Articles | Volume 8, issue 4
https://doi.org/10.5194/esd-8-977-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/esd-8-977-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Review
| Highlight paper
| 
08 Nov 2017
Review | Highlight paper |
| 08 Nov 2017
Towards representing human behavior and decision making in Earth system models – an overview of techniques and approaches
Finn Müller-Hansen
CORRESPONDING AUTHOR
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Department of Physics, Humboldt University Berlin, Newtonstraße 15, 12489 Berlin, Germany
Maja Schlüter
Stockholm Resilience Centre, Stockholm University, Kräftriket 2B, 114 19 Stockholm, Sweden
Michael Mäs
Department of Sociology and ICS, University of Groningen, Grote Rozenstraat 31, 9712 TG Groningen, the Netherlands
Jonathan F. Donges
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Stockholm Resilience Centre, Stockholm University, Kräftriket 2B, 114 19 Stockholm, Sweden
Jakob J. Kolb
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Department of Physics, Humboldt University Berlin, Newtonstraße 15, 12489 Berlin, Germany
Kirsten Thonicke
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Jobst Heitzig
Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, P.O. Box 60 12 03, 14412 Potsdam, Germany
Related authors
Jonathan F. Donges, Jobst Heitzig, Wolfram Barfuss, Marc Wiedermann, Johannes A. Kassel, Tim Kittel, Jakob J. Kolb, Till Kolster, Finn Müller-Hansen, Ilona M. Otto, Kilian B. Zimmerer, and Wolfgang Lucht
Earth Syst. Dynam., 11, 395–413, https://doi.org/10.5194/esd-11-395-2020, https://doi.org/10.5194/esd-11-395-2020, 2020
Short summary
Short summary
We present an open-source software framework for developing so-called
world–Earth modelsthat link physical, chemical and biological processes with social, economic and cultural processes to study the Earth system's future trajectories in the Anthropocene. Due to its modular structure, the software allows interdisciplinary studies of global change and sustainable development that combine stylized model components from Earth system science, climatology, economics, ecology and sociology.
Finn Müller-Hansen, Manoel F. Cardoso, Eloi L. Dalla-Nora, Jonathan F. Donges, Jobst Heitzig, Jürgen Kurths, and Kirsten Thonicke
Nonlin. Processes Geophys., 24, 113–123, https://doi.org/10.5194/npg-24-113-2017, https://doi.org/10.5194/npg-24-113-2017, 2017
Short summary
Short summary
Deforestation and subsequent land uses in the Brazilian Amazon have huge impacts on greenhouse gas emissions, local climate and biodiversity. To better understand these land-cover changes, we apply complex systems methods uncovering spatial patterns in regional transition probabilities between land-cover types, which we estimate using maps derived from satellite imagery. The results show clusters of similar land-cover dynamics and thus complement studies at the local scale.
E. Keith Smith, Marc Wiedermann, Jonathan F. Donges, Jobst Heitzig, and Ricarda Winkelmann
Earth Syst. Dynam., 16, 545–564, https://doi.org/10.5194/esd-16-545-2025, https://doi.org/10.5194/esd-16-545-2025, 2025
Short summary
Short summary
Social tipping dynamics have received recent attention as a potential mechanism for effective climate actions – yet how such tipping dynamics could unfold remains largely unquantified. We explore how social tipping processes can develop by enabling necessary conditions (exemplified by climate change concern) and increased perceptions of localized impacts (sea level rise). The likelihood of social tipping varies regionally, mostly along areas with the highest exposure to persistent risks.
Marie Brunel, Stephen Wirth, Markus Drüke, Kirsten Thonicke, Henrique Barbosa, Jens Heinke, and Susanne Rolinski
EGUsphere, https://doi.org/10.5194/egusphere-2025-922, https://doi.org/10.5194/egusphere-2025-922, 2025
This preprint is open for discussion and under review for Biogeosciences (BG).
Short summary
Short summary
Farmers often use fire to clear dead pasture biomass, impacting vegetation and soil nutrients. This study integrates fire management into a DGVM to assess its effects, focusing on Brazil. The results show that combining grazing and fire management reduces vegetation carbon and soil nitrogen over time. The research highlights the need to include these practices in models to improve pasture management assessments and calls for better data on fire usage and its long-term effects.
Luke Oberhagemann, Maik Billing, Werner von Bloh, Markus Drüke, Matthew Forrest, Simon P. K. Bowring, Jessica Hetzer, Jaime Ribalaygua Batalla, and Kirsten Thonicke
Geosci. Model Dev., 18, 2021–2050, https://doi.org/10.5194/gmd-18-2021-2025, https://doi.org/10.5194/gmd-18-2021-2025, 2025
Short summary
Short summary
Under climate change, the conditions necessary for wildfires to form are occurring more frequently in many parts of the world. To help predict how wildfires will change in future, global fire models are being developed. We analyze and further develop one such model, SPITFIRE. Our work identifies and corrects sources of substantial bias in the model that are important to the global fire modelling field. With this analysis and these developments, we help to provide a basis for future improvements.
Jordan P. Everall, Fabian Tschofenig, Jonathan F. Donges, and Ilona M. Otto
Earth Syst. Dynam., 16, 189–214, https://doi.org/10.5194/esd-16-189-2025, https://doi.org/10.5194/esd-16-189-2025, 2025
Short summary
Short summary
A social tipping process is a large change in a social group that can be started by few people. Does the 80/20 rule apply here? We see if this is the case for human social groups. We find that, if the social conditions allow, change occurs when around 25 % of people engage. While tipping can happen between 10 % and 43 %, most cases tip by 40 %. However, tipping is not guaranteed: when people are resistant, trusted friend groups and context-appropriate messaging help the process along.
Matthew Forrest, Jessica Hetzer, Maik Billing, Simon P. K. Bowring, Eric Kosczor, Luke Oberhagemann, Oliver Perkins, Dan Warren, Fátima Arrogante-Funes, Kirsten Thonicke, and Thomas Hickler
Biogeosciences, 21, 5539–5560, https://doi.org/10.5194/bg-21-5539-2024, https://doi.org/10.5194/bg-21-5539-2024, 2024
Short summary
Short summary
Climate change is causing an increase in extreme wildfires in Europe, but drivers of fire are not well understood, especially across different land cover types. We used statistical models with satellite data, climate data, and socioeconomic data to determine what affects burning in cropland and non-cropland areas of Europe. We found different drivers of burning in cropland burning vs. non-cropland to the point that some variables, e.g. population density, had the complete opposite effects.
Jamir Priesner, Boris Sakschewski, Maik Billing, Werner von Bloh, Sebastian Fiedler, Sarah Bereswill, Kirsten Thonicke, and Britta Tietjen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3066, https://doi.org/10.5194/egusphere-2024-3066, 2024
Short summary
Short summary
Our simulations suggest that increased drought frequencies lead to a drastic reduction in biomass in pine monoculture and mixed forest. Mixed forest eventually recovered, as long as drought frequencies was not too high. The higher resilience of mixed forests was due to higher adaptive capacity. After adaptation mixed forests were mainly composed of smaller, broad-leaved trees with higher wood density and slower growth.This would have strong implications for forestry and other ecosystem services.
Max Bechthold, Wolfram Barfuss, André Butz, Jannes Breier, Sara M. Constantino, Jobst Heitzig, Luana Schwarz, Sanam N. Vardag, and Jonathan F. Donges
EGUsphere, https://doi.org/10.5194/egusphere-2024-2924, https://doi.org/10.5194/egusphere-2024-2924, 2024
Short summary
Short summary
Social norms are a major influence on human behaviour. In natural resource use models, norms are often included in a simplistic way leading to "black or white" sustainability outcomes. We find that a dynamic representation of norms, including social groups, determines more nuanced states of the environment in a stylized model of resource use, while also defining the success of attempts to manage the system, suggesting the importance of well representing both in coupled models.
Viktoria Spaiser, Sirkku Juhola, Sara M. Constantino, Weisi Guo, Tabitha Watson, Jana Sillmann, Alessandro Craparo, Ashleigh Basel, John T. Bruun, Krishna Krishnamurthy, Jürgen Scheffran, Patricia Pinho, Uche T. Okpara, Jonathan F. Donges, Avit Bhowmik, Taha Yasseri, Ricardo Safra de Campos, Graeme S. Cumming, Hugues Chenet, Florian Krampe, Jesse F. Abrams, James G. Dyke, Stefanie Rynders, Yevgeny Aksenov, and Bryan M. Spears
Earth Syst. Dynam., 15, 1179–1206, https://doi.org/10.5194/esd-15-1179-2024, https://doi.org/10.5194/esd-15-1179-2024, 2024
Short summary
Short summary
In this paper, we identify potential negative social tipping points linked to Earth system destabilization and draw on related research to understand the drivers and likelihood of these negative social tipping dynamics, their potential effects on human societies and the Earth system, and the potential for cascading interactions and contribution to systemic risks.
Nico Wunderling, Anna S. von der Heydt, Yevgeny Aksenov, Stephen Barker, Robbin Bastiaansen, Victor Brovkin, Maura Brunetti, Victor Couplet, Thomas Kleinen, Caroline H. Lear, Johannes Lohmann, Rosa Maria Roman-Cuesta, Sacha Sinet, Didier Swingedouw, Ricarda Winkelmann, Pallavi Anand, Jonathan Barichivich, Sebastian Bathiany, Mara Baudena, John T. Bruun, Cristiano M. Chiessi, Helen K. Coxall, David Docquier, Jonathan F. Donges, Swinda K. J. Falkena, Ann Kristin Klose, David Obura, Juan Rocha, Stefanie Rynders, Norman Julius Steinert, and Matteo Willeit
Earth Syst. Dynam., 15, 41–74, https://doi.org/10.5194/esd-15-41-2024, https://doi.org/10.5194/esd-15-41-2024, 2024
Short summary
Short summary
This paper maps out the state-of-the-art literature on interactions between tipping elements relevant for current global warming pathways. We find indications that many of the interactions between tipping elements are destabilizing. This means that tipping cascades cannot be ruled out on centennial to millennial timescales at global warming levels between 1.5 and 2.0 °C or on shorter timescales if global warming surpasses 2.0 °C.
Stephen Björn Wirth, Arne Poyda, Friedhelm Taube, Britta Tietjen, Christoph Müller, Kirsten Thonicke, Anja Linstädter, Kai Behn, Sibyll Schaphoff, Werner von Bloh, and Susanne Rolinski
Biogeosciences, 21, 381–410, https://doi.org/10.5194/bg-21-381-2024, https://doi.org/10.5194/bg-21-381-2024, 2024
Short summary
Short summary
In dynamic global vegetation models (DGVMs), the role of functional diversity in forage supply and soil organic carbon storage of grasslands is not explicitly taken into account. We introduced functional diversity into the Lund Potsdam Jena managed Land (LPJmL) DGVM using CSR theory. The new model reproduced well-known trade-offs between plant traits and can be used to quantify the role of functional diversity in climate change mitigation using different functional diversity scenarios.
Sina Loriani, Yevgeny Aksenov, David Armstrong McKay, Govindasamy Bala, Andreas Born, Cristiano M. Chiessi, Henk Dijkstra, Jonathan F. Donges, Sybren Drijfhout, Matthew H. England, Alexey V. Fedorov, Laura Jackson, Kai Kornhuber, Gabriele Messori, Francesco Pausata, Stefanie Rynders, Jean-Baptiste Salée, Bablu Sinha, Steven Sherwood, Didier Swingedouw, and Thejna Tharammal
EGUsphere, https://doi.org/10.5194/egusphere-2023-2589, https://doi.org/10.5194/egusphere-2023-2589, 2023
Short summary
Short summary
In this work, we draw on paleoreords, observations and modelling studies to review tipping points in the ocean overturning circulations, monsoon systems and global atmospheric circulations. We find indications for tipping in the ocean overturning circulations and the West African monsoon, with potentially severe impacts on the Earth system and humans. Tipping in the other considered systems is considered conceivable but currently not sufficiently supported by evidence.
Jenny Niebsch, Werner von Bloh, Kirsten Thonicke, and Ronny Ramlau
Geosci. Model Dev., 16, 17–33, https://doi.org/10.5194/gmd-16-17-2023, https://doi.org/10.5194/gmd-16-17-2023, 2023
Short summary
Short summary
The impacts of climate change require strategies for climate adaptation. Dynamic global vegetation models (DGVMs) are used to study the effects of multiple processes in the biosphere under climate change. There is a demand for a better computational performance of the models. In this paper, the photosynthesis model in the Lund–Potsdam–Jena managed Land DGVM (4.0.002) was examined. We found a better numerical solution of a nonlinear equation. A significant run time reduction was possible.
Phillip Papastefanou, Christian S. Zang, Zlatan Angelov, Aline Anderson de Castro, Juan Carlos Jimenez, Luiz Felipe Campos De Rezende, Romina C. Ruscica, Boris Sakschewski, Anna A. Sörensson, Kirsten Thonicke, Carolina Vera, Nicolas Viovy, Celso Von Randow, and Anja Rammig
Biogeosciences, 19, 3843–3861, https://doi.org/10.5194/bg-19-3843-2022, https://doi.org/10.5194/bg-19-3843-2022, 2022
Short summary
Short summary
The Amazon rainforest has been hit by multiple severe drought events. In this study, we assess the severity and spatial extent of the extreme drought years 2005, 2010 and 2015/16 in the Amazon. Using nine different precipitation datasets and three drought indicators we find large differences in drought stress across the Amazon region. We conclude that future studies should use multiple rainfall datasets and drought indicators when estimating the impact of drought stress in the Amazon region.
Maria Zeitz, Jan M. Haacker, Jonathan F. Donges, Torsten Albrecht, and Ricarda Winkelmann
Earth Syst. Dynam., 13, 1077–1096, https://doi.org/10.5194/esd-13-1077-2022, https://doi.org/10.5194/esd-13-1077-2022, 2022
Short summary
Short summary
The stability of the Greenland Ice Sheet under global warming is crucial. Here, using PISM, we study how the interplay of feedbacks between the ice sheet, the atmosphere and solid Earth affects the long-term response of the Greenland Ice Sheet under constant warming. Our findings suggest four distinct dynamic regimes of the Greenland Ice Sheet on the route to destabilization under global warming – from recovery via quasi-periodic oscillations in ice volume to ice sheet collapse.
Jonathan F. Donges, Wolfgang Lucht, Sarah E. Cornell, Jobst Heitzig, Wolfram Barfuss, Steven J. Lade, and Maja Schlüter
Earth Syst. Dynam., 12, 1115–1137, https://doi.org/10.5194/esd-12-1115-2021, https://doi.org/10.5194/esd-12-1115-2021, 2021
Boris Sakschewski, Werner von Bloh, Markus Drüke, Anna Amelia Sörensson, Romina Ruscica, Fanny Langerwisch, Maik Billing, Sarah Bereswill, Marina Hirota, Rafael Silva Oliveira, Jens Heinke, and Kirsten Thonicke
Biogeosciences, 18, 4091–4116, https://doi.org/10.5194/bg-18-4091-2021, https://doi.org/10.5194/bg-18-4091-2021, 2021
Short summary
Short summary
This study shows how local adaptations of tree roots across tropical and sub-tropical South America explain patterns of biome distribution, productivity and evapotranspiration on this continent. By allowing for high diversity of tree rooting strategies in a dynamic global vegetation model (DGVM), we are able to mechanistically explain patterns of mean rooting depth and the effects on ecosystem functions. The approach can advance DGVMs and Earth system models.
