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1. Past decade above-ground biomass change comparisons from four multi-temporal global maps

Author

Araza, Arnan, Herold, Martin, de Bruin, Sytze, Ciais, Philippe, Gibbs, David A., Harris, Nancy, Santoro, Maurizio, Wigneron, Jean-Pierre, Yang, Hui, Málaga, Natalia, Nesha, Karimon, Rodriguez-Veiga, Pedro, Brovkina, Olga, Brown, Hugh C.A., Chanev, Milen, Dimitrov, Zlatomir, Filchev, Lachezar, Fridman, Jonas, García, Mariano, Gikov, Alexander, Govaere, Leen, Dimitrov, Petar, Moradi, Fardin, Muelbert, Adriane Esquivel, Novotný, Jan, Pugh, Thomas A.M., Schelhaas, Mart-Jan, Schepaschenko, Dmitry, Stereńczak, Krzysztof, and Hein, Lars

Published
2023
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2. Contributors

Author

Ahlström, Anders, primary, Almeida, Mariana, additional, Andrew, Robbie, additional, Archibeque, Shawn, additional, Basso, Luana, additional, Bastos, Ana, additional, Bezerra, Francisco Gilney, additional, Birdsey, Richard, additional, Bowman, Kevin, additional, Bruhwiler, Lori M., additional, Brunner, Dominik, additional, Bun, Rostyslav, additional, Butman, David E., additional, Campbell, Donovan, additional, Canadell, Josep G., additional, Cardoso, Manoel, additional, Chatterjee, Abhishek, additional, Chevallier, Frédéric, additional, Ciais, Philippe, additional, Commane, Róisín, additional, Crippa, Monica, additional, Cunha-Zeri, Gisleine, additional, Domke, Grant M., additional, Euskirchen, Eugénie S., additional, Fisher, Joshua B., additional, Gilfillan, Dennis, additional, Hayes, Daniel J., additional, Holmquist, James R., additional, Houghton, Richard A., additional, Huntzinger, Deborah, additional, Ilyina, Tatiana, additional, Janardanan, Rajesh, additional, Janssens-Maenhout, Greet, additional, Jones, Matthew W., additional, Keppler, Lydia, additional, Kondo, Masayuki, additional, Kroeger, Kevin D., additional, Kurz, Werner, additional, Landschützer, Peter, additional, Lauerwald, Ronny, additional, Luyssaert, Sebastiaan, additional, MacBean, Natasha, additional, Maksyutov, Shamil, additional, Marland, Eric, additional, Marland, Gregg, additional, Miranda, Marcela, additional, Naipal, Victoria, additional, Naudts, Kim, additional, Neigh, Christopher S.R., additional, Neto, Eráclito Souza, additional, Nevison, Cynthia, additional, Niu, Shuli, additional, Oda, Tomohiro, additional, Ogle, Stephen M., additional, Ometto, Jean Pierre, additional, Ott, Lesley, additional, Pacheco, Felipe S., additional, Parmentier, Frans-Jan W., additional, Patra, Prabir K., additional, Petrescu, A.M. Roxana, additional, Pongratz, Julia, additional, Poulter, Benjamin, additional, Pugh, Thomas A.M., additional, Ramaswami, Anu, additional, Raymond, Peter A., additional, Rezende, Luiz Felipe, additional, Ribeiro, Kelly, additional, Roten, Dustin, additional, Schädel, Christina, additional, Schuur, Edward A.G., additional, Sitch, Stephen, additional, Smith, Pete, additional, Smith, William Kolby, additional, Taboada, Miguel, additional, Thompson, Rona L., additional, Tong, Kangkang, additional, Troxler, Tiffany G., additional, Tubiello, Francesco N., additional, Turner, Alexander J., additional, Villalobos, Yohanna, additional, von Randow, Celso, additional, Watts, Jennifer, additional, Welp, Lisa R., additional, Windham-Myers, Lisamarie, additional, and Zavala-Araiza, Daniel, additional

Published
2022
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4. Strong regional influence of climatic forcing datasets on global crop model ensembles

Author

Ruane, Alex C., Phillips, Meridel, Müller, Christoph, Elliott, Joshua, Jägermeyr, Jonas, Arneth, Almut, Balkovic, Juraj, Deryng, Delphine, Folberth, Christian, Iizumi, Toshichika, Izaurralde, Roberto C., Khabarov, Nikolay, Lawrence, Peter, Liu, Wenfeng, Olin, Stefan, Pugh, Thomas A.M., Rosenzweig, Cynthia, Sakurai, Gen, Schmid, Erwin, Sultan, Benjamin, Wang, Xuhui, de Wit, Allard, and Yang, Hong

Published
2021
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5. Forest demography and biomass accumulation rates are associated with transient mean tree size vs density scaling relations

Author

Yu, Kailiang, primary, Chen, Han Y.H., additional, Gessler, Arthur, additional, Pugh, Thomas A.M., additional, Searle, Eric B., additional, Allen, Robert B., additional, Pretzsch, Hans, additional, Ciais, Philippe, additional, Phillips, Oliver L., additional, Brienen, Roel J.W., additional, Chu, Chengjin, additional, Xie, Shubin, additional, and Ballantyne, Ashley P., additional

Published
2023
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8. Temperature and Tree Size Explain the Mean Time to Fall of Dead Standing Trees across Large Scales

Author

Gärtner, Antje, Jönsson, Anna Maria, Metcalfe, Daniel B., Pugh, Thomas A.M., Tagesson, Torbern, Ahlström, Anders, Gärtner, Antje, Jönsson, Anna Maria, Metcalfe, Daniel B., Pugh, Thomas A.M., Tagesson, Torbern, and Ahlström, Anders

Abstract

Dead standing trees (DSTs) generally decompose slower than wood in contact with the forest floor. In many regions, DSTs are being created at an increasing rate due to accelerating tree mortality caused by climate change. Therefore, factors determining DST fall are crucial for predicting dead wood turnover time but remain poorly constrained. Here, we conduct a re-analysis of published DST fall data to provide standardized information on the mean time to fall (MTF) of DSTs across biomes. We used multiple linear regression to test covariates considered important for DST fall, while controlling for mortality and management effects. DSTs of species killed by fire, insects and other causes stood on average for 48, 13 and 19 years, but MTF calculations were sensitive to how tree size was accounted for. Species’ MTFs differed significantly between DSTs killed by fire and other causes, between coniferous and broadleaved plant functional types (PFTs) and between managed and unmanaged sites, but management did not explain MTFs when we distinguished by mortality cause. Mean annual temperature (MAT) negatively affected MTFs, whereas larger tree size or being coniferous caused DSTs to stand longer. The most important explanatory variables were MAT and tree size, with minor contributions of management and plant functional type depending on mortality cause. Our results provide a basis to improve the representation of dead wood decomposition in carbon cycle assessments.

Published
2023

9. Three billion new trees in the EU’s biodiversity strategy : low ambition, but better environmental outcomes?

Author

Lee, Heera, Pugh, Thomas A.M., Patacca, Marco, Seo, Bumsuk, Winkler, Karina, Rounsevell, Mark, Lee, Heera, Pugh, Thomas A.M., Patacca, Marco, Seo, Bumsuk, Winkler, Karina, and Rounsevell, Mark

Abstract

The EU Biodiversity strategy aims to plant 3 billion trees by 2030, in order to improve ecosystem restoration and biodiversity. Here, we compute the land area that would be required to support this number of newly planted trees by taking account of different tree species and planting regimes across the EU member states. We find that 3 billion trees would require a total land area of between 0.81 and 1.37 Mha (avg. 1.02 Mha). The historic forest expansion in the EU since 2010 was 2.44 Mha, meaning that despite 3 billion trees sounding like a large number this target is considerably lower than historic afforestation rates within the EU, i.e. only 40% of the past trend. Abandoned agricultural land is often proposed as providing capacity for afforestation. We estimate agricultural abandoned land areas from the HIstoric Land Dynamics Assessment+ database using two time thresholds (abandonment since 2009 or 2014) to identify potential areas for tree planting. The area of agricultural abandoned land was 2.6 Mha (potentially accommodating 7.2 billion trees) since 2009 and 0.2 Mha (potentially accommodating 741 million trees) since 2014. Our study highlights that sufficient space could be available to meet the 3 billion tree planting target from abandoned land. However, large-scale afforestation beyond abandoned land could have displacement effects elsewhere in the world because of the embodied deforestation in the import of agricultural crops and livestock. This would negate the expected benefits of EU afforestation. Hence, the EU’s relatively low ambition on tree planting may actually be better in terms of avoiding such displacement effects. We suggest that tree planting targets should be set at a level that considers physical ecosystem dynamics as well as socio-economic conditions.

Published
2023

11. Climate Change Risks to Global Forest Health: Emergence of Unexpected Events of Elevated Tree Mortality Worldwide

Author

Hartmann, Henrik, primary, Bastos, Ana, additional, Das, Adrian J., additional, Esquivel-Muelbert, Adriane, additional, Hammond, William M., additional, Martínez-Vilalta, Jordi, additional, McDowell, Nate G., additional, Powers, Jennifer S., additional, Pugh, Thomas A.M., additional, Ruthrof, Katinka X., additional, and Allen, Craig D., additional

Published
2022
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12. Benchmarking sustainability in cities: The role of indicators and future scenarios

Author

Boyko, Christopher T., Gaterell, Mark R., Barber, Austin R.G., Brown, Julie, Bryson, John R., Butler, David, Caputo, Silvio, Caserio, Maria, Coles, Richard, Cooper, Rachel, Davies, Gemma, Farmani, Raziyeh, Hale, James, Hales, A. Chantal, Hewitt, C. Nicholas, Hunt, Dexter V.L., Jankovic, Lubo, Jefferson, Ian, Leach, Joanne M., Lombardi, D. Rachel, MacKenzie, A. Robert, Memon, Fayyaz A., Pugh, Thomas A.M., Sadler, John P., Weingaertner, Carina, Whyatt, J. Duncan, and Rogers, Christopher D.F.

