Articles | Volume 14, issue 6
https://doi.org/10.5194/gmd-14-4117-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/gmd-14-4117-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
01 Jul 2021
Model description paper |
| 01 Jul 2021
| 01 Jul 2021
CM2Mc-LPJmL v1.0: biophysical coupling of a process-based dynamic vegetation model with managed land to a general circulation model
Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, 14412 Potsdam, Germany
Humboldt University of Berlin, Department of Physics,
12489 Berlin, Germany
Werner von Bloh
Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, 14412 Potsdam, Germany
Stefan Petri
Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, 14412 Potsdam, Germany
Boris Sakschewski
Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, 14412 Potsdam, Germany
Sibyll Schaphoff
Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, 14412 Potsdam, Germany
Matthias Forkel
Institute of Photogrammetry and Remote Sensing, Dresden University of Technology, 01069 Dresden, Germany
Willem Huiskamp
Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, 14412 Potsdam, Germany
Georg Feulner
Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, 14412 Potsdam, Germany
Kirsten Thonicke
Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, 14412 Potsdam, Germany
Related authors
Ricarda Winkelmann, Donovan P. Dennis, Jonathan F. Donges, Sina Loriani, Ann Kristin Klose, Jesse F. Abrams, Jorge Alvarez-Solas, Torsten Albrecht, David Armstrong McKay, Sebastian Bathiany, Javier Blasco Navarro, Victor Brovkin, Eleanor Burke, Gokhan Danabasoglu, Reik V. Donner, Markus Drüke, Goran Georgievski, Heiko Goelzer, Anna B. Harper, Gabriele Hegerl, Marina Hirota, Aixue Hu, Laura C. Jackson, Colin Jones, Hyungjun Kim, Torben Koenigk, Peter Lawrence, Timothy M. Lenton, Hannah Liddy, José Licón-Saláiz, Maxence Menthon, Marisa Montoya, Jan Nitzbon, Sophie Nowicki, Bette Otto-Bliesner, Francesco Pausata, Stefan Rahmstorf, Karoline Ramin, Alexander Robinson, Johan Rockström, Anastasia Romanou, Boris Sakschewski, Christina Schädel, Steven Sherwood, Robin S. Smith, Norman J. Steinert, Didier Swingedouw, Matteo Willeit, Wilbert Weijer, Richard Wood, Klaus Wyser, and Shuting Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1899, https://doi.org/10.5194/egusphere-2025-1899, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
The Tipping Points Modelling Intercomparison Project (TIPMIP) is an international collaborative effort to systematically assess tipping point risks in the Earth system using state-of-the-art coupled and stand-alone domain models. TIPMIP will provide a first global atlas of potential tipping dynamics, respective critical thresholds and key uncertainties, generating an important building block towards a comprehensive scientific basis for policy- and decision-making.
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
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.
Markus Drüke, Wolfgang Lucht, Werner von Bloh, Stefan Petri, Boris Sakschewski, Arne Tobian, Sina Loriani, Sibyll Schaphoff, Georg Feulner, and Kirsten Thonicke
Earth Syst. Dynam., 15, 467–483, https://doi.org/10.5194/esd-15-467-2024, https://doi.org/10.5194/esd-15-467-2024, 2024
Short summary
Short summary
The planetary boundary framework characterizes major risks of destabilization of the Earth system. We use the comprehensive Earth system model POEM to study the impact of the interacting boundaries for climate change and land system change. Our study shows the importance of long-term effects on carbon dynamics and climate, as well as the need to investigate both boundaries simultaneously and to generally keep both boundaries within acceptable ranges to avoid a catastrophic scenario for humanity.
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.
Ricarda Winkelmann, Donovan P. Dennis, Jonathan F. Donges, Sina Loriani, Ann Kristin Klose, Jesse F. Abrams, Jorge Alvarez-Solas, Torsten Albrecht, David Armstrong McKay, Sebastian Bathiany, Javier Blasco Navarro, Victor Brovkin, Eleanor Burke, Gokhan Danabasoglu, Reik V. Donner, Markus Drüke, Goran Georgievski, Heiko Goelzer, Anna B. Harper, Gabriele Hegerl, Marina Hirota, Aixue Hu, Laura C. Jackson, Colin Jones, Hyungjun Kim, Torben Koenigk, Peter Lawrence, Timothy M. Lenton, Hannah Liddy, José Licón-Saláiz, Maxence Menthon, Marisa Montoya, Jan Nitzbon, Sophie Nowicki, Bette Otto-Bliesner, Francesco Pausata, Stefan Rahmstorf, Karoline Ramin, Alexander Robinson, Johan Rockström, Anastasia Romanou, Boris Sakschewski, Christina Schädel, Steven Sherwood, Robin S. Smith, Norman J. Steinert, Didier Swingedouw, Matteo Willeit, Wilbert Weijer, Richard Wood, Klaus Wyser, and Shuting Yang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1899, https://doi.org/10.5194/egusphere-2025-1899, 2025
This preprint is open for discussion and under review for Earth System Dynamics (ESD).
Short summary
Short summary
The Tipping Points Modelling Intercomparison Project (TIPMIP) is an international collaborative effort to systematically assess tipping point risks in the Earth system using state-of-the-art coupled and stand-alone domain models. TIPMIP will provide a first global atlas of potential tipping dynamics, respective critical thresholds and key uncertainties, generating an important building block towards a comprehensive scientific basis for policy- and decision-making.
Jéssica Schüler, Sarah Bereswill, Werner von Bloh, Maik Billing, Boris Sakschewski, Luke Oberhagemann, Kirsten Thonicke, and Mercedes M. C. Bustamante
EGUsphere, https://doi.org/10.5194/egusphere-2025-2225, https://doi.org/10.5194/egusphere-2025-2225, 2025
Short summary
Short summary
We introduced a new plant type into a global vegetation model to better represent the ecology of the Cerrado, South America's second largest biome. This improved the model’s ability to simulate vegetation structure, root systems, and fire dynamics, aligning more closely with observations. Our results enhance understanding of tropical savannas and provide a stronger basis for studying their responses to fire and climate change at regional and global scales.
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
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.
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.
Stephen Björn Wirth, Johanna Braun, Jens Heinke, Sebastian Ostberg, Susanne Rolinski, Sibyll Schaphoff, Fabian Stenzel, Werner von Bloh, Friedhelm Taube, and Christoph Müller
Geosci. Model Dev., 17, 7889–7914, https://doi.org/10.5194/gmd-17-7889-2024, https://doi.org/10.5194/gmd-17-7889-2024, 2024
Short summary
Short summary
We present a new approach to modelling biological nitrogen fixation (BNF) in the Lund–Potsdam–Jena managed Land dynamic global vegetation model. While in the original approach BNF depended on actual evapotranspiration, the new approach considers soil water content and temperature, vertical root distribution, the nitrogen (N) deficit and carbon (C) costs. The new approach improved simulated BNF compared to the scientific literature and the model ability to project future C and N cycle dynamics.
