descombes88 | Valter Di Cecco89 | Michele Di Musciano90 | Jan Dick27 | Romina D. Dimarco91,92 | Jiri Dolezal9,93 | Ellen Dorrepaal94 | This is an open access article under the terms of the Creat ive Commo ns Attri bution-NonCo mmercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. © 2022 The Authors. Global Change Biology published by John Wiley & Sons Ltd. Jonas J. Lembrechts and Johan van den Hoogen should be considered joint first author. Ivan Nijs and Jonathan Lenoir should be considered joint senior author. 13652486, 2022, 9, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16060 by Universidad De Sevilla, Wiley Online Library on [11/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 3111 LEMBRECHTS ET aL. Jiří Dušek95 | Nico Eisenhauer65,66 | Lars Eklundh28 | Todd E. Erickson96,97 | Brigitta Erschbamer98 | Werner Eugster26 | Robert M. Ewers99 | Dan A. Exton100 | Nicolas Fanin101 | Fatih Fazlioglu58 | Iris Feigenwinter26 | Giuseppe Fenu102 | Olga Ferlian65,66 | M. Rosa Fernández Calzado103 | Eduardo Fernández- Pascual104 | Manfred Finckh105 | Rebecca Finger Higgens106 | T'ai G. W. Forte61 | Erika C. Freeman107 | Esther R. Frei108,109,110 | Eduardo Fuentes- Lillo111,1,112 | Rafael A. García111,113 | María B. García114 | Charly Géron1,115 | Mana Gharun26 | Dany Ghosn116 | Khatuna Gigauri117 | Anne Gobin118,119 | Ignacio Goded29 | Mathias Goeckede120 | Felix Gottschall65,66 | Keith Goulding121 | Sanne Govaert7 | Bente Jessen Graae122 | Sarah Greenwood123 | Caroline Greiser80 | Achim Grelle124 | Benoit Guénard125 | Mauro Guglielmin126 | Joannès Guillemot127,128 | Peter Haase129,130 | Sylvia Haider131,65 | Aud H. Halbritter132 | Maroof Hamid133 | Albin Hammerle134 | Arndt Hampe135 | Siri V. Haugum132,136 | Lucia Hederová9 | Bernard Heinesch137 | Carole Helfter138 | Daniel Hepenstrick139 | Maximiliane Herberich140 | Mathias Herbst141 | Luise Hermanutz142 | David S. Hik143 | Raúl Hoffrén144 | Jürgen Homeier145,146 | Lukas Hörtnagl26 | Toke T. Høye147 | Filip Hrbacek148 | Kristoffer Hylander80 | Hiroki Iwata149 | Marcin Antoni Jackowicz- Korczynski150,28 | Hervé Jactel151 | Järvi Järveoja152 | Szymon Jastrzębowski153 | Anke Jentsch46,154 | Juan J. Jiménez155 | Ingibjörg S. Jónsdóttir156 | Tommaso Jucker157 | Alistair S. Jump158 | Radoslaw Juszczak67 | Róbert Kanka37 | Vít Kašpar9,159 | George Kazakis116 | Julia Kelly160 | Anzar A. Khuroo133 | Leif Klemedtsson48 | Marcin Klisz153 | Natascha Kljun160 | Alexander Knohl161 | Johannes Kobler162 | Jozef Kollár37 | Martyna M. Kotowska146 | Bence Kovács163 | Juergen Kreyling164 | Andrea Lamprecht165 | Simone I. Lang166 | Christian Larson167 | Keith Larson168 | Kamil Laska148,169 | Guerric le Maire127,128 | Rachel I. Leihy170 | Luc Lens171 | Bengt Liljebladh48 | Annalea Lohila172,173 | Juan Lorite103,174 | Benjamin Loubet55 | Joshua Lynn132 | Martin Macek9 | Roy Mackenzie73 | Enzo Magliulo175 | Regine Maier26 | Francesco Malfasi60 | František Máliš176 | Matěj Man9 | Giovanni Manca29 | Antonio Manco175 | Tanguy Manise137 | Paraskevi Manolaki177,178,179 | Felipe Marciniak54 | Radim Matula10,180 | Ana Clara Mazzolari32 | Sergiy Medinets181,182,183 | Volodymyr Medinets181 | Camille Meeussen7 | Sonia Merinero80 | Rita de Cássia Guimarães Mesquita184 | Katrin Meusburger185 | Filip J. R. Meysman186 | Sean T. Michaletz187 | Ann Milbau188 | Dmitry Moiseev189 | Pavel Moiseev189 | Andrea Mondoni190 | Ruth Monfries21 | 13652486, 2022, 9, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16060 by Universidad De Sevilla, Wiley Online Library on [11/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 3112 | LEMBRECHTS ET aL. Leonardo Montagnani191 | Mikel Moriana- Armendariz76 | Umberto Morra di Cella192 | Martin Mörsdorf193 | Jonathan R. Mosedale194 | Lena Muffler146 | Miriam Muñoz- Rojas195,196 | Jonathan A. Myers197 | Isla H. Myers- Smith82 | Laszlo Nagy198 | Marianna Nardino199 | Ilona Naujokaitis- Lewis200 | Emily Newling201 | Lena Nicklas98 | Georg Niedrist202 | Armin Niessner203 | Mats B. Nilsson152 | Signe Normand34,35 | Marcelo D. Nosetto204,205 | Yann Nouvellon127,128 | Martin A. Nuñez92,206 | Romà Ogaya207,208 | Jérôme Ogée101 | Joseph Okello39,209,210 | Janusz Olejnik211 | Jørgen Eivind Olesen182 | Øystein H. Opedal212 | Simone Orsenigo190 | Andrej Palaj37 | Timo Pampuch213 | Alexey V. Panov214 | Meelis Pärtel215 | Ada Pastor178 | Aníbal Pauchard111,113 | Harald Pauli165 | Marian Pavelka95 | William D. Pearse216,217 | Matthias Peichl152 | Loïc Pellissier218,219 | Rachel M. Penczykowski197 | Josep Penuelas207,208 | Matteo Petit Bon9,76,166 | Alessandro Petraglia61 | Shyam S. Phartyal220 | Gareth K. Phoenix221 | Casimiro Pio222 | Andrea Pitacco223 | Camille Pitteloud218,219 | Roman Plichta180 | Francesco Porro190 | Miguel Portillo- Estrada1 | Jérôme Poulenard224 | Rafael Poyatos63,225 | Anatoly S. Prokushkin30,214 | Radoslaw Puchalka226,227 | Mihai Pușcaș228,229,230 | Dajana Radujković1 | Krystal Randall5,231 | Amanda Ratier Backes65,131 | Sabine Remmele203 | Wolfram Remmers232 | David Renault40,233 | Anita C. Risch234 | Christian Rixen108,109 | Sharon A. Robinson5,231 | Bjorn J. M. Robroek235 | Adrian V. Rocha236 | Christian Rossi237,238 | Graziano Rossi190 | Olivier Roupsard239,240,241 | Alexey V. Rubtsov30 | Patrick Saccone165 | Clotilde Sagot242 | Jhonatan Sallo Bravo243,244 | Cinthya C. Santos245 | Judith M. Sarneel246 | Tobias Scharnweber213 | Jonas Schmeddes164 | Marius Schmidt247 | Thomas Scholten248 | Max Schuchardt46 | Naomi Schwartz249 | Tony Scott121 | Julia Seeber134,202 | Ana Cristina Segalin de Andrade245 | Tim Seipel167 | Philipp Semenchuk250 | Rebecca A. Senior251 | Josep M. Serra- Diaz252 | Piotr Sewerniak253 | Ankit Shekhar26 | Nikita V. Sidenko214 | Lukas Siebicke161 | Laura Siegwart Collier142,254 | Elizabeth Simpson216 | David P. Siqueira255 | Zuzana Sitková256 | Johan Six26 | Marko Smiljanic213 | Stuart W. Smith122,257 | Sarah Smith- Tripp258 | Ben Somers259 | Mia Vedel Sørensen122 | José João L. L. Souza260 | Bartolomeu Israel Souza261 | Arildo Souza Dias245,262 | Marko J. Spasojevic263 | James D. M. Speed264 | Fabien Spicher265 | Angela Stanisci266 | Klaus Steinbauer165 | Rainer Steinbrecher267 | Michael Steinwandter202 | Michael Stemkovski216 | Jörg G. Stephan268 | 13652486, 2022, 9, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16060 by Universidad De Sevilla, Wiley Online Library on [11/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 3113 LEMBRECHTS ET aL. Christian Stiegler161 | Stefan Stoll232,269 | Martin Svátek180 | Miroslav Svoboda10 | Torbern Tagesson28,270 | Andrew J. Tanentzap107 | Franziska Tanneberger271 | Jean- Paul Theurillat272,273 | Haydn J. D. Thomas82 | Andrew D. Thomas274 | Katja Tielbörger59 | Marcello Tomaselli61 | Urs Albert Treier34,35 | Mario Trouillier213 | Pavel Dan Turtureanu228,230,275 | Rosamond Tutton276 | Vilna A. Tyystjärvi4,277 | Masahito Ueyama278 | Karol Ujházy176 | Mariana Ujházyová72 | Domas Uogintas279 | Anastasiya V. Urban180,214 | Josef Urban30,180 | Marek Urbaniak211 | Tudor- Mihai Ursu280 | Francesco Primo Vaccari281 | Stijn Van de Vondel282 | Liesbeth van den Brink59 | Maarten Van Geel283 | Vigdis Vandvik132 | Pieter Vangansbeke7 | Andrej Varlagin284 | G. F. Veen285 | Elmar Veenendaal286 | Susanna E. Venn287 | Hans Verbeeck288 | Erik Verbrugggen1 | Frank G. A. Verheijen289 | Luis Villar290 | Luca Vitale291 | Pascal Vittoz292 | Maria Vives- Ingla63 | Jonathan von Oppen34,35 | Josefine Walz168 | Runxi Wang125 | Yifeng Wang276 | Robert G. Way276 | Ronja E. M. Wedegärtner122 | Robert Weigel146 | Jan Wild9,159 | Matthew Wilkinson42 | Martin Wilmking213 | Lisa Wingate101 | Manuela Winkler165 | Sonja Wipf108,237 | Georg Wohlfahrt134 | Georgios Xenakis293 | Yan Yang294 | Zicheng Yu295,296 | Kailiang Yu297 | Florian Zellweger110 | Jian Zhang298 | Zhaochen Zhang298 | Peng Zhao152 | Klaudia Ziemblińska211 | Reiner Zimmermann203,299 | Shengwei Zong300 | Viacheslav I. Zyryanov214 | Ivan Nijs1 | Jonathan Lenoir265 1Research Group PLECO (Plants and Ecosystems), University of Antwerp, Wilrijk, Belgium 2Department of Environmental Systems Science, Institute of Integrative Biology, ETH Zürich, Zürich, Switzerland 3Finnish Meteorological Institute, Helsinki, Finland 4Department of Geosciences and Geography, University of Helsinki, Finland 5Centre for Sustainable Ecosystem Solutions, School of Earth, Atmospheric and Life Sciences, University of Wollongong, Wollongong, New South Wales, Australia 6Australian Museum, Sydney, Australia 7Forest & Nature Lab, Department of Environment, Ghent University, Melle- Gontrode, Belgium 8Geography Research Unit, University of Oulu, Oulu, Finland 9Institute of Botany of the Czech Academy of Sciences, Průhonice, Czech Republic 10Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, Prague 