Markus Drüke, Werner von Bloh, Stefan Petri, Boris Sakschewski, Sibyll Schaphoff, Matthias Forkel, Willem Huiskamp, Georg Feulner, and Kirsten Thonicke
Geosci. Model Dev., 14, 4117–4141, https://doi.org/10.5194/gmd-14-4117-2021, https://doi.org/10.5194/gmd-14-4117-2021, 2021
Short summary
Short summary
In this study, we couple the well-established and comprehensively validated state-of-the-art dynamic LPJmL5 global vegetation model to the CM2Mc coupled climate model (CM2Mc-LPJmL v.1.0). Several improvements to LPJmL5 were implemented to allow a fully functional biophysical coupling. The new climate model is able to capture important biospheric processes, including fire, mortality, permafrost, hydrological cycling and the the impacts of managed land (crop growth and irrigation).
Nico Wunderling, Jonathan F. Donges, Jürgen Kurths, and Ricarda Winkelmann
Earth Syst. Dynam., 12, 601–619, https://doi.org/10.5194/esd-12-601-2021, https://doi.org/10.5194/esd-12-601-2021, 2021
Short summary
Short summary
In the Earth system, climate tipping elements exist that can undergo qualitative changes in response to environmental perturbations. If triggered, this would result in severe consequences for the biosphere and human societies. We quantify the risk of tipping cascades using a conceptual but fully dynamic network approach. We uncover that the risk of tipping cascades under global warming scenarios is enormous and find that the continental ice sheets are most likely to initiate these failures.
Sebastian H. R. Rosier, Ronja Reese, Jonathan F. Donges, Jan De Rydt, G. Hilmar Gudmundsson, and Ricarda Winkelmann
The Cryosphere, 15, 1501–1516, https://doi.org/10.5194/tc-15-1501-2021, https://doi.org/10.5194/tc-15-1501-2021, 2021
Short summary
Short summary
Pine Island Glacier has contributed more to sea-level rise over the past decades than any other glacier in Antarctica. Ice-flow modelling studies have shown that it can undergo periods of rapid mass loss, but no study has shown that these future changes could cross a tipping point and therefore be effectively irreversible. Here, we assess the stability of Pine Island Glacier, quantifying the changes in ocean temperatures required to cross future tipping points using statistical methods.
Thomas A. M. Pugh, Tim Rademacher, Sarah L. Shafer, Jörg Steinkamp, Jonathan Barichivich, Brian Beckage, Vanessa Haverd, Anna Harper, Jens Heinke, Kazuya Nishina, Anja Rammig, Hisashi Sato, Almut Arneth, Stijn Hantson, Thomas Hickler, Markus Kautz, Benjamin Quesada, Benjamin Smith, and Kirsten Thonicke
Biogeosciences, 17, 3961–3989, https://doi.org/10.5194/bg-17-3961-2020, https://doi.org/10.5194/bg-17-3961-2020, 2020
Short summary
Short summary
The length of time that carbon remains in forest biomass is one of the largest uncertainties in the global carbon cycle. Estimates from six contemporary models found this time to range from 12.2 to 23.5 years for the global mean for 1985–2014. Future projections do not give consistent results, but 13 model-based hypotheses are identified, along with recommendations for pragmatic steps to test them using existing and novel observations, which would help to reduce large current uncertainty.
Jonathan F. Donges, Jobst Heitzig, Wolfram Barfuss, Marc Wiedermann, Johannes A. Kassel, Tim Kittel, Jakob J. Kolb, Till Kolster, Finn Müller-Hansen, Ilona M. Otto, Kilian B. Zimmerer, and Wolfgang Lucht
Earth Syst. Dynam., 11, 395–413, https://doi.org/10.5194/esd-11-395-2020, https://doi.org/10.5194/esd-11-395-2020, 2020
Short summary
Short summary
We present an open-source software framework for developing so-called
world–Earth modelsthat link physical, chemical and biological processes with social, economic and cultural processes to study the Earth system's future trajectories in the Anthropocene. Due to its modular structure, the software allows interdisciplinary studies of global change and sustainable development that combine stylized model components from Earth system science, climatology, economics, ecology and sociology.
Miguel D. Mahecha, Fabian Gans, Gunnar Brandt, Rune Christiansen, Sarah E. Cornell, Normann Fomferra, Guido Kraemer, Jonas Peters, Paul Bodesheim, Gustau Camps-Valls, Jonathan F. Donges, Wouter Dorigo, Lina M. Estupinan-Suarez, Victor H. Gutierrez-Velez, Martin Gutwin, Martin Jung, Maria C. Londoño, Diego G. Miralles, Phillip Papastefanou, and Markus Reichstein
Earth Syst. Dynam., 11, 201–234, https://doi.org/10.5194/esd-11-201-2020, https://doi.org/10.5194/esd-11-201-2020, 2020
Short summary
Short summary
The ever-growing availability of data streams on different subsystems of the Earth brings unprecedented scientific opportunities. However, researching a data-rich world brings novel challenges. We present the concept of
Earth system data cubesto study the complex dynamics of multiple climate and ecosystem variables across space and time. Using a series of example studies, we highlight the potential of effectively considering the full multivariate nature of processes in the Earth system.
Kirsten Thonicke, Fanny Langerwisch, Matthias Baumann, Pedro J. Leitão, Tomáš Václavík, Ane Alencar, Margareth Simões, Simon Scheiter, Liam Langan, Mercedes Bustamante, Ignacio Gasparri, Marina Hirota, Jan Börner, Raoni Rajao, Britaldo Soares-Filho, Alberto Yanosky, José-Manuel Ochoa-Quinteiro, Lucas Seghezzo, Georgina Conti, and Anne Cristina de la Vega-Leinert
Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-221, https://doi.org/10.5194/bg-2019-221, 2019
Publication in BG not foreseen
Short summary
Short summary
Tropical dry forests and savannas harbor unique biodiversity and provide critical ecosystem services (ES), yet they are under severe pressure globally. We need to improve our understanding of how and when this pressure provokes tipping points in biodiversity and the associated social-ecological systems. We propose an approach to investigate how drivers leading to natural vegetation decline trigger biodiversity tipping and illustrate it using the example of the Dry Diagonal in South America.
Anja Rammig, Jens Heinke, Florian Hofhansl, Hans Verbeeck, Timothy R. Baker, Bradley Christoffersen, Philippe Ciais, Hannes De Deurwaerder, Katrin Fleischer, David Galbraith, Matthieu Guimberteau, Andreas Huth, Michelle Johnson, Bart Krujit, Fanny Langerwisch, Patrick Meir, Phillip Papastefanou, Gilvan Sampaio, Kirsten Thonicke, Celso von Randow, Christian Zang, and Edna Rödig
Geosci. Model Dev., 11, 5203–5215, https://doi.org/10.5194/gmd-11-5203-2018, https://doi.org/10.5194/gmd-11-5203-2018, 2018
Short summary
Short summary
We propose a generic approach for a pixel-to-point comparison applicable for evaluation of models and remote-sensing products. We provide statistical measures accounting for the uncertainty in ecosystem variables. We demonstrate our approach by comparing simulated values of aboveground biomass, woody productivity and residence time of woody biomass from four dynamic global vegetation models (DGVMs) with measured inventory data from permanent plots in the Amazon rainforest.
Steven J. Lade, Jonathan F. Donges, Ingo Fetzer, John M. Anderies, Christian Beer, Sarah E. Cornell, Thomas Gasser, Jon Norberg, Katherine Richardson, Johan Rockström, and Will Steffen
Earth Syst. Dynam., 9, 507–523, https://doi.org/10.5194/esd-9-507-2018, https://doi.org/10.5194/esd-9-507-2018, 2018
Short summary
Short summary
Around half of the carbon that humans emit into the atmosphere each year is taken up on land (by trees) and in the ocean (by absorption). We construct a simple model of carbon uptake that, unlike the complex models that are usually used, can be analysed mathematically. Our results include that changes in atmospheric carbon may affect future carbon uptake more than changes in climate. Our simple model could also study mechanisms that are currently too uncertain for complex models.
Sibyll Schaphoff, Matthias Forkel, Christoph Müller, Jürgen Knauer, Werner von Bloh, Dieter Gerten, Jonas Jägermeyr, Wolfgang Lucht, Anja Rammig, Kirsten Thonicke, and Katharina Waha
Geosci. Model Dev., 11, 1377–1403, https://doi.org/10.5194/gmd-11-1377-2018, https://doi.org/10.5194/gmd-11-1377-2018, 2018
Short summary
Short summary
Here we provide a comprehensive evaluation of the now launched version 4.0 of the LPJmL biosphere, water, and agricultural model. The article is the second part to a comprehensive description of the LPJmL4 model. We have evaluated the model against various datasets of satellite observations, agricultural statistics, and in situ measurements by applying a range of metrics. We are able to show that the LPJmL4 model simulates many parameters and relations reasonably.
Susanne Rolinski, Christoph Müller, Jens Heinke, Isabelle Weindl, Anne Biewald, Benjamin Leon Bodirsky, Alberte Bondeau, Eltje R. Boons-Prins, Alexander F. Bouwman, Peter A. Leffelaar, Johnny A. te Roller, Sibyll Schaphoff, and Kirsten Thonicke
Geosci. Model Dev., 11, 429–451, https://doi.org/10.5194/gmd-11-429-2018, https://doi.org/10.5194/gmd-11-429-2018, 2018
Short summary
Short summary
One-third of the global land area is covered with grasslands which are grazed by or mowed for livestock feed. These areas contribute significantly to the carbon capture from the atmosphere when managed sensibly. To assess the effect of this management, we included different options of grazing and mowing into the global model LPJmL 3.6. We found in polar regions even low grazing pressure leads to soil carbon loss whereas in temperate regions up to 1.4 livestock units per hectare can be sustained.
Matthias Forkel, Wouter Dorigo, Gitta Lasslop, Irene Teubner, Emilio Chuvieco, and Kirsten Thonicke
Geosci. Model Dev., 10, 4443–4476, https://doi.org/10.5194/gmd-10-4443-2017, https://doi.org/10.5194/gmd-10-4443-2017, 2017
Short summary
Short summary
Wildfires affect infrastructures, vegetation, and the atmosphere. However, it is unclear how fires should be accurately represented in global vegetation models. We introduce here a new flexible data-driven fire modelling approach that allows us to explore sensitivities of burned areas to satellite and climate datasets. Our results suggest combining observations with data-driven and process-oriented fire models to better understand the role of fires in the Earth system.
Katja Frieler, Stefan Lange, Franziska Piontek, Christopher P. O. Reyer, Jacob Schewe, Lila Warszawski, Fang Zhao, Louise Chini, Sebastien Denvil, Kerry Emanuel, Tobias Geiger, Kate Halladay, George Hurtt, Matthias Mengel, Daisuke Murakami, Sebastian Ostberg, Alexander Popp, Riccardo Riva, Miodrag Stevanovic, Tatsuo Suzuki, Jan Volkholz, Eleanor Burke, Philippe Ciais, Kristie Ebi, Tyler D. Eddy, Joshua Elliott, Eric Galbraith, Simon N. Gosling, Fred Hattermann, Thomas Hickler, Jochen Hinkel, Christian Hof, Veronika Huber, Jonas Jägermeyr, Valentina Krysanova, Rafael Marcé, Hannes Müller Schmied, Ioanna Mouratiadou, Don Pierson, Derek P. Tittensor, Robert Vautard, Michelle van Vliet, Matthias F. Biber, Richard A. Betts, Benjamin Leon Bodirsky, Delphine Deryng, Steve Frolking, Chris D. Jones, Heike K. Lotze, Hermann Lotze-Campen, Ritvik Sahajpal, Kirsten Thonicke, Hanqin Tian, and Yoshiki Yamagata
Geosci. Model Dev., 10, 4321–4345, https://doi.org/10.5194/gmd-10-4321-2017, https://doi.org/10.5194/gmd-10-4321-2017, 2017
Short summary
Short summary
This paper describes the simulation scenario design for the next phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), which is designed to facilitate a contribution to the scientific basis for the IPCC Special Report on the impacts of 1.5 °C global warming. ISIMIP brings together over 80 climate-impact models, covering impacts on hydrology, biomes, forests, heat-related mortality, permafrost, tropical cyclones, fisheries, agiculture, energy, and coastal infrastructure.
Miguel D. Mahecha, Fabian Gans, Sebastian Sippel, Jonathan F. Donges, Thomas Kaminski, Stefan Metzger, Mirco Migliavacca, Dario Papale, Anja Rammig, and Jakob Zscheischler
Biogeosciences, 14, 4255–4277, https://doi.org/10.5194/bg-14-4255-2017, https://doi.org/10.5194/bg-14-4255-2017, 2017
Short summary
Short summary
We investigate the likelihood of ecological in situ networks to detect and monitor the impact of extreme events in the terrestrial biosphere.
Wolfram Barfuss, Jonathan F. Donges, Marc Wiedermann, and Wolfgang Lucht
Earth Syst. Dynam., 8, 255–264, https://doi.org/10.5194/esd-8-255-2017, https://doi.org/10.5194/esd-8-255-2017, 2017
Short summary
Short summary
Human societies depend on the resources ecosystems provide. We study this coevolutionary relationship by utilizing a stylized model of resource users on a social network. This model demonstrates that social–cultural processes can have a profound influence on the environmental state, such as determining whether the resources collapse from overuse or not. This suggests that social–cultural processes should receive more attention in the modeling of sustainability transitions and the Earth system.
Matthieu Guimberteau, Philippe Ciais, Agnès Ducharne, Juan Pablo Boisier, Ana Paula Dutra Aguiar, Hester Biemans, Hannes De Deurwaerder, David Galbraith, Bart Kruijt, Fanny Langerwisch, German Poveda, Anja Rammig, Daniel Andres Rodriguez, Graciela Tejada, Kirsten Thonicke, Celso Von Randow, Rita C. S. Von Randow, Ke Zhang, and Hans Verbeeck
Hydrol. Earth Syst. Sci., 21, 1455–1475, https://doi.org/10.5194/hess-21-1455-2017, https://doi.org/10.5194/hess-21-1455-2017, 2017
Finn Müller-Hansen, Manoel F. Cardoso, Eloi L. Dalla-Nora, Jonathan F. Donges, Jobst Heitzig, Jürgen Kurths, and Kirsten Thonicke
Nonlin. Processes Geophys., 24, 113–123, https://doi.org/10.5194/npg-24-113-2017, https://doi.org/10.5194/npg-24-113-2017, 2017
Short summary
Short summary
Deforestation and subsequent land uses in the Brazilian Amazon have huge impacts on greenhouse gas emissions, local climate and biodiversity. To better understand these land-cover changes, we apply complex systems methods uncovering spatial patterns in regional transition probabilities between land-cover types, which we estimate using maps derived from satellite imagery. The results show clusters of similar land-cover dynamics and thus complement studies at the local scale.
Fanny Langerwisch, Ariane Walz, Anja Rammig, Britta Tietjen, Kirsten Thonicke, and Wolfgang Cramer
Earth Syst. Dynam., 7, 953–968, https://doi.org/10.5194/esd-7-953-2016, https://doi.org/10.5194/esd-7-953-2016, 2016
Short summary
Short summary
Amazonia is heavily impacted by climate change and deforestation. During annual flooding terrigenous material is imported to the river, converted and finally exported to the ocean or the atmosphere. Changes in the vegetation alter therefore riverine carbon dynamics. Our results show that due to deforestation organic carbon amount will strongly decrease both in the river and exported to the ocean, while inorganic carbon amounts will increase, in the river as well as exported to the atmosphere.
Vera Heck, Jonathan F. Donges, and Wolfgang Lucht
Earth Syst. Dynam., 7, 783–796, https://doi.org/10.5194/esd-7-783-2016, https://doi.org/10.5194/esd-7-783-2016, 2016
Short summary
Short summary
We assess the co-evolutionary dynamics of the Earth's carbon cycle and societal interventions through terrestrial carbon dioxide removal (tCDR) with a conceptual model in a planetary boundary context. The focus on one planetary boundary alone may lead to navigating the Earth system out of the safe operating space due to transgression of other boundaries. The success of tCDR depends on the degree of anticipation of climate change, the potential tCDR rate and the underlying emission pathway.