Published
2012
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15. State of science in carbon budget assessments for temperate forests and grasslands

Author

Kondo, Masayuki, Birdsey, Richard, Pugh, Thomas A.M., Lauerwald, Ronny, Raymond, Peter A., Niu, Shuli, Naudts, Kim, Poulter, Benjamin, Canadell, Josep G., Thompson, Rona L., Poulter, Benjamin, Canadell, Josep G., Thompson, Rona L., and Earth and Climate

Subjects
Temperate forest, Temperate grassland, Eddy covariance flux, Carbon stock change, Land-use change, Regrowth, Atmospheric inversion, Terrestrial biosphere model
Abstract

With the abundance of observations and advancement in modeling, temperate regions allow for a comprehensive comparison of the data-driven and process-based methods of carbon budget estimation. This chapter presents a review of the latest methodologies for carbon budget and component flux estimation, and the key components in the temperate carbon budget, such as forest regrowth, and summarizes uncertainties in the current carbon budget of temperate ecosystems that the research community needs to resolve. Lastly, we describe the key progress made in the carbon budget assessment in past decades, and how it should be further advanced to be useful for policy decision-making.

Published
2022

17. Agricultural breadbaskets shift poleward given adaptive farmer behavior under climate change

Author

Franke, James A., primary, Müller, Christoph, additional, Minoli, Sara, additional, Elliott, Joshua, additional, Folberth, Christian, additional, Gardner, Charles, additional, Hank, Tobias, additional, Izaurralde, Roberto Cesar, additional, Jägermeyr, Jonas, additional, Jones, Curtis D., additional, Liu, Wenfeng, additional, Olin, Stefan, additional, Pugh, Thomas A.M., additional, Ruane, Alex C., additional, Stephens, Haynes, additional, Zabel, Florian, additional, and Moyer, Elisabeth J., additional

Published
2021
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18. Large potential for crop production adaptation depends on available future varieties

Author

Zabel, Florian, primary, Müller, Christoph, additional, Elliott, Joshua, additional, Minoli, Sara, additional, Jägermeyr, Jonas, additional, Schneider, Julia M., additional, Franke, James A., additional, Moyer, Elisabeth, additional, Dury, Marie, additional, Francois, Louis, additional, Folberth, Christian, additional, Liu, Wenfeng, additional, Pugh, Thomas A.M., additional, Olin, Stefan, additional, Rabin, Sam S., additional, Mauser, Wolfram, additional, Hank, Tobias, additional, Ruane, Alex C., additional, and Asseng, Senthold, additional

Published
2021
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19. Author Correction: Tree mode of death and mortality risk factors across Amazon forests

Author

Esquivel-Muelbert, Adriane, Phillips, Oliver L., Brienen, Roel J.W., Fauset, Sophie, Sullivan, Martin J.P., Baker, Timothy R., Chao, Kuo Jung, Feldpausch, Ted R., Gloor, Emanuel, Higuchi, Niro, Houwing-Duistermaat, Jeanne, Lloyd, Jon, Liu, Haiyan, Malhi, Yadvinder, Marimon, Beatriz, Marimon Junior, Ben Hur, Monteagudo-Mendoza, Abel, Poorter, Lourens, Silveira, Marcos, Torre, Emilio Vilanova, Dávila, Esteban Alvarez, del Aguila Pasquel, Jhon, Almeida, Everton, Loayza, Patricia Alvarez, Andrade, Ana, Aragão, Luiz E.O.C., Araujo-Murakami, Alejandro, Arets, Eric, Arroyo, Luzmila, Aymard C, Gerardo A., Baisie, Michel, Baraloto, Christopher, Camargo, Plínio Barbosa, Barroso, Jorcely, Blanc, Lilian, Bonal, Damien, Bongers, Frans, Boot, René, Brown, Foster, Burban, Benoit, Camargo, José Luís, Castro, Wendeson, Moscoso, Victor Chama, Chave, Jerome, Comiskey, James, Valverde, Fernando Cornejo, da Costa, Antonio Lola, Cardozo, Nallaret Davila, Di Fiore, Anthony, Dourdain, Aurélie, Erwin, Terry, Llampazo, Gerardo Flores, Vieira, Ima Célia Guimarães, Herrera, Rafael, Honorio Coronado, Eurídice, Huamantupa-Chuquimaco, Isau, Jimenez-Rojas, Eliana, Killeen, Timothy, Laurance, Susan, Laurance, William, Levesley, Aurora, Lewis, Simon L., Ladvocat, Karina Liana Lisboa Melgaço, Lopez-Gonzalez, Gabriela, Lovejoy, Thomas, Meir, Patrick, Mendoza, Casimiro, Morandi, Paulo, Neill, David, Nogueira Lima, Adriano José, Vargas, Percy Nuñez, de Oliveira, Edmar Almeida, Camacho, Nadir Pallqui, Pardo, Guido, Peacock, Julie, Peña-Claros, Marielos, Peñuela-Mora, Maria Cristina, Pickavance, Georgia, Pipoly, John, Pitman, Nigel, Prieto, Adriana, Pugh, Thomas A.M., Quesada, Carlos, Ramirez-Angulo, Hirma, de Almeida Reis, Simone Matias, Rejou-Machain, Maxime, Correa, Zorayda Restrepo, Bayona, Lily Rodriguez, Rudas, Agustín, Salomão, Rafael, Serrano, Julio, Espejo, Javier Silva, Silva, Natalino, Singh, James, Stahl, Clement, Stropp, Juliana, Swamy, Varun, Talbot, Joey, ter Steege, Hans, Terborgh, John, Thomas, Raquel, Toledo, Marisol, Torres-Lezama, Armando, Gamarra, Luis Valenzuela, van der Heijden, Geertje, van der Meer, Peter, van der Hout, Peter, Martinez, Rodolfo Vasquez, Vieira, Simone Aparecida, Cayo, Jeanneth Villalobos, Vos, Vincent, Zagt, Roderick, Zuidema, Pieter, Galbraith, David, Esquivel-Muelbert, Adriane, Phillips, Oliver L., Brienen, Roel J.W., Fauset, Sophie, Sullivan, Martin J.P., Baker, Timothy R., Chao, Kuo Jung, Feldpausch, Ted R., Gloor, Emanuel, Higuchi, Niro, Houwing-Duistermaat, Jeanne, Lloyd, Jon, Liu, Haiyan, Malhi, Yadvinder, Marimon, Beatriz, Marimon Junior, Ben Hur, Monteagudo-Mendoza, Abel, Poorter, Lourens, Silveira, Marcos, Torre, Emilio Vilanova, Dávila, Esteban Alvarez, del Aguila Pasquel, Jhon, Almeida, Everton, Loayza, Patricia Alvarez, Andrade, Ana, Aragão, Luiz E.O.C., Araujo-Murakami, Alejandro, Arets, Eric, Arroyo, Luzmila, Aymard C, Gerardo A., Baisie, Michel, Baraloto, Christopher, Camargo, Plínio Barbosa, Barroso, Jorcely, Blanc, Lilian, Bonal, Damien, Bongers, Frans, Boot, René, Brown, Foster, Burban, Benoit, Camargo, José Luís, Castro, Wendeson, Moscoso, Victor Chama, Chave, Jerome, Comiskey, James, Valverde, Fernando Cornejo, da Costa, Antonio Lola, Cardozo, Nallaret Davila, Di Fiore, Anthony, Dourdain, Aurélie, Erwin, Terry, Llampazo, Gerardo Flores, Vieira, Ima Célia Guimarães, Herrera, Rafael, Honorio Coronado, Eurídice, Huamantupa-Chuquimaco, Isau, Jimenez-Rojas, Eliana, Killeen, Timothy, Laurance, Susan, Laurance, William, Levesley, Aurora, Lewis, Simon L., Ladvocat, Karina Liana Lisboa Melgaço, Lopez-Gonzalez, Gabriela, Lovejoy, Thomas, Meir, Patrick, Mendoza, Casimiro, Morandi, Paulo, Neill, David, Nogueira Lima, Adriano José, Vargas, Percy Nuñez, de Oliveira, Edmar Almeida, Camacho, Nadir Pallqui, Pardo, Guido, Peacock, Julie, Peña-Claros, Marielos, Peñuela-Mora, Maria Cristina, Pickavance, Georgia, Pipoly, John, Pitman, Nigel, Prieto, Adriana, Pugh, Thomas A.M., Quesada, Carlos, Ramirez-Angulo, Hirma, de Almeida Reis, Simone Matias, Rejou-Machain, Maxime, Correa, Zorayda Restrepo, Bayona, Lily Rodriguez, Rudas, Agustín, Salomão, Rafael, Serrano, Julio, Espejo, Javier Silva, Silva, Natalino, Singh, James, Stahl, Clement, Stropp, Juliana, Swamy, Varun, Talbot, Joey, ter Steege, Hans, Terborgh, John, Thomas, Raquel, Toledo, Marisol, Torres-Lezama, Armando, Gamarra, Luis Valenzuela, van der Heijden, Geertje, van der Meer, Peter, van der Hout, Peter, Martinez, Rodolfo Vasquez, Vieira, Simone Aparecida, Cayo, Jeanneth Villalobos, Vos, Vincent, Zagt, Roderick, Zuidema, Pieter, and Galbraith, David

Abstract

The original version of this Article contained an error in Table 2, where the number of individuals in the “All Amazonia” row was reported as 11,6431 instead of 116,431. Also, the original version of this Article contained an error in the Methods, where the R2 for the proportion of broken/uprooted dead trees increase per year was reported as 0.12, the correct value being 0.06. The original version of this Article contained errors in the author affiliations. The affiliation of Gerardo A. Aymard C. with UNELLEZGuanare, Herbario Universitario (PORT), Portuguesa, Venezuela Compensation International Progress S.A. Ciprogress–Greenlife.