6 - Suchdol, Czech Republic 11Environment and Sustainability Institute, University of Exeter, Penryn Campus, Penryn, UK 12Department of Geography, York St John University, York, UK 13Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium 14School of Natural Resources and Environment, University of Florida, Gainesville, Florida, USA 15Smithsonian Environmental Research Center, Edgewater, Maryland, USA 16Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, USA 17Department of Natural Sciences and Environmental Health, University of South- Eastern Norway, Bø, Norway 18Alpine Ecosystems Research Program, Institute of Ecology, Ilia State University, Tbilisi, Georgia 19Department of Range Management, Faculty of Natural Resources and Marine Sciences, Tarbiat Modares University, Noor, Iran 20Department of Ecological Science, Vrije Universiteit Amsterdam, The Netherlands 21Royal Botanic Garden Edinburgh, Edinburgh, UK 13652486, 2022, 9, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16060 by Universidad De Sevilla, Wiley Online Library on [11/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 3114 | LEMBRECHTS ET aL. 22Environmental Science Center, Qatar University, Doha, Qatar 23Department of Environmental Systems Science, Institute of Integrative Biology, ETH Zurich, Zürich, Switzerland 24Research group ECOBE, University of Antwerp, Wilrijk, Belgium 25Department of Agroecology and Environment, Agroscope Research Institute, Zürich, Switzerland 26Department of Environmental Systems Science, ETH Zurich, Zurich, Switzerland 27UK Centre for Ecology and Hydrology, Penicuik, UK 28Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden 29European Commission, Joint Research Centre (JRC), Ispra, Italy 30Siberian Federal University, Krasnoyarsk, Russia 31Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Cuyo, Mendoza, Argentina 32Instituto Argentino de Nivologiá, Glaciologiá y Ciencias Ambientales (IANIGLA), CONICET, CCT- Mendoza, Mendoza, Argentina 33Natural History Museum, University of Oslo, Oslo, Norway 34Center for Sustainable Landscapes Under Global Change, Department of Biology, Aarhus University, Aarhus C, Denmark 35Center for Biodiversity Dynamics in a Changing World, Department of Biology, Aarhus University, Aarhus C, Denmark 36Ecological Plant Geography, Faculty of Geography, University of Marburg, Marburg, Germany 37Institute of Landscape Ecology Slovak Academy of Sciences, Bratislava, Slovakia 38Faculty of Environmental and Forest Sciences, Agricultural University of Iceland, Reykjavík, Iceland 39Isotope Bioscience Laboratory - ISOFYS, Ghent University, Gent, Belgium 40Université de Rennes, CNRS, EcoBio (Ecosystèmes, biodiversité, évolution) - UMR 6553, Rennes, France 41Department of Sustainable Agroecosystems and Bioresources, Research and Innovation Centre, Fondazione Edmund Mach, San Michele all’Adige, Italy 42Forest Research, Alice Holt Lodge, Wrecclesham, Farnham, UK 43Department of Ecology, Pontificia Universidad Javeriana, Bogota, Colombia 44Jolube Consultor Botánico. C/Mariano R de Ledesma, Jaca, Huesca, Spain 45Institute of Landscape and Plant Ecology, Department of Plant Ecology, University of Hohenheim, Stuttgart, Germany 46Disturbance Ecology, BayCEER, University of Bayreuth, Bayreuth, Germany 47Norwegian Institute for Nature Research, FRAM - High North Research Centre for Climate and the Environment, Tromsø, Norway 48Department of Earth Sciences, University of Gothenburg, Gothenburg, Sweden 49Gothenburg Global Biodiversity Centre, Gothenburg, Sweden 50Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden 51Department of Environmental Science, Policy, and Management, University of California, Berkeley, California, USA 52Alfred Wegener Institute Helmholtz Center for Polar and Marine Research, Telegrafenberg A45, Potsdam, Germany 53Geography Department, Humboldt- Universität zu Berlin, Germany 54Pós- Graduação em Ciências de Florestas Tropicais, Instituto Nacional de Pesquisas da Amazônia, Manaus, Brasil 55UMR ECOSYS INRAE, Uinversité Paris Saclay, AgroParisTech, France 56Biological Dynamics of Forest Fragments Project, BDFFP, Instituto Nacional de Pesquisas da Amazônia, Manaus, Brazil 57Department of Forest Sciences, Federal University of Lavras, Lavras, Brazil 58Faculty of Arts and Sciences, Department of Molecular Biology and Genetics, Ordu University, Ordu, Turkey 59Plant Ecology Group, Department of Evolution and Ecology, University of Tübingen, Tübingen, Germany 60Department of Science and High Technology, Insubria University, Como, Italy 61Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma, Italy 62Department of Evolutionary Biology, Ecology and Environmental Sciences, Biodiversity Research Institute (IRBio), University of Barcelona, Barcelona, Spain 63CREAF, E08193 Bellaterra (Cerdanyola del Vallès), Spain 64Laboratorio de Ecofisiología Vegetal y Cambio Climático, Laboratorio de Ecofisiología Vegetal y Cambio Climático, Departamento de Ciencias Veterinarias y Salud Pública, Universidad Católica de Temuco, Campus Luis Rivas del Canto and Núcleo de Estudios Ambientales (NEA), Facultad de Recursos Naturales, Universidad Católica de Temuco, Temuco, Chile 65German Centre for Integrative Biodiversity Research (iDiv) Halle- Jena- Leipzig, Leipzig, Germany 66Institute of Biology, Leipzig University, Leipzig, Germany 67Laboratory of Bioclimatology, Department of Ecology and Environmental Protection, Poznan University of Life Sciences, Poznan, Poland 68Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LECA, Grenoble, France 69Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LTSER Zone Atelier Alpes, Grenoble, France 70Securing Antarctica's Environmental Future, School of Biological Sciences, Monash University, Melbourne, Victoria, Australia 71Forest Ecology and Conservation Group, Department of Plant Sciences, University of Cambridge, Cambridge, UK 72Faculty of Ecology and Environmental Sciences, Technical University in Zvolen, Zvolen, Slovakia 73Millennium Institute Biodiversity of Antarctic and Subantarctic Ecosystems (BASE), University Austral of Chile, Valdivia, Chile 13652486, 2022, 9, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16060 by Universidad De Sevilla, Wiley Online Library on [11/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 3115 LEMBRECHTS ET aL. 74Cape Horn International Center (CHIC), Puerto Williams, Chile 75British Antarctic Survey, NERC, High Cross, Cambridge, UK 76Department of Arctic and Marine Biology, Faculty of Biosciences Fisheries and Economics, UiT- The Arctic University of Norway, Tromsø, Norway 77Climate Change Unit, Environmental Protection Agency of Aosta Valley, Italy 78Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, USA 79Department of Science, University of Roma Tre, Rome, Italy 80Department of Ecology, Environment and Plant Sciences and Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden 81The County Administrative Board of Västra Götaland, Gothenburg, Sweden 82School of GeoSciences, University of Edinburgh, Edinburgh, UK 83Department of Geology, Geography and Environment, University of Alcalá, Madrid, Spain 84Chair of Geoinformatics, Technische Universität Dresden, Dresden, Germany 85Vegetation Ecology, Institute of Natural Resource Sciences (IUNR), ZHAW Zurich University of Applied Sciences, Wädenswil, Switzerland 86Plant Ecology, Bayreuth Center of Ecology and Environmental Research (BayCEER), University of Bayreuth, Bayreuth, Germany 87VITO- TAP, Mol, Belgium 88Swiss Federal Research Institute WSL, Birmensdorf, Switzerland 89Majella Seed Bank, Majella National Park, Colle Madonna, Lama dei Peligni, Italy 90Department of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Italy 91Grupo de Ecología de Poblaciones de Insectos, IFAB (INTA - CONICET), Bariloche, Argentina 92Department of Biology and Biochemistry, University of Houston, Houston, Texas, USA 93Faculty of Science, Department of Botany, University of South Bohemia, České Budějovice, Czech Republic 94Climate Impacts Research Centre, Department of Ecology and Environmental Science, Umeå University, Abisko, Sweden 95Global Change Research Institute, Academy of Sciences of the Czech Republic, Czech Republic 96School of Biological Sciences, The University of Western Australia, Crawley, Western Australia, Australia 97Kings Park Science, Department of Biodiversity, Conservation and Attractions, Kings Park, Australia 98Department of Botany, Faculty of Biology, University of Innsbruck, Innsbruck, Austria 99Imperial College London, Ascot, UK 100Operation Wallacea, Lincolnshire, UK 101INRAE, Bordeaux Sciences Agro, UMR 1391 ISPA, Villenave d'Ornon, France 102Department of Life and Environmental Sciences, University of Cagliari, Cagliari, Italy 103Department of Botany, University