Jonatan F. Siegmund, Marc Wiedermann, Jonathan F. Donges, and Reik V. Donner
Biogeosciences, 13, 5541–5555, https://doi.org/10.5194/bg-13-5541-2016, https://doi.org/10.5194/bg-13-5541-2016, 2016
Short summary
Short summary
In this study we systematically quantify simultaneities between meteorological extremes and the timing of flowering of four shrub species across Germany by using event coincidence analysis. Our study confirms previous findings of experimental studies, highlighting the impact of early spring temperatures on the flowering of the investigated plants. Additionally, the analysis reveals statistically significant indications of an influence of temperature extremes in the fall preceding the flowering.
F. Langerwisch, A. Walz, A. Rammig, B. Tietjen, K. Thonicke, and W. Cramer
Earth Syst. Dynam., 7, 559–582, https://doi.org/10.5194/esd-7-559-2016, https://doi.org/10.5194/esd-7-559-2016, 2016
Short summary
Short summary
In Amazonia, carbon fluxes are considerably influenced by annual flooding. We applied the newly developed model RivCM to several climate change scenarios to estimate potential changes in riverine carbon. We find that climate change causes substantial changes in riverine organic and inorganic carbon, as well as changes in carbon exported to the atmosphere and ocean. Such changes could have local and regional impacts on the carbon budget of the whole Amazon basin and parts of the Atlantic Ocean.
S. Sippel, F. E. L. Otto, M. Forkel, M. R. Allen, B. P. Guillod, M. Heimann, M. Reichstein, S. I. Seneviratne, K. Thonicke, and M. D. Mahecha
Earth Syst. Dynam., 7, 71–88, https://doi.org/10.5194/esd-7-71-2016, https://doi.org/10.5194/esd-7-71-2016, 2016
Short summary
Short summary
We introduce a novel technique to bias correct climate model output for impact simulations that preserves its physical consistency and multivariate structure. The methodology considerably improves the representation of extremes in climatic variables relative to conventional bias correction strategies. Illustrative simulations of biosphere–atmosphere carbon and water fluxes with a biosphere model (LPJmL) show that the novel technique can be usefully applied to drive climate impact models.
J. Heitzig, T. Kittel, J. F. Donges, and N. Molkenthin
Earth Syst. Dynam., 7, 21–50, https://doi.org/10.5194/esd-7-21-2016, https://doi.org/10.5194/esd-7-21-2016, 2016
Short summary
Short summary
The debate about a safe and just operating space for humanity and the possible pathways towards and within it requires an analysis of the inherent dynamics of the Earth system and of the options for influencing its evolution. We present and illustrate with examples a conceptual framework for performing such an analysis not in a quantitative, optimizing mode, but in a qualitative way that emphasizes the main decision dilemmas that one may face in the sustainable management of the Earth system.
T. Nocke, S. Buschmann, J. F. Donges, N. Marwan, H.-J. Schulz, and C. Tominski
Nonlin. Processes Geophys., 22, 545–570, https://doi.org/10.5194/npg-22-545-2015, https://doi.org/10.5194/npg-22-545-2015, 2015
Short summary
Short summary
The paper reviews the available visualisation techniques and tools for the visual analysis of geo-physical climate networks. The results from a questionnaire with experts from non-linear physics are presented, and the paper surveys recent developments from information visualisation and cartography with respect to their applicability for visual climate network analytics. Several case studies based on own solutions illustrate the potentials of state-of-the-art network visualisation technology.
A. Y. Sun, J. Chen, and J. Donges
Nonlin. Processes Geophys., 22, 433–446, https://doi.org/10.5194/npg-22-433-2015, https://doi.org/10.5194/npg-22-433-2015, 2015
Short summary
Short summary
Terrestrial water storage (TWS) plays a key role in global water and energy cycles. This work applies complex climate networks to analyzing spatial patterns in TWS. A comparative analysis is conducted using a remotely sensed (GRACE) and a model-generated TWS data set. Our results reveal hotspots of TWS anomalies around the global land surfaces. Prospects are offered on using network connectivity as constraints to further improve current global land surface models.
J. F. Donges, R. V. Donner, N. Marwan, S. F. M. Breitenbach, K. Rehfeld, and J. Kurths
Clim. Past, 11, 709–741, https://doi.org/10.5194/cp-11-709-2015, https://doi.org/10.5194/cp-11-709-2015, 2015
Short summary
Short summary
Paleoclimate records from cave deposits allow the reconstruction of Holocene dynamics of the Asian monsoon system, an important tipping element in Earth's climate. Employing recently developed techniques of nonlinear time series analysis reveals several robust and continental-scale regime shifts in the complexity of monsoonal variability. These regime shifts might have played an important role as drivers of migration, cultural change, and societal collapse during the past 10,000 years.
C. Yue, P. Ciais, P. Cadule, K. Thonicke, and T. T. van Leeuwen
Geosci. Model Dev., 8, 1321–1338, https://doi.org/10.5194/gmd-8-1321-2015, https://doi.org/10.5194/gmd-8-1321-2015, 2015
Short summary
Short summary
We conducted parallel simulations using a global land surface model, with and without fires being included, respectively. When the anthropogenic land cover change fire is excluded, we find that natural wildfires have reduced the global land carbon uptake by 0.3Pg C per year over 1901-2012. This is equivalent to 20% of the land carbon uptake in a world without fire. This fire-induced reduction in carbon uptake could be partly explained by climate variability, in particular the ENSO events.
S. Rolinski, A. Rammig, A. Walz, W. von Bloh, M. van Oijen, and K. Thonicke
Biogeosciences, 12, 1813–1831, https://doi.org/10.5194/bg-12-1813-2015, https://doi.org/10.5194/bg-12-1813-2015, 2015
Short summary
Short summary
Extreme weather events can but do not have to cause extreme ecosystem response. Here, we focus on hazardous ecosystem behaviour and identify coinciding weather conditions.
We use a simple probabilistic risk assessment and apply it to terrestrial ecosystems, defining a hazard as negative net biome productivity. In Europe, ecosystems are vulnerable to drought in the Mediterranean and temperate region, whereas vulnerability in Scandinavia is not caused by water shortages.
A. Rammig, M. Wiedermann, J. F. Donges, F. Babst, W. von Bloh, D. Frank, K. Thonicke, and M. D. Mahecha
Biogeosciences, 12, 373–385, https://doi.org/10.5194/bg-12-373-2015, https://doi.org/10.5194/bg-12-373-2015, 2015
D. C. Zemp, C.-F. Schleussner, H. M. J. Barbosa, R. J. van der Ent, J. F. Donges, J. Heinke, G. Sampaio, and A. Rammig
Atmos. Chem. Phys., 14, 13337–13359, https://doi.org/10.5194/acp-14-13337-2014, https://doi.org/10.5194/acp-14-13337-2014, 2014
M. Forkel, N. Carvalhais, S. Schaphoff, W. v. Bloh, M. Migliavacca, M. Thurner, and K. Thonicke
Biogeosciences, 11, 7025–7050, https://doi.org/10.5194/bg-11-7025-2014, https://doi.org/10.5194/bg-11-7025-2014, 2014
M. Van Oijen, J. Balkovi, C. Beer, D. R. Cameron, P. Ciais, W. Cramer, T. Kato, M. Kuhnert, R. Martin, R. Myneni, A. Rammig, S. Rolinski, J.-F. Soussana, K. Thonicke, M. Van der Velde, and L. Xu
Biogeosciences, 11, 6357–6375, https://doi.org/10.5194/bg-11-6357-2014, https://doi.org/10.5194/bg-11-6357-2014, 2014
Short summary
Short summary
We use a new risk analysis method, and six vegetation models, to analyse how climate change may alter drought risks in European ecosystems. The conclusions are (1) drought will pose increasing risks to productivity in the Mediterranean area; (2) this is because severe droughts will become more frequent, not because ecosystems will become more vulnerable; (3) future C sequestration will be at risk because carbon gain in primary productivity will be more affected than carbon loss in respiration.
C. Yue, P. Ciais, P. Cadule, K. Thonicke, S. Archibald, B. Poulter, W. M. Hao, S. Hantson, F. Mouillot, P. Friedlingstein, F. Maignan, and N. Viovy
Geosci. Model Dev., 7, 2747–2767, https://doi.org/10.5194/gmd-7-2747-2014, https://doi.org/10.5194/gmd-7-2747-2014, 2014
Short summary
Short summary
ORCHIDEE-SPITFIRE model could moderately capture the decadal trend and variation of burned area during the 20th century, and the spatial and temporal patterns of contemporary vegetation fires. The model has a better performance in simulating fires for regions dominated by climate-driven fires, such as boreal forests. However, it has limited capability to reproduce the infrequent but important large fires in different ecosystems, where urgent model improvement is needed in the future.
B. Goswami, J. Heitzig, K. Rehfeld, N. Marwan, A. Anoop, S. Prasad, and J. Kurths
Nonlin. Processes Geophys., 21, 1093–1111, https://doi.org/10.5194/npg-21-1093-2014, https://doi.org/10.5194/npg-21-1093-2014, 2014
Short summary
Short summary
We present a new approach to estimating sedimentary proxy records along with the proxy uncertainty. We provide analytical expressions for the proxy record, while transparently propagating uncertainties from the ages to the proxy record. We represent proxies on an error-free, precise timescale. Our approach provides insight into the interrelations between proxy variability and the various uncertainties. We demonstrate our method with synthetic examples and proxy data from the Lonar lake in India.
Related subject area
Dynamics of the Earth system: models
Stable stadial and interstadial states of the last glacial's climate identified in a combined stable water isotope and dust record from Greenland
The modelled climatic response to the 18.6-year lunar nodal cycle and its role in decadal temperature trends
The future of the El Niño–Southern Oscillation: using large ensembles to illuminate time-varying responses and inter-model differences
Regime-oriented causal model evaluation of Atlantic–Pacific teleconnections in CMIP6
Seasonal forecasting skill for the High Mountain Asia region in the Goddard Earth Observing System
Assessing sensitivities of climate model weighting to multiple methods, variables, and domains in the south-central United States
Global and northern-high-latitude net ecosystem production in the 21st century from CMIP6 experiments
Potential for bias in effective climate sensitivity from state-dependent energetic imbalance
Regional dynamical and statistical downscaling temperature, humidity and wind speed for the Beijing region under stratospheric aerosol injection geoengineering
Process-based estimate of global-mean sea-level changes in the Common Era
Present and future European heat wave magnitudes: climatologies, trends, and their associated uncertainties in GCM-RCM model chains
Improving the prediction of the Madden–Julian Oscillation of the ECMWF model by post-processing
Estimating the lateral transfer of organic carbon through the European river network using a land surface model
Effect of the Atlantic Meridional Overturning Circulation on atmospheric pCO2 variations
A methodology for the spatiotemporal identification of compound hazards: wind and precipitation extremes in Great Britain (1979–2019)
MESMER-M: an Earth system model emulator for spatially resolved monthly temperature
Evaluation of convection-permitting extreme precipitation simulations for the south of France
Agricultural management effects on mean and extreme temperature trends
Weakened impact of the Atlantic Niño on the future equatorial Atlantic and Guinea Coast rainfall
The fractional energy balance equation for climate projections through 2100
Climate change in the High Mountain Asia in CMIP6
The sensitivity of the El Niño–Southern Oscillation to volcanic aerosol spatial distribution in the MPI Grand Ensemble
Coupled regional Earth system modeling in the Baltic Sea region
Climate change projections of terrestrial primary productivity over the Hindu Kush Himalayan forests
Bookkeeping estimates of the net land-use change flux – a sensitivity study with the CMIP6 land-use dataset
Climate-controlled root zone parameters show potential to improve water flux simulations by land surface models
Space–time dependence of compound hot–dry events in the United States: assessment using a multi-site multi-variable weather generator
First assessment of the earth heat inventory within CMIP5 historical simulations
The thermal response of small and shallow lakes to climate change: new insights from 3D hindcast modelling
Labrador Sea subsurface density as a precursor of multidecadal variability in the North Atlantic: a multi-model study
How modelling paradigms affect simulated future land use change
Identifying meteorological drivers of extreme impacts: an application to simulated crop yields
Simulating compound weather extremes responsible for critical crop failure with stochastic weather generators
Characterisation of Atlantic meridional overturning hysteresis using Langevin dynamics
Evaluating the dependence structure of compound precipitation and wind speed extremes
Future sea level contribution from Antarctica inferred from CMIP5 model forcing and its dependence on precipitation ansatz
The extremely warm summer of 2018 in Sweden – set in a historical context
Effect of changing ocean circulation on deep ocean temperature in the last millennium
How large does a large ensemble need to be?
Reconstructing coupled time series in climate systems using three kinds of machine-learning methods
An investigation of weighting schemes suitable for incorporating large ensembles into multi-model ensembles
What could we learn about climate sensitivity from variability in the surface temperature record?
Using a nested single-model large ensemble to assess the internal variability of the North Atlantic Oscillation and its climatic implications for central Europe
Climate change in a conceptual atmosphere–phytoplankton model
Variability of surface climate in simulations of past and future
Statistical estimation of global surface temperature response to forcing under the assumption of temporal scaling
Emulating Earth system model temperatures with MESMER: from global mean temperature trajectories to grid-point-level realizations on land
A global semi-empirical glacial isostatic adjustment (GIA) model based on Gravity Recovery and Climate Experiment (GRACE) data
Improvement in the decadal prediction skill of the North Atlantic extratropical winter circulation through increased model resolution
Societal breakdown as an emergent property of large-scale behavioural models of land use change
Keno Riechers, Leonardo Rydin Gorjão, Forough Hassanibesheli, Pedro G. Lind, Dirk Witthaut, and Niklas Boers
Earth Syst. Dynam., 14, 593–607, https://doi.org/10.5194/esd-14-593-2023, https://doi.org/10.5194/esd-14-593-2023, 2023
Short summary
Short summary
Paleoclimate proxy records show that the North Atlantic climate repeatedly transitioned between two regimes during the last glacial interval. This study investigates a bivariate proxy record from a Greenland ice core which reflects past Greenland temperatures and large-scale atmospheric conditions. We reconstruct the underlying deterministic drift by estimating first-order Kramers–Moyal coefficients and identify two separate stable states in agreement with the aforementioned climatic regimes.
Manoj Joshi, Robert A. Hall, David P. Stevens, and Ed Hawkins
Earth Syst. Dynam., 14, 443–455, https://doi.org/10.5194/esd-14-443-2023, https://doi.org/10.5194/esd-14-443-2023, 2023
Short summary
Short summary
The 18.6-year lunar nodal cycle arises from variations in the angle of the Moon's orbital plane and affects ocean tides. In this work we use a climate model to examine the effect of this cycle on the ocean, surface, and atmosphere. The timing of anomalies is consistent with the so-called slowdown in global warming and has implications for when global temperatures will exceed 1.5 ℃ above pre-industrial levels. Regional anomalies have implications for seasonal climate areas such as Europe.
Nicola Maher, Robert C. Jnglin Wills, Pedro DiNezio, Jeremy Klavans, Sebastian Milinski, Sara C. Sanchez, Samantha Stevenson, Malte F. Stuecker, and Xian Wu
Earth Syst. Dynam., 14, 413–431, https://doi.org/10.5194/esd-14-413-2023, https://doi.org/10.5194/esd-14-413-2023, 2023
Short summary
Short summary
Understanding whether the El Niño–Southern Oscillation (ENSO) is likely to change in the future is important due to its widespread impacts. By using large ensembles, we can robustly isolate the time-evolving response of ENSO variability in 14 climate models. We find that ENSO variability evolves in a nonlinear fashion in many models and that there are large differences between models. These nonlinear changes imply that ENSO impacts may vary dramatically throughout the 21st century.