Published
2021

20. Data and R-code from 'Mode of death and mortality risk factors in Amazon trees'. Nature communications. 2020

Author

Esquivel-Muelbert, Adriane, Phillips, Oliver L., Brienen, Roel J.W., Fauset, Sophie, Sullivan, Martin J.P., Baker, Timothy R., Chao, Kuo Jung, Feldpausch, Ted R., Gloor, Emanuel, Higuchi, Niro, Houwing-Duistermaat, Jeanne, Lloyd, Jon, Liu, Haiyan, Malhi, Yadvinder, Marimon, Beatriz, Marimon Junior, Ben Hur, Monteagudo-Mendoza, Abel, Poorter, Lourens, Silveira, Marcos, Torre, Emilio Vilanova, Dávila, Esteban Alvarez, del Aguila Pasquel, Jhon, Almeida, Everton, Loayza, Patricia Alvarez, Andrade, Ana, Aragão, Luiz E.O.C., Araujo-Murakami, Alejandro, Arets, Eric, Arroyo, Luzmila, Aymard C, Gerardo A., Baisie, Michel, Baraloto, Christopher, Camargo, Plínio Barbosa, Barroso, Jorcely, Blanc, Lilian, Bonal, Damien, Bongers, Frans, Boot, René, Brown, Foster, Burban, Benoit, Camargo, José Luís, Castro, Wendeson, Moscoso, Victor Chama, Chave, Jerome, Comiskey, James, Valverde, Fernando Cornejo, da Costa, Antonio Lola, Cardozo, Nallaret Davila, Di Fiore, Anthony, Dourdain, Aurélie, Erwin, Terry, Llampazo, Gerardo Flores, Vieira, Ima Célia Guimarães, Herrera, Rafael, Honorio Coronado, Eurídice, Huamantupa-Chuquimaco, Isau, Jimenez-Rojas, Eliana, Killeen, Timothy, Laurance, Susan, Laurance, William, Levesley, Aurora, Lewis, Simon L., Ladvocat, Karina Liana Lisboa Melgaço, Lopez-Gonzalez, Gabriela, Lovejoy, Thomas, Meir, Patrick, Mendoza, Casimiro, Morandi, Paulo, Neill, David, Nogueira Lima, Adriano José, Vargas, Percy Nuñez, de Oliveira, Edmar Almeida, Camacho, Nadir Pallqui, Pardo, Guido, Peacock, Julie, Peña-Claros, Marielos, Peñuela-Mora, Maria Cristina, Pickavance, Georgia, Pipoly, John, Pitman, Nigel, Prieto, Adriana, Pugh, Thomas A.M., Quesada, Carlos, Ramirez-Angulo, Hirma, de Almeida Reis, Simone Matias, Rejou-Machain, Maxime, Correa, Zorayda Restrepo, Bayona, Lily Rodriguez, Rudas, Agustín, Salomão, Rafael, Serrano, Julio, Espejo, Javier Silva, Silva, Natalino, Singh, James, Stahl, Clement, Stropp, Juliana, Swamy, Varun, Talbot, Joey, ter Steege, Hans, Terborgh, John, Thomas, Raquel, Toledo, Marisol, Torres-Lezama, Armando, Gamarra, Luis Valenzuela, van der Heijden, Geertje, van der Meer, Peter, van der Hout, Peter, Martinez, Rodolfo Vasquez, Vieira, Simone Aparecida, Cayo, Jeanneth Villalobos, Vos, Vincent, Zagt, Roderick, Zuidema, Pieter, Galbraith, David, Esquivel-Muelbert, Adriane, Phillips, Oliver L., Brienen, Roel J.W., Fauset, Sophie, Sullivan, Martin J.P., Baker, Timothy R., Chao, Kuo Jung, Feldpausch, Ted R., Gloor, Emanuel, Higuchi, Niro, Houwing-Duistermaat, Jeanne, Lloyd, Jon, Liu, Haiyan, Malhi, Yadvinder, Marimon, Beatriz, Marimon Junior, Ben Hur, Monteagudo-Mendoza, Abel, Poorter, Lourens, Silveira, Marcos, Torre, Emilio Vilanova, Dávila, Esteban Alvarez, del Aguila Pasquel, Jhon, Almeida, Everton, Loayza, Patricia Alvarez, Andrade, Ana, Aragão, Luiz E.O.C., Araujo-Murakami, Alejandro, Arets, Eric, Arroyo, Luzmila, Aymard C, Gerardo A., Baisie, Michel, Baraloto, Christopher, Camargo, Plínio Barbosa, Barroso, Jorcely, Blanc, Lilian, Bonal, Damien, Bongers, Frans, Boot, René, Brown, Foster, Burban, Benoit, Camargo, José Luís, Castro, Wendeson, Moscoso, Victor Chama, Chave, Jerome, Comiskey, James, Valverde, Fernando Cornejo, da Costa, Antonio Lola, Cardozo, Nallaret Davila, Di Fiore, Anthony, Dourdain, Aurélie, Erwin, Terry, Llampazo, Gerardo Flores, Vieira, Ima Célia Guimarães, Herrera, Rafael, Honorio Coronado, Eurídice, Huamantupa-Chuquimaco, Isau, Jimenez-Rojas, Eliana, Killeen, Timothy, Laurance, Susan, Laurance, William, Levesley, Aurora, Lewis, Simon L., Ladvocat, Karina Liana Lisboa Melgaço, Lopez-Gonzalez, Gabriela, Lovejoy, Thomas, Meir, Patrick, Mendoza, Casimiro, Morandi, Paulo, Neill, David, Nogueira Lima, Adriano José, Vargas, Percy Nuñez, de Oliveira, Edmar Almeida, Camacho, Nadir Pallqui, Pardo, Guido, Peacock, Julie, Peña-Claros, Marielos, Peñuela-Mora, Maria Cristina, Pickavance, Georgia, Pipoly, John, Pitman, Nigel, Prieto, Adriana, Pugh, Thomas A.M., Quesada, Carlos, Ramirez-Angulo, Hirma, de Almeida Reis, Simone Matias, Rejou-Machain, Maxime, Correa, Zorayda Restrepo, Bayona, Lily Rodriguez, Rudas, Agustín, Salomão, Rafael, Serrano, Julio, Espejo, Javier Silva, Silva, Natalino, Singh, James, Stahl, Clement, Stropp, Juliana, Swamy, Varun, Talbot, Joey, ter Steege, Hans, Terborgh, John, Thomas, Raquel, Toledo, Marisol, Torres-Lezama, Armando, Gamarra, Luis Valenzuela, van der Heijden, Geertje, van der Meer, Peter, van der Hout, Peter, Martinez, Rodolfo Vasquez, Vieira, Simone Aparecida, Cayo, Jeanneth Villalobos, Vos, Vincent, Zagt, Roderick, Zuidema, Pieter, and Galbraith, David

Abstract

The carbon sink capacity of tropical forests is substantially affected by tree mortality. However, the main drivers of tropical tree death remain largely unknown. Here we present a pan-Amazonian assessment of how and why trees die, analysing over 120,000 trees representing > 3800 species from 189 long-term RAINFOR forest plots. While tree mortality rates vary greatly Amazon-wide, on average trees are as likely to die standing as they are broken or uprooted—modes of death with different ecological consequences. Species-level growth rate is the single most important predictor of tree death in Amazonia, with faster-growing species being at higher risk. Within species, however, the slowest-growing trees are at greatest risk while the effect of tree size varies across the basin. In the driest Amazonian region species-level bioclimatic distributional patterns also predict the risk of death, suggesting that these forests are experiencing climatic conditions beyond their adaptative limits. These results provide not only a holistic pan-Amazonian picture of tree death but large-scale evidence for the overarching importance of the growth–survival trade-off in driving tropical tree mortality.