of Granada, Granada, Spain 104IMIB – Biodiversity Research Institute, University of Oviedo, Mieres, Spain 105Institute for Plant Science and Microbiology, University of Hamburg, Hamburg, Germany 106Dartmouth College, Hanover, New Hampshire, USA 107Ecosystems and Global Change Group, Department of Plant Sciences, University of Cambridge, Cambridge, UK 108WSL Institute for Snow and Avalanche Research SLF, Davos Dorf, Switzerland 109Climate Change, Extremes and Natural Hazards in Alpine Regions Research Center CERC, Davos Dorf, Switzerland 110Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland 111Laboratorio de Invasiones Biológicas (LIB), Facultad de Ciencias Forestales, Universidad de Concepción, Concepción, Chile 112School of Education and Social Sciences, Adventist University of Chile, Chile 113Instituto de Ecología y Biodiversidad (IEB), Santiago, Chile 114Pyrenean Institute of Ecology (CSIC), Zaragoza, Spain 115Biodiversity and Landscape, TERRA Research Centre, Gembloux Agro- Bio Tech, University of Liège, Gembloux, Belgium 116Department of Geoinformation in Environmental Management, Mediterranean Agronomic Institute of Chania, Chania, Greece 117Department of Environmental Management and Policy, Georgian Institute of Public Affairs, Tbilisi, Georgia 118Flemish Institute for Technological Research, Mol, Belgium 119Department of Earth and Environmental Science, Faculty of BioScience Engineering, KULeuven, Belgium 120Department of Biogeochemical Signals, Max Planck Institute for Biogeochemistry, Jena, Germany 121Sustainable Agricultural Sciences Department, Rothamsted Research, Harpenden, UK 122Department of Biology, Norwegian University of Science and Technology, Trondheim, Norway 123Biodiversity, Wildlife and Ecosystem Health, Biomedical Sciences, University of Edinburgh, Edinburgh, UK 124Department of Ecology, Swedish University of Agricultural Sciences, Uppsala, Sweden 125School of Biological Sciences, The University of Hong Kong, Hong Kong SAR, China 126Department of Theoretical and Applied Sciences, Insubria University, Varese, Italy 13652486, 2022, 9, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16060 by Universidad De Sevilla, Wiley Online Library on [11/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 3116 | LEMBRECHTS ET aL. 127CIRAD, UMR Eco&Sols, Montpellier, France 128Eco&Sols, Univ Montpellier, CIRAD, INRAE, IRD, Montpellier SupAgro, Montpellier, France 129Senckenberg Research Institute and Natural History Museum Frankfurt, Gelnhausen, Germany 130Faculty of Biology, University of Duisburg- Essen, Essen, Germany 131Institute of Biology / Geobotany and Botanical Garden, Martin Luther University Halle- Wittenberg, Halle (Saale), Germany 132Department of Biological Sciences and Bjerknes Centre for Climate Research, University of Bergen, Bergen, Norway 133Centre for Biodiversity and Taxonomy, Department of Botany, University of Kashmir, Srinagar, India 134Department of Ecology, University of Innsbruck, Innsbruck, Austria 135INRAE, Univ. Bordeaux, BIOGECO, Cestas, France 136The Heathland Centre, Alver, Norway 137TERRA Teaching and Research Center, Faculty of Gembloux Agro- Bio Tech, University of Liege, Gembloux, Belgium 138UK Centre for Ecology and Hydrology, Penicuik, UK 139Vegetation Ecology, Institute of Natural Resource Sciences, ZHAW Zurich University of Applied Sciences, Grüental, Switzerland 140Institute for Botany, University of Natural Resources and Life Sciences Vienna (BOKU), Vienna, Austria 141Centre for Agrometeorological Research (ZAMF), German Meteorological Service (DWD), Braunschweig, Germany 142Dept of Biology, Memorial University, St. John's, Newfoundland, Canada 143Department of Biological Sciences, Simon Fraser University, Burnaby, British Columbia, Canada 144Department of Geography, University of Zaragoza, Zaragoza, Spain 145Faculty of Resource Management, HAWK University of Applied Sciences and Arts, Göttingen, Germany 146Plant Ecology, Albrechtvon- Haller- Institute for Plant Sciences, Georg- August University of Göttingen, Göttingen, Germany 147Department of Ecoscience and Arctic Research Centre, Aarhus University, Rønde, Denmark 148Department of Geography, Faculty of Science, Masaryk University, Brno, Czech Republic 149Department of Environmental Science, Shinshu University, Matsumoto, Japan 150Department of Ecoscience and Arctic Research Centre, Aarhus University, Roskilde, Denmark 151INRAE, University of Bordeaux, BIOGECO, Cestas, France 152Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden 153Department of Silviculture and Forest Tree Genetics, Forest Research Institute, Raszyn, Poland 154Bayreuth Center of Ecology and Environmental Research, Bayreuth, Germany 155ARAID/IPECSIC, Pyrenean Institute of Ecology, Avda. Llano de la Victoria, Spain 156Life and Environmental Sciences, University of Iceland, Reykjavík, Iceland 157School of Biological Sciences, University of Bristol, Bristol, UK 158Biological and Environmental Sciences, Faculty of Natural Sciences, University of Stirling, Scotland 159Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague 6 - Suchdol, Czech Republic 160Centre for Environmental and Climate Science, Lund University, Lund, Sweden 161Bioclimatology, University of Göttingen, Göttingen, Germany 162Environment Agency Austria, Vienna, Austria 163Centre for Ecological Research, Institute of Ecology and Botany, Vácrátót, Hungary 164Experimental Plant Ecology, Institute of Botany and Landscape Ecology, University of Greifswald, Greifswald, Germany 165GLORIA Coordination, Institute for Interdisciplinary Mountain Research, Austrian Academy of Sciences (ÖAW) & Department of Integrative Biology and Biodiversity Research, University of Natural Resources and Life Sciences, Vienna, Austria 166Department of Arctic Biology, The University Centre in Svalbard (UNIS), Longyearbyen, Svalbard, Norway 167Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, Montana, USA 168Climate Impacts Research Centre, Department of Ecology and Environmental Sciences, Umeå University, Abisko, Sweden 169Centre for Polar Ecology, Faculty of Science, University of South Bohemia, České Budějovice, Czech Republic 170School of Biological Sciences, Monash University, Melbourne, Victoria, Australia 171Terrestrial Ecology Unit, Department of Biology, Ghent University, Gent, Belgium 172Finnish Meteorological Institute, Climate System Research, Helsinki, Finland 173INAR Institute for Atmospheric and Earth System Research/Physics, Faculty of Science, University of Helsinki, Finland 174Interuniversity Institute for Earth System Research, University of Granada, Granada, Spain 175CNR Institute for Agricultural and Forestry Systems in the Mediterranean, Portici (Napoli), Italy 176Faculty of Forestry, Technical University in Zvolen, Zvolen, Slovakia 177School of Pure & Applied Sciences, Environmental Conservation and Management Programme, Open University of Cyprus, Latsia, Cyprus 178Department of Biology, Aarhus University, Aarhus C, Denmark 13652486, 2022, 9, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16060 by Universidad De Sevilla, Wiley Online Library on [11/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 3117 LEMBRECHTS ET aL. 179Aarhus Institute of Advanced Studies, AIAS Høegh- Guldbergs Gade 6B, Aarhus, Denmark 180Department of Forest Botany, Dendrology and Geobiocoenology, Faculty of Forestry and Wood Technology, Mendel University in Brno, Brno, Czech Republic 181Regional Centre for Integrated Environmental Monitoring, Odesa National I.I. Mechnikov University, Odesa, Ukraine 182Department of Agroecology, Aarhus University, Tjele, Denmark 183NGO New Energy, Kharkiv, Ukraine 184Biological Dynamics of Forest Fragments Project, Coordenação de Dinâmica Ambiental, Instituto Nacional de Pesquisas da Amazônia, Manaus, Brazil 185Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland 186Department of Biology, University of Antwerp, Wilrijk, Belgium 187Department of Botany and Biodiversity Research Centre, University of British Columbia, Vancouver, British Columbia, Canada 188Department of Environment, Province of Antwerp, Antwerpen, Belgium 189Institute of Plant and Animal Ecology of Ural Division of Russian Academy of Science, Ekaterinburg, Russia 190Department of Earth and Environmental Sciences, University of Pavia, Pavia, Italy 191Faculty of Science and Technology, Free University of Bolzano, Bolzano, Italy 192Climate Change Unit, Environmental Protection Agency of Aosta Valley, Saint- Christophe, Italy 193Chair of Geobotany, University of Freiburg, Freiburg, Germany 194Environment and Sustainability Institute, University of Exeter, Penryn Campus, Cornwall, UK 195Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, UNSW Sydney, Sydney, New South Wales, Australia 196Department