Soufiane Karmouche, Evgenia Galytska, Jakob Runge, Gerald A. Meehl, Adam S. Phillips, Katja Weigel, and Veronika Eyring
Earth Syst. Dynam., 14, 309–344, https://doi.org/10.5194/esd-14-309-2023, https://doi.org/10.5194/esd-14-309-2023, 2023
Short summary
Short summary
This study uses a causal discovery method to evaluate the ability of climate models to represent the interactions between the Atlantic multidecadal variability (AMV) and the Pacific decadal variability (PDV). The approach and findings in this study present a powerful methodology that can be applied to a number of environment-related topics, offering tremendous insights to improve the understanding of the complex Earth system and the state of the art of climate modeling.
Elias C. Massoud, Lauren Andrews, Rolf Reichle, Andrea Molod, Jongmin Park, Sophie Ruehr, and Manuela Girotto
Earth Syst. Dynam., 14, 147–171, https://doi.org/10.5194/esd-14-147-2023, https://doi.org/10.5194/esd-14-147-2023, 2023
Short summary
Short summary
In this study, we benchmark the forecast skill of the NASA’s Goddard Earth Observing System subseasonal-to-seasonal (GEOS-S2S version 2) hydrometeorological forecasts in the High Mountain Asia (HMA) region. Hydrometeorological forecast skill is dependent on the forecast lead time, the memory of the variable within the physical system, and the validation dataset used. Overall, these results benchmark the GEOS-S2S system’s ability to forecast HMA hydrometeorology on the seasonal timescale.
Adrienne M. Wootten, Elias C. Massoud, Duane E. Waliser, and Huikyo Lee
Earth Syst. Dynam., 14, 121–145, https://doi.org/10.5194/esd-14-121-2023, https://doi.org/10.5194/esd-14-121-2023, 2023
Short summary
Short summary
Climate projections and multi-model ensemble weighting are increasingly used for climate assessments. This study examines the sensitivity of projections to multi-model ensemble weighting strategies in the south-central United States. Model weighting and ensemble means are sensitive to the domain and variable used. There are numerous findings regarding the improvement in skill with model weighting and the sensitivity associated with various strategies.
Han Qiu, Dalei Hao, Yelu Zeng, Xuesong Zhang, and Min Chen
Earth Syst. Dynam., 14, 1–16, https://doi.org/10.5194/esd-14-1-2023, https://doi.org/10.5194/esd-14-1-2023, 2023
Short summary
Short summary
The carbon cycling in terrestrial ecosystems is complex. In our analyses, we found that both the global and the northern-high-latitude (NHL) ecosystems will continue to have positive net ecosystem production (NEP) in the next few decades under four global change scenarios but with large uncertainties. NHL ecosystems will experience faster climate warming but steadily contribute a small fraction of the global NEP. However, the relative uncertainty of NHL NEP is much larger than the global values.
Benjamin M. Sanderson and Maria Rugenstein
Earth Syst. Dynam., 13, 1715–1736, https://doi.org/10.5194/esd-13-1715-2022, https://doi.org/10.5194/esd-13-1715-2022, 2022
Short summary
Short summary
Equilibrium climate sensitivity (ECS) is a measure of how much long-term warming should be expected in response to a change in greenhouse gas concentrations. It is generally calculated in climate models by extrapolating global average temperatures to a point of where the planet is no longer a net absorber of energy. Here we show that some climate models experience energy leaks which change as the planet warms, undermining the standard approach and biasing some existing model estimates of ECS.
Jun Wang, John C. Moore, Liyun Zhao, Chao Yue, and Zhenhua Di
Earth Syst. Dynam., 13, 1625–1640, https://doi.org/10.5194/esd-13-1625-2022, https://doi.org/10.5194/esd-13-1625-2022, 2022
Short summary
Short summary
We examine how geoengineering using aerosols in the atmosphere might impact urban climate in the greater Beijing region containing over 50 million people. Climate models have too coarse resolutions to resolve regional variations well, so we compare two workarounds for this – an expensive physical model and a cheaper statistical method. The statistical method generally gives a reasonable representation of climate and has limited resolution and a different seasonality from the physical model.
Nidheesh Gangadharan, Hugues Goosse, David Parkes, Heiko Goelzer, Fabien Maussion, and Ben Marzeion
Earth Syst. Dynam., 13, 1417–1435, https://doi.org/10.5194/esd-13-1417-2022, https://doi.org/10.5194/esd-13-1417-2022, 2022
Short summary
Short summary
We describe the contributions of ocean thermal expansion and land-ice melting (ice sheets and glaciers) to global-mean sea-level (GMSL) changes in the Common Era. The mass contributions are the major sources of GMSL changes in the pre-industrial Common Era and glaciers are the largest contributor. The paper also describes the current state of climate modelling, uncertainties and knowledge gaps along with the potential implications of the past variabilities in the contemporary sea-level rise.
Changgui Lin, Erik Kjellström, Renate Anna Irma Wilcke, and Deliang Chen
Earth Syst. Dynam., 13, 1197–1214, https://doi.org/10.5194/esd-13-1197-2022, https://doi.org/10.5194/esd-13-1197-2022, 2022
Short summary
Short summary
This study endorses RCMs' added value on the driving GCMs in representing observed heat wave magnitudes. The future increase of heat wave magnitudes projected by GCMs is attenuated when downscaled by RCMs. Within the downscaling, uncertainties can be attributed almost equally to choice of RCMs and to the driving data associated with different GCMs. Uncertainties of GCMs in simulating heat wave magnitudes are transformed by RCMs in a complex manner rather than simply inherited.
Riccardo Silini, Sebastian Lerch, Nikolaos Mastrantonas, Holger Kantz, Marcelo Barreiro, and Cristina Masoller
Earth Syst. Dynam., 13, 1157–1165, https://doi.org/10.5194/esd-13-1157-2022, https://doi.org/10.5194/esd-13-1157-2022, 2022
Short summary
Short summary
The Madden–Julian Oscillation (MJO) has important socioeconomic impacts due to its influence on both tropical and extratropical weather extremes. In this study, we use machine learning (ML) to correct the predictions of the weather model holding the best performance, developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). We show that the ML post-processing leads to an improved prediction of the MJO geographical location and intensity.
Haicheng Zhang, Ronny Lauerwald, Pierre Regnier, Philippe Ciais, Kristof Van Oost, Victoria Naipal, Bertrand Guenet, and Wenping Yuan
Earth Syst. Dynam., 13, 1119–1144, https://doi.org/10.5194/esd-13-1119-2022, https://doi.org/10.5194/esd-13-1119-2022, 2022
Short summary
Short summary
We present a land surface model which can simulate the complete lateral transfer of sediment and carbon from land to ocean through rivers. Our model captures the water, sediment, and organic carbon discharges in European rivers well. Application of our model in Europe indicates that lateral carbon transfer can strongly change regional land carbon budgets by affecting organic carbon distribution and soil moisture.
Amber Boot, Anna S. von der Heydt, and Henk A. Dijkstra
Earth Syst. Dynam., 13, 1041–1058, https://doi.org/10.5194/esd-13-1041-2022, https://doi.org/10.5194/esd-13-1041-2022, 2022
Short summary
Short summary
Atmospheric pCO2 of the past shows large variability on different timescales. We focus on the effect of the strength of Atlantic Meridional Overturning Circulation (AMOC) on this variability and on the AMOC–pCO2 relationship. We find that climatic boundary conditions and the representation of biology in our model are most important for this relationship. Under certain conditions, we find internal oscillations, which can be relevant for atmospheric pCO2 variability during glacial cycles.
Aloïs Tilloy, Bruce D. Malamud, and Amélie Joly-Laugel
Earth Syst. Dynam., 13, 993–1020, https://doi.org/10.5194/esd-13-993-2022, https://doi.org/10.5194/esd-13-993-2022, 2022
Short summary
Short summary
Compound hazards occur when two different natural hazards impact the same time period and spatial area. This article presents a methodology for the spatiotemporal identification of compound hazards (SI–CH). The methodology is applied to compound precipitation and wind extremes in Great Britain for the period 1979–2019. The study finds that the SI–CH approach can accurately identify single and compound hazard events and represent their spatial and temporal properties.
Shruti Nath, Quentin Lejeune, Lea Beusch, Sonia I. Seneviratne, and Carl-Friedrich Schleussner
Earth Syst. Dynam., 13, 851–877, https://doi.org/10.5194/esd-13-851-2022, https://doi.org/10.5194/esd-13-851-2022, 2022
Short summary
Short summary
Uncertainty within climate model projections on inter-annual timescales is largely affected by natural climate variability. Emulators are valuable tools for approximating climate model runs, allowing for easy exploration of such uncertainty spaces. This study takes a first step at building a spatially resolved, monthly temperature emulator that takes local yearly temperatures as the sole input, thus providing monthly temperature distributions which are of critical value to impact assessments.
Linh N. Luu, Robert Vautard, Pascal Yiou, and Jean-Michel Soubeyroux
Earth Syst. Dynam., 13, 687–702, https://doi.org/10.5194/esd-13-687-2022, https://doi.org/10.5194/esd-13-687-2022, 2022
Short summary
Short summary
This study downscales climate information from EURO-CORDEX (approx. 12 km) output to a higher horizontal resolution (approx. 3 km) for the south of France. We also propose a matrix of different indices to evaluate the high-resolution precipitation output. We find that a higher resolution reproduces more realistic extreme precipitation events at both daily and sub-daily timescales. Our results and approach are promising to apply to other Mediterranean regions and climate impact studies.
Aine M. Gormley-Gallagher, Sebastian Sterl, Annette L. Hirsch, Sonia I. Seneviratne, Edouard L. Davin, and Wim Thiery
Earth Syst. Dynam., 13, 419–438, https://doi.org/10.5194/esd-13-419-2022, https://doi.org/10.5194/esd-13-419-2022, 2022
Short summary
Short summary
Our results show that agricultural management can impact the local climate and highlight the need to evaluate land management in climate models. We use regression analysis on climate simulations and observations to assess irrigation and conservation agriculture impacts on warming trends. This allowed us to distinguish between the effects of land management and large-scale climate forcings such as rising CO2 concentrations and thus gain insight into the impacts under different climate regimes.
Koffi Worou, Hugues Goosse, Thierry Fichefet, and Fred Kucharski
Earth Syst. Dynam., 13, 231–249, https://doi.org/10.5194/esd-13-231-2022, https://doi.org/10.5194/esd-13-231-2022, 2022
Short summary
Short summary
Over the Guinea Coast, the increased rainfall associated with warm phases of the Atlantic Niño is reasonably well simulated by 24 climate models out of 31, for the present-day conditions. In a warmer climate, general circulation models project a gradual decrease with time of the rainfall magnitude associated with the Atlantic Niño for the 2015–2039, 2040–2069 and 2070–2099 periods. There is a higher confidence in these changes over the equatorial Atlantic than over the Guinea Coast.
Roman Procyk, Shaun Lovejoy, and Raphael Hébert
Earth Syst. Dynam., 13, 81–107, https://doi.org/10.5194/esd-13-81-2022, https://doi.org/10.5194/esd-13-81-2022, 2022
Short summary
Short summary
This paper presents a new class of energy balance model that accounts for the long memory within the Earth's energy storage. The model is calibrated on instrumental temperature records and the historical energy budget of the Earth using an error model predicted by the model itself. Our equilibrium climate sensitivity and future temperature projection estimates are consistent with those estimated by complex climate models.
Mickaël Lalande, Martin Ménégoz, Gerhard Krinner, Kathrin Naegeli, and Stefan Wunderle
Earth Syst. Dynam., 12, 1061–1098, https://doi.org/10.5194/esd-12-1061-2021, https://doi.org/10.5194/esd-12-1061-2021, 2021
Short summary
Short summary
Climate change over High Mountain Asia is investigated with CMIP6 climate models. A general cold bias is found in this area, often related to a snow cover overestimation in the models. Ensemble experiments generally encompass the past observed trends, suggesting that even biased models can reproduce the trends. Depending on the future scenario, a warming from 1.9 to 6.5 °C, associated with a snow cover decrease and precipitation increase, is expected at the end of the 21st century.
Benjamin Ward, Francesco S. R. Pausata, and Nicola Maher
Earth Syst. Dynam., 12, 975–996, https://doi.org/10.5194/esd-12-975-2021, https://doi.org/10.5194/esd-12-975-2021, 2021
Short summary
Short summary
Using the largest ensemble of a climate model currently available, the Max Planck Institute Grand Ensemble (MPI-GE), we investigated the impact of the spatial distribution of volcanic aerosols on the El Niño–Southern Oscillation (ENSO) response. By selecting three eruptions with different aerosol distributions, we found that the shift of the Intertropical Convergence Zone (ITCZ) is the main driver of the ENSO response, while other mechanisms commonly invoked seem less important in our model.
Matthias Gröger, Christian Dieterich, Jari Haapala, Ha Thi Minh Ho-Hagemann, Stefan Hagemann, Jaromir Jakacki, Wilhelm May, H. E. Markus Meier, Paul A. Miller, Anna Rutgersson, and Lichuan Wu
Earth Syst. Dynam., 12, 939–973, https://doi.org/10.5194/esd-12-939-2021, https://doi.org/10.5194/esd-12-939-2021, 2021
Short summary
Short summary
Regional climate studies are typically pursued by single Earth system component models (e.g., ocean models and atmosphere models). These models are driven by prescribed data which hamper the simulation of feedbacks between Earth system components. To overcome this, models were developed that interactively couple model components and allow an adequate simulation of Earth system interactions important for climate. This article reviews recent developments of such models for the Baltic Sea region.
Halima Usman, Thomas A. M. Pugh, Anders Ahlström, and Sofia Baig
Earth Syst. Dynam., 12, 857–870, https://doi.org/10.5194/esd-12-857-2021, https://doi.org/10.5194/esd-12-857-2021, 2021
Short summary
Short summary
The study assesses the impacts of climate change on forest productivity in the Hindu Kush Himalayan region. LPJ-GUESS was simulated from 1851 to 2100. In first approach, the model was compared with observational estimates. The comparison showed a moderate agreement. In the second approach, the model was assessed for the temporal and spatial trends of net biome productivity and its components along with carbon pool. Increases in both variables were predicted in 2100.
Kerstin Hartung, Ana Bastos, Louise Chini, Raphael Ganzenmüller, Felix Havermann, George C. Hurtt, Tammas Loughran, Julia E. M. S. Nabel, Tobias Nützel, Wolfgang A. Obermeier, and Julia Pongratz
Earth Syst. Dynam., 12, 763–782, https://doi.org/10.5194/esd-12-763-2021, https://doi.org/10.5194/esd-12-763-2021, 2021
Short summary
Short summary
In this study, we model the relative importance of several contributors to the land-use and land-cover change (LULCC) flux based on a LULCC dataset including uncertainty estimates. The uncertainty of LULCC is as relevant as applying wood harvest and gross transitions for the cumulative LULCC flux over the industrial period. However, LULCC uncertainty matters less than the other two factors for the LULCC flux in 2014; historical LULCC uncertainty is negligible for estimates of future scenarios.
Fransje van Oorschot, Ruud J. van der Ent, Markus Hrachowitz, and Andrea Alessandri
Earth Syst. Dynam., 12, 725–743, https://doi.org/10.5194/esd-12-725-2021, https://doi.org/10.5194/esd-12-725-2021, 2021
Short summary
Short summary
The roots of vegetation largely control the Earth's water cycle by transporting water from the subsurface to the atmosphere but are not adequately represented in land surface models, causing uncertainties in modeled water fluxes. We replaced the root parameters in an existing model with more realistic ones that account for a climate control on root development and found improved timing of modeled river discharge. Further extension of our approach could improve modeled water fluxes globally.
