Articles | Volume 12, issue 10
https://doi.org/10.5194/gmd-12-4309-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-12-4309-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| Highlight paper
| 
14 Oct 2019
Model description paper | Highlight paper |
| 14 Oct 2019
| 14 Oct 2019
The biophysics, ecology, and biogeochemistry of functionally diverse, vertically and horizontally heterogeneous ecosystems: the Ecosystem Demography model, version 2.2 – Part 1: Model description
Harvard University, Cambridge, MA, USA
Embrapa Agricultural Informatics, Campinas, SP, Brazil
Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Ryan G. Knox
Massachusetts Institute of Technology, Cambridge, MA, USA
Lawrence Berkeley National Laboratory, Berkeley, CA, USA
David M. Medvigy
University of Notre Dame, Notre Dame, IN, USA
Naomi M. Levine
University of Southern California, Los Angeles, CA, USA
Michael C. Dietze
Boston University, Boston, MA, USA
Yeonjoo Kim
Department of Civil and Environmental Engineering, Yonsei University, Seoul, Korea
Abigail L. S. Swann
University of Washington, Seattle, WA, USA
Hohai University, Nanjing, Jiangsu, China
Christine R. Rollinson
The Morton Arboretum, Lisle, IL, USA
Rafael L. Bras
Georgia Institute of Technology, Atlanta, GA, USA
Steven C. Wofsy
Harvard University, Cambridge, MA, USA
Paul R. Moorcroft
Harvard University, Cambridge, MA, USA
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We use a microbial ecosystem model to quantitatively explain the mechanisms controlling observed relative abundances and nitrification rates of ammonia- and nitrite-oxidizing microorganisms in the ocean. We also estimate how much global carbon fixation can be associated with chemoautotrophic nitrification. Our results improve our understanding of the controls on nitrification, laying the groundwork for more accurate predictions in global climate models.
Yilin Fang, L. Ruby Leung, Ryan Knox, Charlie Koven, and Ben Bond-Lamberty
Geosci. Model Dev., 15, 6385–6398, https://doi.org/10.5194/gmd-15-6385-2022, https://doi.org/10.5194/gmd-15-6385-2022, 2022
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Accounting for water movement in the soil and water transport within the plant is important for plant growth in Earth system modeling. We implemented different numerical approaches for a plant hydrodynamic model and compared their impacts on the simulated aboveground biomass (AGB) at single points and globally. We found care should be taken when discretizing the number of soil layers for numerical simulations as it can significantly affect AGB if accuracy and computational costs are of concern.
Suyeon Choi and Yeonjoo Kim
Geosci. Model Dev., 15, 5967–5985, https://doi.org/10.5194/gmd-15-5967-2022, https://doi.org/10.5194/gmd-15-5967-2022, 2022
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Here we present the cGAN-based precipitation nowcasting model, named Rad-cGAN, trained to predict a radar reflectivity map with a lead time of 10 min. Rad-cGAN showed superior performance at a lead time of up to 90 min compared with the reference models. Furthermore, we demonstrate the successful implementation of the transfer learning strategies using pre-trained Rad-cGAN to develop the models for different dam domains.
Jiaying Zhang, Rafael L. Bras, Marcos Longo, and Tamara Heartsill Scalley
Geosci. Model Dev., 15, 5107–5126, https://doi.org/10.5194/gmd-15-5107-2022, https://doi.org/10.5194/gmd-15-5107-2022, 2022
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We implemented hurricane disturbance in a vegetation dynamics model and calibrated the model with observations of a tropical forest. We used the model to study forest recovery from hurricane disturbance and found that a single hurricane disturbance enhances AGB and BA in the long term compared with a no-hurricane situation. The model developed and results presented in this study can be utilized to understand the impact of hurricane disturbances on forest recovery under the changing climate.
Hamze Dokoohaki, Bailey D. Morrison, Ann Raiho, Shawn P. Serbin, Katie Zarada, Luke Dramko, and Michael Dietze
Geosci. Model Dev., 15, 3233–3252, https://doi.org/10.5194/gmd-15-3233-2022, https://doi.org/10.5194/gmd-15-3233-2022, 2022
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We present a new terrestrial carbon cycle data assimilation system, built on the PEcAn model–data eco-informatics system, and its application for the development of a proof-of-concept carbon
reanalysisproduct that harmonizes carbon pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986–2019. Here, we build on a decade of work on uncertainty propagation to generate the most complete and robust uncertainty accounting available to date.
Elias C. Massoud, A. Anthony Bloom, Marcos Longo, John T. Reager, Paul A. Levine, and John R. Worden
Hydrol. Earth Syst. Sci., 26, 1407–1423, https://doi.org/10.5194/hess-26-1407-2022, https://doi.org/10.5194/hess-26-1407-2022, 2022
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The water balance on river basin scales depends on a number of soil physical processes. Gaining information on these quantities using observations is a key step toward improving the skill of land surface hydrology models. In this study, we use data from the Gravity Recovery and Climate Experiment (NASA-GRACE) to inform and constrain these hydrologic processes. We show that our model is able to simulate the land hydrologic cycle for a watershed in the Amazon from January 2003 to December 2012.
Taylor S. Jones, Jonathan E. Franklin, Jia Chen, Florian Dietrich, Kristian D. Hajny, Johannes C. Paetzold, Adrian Wenzel, Conor Gately, Elaine Gottlieb, Harrison Parker, Manvendra Dubey, Frank Hase, Paul B. Shepson, Levi H. Mielke, and Steven C. Wofsy
Atmos. Chem. Phys., 21, 13131–13147, https://doi.org/10.5194/acp-21-13131-2021, https://doi.org/10.5194/acp-21-13131-2021, 2021
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Methane emissions from leaks in natural gas pipes are often a large source in urban areas, but they are difficult to measure on a city-wide scale. Here we use an array of innovative methane sensors distributed around the city of Indianapolis and a new method of combining their data with an atmospheric model to accurately determine the magnitude of these emissions, which are about 70 % larger than predicted. This method can serve as a framework for cities trying to account for their emissions.
Wu Ma, Lu Zhai, Alexandria Pivovaroff, Jacquelyn Shuman, Polly Buotte, Junyan Ding, Bradley Christoffersen, Ryan Knox, Max Moritz, Rosie A. Fisher, Charles D. Koven, Lara Kueppers, and Chonggang Xu
Biogeosciences, 18, 4005–4020, https://doi.org/10.5194/bg-18-4005-2021, https://doi.org/10.5194/bg-18-4005-2021, 2021
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We use a hydrodynamic demographic vegetation model to estimate live fuel moisture dynamics of chaparral shrubs, a dominant vegetation type in fire-prone southern California. Our results suggest that multivariate climate change could cause a significant net reduction in live fuel moisture and thus exacerbate future wildfire danger in chaparral shrub systems.
Elizabeth B. Wiggins, Arlyn Andrews, Colm Sweeney, John B. Miller, Charles E. Miller, Sander Veraverbeke, Roisin Commane, Steven Wofsy, John M. Henderson, and James T. Randerson
Atmos. Chem. Phys., 21, 8557–8574, https://doi.org/10.5194/acp-21-8557-2021, https://doi.org/10.5194/acp-21-8557-2021, 2021
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We analyzed high-resolution trace gas measurements collected from a tower in Alaska during a very active fire season to improve our understanding of trace gas emissions from boreal forest fires. Our results suggest previous studies may have underestimated emissions from smoldering combustion in boreal forest fires.
Alexey N. Shiklomanov, Michael C. Dietze, Istem Fer, Toni Viskari, and Shawn P. Serbin
Geosci. Model Dev., 14, 2603–2633, https://doi.org/10.5194/gmd-14-2603-2021, https://doi.org/10.5194/gmd-14-2603-2021, 2021
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Airborne and satellite images are a great resource for calibrating and evaluating computer models of ecosystems. Typically, researchers derive ecosystem properties from these images and then compare models against these derived properties. Here, we present an alternative approach where we modify a model to predict what the satellite would see more directly. We then show how this approach can be used to calibrate model parameters using airborne data from forest sites in the northeastern US.
Kathryn I. Wheeler and Michael C. Dietze
Biogeosciences, 18, 1971–1985, https://doi.org/10.5194/bg-18-1971-2021, https://doi.org/10.5194/bg-18-1971-2021, 2021
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Monitoring leaf phenology (i.e., seasonality) allows for tracking the progression of climate change and seasonal variations in a variety of organismal and ecosystem processes. Recent versions of the Geostationary Operational Environmental Satellites allow for the monitoring of a phenological-sensitive index at a high temporal frequency (5–10 min) throughout most of the western hemisphere. Here we show the high potential of these new data to measure the phenology of deciduous forests.
Junjie Liu, Latha Baskaran, Kevin Bowman, David Schimel, A. Anthony Bloom, Nicholas C. Parazoo, Tomohiro Oda, Dustin Carroll, Dimitris Menemenlis, Joanna Joiner, Roisin Commane, Bruce Daube, Lucianna V. Gatti, Kathryn McKain, John Miller, Britton B. Stephens, Colm Sweeney, and Steven Wofsy
Earth Syst. Sci. Data, 13, 299–330, https://doi.org/10.5194/essd-13-299-2021, https://doi.org/10.5194/essd-13-299-2021, 2021
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On average, the terrestrial biosphere carbon sink is equivalent to ~ 20 % of fossil fuel emissions. Understanding where and why the terrestrial biosphere absorbs carbon from the atmosphere is pivotal to any mitigation policy. Here we present a regionally resolved satellite-constrained net biosphere exchange (NBE) dataset with corresponding uncertainties between 2010–2018: CMS-Flux NBE 2020. The dataset provides a unique perspective on monitoring regional contributions to the CO2 growth rate.
Maoyi Huang, Yi Xu, Marcos Longo, Michael Keller, Ryan G. Knox, Charles D. Koven, and Rosie A. Fisher
Biogeosciences, 17, 4999–5023, https://doi.org/10.5194/bg-17-4999-2020, https://doi.org/10.5194/bg-17-4999-2020, 2020
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The Functionally Assembled Terrestrial Ecosystem Simulator (FATES) is enhanced to mimic the ecological, biophysical, and biogeochemical processes following a logging event. The model can specify the timing and aerial extent of logging events; determine the survivorship of cohorts in the disturbed forest; and modifying the biomass, coarse woody debris, and litter pools. This study lays the foundation to simulate land use change and forest degradation in FATES as part of an Earth system model.
Charles D. Koven, Ryan G. Knox, Rosie A. Fisher, Jeffrey Q. Chambers, Bradley O. Christoffersen, Stuart J. Davies, Matteo Detto, Michael C. Dietze, Boris Faybishenko, Jennifer Holm, Maoyi Huang, Marlies Kovenock, Lara M. Kueppers, Gregory Lemieux, Elias Massoud, Nathan G. McDowell, Helene C. Muller-Landau, Jessica F. Needham, Richard J. Norby, Thomas Powell, Alistair Rogers, Shawn P. Serbin, Jacquelyn K. Shuman, Abigail L. S. Swann, Charuleka Varadharajan, Anthony P. Walker, S. Joseph Wright, and Chonggang Xu
Biogeosciences, 17, 3017–3044, https://doi.org/10.5194/bg-17-3017-2020, https://doi.org/10.5194/bg-17-3017-2020, 2020
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Tropical forests play a crucial role in governing climate feedbacks, and are incredibly diverse ecosystems, yet most Earth system models do not take into account the diversity of plant traits in these forests and how this diversity may govern feedbacks. We present an approach to represent diverse competing plant types within Earth system models, test this approach at a tropical forest site, and explore how the representation of disturbance and competition governs traits of the forest community.
Christopher P. O. Reyer, Ramiro Silveyra Gonzalez, Klara Dolos, Florian Hartig, Ylva Hauf, Matthias Noack, Petra Lasch-Born, Thomas Rötzer, Hans Pretzsch, Henning Meesenburg, Stefan Fleck, Markus Wagner, Andreas Bolte, Tanja G. M. Sanders, Pasi Kolari, Annikki Mäkelä, Timo Vesala, Ivan Mammarella, Jukka Pumpanen, Alessio Collalti, Carlo Trotta, Giorgio Matteucci, Ettore D'Andrea, Lenka Foltýnová, Jan Krejza, Andreas Ibrom, Kim Pilegaard, Denis Loustau, Jean-Marc Bonnefond, Paul Berbigier, Delphine Picart, Sébastien Lafont, Michael Dietze, David Cameron, Massimo Vieno, Hanqin Tian, Alicia Palacios-Orueta, Victor Cicuendez, Laura Recuero, Klaus Wiese, Matthias Büchner, Stefan Lange, Jan Volkholz, Hyungjun Kim, Joanna A. Horemans, Friedrich Bohn, Jörg Steinkamp, Alexander Chikalanov, Graham P. Weedon, Justin Sheffield, Flurin Babst, Iliusi Vega del Valle, Felicitas Suckow, Simon Martel, Mats Mahnken, Martin Gutsch, and Katja Frieler
Earth Syst. Sci. Data, 12, 1295–1320, https://doi.org/10.5194/essd-12-1295-2020, https://doi.org/10.5194/essd-12-1295-2020, 2020
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Process-based vegetation models are widely used to predict local and global ecosystem dynamics and climate change impacts. Due to their complexity, they require careful parameterization and evaluation to ensure that projections are accurate and reliable. The PROFOUND Database provides a wide range of empirical data to calibrate and evaluate vegetation models that simulate climate impacts at the forest stand scale to support systematic model intercomparisons and model development in Europe.