of Plant Biology and Ecology, University of Seville, Seville, Spain 197Department of Biology, Washington University in St. Louis, St. Louis, Missouri, USA 198Department of Animal Biology, Institute of Biology, University of Campinas, Campinas, Brazil 199CNR Institute of BioEconomy, Bologna, Italy 200National Wildlife Research Centre, Environment and Climate Change Canada, Carleton University, Ottawa, Ontario, Canada 201School of Life and Environmental Sciences, Deakin University, Burwood, Victoria, Australia 202Institute for Alpine Environment, Eurac Research, Bozen/Bolzano, Italy 203Institute of Biology, Department of Molecular Botany, University of Hohenheim, Stuttgart, Germany 204Instituto de Matemática Aplicada San Luis, IMASL, CONICET and Universidad Nacional de San Luis, San Luis, Argentina 205Cátedra de Climatología Agrícola (FCAUNER), Entre Ríos, Argentina 206Grupo de Ecología de Invasiones, INIBIOMA, CONICET/ Universidad Nacional del Comahue, Bariloche, Argentina 207CSIC, Global Ecology Unit CREAFCSIC- UAB, Bellaterra, Spain 208CREAF, Spain 209Mountains of the Moon University, Fort Portal, Uganda 210National Agricultural Research Organisation, Mbarara Zonal Agricultural Research and Development Institute, Mbarara, Uganda 211Laboratory of Meteorology, Department of Construction and Geoengineering, Faculty of Environmental Engineering and Mechanical Engineering, Poznan University of Life Sciences, Poznan, Poland 212Department of Biology, Lund University, Lund, Sweden 213Institute of Botany and Landscape Ecology, University Greifswald, Greifswald, Germany 214V.N. Sukachev Institute of Forest SB RAS, Krasnoyarsk, Russia 215Institute of Ecology and Earth Sciences, University of Tartu, Tartu, Estonia 216Department of Biology and Ecology Center, Utah State University, Logan, Utah, USA 217Department of Life Sciences, Imperial College, Ascot, Berkshire, UK 218Landscape Ecology, Institute of Terrestrial Ecosystems, Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland 219Unit of Land Change Science, Swiss Federal Research Institute WSL, Birmensdorf, Switzerland 220School of Ecology and Environment Studies, Nalanda University, Rajgir, India 221School of Biosciences, University of Sheffield, Sheffield, UK 222CESAM & Department of Environment, University of Aveiro, Aveiro, Portugal 223Department of Agronomy, Food, Natural resources, Animals and Environment - University of Padua, Legnaro, Italy 224Univ. Savoie Mont Blanc, CNRS, Univ. Grenoble Alpes, EDYTEM, Chambéry, France 225Universitat Autònoma de Barcelona, Spain 226Department of Ecology and Biogeography, Faculty of Biological and Veterinary Sciences, Nicolaus Copernicus University, Toruń, Poland 227Centre for Climate Change Research, Nicolaus Copernicus University, Toruń, Poland 228A. Borza Botanic Garden, Babeș- Bolyai University, Cluj- Napoca, Romania 229Faculty of Biology and Geology, Department of Taxonomy and Ecology, Babeș- Bolyai University, Cluj- Napoca, Romania 230E. G. Racoviță Institute, Babeș- Bolyai University, Cluj- Napoca, Romania 13652486, 2022, 9, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16060 by Universidad De Sevilla, Wiley Online Library on [11/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 3118 | LEMBRECHTS ET aL. 231Securing Antarctica's Environmental Future, School of Earth, Atmospheric and Life Sciences, University of Wollongong, Wollongong, New South Wales, Australia 232University of Applied Sciences Trier, Environmental Campus Birkenfeld, Birkenfeld, Germany 233Institut Universitaire de France, Paris, France 234Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland 235Aquatic Ecology and Environmental Biology, Radboud Institute for Environmental and Biological Sciences, Radboud University Nijmegen, Nijmegen, The Netherlands 236Department of Biological Sciences and the Environmental Change Initiative, University of Notre Dame, Notre Dame, Indiana, USA 237Swiss National Park, Chastè Planta- Wildenberg, Zernez, Switzerland 238Remote Sensing Laboratories, Department of Geography, University of Zurich, Zurich, Switzerland 239CIRAD, UMR Eco&Sols, Dakar, Senegal 240Eco&Sols, Univ Montpellier, CIRAD, INRAE, IRD, Institut Agro, Montpellier, France 241LMI IESOL, Centre IRDISRA de Bel Air, Dakar, Senegal 242Parc national des Ecrins - Domaine de Charance, France 243Universidad Nacional de San Antonio Abad del Cusco, Cusco, Perú 244Centro de Investigación de la Biodiversidad Wilhelm L. Johannsen, Cusco, Perú 245Biological Dynamics of Forest Fragments Project, PDBFF, Instituto Nacional de Pesquisas da Amazônia, Manaus, Brazil 246Department of Ecology and Environmental Science, Umeå University, Umeå, Sweden 247Institute of Bio- and Geosciences (IBG- 3): Agrosphere, Forschungszentrum Jülich GmbH, Jülich, Germany 248Chair of Soil Science and Geomorphology, Department of Geosciences, University of Tuebingen, Tuebingen, Germany 249Department of Geography, The University of British Columbia, Vancouver, British Columbia, Canada 250Department of Botany and Biodiversity Research, Vienna, Austria 251Princeton School of Public and International Affairs, Princeton University, Princeton, New Jersey, USA 252Université de Lorraine, AgroParisTech, INRAE, Nancy, France 253Department of Soil Science and Landscape Management, Faculty of Earth Sciences and Spatial Management, Nicolaus Copernicus University, Toruń, Poland 254Terra Nova National Park, Parks Canada Agency, Glovertown, Newfoundland, Canada 255Universidade Estadual do Norte Fluminense Darcy Ribeiro, Rio de Janeiro, Brazil 256National Forest Centre, Forest Research Institute Zvolen, Zvolen, Slovakia 257Department of Physical Geography, Stockholm University, Stockholm, Sweden 258Department of Geography, University of British Columbia, Vancouver, British Columbia, Canada 259Department of Earth and Environmental Sciences, Leuven, Belgium 260Soil Science Department, Federal University of Viçosa, Viçosa- MG, Brazil 261Departamento de Geociências. Cidade Universitária, Universidade Federal da Paraíba, João Pessoa - PB, Brasil 262Department of Physical Geography, Goethe- Universität Frankfurt, Frankfurt am Main, Germany 263Department of Evolution, Ecology, and Organismal Biology, University of California Riverside, Riverside, California, USA 264Department of Natural History, NTNU University Museum, Norwegian University of Science and Technology, Trondheim, Norway 265UMR 7058 CNRS ‘Ecologie et Dynamique des Systèmes Anthropisés’ (EDYSAN), Univ. de Picardie Jules Verne, Amiens, France 266EnvixLab, Dipartimento di Bioscienze e Territorio, Università degli Studi del Molise, Termoli, Italy 267Institute of Meteorology and Climate Research (IMK), Department of Atmospheric Environmental Research (IFU), Karlsruhe Institute of Technology (KIT), Garmisch- Partenkirchen, Germany 268Swedish University of Agricultural Sciences, SLU Swedish Species Information Centre, Uppsala, Sweden 269Faculty for Biology, University Duisburg- Essen, Essen, Germany 270Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark 271Experimental Plant Ecology, Institute of Botany and Landscape Ecology, University of Greifswald, partner in the Greifswald Mire Centre, Greifswald, Germany 272Foundation J.- M. Aubert, Champex- Lac, Switzerland 273Département de Botanique et Biologie végétale, Université de Genève, Chambésy, Switzerland 274Department of Geography and Earth Sciences, Aberystwyth University, Wales, UK 275Center for Systematic Biology, Biodiversity and Bioresources - 3B, Babeș- Bolyai University, Cluj- Napoca, Romania 276Northern Environmental Geoscience Laboratory, Department of Geography and Planning, Queen's University, Kingston, Ontario, Canada 277Finnish Meteorological Inst, Helsinki, Finland 278Graduate School of Life and Environmental Sciences, Osaka Prefecture University, Japan 279Nature Research Centre, Vilnius, Lithuania 280Institute of Biological Research Cluj- Napoca, National Institute of Research and Development for Biological Sciences, Bucharest, Romania 13652486, 2022, 9, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16060 by Universidad De Sevilla, Wiley Online Library on [11/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 3125 LEMBRECHTS ET aL. Klinges, 2021), as the latter require highly detailed physical input parameters for calibration, and current computational barriers preclude global assessments at a 1 km2 resolution and over multiple decades. Nevertheless, we urge future endeavours to compare and potentially improve our results with estimates based on such mechanistic models. FIGURE 2 Global modelled temperature offsets between soil and air temperature show strong spatiotemporal variation across months. Modelled annual (a) and monthly (b– m) temperature offset (in °C) between in situ measured