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.
Markus Drüke, Wolfgang Lucht, Werner von Bloh, Stefan Petri, Boris Sakschewski, Arne Tobian, Sina Loriani, Sibyll Schaphoff, Georg Feulner, and Kirsten Thonicke
Earth Syst. Dynam., 15, 467–483, https://doi.org/10.5194/esd-15-467-2024, https://doi.org/10.5194/esd-15-467-2024, 2024
Short summary
Short summary
The planetary boundary framework characterizes major risks of destabilization of the Earth system. We use the comprehensive Earth system model POEM to study the impact of the interacting boundaries for climate change and land system change. Our study shows the importance of long-term effects on carbon dynamics and climate, as well as the need to investigate both boundaries simultaneously and to generally keep both boundaries within acceptable ranges to avoid a catastrophic scenario for humanity.
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024, https://doi.org/10.5194/gmd-17-3235-2024, 2024
Short summary
Short summary
We provide an R package to compute two biosphere integrity metrics that can be applied to simulations of vegetation growth from the dynamic global vegetation model LPJmL. The pressure metric BioCol indicates that we humans modify and extract > 20 % of the potential preindustrial natural biomass production. The ecosystems state metric EcoRisk shows a high risk of ecosystem destabilization in many regions as a result of climate change and land, water, and fertilizer use.
Adrianus de Laat, Vincent Huijnen, Niels Andela, and Matthias Forkel
EGUsphere, https://doi.org/10.5194/egusphere-2024-732, https://doi.org/10.5194/egusphere-2024-732, 2024
Preprint archived
Short summary
Short summary
This study assesses state-of-the art and more advanced and innovative satellite-observation-based (bottom-up) wildfire emission estimates. They are evaluated by comparison with satellite observation of single fire emission plumes. Results indicate that more advanced fire emission estimates – more information – are more realistic but that especially for a limited number of very large fires certain differences remain – for unknown reasons.
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.
Julius Eberhard, Oliver E. Bevan, Georg Feulner, Stefan Petri, Jeroen van Hunen, and James U. L. Baldini
Clim. Past, 19, 2203–2235, https://doi.org/10.5194/cp-19-2203-2023, https://doi.org/10.5194/cp-19-2203-2023, 2023
Short summary
Short summary
During at least two phases in its past, Earth was more or less covered in ice. These “snowball Earth” events probably started suddenly upon undercutting a certain threshold in the carbon-dioxide concentration. This threshold can vary considerably under different conditions. In our study, we find the thresholds for different distributions of continents, geometries of Earth’s orbit, and volcanic eruptions. The results show that the threshold might have varied by up to 46 %.
Sebastian Ostberg, Christoph Müller, Jens Heinke, and Sibyll Schaphoff
Geosci. Model Dev., 16, 3375–3406, https://doi.org/10.5194/gmd-16-3375-2023, https://doi.org/10.5194/gmd-16-3375-2023, 2023
Short summary
Short summary
We present a new toolbox for generating input datasets for terrestrial ecosystem models from diverse and partially conflicting data sources. The toolbox documents the sources and processing of data and is designed to make inconsistencies between source datasets transparent so that users can make their own decisions on how to resolve these should they not be content with our default assumptions. As an example, we use the toolbox to create input datasets at two different spatial resolutions.
Georg Feulner, Mona Bukenberger, and Stefan Petri
Earth Syst. Dynam., 14, 533–547, https://doi.org/10.5194/esd-14-533-2023, https://doi.org/10.5194/esd-14-533-2023, 2023
Short summary
Short summary
One limit of planetary habitability is defined by the threshold of global glaciation. If Earth cools, growing ice cover makes it brighter, leading to further cooling, since more sunlight is reflected, eventually leading to global ice cover (Snowball Earth). We study how much carbon dioxide is needed to prevent global glaciation in Earth's history given the slow increase in the Sun's brightness. We find an unexpected change in the characteristics of climate states close to the Snowball limit.
Hoontaek Lee, Martin Jung, Nuno Carvalhais, Tina Trautmann, Basil Kraft, Markus Reichstein, Matthias Forkel, and Sujan Koirala
Hydrol. Earth Syst. Sci., 27, 1531–1563, https://doi.org/10.5194/hess-27-1531-2023, https://doi.org/10.5194/hess-27-1531-2023, 2023
Short summary
Short summary
We spatially attribute the variance in global terrestrial water storage (TWS) interannual variability (IAV) and its modeling error with two data-driven hydrological models. We find error hotspot regions that show a disproportionately large significance in the global mismatch and the association of the error regions with a smaller-scale lateral convergence of water. Our findings imply that TWS IAV modeling can be efficiently improved by focusing on model representations for the error hotspots.
Luisa Schmidt, Matthias Forkel, Ruxandra-Maria Zotta, Samuel Scherrer, Wouter A. Dorigo, Alexander Kuhn-Régnier, Robin van der Schalie, and Marta Yebra
Biogeosciences, 20, 1027–1046, https://doi.org/10.5194/bg-20-1027-2023, https://doi.org/10.5194/bg-20-1027-2023, 2023
Short summary
Short summary
Vegetation attenuates natural microwave emissions from the land surface. The strength of this attenuation is quantified as the vegetation optical depth (VOD) parameter and is influenced by the vegetation mass, structure, water content, and observation wavelength. Here we model the VOD signal as a multi-variate function of several descriptive vegetation variables. The results help in understanding the effects of ecosystem properties on VOD.
Matthias Forkel, Luisa Schmidt, Ruxandra-Maria Zotta, Wouter Dorigo, and Marta Yebra
Hydrol. Earth Syst. Sci., 27, 39–68, https://doi.org/10.5194/hess-27-39-2023, https://doi.org/10.5194/hess-27-39-2023, 2023
Short summary
Short summary
The live fuel moisture content (LFMC) of vegetation canopies is a driver of wildfires. We investigate the relation between LFMC and passive microwave satellite observations of vegetation optical depth (VOD) and develop a method to estimate LFMC from VOD globally. Our global VOD-based estimates of LFMC can be used to investigate drought effects on vegetation and fire risks.
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.