Marcos Longo, Ryan G. Knox, Naomi M. Levine, Abigail L. S. Swann, David M. Medvigy, Michael C. Dietze, Yeonjoo Kim, Ke Zhang, Damien Bonal, Benoit Burban, Plínio B. Camargo, Matthew N. Hayek, Scott R. Saleska, Rodrigo da Silva, Rafael L. Bras, Steven C. Wofsy, and Paul R. Moorcroft
Geosci. Model Dev., 12, 4347–4374, https://doi.org/10.5194/gmd-12-4347-2019, https://doi.org/10.5194/gmd-12-4347-2019, 2019
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The Ecosystem Demography model calculates the fluxes of heat, water, and carbon between plants and ground and the air, and the life cycle of plants in different climates. To test if our calculations were reasonable, we compared our results with field and satellite measurements. Our model predicts well the extent of the Amazon forest, how much light forests absorb, and how much water forests release to the air. However, it must improve the tree growth rates and how fast dead plants decompose.
Elias C. Massoud, Chonggang Xu, Rosie A. Fisher, Ryan G. Knox, Anthony P. Walker, Shawn P. Serbin, Bradley O. Christoffersen, Jennifer A. Holm, Lara M. Kueppers, Daniel M. Ricciuto, Liang Wei, Daniel J. Johnson, Jeffrey Q. Chambers, Charlie D. Koven, Nate G. McDowell, and Jasper A. Vrugt
Geosci. Model Dev., 12, 4133–4164, https://doi.org/10.5194/gmd-12-4133-2019, https://doi.org/10.5194/gmd-12-4133-2019, 2019
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We conducted a comprehensive sensitivity analysis to understand behaviors of a demographic vegetation model within a land surface model. By running the model 5000 times with changing input parameter values, we found that (1) the photosynthetic capacity controls carbon fluxes, (2) the allometry is important for tree growth, and (3) the targeted carbon storage is important for tree survival. These results can provide guidance on improved model parameterization for a better fit to observations.
Marcos A. S. Scaranello, Michael Keller, Marcos Longo, Maiza N. dos-Santos, Veronika Leitold, Douglas C. Morton, Ekena R. Pinagé, and Fernando Del Bon Espírito-Santo
Biogeosciences, 16, 3457–3474, https://doi.org/10.5194/bg-16-3457-2019, https://doi.org/10.5194/bg-16-3457-2019, 2019
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The coarse dead wood component of the tropical forest carbon pool is rarely measured. For the first time, we developed models for predicting coarse dead wood in Amazonian forests by using airborne laser scanning data. Our models produced site-based estimates similar to independent field estimates found in the literature. Our study provides an approach for estimating coarse dead wood pools from remotely sensed data and mapping those pools over large scales in intact and degraded forests.
Muhammad Shafqat Mehboob, Yeonjoo Kim, Jaehyeong Lee, Myoung-Jin Um, Amir Erfanian, and Guiling Wang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-319, https://doi.org/10.5194/hess-2019-319, 2019
Manuscript not accepted for further review
Yingchun Huang, András Bárdossy, and Ke Zhang
Hydrol. Earth Syst. Sci., 23, 2647–2663, https://doi.org/10.5194/hess-23-2647-2019, https://doi.org/10.5194/hess-23-2647-2019, 2019
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This study investigates whether higher temporal and spatial resolution of rainfall can lead to improved model performance. Four rainfall datasets were used to drive lumped and distributed HBV models to simulate daily discharges. Results show that a higher temporal resolution of rainfall improves the model performance if the station density is high. A combination of observed high temporal resolution observations with disaggregated daily rainfall leads to further improvement of the tested models.
Hocheol Seo and Yeonjoo Kim
Geosci. Model Dev., 12, 457–472, https://doi.org/10.5194/gmd-12-457-2019, https://doi.org/10.5194/gmd-12-457-2019, 2019
Benjamin Gaubert, Britton B. Stephens, Sourish Basu, Frédéric Chevallier, Feng Deng, Eric A. Kort, Prabir K. Patra, Wouter Peters, Christian Rödenbeck, Tazu Saeki, David Schimel, Ingrid Van der Laan-Luijkx, Steven Wofsy, and Yi Yin
Biogeosciences, 16, 117–134, https://doi.org/10.5194/bg-16-117-2019, https://doi.org/10.5194/bg-16-117-2019, 2019
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We have compared global carbon budgets calculated from numerical inverse models and CO2 observations, and evaluated how these systems reproduce vertical gradients in atmospheric CO2 from aircraft measurements. We found that available models have converged on near-neutral tropical total fluxes for several decades, implying consistent sinks in intact tropical forests, and that assumed fossil fuel emissions and predicted atmospheric growth rates are now the dominant axes of disagreement.
Ke Zhang, Sheng Wang, Hongjun Bao, and Xiaomeng Zhao
Nat. Hazards Earth Syst. Sci., 19, 93–105, https://doi.org/10.5194/nhess-19-93-2019, https://doi.org/10.5194/nhess-19-93-2019, 2019
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We investigated the spatiotemporal characteristics of landslide and debris flow hazards in Shaanxi Province and quantified the relationships between the occurrence rates of the two hazards and their influencing factors, including antecedent rainfall amount, rainfall duration, rainfall intensity, terrain slope, land cover type and soil type. Rainfall amount, duration, and intensity and slope are the dominant factors controlling slope stability across this region.
Elizabeth N. Teel, Xiao Liu, Bridget N. Seegers, Matthew A. Ragan, William Z. Haskell, Burton H. Jones, and Naomi M. Levine
Biogeosciences, 15, 6151–6165, https://doi.org/10.5194/bg-15-6151-2018, https://doi.org/10.5194/bg-15-6151-2018, 2018
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Time-series sites have been instrumental in providing insight into how the ocean functions. However, to extrapolate the results from a single site to a larger region, the dynamics at the site must be placed into the context of regional-scale dynamics. We develop a framework for determining the spatial domain of a time-series site using high-resolution data. This framework quantifies the representativeness of the site and can be used to improve sampling to better capture the dynamics at the site.
Istem Fer, Ryan Kelly, Paul R. Moorcroft, Andrew D. Richardson, Elizabeth M. Cowdery, and Michael C. Dietze
Biogeosciences, 15, 5801–5830, https://doi.org/10.5194/bg-15-5801-2018, https://doi.org/10.5194/bg-15-5801-2018, 2018
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The computer models we use to understand and forecast the ecosystem changes have multiple components that determine their outcomes. Due to our limited observation capacities, these components bear uncertainties that in return affect our predictions. While there are techniques for reducing these uncertainties, they are not applicable to every model due to computational and statistical barriers. This research presents a method that lowers those barriers and allows us to improve model predictions.
Matthew N. Hayek, Marcos Longo, Jin Wu, Marielle N. Smith, Natalia Restrepo-Coupe, Raphael Tapajós, Rodrigo da Silva, David R. Fitzjarrald, Plinio B. Camargo, Lucy R. Hutyra, Luciana F. Alves, Bruce Daube, J. William Munger, Kenia T. Wiedemann, Scott R. Saleska, and Steven C. Wofsy
Biogeosciences, 15, 4833–4848, https://doi.org/10.5194/bg-15-4833-2018, https://doi.org/10.5194/bg-15-4833-2018, 2018
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We investigated the roles that weather and forest disturbances like drought play in shaping changes in ecosystem photosynthesis and carbon exchange in an Amazon forest. We discovered that weather largely influenced differences between years, but a prior drought, which occurred 3 years before measurements started, likely hampered photosynthesis in the first year. This is the first atmospheric evidence that drought can have legacy impacts on Amazon forest photosynthesis.
Anna T. Trugman, David Medvigy, William A. Hoffmann, and Adam F. A. Pellegrini
Biogeosciences, 15, 233–243, https://doi.org/10.5194/bg-15-233-2018, https://doi.org/10.5194/bg-15-233-2018, 2018
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Tree fire tolerance strategies may significantly impact woody carbon stability and the existence of tropical savannas under global climate change. We used a numerical ecosystem model to test the impacts of fire survival strategy under differing fire and rainfall regimes. We found that the high survival rate of large fire-tolerant trees reduced carbon losses with increasing fire frequency, and reduced the range of conditions leading to either complete tree loss or complete grass loss.
Jennifer R. Marlon, Neil Pederson, Connor Nolan, Simon Goring, Bryan Shuman, Ann Robertson, Robert Booth, Patrick J. Bartlein, Melissa A. Berke, Michael Clifford, Edward Cook, Ann Dieffenbacher-Krall, Michael C. Dietze, Amy Hessl, J. Bradford Hubeny, Stephen T. Jackson, Jeremiah Marsicek, Jason McLachlan, Cary J. Mock, David J. P. Moore, Jonathan Nichols, Dorothy Peteet, Kevin Schaefer, Valerie Trouet, Charles Umbanhowar, John W. Williams, and Zicheng Yu
Clim. Past, 13, 1355–1379, https://doi.org/10.5194/cp-13-1355-2017, https://doi.org/10.5194/cp-13-1355-2017, 2017
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To improve our understanding of paleoclimate in the northeastern (NE) US, we compiled data from pollen, tree rings, lake levels, testate amoeba from bogs, and other proxies from the last 3000 years. The paleoclimate synthesis supports long-term cooling until the 1800s and reveals an abrupt transition from wet to dry conditions around 550–750 CE. Evidence suggests the region is now becoming warmer and wetter, but more calibrated data are needed, especially to capture multidecadal variability.
Myoung-Jin Um, Yeonjoo Kim, Daeryong Park, and Jeongbin Kim
Hydrol. Earth Syst. Sci., 21, 4989–5007, https://doi.org/10.5194/hess-21-4989-2017, https://doi.org/10.5194/hess-21-4989-2017, 2017
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This study aims to understand how different reference periods (i.e., calibration periods) of climate data for estimating the drought index influence regional drought assessments. Specifically, we investigate the influence of different reference periods on historical drought characteristics such as trends, frequency, intensity and spatial extents using the Standard Precipitation Evapotranspiration Index (SPEI) estimated from the two widely used global datasets.
Fabio F. Pereira, Fabio Farinosi, Mauricio E. Arias, Eunjee Lee, John Briscoe, and Paul R. Moorcroft
Hydrol. Earth Syst. Sci., 21, 4629–4648, https://doi.org/10.5194/hess-21-4629-2017, https://doi.org/10.5194/hess-21-4629-2017, 2017
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ED2 is a terrestrial biosphere model (TBM) suited for investigating combined impacts of changes in climate, atmospheric CO2, and land cover on the water cycle. In this study, we describe the integration of ED2 with a hydrological routing scheme. The resulting ED2+R model calculates the lateral propagation of surface and subsurface runoff resulting from the TBM and determines spatiotemporal patterns of river flows. We successfully evaluated the ED2+R model in the Tapajós, Brazilian Amazon.
Michael J. Prather, Xin Zhu, Clare M. Flynn, Sarah A. Strode, Jose M. Rodriguez, Stephen D. Steenrod, Junhua Liu, Jean-Francois Lamarque, Arlene M. Fiore, Larry W. Horowitz, Jingqiu Mao, Lee T. Murray, Drew T. Shindell, and Steven C. Wofsy
Atmos. Chem. Phys., 17, 9081–9102, https://doi.org/10.5194/acp-17-9081-2017, https://doi.org/10.5194/acp-17-9081-2017, 2017
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We present a new approach for comparing atmospheric chemistry models with measurements based on what these models are used to do, i.e., calculate changes in ozone and methane, prime greenhouse gases. This method anticipates a new type of measurements from the NASA Atmospheric Tomography (ATom) mission. In comparing the mixture of species within air parcels, we focus on those responsible for key chemical changes and weight these parcels by their chemical reactivity.
Jochen Stutz, Bodo Werner, Max Spolaor, Lisa Scalone, James Festa, Catalina Tsai, Ross Cheung, Santo F. Colosimo, Ugo Tricoli, Rasmus Raecke, Ryan Hossaini, Martyn P. Chipperfield, Wuhu Feng, Ru-Shan Gao, Eric J. Hintsa, James W. Elkins, Fred L. Moore, Bruce Daube, Jasna Pittman, Steven Wofsy, and Klaus Pfeilsticker
Atmos. Meas. Tech., 10, 1017–1042, https://doi.org/10.5194/amt-10-1017-2017, https://doi.org/10.5194/amt-10-1017-2017, 2017
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A new limb-scanning Differential Optical Absorption Spectroscopy (DOAS) instrument was developed for NASA’s Global Hawk unmanned aerial system during the Airborne Tropical TRopopause EXperiment to study trace gases in the tropical tropopause layer. A new technique that uses in situ and DOAS O3 observations together with radiative transfer calculations allows the retrieval of mixing ratios from the slant column densities of BrO and NO2 at high accuracies of 0.5 ppt and 15 ppt, respectively.