soil temperature (topsoil, 0– 5 cm) and gridded air temperature. Positive (red) values indicate soils that are warmer than the air. Dark grey represents regions outside the modelling area 13652486, 2022, 9, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16060 by Universidad De Sevilla, Wiley Online Library on [11/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 3126 | LEMBRECHTS ET aL. We performed a grid search procedure to tune the RF models across a range of 52 hyperparameter settings (variables per split: 2– 14, minimum leaf population: 2– 5, in all combinations adding up to 52 models, each time with 250 trees). During this procedure, we assessed each of the 52 model's performance using kfold crossvalidation (k = 10; folds assigned randomly, stratified per biome). The models’ mean and standard deviation values were the basis for choosing the best of all evaluated models. This procedure was repeated for each month separately for the two soil depth layers (0– 5 cm, 5– 15 cm), for offsets in mean, minimum and maximum temperature. The importance of predictor variables was assessed using the variable importance and ordered by mean variable importance across all models. This variable importance adds up the decreases in the impurity criterion (i.e. the measure on which the local optimal condition is chosen) at each split of a node for each individual variable over all trees in the forest (van den Hoogen et al., 2021). 2.6 | Soil bioclimatic variables The resulting global maps of the annual and monthly offsets between mean, minimum and maximum soil and air temperature were used to calculate relevant bioclimatic variables following the definition used in CHELSA, BIOCLIM, ANUCLIM and WorldClim (Booth et al., 2014; Fick & Hijmans, 2017; Karger et al., 2017a; Xu & Hutchinson, 2011) (Table 1, Figures 3– 4). First, we calculated monthly soil mean, maximum and minimum temperature by adding monthly temperature offsets to the respective CHELSA monthly mean, maximum and minimum temperature (Karger et al., 2017a). Next, we used these soil temperature layers to compute 11 soil bioclimatic layers (SBIO, Table 1) (O’Donnell & Ignizio, 2012). Wettest and driest quarters were identified for each pixel based on CHELSA’s monthly values. 2.7 | Model uncertainty To assess the uncertainty in the monthly models, we performed a stratified bootstrapping procedure, with total size of the bootstrap samples equal to the original training data (van den Hoogen et al., 2021). Using biomes as a stratification category, we ensured the samples included in each of the bootstrap training collections were proportionally representative of each biome's total area. Next, we trained RF models (with the same hyperparameters as selected during the gridsearch procedure) using each of 100 bootstrap iterations. Each of these trained RF models was then used to classify the predictor layer stack, to generate perpixel 95% confidence intervals and standard deviation for the modelled monthly offsets (Figure 5a, Figure S6a). The mean R² value of the RF models for the monthly mean temperature offset was 0.70 (from 0.64 to 0.78) at 0– 5 cm and 0.76 (0.63– 0.85) at 5 to 15 cm across all 12 monthly models. Mean RMSE of the models was 2.20°C (1.94– 2.51°C) at 0– 5 cm, and 2.06°C (1.67– 2.35°C) at 5– 15 cm. Importantly, model uncertainty as reported in Figure 5a and Figure S6a comes on top of existing uncertainties in (1) in situ soil temperature measurements and (2) the ERA5L macroclimate models as used in our models. However, both of those are usually under 1°C (Copernicus Climate Change Service (C3S), 2019; Wild et al., 2019). To assess the spatial extent of extrapolation, which is necessary due to the incomplete global coverage of the training data, we first performed a principal component analysis (PCA) on the full environmental space covered by the monthly training data, including all explanatory variables as used in the models, and then transformed the composite image into the same principal comonents' (PC) spaces as of the sampled data (van den Hoogen et al., 2019). Next, we created convex hulls for each of the bivariate combinations from the first 10 to 12 PCs, covering at least 90% of the sample space variation, with the number of PCs depending on the month. Using the coordinates of these convex hulls, we assessed whether each pixel fell within or outside each of these convex hulls and calculated the percentage of bivariate combinations for which this was the case (Figure 5b, Figure S6b). This process was repeated for each month and for each of the two soil depths separately. These uncertainty maps are important because one should be careful with extrapolation beyond the range of conditions covered by the environmental variables included in the original calibration data set, especially in the case of nonlinear patterns such as modelled here. The maps are provided as spatial masks to Bioclimatic variable Meaning SBIO1 annual mean temperature SBIO2 mean diurnal range (mean of monthly (max temp - min temp)) SBIO3 isothermality (SBIO2/SBIO7) (×100) SBIO4 temperature seasonality (standard deviation ×100) SBIO5 max temperature of warmest month SBIO6 min temperature of coldest month SBIO7 temperature annual range (SBIO5- SBIO6) SBIO8 mean temperature of wettest quarter SBIO9 mean temperature of driest quarter SBIO10 mean temperature of warmest quarter SBIO11 mean temperature of coldest quarter TABLE 1 Overview of soil bioclimatic variables as calculated in this study 13652486, 2022, 9, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16060 by Universidad De Sevilla, Wiley Online Library on [11/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 3127 LEMBRECHTS ET aL. remove or reduce the weighting of the pixels for which predictions are beyond the range of values covered by the models during calibration. To assess this further, we used a spatial leaveone- out crossvalidation analysis to test for spatial autocorrelation in the data set (Figure S7) (van den Hoogen et al., 2021). This approach trains a model for each sample in the data set on all remaining samples, excluding data points that fall within an increasingly large buffer around that focal sample. Results show lowest confidence for May to September at 5– 15 cm, likely driven by uneven global coverage of data points. Finally, we compared the modelled mean annual temperature (SBIO1, topsoil layer) with a similar product based on monthly ERA5L topsoil (0– 7 cm) temperature with a spatial resolution of 0.08 × 0.08 degrees (≈9 × 9 km at the equator, Copernicus Climate Change Service (C3S), 2019). The corresponding SBIO1 based on ERA5L was calculated using the means of the monthly averages for each month over the period 1981 to 2016, and averaging these 12 monthly values into one annual product. We then visualized spatial differences between SBIO1 and ERA5, as well as differences across the macroclimatic gradient, to identify mismatches between both data sets. FIGURE 3 Soil bioclimatic variables. Global maps of bioclimatic variables for topsoil (0– 5 cm depth) climate, calculated using the maps of the monthly offsets between soil and air temperature (see Figure 2), and the bioclimatic variables for air temperature from CHELSA 13652486, 2022, 9, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16060 by Universidad De Sevilla, Wiley Online Library on [11/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 3128 | LEMBRECHTS ET aL. All geospatial modelling was performed using the Python API in Google Earth Engine (Gorelick et al., 2017). The R statistical software, version 4.0.2 (R Core Team, 2020), was used for data visualizations. All maps were plotted using the Mollweide projection, which preserves relative areas, to avoid large distortions at high latitudes. 