Benjamin Wild, Irene Teubner, Leander Moesinger, Ruxandra-Maria Zotta, Matthias Forkel, Robin van der Schalie, Stephen Sitch, and Wouter Dorigo
Earth Syst. Sci. Data, 14, 1063–1085, https://doi.org/10.5194/essd-14-1063-2022, https://doi.org/10.5194/essd-14-1063-2022, 2022
Short summary
Short summary
Gross primary production (GPP) describes the conversion of CO2 to carbohydrates and can be seen as a filter for our atmosphere of the primary greenhouse gas CO2. We developed VODCA2GPP, a GPP dataset that is based on vegetation optical depth from microwave remote sensing and temperature. Thus, it is mostly independent from existing GPP datasets and also available in regions with frequent cloud coverage. Analysis showed that VODCA2GPP is able to complement existing state-of-the-art GPP datasets.
Vera Porwollik, Susanne Rolinski, Jens Heinke, Werner von Bloh, Sibyll Schaphoff, and Christoph Müller
Biogeosciences, 19, 957–977, https://doi.org/10.5194/bg-19-957-2022, https://doi.org/10.5194/bg-19-957-2022, 2022
Short summary
Short summary
The study assesses impacts of grass cover crop cultivation on cropland during main-crop off-season periods applying the global vegetation model LPJmL (V.5.0-tillage-cc). Compared to simulated bare-soil fallowing practices, cover crops led to increased soil carbon content and reduced nitrogen leaching rates on the majority of global cropland. Yield responses of main crops following cover crops vary with location, duration of altered management, crop type, water regime, and tillage practice.
Willem Huiskamp and Shayne McGregor
Clim. Past, 17, 1819–1839, https://doi.org/10.5194/cp-17-1819-2021, https://doi.org/10.5194/cp-17-1819-2021, 2021
Short summary
Short summary
This study investigates the reliability of paleo-reconstructions of the Southern Annular Mode (SAM) using climate model data. We find that reconstructions are able to capture ~ 60 % of the SAM variability at best, with poorer reconstructions managing only 35 %. Reconstructions perform best when they use more proxies sourced from the entire Southern Hemisphere land mass. Future reconstructions should endeavour to address both sampling and proxy–SAM correlation stability uncertainties.
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.
Alexander Kuhn-Régnier, Apostolos Voulgarakis, Peer Nowack, Matthias Forkel, I. Colin Prentice, and Sandy P. Harrison
Biogeosciences, 18, 3861–3879, https://doi.org/10.5194/bg-18-3861-2021, https://doi.org/10.5194/bg-18-3861-2021, 2021
Short summary
Short summary
Along with current climate, vegetation, and human influences, long-term accumulation of biomass affects fires. Here, we find that including the influence of antecedent vegetation and moisture improves our ability to predict global burnt area. Additionally, the length of the preceding period which needs to be considered for accurate predictions varies across regions.
Moritz Kreuzer, Ronja Reese, Willem Nicholas Huiskamp, Stefan Petri, Torsten Albrecht, Georg Feulner, and Ricarda Winkelmann
Geosci. Model Dev., 14, 3697–3714, https://doi.org/10.5194/gmd-14-3697-2021, https://doi.org/10.5194/gmd-14-3697-2021, 2021
Short summary
Short summary
We present the technical implementation of a coarse-resolution coupling between an ice sheet model and an ocean model that allows one to simulate ice–ocean interactions at timescales from centuries to millennia. As ice shelf cavities cannot be resolved in the ocean model at coarse resolution, we bridge the gap using an sub-shelf cavity module. It is shown that the framework is computationally efficient, conserves mass and energy, and can produce a stable coupled state under present-day forcing.
Irene E. Teubner, Matthias Forkel, Benjamin Wild, Leander Mösinger, and Wouter Dorigo
Biogeosciences, 18, 3285–3308, https://doi.org/10.5194/bg-18-3285-2021, https://doi.org/10.5194/bg-18-3285-2021, 2021
Short summary
Short summary
Vegetation optical depth (VOD), which contains information on vegetation water content and biomass, has been previously shown to be related to gross primary production (GPP). In this study, we analyzed the impact of adding temperature as model input and investigated if this can reduce the previously observed overestimation of VOD-derived GPP. In addition, we could show that the relationship between VOD and GPP largely holds true along a gradient of dry or wet conditions.
Yvonne Jans, Werner von Bloh, Sibyll Schaphoff, and Christoph Müller
Hydrol. Earth Syst. Sci., 25, 2027–2044, https://doi.org/10.5194/hess-25-2027-2021, https://doi.org/10.5194/hess-25-2027-2021, 2021
Short summary
Short summary
Growth of and irrigation water demand on cotton may be challenged by future climate change. To analyze the global cotton production and irrigation water consumption under spatially varying present and future climatic conditions, we use the global terrestrial biosphere model LPJmL. Our simulation results suggest that the beneficial effects of elevated [CO2] on cotton yields overcompensate yield losses from direct climate change impacts, i.e., without the beneficial effect of [CO2] fertilization.
Gerilyn S. Soreghan, Laurent Beccaletto, Kathleen C. Benison, Sylvie Bourquin, Georg Feulner, Natsuko Hamamura, Michael Hamilton, Nicholas G. Heavens, Linda Hinnov, Adam Huttenlocker, Cindy Looy, Lily S. Pfeifer, Stephane Pochat, Mehrdad Sardar Abadi, James Zambito, and the Deep Dust workshop participants
Sci. Dril., 28, 93–112, https://doi.org/10.5194/sd-28-93-2020, https://doi.org/10.5194/sd-28-93-2020, 2020
Short summary
Short summary
The events of the Permian — the orogenies, biospheric turnovers, icehouse and greenhouse antitheses, and Mars-analog lithofacies — boggle the imagination and present us with great opportunities to explore Earth system behavior. Here we outline results of workshops to propose continuous coring of continental Permian sections in western (Anadarko Basin) and eastern (Paris Basin) equatorial Pangaea to retrieve continental records spanning 50 Myr of Earth's history.
Cited articles
Adcroft, A., Anderson, W., Balaji, V., Blanton, C., Bushuk, M., Dufour, C. O.,
Dunne, J. P., Griffies, S. M., Hallberg, R., Harrison, M. J., Held, I. M.,
Jansen, M. F., John, J. G., Krasting, J. P., Langenhorst, A. R., Legg, S.,
Liang, Z., McHugh, C., Radhakrishnan, A., Reichl, B. G., Rosati, T., Samuels,
B. L., Shao, A., Stouffer, R., Winton, M., Wittenberg, A. T., Xiang, B.,
Zadeh, N., and Zhang, R.: The GFDL Global Ocean and Sea Ice Model OM4.0:
Model Description and Simulation Features, J. Adv. Model.