Manuela I. Brunner, Eric Gilleland, and Andrew W. Wood
Earth Syst. Dynam., 12, 621–634, https://doi.org/10.5194/esd-12-621-2021, https://doi.org/10.5194/esd-12-621-2021, 2021
Short summary
Short summary
Compound hot and dry events can lead to severe impacts whose severity may depend on their timescale and spatial extent. Here, we show that the spatial extent and timescale of compound hot–dry events are strongly related, spatial compound event extents are largest at
sub-seasonal timescales, and short events are driven more by high temperatures, while longer events are more driven by low precipitation. Future climate impact studies should therefore be performed at different timescales.
Francisco José Cuesta-Valero, Almudena García-García, Hugo Beltrami, and Joel Finnis
Earth Syst. Dynam., 12, 581–600, https://doi.org/10.5194/esd-12-581-2021, https://doi.org/10.5194/esd-12-581-2021, 2021
Short summary
Short summary
The current radiative imbalance at the top of the atmosphere is increasing the heat stored in the oceans, atmosphere, continental subsurface and cryosphere, with consequences for societies and ecosystems (e.g. sea level rise). We performed the first assessment of the ability of global climate models to represent such heat storage in the climate subsystems. Models are able to reproduce the observed atmosphere heat content, with biases in the simulation of heat content in the rest of components.
Francesco Piccioni, Céline Casenave, Bruno Jacques Lemaire, Patrick Le Moigne, Philippe Dubois, and Brigitte Vinçon-Leite
Earth Syst. Dynam., 12, 439–456, https://doi.org/10.5194/esd-12-439-2021, https://doi.org/10.5194/esd-12-439-2021, 2021
Short summary
Short summary
Small lakes are ecosystems highly impacted by climate change. Here, the thermal regime of a small, shallow lake over the past six decades was reconstructed via 3D modelling. Significant changes were found: strong water warming in spring and summer (0.7 °C/decade) as well as increased stratification and thermal energy for cyanobacteria growth, especially in spring. The strong spatial patterns detected for stratification might create local conditions particularly favourable to cyanobacteria bloom.
Pablo Ortega, Jon I. Robson, Matthew Menary, Rowan T. Sutton, Adam Blaker, Agathe Germe, Jöel J.-M. Hirschi, Bablu Sinha, Leon Hermanson, and Stephen Yeager
Earth Syst. Dynam., 12, 419–438, https://doi.org/10.5194/esd-12-419-2021, https://doi.org/10.5194/esd-12-419-2021, 2021
Short summary
Short summary
Deep Labrador Sea densities are receiving increasing attention because of their link to many of the processes that govern decadal climate oscillations in the North Atlantic and their potential use as a precursor of those changes. This article explores those links and how they are represented in global climate models, documenting the main differences across models. Models are finally compared with observational products to identify the ones that reproduce the links more realistically.
Calum Brown, Ian Holman, and Mark Rounsevell
Earth Syst. Dynam., 12, 211–231, https://doi.org/10.5194/esd-12-211-2021, https://doi.org/10.5194/esd-12-211-2021, 2021
Short summary
Short summary
The variety of human and natural processes in the land system can be modelled in many different ways. However, little is known about how and why basic model assumptions affect model results. We compared two models that represent land use in completely distinct ways and found several results that differed greatly. We identify the main assumptions that caused these differences and therefore key issues that need to be addressed for more robust model development.
Johannes Vogel, Pauline Rivoire, Cristina Deidda, Leila Rahimi, Christoph A. Sauter, Elisabeth Tschumi, Karin van der Wiel, Tianyi Zhang, and Jakob Zscheischler
Earth Syst. Dynam., 12, 151–172, https://doi.org/10.5194/esd-12-151-2021, https://doi.org/10.5194/esd-12-151-2021, 2021
Short summary
Short summary
We present a statistical approach for automatically identifying multiple drivers of extreme impacts based on LASSO regression. We apply the approach to simulated crop failure in the Northern Hemisphere and identify which meteorological variables including climate extreme indices and which seasons are relevant to predict crop failure. The presented approach can help unravel compounding drivers in high-impact events and could be applied to other impacts such as wildfires or flooding.
Peter Pfleiderer, Aglaé Jézéquel, Juliette Legrand, Natacha Legrix, Iason Markantonis, Edoardo Vignotto, and Pascal Yiou
Earth Syst. Dynam., 12, 103–120, https://doi.org/10.5194/esd-12-103-2021, https://doi.org/10.5194/esd-12-103-2021, 2021
Short summary
Short summary
In 2016, northern France experienced an unprecedented wheat crop loss. This crop loss was likely due to an extremely warm December 2015 and abnormally high precipitation during the following spring season. Using stochastic weather generators we investigate how severe the metrological conditions leading to the crop loss could be in current climate conditions. We find that December temperatures were close to the plausible maximum but that considerably wetter springs would be possible.
Jelle van den Berk, Sybren Drijfhout, and Wilco Hazeleger
Earth Syst. Dynam., 12, 69–81, https://doi.org/10.5194/esd-12-69-2021, https://doi.org/10.5194/esd-12-69-2021, 2021
Short summary
Short summary
A collapse of the Atlantic Meridional Overturning Circulation can be described by six parameters and Langevin dynamics. These parameters can be determined from collapses seen in climate models of intermediate complexity. With this parameterisation, it might be possible to estimate how much fresh water is needed to observe a collapse in more complicated models and reality.
Jakob Zscheischler, Philippe Naveau, Olivia Martius, Sebastian Engelke, and Christoph C. Raible
Earth Syst. Dynam., 12, 1–16, https://doi.org/10.5194/esd-12-1-2021, https://doi.org/10.5194/esd-12-1-2021, 2021
Short summary
Short summary
Compound extremes such as heavy precipitation and extreme winds can lead to large damage. To date it is unclear how well climate models represent such compound extremes. Here we present a new measure to assess differences in the dependence structure of bivariate extremes. This measure is applied to assess differences in the dependence of compound precipitation and wind extremes between three model simulations and one reanalysis dataset in a domain in central Europe.
Christian B. Rodehacke, Madlene Pfeiffer, Tido Semmler, Özgür Gurses, and Thomas Kleiner
Earth Syst. Dynam., 11, 1153–1194, https://doi.org/10.5194/esd-11-1153-2020, https://doi.org/10.5194/esd-11-1153-2020, 2020
Short summary
Short summary
In the warmer future, Antarctica's ice sheet will lose more ice due to enhanced iceberg calving and a warming ocean that melts more floating ice from below. However, the hydrological cycle is also stronger in a warmer world. Hence, more snowfall will precipitate on Antarctica and may balance the amplified ice loss. We have used future climate scenarios from various global climate models to perform numerous ice sheet simulations to show that precipitation may counteract mass loss.
Renate Anna Irma Wilcke, Erik Kjellström, Changgui Lin, Daniela Matei, Anders Moberg, and Evangelos Tyrlis
Earth Syst. Dynam., 11, 1107–1121, https://doi.org/10.5194/esd-11-1107-2020, https://doi.org/10.5194/esd-11-1107-2020, 2020
Short summary
Short summary
Two long-lasting high-pressure systems in summer 2018 led to heat waves over Scandinavia and an extended summer period with devastating impacts on both agriculture and human life. Using five climate model ensembles, the unique 263-year Stockholm temperature time series and a composite 150-year time series for the whole of Sweden, we found that anthropogenic climate change has strongly increased the probability of a warm summer, such as the one observed in 2018, occurring in Sweden.
Jeemijn Scheen and Thomas F. Stocker
Earth Syst. Dynam., 11, 925–951, https://doi.org/10.5194/esd-11-925-2020, https://doi.org/10.5194/esd-11-925-2020, 2020
Short summary
Short summary
Variability of sea surface temperatures (SST) in 1200–2000 CE is quite well-known, but the history of deep ocean temperatures is not. Forcing an ocean model with these SSTs, we simulate temperatures in the ocean interior. The circulation changes alter the amplitude and timing of deep ocean temperature fluctuations below 2 km depth, e.g. delaying the atmospheric signal by ~ 200 years in the deep Atlantic. Thus ocean circulation changes are shown to be as important as SST changes at these depths.
Sebastian Milinski, Nicola Maher, and Dirk Olonscheck
Earth Syst. Dynam., 11, 885–901, https://doi.org/10.5194/esd-11-885-2020, https://doi.org/10.5194/esd-11-885-2020, 2020
Short summary
Short summary
Initial-condition large ensembles with ensemble sizes ranging from 30 to 100 members have become a commonly used tool to quantify the forced response and internal variability in various components of the climate system, but there is no established method to determine the required ensemble size for a given problem. We propose a new framework that can be used to estimate the required ensemble size from a model's control run or an existing large ensemble.
Yu Huang, Lichao Yang, and Zuntao Fu
Earth Syst. Dynam., 11, 835–853, https://doi.org/10.5194/esd-11-835-2020, https://doi.org/10.5194/esd-11-835-2020, 2020
Short summary
Short summary
We investigate the applicability of machine learning (ML) on time series reconstruction and find that the dynamical coupling relation and nonlinear causality are crucial for the application of ML. Our results could provide insights into causality and ML approaches for paleoclimate reconstruction, parameterization schemes, and prediction in climate studies.
Anna Louise Merrifield, Lukas Brunner, Ruth Lorenz, Iselin Medhaug, and Reto Knutti
Earth Syst. Dynam., 11, 807–834, https://doi.org/10.5194/esd-11-807-2020, https://doi.org/10.5194/esd-11-807-2020, 2020
Short summary
Short summary
Justifiable uncertainty estimates of future change in northern European winter and Mediterranean summer temperature can be obtained by weighting a multi-model ensemble comprised of projections from different climate models and multiple projections from the same climate model. Weights reduce the influence of model biases and handle dependence by identifying a projection's model of origin from historical characteristics; contributions from the same model are scaled by the number of members.
James D. Annan, Julia C. Hargreaves, Thorsten Mauritsen, and Bjorn Stevens
Earth Syst. Dynam., 11, 709–719, https://doi.org/10.5194/esd-11-709-2020, https://doi.org/10.5194/esd-11-709-2020, 2020
Short summary
Short summary
In this paper we explore the potential of variability for constraining the equilibrium response of the climate system to external forcing. We show that the constraint is inherently skewed, with a long tail to high sensitivity, and that while the variability may contain some useful information, it is unlikely to generate a tight constraint.
Andrea Böhnisch, Ralf Ludwig, and Martin Leduc
Earth Syst. Dynam., 11, 617–640, https://doi.org/10.5194/esd-11-617-2020, https://doi.org/10.5194/esd-11-617-2020, 2020
Short summary
Short summary
North Atlantic air pressure variations influencing European climate variables are simulated in coarse-resolution global climate models (GCMs). As single-model runs do not sufficiently describe variations of their patterns, several model runs with slightly diverging initial conditions are analyzed. The study shows that GCM and regional climate model (RCM) patterns vary in a similar range over the same domain, while RCMs add consistent fine-scale information due to their higher spatial resolution.
György Károlyi, Rudolf Dániel Prokaj, István Scheuring, and Tamás Tél
Earth Syst. Dynam., 11, 603–615, https://doi.org/10.5194/esd-11-603-2020, https://doi.org/10.5194/esd-11-603-2020, 2020
Short summary
Short summary
We construct a conceptual model to understand the interplay between the atmosphere and the ocean biosphere in a climate change framework, including couplings between extraction of carbon dioxide by phytoplankton and climate change, temperature and carrying capacity of phytoplankton, and wind energy and phytoplankton production. We find that sufficiently strong mixing can result in decaying global phytoplankton content.
Kira Rehfeld, Raphaël Hébert, Juan M. Lora, Marcus Lofverstrom, and Chris M. Brierley
Earth Syst. Dynam., 11, 447–468, https://doi.org/10.5194/esd-11-447-2020, https://doi.org/10.5194/esd-11-447-2020, 2020
Short summary
Short summary
Under continued anthropogenic greenhouse gas emissions, it is likely that global mean surface temperature will continue to increase. Little is known about changes in climate variability. We analyze surface climate variability and compare it to mean change in colder- and warmer-than-present climate model simulations. In most locations, but not on subtropical land, simulated temperature variability up to decadal timescales decreases with mean temperature, and precipitation variability increases.
Eirik Myrvoll-Nilsen, Sigrunn Holbek Sørbye, Hege-Beate Fredriksen, Håvard Rue, and Martin Rypdal
Earth Syst. Dynam., 11, 329–345, https://doi.org/10.5194/esd-11-329-2020, https://doi.org/10.5194/esd-11-329-2020, 2020
Short summary
Short summary
This paper presents efficient Bayesian methods for linear response models of global mean surface temperature that take into account long-range dependence. We apply the methods to the instrumental temperature record and historical model runs in the CMIP5 ensemble to provide estimates of the transient climate response and temperature projections under the Representative Concentration Pathways.
Lea Beusch, Lukas Gudmundsson, and Sonia I. Seneviratne
Earth Syst. Dynam., 11, 139–159, https://doi.org/10.5194/esd-11-139-2020, https://doi.org/10.5194/esd-11-139-2020, 2020
Short summary
Short summary
Earth system models (ESMs) are invaluable to study the climate system but expensive to run. Here, we present a statistical tool which emulates ESMs at a negligible computational cost by creating stochastic realizations of yearly land temperature field time series. Thereby, 40 ESMs are considered, and for each ESM, a single simulation is required to train the tool. The resulting ESM-specific realizations closely resemble ESM simulations not employed during training at point to regional scales.
Yu Sun and Riccardo E. M. Riva
Earth Syst. Dynam., 11, 129–137, https://doi.org/10.5194/esd-11-129-2020, https://doi.org/10.5194/esd-11-129-2020, 2020
Short summary
Short summary
The solid Earth is still deforming because of the effect of past ice sheets through glacial isostatic adjustment (GIA). Satellite gravity observations by the Gravity Recovery and Climate Experiment (GRACE) mission are sensitive to those signals but are superimposed on the redistribution effect of water masses by the hydrological cycle. We propose a method separating the two signals, providing new constraints for forward GIA models and estimating the global water cycle's patterns and magnitude.
Mareike Schuster, Jens Grieger, Andy Richling, Thomas Schartner, Sebastian Illing, Christopher Kadow, Wolfgang A. Müller, Holger Pohlmann, Stephan Pfahl, and Uwe Ulbrich
Earth Syst. Dynam., 10, 901–917, https://doi.org/10.5194/esd-10-901-2019, https://doi.org/10.5194/esd-10-901-2019, 2019
Short summary
Short summary
Decadal climate predictions are valuable to society as they allow us to estimate climate conditions several years in advance. We analyze the latest version of the German MiKlip prediction system (https://www.fona-miklip.de) and assess the effect of the model resolution on the skill of the system. The increase in the resolution of the system reduces the bias and significantly improves the forecast skill for North Atlantic extratropical winter dynamics for lead times of two to five winters.
Calum Brown, Bumsuk Seo, and Mark Rounsevell
Earth Syst. Dynam., 10, 809–845, https://doi.org/10.5194/esd-10-809-2019, https://doi.org/10.5194/esd-10-809-2019, 2019
Short summary
Short summary
Concerns are growing that human activity will lead to social and environmental breakdown, but it is hard to anticipate when and where such breakdowns might occur. We developed a new model of land management decisions in Europe to explore possible future changes and found that decision-making that takes into account social and environmental conditions can produce unexpected outcomes that include societal breakdown in challenging conditions.
Cited articles
Acemoglu, D.: Introduction to Modern Economic Growth, Princeton University Press, Princeton, NJ, 2009.
Ackermann, K. A., Fleiß, J., and Murphy, R. O.: Reciprocity as an individual difference, J. Conflict Resolut., 60, 340–367, https://doi.org/10.1177/0022002714541854, 2016.