2.8 | Sources of uncertainty The temporal mismatch between the period covered by CHELSA (1979– 2013) and our in situ measurements (2000– 2020) prevented us from directly using CHELSA climate to calculate the temperature offsets used in our models. This temporal mismatch might affect the offsets calculated here because the relationship between temperature offset and macroclimate will change through time as the climate warms. Similarly, interannual differences in offsets due to specific weather conditions cannot be implemented in the used approach. However, we are confident that, at the relatively coarse spatial (1 km2) and temporal (monthly averages) resolution we are working at, our results are sufficiently robust to withstand these temporal issues, given that we found high consistency in offset patterns between the different time frames and air temperature data sets examined (Figures S2– S5). Nevertheless, we strongly urge future research to disentangle FIGURE 4 Mean annual soil temperature shows significantly lower spatial variability than air temperature. (a) Global map of mean annual topsoil temperature (SBIO1, 0– 5 cm depth, in °C), created by adding the monthly offset between soil and air temperature for the period 2000– 2020 (Figure 2) to the monthly air temperature from CHELSA. A black mask is used to exclude regions where our models are extrapolating (i.e. interpolation values in Figure 5 are <0.9, 18% of pixels). Dark grey represents regions outside the modelling area. (b– c) Density plots of mean annual soil temperature across the globe (b) and for each Whittaker biome separately (c) for SBIO1 (dark grey, soil temperature), compared with BIO1 from CHELSA (light grey, air temperature), created by extracting 1,000,000 random points from the 1- km² gridded bioclimatic products. The numbers in (c) represent the standard deviations of air temperature (light grey) and soil temperature (dark grey). Biomes are ordered according to the median annual soil temperature values (vertical black line) from the highest temperature (subtropical desert) to the lowest (tundra) 13652486, 2022, 9, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16060 by Universidad De Sevilla, Wiley Online Library on [11/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 3129 LEMBRECHTS ET aL. these potential temporal dynamics, especially given the increasing rate at which the climate is warming (GISTEMP Team, 2021; Xu et al., 2018). Similarly, a potential bias could result from the mismatch in method and resolution between ERA5L— used to calculate the temperature offsets— and CHELSA, which was used to create the bioclimatic variables. However, even though temperature offsets have slightly larger variation when based on the coarsergrained ERA5L- data than on the finergrained CHELSA- data, Figures S2– S5 show that relationships between soil and air temperature are largely consistent in all biomes and across the whole global temperature gradient. Therefore, the larger offsets created additional random scatter, yet no consistent bias. Finally, we acknowledge that the 1- km² resolution gridded products might not be representative of conditions at the in situ measurement locations within each pixel. This issue could be particularly significant for different vegetation types (here proxied at the pixel level using total aboveground biomass (unit: tons/ha i.e., Mg/ha, for the year 2010; Santoro, 2018) and NDVI (MODIS NDVI product MYD13Q1, averaged over 2015– 2019). To verify this, we compared a pixel's estimated aboveground biomass with the dominant in situ habitat (forest versus open) surrounding the sensors in that pixel (Table S6). Importantly, all sensors installed in forests fell indeed in pixels with more than 1 ton/ha aboveground biomass. Similarly, 75% or more of sensors in open terrain fell in pixels with biomass estimates of less than 1 ton/ha. Only in the temperate woodland biome was the match between in situ habitat estimates and pixellevel aboveground biomass lower, with less than 95% of sensors in forested locations correctly placed in pixels with more than 1 ton/ha biomass, and less than 50% of open terrain sensors in pixels with less than 1 ton/ha biomass. While our predictions will thus not be accurate for locations within a pixel that largely deviate from average conditions (e.g. open terrain in pixels identified as largely forested, or vice versa), they should be largely representative for those pixellevel averages. 3 | RESULTS 3.1 | Biomewide patterns in the temperature offset We found positive and negative temperature offsets of up to 10°C between in situ measured mean annual topsoil temperature and gridded air temperature (mean = 3.0 ± 2.1°C standard deviation, Figure 1, 0– 5 cm, depth; 5– 15 cm is available in Figures S2, S5). The magnitude and direction of these temperature offsets varied considerably within and across biomes. Mean annual topsoil temperature was on average 3.6 ± 2.3°C higher than gridded air temperature in cold and/or FIGURE 5 Models of the temperature offset between soil and air temperature have low standard deviations and good global coverage. Analyses for the temperature offset between in situ measured topsoil (0– 5 cm depth) temperature and gridded air temperature. (a) Standard deviation (in °C) over the predictions from a crossvalidation analysis that iteratively varied the set of covariates (explanatory data layers) and model hyperparameters across 100 models and evaluated model strength using 10- fold crossvalidation, for January (left) and July (right), as examples of the two most contrasting months. (b) The fraction of axes in the multidimensional environmental space for which the pixel lies inside the range of data covered by the sensors in the database. Low values indicate increased extrapolation 13652486, 2022, 9, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16060 by Universidad De Sevilla, Wiley Online Library on [11/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 3130 | LEMBRECHTS ET aL. dry biomes, namely tundra, boreal forests, temperate grasslands and subtropical deserts. In contrast, offsets were slightly negative in warm and wet biomes (tropical savannas, temperate forests and tropical rainforests) where soils were, on average, 0.7 ± 2.7°C cooler than gridded air temperature (Figure 1b, Figures S2 and S5; note, however, the lower spatial coverage in these biomes in Figure 1a,c,d, Table S4). Temperature offsets in annual minimum and maximum temperature amounted to c. 10°C maximum. While annual soil temperature minima were on average higher than corresponding gridded air temperature minima in all biomes, temperature offsets of annual maxima followed largely the same biomerelated trends as seen for the annual means, albeit with the higher variability expected for temperature extremes (Figures S2g, S2h, S4g, S4h). Using different air temperature data sources did not alter the annual temperature offset and biomerelated patterns (see Methods and Figures S2– S5). Soils in the temperate seasonal forest biome were on average 0.8°C (±2.2°C) cooler than air temperature within 1- km2 grid cells of forested habitats, and 1.0°C (±4.0°C) warmer than the air within 1 - k m 2 grid cells of nonforested habitats, resulting in a biomewide average of 0.5°C (Table S7). Similar patterns were observed in other biomes. 3.2 | Temporal and spatial variation in temperature offsets Our RF outputs highlighted a strong seasonality in monthly temperature offsets, especially towards higher latitudes (Figure 2). Highlatitude soils were found to be several degrees warmer than the air (monthly offsets of up to 25°C) during their respective winter months, and cooler (up to 10°C) in summer months, both at 0– 5 cm (Figure 2) and 5– 15 cm (Figure S8) soil depths. In the tropics and subtropics, soils in dry biomes (e.g. in the Sahara Desert or southern Africa) were predicted to be warmer than air throughout most of the year, while soils in mesic biomes (e.g. tropical biomes in South America, central Africa and Southeast Asia) were modelled to be consistently cooler, at both soil depths. These global gridded products were then used to create temperaturebased global bioclimatic variables for soils (SBIO, Figure 3, Figure S9). 3.3 | Global variation in soil temperature We observed 17% less spatial variation in mean annual soil temperature globally (expressed by the standard deviation) than in air temperature, largely driven by the positive offset between soil and air temperature in cold environments (Figure 4). Importantly, our machine learning models slightly (up to 1°C, or around 10% of variation) underestimated temperature offsets at both extremes of the temperature gradient at the 1- km² resolution (Figure S10) and likely even more in comparison with finerresolution products. Estimates of the reduction in variation across space are thus conservative, especially in the coldest biomes. The reduction in spatial temperature variation was observed in all cold and cool biomes, with tundra and boreal forests having both a significant positive mean temperature offset and a reduction of 20% and 22% in variation, respectively (Figure 4c). In the warmest biomes (e.g. tropical savanna and subtropical desert), however, we found an increase in variation of, on average, 10%. Our bootstrap approach to validate modelled monthly offsets indicated high consistency among the outcomes of 100 bootstrapped models (Figure 5, Figure S6a), with standard deviations in most months and across most parts of the globe around or below ±1°C. One exception to this was the temperature offset at high latitudes of the Northern Hemisphere during winter months (standard deviation up to ±5°C in the 0– 5 cm