Earth Sy., 11, 3167–3211, https://doi.org/10.1029/2019MS001726, 2019. a
Alkama, R. and Cescatti, A.: Climate change: Biophysical climate impacts of
recent changes in global forest cover, Science, 351, 600–604,
https://doi.org/10.1126/science.aac8083, 2016. a
Anav, A., Friedlingstein, P., Kidston, M., Bopp, L., Ciais, P., Cox, P., Jones,
C., Jung, M., Myneni, R., and Zhu, Z.: Evaluating the land and ocean
components of the global carbon cycle in the CMIP5 earth system models,
J. Climate, 26, 6801–6843, https://doi.org/10.1175/JCLI-D-12-00417.1, 2013. a
Anderson, J. L., Balaji, V., Broccoli, A. J., Cooke, W. F., Delworth, T. L.,
Dixon, K. W., Donner, L. J., Dunne, K. A., Freidenreich, S. M., Garner,
S. T., Gudgel, R. G., Gordon, C. T., Held, I. M., Hemler, R. S., Horowitz,
L. W., Klein, S. A., Knutson, T. R., Kushner, P. J., Langenhost, A. R., Lau,
N. C., Liang, Z., Malyshev, S. L., Milly, P. C. D., Nath, M. J., Ploshay,
J. J., Ramaswamy, V., Schwarzkopf, M. D., Shevliakova, E., Sirutis, J. J.,
Soden, B. J., Stern, W. F., Thompson, L. A., Wilson, R. J., Wittenberg,
A. T., and Wyman, B. L.: The new GFDL global atmosphere and land model
AM2-LM2: Evaluation with prescribed SST simulations, J. Climate, 17,
4641–4673, https://doi.org/10.1175/JCLI-3223.1, 2004. a, b, c, d, e
Best, M. J., Pryor, M., Clark, D. B., Rooney, G. G., Essery, R. L. H., Ménard, C. B., Edwards, J. M., Hendry, M. A., Porson, A., Gedney, N., Mercado, L. M., Sitch, S., Blyth, E., Boucher, O., Cox, P. M., Grimmond, C. S. B., and Harding, R. J.: The Joint UK Land Environment Simulator (JULES), model description – Part 1: Energy and water fluxes, Geosci. Model Dev., 4, 677–699, https://doi.org/10.5194/gmd-4-677-2011, 2011. a
Bonan, G.
B., Levis, S., Sitch, S., Vertenstein, M., and Oleson, K. W.: A dynamic global vegetation
model for use with climate models: Concepts and description of simulated vegetation
dynamics, Global Change Biol., 9, https://doi.org/10.1046/j.1365-2486.2003.00681.x, 2003. a
Bondeau, A., Smith, P. C., Zaehle, S., Schaphoff, S., Lucht, W., Cramer, W.,
Gerten, D., Lotze-Campen, H., Müller, C., Reichstein, M., and Smith,
B.: Modelling the role of agriculture for the 20th century global
terrestrial carbon balance, Global Change Biol., 13, 679–706,
https://doi.org/10.1111/j.1365-2486.2006.01305.x, 2007. a, b
Boysen, L. R., Brovkin, V., Pongratz, J., Lawrence, D. M., Lawrence, P., Vuichard, N., Peylin, P., Liddicoat, S., Hajima, T., Zhang, Y., Rocher, M., Delire, C., Séférian, R., Arora, V. K., Nieradzik, L., Anthoni, P., Thiery, W., Laguë, M. M., Lawrence, D., and Lo, M.-H.: Global climate response to idealized deforestation in CMIP6 models, Biogeosciences, 17, 5615–5638, https://doi.org/10.5194/bg-17-5615-2020, 2020. a, b
Chapin, F. S., Randerson, J. T., McGuire, A. D., Foley, J. A., and Field,
C. B.: Changing feedbacks in the climate-biosphere system, Front.
Ecol. Environ., 6, 313–320, https://doi.org/10.1890/080005, 2008. a
Christian, H. J., Blakeslee, R. J., Boccippio, D. J., Boeck, W. L., Buechler,
D. E., Driscoll, K. T., Goodman, S. J., Hall, J. M., Koshak, W. J., Mach,
D. M., and Stewart, M. F.: Global frequency and distribution of lightning as
observed from space by the Optical Transient Detector, J. Geophys. Res.-Atmos., 108, 4–1, https://doi.org/10.1029/2002JD002347, 2003. a
Clark, D. A., Clark, D. B., and Oberbauer, S. F.: Field-quantified responses
of tropical rainforest aboveground productivity to increasing CO2 and
climatic stress, 1997–2009, J. Geophys. Res.-Biogeo.,
118, 783–794, https://doi.org/10.1002/jgrg.20067, 2013. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler,
M., Matricardi, M., Mcnally, A. P., Monge-Sanz, B. M., Morcrette, J. J.,
Park, B. K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J. N.,
and Vitart, F.: The ERA-Interim reanalysis: Configuration and performance of
the data assimilation system, Q. J. Roy. Meteor.
Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
De Kauwe, M. G., Kala, J., Lin, Y.-S., Pitman, A. J., Medlyn, B. E., Duursma, R. A., Abramowitz, G., Wang, Y.-P., and Miralles, D. G.: A test of an optimal stomatal conductance scheme within the CABLE land surface model, Geosci. Model Dev., 8, 431–452, https://doi.org/10.5194/gmd-8-431-2015, 2015. a
Delworth, T. L., Broccoli, A. J., Rosati, A., Stouffer, R. J., Balaji, V., Beesley, J. A.,
Cooke, W. F., Dixon, K. W., Dunne, J., Dunne, K. A., Durachta, J. W., Findell, K. L., Ginoux,
P., Gnanadesikan, A., Gordon, C. T., Griffies, S. M., Gudgel, R., Harrison, M. J., Held, I. M.,
Hemler, R. S., Horowitz, L. W., Klein, S. A., Knutson, T. R., Kushner, P. J., Langenhorst, A.