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
Bodo Werner, Jochen Stutz, Max Spolaor, Lisa Scalone, Rasmus Raecke, James Festa, Santo Fedele Colosimo, Ross Cheung, Catalina Tsai, Ryan Hossaini, Martyn P. Chipperfield, Giorgio S. Taverna, Wuhu Feng, James W. Elkins, David W. Fahey, Ru-Shan Gao, Erik J. Hintsa, Troy D. Thornberry, Free Lee Moore, Maria A. Navarro, Elliot Atlas, Bruce C. Daube, Jasna Pittman, Steve Wofsy, and Klaus Pfeilsticker
Atmos. Chem. Phys., 17, 1161–1186, https://doi.org/10.5194/acp-17-1161-2017, https://doi.org/10.5194/acp-17-1161-2017, 2017
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The paper reports on inorganic and organic bromine measured in the tropical tropopause layer (TTL) over the eastern Pacific in early 2013. Bryinorg is found to increase from a mean of 2.63 ± 1.04 ppt for θ in the range of 350–360 K to 5.11 ± 1.57 ppt for θ=390 ± 400 K, whereas in the subtropical lower stratosphere, it reaches 7.66 ± 2.95 ppt for θ in the range of 390–400 K. Within the TTL, total bromine is found to range from 20.3 ppt to 22.3 ppt.
Saulo R. Freitas, Jairo Panetta, Karla M. Longo, Luiz F. Rodrigues, Demerval S. Moreira, Nilton E. Rosário, Pedro L. Silva Dias, Maria A. F. Silva Dias, Enio P. Souza, Edmilson D. Freitas, Marcos Longo, Ariane Frassoni, Alvaro L. Fazenda, Cláudio M. Santos e Silva, Cláudio A. B. Pavani, Denis Eiras, Daniela A. França, Daniel Massaru, Fernanda B. Silva, Fernando C. Santos, Gabriel Pereira, Gláuber Camponogara, Gonzalo A. Ferrada, Haroldo F. Campos Velho, Isilda Menezes, Julliana L. Freire, Marcelo F. Alonso, Madeleine S. Gácita, Maurício Zarzur, Rafael M. Fonseca, Rafael S. Lima, Ricardo A. Siqueira, Rodrigo Braz, Simone Tomita, Valter Oliveira, and Leila D. Martins
Geosci. Model Dev., 10, 189–222, https://doi.org/10.5194/gmd-10-189-2017, https://doi.org/10.5194/gmd-10-189-2017, 2017
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We present a new version of the Brazilian developments on the Regional Atmospheric Modeling System (BRAMS) where different previous versions for weather, chemistry, and the carbon cycle were unified in a single harmonized software system. This version also has a new set of state-of-the-art physical parametrizations and higher computational parallel and memory usage efficiency. BRAMS has been applied for research and operational weather and air quality forecasting, largely in South America.
Ke Zhang, Xianwu Xue, Yang Hong, Jonathan J. Gourley, Ning Lu, Zhanming Wan, Zhen Hong, and Rick Wooten
Hydrol. Earth Syst. Sci., 20, 5035–5048, https://doi.org/10.5194/hess-20-5035-2016, https://doi.org/10.5194/hess-20-5035-2016, 2016
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We developed a new approach to couple a distributed hydrological model, CREST, to a geotechnical landslide model, TRIGRS, to simulate both flood- and rainfall-triggered landslide hazards. By implementing more sophisticated and realistic representations of hydrological processes in the coupled model system, it shows better performance than the standalone landslide model in the case study. It highlights the important physical connection between rainfall, hydrological processes and slope stability.
Xiyan Xu, William J. Riley, Charles D. Koven, Dave P. Billesbach, Rachel Y.-W. Chang, Róisín Commane, Eugénie S. Euskirchen, Sean Hartery, Yoshinobu Harazono, Hiroki Iwata, Kyle C. McDonald, Charles E. Miller, Walter C. Oechel, Benjamin Poulter, Naama Raz-Yaseef, Colm Sweeney, Margaret Torn, Steven C. Wofsy, Zhen Zhang, and Donatella Zona
Biogeosciences, 13, 5043–5056, https://doi.org/10.5194/bg-13-5043-2016, https://doi.org/10.5194/bg-13-5043-2016, 2016
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Wetlands are the largest global natural methane source. Peat-rich bogs and fens lying between 50°N and 70°N contribute 10–30% to this source. The predictive capability of the seasonal methane cycle can directly affect the estimation of global methane budget. We present multiscale methane seasonal emission by observations and modeling and find that the uncertainties in predicting the seasonal methane emissions are from the wetland extent, cold-season CH4 production and CH4 transport processes.
Douglas C. Morton, Jérémy Rubio, Bruce D. Cook, Jean-Philippe Gastellu-Etchegorry, Marcos Longo, Hyeungu Choi, Maria Hunter, and Michael Keller
Biogeosciences, 13, 2195–2206, https://doi.org/10.5194/bg-13-2195-2016, https://doi.org/10.5194/bg-13-2195-2016, 2016
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Seasonal dynamics of tropical forest productivity remain an important source of uncertainty in assessments of the land carbon sink. This study confirms the potential for canopy structure and illumination geometry to alter the seasonal availability of light for canopy photosynthesis without changes in canopy composition. Our results point to the need for 3-D forest structure in ecosystem models to account the impact of changing illumination geometry on tropical forest productivity.
Y. Kim, P. R. Moorcroft, I. Aleinov, M. J. Puma, and N. Y. Kiang
Geosci. Model Dev., 8, 3837–3865, https://doi.org/10.5194/gmd-8-3837-2015, https://doi.org/10.5194/gmd-8-3837-2015, 2015
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The Ent Terrestrial Biosphere Model is a mixed-canopy dynamic global vegetation model developed specifically for coupling with land surface hydrology and general circulation models. This study describes the leaf phenology submodel implemented in the Ent TBM. We evaluate the performance in reproducing observed leaf seasonal growth as well as water and carbon fluxes for four plant functional types at five Fluxnet sites.
R. A. Fisher, S. Muszala, M. Verteinstein, P. Lawrence, C. Xu, N. G. McDowell, R. G. Knox, C. Koven, J. Holm, B. M. Rogers, A. Spessa, D. Lawrence, and G. Bonan
Geosci. Model Dev., 8, 3593–3619, https://doi.org/10.5194/gmd-8-3593-2015, https://doi.org/10.5194/gmd-8-3593-2015, 2015
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Predicting the distribution of vegetation under novel climates is important, both to understand how climate change will impact ecosystem services, but also to understand how vegetation changes might affect the carbon, energy and water cycles. Historically, predictions have been heavily dependent upon observations of existing vegetation boundaries. In this paper, we attempt to predict ecosystem boundaries from the ``bottom up'', and illustrate the complexities and promise of this approach.
C. D. Koven, J. Q. Chambers, K. Georgiou, R. Knox, R. Negron-Juarez, W. J. Riley, V. K. Arora, V. Brovkin, P. Friedlingstein, and C. D. Jones
Biogeosciences, 12, 5211–5228, https://doi.org/10.5194/bg-12-5211-2015, https://doi.org/10.5194/bg-12-5211-2015, 2015
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Terrestrial carbon feedbacks are a large uncertainty in climate change. We separate modeled feedback responses into those governed by changed carbon inputs (productivity) and changed outputs (turnover). The disaggregated responses show that both are important in controlling inter-model uncertainty. Interactions between productivity and turnover are also important, and research must focus on these interactions for more accurate projections of carbon cycle feedbacks.
L. Rowland, A. Harper, B. O. Christoffersen, D. R. Galbraith, H. M. A. Imbuzeiro, T. L. Powell, C. Doughty, N. M. Levine, Y. Malhi, S. R. Saleska, P. R. Moorcroft, P. Meir, and M. Williams
Geosci. Model Dev., 8, 1097–1110, https://doi.org/10.5194/gmd-8-1097-2015, https://doi.org/10.5194/gmd-8-1097-2015, 2015
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This study evaluates the capability of five vegetation models to simulate the response of forest productivity to changes in temperature and drought, using data collected from an Amazonian forest. This study concludes that model consistencies in the responses of net canopy carbon production to temperature and precipitation change were the result of inconsistently modelled leaf-scale process responses and substantial variation in modelled leaf area responses.
R. G. Knox, M. Longo, A. L. S. Swann, K. Zhang, N. M. Levine, P. R. Moorcroft, and R. L. Bras
Hydrol. Earth Syst. Sci., 19, 241–273, https://doi.org/10.5194/hess-19-241-2015, https://doi.org/10.5194/hess-19-241-2015, 2015
R. G. Knox, M. Longo, A. L. S. Swann, K. Zhang, N. M. Levine, P. R. Moorcroft, and R. L. Bras
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hessd-10-15295-2013, https://doi.org/10.5194/hessd-10-15295-2013, 2013
Preprint withdrawn
P. C. Stoy, M. C. Dietze, A. D. Richardson, R. Vargas, A. G. Barr, R. S. Anderson, M. A. Arain, I. T. Baker, T. A. Black, J. M. Chen, R. B. Cook, C. M. Gough, R. F. Grant, D. Y. Hollinger, R. C. Izaurralde, C. J. Kucharik, P. Lafleur, B. E. Law, S. Liu, E. Lokupitiya, Y. Luo, J. W. Munger, C. Peng, B. Poulter, D. T. Price, D. M. Ricciuto, W. J. Riley, A. K. Sahoo, K. Schaefer, C. R. Schwalm, H. Tian, H. Verbeeck, and E. Weng
Biogeosciences, 10, 6893–6909, https://doi.org/10.5194/bg-10-6893-2013, https://doi.org/10.5194/bg-10-6893-2013, 2013
C. Lepore, E. Arnone, L. V. Noto, G. Sivandran, and R. L. Bras
Hydrol. Earth Syst. Sci., 17, 3371–3387, https://doi.org/10.5194/hess-17-3371-2013, https://doi.org/10.5194/hess-17-3371-2013, 2013
Related subject area
Biogeosciences
Soil nitrous oxide emissions from global land ecosystems and their drivers within the LPJ-GUESS model (v4.1)
Parameterization toolbox for a physical–biogeochemical model compatible with FABM – a case study: the coupled 1D GOTM–ECOSMO E2E for the Sylt–Rømø Bight, North Sea
H2MV (v1.0): global physically constrained deep learning water cycle model with vegetation
NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks
China Wildfire Emission Dataset (ChinaWED v1) for the period 2012–2022
Process-based modeling of solar-induced chlorophyll fluorescence with VISIT-SIF version 1.0
Including the phosphorus cycle into the LPJ-GUESS dynamic global vegetation model (v4.1, r10994) – global patterns and temporal trends of N and P primary production limitation
A comprehensive land-surface vegetation model for multi-stream data assimilation, D&B v1.0
Sources of uncertainty in the SPITFIRE global fire model: development of LPJmL-SPITFIRE1.9 and directions for future improvements
The unicellular NUM v.0.91: a trait-based plankton model evaluated in two contrasting biogeographic provinces
FESOM2.1-REcoM3-MEDUSA2: an ocean–sea ice–biogeochemistry model coupled to a sediment model
Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)
Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application
Systematic underestimation of type-specific ecosystem process variability in the Community Land Model v5 over Europe
pyVPRM: A next-generation Vegetation Photosynthesis and Respiration Model for the post-MODIS era
Lambda-PFLOTRAN 1.0: a workflow for incorporating organic matter chemistry informed by ultra high resolution mass spectrometry into biogeochemical modeling
An improved model for air–sea exchange of elemental mercury in MITgcm-ECCOv4-Hg: the role of surfactants and waves
BOATSv2: new ecological and economic features improve simulations of high seas catch and effort
A dynamical process-based model for quantifying global agricultural ammonia emissions – AMmonia–CLIMate v1.0 (AMCLIM v1.0) – Part 1: Land module for simulating emissions from synthetic fertilizer use
Simulating the drought response of European tree species with the dynamic vegetation model LPJ-GUESS (v4.1, 97c552c5)
Simulating Ips typographus L. outbreak dynamics and their influence on carbon balance estimates with ORCHIDEE r8627
Biological nitrogen fixation of natural and agricultural vegetation simulated with LPJmL 5.7.9
Learning from conceptual models – a study of the emergence of cooperation towards resource protection in a social–ecological system
Development and assessment of the physical-biogeochemical ocean regional model in the Northwest Pacific: NPRT v1.0 (ROMS v3.9–TOPAZ v2.0)
TROLL 4.0: representing water and carbon fluxes, leaf phenology and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 1: Model description
The biogeochemical model Biome-BGCMuSo v6.2 provides plausible and accurate simulations of the carbon cycle in central European beech forests
TROLL 4.0: representing water and carbon fluxes, leaf phenology, and intraspecific trait variation in a mixed-species individual-based forest dynamics model – Part 2: Model evaluation for two Amazonian sites
Estimation of above- and below-ground ecosystem parameters for the DVM-DOS-TEM v0.7.0 model using MADS v1.7.3: a synthetic case study
DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology
Impacts of land-use change on biospheric carbon: an oriented benchmark using the ORCHIDEE land surface model
Implementing the iCORAL (version 1.0) coral reef CaCO3 production module in the iLOVECLIM climate model
Assimilation of carbonyl sulfide (COS) fluxes within the adjoint-based data assimilation system – Nanjing University Carbon Assimilation System (NUCAS v1.0)
ML4Fire-XGBv1.0: Improving North American wildfire prediction by integrating a machine-learning fire model in a land surface model
Alquimia v1.0: A generic interface to biogeochemical codes – A tool for interoperable development, prototyping and benchmarking for multiphysics simulators
Quantifying the role of ozone-caused damage to vegetation in the Earth system: a new parameterization scheme for photosynthetic and stomatal responses
Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models
Exploring the potential of history matching for land surface model calibration
EAT v1.0.0: a 1D test bed for physical–biogeochemical data assimilation in natural waters
Using deep learning to integrate paleoclimate and global biogeochemistry over the Phanerozoic Eon
Modelling boreal forest's mineral soil and peat C dynamics with the Yasso07 model coupled with the Ricker moisture modifier
Dynamic ecosystem assembly and escaping the “fire trap” in the tropics: insights from FATES_15.0.0
In silico calculation of soil pH by SCEPTER v1.0
Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes
A global behavioural model of human fire use and management: WHAM! v1.0
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Inferring the tree regeneration niche from inventory data using a dynamic forest model
Jianyong Ma, Almut Arneth, Benjamin Smith, Peter Anthoni, Xu-Ri, Peter Eliasson, David Wårlind, Martin Wittenbrink, and Stefan Olin
Geosci. Model Dev., 18, 3131–3155, https://doi.org/10.5194/gmd-18-3131-2025, https://doi.org/10.5194/gmd-18-3131-2025, 2025
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Nitrous oxide (N2O) is a powerful greenhouse gas mainly released from natural and agricultural soils. This study examines how global soil N2O emissions changed from 1961 to 2020 and identifies key factors driving these changes using an ecological model. The findings highlight croplands as the largest source, with factors like fertilizer use and climate change enhancing emissions. Rising CO2 levels, however, can partially mitigate N2O emissions through increased plant nitrogen uptake.