layer). Predictive performance was comparable across biomes, although with large variation in data availability (Figure S11). The importance of predictor variables in the RF models was largely consistent across months. Macroclimatic variables such as incoming solar radiation as well as longterm averages in air temperature and precipitation were by far the most influential explanatory variables in the spatial models of the monthly temperature offset (Figures S12 and S13). We highlight that the current availability of in situ soil temperature measurements is significantly lower in the tropics (Table S5), where our model had to extrapolate temperatures beyond the range used to calibrate the model (Figure 5b, Figure S6b). Finally, our comparison with a mean annual soil temperature product derived from the coarseresolution ERA5L topsoil temperature showed that spatial variability, for example, driven by topographic heterogeneity, is much better captured here than in the coarser resolution of the ERA5L- based product (Figure 6c- e). Nevertheless, our predictions at the coarse scale showed to be condensed within a 5°C range of values from the ERA5L- predictions, for more than 95% of pixels globally. Noteworthy, our predictions resulted in consistently cooler soil temperature predictions than topsoil conditions provided by ERA5L across large areas, such as the boreal and tropical forest biomes (Figure 6a,b). Additionally, our models predicted lower values for SBIO1 than ERA5L in all regions with mean annual soil temperature below 0°C, except for a few locations around Greenland and Svalbard (Figure 6a,b). 4 | DISCUSSION 4.1 | Global patterns in soil temperature We observed large spatiotemporal heterogeneity in the global offset between soil and air temperature, often in the order of several degrees annually and up to more than 20°C during winter months at high latitudes. These values are in line with empirical data from regional studies (Lembrechts et al., 2019; Obu et al., 2019; Zhang et al., 2018). Both annual and monthly offsets showed clear discrepancies between cold and dry versus warm and wet biomes. The modelled monthly offsets covaried strongly negatively with both longterm averages in freeair temperature and solar radiation, linking to the wellknown decoupling of soil from air temperature due to snow (for cold extremes in cold and 13652486, 2022, 9, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16060 by Universidad De Sevilla, Wiley Online Library on [11/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 3131 LEMBRECHTS ET aL. cool biomes) (Grundstein et al., 2005). However, the secondary importance of variables related to precipitation and soil structure hints to the additional distinction between wet and dry biomes at the warm end of the temperature gradient. There, buffering due to shading, evapotranspiration and the specific heat of water (mostly against warm extremes in warm and wet biomes) results in cooler soil temperature (De Frenne et al., 2013; Geiger, 1950; Grünberg et al., 2020; Grundstein et al., 2005; Hennon et al., 2010; Wang & Dickinson, 2012), while such buffering is not as strong in warm and dry biomes due to the lower water availability (Greiser et al., 2018; Wang & Dickinson, 2012; Zhou et al., 2021). As such, these results highlight strong macroclimatic impacts on the soil microclimate across the globe (see also De Frenne et al., 2019), yet with soil temperature importantly nonlinearly related to air temperature at the global scale. This confirms that the latter is not sufficient as a proxy for temperature conditions near or in the soil. With our soilspecific global bioclimatic products, we have provided the means to correct for these important regionspecific, nonlinear differences between soil and air temperature at an unprecedented spatial resolution. 4.2 | Drivers of the temperature offset Our empirical modelling approach enabled us to accurately map global patterns in soil temperature. In doing so we did not aim to disentangle the mechanisms governing the temperature offset: such an endeavour would require modelling the biophysics of energy exchange at the soil surface across biomes (Kearney et al., FIGURE 6 The mean annual soil temperature (SBIO1, 1 x 1 km resolution) modelled here is consistently cooler than ERA5L (9 x 9 km) soil temperature in forested areas. (a) Spatial representation of the difference between SBIO1 based on our model and based on ERA5L soil temperature data. Negative values (blue colours) indicate areas where our model predicts cooler soil temperature. Dark grey areas (Greenland and Antarctica) are excluded from our models. Asterisk in Scandinavia indicates the highlighted area in panels d to f (see below). (b) Distribution of the difference between SBIO1 and ERA5L along the macroclimatic gradient (represented by SBIO1 itself) based on a random subsample of 50,000 points from the map in a). Red line from a Generalized Additive Model (GAM) with k = 4. (c- e) Highresolution zoomed panels of an area of high elevational contrast in Norway (from 66.0– 66.4°N, 15.0– 16.0°E) visualizing SBIO1 (c), ERA5L (d) and their difference (e), to highlight the higher spatial resolution as obtained with SBIO1 13652486, 2022, 9, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16060 by Universidad De Sevilla, Wiley Online Library on [11/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 3132 | LEMBRECHTS ET aL. 2019; Maclean & Klinges, 2021; Maclean et al., 2019). Importantly, many of the predictor variables used in our study (e.g. longterm averages in macroclimatic conditions or solar radiation) are unlikely to represent direct causal relationships underlying the temperature offset, but may rather indirectly relate to many ensuing factors that affect the functioning of ecosystems at fine spatial scales which, in turn, feedback on local temperature offsets, such as energy and water balances, snow cover, wind intensity and vegetation cover (De Frenne et al., 2021). For example, while increased solar radiation itself would theoretically result in soils warming more than the air, high solar radiation at the global scale often coincides with high vegetation cover blocking radiation input to the soil, thus correlating with relatively cooler soils (De Frenne et al., 2021). Our results highlight, however, that the complex relationship between microclimatic soil temperature and macroclimatic air temperature is predictable across large spatial extents thanks to broad scale patterns, even if this is governed by a multitude of localscale factors involving fine spatiotemporal resolutions. Nevertheless, the predictive quality of our models was lower in high latitude regions, where high variation in the in situ measured offsets— likely driven by the interactions between snow, local topography and vegetation— reduced predictive power of the models at the 1- km2 resolution (Greiser et al., 2018; Grünberg et al., 2020; Myers- Smith et al., 2020; Niittynen et al., 2020; Way & Lewkowicz, 2018). 4.3 | Implications for microclimate warming Our results highlight clear biomespecific differences in mean annual temperature between air and soil temperatures, as well as a significant reduction in the spatial variation in temperature in the soil or near the soil surface, especially in cold and cool biomes (Figure 4). These patterns remain even despite the presence of often strongly opposing monthly offset trends (Figure 2). The observed correlation between longterm averages in macroclimatic conditions and the annual temperature offset illustrates that soil temperature is unlikely to warm at the same rate as air temperature when macroclimate warms. Indeed, one degree of air temperature warming could result in either a bigger or smaller soil temperature change, depending on where along the macroclimatic gradient this is happening. These effects might be seen in cold biome soils most strongly, as they not only experience the largest (positive) temperature offsets and reductions in climate range compared to air temperature (Figure 4b,c), but they are also expected to experience the strongest magnitude of macroclimate warming (Chen et al., 2021; Cooper, 2014; GISTEMP Team, 2021; Overland et al., 2014). As a result, mean annual temperatures in cold climate soils can be expected to warm slower than the corresponding macroclimate as offsets shrink with increasing macroclimate warming. Contrastingly, predicted climate warming in hot and dry biomes could be amplified in the topsoil, where we show soils to become increasingly warmer than the air at higher temperatures. Similarly, changes in precipitation regimes— and thus soil moisture— can significantly alter the relationship between air and soil temperature, with critical implications for soil moistureatmosphere feedbacks, especially in hot biomes (Zhou et al., 2021). Indeed, as precipitation decreases, offsets could turn more positive and soil temperatures might warm even faster than the observed macroclimate warming. Therefore, future research should not only use soil temperature data as provided here to study belowground ecological processes (De Frenne et al., 2013; Lembrechts et al., 2020), it should also urgently investigate future scenarios of soil climate warming in light of changing air temperature and precipitation, at ecologically relevant spatial and temporal resolutions to incorporate the nonlinear relationships exposed so far (Lembrechts & Nijs, 2020). 