Published
2020

21. Tree mode of death and mortality risk factors across Amazon forests

Author

Esquivel-Muelbert, Adriane, Phillips, Oliver L., Brienen, Roel J.W., Fauset, Sophie, Sullivan, Martin J.P., Baker, Timothy R., Chao, Kuo Jung, Feldpausch, Ted R., Gloor, Emanuel, Higuchi, Niro, Houwing-Duistermaat, Jeanne, Lloyd, Jon, Liu, Haiyan, Malhi, Yadvinder, Marimon, Beatriz, Marimon Junior, Ben Hur, Monteagudo-Mendoza, Abel, Poorter, Lourens, Silveira, Marcos, Torre, Emilio Vilanova, Dávila, Esteban Alvarez, del Aguila Pasquel, Jhon, Almeida, Everton, Loayza, Patricia Alvarez, Andrade, Ana, Aragão, Luiz E.O.C., Araujo-Murakami, Alejandro, Arets, Eric, Arroyo, Luzmila, Aymard C, Gerardo A., Baisie, Michel, Baraloto, Christopher, Camargo, Plínio Barbosa, Barroso, Jorcely, Blanc, Lilian, Bonal, Damien, Bongers, Frans, Boot, René, Brown, Foster, Burban, Benoit, Camargo, José Luís, Castro, Wendeson, Moscoso, Victor Chama, Chave, Jerome, Comiskey, James, Valverde, Fernando Cornejo, da Costa, Antonio Lola, Cardozo, Nallaret Davila, Di Fiore, Anthony, Dourdain, Aurélie, Erwin, Terry, Llampazo, Gerardo Flores, Vieira, Ima Célia Guimarães, Herrera, Rafael, Honorio Coronado, Eurídice, Huamantupa-Chuquimaco, Isau, Jimenez-Rojas, Eliana, Killeen, Timothy, Laurance, Susan, Laurance, William, Levesley, Aurora, Lewis, Simon L., Ladvocat, Karina Liana Lisboa Melgaço, Lopez-Gonzalez, Gabriela, Lovejoy, Thomas, Meir, Patrick, Mendoza, Casimiro, Morandi, Paulo, Neill, David, Nogueira Lima, Adriano José, Vargas, Percy Nuñez, de Oliveira, Edmar Almeida, Camacho, Nadir Pallqui, Pardo, Guido, Peacock, Julie, Peña-Claros, Marielos, Peñuela-Mora, Maria Cristina, Pickavance, Georgia, Pipoly, John, Pitman, Nigel, Prieto, Adriana, Pugh, Thomas A.M., Quesada, Carlos, Ramirez-Angulo, Hirma, de Almeida Reis, Simone Matias, Rejou-Machain, Maxime, Correa, Zorayda Restrepo, Bayona, Lily Rodriguez, Rudas, Agustín, Salomão, Rafael, Serrano, Julio, Espejo, Javier Silva, Silva, Natalino, Singh, James, Stahl, Clement, Stropp, Juliana, Swamy, Varun, Talbot, Joey, ter Steege, Hans, Terborgh, John, Thomas, Raquel, Toledo, Marisol, Torres-Lezama, Armando, Gamarra, Luis Valenzuela, van der Heijden, Geertje, van der Meer, Peter, van der Hout, Peter, Martinez, Rodolfo Vasquez, Vieira, Simone Aparecida, Cayo, Jeanneth Villalobos, Vos, Vincent, Zagt, Roderick, Zuidema, Pieter, Galbraith, David, Esquivel-Muelbert, Adriane, Phillips, Oliver L., Brienen, Roel J.W., Fauset, Sophie, Sullivan, Martin J.P., Baker, Timothy R., Chao, Kuo Jung, Feldpausch, Ted R., Gloor, Emanuel, Higuchi, Niro, Houwing-Duistermaat, Jeanne, Lloyd, Jon, Liu, Haiyan, Malhi, Yadvinder, Marimon, Beatriz, Marimon Junior, Ben Hur, Monteagudo-Mendoza, Abel, Poorter, Lourens, Silveira, Marcos, Torre, Emilio Vilanova, Dávila, Esteban Alvarez, del Aguila Pasquel, Jhon, Almeida, Everton, Loayza, Patricia Alvarez, Andrade, Ana, Aragão, Luiz E.O.C., Araujo-Murakami, Alejandro, Arets, Eric, Arroyo, Luzmila, Aymard C, Gerardo A., Baisie, Michel, Baraloto, Christopher, Camargo, Plínio Barbosa, Barroso, Jorcely, Blanc, Lilian, Bonal, Damien, Bongers, Frans, Boot, René, Brown, Foster, Burban, Benoit, Camargo, José Luís, Castro, Wendeson, Moscoso, Victor Chama, Chave, Jerome, Comiskey, James, Valverde, Fernando Cornejo, da Costa, Antonio Lola, Cardozo, Nallaret Davila, Di Fiore, Anthony, Dourdain, Aurélie, Erwin, Terry, Llampazo, Gerardo Flores, Vieira, Ima Célia Guimarães, Herrera, Rafael, Honorio Coronado, Eurídice, Huamantupa-Chuquimaco, Isau, Jimenez-Rojas, Eliana, Killeen, Timothy, Laurance, Susan, Laurance, William, Levesley, Aurora, Lewis, Simon L., Ladvocat, Karina Liana Lisboa Melgaço, Lopez-Gonzalez, Gabriela, Lovejoy, Thomas, Meir, Patrick, Mendoza, Casimiro, Morandi, Paulo, Neill, David, Nogueira Lima, Adriano José, Vargas, Percy Nuñez, de Oliveira, Edmar Almeida, Camacho, Nadir Pallqui, Pardo, Guido, Peacock, Julie, Peña-Claros, Marielos, Peñuela-Mora, Maria Cristina, Pickavance, Georgia, Pipoly, John, Pitman, Nigel, Prieto, Adriana, Pugh, Thomas A.M., Quesada, Carlos, Ramirez-Angulo, Hirma, de Almeida Reis, Simone Matias, Rejou-Machain, Maxime, Correa, Zorayda Restrepo, Bayona, Lily Rodriguez, Rudas, Agustín, Salomão, Rafael, Serrano, Julio, Espejo, Javier Silva, Silva, Natalino, Singh, James, Stahl, Clement, Stropp, Juliana, Swamy, Varun, Talbot, Joey, ter Steege, Hans, Terborgh, John, Thomas, Raquel, Toledo, Marisol, Torres-Lezama, Armando, Gamarra, Luis Valenzuela, van der Heijden, Geertje, van der Meer, Peter, van der Hout, Peter, Martinez, Rodolfo Vasquez, Vieira, Simone Aparecida, Cayo, Jeanneth Villalobos, Vos, Vincent, Zagt, Roderick, Zuidema, Pieter, and Galbraith, David

Abstract

The carbon sink capacity of tropical forests is substantially affected by tree mortality. However, the main drivers of tropical tree death remain largely unknown. Here we present a pan-Amazonian assessment of how and why trees die, analysing over 120,000 trees representing > 3800 species from 189 long-term RAINFOR forest plots. While tree mortality rates vary greatly Amazon-wide, on average trees are as likely to die standing as they are broken or uprooted—modes of death with different ecological consequences. Species-level growth rate is the single most important predictor of tree death in Amazonia, with faster-growing species being at higher risk. Within species, however, the slowest-growing trees are at greatest risk while the effect of tree size varies across the basin. In the driest Amazonian region species-level bioclimatic distributional patterns also predict the risk of death, suggesting that these forests are experiencing climatic conditions beyond their adaptative limits. These results provide not only a holistic pan-Amazonian picture of tree death but large-scale evidence for the overarching importance of the growth–survival trade-off in driving tropical tree mortality.

Published
2020

24. Agricultural breadbaskets shift poleward given adaptive farmer behavior under climate change.

Author

Franke, James A., Müller, Christoph, Minoli, Sara, Elliott, Joshua, Folberth, Christian, Gardner, Charles, Hank, Tobias, Izaurralde, Roberto Cesar, Jägermeyr, Jonas, Jones, Curtis D., Liu, Wenfeng, Olin, Stefan, Pugh, Thomas A.M., Ruane, Alex C., Stephens, Haynes, Zabel, Florian, and Moyer, Elisabeth J.

Subjects
WINTER wheat, CLIMATE change, RICE, WHEAT, GROWING season, FARMERS, FOOD production
Abstract

Modern food production is spatially concentrated in global "breadbaskets." A major unresolved question is whether these peak production regions will shift poleward as the climate warms, allowing some recovery of potential climate‐related losses. While agricultural impacts studies to date have focused on currently cultivated land, the Global Gridded Crop Model Intercomparison Project (GGCMI) Phase 2 experiment allows us to assess changes in both yields and the location of peak productivity regions under warming. We examine crop responses under projected end of century warming using seven process‐based models simulating five major crops (maize, rice, soybeans, and spring and winter wheat) with a variety of adaptation strategies. We find that in no‐adaptation cases, when planting date and cultivar choices are held fixed, regions of peak production remain stationary and yield losses can be severe, since growing seasons contract strongly with warming. When adaptations in management practices are allowed (cultivars that retain growing season length under warming and modified planting dates), peak productivity zones shift poleward and yield losses are largely recovered. While most growing‐zone shifts are ultimately limited by geography, breadbaskets studied here move poleward over 600 km on average by end of the century under RCP 8.5. These results suggest that agricultural impacts assessments can be strongly biased if restricted in spatial area or in the scope of adaptive behavior considered. Accurate evaluation of food security under climate change requires global modeling and careful treatment of adaptation strategies. [ABSTRACT FROM AUTHOR]

Published
2022
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25. State-of-the-art global models underestimate impacts from climate extremes

Author

Schewe, Jacob, Gosling, Simon N., Reyer, Christopher P.O., Zhao, Fang, Ciais, Philippe, Elliott, Joshua, François, Louis, Huber, Veronika, Lotze, Heike, Seneviratne, Sonia I., van Vliet, Michelle T.H., Vautard, Robert, Wada, Yoshihide, Breuer, Lutz, Büchner, Matthias, Carozza, David A., Chang, Jinfeng, Coll, Marta, Deryng, Delphine, De Wit, Allard, Eddy, Tyler D., Folberth, Christian, Frieler, Katja, Friend, Andrew D., Gerten, Dieter, Gudmundsson, Lukas, Hanasaki, Naota, Ito, Akihiko, Khabarov, Nikolay, Kim, Hyungjun, Lawrence, Peter J., Morfopoulos, Catherine, Müller, Christoph, Müller Schmied, Hannes, Orth, René, Pokhrel, Yadu, Pugh, Thomas A.M., Sakurai, Gen, Satoh, Yusuke, Schmid, Erwin, Stacke, Tobias, Steenbeek, Jeroen, Steinkamp, Joerg, Tang, Qiuhong, Tian, Hanqin, Tittensor, Derek P., Volkholz, Jan, Wang, Xuhui, and Warszawski, Lila