Aghion, P. and Howitt, P.: Endogenous Growth Theory, MIT Press, Cambridge, Massachusetts and London, UK, 1998.
Ainslie, G. and Haslam, N.: Hyperbolic Discounting, in: Choice over time, edited by: Loewenstein, G. and Elster, J., Russell Sage Foundation, New York, 57–92, 1992.
Akers, R. L., Krohn, M. D., Lanza-Kaduce, L., and Radosevich, M.: Social Learning and Deviant Behavior: A specific Test of a general Theory, Am. Sociol. Rev., 44, 636–655, https://doi.org/10.2307/2094592, 1979.
An, L.: Modeling human decisions in coupled human and natural systems: Review of agent-based models, Ecol. Model., 229, 25–36, https://doi.org/10.1016/j.ecolmodel.2011.07.010, 2012.
Arneth, A., Brown, C., and Rounsevell, M. D. A.: Global models of human decision-making for land-based mitigation and adaptation assessment, Nature Climate Change, 4, 550–557, https://doi.org/10.1038/nclimate2250, 2014.
Arrow, K. J. and Debreu, G.: Existence of an Equilibrium for a Competitive Economy, Econometrica, 22, 265–290, https://doi.org/10.2307/1907353, 1954.
Auer, S., Heitzig, J., Kornek, U., Schöll, E., Kurths, J., Scholl, E., Kurths, J., Schöll, E., and Kurths, J.: The Dynamics of Coalition Formation on Complex Networks, Nature Scientific Reports, 5, 13386, https://doi.org/10.1038/srep13386, 2015.
Aumann, R. J.: War and peace, P. Natl. Acad. Sci. USA, 103, 17075–17078, https://doi.org/10.1073/pnas.0608329103, 2006.
Axelrod, R.: The evolution of cooperation, Basic Books, New York, 1984.
Axelrod, R.: The dissemination of culture: A model with local convergence and global polarization, J. Conflict Resolut., 41, 203–226, https://doi.org/10.1177/0022002797041002001, 1997.
Ayres, R. U. and Ayres, L. (Eds.): A Handbook of Industrial Ecology, Edward Elgar, Cheltenham, UK, 2002.
Babad, E. and Katz, Y.: Wishful Thinking – Against All Odds, J. Appl. Soc. Psychol., 21, 1921–1938, https://doi.org/10.1111/j.1559-1816.1991.tb00514.x, 1991.
Babiker, M., Gurgel, A., Paltsev, S., and Reilly, J.: Forward-looking versus recursive-dynamic modeling in climate policy analysis: A comparison, Econ. Model., 26, 1341–1354, https://doi.org/10.1016/j.econmod.2009.06.009, 2009.
Baker, W. L.: A review of models of landscape change, Landscape Ecol., 2, 111–133, https://doi.org/10.1007/bf00137155, 1989.
Balint, T., Lamperti, F., Mandel, A., Napoletano, M., Roventini, A., and Sapio, A.: Complexity and the Economics of Climate Change: A Survey and a Look Forward, Ecol. Econ., 138, 252–265, https://doi.org/10.1016/j.ecolecon.2017.03.032, 2017.
Balke, T. and Gilbert, N.: How Do Agents Make Decisions? A Survey, JASSS-J. Artif. Soc. S., 17, 13, https://doi.org/10.18564/jasss.2687, 2014.
Barfuss, W., Donges, J. F., Wiedermann, M., and Lucht, W.: Sustainable use of renewable resources in a stylized social-ecological network model under heterogeneous resource distribution, Earth Syst. Dynam., 8, 255–264, https://doi.org/10.5194/esd-8-255-2017, 2017.
Bellman, R.: A Markovian decision process, Indiana U. Math. J., 6, 679–684, https://doi.org/10.1512/iumj.1957.6.56038, 1957.
Berg, M., Hartley, B., and Richters, O.: A stock-flow consistent input-output model with applications to energy price shocks, interest rates, and heat emissions, New J. Phys., 17, 15011, https://doi.org/10.1088/1367-2630/17/1/015011, 2015.
Bierkandt, R., Wenz, L., Willner, S. N., and Levermann, A.: Acclimate - a model for economic damage propagation. Part I: basic formulation of damage transfer within a global supply network and damage conserving dynamics, Environment Systems and Decisions, 34, 507–524, https://doi.org/10.1007/s10669-014-9523-4, 2014.
Bikhchandani, S., Hirshleifer, D., and Welch, I.: A Theory of Fads, Fashion, Custom, and Cultural-Change as Informational Cascades, J. Polit. Econ., 100, 992–1026, https://doi.org/10.1086/261849, 1992.
Boccaletti, S., Bianconi, G., Criado, R., del Genio, C. I., Gómez-Gardeñes, J., Romance, M., Sendiña-Nadal, I., Wang, Z., and Zanin, M.: The structure and dynamics of multilayer networks, Phys. Rep., 544, 1–122, https://doi.org/10.1016/j.physrep.2014.07.001, 2014.
Boudon, R.: The Logic of Social Action. An Introduction to Sociological Analysis, Routledge & Kegan Paul, London, 1981.
Boumans, R., Costanza, R., Farley, J., Wilson, M. A., Portela, R., Rotmans, J., Villa, F., and Grasso, M.: Modeling the dynamics of the integrated earth system and the value of global ecosystem services using the GUMBO model, Ecol. Econ., 41, 529–560, https://doi.org/10.1016/S0921-8009(02)00098-8, 2002.
Bousquet, F. and Le Page, C.: Multi-agent simulations and ecosystem management: a review, Ecol. Model., 176, 313–332, https://doi.org/10.1016/j.ecolmodel.2004.01.011, 2004.
Bowles, S.: Endogenous Preferences: The Cultural Consequences of Markets and Other Economic Institutions, J. Econ. Lit., 36, 75–111, 1998.
Brander, J. A. and Taylor, M. S.: The Simple Economics of Easter Island: A Ricardo-Malthus Model of Renewable Resource Use, A. Econ. Rev., 88, 119–138, 1998.
Brown, C., Brown, K., and Rounsevell, M.: A philosophical case for process-based modelling of land use change, Modeling Earth Systems and Environment, 2, 50, https://doi.org/10.1007/s40808-016-0102-1, 2016.
Brown, C., Alexander, P., Holzhauer, S., and Rounsevell, M. D.: Behavioural models of climate change adaptation and mitigation in land-based sectors, WIREs Climate Change, 8, e448, https://doi.org/10.1002/wcc.448, 2017.
Brown, D. G., Walker, R., Manson, S., and Seto, K.: Modeling Land-Use and Land-Cover Change, in: Land Change Science: Observing, Monitoring and Understanding Trajectories of Change on the Earth's Surface, edited by: Gutman, G., Janetos, A. C., Justice, C. O., Moran, E. F., Mustard, J. F., Rindfuss, R. R., Skole, D., Turner II, B. L., and Cochrane, M. A., Springer, Dordrecht, the Netherlands, chap. 23, 395–409, https://doi.org/10.1007/978-1-4020-2562-4_23, 2004.
Bruhin, A., Fehr-Duda, H., and Epper, T.: Risk and Rationality: Uncovering Heterogeneity in Probability Distortion, Econometrica, 78, 1375–1412, https://doi.org/10.3982/ECTA7139, 2010.
Camerer, C. and Ho, T. H.: Experience-weighted attraction learning in normal form games, Econometrica, 67, 827–874, https://doi.org/10.1111/1468-0262.00054, 1999.
Castellano, C., Fortunato, S., and Loreto, V.: Statistical physics of social dynamics, Rev. Mod. Phys., 81, 591–646, https://doi.org/10.1103/RevModPhys.81.591, 2009.
Chong, E. K. P. and Zak, S. H.: An Introduction to Optimization, 4th Edn., Wiley, Hoboken, NJ, 2013.
Clarke, L., Jiang, K., Akimoto, K., Babiker, M., Blanford, G., Fisher-Vanden, K., Hourcade, J.-C., Krey, V., Kriegler, E., Löschel, A., McCollum, D., Paltsev, S., Rose, S., Shukla, P., Tavoni, M., van der Zwaan, B., and van Vuuren, D. P.: Assessing Transformation Pathways, in: Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Farahani, E., Kadner, S., Seyboth, K., Adler, A., Baum, I., Brunner, S., Eickemeier, P., Kriemann, B., Savolainen, J., Schlömer, S., von Stechow, C., Zwickel, T., Minx, J., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2014.
Coleman, J. S.: Foundations of social theory, The Belknap Press of Harvard University Press, Cambridge, MA, and London, UK, 1994.
Conte, R. and Paolucci, M.: On agent-based modeling and computational social science, Frontiers in Psychology, 5, 668, https://doi.org/10.3389/fpsyg.2014.00668, 2014.
Cooke, I. R., Queenborough, S. A., Mattison, E. H. A., Bailey, A. P., Sandars, D. L., Graves, A. R., Morris, J., Atkinson, P. W., Trawick, P., Freckleton, R. P., Watkinson, A. R., and Sutherland, W. J.: Integrating socio-economics and ecology: A taxonomy of quantitative methods and a review of their use in agro-ecology, J. Appl. Ecol., 46, 269–277, https://doi.org/10.1111/j.1365-2664.2009.01615.x, 2009.
Couzin, I. D.: Collective cognition in animal groups, Trends Cogn. Sci., 13, 36–43, https://doi.org/10.1016/j.tics.2008.10.002, 2009.
Crutzen, P. J.: Geology of mankind, Nature, 415, p. 23, https://doi.org/10.1038/415023a, 2002.
Currarini, S., Marchiori, C., and Tavoni, A.: Network Economics and the Environment: Insights and Perspectives, Environmental and Resource Economics, 65, 159–189, https://doi.org/10.1007/s10640-015-9953-6, 2016.
Dafermos, Y., Nikolaidi, M., and Galanis, G.: A stock-flow-fund ecological macroeconomic model, Ecol. Econ., 131, 191–207, https://doi.org/10.1016/j.ecolecon.2016.08.013, 2017.
Dagum, C.: On the Relationship between Income Inequality measures and social welfare functions, Journal of Economectrics, 43, 91–102, https://doi.org/10.1016/0304-4076(90)90109-7, 1990.
de Marchi, S. and Page, S. E.: Agent-Based Models, Annu. Rev. Polit. Sci., 17, 1–20, https://doi.org/10.1146/annurev-polisci-080812-191558, 2014.
Deadman, P., Robinson, D., Moran, E., and Brondizio, E.: Colonist household decisionmaking and land-use change in the Amazon Rainforest: An agent-based simulation, Environ. Plann. B, 31, 693–709, https://doi.org/10.1068/b3098, 2004.
Deffuant, G., Huet, S., and Amblard, F.: An Individual-Based Model of Innovation Diffusion Mixing Social Value and Individual Benefit, Am. J. Sociol., 110, 1041–1069, https://doi.org/10.1086/430220, 2005.
DeGroot, M. H.: Reaching a Consensus, J. Am. Stat. Assoc., 69, 118–121, https://doi.org/10.1080/01621459.1974.10480137, 1974.
Demirel, G., Vazquez, F., Böhme, G. A., and Gross, T.: Moment-closure approximations for discrete adaptive networks, Physica D, 267, 68–80, https://doi.org/10.1016/j.physd.2013.07.003, 2014.
Dhami, M. K. and Ayton, P.: Bailing and jailing the fast and frugal way, J. Behav. Decis. Making, 14, 141–168, https://doi.org/10.1002/bdm.371, 2001.
Dhami, M. K. and Harries, C.: Fast and frugal versus regression models of human judgement, Think. Reasoning, 7, 5–27, https://doi.org/10.1080/13546780042000019, 2001.
Do, A.-L. and Gross, T.: Contact processes and moment closure on adaptive networks, in: Adaptive Networks: Theory, Models and Applications, edited by: Gross, T. and Sayama, H., Springer and NECSI, Cambridge, Massachusetts, chap. 9, 191–208, https://doi.org/10.1007/978-3-642-01284-6_9, 2009.
Donges, J. F., Lucht, W., Heitzig, J., Cornell, S., Lade, S. J., Schlüter, M., and Barfuss, W.: A taxonomy of co-evolutionary interactions in models of the World-Earth system, in preparation, Earth System Dynamics, 2017a.
Donges, J. F., Lucht, W., Müller-Hansen, F., and Steffen, W.: The technosphere in Earth system analysis: a coevolutionary perspective, The Anthropocene Review, 4, 23–33, https://doi.org/10.1177/2053019616676608, 2017b.
Donges, J. F., Winkelmann, R., Lucht, W., Cornell, S. E., Dyke, J. G., Rockström, J., Heitzig, J., and Schellnhuber, H.-J.: Closing the loop: reconnecting human dynamics to Earth system science, The Anthropocene Review, 4, 151–157, https://doi.org/10.1177/2053019617725537, 2017c.
Durkheim, E.: The rules of sociological method. And selected texts on sociology and its method, The Free Press, New York, 2014.
Edmonds, B. and Meyer, R.: Simulating Social Complexity. A Handbook, Springer, Berlin, New York, https://doi.org/10.1007/978-3-540-93813-2, 2013.
Epstein, J. M.: Agent-based computational models and generative social science, Complexity, 4, 41–60, https://doi.org/10.1002/(SICI)1099-0526(199905/06)4:5<41::AID-CPLX9>3.0.CO;2-F, 1999.
Farmer, J. D., Hepburn, C., Mealy, P., and Teytelboym, A.: A Third Wave in the Economics of Climate Change, Environmental and Resource Economics, 62, 329–357, https://doi.org/10.1007/s10640-015-9965-2, 2015.
Fehr, E. and Fischbacher, U.: The nature of human altruism, Nature, 425, 785–791, https://doi.org/10.1038/nature02043, 2003.
Fehr, E. and Schmidt, K. M.: A Theory of Fairness, Competition, and Cooperation, The Quarterly Journal of Economics, 114, 817–868, https://doi.org/10.1162/003355399556151, 1999.
Feldman, A. M. and Serrano, R.: Welfare economics and social choice theory, 2nd Edn., Springer, New York, 2006.
Feldman, L.: The Opinion Factor: The Effects of Opinionated News on Information Processing and Attitude Change, Polit. Commun., 28, 163–181, https://doi.org/10.1080/10584609.2011.565014, 2011.
Festinger, L., Schachter, S., and Back, K.: Social Pressures in Informal Groups: A Study of Human Factors in Housing, Stanford University Press, Stanford, CA, 1950.
Fischer-Kowalski, M. and Haberl, H.: Tons, joules, and money: Modes of production and their sustainability problems, Soc. Natur. Resour., 10, 61–85, https://doi.org/10.1080/08941929709381009, 1997.
Fishburn, P. C.: Utility Theory, Manage. Sci., 14, 335–378, https://doi.org/10.1287/mnsc.14.5.335, 1968.
Flache, A. and Macy, M. W.: Local Convergence and Global Diversity: From Interpersonal to Social Influence, J. Conflict Resolut., 55, 970–995, https://doi.org/10.1177/0022002711414371, 2011.
Foster, D. and Young, P.: Stochastic evolutionary game dynamics, Theor. Popul. Biol., 38, 219–232, https://doi.org/10.1016/0040-5809(90)90011-J, 1990.
French, J. R. P.: A Formal Theory of Social Power, Psychol. Rev., 63, 181–194, https://doi.org/10.1037/h0046123, 1956.
Friedkin, N. E. and Johnsen, E. C.: Social Influence Network Theory, Cambridge University Press, New York, 2011.
Fudenberg, D. and Levine, D. K.: The Theory of Learning in Games, MIT Press, Cambridge, Massachusetts, 1998.
Garrett, T. J.: Long-run evolution of the global economy – Part 2: Hindcasts of innovation and growth, Earth Syst. Dynam., 6, 673–688, https://doi.org/10.5194/esd-6-673-2015, 2015.