Hoa Nguyen, Ute Daewel, Neil Banas, and Corinna Schrum
Geosci. Model Dev., 18, 2961–2982, https://doi.org/10.5194/gmd-18-2961-2025, https://doi.org/10.5194/gmd-18-2961-2025, 2025
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Parameterization is key in modeling to reproduce observations well but is often done manually. This study presents a particle-swarm-optimizer-based toolbox for marine ecosystem models, compatible with the Framework for Aquatic Biogeochemical Models, thus enhancing its reusability. Applied to the Sylt ecosystem, the toolbox effectively (1) identified multiple parameter sets that matched observations well, providing different insights into ecosystem dynamics, and (2) optimized model complexity.
Zavud Baghirov, Martin Jung, Markus Reichstein, Marco Körner, and Basil Kraft
Geosci. Model Dev., 18, 2921–2943, https://doi.org/10.5194/gmd-18-2921-2025, https://doi.org/10.5194/gmd-18-2921-2025, 2025
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We use an innovative approach to studying the Earth's water cycle by integrating advanced machine learning techniques with a traditional water cycle model. Our model is designed to learn from observational data, with a particular emphasis on understanding the influence of vegetation on water movement. By closely aligning with real-world observations, our model offers new possibilities for enhancing our understanding of the water cycle and its interactions with vegetation.
Naveenkumar Parameswaran, Everardo González, Ewa Burwicz-Galerne, Malte Braack, and Klaus Wallmann
Geosci. Model Dev., 18, 2521–2544, https://doi.org/10.5194/gmd-18-2521-2025, https://doi.org/10.5194/gmd-18-2521-2025, 2025
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Our research uses deep learning to predict organic carbon stocks in ocean sediments, which is crucial for understanding their role in the global carbon cycle. By analysing over 22 000 samples and various seafloor characteristics, our model gives more accurate results than traditional methods. We estimate that the top 10 cm of ocean sediments hold about 156 Pg of carbon. This work enhances carbon stock estimates and helps plan future sampling strategies to better understand oceanic carbon burial.
Zhengyang Lin, Ling Huang, Hanqin Tian, Anping Chen, and Xuhui Wang
Geosci. Model Dev., 18, 2509–2520, https://doi.org/10.5194/gmd-18-2509-2025, https://doi.org/10.5194/gmd-18-2509-2025, 2025
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The China Wildfire Emission Dataset (ChinaWED v1) estimated wildfire emissions in China during 2012–2022 as 78.13 Tg CO2, 279.47 Gg CH4, and 6.26 Gg N2O annually. Agricultural fires dominated emissions, while forest and grassland emissions decreased. Seasonal peaks occurred in late spring, with hotspots in northeast, southwest, and east China. The model emphasizes the importance of using localized emission factors and high-resolution fire estimates for accurate assessments.
Tatsuya Miyauchi, Makoto Saito, Hibiki M. Noda, Akihiko Ito, Tomomichi Kato, and Tsuneo Matsunaga
Geosci. Model Dev., 18, 2329–2347, https://doi.org/10.5194/gmd-18-2329-2025, https://doi.org/10.5194/gmd-18-2329-2025, 2025
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Solar-induced chlorophyll fluorescence (SIF) is an effective indicator for monitoring photosynthetic activity. This paper introduces VISIT-SIF, a biogeochemical model developed based on the Vegetation Integrative Simulator for Trace gases (VISIT) to represent satellite-observed SIF. Our simulations reproduced the global distribution and seasonal variations in observed SIF. VISIT-SIF helps to improve photosynthetic processes through a combination of biogeochemical modeling and observed SIF.
Mateus Dantas de Paula, Matthew Forrest, David Warlind, João Paulo Darela Filho, Katrin Fleischer, Anja Rammig, and Thomas Hickler
Geosci. Model Dev., 18, 2249–2274, https://doi.org/10.5194/gmd-18-2249-2025, https://doi.org/10.5194/gmd-18-2249-2025, 2025
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Our study maps global nitrogen (N) and phosphorus (P) availability and how they changed from 1901 to 2018. We find that tropical regions are mostly P-limited, while temperate and boreal areas face N limitations. Over time, P limitation increased, especially in the tropics, while N limitation decreased. These shifts are key to understanding global plant growth and carbon storage, highlighting the importance of including P dynamics in ecosystem models.
Wolfgang Knorr, Matthew Williams, Tea Thum, Thomas Kaminski, Michael Voßbeck, Marko Scholze, Tristan Quaife, T. Luke Smallman, Susan C. Steele-Dunne, Mariette Vreugdenhil, Tim Green, Sönke Zaehle, Mika Aurela, Alexandre Bouvet, Emanuel Bueechi, Wouter Dorigo, Tarek S. El-Madany, Mirco Migliavacca, Marika Honkanen, Yann H. Kerr, Anna Kontu, Juha Lemmetyinen, Hannakaisa Lindqvist, Arnaud Mialon, Tuuli Miinalainen, Gaétan Pique, Amanda Ojasalo, Shaun Quegan, Peter J. Rayner, Pablo Reyes-Muñoz, Nemesio Rodríguez-Fernández, Mike Schwank, Jochem Verrelst, Songyan Zhu, Dirk Schüttemeyer, and Matthias Drusch
Geosci. Model Dev., 18, 2137–2159, https://doi.org/10.5194/gmd-18-2137-2025, https://doi.org/10.5194/gmd-18-2137-2025, 2025
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When it comes to climate change, the land surface is where the vast majority of impacts happen. The task of monitoring those impacts across the globe is formidable and must necessarily rely on satellites – at a significant cost: the measurements are only indirect and require comprehensive physical understanding. We have created a comprehensive modelling system that we offer to the research community to explore how satellite data can be better exploited to help us capture the changes that happen on our lands.
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
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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.
Trine Frisbæk Hansen, Donald Eugene Canfield, Ken Haste Andersen, and Christian Jannik Bjerrum
Geosci. Model Dev., 18, 1895–1916, https://doi.org/10.5194/gmd-18-1895-2025, https://doi.org/10.5194/gmd-18-1895-2025, 2025
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We describe and test the size-based Nutrient-Unicellular-Multicellular model, which defines unicellular plankton using a single set of parameters, on a eutrophic and oligotrophic ecosystem. The results demonstrate that both sites can be modeled with similar parameters and robust performance over a wide range of parameters. The study shows that the model is useful for non-experts and applicable for modeling ecosystems with limited data. It holds promise for evolutionary and deep-time climate models.
Ying Ye, Guy Munhoven, Peter Köhler, Martin Butzin, Judith Hauck, Özgür Gürses, and Christoph Völker
Geosci. Model Dev., 18, 977–1000, https://doi.org/10.5194/gmd-18-977-2025, https://doi.org/10.5194/gmd-18-977-2025, 2025
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Many biogeochemistry models assume all material reaching the seafloor is remineralized and returned to solution, which is sufficient for studies on short-term climate change. Under long-term climate change, the carbon storage in sediments slows down carbon cycling and influences feedbacks in the atmosphere–ocean–sediment system. This paper describes the coupling of a sediment model to an ocean biogeochemistry model and presents results under the pre-industrial climate and under CO2 perturbation.
Juliette Bernard, Elodie Salmon, Marielle Saunois, Shushi Peng, Penélope Serrano-Ortiz, Antoine Berchet, Palingamoorthy Gnanamoorthy, Joachim Jansen, and Philippe Ciais
Geosci. Model Dev., 18, 863–883, https://doi.org/10.5194/gmd-18-863-2025, https://doi.org/10.5194/gmd-18-863-2025, 2025
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Despite their importance, uncertainties remain in the evaluation of the drivers of temporal variability of methane emissions from wetlands on a global scale. Here, a simplified global model is developed, taking advantage of advances in remote-sensing data and in situ observations. The model reproduces the large spatial and temporal patterns of emissions, albeit with limitations in the tropics due to data scarcity. This model, while simple, can provide valuable insights into sensitivity analyses.
Carolina Natel, David Martin Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth
EGUsphere, https://doi.org/10.5194/egusphere-2024-4064, https://doi.org/10.5194/egusphere-2024-4064, 2025
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Complex models predict forest carbon responses to future climate change but are slow and computationally intensive, limiting large-scale analyses. We used machine learning to accelerate predictions from the LPJ-GUESS vegetation model. Our emulators, based on random forests and neural networks, achieved 97 % faster simulations. This approach enables rapid exploration of climate mitigation strategies and supports informed policy decisions.
Christian Poppe Terán, Bibi S. Naz, Harry Vereecken, Roland Baatz, Rosie A. Fisher, and Harrie-Jan Hendricks Franssen
Geosci. Model Dev., 18, 287–317, https://doi.org/10.5194/gmd-18-287-2025, https://doi.org/10.5194/gmd-18-287-2025, 2025
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Carbon and water exchanges between the atmosphere and the land surface contribute to water resource availability and climate change mitigation. Land surface models, like the Community Land Model version 5 (CLM5), simulate these. This study finds that CLM5 and other data sets underestimate the magnitudes of and variability in carbon and water exchanges for the most abundant plant functional types compared to observations. It provides essential insights for further research into these processes.
Theo Glauch, Julia Marshall, Christoph Gerbig, Santiago Botía, Michał Gałkowski, Sanam N. Vardag, and André Butz
EGUsphere, https://doi.org/10.5194/egusphere-2024-3692, https://doi.org/10.5194/egusphere-2024-3692, 2025
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The Vegetation Photosynthesis and Respiration Model (VPRM) estimates carbon exchange between the atmosphere and biosphere by modeling gross primary production and respiration using satellite data and weather variables. Our new version, pyVPRM, supports diverse satellite products like Sentinel-2, MODIS, VIIRS and new land cover maps, enabling high spatial and temporal resolution. This improves flux estimates, especially in complex landscapes, and ensures continuity as MODIS nears decommissioning.
Katherine A. Muller, Peishi Jiang, Glenn Hammond, Tasneem Ahmadullah, Hyun-Seob Song, Ravi Kukkadapu, Nicholas Ward, Madison Bowe, Rosalie K. Chu, Qian Zhao, Vanessa A. Garayburu-Caruso, Alan Roebuck, and Xingyuan Chen
Geosci. Model Dev., 17, 8955–8968, https://doi.org/10.5194/gmd-17-8955-2024, https://doi.org/10.5194/gmd-17-8955-2024, 2024
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The new Lambda-PFLOTRAN workflow incorporates organic matter chemistry into reaction networks to simulate aerobic respiration and biogeochemistry. Lambda-PFLOTRAN is a Python-based workflow in a Jupyter notebook interface that digests raw organic matter chemistry data via Fourier transform ion cyclotron resonance mass spectrometry, develops a representative reaction network, and completes a biogeochemical simulation with the open-source, parallel-reactive-flow, and transport code PFLOTRAN.