Abstract

Global impact models represent process-level understanding of how natural and human systems may be affected by climate change. Their projections are used in integrated assessments of climate change. Here we test, for the first time, systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions. Using the 2003 European heat wave and drought as a historical analogue for comparable events in the future, we find that a majority of models underestimate the extremeness of impacts in important sectors such as agriculture, terrestrial ecosystems, and heat-related human mortality, while impacts on water resources and hydropower are overestimated in some river basins; and the spread across models is often large. This has important implications for economic assessments of climate change impacts that rely on these models. It also means that societal risks from future extreme events may be greater than previously thought. ISSN:2041-1723

Published
2019

26. State-of-the-art global models underestimate impacts from climate extremes

Author

European Commission, Federal Ministry of Education and Research (Germany), Schewe, Jacob, Gosling, Simon, Reyer, Christopher, Zhao, Fang, Ciais, Philippe, Elliott, Joshua, Francois, Louis M., Huber, Veronika, Lotze, Heike K., Seneviratne, Sonia I., van Vliet, Michelle, Vautard, Robert, Wada, Yoshihide, Breuer, Lutz, Büchner, Matthias, Carozza, David A., Chang, Jinfeng, Coll, Marta, Deryng, Delphine, de Wit, Allard, Eddy, Tyler D., Folberth, Christian, Frieler, Katja, Friend, Andrew D., Gerten, Dieter, Gudmundsson, Lukas, Hanasaki, Naota, Ito, Akihiko, Khabarov, Nikolay, Kim, Hyungjun, Lawrence, Peter, Morfopoulos, Catherine, Müller, Christoph, Schmied, Hannes Müller, Orth, René, Ostberg, Sebastian, Pokhrel, Yadu, Pugh, Thomas A.M., Sakurai, Gen, Satoh, Yusuke, Schmid, Erwin, Stacke, Tobias, Steenbeek, Jeroen, Steinkamp, Jörg, Tang, Qiuhong, Tian, Hanqin, Tittensor, Derek P., Volkholz, Jan, Wang, Xuhui, Warszawski, Lila, European Commission, Federal Ministry of Education and Research (Germany), Schewe, Jacob, Gosling, Simon, Reyer, Christopher, Zhao, Fang, Ciais, Philippe, Elliott, Joshua, Francois, Louis M., Huber, Veronika, Lotze, Heike K., Seneviratne, Sonia I., van Vliet, Michelle, Vautard, Robert, Wada, Yoshihide, Breuer, Lutz, Büchner, Matthias, Carozza, David A., Chang, Jinfeng, Coll, Marta, Deryng, Delphine, de Wit, Allard, Eddy, Tyler D., Folberth, Christian, Frieler, Katja, Friend, Andrew D., Gerten, Dieter, Gudmundsson, Lukas, Hanasaki, Naota, Ito, Akihiko, Khabarov, Nikolay, Kim, Hyungjun, Lawrence, Peter, Morfopoulos, Catherine, Müller, Christoph, Schmied, Hannes Müller, Orth, René, Ostberg, Sebastian, Pokhrel, Yadu, Pugh, Thomas A.M., Sakurai, Gen, Satoh, Yusuke, Schmid, Erwin, Stacke, Tobias, Steenbeek, Jeroen, Steinkamp, Jörg, Tang, Qiuhong, Tian, Hanqin, Tittensor, Derek P., Volkholz, Jan, Wang, Xuhui, and Warszawski, Lila

Abstract

Global impact models represent process-level understanding of how natural and human systems may be affected by climate change. Their projections are used in integrated assessments of climate change. Here we test, for the first time, systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions. Using the 2003 European heat wave and drought as a historical analogue for comparable events in the future, we find that a majority of models underestimate the extremeness of impacts in important sectors such as agriculture, terrestrial ecosystems, and heat-related human mortality, while impacts on water resources and hydropower are overestimated in some river basins; and the spread across models is often large. This has important implications for economic assessments of climate change impacts that rely on these models. It also means that societal risks from future extreme events may be greater than previously thought

Published
2019

27. The Global Gridded Crop Model Intercomparison phase 1 simulation dataset

Author

Müller, Christoph, Elliott, Joshua, Kelly, David, Arneth, Almut, Balkovic, Juraj, Ciais, Philippe, Deryng, Delphine, Folberth, Christian, Hoek, Steven, Izaurralde, Roberto C., Jones, Curtis D., Khabarov, Nikolay, Lawrence, Peter, Liu, Wenfeng, Olin, Stefan, Pugh, Thomas A.M., Reddy, Ashwan, Rosenzweig, Cynthia, Ruane, Alex C., Sakurai, Gen, Schmid, Erwin, Skalsky, Rastislav, Wang, Xuhui, de Wit, Allard, Yang, Hong, Müller, Christoph, Elliott, Joshua, Kelly, David, Arneth, Almut, Balkovic, Juraj, Ciais, Philippe, Deryng, Delphine, Folberth, Christian, Hoek, Steven, Izaurralde, Roberto C., Jones, Curtis D., Khabarov, Nikolay, Lawrence, Peter, Liu, Wenfeng, Olin, Stefan, Pugh, Thomas A.M., Reddy, Ashwan, Rosenzweig, Cynthia, Ruane, Alex C., Sakurai, Gen, Schmid, Erwin, Skalsky, Rastislav, Wang, Xuhui, de Wit, Allard, and Yang, Hong

Abstract

The Global Gridded Crop Model Intercomparison (GGCMI) phase 1 dataset of the Agricultural Model Intercomparison and Improvement Project (AgMIP) provides an unprecedentedly large dataset of crop model simulations covering the global ice-free land surface. The dataset consists of annual data fields at a spatial resolution of 0.5 arc-degree longitude and latitude. Fourteen crop modeling groups provided output for up to 11 historical input datasets spanning 1901 to 2012, and for up to three different management harmonization levels. Each group submitted data for up to 15 different crops and for up to 14 output variables. All simulations were conducted for purely rainfed and near-perfectly irrigated conditions on all land areas irrespective of whether the crop or irrigation system is currently used there. With the publication of the GGCMI phase 1 dataset we aim to promote further analyses and understanding of crop model performance, potential relationships between productivity and environmental impacts, and insights on how to further improve global gridded crop model frameworks. We describe dataset characteristics and individual model setup narratives.

Published
2019

28. Actual European forest management by region, tree species and owner based on 714,000 re-measured trees in national forest inventories

Author

Schelhaas, Mart-Jan, Fridman, Jonas, Hengeveld, Geerten M., Henttonen, Helena M., Lehtonen, Aleksi, Kies, Uwe, Krajnc, Nike, Lerink, Bas, Ní Dhubháin, Áine, Polley, Heino, Pugh, Thomas A.M., Redmond, John J., Rohner, Brigitte, Temperli, Cristian, Vayreda, Jordi, Nabuurs, Gert-Jan, and Hanewinkel, Marc

Subjects
0106 biological sciences, 010504 meteorology & atmospheric sciences, Bos- en Landschapsecologie, lcsh:Medicine, Plant Science, Forests, 01 natural sciences, Trees, Geographical Locations, Bos- en Natuurbeleid, Land use, land-use change and forestry, Forest and Landscape Ecology, lcsh:Science, Finland, Netherlands, Multidisciplinary, Ecology, Agroforestry, Plant Anatomy, Eukaryota, Subsidy, Forestry, Plants, PE&RC, Terrestrial Environments, Wood, Europe, Biometris, Vegetatie, Bos- en Landschapsecologie, Research Article, Conservation of Natural Resources, Death Rates, Yield (finance), Forest management, 010603 evolutionary biology, Ecosystems, Forest and Nature Conservation Policy, Population Metrics, Life Science, European Union, Baseline (configuration management), Productivity, Vegetatie, 0105 earth and related environmental sciences, Tree measurement, Vegetation, Land use, Population Biology, lcsh:R, Ecology and Environmental Sciences, Ownership, Organisms, Biology and Life Sciences, 15. Life on land, People and Places, lcsh:Q, Water Systems and Global Change, Vegetation, Forest and Landscape Ecology, Pines
Abstract

BACKGROUND:European forests have a long record of management. However, the diversity of the current forest management across nations, tree species and owners, is hardly understood. Often when trying to simulate future forest resources under alternative futures, simply the yield table style of harvesting is applied. It is now crucially important to come to grips with actual forest management, now that demand for wood is increasing and the EU Land Use, Land Use Change and Forestry Regulation has been adopted requiring 'continuation of current management practices' as a baseline to set the Forest Reference Level carbon sink. METHODS:Based on a large dataset of 714,000 re-measured trees in National Forest inventories from 13 regions, we are now able to analyse actual forest harvesting. CONCLUSIONS:From this large set of repeated tree measurements we can conclude that there is no such thing as yield table harvesting in Europe. We found general trends of increasing harvest probability with higher productivity of the region and the species, but with important deviations related to local conditions like site accessibility, state of the forest resource (like age), specific subsidies, importance of other forest services, and ownership of the forest. As a result, we find a huge diversity in harvest regimes. Over the time period covered in our inventories, the average harvest probability over all regions was 2.4% yr-1 (in number of trees) and the mortality probability was 0.4% yr-1. Our study provides underlying and most actual data that can serve as a basis for quantifying 'continuation of current forest management'. It can be used as a cornerstone for the base period as required for the Forest Reference Level for EU Member States.