R., Lee, H., Lin, S., Lu, J., Malyshev, S. L., Milly, P. C. D., Ramaswamy, V., Russell, J.,
Schwarzkopf, M. D., Shevliakova, E., Sirutis, J. J., Spelman, M. J., Stern, W. F., Winton, M.,
Wittenberg, A. T., Wyman, B., Zeng, F., and Zhang, R.: GFDL's CM2 global coupled climate models. Part I: Formulation and
simulation characteristics, J. Climate, 19, 643–674, 2006. a, b, c
Drüke, M.: Output data for the GMD publication gmd-2020-436 [data set], Zenodo, https://doi.org/10.5281/zenodo.4683086, 2021. a
Drüke, M., Forkel, M., von Bloh, W., Sakschewski, B., Cardoso, M., Bustamante, M., Kurths, J., and Thonicke, K.: Improving the LPJmL4-SPITFIRE vegetation–fire model for South America using satellite data, Geosci. Model Dev., 12, 5029–5054, https://doi.org/10.5194/gmd-12-5029-2019, 2019. a, b, c
Drüke, M., Petri, S., von Bloh, W., and Schaphoff, S.: Model code for the GMD publication gmd-2020-436 (Version 1.0) [code], Zenodo, https://doi.org/10.5281/zenodo.4700270, 2021. a
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016. a
Fader, M., Rost, S., Mueller, C., Bondeau, A., and Gerten, D.: Virtual water
content of temperate cereals and maize: Present and potential future
patterns, J. Hydrol., 384, 218–231, https://doi.org/10.1016/j.jhydrol.2009.12.011,
2010. a, b
Fisher, R. A., Koven, C. D., Anderegg, W. R., Christoffersen, B. O., Dietze,
M. C., Farrior, C. E., Holm, J. A., Hurtt, G. C., Knox, R. G., Lawrence,
P. J., Lichstein, J. W., Longo, M., Matheny, A. M., Medvigy, D.,
Muller-Landau, H. C., Powell, T. L., Serbin, S. P., Sato, H., Shuman, J. K.,
Smith, B., Trugman, A. T., Viskari, T., Verbeeck, H., Weng, E., Xu, C., Xu,
X., Zhang, T., and Moorcroft, P. R.: Vegetation demographics in Earth System
Models: A review of progress and priorities, Global Change Biol., 24,
35–54, https://doi.org/10.1111/gcb.13910, 2018. a
Forkel, M., Carvalhais, N., Schaphoff, S., v. Bloh, W., Migliavacca, M., Thurner, M., and Thonicke, K.: Identifying environmental controls on vegetation greenness phenology through model–data integration, Biogeosciences, 11, 7025–7050, https://doi.org/10.5194/bg-11-7025-2014, 2014. a, b
Forkel, M., Drüke, M., Thurner, M., Dorigo, W., Schaphoff, S.,
Thonicke, K., von Bloh, W., and Carvalhais, N.: Constraining modelled global vegetation
dynamics and carbon turnover using multiple satellite observations, Sci. Rep., 9, 18757, https://doi.org/10.1038/s41598-019-55187-7, 2019. a
Forrest, M., Tost, H., Lelieveld, J., and Hickler, T.: Including vegetation dynamics in an atmospheric chemistry-enabled general circulation model: linking LPJ-GUESS (v4.0) with the EMAC modelling system (v2.53), Geosci. Model Dev., 13, 1285–1309, https://doi.org/10.5194/gmd-13-1285-2020, 2020. a, b
Frieler, K., Lange, S., Piontek, F., Reyer, C. P. O., Schewe, J., Warszawski,
L., Zhao, F., Chini, L., Denvil, S., Emanuel, K., Geiger, T., Halladay, K.,
Hurtt, G., Mengel, M., Murakami, D., Ostberg, S., Popp, A., Riva, R.,
Stevanovic, M., Suzuki, T., Volkholz, J., Burke, E., Ciais, P., Ebi, K.,
Eddy, T. D., Elliott, J., Galbraith, E., Gosling, S. N., Hattermann, F.,
Hickler, T., Hinkel, J., Hof, C., Huber, V., Jägermeyr, J., Krysanova,
V., Marc, R., Müller Schmied, H., Mouratiadou, I., Pierson, D.,
Tittensor, D. P., Vautard, R., van Vliet, M., Biber, M. F., Betts, R. A.,
Bodirsky, B. L., Deryng, D., Frolking, S., Jones, C. D., Lotze, H. K.,
Lotze-Campen, H., Sahajpal, R., Thonicke, K., Tian, H., and Yamagata, Y.:
Assessing the impacts of 1.5 ∘C global warming – simulation
protocol of the Inter-Sectoral Impact Model Intercomparison Project
(ISIMIP2b), European Geosciences Union,
available at: http://eprints.nottingham.ac.uk/48771 (last access: 30 November 2020), 2017. a
Galbraith, E. D., Kwon, E. Y., Gnanadesikan, A., Rodgers, K. B., Griffies,
S. M., Bianchi, D., Sarmiento, J. L., Dunne, J. P., Simeon, J., Slater,
R. D., Wittenberg, A. T., and Held, I. M.: Climate variability and
radiocarbon in the CM2Mc earth system model, J. Climate, 24,
4230–4254, https://doi.org/10.1175/2011JCLI3919.1, 2011. a, b, c, d, e, f, g, h, i, j, k, l
Gelfan, A. N., Pomeroy, J. W., and Kuchment, L. S.: Modeling forest cover
influences on snow accumulation, sublimation, and melt, J.
Hydrometeorol., 5, 785–803,
https://doi.org/10.1175/1525-7541(2004)005<0785:MFCIOS>2.0.CO;2, 2004. a
Gerten, D., Schaphoff, S., Haberlandt, U., Lucht, W., and Sitch, S.:
Terrestrial vegetation and water balance – hydrological evaluation of a
dynamic global vegetation model, J. Hydrol., 286, 249–270,
https://doi.org/10.1016/j.jhydrol.2003.09.029, 2004. a, b
Gkatsopoulos, P.: A Methodology for Calculating Cooling from Vegetation
Evapotranspiration for Use in Urban Space Microclimate Simulations, Proc.