Ling Li, Peipei Wu, Peng Zhang, Shaojian Huang, and Yanxu Zhang
Geosci. Model Dev., 17, 8683–8695, https://doi.org/10.5194/gmd-17-8683-2024, https://doi.org/10.5194/gmd-17-8683-2024, 2024
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In this study, we incorporate sea surfactants and wave-breaking processes into MITgcm-ECCOv4-Hg. The updated model shows increased fluxes in high-wind-speed and high-wave regions and vice versa, enhancing spatial heterogeneity. It shows that elemental mercury (Hg0) transfer velocity is more sensitive to wind speed. These findings may elucidate the discrepancies in previous estimations and offer insights into global Hg cycling.
Jerome Guiet, Daniele Bianchi, Kim J. N. Scherrer, Ryan F. Heneghan, and Eric D. Galbraith
Geosci. Model Dev., 17, 8421–8454, https://doi.org/10.5194/gmd-17-8421-2024, https://doi.org/10.5194/gmd-17-8421-2024, 2024
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The BiOeconomic mArine Trophic Size-spectrum (BOATSv2) model dynamically simulates global commercial fish populations and their coupling with fishing activity, as emerging from environmental and economic drivers. New features, including separate pelagic and demersal populations, iron limitation, and spatial variation of fishing costs and management, improve the accuracy of high seas fisheries. The updated model code is available to simulate both historical and future scenarios.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
Geosci. Model Dev., 17, 8181–8222, https://doi.org/10.5194/gmd-17-8181-2024, https://doi.org/10.5194/gmd-17-8181-2024, 2024
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A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use and also taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers was lost due to NH3 emissions. Hot and dry conditions and regions with high-pH soils can expect higher NH3 emissions.
Benjamin Franklin Meyer, João Paulo Darela-Filho, Konstantin Gregor, Allan Buras, Qiao-Lin Gu, Andreas Krause, Daijun Liu, Phillip Papastefanou, Sijeh Asuk, Thorsten E. E. Grams, Christian S. Zang, and Anja Rammig
EGUsphere, https://doi.org/10.5194/egusphere-2024-3352, https://doi.org/10.5194/egusphere-2024-3352, 2024
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Climate change has increased the likelihood of drought events across Europe, potentially threatening European forest carbon sink. Dynamic vegetation models with mechanistic plant hydraulic architecture are needed to model these developments. We evaluate the plant hydraulic architecture version of LPJ-GUESS and show it's capability at capturing species-specific evapotranspiration responses to drought and reproducing flux observations of both gross primary production and evapotranspiration.
Guillaume Marie, Jina Jeong, Hervé Jactel, Gunnar Petter, Maxime Cailleret, Matthew J. McGrath, Vladislav Bastrikov, Josefine Ghattas, Bertrand Guenet, Anne Sofie Lansø, Kim Naudts, Aude Valade, Chao Yue, and Sebastiaan Luyssaert
Geosci. Model Dev., 17, 8023–8047, https://doi.org/10.5194/gmd-17-8023-2024, https://doi.org/10.5194/gmd-17-8023-2024, 2024
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This research looks at how climate change influences forests, and particularly how altered wind and insect activities could make forests emit instead of absorb carbon. We have updated a land surface model called ORCHIDEE to better examine the effect of bark beetles on forest health. Our findings suggest that sudden events, such as insect outbreaks, can dramatically affect carbon storage, offering crucial insights into tackling climate change.
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
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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.
Saeed Harati-Asl, Liliana Perez, and Roberto Molowny-Horas
Geosci. Model Dev., 17, 7423–7443, https://doi.org/10.5194/gmd-17-7423-2024, https://doi.org/10.5194/gmd-17-7423-2024, 2024
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Social–ecological systems are the subject of many sustainability problems. Because of the complexity of these systems, we must be careful when intervening in them; otherwise we may cause irreversible damage. Using computer models, we can gain insight about these complex systems without harming them. In this paper we describe how we connected an ecological model of forest insect infestation with a social model of cooperation and simulated an intervention measure to save a forest from infestation.
Daehyuk Kim, Hyun-Chae Jung, Jae-Hong Moon, and Na-Hyeon Lee
EGUsphere, https://doi.org/10.5194/egusphere-2024-1509, https://doi.org/10.5194/egusphere-2024-1509, 2024
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Physical–biogeochemical ocean global models is difficult to analyze oceanic environmental systems. To accurately understand the physical–biogeochemical processes at the regional scale, physical and biogeochemical models were coupled at a high resolution. The results successfully simulated the seasonal variations of chlorophyll and nutrients, particularly in the marginal seas, which were not captured by global models. The model is an important tool for studying physical–biogeochemical processes.
Isabelle Maréchaux, Fabian Jörg Fischer, Sylvain Schmitt, and Jérôme Chave
EGUsphere, https://doi.org/10.5194/egusphere-2024-3104, https://doi.org/10.5194/egusphere-2024-3104, 2024
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We describe TROLL 4.0, a simulator of forest dynamics that represents trees in a virtual space at one-meter resolution. Tree birth, growth, death and the underlying physiological processes such as carbon assimilation, water transpiration and leaf phenology depend on plant traits that are measured in the field for many individuals and species. The model is thus capable of jointly simulating forest structure, diversity and ecosystem functioning, a major challenge in modelling vegetation dynamics.
Katarína Merganičová, Ján Merganič, Laura Dobor, Roland Hollós, Zoltán Barcza, Dóra Hidy, Zuzana Sitková, Pavel Pavlenda, Hrvoje Marjanovic, Daniel Kurjak, Michal Bošel'a, Doroteja Bitunjac, Maša Zorana Ostrogović Sever, Jiří Novák, Peter Fleischer, and Tomáš Hlásny
Geosci. Model Dev., 17, 7317–7346, https://doi.org/10.5194/gmd-17-7317-2024, https://doi.org/10.5194/gmd-17-7317-2024, 2024
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We developed a multi-objective calibration approach leading to robust parameter values aiming to strike a balance between their local precision and broad applicability. Using the Biome-BGCMuSo model, we tested the calibrated parameter sets for simulating European beech forest dynamics across large environmental gradients. Leveraging data from 87 plots and five European countries, the results demonstrated reasonable local accuracy and plausible large-scale productivity responses.
Sylvain Schmitt, Fabian Fischer, James Ball, Nicolas Barbier, Marion Boisseaux, Damien Bonal, Benoit Burban, Xiuzhi Chen, Géraldine Derroire, Jeremy Lichstein, Daniela Nemetschek, Natalia Restrepo-Coupe, Scott Saleska, Giacomo Sellan, Philippe Verley, Grégoire Vincent, Camille Ziegler, Jérôme Chave, and Isabelle Maréchaux
EGUsphere, https://doi.org/10.5194/egusphere-2024-3106, https://doi.org/10.5194/egusphere-2024-3106, 2024
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We evaluate the capability of TROLL 4.0, a simulator of forest dynamics, to represent tropical forest structure, diversity and functioning in two Amazonian forests. Evaluation data include forest inventories, carbon and water fluxes between the forest and the atmosphere, and leaf area and canopy height from remote-sensing products. The model realistically predicts the structure and composition, and the seasonality of carbon and water fluxes at both sites.
Elchin E. Jafarov, Helene Genet, Velimir V. Vesselinov, Valeria Briones, Aiza Kabeer, Andrew L. Mullen, Benjamin Maglio, Tobey Carman, Ruth Rutter, Joy Clein, Chu-Chun Chang, Dogukan Teber, Trevor Smith, Joshua M. Rady, Christina Schädel, Jennifer D. Watts, Brendan M. Rogers, and Susan M. Natali
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-158, https://doi.org/10.5194/gmd-2024-158, 2024
Revised manuscript accepted for GMD
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Thawing permafrost could greatly impact global climate. Our study improves modeling of carbon cycling in Arctic ecosystems. We developed an automated method to fine-tune a model that simulates carbon and nitrogen flows, using computer-generated data. Using computer-generated data, we tested our method and found it enhances accuracy and reduces the time needed for calibration. This work helps make climate predictions more reliable in sensitive permafrost regions.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew D. Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander J. Winkler
Geosci. Model Dev., 17, 6683–6701, https://doi.org/10.5194/gmd-17-6683-2024, https://doi.org/10.5194/gmd-17-6683-2024, 2024
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Our study employs long short-term memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. The LSTM model outperforms traditional methods, enhancing accuracy in predicting greenness dynamics and phenological transitions across plant functional types. Highlighting the importance of multi-variate meteorological memory effects, our research pioneers unlock the secrets of vegetation phenology responses to climate change with deep learning techniques.
Thi Lan Anh Dinh, Daniel Goll, Philippe Ciais, and Ronny Lauerwald
Geosci. Model Dev., 17, 6725–6744, https://doi.org/10.5194/gmd-17-6725-2024, https://doi.org/10.5194/gmd-17-6725-2024, 2024
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The study assesses the performance of the dynamic global vegetation model (DGVM) ORCHIDEE in capturing the impact of land-use change on carbon stocks across Europe. Comparisons with observations reveal that the model accurately represents carbon fluxes and stocks. Despite the underestimations in certain land-use conversions, the model describes general trends in soil carbon response to land-use change, aligning with the site observations.
Nathaelle Bouttes, Lester Kwiatkowski, Manon Berger, Victor Brovkin, and Guy Munhoven
Geosci. Model Dev., 17, 6513–6528, https://doi.org/10.5194/gmd-17-6513-2024, https://doi.org/10.5194/gmd-17-6513-2024, 2024
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Coral reefs are crucial for biodiversity, but they also play a role in the carbon cycle on long time scales of a few thousand years. To better simulate the future and past evolution of coral reefs and their effect on the global carbon cycle, hence on atmospheric CO2 concentration, it is necessary to include coral reefs within a climate model. Here we describe the inclusion of coral reef carbonate production in a carbon–climate model and its validation in comparison to existing modern data.
Huajie Zhu, Mousong Wu, Fei Jiang, Michael Vossbeck, Thomas Kaminski, Xiuli Xing, Jun Wang, Weimin Ju, and Jing M. Chen
Geosci. Model Dev., 17, 6337–6363, https://doi.org/10.5194/gmd-17-6337-2024, https://doi.org/10.5194/gmd-17-6337-2024, 2024
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In this work, we developed the Nanjing University Carbon Assimilation System (NUCAS v1.0). Data assimilation experiments were conducted to demonstrate the robustness and investigate the feasibility and applicability of NUCAS. The assimilation of ecosystem carbonyl sulfide (COS) fluxes improved the model performance in gross primary productivity, evapotranspiration, and sensible heat, showing that COS provides constraints on parameters relevant to carbon-, water-, and energy-related processes.
Ye Liu, Huilin Huang, Sing-Chun Wang, Tao Zhang, Donghui Xu, and Yang Chen
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-151, https://doi.org/10.5194/gmd-2024-151, 2024
Revised manuscript accepted for GMD
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This study integrates machine learning with a land surface model to improve wildfire predictions in North America. Traditional models struggle with accurately simulating burned areas due to simplified processes. By combining the predictive power of machine learning with a land model, our hybrid framework better captures fire dynamics. This approach enhances our understanding of wildfire behavior and aids in developing more effective climate and fire management strategies.
Sergi Molins, Benjamin Andre, Jeffrey Johnson, Glenn Hammond, Benjamin Sulman, Konstantin Lipnikov, Marcus Day, James Beisman, Daniil Svyatsky, Hang Deng, Peter Lichtner, Carl Steefel, and David Moulton
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-108, https://doi.org/10.5194/gmd-2024-108, 2024
Revised manuscript accepted for GMD
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Developing scientific software and making sure it functions properly requires a significant effort. As we advance our understanding of natural systems, however, there is the need to develop yet more complex models and codes. In this work, we present a piece of software that facilitates this work, specifically with regard to reactive processes. Existing tried-and-true codes are made available via this new interface, freeing up resources to focus on the new aspects of the problems at hand.
Fang Li, Zhimin Zhou, Samuel Levis, Stephen Sitch, Felicity Hayes, Zhaozhong Feng, Peter B. Reich, Zhiyi Zhao, and Yanqing Zhou
Geosci. Model Dev., 17, 6173–6193, https://doi.org/10.5194/gmd-17-6173-2024, https://doi.org/10.5194/gmd-17-6173-2024, 2024
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A new scheme is developed to model the surface ozone damage to vegetation in regional and global process-based models. Based on 4210 data points from ozone experiments, it accurately reproduces statistically significant linear or nonlinear photosynthetic and stomatal responses to ozone in observations for all vegetation types. It also enables models to implicitly capture the variability in plant ozone tolerance and the shift among species within a vegetation type.