Published
2018

29. Large uncertainty in carbon uptake potential of land-based climate-change mitigation efforts

Author

Environmental Sciences, Section Economic Urban Transitions, Krause, Andreas, Pugh, Thomas A.M., Bayer, Anita D., Li, Wei, Leung, Felix, Bondeau, Alberte, Doelman, Jonathan C., Humpenöder, Florian, Anthoni, Peter, Bodirsky, Benjamin Leon, Ciais, Philippe, Müller, Christoph, Murray-Tortarolo, Guillermo, Olin, Stefan, Popp, Alexander, Sitch, Stephen, Stehfest, Elke, Arneth, Almut, Environmental Sciences, Section Economic Urban Transitions, Krause, Andreas, Pugh, Thomas A.M., Bayer, Anita D., Li, Wei, Leung, Felix, Bondeau, Alberte, Doelman, Jonathan C., Humpenöder, Florian, Anthoni, Peter, Bodirsky, Benjamin Leon, Ciais, Philippe, Müller, Christoph, Murray-Tortarolo, Guillermo, Olin, Stefan, Popp, Alexander, Sitch, Stephen, Stehfest, Elke, and Arneth, Almut

Published
2018

30. Uncertainties in the land-use flux resulting from land-use change reconstructions and gross land transitions

Author

Bayer, Anita D., Lindeskog, Mats, Pugh, Thomas A.M., Anthoni, Peter M., Fuchs, Richard, Arneth, Almut, and Earth and Climate

Subjects
Earth sciences, Laboratory of Geo-information Science and Remote Sensing, ddc:550, Life Science, Laboratorium voor Geo-informatiekunde en Remote Sensing, PE&RC
Abstract

Land-use and land-cover (LUC) changes are a key uncertainty when attributing changes in measured atmospheric CO2 concentration to its sinks and sources and must also be much better understood to determine the possibilities for land-based climate change mitigation, especially in the light of human demand on other land-based resources. On the spatial scale typically used in terrestrial ecosystem models (0.5 or 1°) changes in LUC over time periods of a few years or more can include bidirectional changes on the sub-grid level, such as the parallel expansion and abandonment of agricultural land (e.g. in shifting cultivation) or cropland–grassland conversion (and vice versa). These complex changes between classes within a grid cell have often been neglected in previous studies, and only net changes of land between natural vegetation cover, cropland and pastures accounted for, mainly because of a lack of reliable high-resolution historical information on gross land transitions, in combination with technical limitations within the models themselves. In the present study we applied a state-of-the-art dynamic global vegetation model with a detailed representation of croplands and carbon–nitrogen dynamics to quantify the uncertainty in terrestrial ecosystem carbon stocks and fluxes arising from the choice between net and gross representations of LUC. We used three frequently applied global, one recent global and one recent European LUC datasets, two of which resolve gross land transitions, either in Europe or in certain tropical regions. When considering only net changes, land-use-transition uncertainties (expressed as 1 standard deviation around decadal means of four models) in global carbon emissions from LUC (ELUC) are ±0.19, ±0.66 and ±0.47 Pg C a−1 in the 1980s, 1990s and 2000s, respectively, or between 14 and 39 % of mean ELUC. Carbon stocks at the end of the 20th century vary by ±11 Pg C for vegetation and ±37 Pg C for soil C due to the choice of LUC reconstruction, i.e. around 3 % of the respective C pools. Accounting for sub-grid (gross) land conversions significantly increased the effect of LUC on global and European carbon stocks and fluxes, most noticeably enhancing global cumulative ELUC by 33 Pg C (1750–2014) and entailing a significant reduction in carbon stored in vegetation, although the effect on soil C stocks was limited. Simulations demonstrated that assessments of historical carbon stocks and fluxes are highly uncertain due to the choice of LUC reconstruction and that the consideration of different contrasting LUC reconstructions is needed to account for this uncertainty. The analysis of gross, in addition to net, land-use changes showed that the full complexity of gross land-use changes is required in order to accurately predict the magnitude of LUC change emissions. This introduces technical challenges to process-based models and relies on extensive information regarding historical land-use transitions.

Published
2017

31. Potential yield simulated by Global Gridded Crop Models: a process-based emulator to explain their differences.

Author

Ringeval, Bruno, Müller, Christoph, Pugh, Thomas A.M., Mueller, Nathaniel D., Ciais, Philippe, Folberth, Christian, Liu, Wenfeng, Debaeke, Philippe, and Pellerin, Sylvain

Subjects
BEER-Lambert law, RADIATION absorption, PRIMARY productivity (Biology), GROWING season, CROPS, BIOMASS, BIOMASS conversion
Abstract

How Global Gridded Crop Models (GGCMs) differ in their simulation of potential yield and reasons for those differences have never been assessed. The GGCM Inter-comparison (GGCMI) offers a good framework for this assessment. Here, we built an emulator (called SMM for Simple Mechanistic Model) of GGCMs based on generic and simplified formalism. The SMM equations describe crop phenology by a sum of growing degree days, canopy radiation absorption by the Beer-Lambert law, and its conversion into aboveground biomass by a radiation use efficiency (RUE). We fitted the parameters of this emulator against gridded aboveground maize biomass at the end of the growing season simulated by eight different GGCMs in a given year (2000). Our assumption is that the simple set of equations of SMM, after calibration, could reproduce the response of most GGCMs, so that differences between GGCMs can be attributed to the parameters related to processes captured by the emulator. Despite huge differences between GGCMs, we show that if we fit both a parameter describing the thermal requirement for leaf emergence by adjusting its value to each grid-point in space, as done by GGCM modellers following the GGCMI protocol, and a GGCM-dependent globally uniform RUE, then the simple set of equations of the SMM emulator is sufficient to reproduce the spatial distribution of the original aboveground biomass simulated by most GGCMs. The grain filling is simulated in SMM by considering a fixed in time fraction of net primary productivity allocated to the grain (frac) once a threshold in leaves number (nthresh) is reached. Once calibrated, these two parameters allow to capture the relationship between potential yield and final aboveground biomass of each GGCM. It is particularly important as the divergence among GGCMs is larger for yield than for aboveground biomass. Thus, we showed that the divergence between GGCMs can be summarized by the differences in few parameters. Our simple but mechanistic model could also be an interesting tool to test new developments in order to improve the simulation of potential yield at the global scale. [ABSTRACT FROM AUTHOR]

Published
2020
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32. Land-use and land-cover change carbon emissions between 1901 and 2012 constrained by biomass observations

Author

Li, Wei, Ciais, Philippe, Peng, Shushi, Yue, Chao, Wang, Yilong, Thurner, Martin, Saatchi, Sassan S., Arneth, Almut, Avitabile, Valerio, Carvalhais, Nuno, Harper, Anna B., Kato, Etsushi, Koven, Charles, Liu, Yi Y., Nabel, Julia E.M.S., Pan, Yude, Pongratz, Julia, Poulter, Benjamin, Pugh, Thomas A.M., Santoro, Maurizio, Sitch, Stephen, Stocker, Benjamin D., Viovy, Nicolas, Wiltshire, Andy, Yousefpour, Rasoul, Zaehle, Sönke, Li, Wei, Ciais, Philippe, Peng, Shushi, Yue, Chao, Wang, Yilong, Thurner, Martin, Saatchi, Sassan S., Arneth, Almut, Avitabile, Valerio, Carvalhais, Nuno, Harper, Anna B., Kato, Etsushi, Koven, Charles, Liu, Yi Y., Nabel, Julia E.M.S., Pan, Yude, Pongratz, Julia, Poulter, Benjamin, Pugh, Thomas A.M., Santoro, Maurizio, Sitch, Stephen, Stocker, Benjamin D., Viovy, Nicolas, Wiltshire, Andy, Yousefpour, Rasoul, and Zaehle, Sönke

Abstract

The use of dynamic global vegetation models (DGVMs) to estimate CO2 emissions from land-use and land-cover change (LULCC) offers a new window to account for spatial and temporal details of emissions and for ecosystem processes affected by LULCC. One drawback of LULCC emissions from DGVMs, however, is lack of observation constraint. Here, we propose a new method of using satellite- and inventory-based biomass observations to constrain historical cumulative LULCC emissions (ELUCc) from an ensemble of nine DGVMs based on emerging relationships between simulated vegetation biomass and ELUCc. This method is applicable on the global and regional scale. The original DGVM estimates of ELUCc range from 94 to 273 PgC during 1901–2012. After constraining by current biomass observations, we derive a best estimate of 155 ± 50 PgC (1σ Gaussian error). The constrained LULCC emissions are higher than prior DGVM values in tropical regions but significantly lower in North America. Our emergent constraint approach independently verifies the median model estimate by biomass observations, giving support to the use of this estimate in carbon budget assessments. The uncertainty in the constrained ELUCc is still relatively large because of the uncertainty in the biomass observations, and thus reduced uncertainty in addition to increased accuracy in biomass observations in the future will help improve the constraint. This constraint method can also be applied to evaluate the impact of land-based mitigation activities.