4.4 | Withinpixel heterogeneity We chose to use a 1- km² resolution spatial grid to model mismatches between soil and air temperature, aggregating all values from different microhabitats within the same 1- km2 grid cell (e.g. sensors in forested versus open patches) as well as all daily and diurnal variation within a month. Additionally, we used coarsegrained freeair temperature rather than in situ measured air temperatures. We are aware that higher spatiotemporal resolutions would likely reveal the importance of locally heterogeneous variables. Finerscale factors that affect the local radiation balance and wind (e.g. topography, snow and vegetation cover, urbanization) at the landscape to local scales and those that directly affect neighbouring locations (e.g. topographic shading and coldair drainage, Ashcroft & Gollan, 2012; Lembrechts et al., 2020; Whiteman, 1982) would probably have emerged as more important drivers at regional scales and with higher spatiotemporal resolutions than those used here (Figure S12). The latter is illustrated by the multidegree Celsius difference in mean annual temperature between forested and nonforested locations within the same biome (Table S7), as well as the lower accuracy obtained during winter months at high latitudes, where and when finescale spatial heterogeneity in snow cover and depth probably lowers models’ predictability at the 1- km2 resolution. In situ measurements were largely from areas with a representative vegetation type, supporting the reliability of our predictions for the dominant habitat type within a pixel. However, improved accuracy at high latitudes will depend on the future development of highresolution snow depth and/or snow water equivalent estimates (Luojus et al., 2010). The SoilTemp database (Lembrechts et al., 2020) will facilitate the necessary steps towards mapping soil temperature at higher spatiotemporal resolutions in the future, with its georeferenced time series of in situ measured soil and nearsurface temperature and associated metadata. Nevertheless, compared with existing soil temperature products such as those from ERA5L (Copernicus Climate Change Service (C3S), 2019), we 13652486, 2022, 9, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16060 by Universidad De Sevilla, Wiley Online Library on [11/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 3133 LEMBRECHTS ET aL. emphasize that the increased resolution of our data products already provides a major technical advance, even though substantial finer withinpixel variation is still lost through spatiotemporal aggregation. 5 | CONCLUSIONS The spatial (biomespecific) and temporal (seasonally variable) offsets between air and soil temperature quantified here likely bias predictions of current and future climate impacts on species and ecosystems (Bergstrom et al., 2021; Cooper, 2014; Graae et al., 2018; Kearney et al., 2009; Körner & Paulsen, 2004; Opedal et al., 2015; Zellweger et al., 2020). Temperature in the topsoil rather than in the air ultimately defines the distribution and performance of most terrestrial species, as well as many ecosystem functions at or below the soil surface (Gottschall et al., 2019; Hursh et al., 2017; Pleim & Gilliam, 2009; Portillo- Estrada et al., 2016). As many ecosystem functions are highly correlated with temperature (yet often nonlineary, Johnston et al., 2021), soil temperature rather than air temperature should in those instances be the preferred predictor for estimating their rates and temperature thresholds (Coûteaux et al., 1995; Rosenberg et al., 1990; Schimel et al., 1996). Correcting for the nonlinear relationship between air and soil temperature identified here is, thus, vital for all fields investigating abiotic and biotic processes relating to terrestrial environments (White et al., 2020). Indeed, soil temperature, macroclimate and landuse change will interact to define the future climate as experienced by organisms, and highresolution soil temperature data are needed to tackle current and future challenges. By making our global soil temperature maps and the underlying monthly offset data openly available, we offer gridded soil temperature data for climate research, ecology, agronomy and other life and environmental sciences. Future research has the important task of further improving the spatial and temporal resolution of global microclimate products as microclimate operates at much higher temporal resolutions, with temporal variation over hours, days, seasons and years (Bütikofer et al., 2020; Potter et al., 2013), as well as to confirm accuracy of predictions in undersampled regions in the underlying maps (Lembrechts et al., 2021). However, we are convinced that the maps presented here bring us one step closer to having accessible climate data exactly where it matters most for many terrestrial organisms (Ashcroft et al., 2014; Kearney & Porter, 2009; Lembrechts & Lenoir, 2019; Niittynen & Luoto, 2018; Pincebourde et al., 2016). We, nevertheless, highlight that there is still a long way to go towards global soil microclimate data with an optimal spatiotemporal resolution. We, therefore, urge all scientists to submit their microclimate time series to the SoilTemp database to fill data gaps and help to increase the spatial resolution until it matches with the scale at which ecological processes take place (Bütikofer et al., 2020; Lembrechts et al., 2020). ACKNOWLEDGEMENTS 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, Nordenskiöld 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. QUEXCAS- QPRD- 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 INTEREXCELLENCE, subprogram INTERACTION) 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 Dirección General de Cambio Climático del Gobierno de Aragón; JLBA acknowledges fieldwork assistance by Ana Acín, the Ordesa y Monte Perdido National Park, and the Servicio de Medio Ambiente de Soria de la Junta de Castilla y León. 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 Skłodowska- 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 13652486, 2022, 9, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16060 by Universidad De Sevilla, Wiley Online Library on [11/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 3134 | LEMBRECHTS ET aL. 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 Amazônia, Brazil were supported by the MCTI/ CNPq/FNDCT – Ação Transversal n°68/2013 – Programa de Grande Escala da Biosfera- Atmosfera na Amazônia – LBA; Project ‘Como as florestas da Amazônia Central respondem às variações climáticas? Efeitos sobre dinâmica florestal e sinergia com a fragmentação 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. ANIDFONDECYT 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, MČ, KU and MU received funding from Slovak Research and Development Agency (no. APVV- 19- 0319). Instituto Antartico Chileno (INACH_RT- 48_16), Iniciativa Científica Milenio Núcleo Milenio de Salmónidos 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 Alcalá 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: UKSCAPE; 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 GLORIAEU 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 Jardín Botánico Atlántico (SV- 20- GIJONJBA). 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 (ICOSCH 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 LTERCWN (FFG, F&E Infrastrukturförderung, 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- Württemberg Ministry of Science, Research and Arts via the project DRIeR (Drought impacts, processes and resilience: making the invisible 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 (NÝR- 11 - 2019, NÝR- 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 13652486, 2022, 9, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/gcb.16060 by Universidad De Sevilla, Wiley Online Library on [11/11/2022]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License | 3141 LEMBRECHTS ET aL. Ashcroft, M. B., Chisholm, L. A., & French, K. O. (2008). 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