Environ. Sci., 38, 477–484, https://doi.org/10.1016/j.proenv.2017.03.139,
2017. a
Goldewijk, K. K., Beusen, A., van Drecht, G., and de Vos, M.: The HYDE 3.1
spatially explicit database of human-induced global land-use change over the
past 12 000 years, Global Ecol. Biogeogr., 20, 73–86,
https://doi.org/10.1111/j.1466-8238.2010.00587.x, 2011. a
Green, J. K., Konings, A. G., Alemohammad, S. H., Berry, J., Entekhabi, D.,
Kolassa, J., Lee, J. E., and Gentine, P.: Regionally strong feedbacks
between the atmosphere and terrestrial biosphere, Nat. Geosci., 10,
410–414, https://doi.org/10.1038/ngeo2957, 2017. a, b
Griffies, S. M., Gnanadesikan, A., Dixon, K. W., Dunne, J. P., Gerdes, R., Harrison, M. J., Rosati, A., Russell, J. L., Samuels, B. L., Spelman, M. J., Winton, M., and Zhang, R.: Formulation of an ocean model for global climate simulations, Ocean Sci., 1, 45–79, https://doi.org/10.5194/os-1-45-2005, 2005. a
Hajima, T., Watanabe, M., Yamamoto, A., Tatebe, H., Noguchi, M. A., Abe, M., Ohgaito, R., Ito, A., Yamazaki, D., Okajima, H., Ito, A., Takata, K., Ogochi, K., Watanabe, S., and Kawamiya, M.: Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks, Geosci. Model Dev., 13, 2197–2244, https://doi.org/10.5194/gmd-13-2197-2020, 2020. a, b
Harper, A. B., Wiltshire, A. J., Cox, P. M., Friedlingstein, P., Jones, C. D., Mercado, L. M., Sitch, S., Williams, K., and Duran-Rojas, C.: Vegetation distribution and terrestrial carbon cycle in a carbon cycle configuration of JULES4.6 with new plant functional types, Geosci. Model Dev., 11, 2857–2873, https://doi.org/10.5194/gmd-11-2857-2018, 2018. a
Heyder, U., Schaphoff, S., Gerten, D., and Lucht, W.: Risk of severe
climate change impact on the terrestrial biosphere, Environ. Res. Lett., 6, 034036, https://doi.org/10.1088/1748-9326/6/3/034036, 2011. a
Hoffmann, W. A. and Jackson, R. B.: Vegetation-climate feedbacks in the
conversion of tropical savanna to Grassland, J. Climate, 13,
1593–1602, https://doi.org/10.1175/1520-0442(2000)013<1593:VCFITC>2.0.CO;2, 2000. a
Huntingford, C. and Monteith, J. L.: The behaviour of a mixed-layer model of
the convective boundary layer coupled to a big leaf model of surface energy
partitioning, Bound.-Lay. Meteorol., 88, 87–101,
https://doi.org/10.1023/A:1001110819090, 1998. a
Kattsov, V., Federation, R., Reason, C., Africa, S., Uk, A. A., Uk, T. A.,
Baehr, J., Uk, A. B.-s., Catto, J., Canada, J. S., and Uk, A. S.: Evaluation
of climate models (AR5), Climate Change 2013 the Physical Science Basis:
Working Group I Contribution to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change, 9781107057, 741–866,
https://doi.org/10.1017/CBO9781107415324.020, 2013. a, b, c, d, e, f
Kelley, D. I., Prentice, I. C., Harrison, S. P., Wang, H., Simard, M., Fisher, J. B., and Willis, K. O.: A comprehensive benchmarking system for evaluating global vegetation models, Biogeosciences, 10, 3313–3340, https://doi.org/10.5194/bg-10-3313-2013, 2013. a
Kim, H., Lee, M. I., Cha, D. H., Lim, Y. K., and Putman, W. M.: Improved
representation of the diurnal variation of warm season precipitation by an
atmospheric general circulation model at a 10 km horizontal resolution,
Clim. Dynam., 53, 6523–6542, https://doi.org/10.1007/s00382-019-04943-6, 2019. a
Körner, C.: CO2 Fertilization: The Great Uncertainty in Future
Vegetation Development, in: Vegetation Dynamics & Global Change, pp.
53–70, Springer US, https://doi.org/10.1007/978-1-4615-2816-6_3, 1993. a
Krinner, G., Viovy, N., de Noblet-Ducoudré, N., Ogée, J., Polcher,
J., Friedlingstein, P., Ciais, P., Sitch, S., and Prentice, I. C.: A dynamic
global vegetation model for studies of the coupled atmosphere-biosphere
system, Global Biogeochem. Cycles, 19, 1–33, https://doi.org/10.1029/2003GB002199,
2005. a
Kueppers, L. M., Snyder, M. A., and Sloan, L. C.: Irrigation cooling effect:
Regional climate forcing by land-use change, Geophys. Res. Lett.,
34, 1–5, https://doi.org/10.1029/2006GL028679, 2007. a
Lenssen, N. J., Schmidt, G. A., Hansen, J. E., Menne, M. J., Persin, A., Ruedy,
R., and Zyss, D.: Improvements in the GISTEMP Uncertainty Model, J.
Geophys. Res.-Atmos., 124, 6307–6326,
https://doi.org/10.1029/2018JD029522, 2019. a, b, c
Le Quéré, C., Moriarty, R., Andrew, R. M., Canadell, J. G., Sitch, S., Korsbakken, J. I., Friedlingstein, P., Peters, G. P., Andres, R. J., Boden, T. A., Houghton, R. A., House, J. I., Keeling, R. F., Tans, P., Arneth, A., Bakker, D. C. E., Barbero, L., Bopp, L., Chang, J., Chevallier, F., Chini, L. P., Ciais, P., Fader, M., Feely, R. A., Gkritzalis, T., Harris, I., Hauck, J., Ilyina, T., Jain, A. K., Kato, E., Kitidis, V., Klein Goldewijk, K., Koven, C., Landschützer, P., Lauvset, S. K., Lefèvre, N., Lenton, A., Lima, I. D., Metzl, N., Millero, F., Munro, D. R., Murata, A., Nabel, J. E. M. S., Nakaoka, S., Nojiri, Y., O'Brien, K., Olsen, A., Ono, T., Pérez, F. F., Pfeil, B., Pierrot, D., Poulter, B., Rehder, G., Rödenbeck, C., Saito, S., Schuster, U., Schwinger, J., Séférian, R., Steinhoff, T., Stocker, B. D., Sutton, A. J., Takahashi, T., Tilbrook, B., van der Laan-Luijkx, I. T., van der Werf, G. R., van Heuven, S., Vandemark, D., Viovy, N., Wiltshire, A., Zaehle, S., and Zeng, N.: Global Carbon Budget 2015, Earth Syst. Sci. Data, 7, 349–396, https://doi.org/10.5194/essd-7-349-2015, 2015. a
Levis, S.: Modeling vegetation and land use in models of the Earth System,
Wiley Interdisciplinary Reviews: Climate Change, 1, 840–856,
https://doi.org/10.1002/wcc.83, 2010. a, b
Li, W., MacBean, N., Ciais, P., Defourny, P., Lamarche, C., Bontemps, S., Houghton, R. A., and Peng, S.: Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992–2015), Earth Syst. Sci. Data, 10, 219–234, https://doi.org/10.5194/essd-10-219-2018, 2018. a
Lin, S. J.: A “vertically Lagrangian” finite-volume dynamical core for global
models, Mon. Weather Rev., 132, 2293–2307,
https://doi.org/10.1175/1520-0493(2004)132<2293:AVLFDC>2.0.CO;2, 2004. a
Lutz, F., Herzfeld, T., Heinke, J., Rolinski, S., Schaphoff, S., von Bloh, W., Stoorvogel, J. J., and Müller, C.: Simulating the effect of tillage practices with the global ecosystem model LPJmL (version 5.0-tillage), Geosci. Model Dev., 12, 2419–2440, https://doi.org/10.5194/gmd-12-2419-2019, 2019. a
Luyssaert, S., Jammet, M., Stoy, P. C., Estel, S., Pongratz, J., Ceschia, E.,
Churkina, G., Don, A., Erb, K., Ferlicoq, M., Gielen, B., Grünwald, T.,
Houghton, R. A., Klumpp, K., Knohl, A., Kolb, T., Kuemmerle, T., Laurila, T.,
Lohila, A., Loustau, D., McGrath, M. J., Meyfroidt, P., Moors, E. J., Naudts,
K., Novick, K., Otto, J., Pilegaard, K., Pio, C. A., Rambal, S., Rebmann, C.,
Ryder, J., Suyker, A. E., Varlagin, A., Wattenbach, M., and Dolman, A. J.:
Land management and land-cover change have impacts of similar magnitude on
surface temperature, Nat. Clim. Change, 4, 389–393,
https://doi.org/10.1038/nclimate2196, 2014. a, b
Medlyn, B. E., Duursma, R. A., Eamus, D., Ellsworth, D. S., Prentice, I. C.,
Barton, C. V. M., Crous, K. Y., De Angelis, P., Freeman, M., and Wingate, L.:
Reconciling the optimal and empirical approaches to modelling stomatal
conductance, Global Change Biol., 17, 2134–2144,
https://doi.org/10.1111/j.1365-2486.2010.02375.x, 2011. a
Monteith, J. L.: Rothamsted Repository Download, Symposia of the Society for
Experimental Biology, Cambridge University Press (CUP) Cambridge, 205–234, 1965. a
Mueller, B. and Seneviratne, S. I.: Systematic land climate and
evapotranspiration biases in CMIP5 simulations, Geophys. Res.