Georgescu-Roegen, N.: The Entropy Law and the Economic Process, Harvard University Press, Cambridge, MA, 1971.
Gibson, C. C., Ostrom, E., and Ahn, T. K.: The concept of scale and the human dimensions of global change: A survey, Ecol. Econ., 32, 217–239, https://doi.org/10.1016/S0921-8009(99)00092-0, 2000.
Gigerenzer, G. and Gaissmaier, W.: Heuristic decision making, Annu. Rev. Psychol., 62, 451–482, https://doi.org/10.1146/annurev-psych-120709-145346, 2011.
Gigerenzer, G. and Selten, R. (Eds.): Bounded rationality: The adaptive toolbox, MIT Press, Cambridge, MA and London, UK, 2002.
Gigerenzer, G. and Todd, P. M.: Simple heuristics that make us smart, Oxford University Press, New York, 1999.
Gigerenzer, G., Hoffrage, U., and Goldstein, D. G.: Fast and frugal heuristics are plausible models of cognition: reply to Dougherty, Franco-Watkins, and Thomas (2008), Psychol. Rev., 115, 230–239, https://doi.org/10.1037/0033-295X.115.1.230, 2008.
Gilbert, N.: Agent-based models, Sage, Thousand Oaks, CA, USA, 2008.
Gillespie, C.: Moment-closure approximations for mass-action models, IET Syst. Biol., 3, 52–58, https://doi.org/10.1049/iet-syb:20070031, 2009.
Gintis, H.: The Bounds of Reason: Game Theory and the Unification of the Behavioral Sciences, Princeton University Press, Princeton and Oxford, 2009.
Godley, W. and Lavoie, M.: Monetary Economics. An Integrated Approach to Credit, Money, Income, Production and Wealth, Palgrave Macmillan, New York, 2007.
Goodman, L. A.: Population Growth of the Sexes, Biometrics, 9, 212–225, https://doi.org/10.2307/3001852, 1953.
Grimm, V. and Railsback, S. F.: Individual-based modeling and ecology, Princeton Series in Theoretical and Computational Biology, Princeton University Press, Princeton, NJ, 2005.
Groeneveld, J., Müller, B., Buchmann, C., Dressler, G., Guo, C., Hase, N., Hoffmann, F., John, F., Klassert, C., Lauf, T., Liebelt, V., Nolzen, H., Pannicke, N., Schulze, J., Weise, H., and Schwarz, N.: Theoretical foundations of human decision-making in agent-based land use models – A review, Environ. Modell. Softw., 87, 39–48, https://doi.org/10.1016/j.envsoft.2016.10.008, 2017.
Gross, T. and Blasius, B.: Adaptive coevolutionary networks: a review, J. R. Soc. Interface, 5, 259–271, https://doi.org/10.1098/rsif.2007.1229, 2008.
Gross, T., D'Lima, C. J. D., and Blasius, B.: Epidemic dynamics on an adaptive network, Phys. Rev. Lett., 96, 208701, https://doi.org/10.1103/PhysRevLett.96.208701, 2006.
Hamill, L. and Gilbert, N.: Agent-Based Modelling in Economics, Wiley, Chichester, UK, 2016.
Hansson, S. O.: Social Choice with procedural preferences, Soc. Choice Welfare, 13, 215–230, https://doi.org/10.1007/BF00183352, 1996.
Hardt, L. and O'Neill, D. W.: Ecological Macroeconomic Models: Assessing Current Developments, Ecol. Econ., 134, 198–211, https://doi.org/10.1016/j.ecolecon.2016.12.027, 2017.
Harsanyi, J. C. and Selten, R.: A General Theory of Equilibrium Selection in Games, MIT Press, Cambridge, MA, 1988.
Hauser, J. R., Ding, M., and Gaskin, S. P.: Non-compensatory (and Compensatory) Models of Consideration-Set Decisions, in: Proceedings of the Sawtooth Software Conference, May, 2009.
Havlík, P., Schneider, U. A., Schmid, E., Böttcher, H., Fritz, S., Skalský, R., Aoki, K., Cara, S. D., Kindermann, G., Kraxner, F., Leduc, S., McCallum, I., Mosnier, A., Sauer, T., and Obersteiner, M.: Global land-use implications of first and second generation biofuel targets, Energ. Policy, 39, 5690–5702, https://doi.org/10.1016/j.enpol.2010.03.030, 2011.
Heckbert, S.: MayaSim: An Agent-Based Model of the Ancient Maya Social-Ecological System, JASSS-J. Artif. Soc. S., 16, 11, https://doi.org/10.18564/jasss.2305, 2013.
Heckbert, S., Baynes, T., and Reeson, A.: Agent-based modeling in ecological economics, Ann. NY Acad. Sci., 1185, 39–53, https://doi.org/10.1111/j.1749-6632.2009.05286.x, 2010.
Hedström, P.: Dissecting the social: On the principles of analytical sociology, Cambridge University Press, Cambridge, UK, 2005.
Hedström, P. and Udehn, L.: Analytical Sociology and Theories of the Middle Range, in: The Oxford Handbook of Analytical Sociology, edited by: Hedström, P. and Bearman, P., Oxford University Press, Oxford and New York, chap. 2, 25–47, 2009.
Hedström, P. and Ylikoski, P.: Causal Mechanisms in the Social Sciences, Annu. Rev. Sociol., 36, 49–67, https://doi.org/10.1146/annurev.soc.012809.102632, 2010.
Hegselmann, R. and Krause, U.: Opinion Dynamics and Bounded Confidence Models, Analysis, and Simulation, JASSS-J. Artif. Soc. S., 5, 1–33, 2002.
Heitzig, J.: Bottom-Up Strategic Linking of Carbon Markets: Which Climate Coalitions Would Farsighted Players Form?, FEEM Working Paper No. 48.2013, https://doi.org/10.2139/ssrn.2274724, 2013.
Heitzig, J. and Simmons, F. W.: Some chance for consensus: voting methods for which consensus is an equilibrium, Soc. Choice Welfare, 38, 43–57, https://doi.org/10.1007/s00355-010-0517-y, 2012.
Heitzig, J., Lessmann, K., and Zou, Y.: Self-enforcing strategies to deter free-riding in the climate change mitigation game and other repeated public good games, P. Natl. Acad. Sci. USA, 108, 15739–15744, https://doi.org/10.1073/pnas.1106265108, 2011.
Hertel, T. W., Rose, S. K., and Tol, R. S. J. (Eds.): Economic analysis of land use in global climate change policy, Routledge, London and New York, 2009.
Hertwig, R. and Herzog, S. M.: Fast and Frugal Heuristics: Tools of Social Rationality, Soc. Cognition, 27, 661–698, https://doi.org/10.1521/soco.2009.27.5.661, 2009.
Hilbert, M.: Toward a Synthesis of Cognitive Biases: How Noisy Information Processing Can Bias Human Decision Making, Psychol. Bull., 138, 211–237, https://doi.org/10.1037/a0025940, 2012.
Hodgson, G. M.: Meanings of methodological individualism, Journal of Economic Methodology, 14, 211–226, https://doi.org/10.1080/13501780701394094, 2007.
Holme, P. and Newman, M. E. J.: Nonequilibrium phase transition in the coevolution of networks and opinions, Phys. Rev. E, 74, 056108, https://doi.org/10.1103/PhysRevE.74.056108, 2006.
Holme, P. and Saramäki, J.: Temporal networks, Phys. Rep., 519, 97–125, https://doi.org/10.1016/j.physrep.2012.03.001, 2012.
Homans, G. C.: The human group, Harcourt, Brace & World, New York, 1950.
Huckfeldt, R., Johnson, P. E., and Sprague, J.: Political Disagreement. The Survival of Diverse Opinions within Communication Networks, Cambridge University Press, Cambridge, UK, 2004.
Hughes, B.: International Futures: Choices in the face of uncertainty, 3rd Edn., Westview Press, Boulder, CO, 1999.
IPCC: Climate Change 2014: Synthesis Report. Contribution of Working Group I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, IPCC, Geneva, 2014.
Jamison, D. T. and Jamison, J.: Characterizing the Amount and Speed of Discounting Procedures, Journal of Benefit-Cost Analysis, 2, 1, https://doi.org/10.2202/2152-2812.1031, 2011.
Jarvis, A. J., Jarvis, S. J., and Hewitt, C. N.: Resource acquisition, distribution and end-use efficiencies and the growth of industrial society, Earth Syst. Dynam., 6, 689–702, https://doi.org/10.5194/esd-6-689-2015, 2015.
Jones, D. A.: The polarizing effect of new media messages, International Journal of Public Opinion Research, 14, 158–174, https://doi.org/10.1093/Ijpor/14.2.158, 2002.
Kahneman, D. and Tversky, A.: Prospect Theory: An Analysis of Decision under Risk, Econometrica, 47, 263–292, https://doi.org/10.2307/1914185, 1979.
Kamien, M. I. and Schwartz, N. L.: Dynamic Optimization: The Calculus of Variations and Optimal Control in Economics and Management, 2nd Edn., Dover Publications, Mineola, New York, 2012.
Kandori, M., Mailath, G. J., and Rob, R.: Learning, mutation, and long run equilibria in games, Econometrica, 61, 29–56, https://doi.org/10.2307/2951777, 1993.
Keeling, M. J.: Multiplicative moments and measures of persistence in ecology., J. Theor. Biol., 205, 269–81, https://doi.org/10.1006/jtbi.2000.2066, 2000.
Keller, N., Czienskowski, U., and Feufel, M. A.: Tying up loose ends: a method for constructing and evaluating decision aids that meet blunt and sharp-end goals, Ergonomics, 57, 1127–1139, https://doi.org/10.1080/00140139.2014.917204, 2014.
Kellie-Smith, O. and Cox, P. M.: Emergent dynamics of the climate-economy system in the Anthropocene, Philos. T. R. Soc. A, 369, 868–86, https://doi.org/10.1098/rsta.2010.0305, 2011.
Kennedy, W. G. and Bassett, J. K.: Implementing a “Fast and Frugal” Cognitive Model within a Computational Social Simulation, Proceedings of the Second Annual Meeting of the Computational Social Science Society of the Americas, http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.399.2304&rep=rep1&type=pdf (last access: 27 October 2017), 2011.
Kirman, A. P.: Whom and What Does the Representative Individual Represent?, J. Econ. Perspect., 6, 117–136, https://doi.org/10.1257/jep.6.2.117, 1992.
Kirman, A.: Complex Economics. Individual and collective rationality, Taylor & Francis, New York, 2011.
Kurahashi-Nakamura, T., Mäs, M., and Lorenz, J.: Robust clustering in generalized bounded confidence models, JASSS-J. Artif. Soc. S., 19, 7, https://doi.org/10.18564/jasss.3220, 2016.
Kurths, J., Heitzig, J., and Marwan, N.: Approaching cooperation via complexity, in: Global cooperation and the human factor in international relations, edited by: Messner, D. and Weinlich, S., Taylor & Francis, London and New York, chap. 7, 155–180, 2015.
Lade, S., Bodin, Ö., Donges, J. F., Kautsky, E. E., Galafassi, D., Olsson, P., and Schlüter, M.: Modelling social-ecological transformations: an adaptive network proposal, https://arxiv.org/ftp/arxiv/papers/1704/1704.06135.pdf, last access: 27 October 2017.
Lau, R. R. and Redlawsk, D. P.: How voters decide: Information processing in election campaigns, Cambridge University Press, New York, 2006.
Lazarsfeld, P. F. and Merton, R. K.: Friendship and Social Process: A Substantive and Methodological Analysis, in: Freedom and Control in Modern Society, edited by: Berger, M., Abel, T., and Page, C. H., Van Nostrand, New York, Toronto, London, 18–66, 1954.
Leontief, W.: Input-Output Economics, 2 Edn., Oxford University Press, New York, 1986.
Lindenberg, S.: An assessment of the new political economy: Its potential for the social sciences and for sociology in particular, Sociol. Theor., 3, 99–114, https://doi.org/10.2307/202177, 1985.
Lindenberg, S.: Homo socio-oeconomicus: The emergence of a general model of man in the social sciences, J. Inst. Theor. Econ., 146, 727–748, 1990.
Lindenberg, S.: Social rationality as a unified model of man (including bounded rationality), Journal of Management and Governance, 5, 239–251, https://doi.org/10.1023/A:1014036120725, 2001.
Lisowski, M.: Playing the Two-level Game: US President Bush's Decision to Repudiate the Kyoto Protocol, Environ. Polit., 11, 101–119, https://doi.org/10.1080/714000641, 2002.
Loewenstein, G. and Lerner, J. S.: The role of affect in decision making, in: Handbook of Affective Sciences, edited by: Davidson, R. J., Scherer, K. R., and Goldsmith, H. H., Oxford University Press, New York, chap. 31, 619–642, 2003.
Loock, M. and Hinnen, G.: Heuristics in organizations: A review and a research agenda, J. Bus. Res., 68, 2027–2036, https://doi.org/10.1016/j.jbusres.2015.02.016, 2015.
Lorenz, J.: A stabilization theorem for dynamics of continuous opinions, Physica A, 355, 217–223, https://doi.org/10.1016/j.physa.2005.02.086, 2005.
Lotze-Campen, H., Müller, C., Bondeau, A., Rost, S., Popp, A., and Lucht, W.: Global food demand, productivity growth, and the scarcity of land and water resources: A spatially explicit mathematical programming approach, Agr. Econ., 39, 325–338, https://doi.org/10.1111/j.1574-0862.2008.00336.x, 2008.
Macy, M., Flache, A., and Benard, S.: Learning, in: Simulating Social Complexity. A Handbook, edited by: Edmonds, B. and Meyer, R., Springer, New York, chap. 17, 431–452, https://doi.org/10.1007/978-3-540-93813-2_17, 2013.
Macy, M. W. and Flache, A.: Learning dynamics in social dilemmas, P. Natl. Acad. Sci. USA, 99, 7229–7236, https://doi.org/10.1073/pnas.092080099, 2002.
Martin, R., Linstädter, A., Frank, K., and Müller, B.: Livelihood security in face of drought – Assessing the vulnerability of pastoral households, Environ. Modell. Softw., 75, 414–423, https://doi.org/10.1016/j.envsoft.2014.10.012, 2016.
Martins, A. C. R.: Continuous Opinions and Discrete Actions in Opinion Dynamics Problems, Int. J. Mod. Phys. C, 19, 617–624, https://doi.org/10.1142/S0129183108012339, 2008.
Mäs, M. and Flache, A.: Differentiation without distancing. Explaining opinion bi-polarization without assuming negative influence, PLoS ONE, 8, e74516, https://doi.org/10.1371/journal.pone.0074516, 2013.
Mäs, M., Flache, A., and Helbing, D.: Individualization as Driving Force of Clustering Phenomena in Humans, PLoS Comput. Biol., 6, e1000959, https://doi.org/10.1371/journal.pcbi.1000959, 2010.
Mäs, M., Flache, A., Takács, K., and Jehn, K.: In the short term we divide, in the long term we unite. Crisscrossing work team members and the effects of faultlines on intergroup polarization, Organ. Sci., 24, 716–736, https://doi.org/10.1287/orsc.1120.0767, 2013.
Maslin, M. A. and Lewis, S. L.: Anthropocene: Earth System, geological, philosophical and political paradigm shifts, The Anthropocene Review, 2, 108–116, https://doi.org/10.1177/2053019615588791, 2015.
Mathesius, S., Hofmann, M., Caldeira, K., and Schellnhuber, H. J.: Long-term response of oceans to CO2 removal from the atmosphere, Nature Climate Change, 5, 1107–1113, https://doi.org/10.1038/nclimate2729, 2015.
Matthews, R. B., Gilbert, N. G., Roach, A., Polhill, J. G., and Gotts, N. M.: Agent-based land-use models: a review of applications, Landscape Ecol., 22, 1447–1459, https://doi.org/10.1007/s10980-007-9135-1, 2007.