Published
2017

33. Consistent negative response of US crops to high temperatures in observations and crop models

Author

Schauberger, Bernhard, Archontoulis, Sotirios, Arneth, Almut, Balkovic, Juraj, Ciais, Philippe, Deryng, Delphine, Elliott, Joshua, Folberth, Christian, Khabarov, Nikolay, Müller, Christoph, Pugh, Thomas A.M., Rolinski, Susanne, Schaphoff, Sibyll, Schmid, Erwin, Wang, Xuhui, Schlenker, Wolfram, Frieler, Katja, Schauberger, Bernhard, Archontoulis, Sotirios, Arneth, Almut, Balkovic, Juraj, Ciais, Philippe, Deryng, Delphine, Elliott, Joshua, Folberth, Christian, Khabarov, Nikolay, Müller, Christoph, Pugh, Thomas A.M., Rolinski, Susanne, Schaphoff, Sibyll, Schmid, Erwin, Wang, Xuhui, Schlenker, Wolfram, and Frieler, Katja

Abstract

High temperatures are detrimental to crop yields and could lead to global warming-driven reductions in agricultural productivity. To assess future threats, the majority of studies used process-based crop models, but their ability to represent effects of high temperature has been questioned. Here we show that an ensemble of nine crop models reproduces the observed average temperature responses of US maize, soybean and wheat yields. Each day 430 C diminishes maize and soybean yields by up to 6% under rainfed conditions. Declines observed in irrigated areas, or simulated assuming full irrigation, are weak. This supports the hypothesis that water stress induced by high temperatures causes the decline. For wheat a negative response to high temperature is neither observed nor simulated under historical conditions, since critical temperatures are rarely exceeded during the growing season. In the future, yields are modelled to decline for all three crops at temperatures 430 C. Elevated CO2 can only weakly reduce these yield losses, in contrast to irrigation., Peer Reviewed

Published
2017

34. Global gridded crop model evaluation : Benchmarking, skills, deficiencies and implications

Author

Müller, Christoph, Elliott, Joshua, Chryssanthacopoulos, James, Arneth, Almut, Balkovic, Juraj, Ciais, Philippe, Deryng, Delphine, Folberth, Christian, Glotter, Michael, Hoek, Steven, Iizumi, Toshichika, Izaurralde, Roberto C., Jones, Curtis, Khabarov, Nikolay, Lawrence, Peter, Liu, Wenfeng, Olin, Stefan, Pugh, Thomas A.M., Ray, Deepak K., Reddy, Ashwan, Rosenzweig, Cynthia, Ruane, Alex C., Sakurai, Gen, Schmid, Erwin, Skalsky, Rastislav, Song, Carol X., Wang, Xuhui, De Wit, Allard, Yang, Hong, Müller, Christoph, Elliott, Joshua, Chryssanthacopoulos, James, Arneth, Almut, Balkovic, Juraj, Ciais, Philippe, Deryng, Delphine, Folberth, Christian, Glotter, Michael, Hoek, Steven, Iizumi, Toshichika, Izaurralde, Roberto C., Jones, Curtis, Khabarov, Nikolay, Lawrence, Peter, Liu, Wenfeng, Olin, Stefan, Pugh, Thomas A.M., Ray, Deepak K., Reddy, Ashwan, Rosenzweig, Cynthia, Ruane, Alex C., Sakurai, Gen, Schmid, Erwin, Skalsky, Rastislav, Song, Carol X., Wang, Xuhui, De Wit, Allard, and Yang, Hong

Abstract

Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all c

Published
2017

35. Large uncertainty in carbon uptake potential of land-based climate-change mitigation efforts

Author

Krause, Andreas, Pugh, Thomas A.M., Bayer, Anita D., Li, Wei, Leung, Felix, Bondeau, Alberte, Doelman, Jonathan C., Humpenöder, Florian, Anthoni, Peter, Bodirsky, Benjamin Leon, Ciais, Philippe, Müller, Christoph, Murray-Tortarolo, Guillermo, Olin, Stefan, Popp, Alexander, Sitch, Stephen, Stehfest, Elke, Arneth, Almut, Environmental Sciences, Section Economic Urban Transitions, Environmental Sciences, Section Economic Urban Transitions, Karlsruher Institut für Technologie (KIT), Institute for Meteorology and Climate Research (IMK), Karlsruhe Institute of Technology (KIT), School of Geography, Earth and Environmental Sciences and Birmingham Institute of Forest Research, University of Birmingham [Birmingham], Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), University of Exeter, Institut méditerranéen de biodiversité et d'écologie marine et continentale (IMBE), Avignon Université (AU)-Aix Marseille Université (AMU)-Institut de recherche pour le développement [IRD] : UMR237-Centre National de la Recherche Scientifique (CNRS), PBL Netherlands Environmental Assessment Agency, Potsdam Institute for Climate Impact Research (PIK), ICOS-ATC (ICOS-ATC), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Catedra CONACyT comisionado al Instituto de Investigaciones en Ecosistemas y Sustentabilidad, Universidad Nacional Autonoma de Mexico, Netherlands Environmental Assessment Agency, Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Centre for Ecology and Conservation, College of Life and Environmental Sciences, University of Exeter, Falmouth, United Kingdom, Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), College of Life and Environmental Sciences, University of Exeter, and Exeter Climate Systems, University of Exeter, Exeter, United Kingdom

Subjects
0106 biological sciences, Crops, Agricultural, Carbon Sequestration, Conservation of Natural Resources, 010504 meteorology & atmospheric sciences, Climate Change, Land management, Climate change, Forests, Atmospheric sciences, 7. Clean energy, 010603 evolutionary biology, 01 natural sciences, Carbon Cycle, Soil, Deforestation, land-basedmitigation, Environmental Science(all), Carbon capture and storage, BECCS, Environmental Chemistry, Biomass, land‐based mitigation, carbon dioxide removal, 0105 earth and related environmental sciences, General Environmental Science, 2. Zero hunger, Global and Planetary Change, Global temperature, Ecology, Uncertainty, Bio-energy with carbon capture and storage, Soil carbon, 15. Life on land, Carbon Dioxide, Carbon, negative emissions, Climate change mitigation, 13. Climate action, [SDE]Environmental Sciences, Environmental science, avoided deforestation and afforestation
Abstract

International audience; Most climate mitigation scenarios involve negative emissions, especially those that aim to limit global temperature increase to 2°C or less. However, the carbon uptake potential in land‐based climate change mitigation efforts is highly uncertain. Here, we address this uncertainty by using two land‐based mitigation scenarios from two land‐use models (IMAGE and MAgPIE) as input to four dynamic global vegetation models (DGVMs; LPJ‐GUESS, ORCHIDEE, JULES, LPJmL). Each of the four combinations of land‐use models and mitigation scenarios aimed for a cumulative carbon uptake of ~130 GtC by the end of the century, achieved either via the cultivation of bioenergy crops combined with carbon capture and storage (BECCS) or avoided deforestation and afforestation (ADAFF). Results suggest large uncertainty in simulated future land demand and carbon uptake rates, depending on the assumptions related to land use and land management in the models. Total cumulative carbon uptake in the DGVMs is highly variable across mitigation scenarios, ranging between 19 and 130 GtC by year 2099. Only one out of the 16 combinations of mitigation scenarios and DGVMs achieves an equivalent or higher carbon uptake than achieved in the land‐use models. The large differences in carbon uptake between the DGVMs and their discrepancy against the carbon uptake in IMAGE and MAgPIE are mainly due to different model assumptions regarding bioenergy crop yields and due to the simulation of soil carbon response to land‐use change. Differences between land‐use models and DGVMs regarding forest biomass and the rate of forest regrowth also have an impact, albeit smaller, on the results. Given the low confidence in simulated carbon uptake for a given land‐based mitigation scenario, and that negative emissions simulated by the DGVMs are typically lower than assumed in scenarios consistent with the 2°C target, relying on negative emissions to mitigate climate change is a highly uncertain strategy.

Published
2018

36. Spatial and temporal uncertainty of crop yield aggregations

Author

Porwollik, Vera, primary, Müller, Christoph, additional, Elliott, Joshua, additional, Chryssanthacopoulos, James, additional, Iizumi, Toshichika, additional, Ray, Deepak K., additional, Ruane, Alex C., additional, Arneth, Almut, additional, Balkovič, Juraj, additional, Ciais, Philippe, additional, Deryng, Delphine, additional, Folberth, Christian, additional, Izaurralde, Roberto C., additional, Jones, Curtis D., additional, Khabarov, Nikolay, additional, Lawrence, Peter J., additional, Liu, Wenfeng, additional, Pugh, Thomas A.M., additional, Reddy, Ashwan, additional, Sakurai, Gen, additional, Schmid, Erwin, additional, Wang, Xuhui, additional, de Wit, Allard, additional, and Wu, Xiuchen, additional

Published
2017
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38. Global change pressures on soils from land use and management

Author

Smith, Pete, House, Joanna I., Bustamante, Mercedes, Sobocká, Jaroslava, Harper, Richard, Pan, Genxing, West, Paul C., Clark, Joanna M., Adhya, Tapan, Rumpel, Cornelia, Paustian, Keith, Kuikman, Peter, Cotrufo, M. Francesca, Elliott, Jane A., McDowell, Richard, Griffiths, Robert I., Asakawa, Susumu, Bondeau, Alberte, Jain, Atul K., Meersmans, Jeroen, Pugh, Thomas A.M., Smith, Pete, House, Joanna I., Bustamante, Mercedes, Sobocká, Jaroslava, Harper, Richard, Pan, Genxing, West, Paul C., Clark, Joanna M., Adhya, Tapan, Rumpel, Cornelia, Paustian, Keith, Kuikman, Peter, Cotrufo, M. Francesca, Elliott, Jane A., McDowell, Richard, Griffiths, Robert I., Asakawa, Susumu, Bondeau, Alberte, Jain, Atul K., Meersmans, Jeroen, and Pugh, Thomas A.M.