Alexander S. Brunmayr, Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven
Geosci. Model Dev., 17, 5961–5985, https://doi.org/10.5194/gmd-17-5961-2024, https://doi.org/10.5194/gmd-17-5961-2024, 2024
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A new generation of soil models promises to more accurately predict the carbon cycle in soils under climate change. However, measurements of 14C (the radioactive carbon isotope) in soils reveal that the new soil models face similar problems to the traditional models: they underestimate the residence time of carbon in soils and may therefore overestimate the net uptake of CO2 by the land ecosystem. Proposed solutions include restructuring the models and calibrating model parameters with 14C data.
Nina Raoult, Simon Beylat, James M. Salter, Frédéric Hourdin, Vladislav Bastrikov, Catherine Ottlé, and Philippe Peylin
Geosci. Model Dev., 17, 5779–5801, https://doi.org/10.5194/gmd-17-5779-2024, https://doi.org/10.5194/gmd-17-5779-2024, 2024
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We use computer models to predict how the land surface will respond to climate change. However, these complex models do not always simulate what we observe in real life, limiting their effectiveness. To improve their accuracy, we use sophisticated statistical and computational techniques. We test a technique called history matching against more common approaches. This method adapts well to these models, helping us better understand how they work and therefore how to make them more realistic.
Jorn Bruggeman, Karsten Bolding, Lars Nerger, Anna Teruzzi, Simone Spada, Jozef Skákala, and Stefano Ciavatta
Geosci. Model Dev., 17, 5619–5639, https://doi.org/10.5194/gmd-17-5619-2024, https://doi.org/10.5194/gmd-17-5619-2024, 2024
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To understand and predict the ocean’s capacity for carbon sequestration, its ability to supply food, and its response to climate change, we need the best possible estimate of its physical and biogeochemical properties. This is obtained through data assimilation which blends numerical models and observations. We present the Ensemble and Assimilation Tool (EAT), a flexible and efficient test bed that allows any scientist to explore and further develop the state of the art in data assimilation.
Dongyu Zheng, Andrew S. Merdith, Yves Goddéris, Yannick Donnadieu, Khushboo Gurung, and Benjamin J. W. Mills
Geosci. Model Dev., 17, 5413–5429, https://doi.org/10.5194/gmd-17-5413-2024, https://doi.org/10.5194/gmd-17-5413-2024, 2024
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This study uses a deep learning method to upscale the time resolution of paleoclimate simulations to 1 million years. This improved resolution allows a climate-biogeochemical model to more accurately predict climate shifts. The method may be critical in developing new fully continuous methods that are able to be applied over a moving continental surface in deep time with high resolution at reasonable computational expense.
Boris Ťupek, Aleksi Lehtonen, Alla Yurova, Rose Abramoff, Bertrand Guenet, Elisa Bruni, Samuli Launiainen, Mikko Peltoniemi, Shoji Hashimoto, Xianglin Tian, Juha Heikkinen, Kari Minkkinen, and Raisa Mäkipää
Geosci. Model Dev., 17, 5349–5367, https://doi.org/10.5194/gmd-17-5349-2024, https://doi.org/10.5194/gmd-17-5349-2024, 2024
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Updating the Yasso07 soil C model's dependency on decomposition with a hump-shaped Ricker moisture function improved modelled soil organic C (SOC) stocks in a catena of mineral and organic soils in boreal forest. The Ricker function, set to peak at a rate of 1 and calibrated against SOC and CO2 data using a Bayesian approach, showed a maximum in well-drained soils. Using SOC and CO2 data together with the moisture only from the topsoil humus was crucial for accurate model estimates.
Jacquelyn K. Shuman, Rosie A. Fisher, Charles Koven, Ryan Knox, Lara Kueppers, and Chonggang Xu
Geosci. Model Dev., 17, 4643–4671, https://doi.org/10.5194/gmd-17-4643-2024, https://doi.org/10.5194/gmd-17-4643-2024, 2024
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We adapt a fire behavior and effects module for use in a size-structured vegetation demographic model to test how climate, fire regime, and fire-tolerance plant traits interact to determine the distribution of tropical forests and grasslands. Our model captures the connection between fire disturbance and plant fire-tolerance strategies in determining plant distribution and provides a useful tool for understanding the vulnerability of these areas under changing conditions across the tropics.
Yoshiki Kanzaki, Isabella Chiaravalloti, Shuang Zhang, Noah J. Planavsky, and Christopher T. Reinhard
Geosci. Model Dev., 17, 4515–4532, https://doi.org/10.5194/gmd-17-4515-2024, https://doi.org/10.5194/gmd-17-4515-2024, 2024
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Soil pH is one of the most commonly measured agronomical and biogeochemical indices, mostly reflecting exchangeable acidity. Explicit simulation of both porewater and bulk soil pH is thus crucial to the accurate evaluation of alkalinity required to counteract soil acidification and the resulting capture of anthropogenic carbon dioxide through the enhanced weathering technique. This has been enabled by the updated reactive–transport SCEPTER code and newly developed framework to simulate soil pH.
David Sandoval, Iain Colin Prentice, and Rodolfo L. B. Nóbrega
Geosci. Model Dev., 17, 4229–4309, https://doi.org/10.5194/gmd-17-4229-2024, https://doi.org/10.5194/gmd-17-4229-2024, 2024
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Numerous estimates of water and energy balances depend on empirical equations requiring site-specific calibration, posing risks of "the right answers for the wrong reasons". We introduce novel first-principles formulations to calculate key quantities without requiring local calibration, matching predictions from complex land surface models.
Oliver Perkins, Matthew Kasoar, Apostolos Voulgarakis, Cathy Smith, Jay Mistry, and James D. A. Millington
Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, https://doi.org/10.5194/gmd-17-3993-2024, 2024
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Wildfire is often presented in the media as a danger to human life. Yet globally, millions of people’s livelihoods depend on using fire as a tool. So, patterns of fire emerge from interactions between humans, land use, and climate. This complexity means scientists cannot yet reliably say how fire will be impacted by climate change. So, we developed a new model that represents globally how people use and manage fire. The model reveals the extent and diversity of how humans live with and use fire.
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024, https://doi.org/10.5194/gmd-17-3733-2024, 2024
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We have developed the Terrestrial Ecosystem Model in R (TEMIR), which simulates plant carbon and pollutant uptake and predicts their response to varying atmospheric conditions. This model is designed to couple with an atmospheric chemistry model so that questions related to plant–atmosphere interactions, such as the effects of climate change, rising CO2, and ozone pollution on forest carbon uptake, can be addressed. The model has been well validated with both ground and satellite observations.
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
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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.
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024, https://doi.org/10.5194/gmd-17-2929-2024, 2024
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By including microbial processes in soil models, we learn how the soil system interacts with its environment and responds to climate change. We present a soil process model, MIMICS+, which is able to reproduce carbon stocks found in boreal forest soils better than a conventional land model. With the model we also find that when adding nitrogen, the relationship between soil microbes changes notably. Coupling the model to a vegetation model will allow for further study of these mechanisms.
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024, https://doi.org/10.5194/gmd-17-2705-2024, 2024
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Soil microbes provide a strong link for elemental fluxes in the earth system. The SESAM model applies an optimality assumption to model those linkages and their adaptation. We found that a previous heuristic description was a special case of a newly developed more rigorous description. The finding of new behaviour at low microbial biomass led us to formulate the constrained enzyme hypothesis. We now can better describe how microbially mediated linkages of elemental fluxes adapt across decades.
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024, https://doi.org/10.5194/gmd-17-2683-2024, 2024
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Canadian forests are responding to fire, harvest, and climate change. Models need to quantify these processes and their carbon and energy cycling impacts. We develop a scheme that, based on satellite records, represents fire, harvest, and the sparsely vegetated areas that these processes generate. We evaluate model performance and demonstrate the impacts of disturbance on carbon and energy cycling. This work has implications for land surface modeling and assessing Canada’s terrestrial C cycle.
Yannek Käber, Florian Hartig, and Harald Bugmann
Geosci. Model Dev., 17, 2727–2753, https://doi.org/10.5194/gmd-17-2727-2024, https://doi.org/10.5194/gmd-17-2727-2024, 2024
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Many forest models include detailed mechanisms of forest growth and mortality, but regeneration is often simplified. Testing and improving forest regeneration models is challenging. We address this issue by exploring how forest inventories from unmanaged European forests can be used to improve such models. We find that competition for light among trees is captured by the model, unknown model components can be informed by forest inventory data, and climatic effects are challenging to capture.
Cited articles
Ahlström, A., Schurgers, G., Arneth, A., and Smith, B.: Robustness and
uncertainty in terrestrial ecosystem carbon response to CMIP5 climate
change projections, Environ. Res. Lett., 7, 044008,
https://doi.org/10.1088/1748-9326/7/4/044008, 2012. a
Amthor, J. S.: The role of maintenance respiration in plant growth, Plant Cell
Environ., 7, 561–569, https://doi.org/10.1111/1365-3040.ep11591833, 1984. a
Antonarakis, A. S., Saatchi, S. S., Chazdon, R. L., and Moorcroft, P. R.: Using
Lidar and Radar measurements to constrain predictions of forest ecosystem
structure and function, Ecol. Appl., 21, 1120–1137, https://doi.org/10.1890/10-0274.1,
2011. a, b
Antonarakis, A. S., Munger, J. W., and Moorcroft, P. R.: Imaging spectroscopy-
and lidar-derived estimates of canopy composition and structure to improve
predictions of forest carbon fluxes and ecosystem dynamics, Geophys. Res.
Lett., 41, 2535–2542, https://doi.org/10.1002/2013GL058373, 2014. a
Arias, M. E., Lee, E., Farinosi, F., Pereira, F. F., and Moorcroft, P. R.:
Decoupling the effects of deforestation and climate variability in the
Tapajós river basin in the Brazilian Amazon, Hydrol. Process., 32,
1648–1663, https://doi.org/10.1002/hyp.11517, 2018. a
Avissar, R. and Mahrer, Y.: Mapping frost-sensitive areas with a
three-dimensional local-scale numerical model. Part I. Physical and
numerical aspects, J. Appl. Meteor., 27, 400–413,
https://doi.org/10.1175/1520-0450(1988)027<0400:MFSAWA>2.0.CO;2, 1988. a
Baker, I., Denning, A. S., Hanan, N., Prihodko, L., Uliasz, M., Vidale, P.-L.,
Davis, K., and Bakwin, P.: Simulated and observed fluxes of sensible and
latent heat and CO2 at the WLEF-TV tower using SiB2.5, Glob.
Change Biol., 9, 1262–1277, https://doi.org/10.1046/j.1365-2486.2003.00671.x, 2003. a
Bazzaz, F. A.: The physiological ecology of plant succession, Annu. Rev. Ecol.
Syst., 10, 351–371, https://doi.org/10.1146/annurev.es.10.110179.002031, 1979. a
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, b
Betts, A. K. and Silva Dias, M. A. F.: Progress in understanding
land-surface-atmosphere coupling from LBA research, J. Adv. Model. Earth
Syst., 2, 6, https://doi.org/10.3894/JAMES.2010.2.6, 2010. a
Beven, K. and Freer, J.: A dynamic TOPMODEL, Hydrol. Process., 15,
1993–2011, https://doi.org/10.1002/hyp.252, 2001. a
Blyth, E., Clark, D. B., Ellis, R., Huntingford, C., Los, S., Pryor, M., Best,
M., and Sitch, S.: A comprehensive set of benchmark tests for a land surface
model of simultaneous fluxes of water and carbon at both the global and
seasonal scale, Geosci. Model Dev., 4, 255–269,
https://doi.org/10.5194/gmd-4-255-2011, 2011. a
Bogan, S. A., Antonarakis, A. S., and Moorcroft, P. R.: Imaging
spectrometry-derived estimates of regional ecosystem composition for the
Sierra Nevada, California, Remote Sens. Environ., 228, 14–30,
https://doi.org/10.1016/j.rse.2019.03.031, 2019. a
Bolker, B. M., Pacala, S. W., and Parton, W. J.: Linear analysis of soil
decomposition: insights from the CENTURY model, Ecol. Appl., 8, 425–439,
https://doi.org/10.1890/1051-0761(1998)008[0425:LAOSDI]2.0.CO;2, 1998. a, b, c, d
Bonan, G. B.: Land-atmosphere CO2 exchange simulated by a land surface process
model coupled to an atmospheric general circulation model, J. Geophys.
Res.-Atmos., 100, 2817–2831, https://doi.org/10.1029/94JD02961, 1995. a
Both, S., Riutta, T., Paine, C. E. T., Elias, D. M. O., Cruz, R. S., Jain, A.,
Johnson, D., Kritzler, U. H., Kuntz, M., Majalap-Lee, N., Mielke, N.,
Montoya Pillco, M. X., Ostle, N. J., Arn Teh, Y., Malhi, Y., and Burslem, D.