Lett., 41, 128–134, https://doi.org/10.1002/2013GL058055, 2014. a
Murray, R. J.: Explicit generation of orthogonal grids for ocean models,
J. Comput. Phys., 126, 251–273,
https://doi.org/10.1006/jcph.1996.0136, 1996. a
Nachtergaele, F. O., van Velthuizen, H. T., and Verelst, L.: Harmonized World
Soil Database, available at: http://pure.iiasa.ac.at/id/eprint/8958 (last access: 30 November 2020), 2009. a
Nyawira, S. S., Nabel, J. E. M. S., Don, A., Brovkin, V., and Pongratz, J.: Soil carbon response to land-use change: evaluation of a global vegetation model using observational meta-analyses, Biogeosciences, 13, 5661–5675, https://doi.org/10.5194/bg-13-5661-2016, 2016. a
Pokhrel, Y. N., Hanasaki, N., Wada, Y., and Kim, H.: Recent progresses in
incorporating human land-water management into global land surface models
toward their integration into Earth system models, Wiley Interdisciplinary
Reviews: Water, 3, 548–574, https://doi.org/10.1002/wat2.1150, 2016. a, b
Prentice, I. C., Bondeau, A., Cramer, W., Harrison,
S. P., Hickler, T., Lucht, W., Sitch, S., Smith, B., and Sykes, M. T.: Dynamic Global
Vegetation Modeling: Quantifying Terrestrial Ecosystem Responses to Large-Scale
Environmental Change, in: Terrestrial
Ecosystems in a Changing World. Global Change – The IGBP Series, edited by: Canadell, J. G., Pataki, D. E., and Pitelka, L. F., Springer, Berlin,
Heidelberg, https://doi.org/10.1007/978-3-540-32730-1_15, 2007. a
Quillet, A., Peng, C., and Garneau, M.: Toward dynamic global vegetation
models for simulating vegetation-climate interactions and feedbacks: Recent
developments, limitations, and future challenges, Environ. Rev., 18,
333–353, https://doi.org/10.1139/A10-016, 2010. a, b
Randall, D. A., Harshvardhan, and Dazlich, D. A.: Diurnal variability of the
hydrologic cycle in a general circulation model, J. Atmos.
Sci., 48, 40–62, https://doi.org/10.1175/1520-0469(1991)048<0040:DVOTHC>2.0.CO;2,
1991. a
Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin,
J. K., Walker, J. P., Lohmann, D., Toll, D., Rodell, M., Houser, P. R.,
Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.-J., Arsenault, K.,
Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J. K., Walker, J. P.,
Lohmann, D., and Toll, D.: The Global Land Data Assimilation System, B.
Am. Meteorol. Soc., 85, 381–394, https://doi.org/10.1175/BAMS-85-3-381, 2004. a
Rolinski, S., Müller, C., Heinke, J., Weindl, I., Biewald, A., Bodirsky, B. L., Bondeau, A., Boons-Prins, E. R., Bouwman, A. F., Leffelaar, P. A., te Roller, J. A., Schaphoff, S., and Thonicke, K.: Modeling vegetation and carbon dynamics of managed grasslands at the global scale with LPJmL 3.6, Geosci. Model Dev., 11, 429–451, https://doi.org/10.5194/gmd-11-429-2018, 2018. a
Ronda, R. J., Haarsma, R. J., and Holtslag, A. A.: Representing the
atmospheric boundary layer in climate models of intermediate complexity,
Clim. Dynam., 21, 327–335, https://doi.org/10.1007/s00382-003-0338-0, 2003. a
Sakschewski, B., von Bloh, W., Drüke, M., Sörensson, A. A., Ruscica, R., Langerwisch, F., Billing, M., Bereswill, S., Hirota, M., Oliveira, R. S., Heinke, J., and Thonicke, K.: Variable tree rooting strategies improve tropical productivity and evapotranspiration in a dynamic global vegetation model, Biogeosciences Discuss. [preprint], https://doi.org/10.5194/bg-2020-97, in review, 2020. a, b
Santoro, M.: GlobBiomass – global datasets of forest biomass, PANGAEA,
https://doi.org/10.1594/PANGAEA.894711, 2018. a
Santoro, M., Cartus, O., Carvalhais, N., Rozendaal, D., Avitabilie, V., Araza, A., de Bruin, S., Herold, M., Quegan, S., Rodríguez Veiga, P., Balzter, H., Carreiras, J., Schepaschenko, D., Korets, M., Shimada, M., Itoh, T., Moreno Martínez, Á., Cavlovic, J., Cazzolla Gatti, R., da Conceição Bispo, P., Dewnath, N., Labrière, N., Liang, J., Lindsell, J., Mitchard, E. T. A., Morel, A., Pacheco Pascagaza, A. M., Ryan, C. M., Slik, F., Vaglio Laurin, G., Verbeeck, H., Wijaya, A., and Willcock, S.: The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2020-148, in review, 2020. a
Schaphoff, S., Heyder, U., Ostberg, S., Gerten, D., Heinke, J., and Lucht, W.:
Contribution of permafrost soils to the global carbon budget, Environ. Res.