Abstract

Soils are subject to varying degrees of direct or indirect human disturbance, constituting a major global change driver. Factoring out natural from direct and indirect human influence is not always straightforward, but some human activities have clear impacts. These include land-use change, land management and land degradation (erosion, compaction, sealing and salinization). The intensity of land use also exerts a great impact on soils, and soils are also subject to indirect impacts arising from human activity, such as acid deposition (sulphur and nitrogen) and heavy metal pollution. In this critical review, we report the state-of-the-art understanding of these global change pressures on soils, identify knowledge gaps and research challenges and highlight actions and policies to minimize adverse environmental impacts arising from these global change drivers. Soils are central to considerations of what constitutes sustainable intensification. Therefore, ensuring that vulnerable and high environmental value soils are considered when protecting important habitats and ecosystems, will help to reduce the pressure on land from global change drivers. To ensure that soils are protected as part of wider environmental efforts, a global soil resilience programme should be considered, to monitor, recover or sustain soil fertility and function, and to enhance the ecosystem services provided by soils. Soils cannot, and should not, be considered in isolation of the ecosystems that they underpin and vice versa. The role of soils in supporting ecosystems and natural capital needs greater recognition. The lasting legacy of the International Year of Soils in 2015 should be to put soils at the centre of policy supporting environmental protection and sustainable development.

Published
2016

39. Multisectoral climate impact hotspots in a warming world

Author

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

Abstract

The impacts of global climate change on different aspects of humanity’s diverse life-support systems are complex and often difficult to predict. To facilitate policy decisions on mitigation and adaptation strategies, it is necessary to understand, quantify, and synthesize these climate-change impacts, taking into account their uncertainties. Crucial to these decisions is an understanding of how impacts in different sectors overlap, as overlapping impacts increase exposure, lead to interactions of impacts, and are likely to raise adaptation pressure. As a first step we develop herein a framework to study coinciding impacts and identify regional exposure hotspots. This framework can then be used as a starting point for regional case studies on vulnerability and multifaceted adaptation strategies. We consider impacts related to water, agriculture, ecosystems, and malaria at different levels of global warming. Multisectoral overlap starts to be seen robustly at a mean global warming of 3 °C above the 1980–2010 mean, with 11% of the world population subject to severe impacts in at least two of the four impact sectors at 4 °C. Despite these general conclusions, we find that uncertainty arising from the impact models is considerable, and larger than that from the climate models. In a low probability-high impact worst-case assessment, almost the whole inhabited world is at risk for multisectoral pressures. Hence, there is a pressing need for an increased research effort to develop a more comprehensive understanding of impacts, as well as for the development of policy measures under existing uncertainty.

Published
2014

40. Modelling chemistry in the nocturnal boundary layer above tropical rainforest and a generalised effective nocturnal ozone deposition velocity for sub-ppbv NOx conditions

Author

Pugh, Thomas A.M., Ryder, James, MacKenzie, A. Robert, Moller, Sarah J., Lee, James D., Helfter, Carole, Nemitz, Eiko, Lowe, Douglas, Hewitt, C. Nicholas, Pugh, Thomas A.M., Ryder, James, MacKenzie, A. Robert, Moller, Sarah J., Lee, James D., Helfter, Carole, Nemitz, Eiko, Lowe, Douglas, and Hewitt, C. Nicholas

Abstract

Measurements of atmospheric composition have been made over a remote rainforest landscape. A box model has previously been demonstrated to model the observed daytime chemistry well. However the box model is unable to explain the nocturnal measurements of relatively high [NO] and [O3], but relatively low observed [NO2]. It is shown that a one-dimensional (1-D) column model with simple O3-NOx chemistry and a simple representation of vertical transport is able to explain the observed nocturnal concentrations and predict the likely vertical profiles of these species in the nocturnal boundary layer (NBL). Concentrations of tracers carried over from the end of the night can affect the atmospheric chemistry of the following day. To ascertain the anomaly introduced by using the box model to represent the NBL, vertically-averaged NBL concentrations at the end of the night are compared between the 1-D model and the box model. It is found that, under low to medium [NOx] conditions (NOx < 1 ppbv), a simple parametrisation can be used to modify the box model deposition velocity of ozone, in order to achieve good agreement between the box and 1-D models for these end-of-night concentrations of NOx and O3. This parametrisation would could also be used in global climate-chemistry models with limited vertical resolution near the surface. Box-model results for the following day differ substantially if this effective nocturnal deposition velocity for ozone is implemented; for instance, there is a 9% increase in the following days peak ozone concentration. However under medium to high [NOx] conditions (NOx > 1 ppbv), the effect on the chemistry due to the vertical distribution of the species means no box model can adequately represent chemistry in the NBL without modifying reaction rate coefficients.

Published
2010

41. Land use change and El Niño-Southern Oscillation drive decadal carbon balance shifts in Southeast Asia

Author

Kondo, Masayuki, Ichii, Kazuhito, Patra, Prabir K., Canadell, Joseph G., Poulter, Benjamin, Sitch, Stephen, Calle, Leonardo, Liu, Yi Y., Van Dijk, Albert I. J. M., Saeki, Tazu, Saigusa, Nobuko, Friedlingstein, Pierre, Arneth, Almuth, Harper, Anna B., Jain, Atul K., Kato, Etsushi, Koven, Charles D., Li, Fang, Pugh, Thomas A.M., Zaehle, Sönke, Wiltshire, Andy J., Chevallier, Frédéric, Maki, Takashi, Nakamura, Takashi, Niwa, Yosuke, and Rödenbeck, Christian

Subjects
13. Climate action, 15. Life on land

42. State-of-the-art global models underestimate impacts from climate extremes

Author

Schewe, Jacob, Gosling, Simon N., Reyer, Christopher P.O., Zhao, Fang, Ciais, Philippe, Elliott, Joshua, François, Louis, Huber, Veronika, Lotze, Heike, Seneviratne, Sonia I., van Vliet, Michelle T.H., Vautard, Robert, Wada, Yoshihide, Breuer, Lutz, Büchner, Matthias, Carozza, David A., Chang, Jinfeng, Coll, Marta, Deryng, Delphine, De Wit, Allard, Eddy, Tyler D., Folberth, Christian, Frieler, Katja, Friend, Andrew D., Gerten, Dieter, Gudmundsson, Lukas, Hanasaki, Naota, Ito, Akihiko, Khabarov, Nikolay, Kim, Hyungjun, Lawrence, Peter J., Morfopoulos, Catherine, Müller, Christoph, Müller Schmied, Hannes, Orth, René, Pokhrel, Yadu, Pugh, Thomas A.M., Sakurai, Gen, Satoh, Yusuke, Schmid, Erwin, Stacke, Tobias, Steenbeek, Jeroen, Steinkamp, Joerg, Tang, Qiuhong, Tian, Hanqin, Tittensor, Derek P., Volkholz, Jan, Wang, Xuhui, and Warszawski, Lila

Subjects
13. Climate action, 15. Life on land
Abstract

Global impact models represent process-level understanding of how natural and human systems may be affected by climate change. Their projections are used in integrated assessments of climate change. Here we test, for the first time, systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions. Using the 2003 European heat wave and drought as a historical analogue for comparable events in the future, we find that a majority of models underestimate the extremeness of impacts in important sectors such as agriculture, terrestrial ecosystems, and heat-related human mortality, while impacts on water resources and hydropower are overestimated in some river basins; and the spread across models is often large. This has important implications for economic assessments of climate change impacts that rely on these models. It also means that societal risks from future extreme events may be greater than previously thought., Nature Communications, 10 (1), ISSN:2041-1723

43. Impacts of land-use history on the recovery of ecosystems after agricultural abandonment

Author

Krause, Andreas, Pugh, Thomas A.M., Bayer, Anita D., Lindeskog, Mats, and Arneth, Almut

Subjects
13. Climate action, 15. Life on land
Abstract

Land-use changes have been shown to have large effects on climate and biogeochemical cycles, but so far most studies have focused on the effects of conversion of natural vegetation to croplands and pastures. By contrast, relatively little is known about the long-term influence of past agriculture on vegetation regrowth and carbon sequestration following land abandonment. We used the LPJ-GUESS dynamic vegetation model to study the legacy effects of different land-use histories (in terms of type and duration) across a range of ecosystems. To this end, we performed six idealized simulations for Europe and Africa in which we made a transition from natural vegetation to either pasture or cropland, followed by a transition back to natural vegetation after 20, 60 or 100 years. The simulations identified substantial differences in recovery trajectories of four key variables (vegetation composition, vegetation carbon, soil carbon, net biome productivity) after agricultural cessation. Vegetation carbon and composition typically recovered faster than soil carbon in subtropical, temperate and boreal regions, and vice versa in the tropics. While the effects of different land-use histories on recovery periods of soil carbon stocks often differed by centuries across our simulations, differences in recovery times across simulations were typically small for net biome productivity (a few decades) and modest for vegetation carbon and composition (several decades). Spatially, we found the greatest sensitivity of recovery times to prior land use in boreal forests and subtropical grasslands, where post-agricultural productivity was strongly affected by prior land management. Our results suggest that land-use history is a relevant factor affecting ecosystems long after agricultural cessation, and it should be considered not only when assessing historical or future changes in simulations of the terrestrial carbon cycle but also when establishing long-term monitoring networks and interpreting data derived therefrom, including analysis of a broad range of ecosystem properties or local climate effects related to land cover changes.

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