McPherson, M., Smith-Lovin, L., and Cook, J. M.: Birds of a Feather: Homophily in Social Networks, Annu. Rev. Sociol., 27, 415–444, https://doi.org/10.1146/annurev.soc.27.1.415, 2001.
Meadows, D. H., Meadows, D. L., Randers, J., and Behrens, W. W.: The Limits to Growth, Universe Books, New York, 1972.
Meadows, D. H., Randers, J., and Meadows, D. L.: Limits to Growth: The 30-Year Update, Earthscan, London, 2004.
Merton, R. K.: Social Theory and Social Structure, The Free Press, New York, 1957.
Meyfroidt, P.: Environmental cognitions, land change, and social–ecological feedbacks: an overview, Journal of Land Use Science, 8, 341–367, https://doi.org/10.1080/1747423X.2012.667452, 2013.
Michetti, M.: Modelling Land Use, Land-Use Change, and Forestry in Climate Change: A Review of Major Approaches, FEEM Working Paper No. 46.2012, https://doi.org/10.2139/ssrn.2122298, 2012.
Miller, R. E. and Blair, P. D.: Input-Output Analysis: Foundations and Extensions, 2nd Edn., Cambridge University Press, Cambridge, UK, 2009.
Millner, A.: On welfare frameworks and catastrophic climate risks, J. Environ. Econ. Manag., 65, 310–325, https://doi.org/10.1016/j.jeem.2012.09.006, 2013.
Moss, R., Edmonds, J., Hibbard, K., Manning, M., Rose, S., Van Vuuren, D., Carter, T., Emori, S., Kainuma, M., Kram, T., Meehl, G., Mitchell, J., Nakicenovic, N., Riahi, K., Smith, S., Stouffer, R., Thomson, A., Weyant, J., and Wilbanks, T.: The next generation of scenarios for climate change research and assessment, Nature, 463, 747–756, https://doi.org/10.1038/nature08823, 2010.
Mueller, D. C.: Public Choice III, Cambridge University Press, Cambridge, UK, 2003.
Murray-Rust, D., Brown, C., van Vliet, J., Alam, S. J., Robinson, D. T., Verburg, P. H., and Rounsevell, M.: Combining agent functional types, capitals and services to model land use dynamics, Environ. Modell. Softw., 59, 187–201, https://doi.org/10.1016/j.envsoft.2014.05.019, 2014.
Myers, D. G.: Polarizing Effects of Social Interaction, in: Group Decision Making, edited by: Brandstätter, H., Davis, J. H., and Stocker-Kreichgauer, G., Academic Press, London, chap. 6, 125–161, 1982.
Nowak, A., Szamrej, J., and Latané, B.: From Private Attitude to Public Opinion: A Dynamic Theory of Social Impact, Psychol. Rev., 97, 362–376, https://doi.org/10.1037/0033-295X.97.3.362, 1990.
Nowak, M. A.: Evolutionary Dynamics – Exploring the Equations of Life, The Belknap Press of Harvad University Press, Cambridge, MA and London, UK, 2006.
Opp, K.-D.: Contending conceptions of the theory of rational action, J. Theor. Polit., 11, 171–202, https://doi.org/10.1177/0951692899011002002, 1999.
Ordeshook, P. C.: Game theory and political theory. An Introduction, Cambridge University Press, Cambridge, UK, New York and Melbourne, 1986.
Ostrom, E.: Governing the Commons: The Evolution of Institutions for Collective Action, Cambridge University Press, Cambridge, UK, 1990.
Page, S. E.: What Sociologists Should Know About Complexity, Ann. Rev. Sociol., 41, 21–41, https://doi.org/10.1146/annurev-soc-073014-112230, 2015.
Palmer, P. I. and Smith, M. J.: Model human adaptation to climate change, Nature, 512, 365–366, https://doi.org/10.1038/512365a, 2014.
Parker, D. C., Manson, S. M., Janssen, M. a., Hoffmann, M. J., and Deadman, P.: Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review, Ann. Assoc. Am. Geogr., 93, 314–337, https://doi.org/10.1111/1467-8306.9302004, 2003.
Perc, M. and Szolnoki, A.: Coevolutionary games-A mini review, BioSystems, 99, 109–125, https://doi.org/10.1016/j.biosystems.2009.10.003, 2010.
Perman, R., Ma, Y., McGilvray, J., and Common, M.: Natural resource and environmental economics, 3rd Edn., Pearson Education, Essex, UK, 2003.
Pineda, M., Toral, R., and Hernández-García, E.: Noisy continuous-opinion dynamics, J. Stat. Mech.-Theory E., 08, P08001, https://doi.org/10.1088/1742-5468/2009/08/P08001, 2009.
Puga, J. L., Krzywinski, M., and Altman, N.: Points of Significance: Bayes' theorem, Nat. Methods, 12, 277–278, https://doi.org/10.1038/nmeth.3335, 2015.
Putnam, R. D.: Diplomacy and domestic politics: the logic of two-level games, Int. Organ., 42, 427–460, https://doi.org/10.1017/S0020818300027697, 1988.
Rabin, M.: A perspective on psychology and economics, Eur. Econ. Rev., 46, 657–685, https://doi.org/10.1016/S0014-2921(01)00207-0, 2002.
Reif, F.: Fundamentals of statistical and thermal physics, McGraw-Hill, New York, 1965.
Rizvi, S. A. T.: The microfoundations project in general equilibrium theory, Cambridge J. Econ., 18, 357–377, https://doi.org/10.1093/oxfordjournals.cje.a035280, 1994.
Rogers, T., Clifford-Brown, W., Mills, C., and Galla, T.: Stochastic oscillations of adaptive networks: application to epidemic modelling, J. Stat. Mech.-Theory E, 2012, P08018, https://doi.org/10.1088/1742-5468/2012/08/P08018, 2012.
Romer, P. M.: Increasing Returns and Long-Run Growth, J. Polit. Econ., 94, 1002–1037, https://doi.org/10.1086/261420, 1986.
Rosenberg, A.: Philosophy of Social Science, 4th Edn., Westview Press, Boulder, CO, 2012.
Schelling, T. C.: Micromotives and Macrobehavior, W. W. Norton and Company, New York, 1978.
Schleussner, C.-F., Donges, J. F., Engemann, D. A., and Levermann, A.: Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure, Nature Scientific Reports, 6, 30790, https://doi.org/10.1038/srep30790, 2016.
Schlüter, M., Mcallister, R. R. J., Arlinghaus, R., Bunnefeld, N., Eisenack, K., Hölker, F., Milner-Gulland, E. J., Müller, B., Nicholson, E., Quaas, M., and Stöven, M.: New Horizons for Managing the Environment: A Review of Coupled Social-Ecological Systems Modeling, Nat. Resour. Model., 25, 219–272, https://doi.org/10.1111/j.1939-7445.2011.00108.x, 2012.
Schlüter, M., Tavoni, A., and Levin, S.: Robustness of norm-driven cooperation in the commons, P. Roy. Soc, B-Biol. Sci., 283, 20152431, https://doi.org/10.1098/rspb.2015.2431, 2016.
Schlüter, M., Baeza, A., Dressler, G., Frank, K., Gröneveld, J., Jager, W., Janssen, M., McAllister, R., Müller, B., Orach, K., Schwarz, N., and Wijermans, N.: A framework for mapping and comparing behavioral theories in models of social-ecological systems, Ecol. Econ., 131, 21–35, https://doi.org/10.1016/j.ecolecon.2016.08.008, 2017.
Simon, H. A.: Rational choice and the structure of the environment, Psychol. Rev., 63, 129–138, https://doi.org/10.1037/h0042769, 1956.
Simon, H. A.: Administrative Behavior: A Study of Decision-Making Processes in Administrative Organisations, 4th Edn., Free Press, New York, 1997.
Smajgl, A. and Barreteau, O. (Eds.): Empirical Agent-Based Modelling – Challenges and Solutions, Springer, New York, https://doi.org/10.1007/978-1-4614-6134-0, 2014.
Stanilov, K.: Space in Agent-Based Models, in: Agent-Based Models of Geographical Systems, edited by: Heppenstall, A. J., Crooks, A. T., See, L. M., and Batty, M., Springer, Dordrecht, the Netherlands, chap. 13, https://doi.org/10.1007/978-90-481-8927-4, 2012.
Stroud, N. J.: Polarization and Partisan Selective Exposure, J. Commun., 60, 556–576, https://doi.org/10.1111/J.1460-2466.2010.01497.X, 2010.
Suh, S. (Ed.): Handbook of Input-Output Economics in Industrial Ecology, Eco-Efficiency in Industry and Science 23, Springer, Dordrecht, the Netherlands, 2009.
Sutton, R. S. and Barto, A. G.: Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA, 1998.
Szabó, G. and Fáth, G.: Evolutionary games on graphs, Phys. Rep., 446, 97–216, https://doi.org/10.1016/j.physrep.2007.04.004, 2007.
Sznajd-Weron, K. and Sznajd, J.: Opinion Evolution in Closed Community, Int. J. Mod. Phys. C, 11, 1157–1165, https://doi.org/10.1142/S0129183100000936, 2000.
Takács, K., Flache, A., and Mäs, M.: Discrepancy and disliking do not induce negative opinion shifts, PLoS ONE, 11, e0157948, https://doi.org/10.1371/journal.pone.0157948, 2016.
Ten Raa, T.: The economics of input-output analysis, Cambridge University Press, Cambridge, UK, 2005.
Tesfatsion, L.: Agent-Based Computational Economics: A Constructive Approach to Economic Theory, in: Handbook of Computational Economics Volume 2: Agent-Based Computational Economics, edited by Tesfatsion, L. and Judd, K. L., vol. 2, 831–880, North Holland, Amsterdam, https://doi.org/10.1016/S1574-0021(05)02016-2, 2006.
Thaler, R. H. and Sunstein, C. R.: Nudge: Improving Decisions About Health, Wealth, and Happiness, Penguin Books, New York, 2009.
The World Bank: World Development Report 2015: Mind, society, and behavior, Tech. rep., https://doi.org/10.1596/978-1-4648-0342-0, 2015.
Thornton, P. E., Calvin, K., Jones, A. D., Di Vittorio, A. V., Bond-Lamberty, B., Chini, L., Shi, X., Mao, J., Collins, W. D., Edmonds, J., Thomson, A., Truesdale, J., Craig, A., Branstetter, M. L., and Hurtt, G.: Biospheric feedback effects in a synchronously coupled model of human and Earth systems, Nature Climate Change, 7, 496–500, https://doi.org/10.1038/nclimate3310, 2017.
Todd, P. M. and Gigerenzer, G.: Environments That Make Us Smart: Ecological Rationality, Curr. Dir. Psychol. Sci., 16, 167–171, https://doi.org/10.1111/j.1467-8721.2007.00497.x, 2007.
Tversky, A. and Kahneman, D.: Judgment under Uncertainty: Heuristics and Biases, Science, 185, 1124–1131, https://doi.org/10.1126/science.185.4157.1124, 1974.
Tversky, A. and Kahneman, D.: The framing of decisions and the psychology of choice, in: Environmental Impact Assessment, Technology Assessment, and Risk Analysis: Contributions from the Psychological and Decision Sciences, edited by: Covello, V. T., Mumpower, J. L., Stallen, P. J. M., and Uppuluri, V. R. R., Springer, Berlin, Heidelberg, 107–129, https://doi.org/10.1007/978-3-642-70634-9_6, 1985.
Udehn, L.: The changing face of methodological individualism, Annu. Rev. Sociol., 28, 479–507, https://doi.org/10.1146/annurev.soc.28.110601.140938, 2002.
United Nations General Assembly: Transforming our world: The 2030 agenda for sustainable development, https://sustainabledevelopment.un.org/content/documents/21252030 Agenda for Sustainable Development web.pdf (last access: 27 October 2017), 2015.
van den Bergh, J. C.: Ecological economics: themes, approaches, and differences with environmental economics, Reg. Environ. Change, 2, 13–23, https://doi.org/10.1007/s101130000020, 2001.
van Vuuren, D. P., Bayer, L. B., Chuwah, C., Ganzeveld, L., Hazeleger, W., van den Hurk, B., van Noije, T., O'Neill, B., and Strengers, B. J.: A comprehensive view on climate change: coupling of earth system and integrated assessment models, Environ. Res. Lett., 7, 024012, https://doi.org/10.1088/1748-9326/7/2/024012, 2012.
van Vuuren, D. P., Lucas, P. L., Häyhä, T., Cornell, S. E., and Stafford-Smith, M.: Horses for courses: analytical tools to explore planetary boundaries, Earth Syst. Dynam., 7, 267–279, https://doi.org/10.5194/esd-7-267-2016, 2016.
Varian, H. R.: Intermediate Microeconomics, 8th Edn., W. W. Norton & Company, New York and London, 2010.
Verburg, P. H., Dearing, J. A., Dyke, J. G., Leeuw, S. V. D., Seitzinger, S., Steffen, W., and Syvitski, J.: Methods and approaches to modelling the Anthropocene, Global Environ. Chang., 39, 328–340, https://doi.org/10.1016/j.gloenvcha.2015.08.007, 2016.
Vlassis, N., Ghavamzadeh, M., Mannor, S., and Poupart, P.: Bayesian Reinforcement Learning, in: Reinforcement Learning: State-of-the-Art, edited by: Wiering, M. and van Otterlo, M., Springer, Berlin, Heidelberg, 359–386, https://doi.org/10.1007/978-3-642-27645-3_11, 2012.
Von Neumann, J. and Morgenstern, O.: Theory of games and economic behavior, 3rd Edn., Princeton University Press, Princeton, NJ, 1953.
Weyant, J., Davidson, O., Dowlabathi, H., Edmonds, J., Grubb, M., Parson, E., Richels, R., Rotmans, J., Shukla, P., Tol, R., Cline, W., and Fankhauser, S.: Integrated Assessment of Climate Change: An Overview and Comparison of Approaches and Results, in: Climate Change 1995: Economic and Social Dimensions of Climate Change – Contribution of Working Group III to the Second Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Bruce, J. P., Lee, H., and Haites, E. F., Cambridge University Press, Cambridge, UK, New York and Melbourne, 367–396, 1996.
Wickens, M.: Macroeconomic Theory. A Dynamic General Equilibrium Approach, Princeton University Press, Princeton and Oxford, 2008.
Wiedermann, M., Donges, J. F., Heitzig, J., Lucht, W., and Kurths, J.: Macroscopic description of complex adaptive networks coevolving with dynamic node states, Phys. Rev. E, 91, 052801, https://doi.org/10.1103/PhysRevE.91.052801, 2015.
Wiedmann, T.: A review of recent multi-region input-output models used for consumption-based emission and resource accounting, Ecol. Econ., 69, 211–222, https://doi.org/10.1016/j.ecolecon.2009.08.026, 2009.
Wimmer, A. and Lewis, K.: Beyond and Below Racial Homophily: ERG Models of a Friendship Network Documented on Facebook, Am. J. Sociol., 116, 583–642, https://doi.org/10.1086/653658, 2010.
Wood, W.: Attitude Change: Persuasion and Social Influence, Annu. Rev. Psychol., 51, 539–570, https://doi.org/10.1146/annurev.psych.51.1.539, 2000.
Short summary
Today, human interactions with the Earth system lead to complex feedbacks between social and ecological dynamics. Modeling such feedbacks explicitly in Earth system models (ESMs) requires making assumptions about individual decision making and behavior, social interaction, and their aggregation. In this overview paper, we compare different modeling approaches and techniques and highlight important consequences of modeling assumptions. We illustrate them with examples from land-use modeling.
Today, human interactions with the Earth system lead to complex feedbacks between social and...
Altmetrics
Final-revised paper
Preprint