Lett., 8, 14026, https://doi.org/10.1088/1748-9326/8/1/014026, 2013. a
Schaphoff, S., Forkel, M., Müller, C., Knauer, J., von Bloh, W., Gerten, D., Jägermeyr, J., Lucht, W., Rammig, A., Thonicke, K., and Waha, K.: LPJmL4 – a dynamic global vegetation model with managed land – Part 2: Model evaluation, Geosci. Model Dev., 11, 1377–1403, https://doi.org/10.5194/gmd-11-1377-2018, 2018a. a, b
Schaphoff, S., von Bloh, W., Rammig, A., Thonicke, K., Biemans, H., Forkel, M., Gerten, D., Heinke, J., Jägermeyr, J., Knauer, J., Langerwisch, F., Lucht, W., Müller, C., Rolinski, S., and Waha, K.: LPJmL4 – a dynamic global vegetation model with managed land – Part 1: Model description, Geosci. Model Dev., 11, 1343–1375, https://doi.org/10.5194/gmd-11-1343-2018, 2018b. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W.,
Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., Thonicke, K., and
Venevsky, S.: Evaluation of ecosystem dynamics, plant geography and
terrestrial carbon cycling in the LPJ dynamic global vegetation model,
Global Change Biol., 9, 161–185, https://doi.org/10.1046/j.1365-2486.2003.00569.x,
2003. a, b
Strengers, B. J., Müller, C., Schaeffer, M., Haarsma, R. J., Severijns,
C., Gerten, D., Schaphoff, S., Van Den Houdt, R., and Oostenrijk, R.:
Assessing 20th century climate-vegetation feedbacks of land-use change and
natural vegetation dynamics in a fully coupled vegetation-climate model,
Int. J. Climatol., 30, 2055–2065, https://doi.org/10.1002/joc.2132,
2010. a, b, c, d, e
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and the
experiment design, B. Am. Meteorol. Soc., 93, 485–498, https://doi.org/10.1175/BAMS-D-11-00094.1, 2012. a, b
Thonicke, K., Spessa, A., Prentice, I. C., Harrison, S. P., Dong, L., and Carmona-Moreno, C.: The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model, Biogeosciences, 7, 1991–2011, https://doi.org/10.5194/bg-7-1991-2010, 2010. a, b, c, d
Unger, N.: Human land-use-driven reduction of forest volatiles cools global
climate, Nat. Clim. Change, 4, 907–910, https://doi.org/10.1038/nclimate2347,
2014. a
Verheijen, L. M., Brovkin, V., Aerts, R., Bönisch, G., Cornelissen, J. H. C., Kattge, J., Reich, P. B., Wright, I. J., and van Bodegom, P. M.: Impacts of trait variation through observed trait–climate relationships on performance of an Earth system model: a conceptual analysis, Biogeosciences, 10, 5497–5515, https://doi.org/10.5194/bg-10-5497-2013, 2013. a
Viterbo, P.: A review of parametrization schemes for land surface processes,
Training Course Lecture Series, ECMWF, 1–49,
available at: http://193.63.95.1/newsevents/training/rcourse_notes/pdf_files/Land_surface_processes.pdf (last access: 30 November 2020),
2002.
a
von Bloh, W., Schaphoff, S., Müller, C., Rolinski, S., Waha, K., and Zaehle, S.: Implementing the nitrogen cycle into the dynamic global vegetation, hydrology, and crop growth model LPJmL (version 5.0), Geosci. Model Dev., 11, 2789–2812, https://doi.org/10.5194/gmd-11-2789-2018, 2018. a, b, c, d, e, f, g
Winkelmann, R., Martin, M. A., Haseloff, M., Albrecht, T., Bueler, E., Khroulev, C., and Levermann, A.: The Potsdam Parallel Ice Sheet Model (PISM-PIK) – Part 1: Model description, The Cryosphere, 5, 715–726, https://doi.org/10.5194/tc-5-715-2011, 2011. a
Zhao, M., Golaz, J. C., Held, I. M., Guo, H., Balaji, V., Benson, R., Chen,
J. H., Chen, X., Donner, L. J., Dunne, J. P., Dunne, K., Durachta, J., Fan,
S. M., Freidenreich, S. M., Garner, S. T., Ginoux, P., Harris, L. M.,
Horowitz, L. W., Krasting, J. P., Langenhorst, A. R., Liang, Z., Lin, P.,
Lin, S. J., Malyshev, S. L., Mason, E., Milly, P. C., Ming, Y., Naik, V.,
Paulot, F., Paynter, D., Phillipps, P., Radhakrishnan, A., Ramaswamy, V.,
Robinson, T., Schwarzkopf, D., Seman, C. J., Shevliakova, E., Shen, Z., Shin,
H., Silvers, L. G., Wilson, J. R., Winton, M., Wittenberg, A. T., Wyman, B.,
and Xiang, B.: The GFDL Global Atmosphere and Land Model AM4.0/LM4.0: 1.
Simulation Characteristics With Prescribed SSTs, J. Adv.
Model. Earth Sy., 10, 691–734, https://doi.org/10.1002/2017MS001208, 2018. a
Zhou, M. C., Ishidaira, H., and Takeuchi, K.: Estimation of potential
evapotranspiration over the Yellow River basin: Reference crop evaporation or
Shuttleworth-Wallace?, Hydrol. Process., 21, 1860–1874,
https://doi.org/10.1002/hyp.6339, 2006. a
Zhu, Z., Piao, S., Myneni, R. B., Huang, M., Zeng, Z., Canadell, J. G., Ciais,
P., Sitch, S., Friedlingstein, P., Arneth, A., Cao, C., Cheng, L., Kato, E.,
Koven, C., Li, Y., Lian, X., Liu, Y., Liu, R., Mao, J., Pan, Y., Peng, S.,
Peuelas, J., Poulter, B., Pugh, T. A., Stocker, B. D., Viovy, N., Wang, X.,
Wang, Y., Xiao, Z., Yang, H., Zaehle, S., and Zeng, N.: Greening of the
Earth and its drivers, Nat. Clim. Change, 6, 791–795,
https://doi.org/10.1038/nclimate3004, 2016. a
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).
In this study, we couple the well-established and comprehensively validated state-of-the-art...
Special issue