F. R. P.: Logging and soil nutrients independently explain plant trait
expression in tropical forests, New Phytol., 221, 1853–1865,
https://doi.org/10.1111/nph.15444, 2019. a
Brooks, R. H. and Corey, A. T.: Hydraulic properties of porous media, Hydrology
Papers 3, Colorado State University, Fort Collins, USA, 1964. a
Bruelheide, H., Dengler, J., Purschke, O., Lenoir, J., Jiménez-Alfaro,
B., Hennekens, S. M., Botta-Dukát, Z., Chytrý, M., Field,
R., Jansen, F., Kattge, J., Pillar, V. D., Schrodt, F., Mahecha, M. D., Peet,
R. K., Sandel, B., van Bodegom, P., Altman, J., Alvarez-Dávila,
E., Arfin Khan, M. A. S., Attorre, F., Aubin, I., Baraloto, C., Barroso,
J. G., Bauters, M., Bergmeier, E., Biurrun, I., Bjorkman, A. D., Blonder, B.,
Čarni, A., Cayuela, L., Černý, T., Cornelissen, J.
H. C., Craven, D., Dainese, M., Derroire, G., De Sanctis, M., DÍaz,
S., Doležal, J., Farfan-Rios, W., Feldpausch, T. R., Fenton, N. J.,
Garnier, E., Guerin, G. R., Gutiérrez, A. G., Haider, S., Hattab, T.,
Henry, G., Hérault, B., Higuchi, P., Hölzel, N., Homeier, J.,
Jentsch, A., Jürgens, N., Ka̧cki, Z., Karger, D. N., Kessler,
M., Kleyer, M., Knollová, I., Korolyuk, A. Y., Kühn, I.,
Laughlin, D. C., Lens, F., Loos, J., Louault, F., Lyubenova, M. I., Malhi,
Y., Marcenò, C., Mencuccini, M., Müller, J. V., Munzinger,
J., Myers-Smith, I. H., Neill, D. A., Niinemets, Ü., Orwin, K. H.,
Ozinga, W. A., Penuelas, J., Pérez-Haase, A., Petřík,
P., Phillips, O. L., Pärtel, M., Reich, P. B., Römermann, C.,
Rodrigues, A. V., Sabatini, F. M., Sardans, J., Schmidt, M., Seidler, G.,
Silva Espejo, J. E., Silveira, M., Smyth, A., Sporbert, M., Svenning, J.-C.,
Tang, Z., Thomas, R., Tsiripidis, I., Vassilev, K., Violle, C., Virtanen, R.,
Weiher, E., Welk, E., Wesche, K., Winter, M., Wirth, C., and Jandt, U.:
Global trait–environment relationships of plant communities, Nat. Ecol.
Evol., 2, 1906–1917, https://doi.org/10.1038/s41559-018-0699-8, 2018. a
Bugmann, H.: A Review of Forest Gap Models, Clim. Change, 51, 259–305,
https://doi.org/10.1023/A:1012525626267, 2001. a
Cardinale, B. J., Wright, J. P., Cadotte, M. W., Carroll, I. T., Hector, A.,
Srivastava, D. S., Loreau, M., and Weis, J. J.: Impacts of plant diversity on
biomass production increase through time because of species complementarity,
P. Natl. Acad. Sci. USA, 104, 18123–18128,
https://doi.org/10.1073/pnas.0709069104, 2007. a
Castanho, A. D. A., Galbraith, D., Zhang, K., Coe, M. T., Costa, M. H., and
Moorcroft, P.: Changing Amazon biomass and the role of atmospheric
CO2 concentration, climate and land use, Global Biogeochem. Cy.,
30, 18–39, https://doi.org/10.1002/2015GB005135, 2016. a
Cavanaugh, K. C., Gosnell, J. S., Davis, S. L., Ahumada, J., Boundja, P.,
Clark, D. B., Mugerwa, B., Jansen, P. A., O'Brien, T. G., Rovero, F.,
Sheil, D., Vasquez, R., and Andelman, S.: Carbon storage in tropical forests
correlates with taxonomic diversity and functional dominance on a global
scale, Global Ecol. Biogeogr., 23, 563–573, https://doi.org/10.1111/geb.12143, 2014. a
Chen, J. and Black, T.: Foliage area and architecture of plant canopies from
sunfleck size distributions, Agr. Forest Meteorol., 60, 249–266,
https://doi.org/10.1016/0168-1923(92)90040-B, 1992. a
Clark, D. B., Mercado, L. M., Sitch, S., Jones, C. D., Gedney, N., Best, M. J.,
Pryor, M., Rooney, G. G., Essery, R. L. H., Blyth, E., Boucher, O., Harding,
R. J., Huntingford, C., and Cox, P. M.: The Joint UK Land Environment
Simulator (JULES), model description – Part 2: Carbon fluxes and
vegetation dynamics, Geosci. Model Dev., 4, 701–722,
https://doi.org/10.5194/gmd-4-701-2011, 2011. a
Collatz, G., Ribas-Carbo, M., and Berry, J.: Coupled photosynthesis-stomatal
conductance model for leaves of C4 plants, Aust. J. Plant Physiol.,
19, 519–538, https://doi.org/10.1071/PP9920519, 1992. a, b, c
Collatz, G. J., Ball, J., Grivet, C., and Berry, J. A.: Physiological and
environmental regulation of stomatal conductance, photosynthesis and
transpiration: a model that includes a laminar boundary layer, Agr. Forest
Meteorol., 54, 107–136, https://doi.org/10.1016/0168-1923(91)90002-8, 1991. a, b
Cowan, I. and Troughton, J.: The relative role of stomata in transpiration and
assimilation, Planta, 97, 325–336, https://doi.org/10.1007/BF00390212, 1971. 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. M., 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. Meteorol. Soc., 137, 553–597,
https://doi.org/10.1002/qj.828, 2011. a
di Porcia e Brugnera, M., Meunier, F., Longo, M., Moorthy, S., De Deurwaerder, H., Schnitzer, S. A., Bonal, D., Faybishenko, B., and Verbeeck, H.: Modelling the impact of liana infestation on the demography and carbon cycle of tropical forests, Global Change Biol., 25, 3767–3780, https://doi.org/10.1111/gcb.14769, 2019. a
Dickinson, R., Henderson-Sellers, A., Kennedy, P., and Wilson, M.:
Biosphere-atmosphere transfer scheme (BATS) for the NCAR community
climate model, Technical Note NCAR/TN-275+STR, NCAR, Boulder, CO,
https://doi.org/10.5065/D6668B58, 1986. a
Dietze, M. C., Serbin, S. P., Davidson, C., Desai, A. R., Feng, X., Kelly, R.,
Kooper, R., LeBauer, D., Mantooth, J., McHenry, K., and Wang, D.: A
quantitative assessment of a terrestrial biosphere model's data needs across
North American biomes, J. Geophys. Res.-Biogeosci., 119, 286–300,
https://doi.org/10.1002/2013JG002392, 2014. a
Dufour, L. and van Mieghem, J.: Thermodynamique de l'atmosphère, Institut
Royal Météorologique de Belgique, Gembloux, Belgium, 2 edn., in
French, 1975. a
Evans, M. R.: Modelling ecological systems in a changing world, Philos. Trans.
R. Soc. B-Biol. Sci., 367, 181–190, https://doi.org/10.1098/rstb.2011.0172, 2012. a, b
Farquhar, G., von Caemmerer, S., and Berry, J.: A biochemical model of
photosynthetic CO2 assimilation in leaves of C3
species, Planta, 149, 78–90, https://doi.org/10.1007/BF00386231, 1980. a, b
Feldpausch, T. R., Jirka, S., Passos, C. A. M., Jasper, F., and Riha, S. J.:
When big trees fall: Damage and carbon export by reduced impact logging in
southern Amazonia, Forest Ecol. Manag., 219, 199–215,
https://doi.org/10.1016/j.foreco.2005.09.003, 2005. a
Feng, X., Uriarte, M., González, G., Reed, S., Thompson, J., Zimmerman,
J. K., and Murphy, L.: Improving predictions of tropical forest response to
climate change through integration of field studies and ecosystem modeling,
Global Change Biol., 24, e213–e232, https://doi.org/10.1111/gcb.13863, 2018. a
Fischer, R., Bohn, F., de Paula, M. D., Dislich, C., Groeneveld, J.,
Gutiérrez, A. G., Kazmierczak, M., Knapp, N., Lehmann, S., Paulick, S.,
Pütz, S., Rödig, E., Taubert, F., Köhler, P., and Huth, A.:
Lessons learned from applying a forest gap model to understand ecosystem and
carbon dynamics of complex tropical forests, Ecol. Model., 326, 124–133,
https://doi.org/10.1016/j.ecolmodel.2015.11.018, 2016. a, b
Fisher, J. B., Huntzinger, D. N., Schwalm, C. R., and Sitch, S.: Modeling the
terrestrial biosphere, Ann. Rev. Environ. Res., 39, 91–123,
https://doi.org/10.1146/annurev-environ-012913-093456, 2014. a
Fisher, R., McDowell, N., Purves, D., Moorcroft, P., Sitch, S., Cox, P.,
Huntingford, C., Meir, P., and Ian Woodward, F.: Assessing uncertainties in a
second-generation dynamic vegetation model caused by ecological scale
limitations, New Phytol., 187, 666–681,
https://doi.org/10.1111/j.1469-8137.2010.03340.x, 2010. a
Fisher, R. A., Muszala, S., Vertenstein, M., Lawrence, P., Xu, C., McDowell,
N. G., Knox, R. G., Koven, C., Holm, J., Rogers, B. M., Lawrence, D., and
Bonan, G.: Taking off the training wheels: the properties of a dynamic
vegetation model without climate envelopes, Geosci. Model Dev., 8,
3593–3619, https://doi.org/10.5194/gmd-8-3593-2015, 2015. a, b
Fisher, R. A., Koven, C. D., Anderegg, W. R. L., Christoffersen, B. O., Dietze,
M. C., Farrior, C., Holm, J. A., Hurtt, G., Knox, R. G., Lawrence, P. J.,
Lichststein, J. W., Longo, M., Matheny, A. M., Medvigy, D., Muller-Landau,
H. C., Powell, T. L., Serbin, S. P., Sato, H., Shuman, J., Smith, B.,
Trugman, A. T., Viskari, T., Verbeeck, H., Weng, E., Xu, C., Xu, X., Zhang,
T., and Moorcroft, P.: 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, b, c, d
Foken, T.: 50 years of the Monin–Obukhov similarity theory, Bound.-Lay.
Meteorol., 119, 431–447, https://doi.org/10.1007/s10546-006-9048-6, 2006. a, b
Foley, J. A., Prentice, I. C., Ramankutty, N., Levis, S., Pollard, D., Sitch,
S., and Haxeltine, A.: An integrated biosphere model of land surface
processes, terrestrial carbon balance, and vegetation dynamics, Global
Biogeochem. Cy., 10, 603–628, https://doi.org/10.1029/96GB02692, 1996. a, b, c, d
Fortunel, C., Fine, P. V. A., and Baraloto, C.: Leaf, stem and root tissue
strategies across 758 Neotropical tree species, Funct. Ecol., 26,
1153–1161, https://doi.org/10.1111/j.1365-2435.2012.02020.x, 2012. a
Freitas, S. R., Panetta, J., Longo, K. M., Rodrigues, L. F., Moreira, D. S.,
Rosário, N. E., Silva Dias, P. L., Silva Dias, M. A. F., Souza, E. P.,
Freitas, E. D., Longo, M., Frassoni, A., Fazenda, A. L., Santos e Silva,
C. M., Pavani, C. A. B., Eiras, D., França, D. A., Massaru, D., Silva,
F. B., Santos, F. C., Pereira, G., Camponogara, G., Ferrada, G. A.,
Campos Velho, H. F., Menezes, I., Freire, J. L., Alonso, M. F., Gácita,
M. S., Zarzur, M., Fonseca, R. M., Lima, R. S., Siqueira, R. A., Braz, R.,
Tomita, S., Oliveira, V., and Martins, L. D.: The Brazilian developments on
the Regional Atmospheric Modeling System (BRAMS 5.2): an integrated
environmental model tuned for tropical areas, Geosci. Model Dev., 10,
189–222, https://doi.org/10.5194/gmd-10-189-2017, 2017. a
Friedlingstein, P., Meinshausen, M., Arora, V. K., Jones, C. D., Anav, A.,
Liddicoat, S. K., and Knutti, R.: Uncertainties in CMIP5 climate
projections due to carbon cycle feedbacks, J. Climate, 27, 511–526,
https://doi.org/10.1175/JCLI-D-12-00579.1, 2014. a
Friend, A. D., Stevens, A. K., Knox, R. G., and Cannell, M. G. R.: A
process-based, terrestrial biosphere model of ecosystem dynamics (Hybrid
v3.0), Ecol. Model., 95, 249–287, https://doi.org/10.1016/S0304-3800(96)00034-8, 1997. a, b
García-Palacios, P., Gross, N., Gaitán, J., and Maestre, F. T.:
Climate mediates the biodiversity–ecosystem stability
relationship globally, P. Natl. Acad. Sci. USA, 115, 8400–8405,
https://doi.org/10.1073/pnas.1800425115, 2018. a
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs,
L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K.,
Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da
Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D.,
Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert,
S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective
Analysis for Research and Applications, version 2 (MERRA-2), J.
Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017. a
Global Soil Data Task: Global Soil Data Products CD-ROM Contents (IGBP-DIS), https://doi.org/10.3334/ORNLDAAC/565, Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA, 2000. a
Good, P., Jones, C., Lowe, J., Betts, R., Booth, B., and Huntingford, C.:
Quantifying Environmental Drivers of Future Tropical Forest Extent, J.
Climate, 24, 1337–1349, https://doi.org/10.1175/2010JCLI3865.1, 2011. a, b
Haddad, N. M., Brudvig, L. A., Clobert, J., Davies, K. F., Gonzalez, A., Holt,
R. D., Lovejoy, T. E., Sexton, J. O., Austin, M. P., Collins, C. D., Cook,
W. M., Damschen, E. I., Ewers, R. M., Foster, B. L., Jenkins, C. N., King,
A. J., Laurance, W. F., Levey, D. J., Margules, C. R., Melbourne, B. A.,
Nicholls, A. O., Orrock, J. L., Song, D.-X., and Townshend, J. R.: Habitat
fragmentation and its lasting impact on Earth's ecosystems, Science
Advances, 1, e1500052, https://doi.org/10.1126/sciadv.1500052, 2015. a
Haverd, V., Cuntz, M., Leuning, R., and Keith, H.: Air and biomass heat storage
fluxes in a forest canopy: Calculation within a soil vegetation atmosphere
transfer model, Agr. Forest Meteorol., 147, 125–139,
https://doi.org/10.1016/j.agrformet.2007.07.006, 2007. a
Haxeltine, A. and Prentice, I. C.: BIOME3: An equilibrium terrestrial biosphere
model based on ecophysiological constraints, resource availability, and
competition among plant functional types, Global Biogeochem. Cy., 10,
693–709, https://doi.org/10.1029/96GB02344, 1996. a
Hengl, T., de Jesus, J. M., Heuvelink, G. B. M., Ruiperez Gonzalez, M.,
Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X.,
Bauer Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A.,
Batjes, N. H., Leenaars, J. G. B., Ribeiro, E., Wheeler, I., Mantel, S., and
Kempen, B.: SoilGrids250m: Global gridded soil information based on machine
learning, PLoS One, 12, 1–40, https://doi.org/10.1371/journal.pone.0169748, 2017. a
Huang, M., Xu, Y., Longo, M., Keller, M., Knox, R., Koven, C., and Fisher, R.: Assessing impacts of selective logging on water, energy, and carbon budgets and ecosystem dynamics in Amazon forests using the Functionally Assembled Terrestrial Ecosystem Simulator, Biogeosciences Discuss., https://doi.org/10.5194/bg-2019-129, in review, 2019. a
Huang, Y., Chen, Y., Castro-Izaguirre, N., Baruffol, M., Brezzi, M., Lang, A.,
Li, Y., Härdtle, W., von Oheimb, G., Yang, X., Liu, X., Pei, K.,
Both, S., Yang, B., Eichenberg, D., Assmann, T., Bauhus, J., Behrens, T.,
Buscot, F., Chen, X.-Y., Chesters, D., Ding, B.-Y., Durka, W., Erfmeier, A.,
Fang, J., Fischer, M., Guo, L.-D., Guo, D., Gutknecht, J. L. M., He, J.-S.,
He, C.-L., Hector, A., Hönig, L., Hu, R.-Y., Klein, A.-M., Kühn,
P., Liang, Y., Li, S., Michalski, S., Scherer-Lorenzen, M., Schmidt, K.,
Scholten, T., Schuldt, A., Shi, X., Tan, M.-Z., Tang, Z., Trogisch, S., Wang,
Z., Welk, E., Wirth, C., Wubet, T., Xiang, W., Yu, M., Yu, X.-D., Zhang, J.,
Zhang, S., Zhang, N., Zhou, H.-Z., Zhu, C.-D., Zhu, L., Bruelheide, H., Ma,
K., Niklaus, P. A., and Schmid, B.: Impacts of species richness on
productivity in a large-scale subtropical forest experiment, Science, 362,
80–83, https://doi.org/10.1126/science.aat6405, 2018. a
Hughes, J. K., Valdes, P. J., and Betts, R. A.: Dynamical properties of the
TRIFFID dynamic global vegetation model, Technical Note HCTN, No. 56, U.K.
Met Office Hadley Centre, Exeter, UK, 2004. a
Hurtt, G. C., Moorcroft, P. R., Pacala, S. W., and Levin, S. A.: Terrestrial
models and global change: challenges for the future, Global Change Biol., 4,
581–590, https://doi.org/10.1046/j.1365-2486.1998.t01-1-00203.x, 1998. a
Hurtt, G. C., Pacala, S. W., Moorcroft, P. R., Caspersen, J., Shevliakova, E.,
Houghton, R. A., and Moore, B.: Projecting the future of the U.S. carbon
sink, P. Natl. Acad. Sci. USA, 99, 1389–1394,
https://doi.org/10.1073/pnas.012249999, 2002. a, b, c
Hurtt, G. C., Frolking, S., Fearon, M. G., Moore, B., Shevliakova, E.,
Malyshev, S., Pacala, S. W., and Houghton, R. A.: The underpinnings of
land-use history: three centuries of global gridded land-use transitions,
wood-harvest activity, and resulting secondary lands., Global Change Biol.,
12, 1208–1229, https://doi.org/10.1111/j.1365-2486.2006.01150.x, 2006. a
Hutchings, M. J.: The Structure of Plant Populations, chap. 11, 325–358,
Wiley-Blackwell, Oxford, U.K., 2nd Edn.,
https://doi.org/10.1002/9781444313642.ch11, 1997. a
IPCC: Climate change 2014: impacts, adaptation, and vulnerability. Part A:
global and sectoral aspects, Cambridge Univ. Press, Cambridge, UK and New
York, NY, USA, 2014. a
Ise, T., Dunn, A. L., Wofsy, S. C., and Moorcroft, P. R.: High sensitivity of
peat decomposition to climate change through water-table feedback, Nat.
Geosci., 1, 763–766, https://doi.org/10.1038/ngeo331, 2008. a
Jin, J., Gao, X., Sorooshian, S., Yang, Z.-L., Bales, R., Dickinson, R. E.,
Sun, S.-F., and Wu, G.-X.: One-dimensional snow water and energy balance
model for vegetated surfaces, Hydrol. Process., 13, 2467–2482,
https://doi.org/10.1002/(SICI)1099-1085(199910)13:14/15<2467::AID-HYP861>3.0.CO;2-J,
1999. a
Jucker, T. and Coomes, D. A.: Comment on “Plant Species Richness and Ecosystem
Multifunctionality in Global Drylands”, Science, 337, 155,
https://doi.org/10.1126/science.1220473, 2012. a
Kim, Y., Knox, R. G., Longo, M., Medvigy, D., Hutyra, L. R., Pyle, E. H.,
Wofsy, S. C., Bras, R. L., and Moorcroft, P. R.: Seasonal carbon dynamics and
water fluxes in an Amazon rainforest, Global Change Biol., 18, 1322–1334,
https://doi.org/10.1111/j.1365-2486.2011.02629.x, 2012. a
Knox, R. G., Longo, M., Swann, A. L. S., Zhang, K., Levine, N. M., Moorcroft,
P. R., and Bras, R. L.: Hydrometeorological effects of historical
land-conversion in an ecosystem-atmosphere model of Northern South
America, Hydrol. Earth Syst. Sci., 19, 241–273,
https://doi.org/10.5194/hess-19-241-2015, 2015. a, b, c, d
Lambers, H., Chapin III, F. S., and Pons, T. L.: Plant physiological ecology,
Springer, New York, USA, 2nd Edn.,
https://doi.org/10.1007/978-0-387-78341-3, 2008. a, b, c, d
LeBauer, D. S., Wang, D., Richter, K. T., Davidson, C. C., and Dietze, M. C.:
Facilitating feedbacks between field measurements and ecosystem models, Ecol.
Monogr., 83, 133–154, https://doi.org/10.1890/12-0137.1, 2013. a
Le Page, Y., Morton, D., Bond-Lamberty, B., Pereira, J. M. C., and Hurtt, G.:
HESFIRE: a global fire model to explore the role of anthropogenic and
weather drivers, Biogeosciences, 12, 887–903, https://doi.org/10.5194/bg-12-887-2015,
2015. a
Le Quéré, C., Andrew, R. M., Friedlingstein, P., Sitch, S., Hauck,
J., Pongratz, J., Pickers, P. A., Korsbakken, J. I., Peters, G. P., Canadell,
J. G., Arneth, A., Arora, V. K., Barbero, L., Bastos, A., Bopp, L.,
Chevallier, F., Chini, L. P., Ciais, P., Doney, S. C., Gkritzalis, T., Goll,
D. S., Harris, I., Haverd, V., Hoffman, F. M., Hoppema, M., Houghton, R. A.,
Hurtt, G., Ilyina, T., Jain, A. K., Johannessen, T., Jones, C. D., Kato, E.,
Keeling, R. F., Goldewijk, K. K., Landschützer, P., Lefèvre, N.,
Lienert, S., Liu, Z., Lombardozzi, D., Metzl, N., Munro, D. R., Nabel, J. E.
M. S., Nakaoka, S.-I., Neill, C., Olsen, A., Ono, T., Patra, P., Peregon, A.,
Peters, W., Peylin, P., Pfeil, B., Pierrot, D., Poulter, B., Rehder, G.,
Resplandy, L., Robertson, E., Rocher, M., Rödenbeck, C., Schuster, U.,
Schwinger, J., Séférian, R., Skjelvan, I., Steinhoff, T., Sutton,
A., Tans, P. P., Tian, H., Tilbrook, B., Tubiello, F. N., van der Laan
Luijkx, I. T., van der Werf, G. R., Viovy, N., Walker, A. P.,
Wiltshire, A. J., Wright, R., Zaehle, S., and Zheng, B.: Global carbon budget
2018, Earth Syst. Sci. Data, 10, 2141–2194, https://doi.org/10.5194/essd-10-2141-2018,
2018. a, b
Lee, T. J. and Pielke, R. A.: Estimating the Soil Surface Specific Humidity, J. Appl. Meteor., 31, 480–484,
https://doi.org/10.1175/1520-0450(1992)031<0480:ETSSSH>2.0.CO;2, 1992. a
Leuning, R.: A critical appraisal of a combined stomatal-photosynthesis model
for C3 plants, Plant Cell Environ., 18, 339–355,
https://doi.org/10.1111/j.1365-3040.1995.tb00370.x, 1995. a, b, c
Levine, N. M., Zhang, K., Longo, M., Baccini, A., Phillips, O. L., Lewis,
S. L., Alvarez, E., de Andrade, A. C. S., Brienen, R., Erwin, T., Feldpausch,
T. R., Mendoza, A. L. M., Vargas, P. N., Prieto, A., Espejo, J. E. S., Malhi,
Y., and Moorcroft, P. R.: Ecosystem heterogeneity determines the resilience
of the Amazon to climate change, P. Natl. Acad. Sci. USA, 113,
793–797, https://doi.org/10.1073/pnas.1511344112, 2016. a, b, c, d
Levis, S.: Modeling vegetation and land use in models of the Earth System,
WIREs Clim. Change, 1, 840–856, https://doi.org/10.1002/wcc.83, 2010. a
Levis, S., Bonan, G., Vertenstein, M., and Oleson, K.: The Community Land
Model's Dynamic Global Vegetation Model (CLM-DGVM): Technical description and
user's guide, Technical Note NCAR/TN-459+IA, NCAR, Boulder, CO,
https://doi.org/10.5065/D6P26W36, 2004. a
Lewis, S. L., Edwards, D. P., and Galbraith, D.: Increasing human dominance of
tropical forests, Science, 349, 827–832, https://doi.org/10.1126/science.aaa9932,
2015. a, b
Liang, J., Crowther, T. W., Picard, N., Wiser, S., Zhou, M., Alberti, G.,
Schulze, E.-D., McGuire, A. D., Bozzato, F., Pretzsch, H., de Miguel, S.,
Paquette, A., Hérault, B., Scherer-Lorenzen, M., Barrett, C. B., Glick,
H. B., Hengeveld, G. M., Nabuurs, G.-J., Pfautsch, S., Viana, H., Vibrans,
A. C., Ammer, C., Schall, P., Verbyla, D., Tchebakova, N., Fischer, M.,
Watson, J. V., Chen, H. Y. H., Lei, X., Schelhaas, M.-J., Lu, H., Gianelle,
D., Parfenova, E. I., Salas, C., Lee, E., Lee, B., Kim, H. S., Bruelheide,
H., Coomes, D. A., Piotto, D., Sunderland, T., Schmid, B., Gourlet-Fleury,
S., Sonké, B., Tavani, R., Zhu, J., Brandl, S., Vayreda, J., Kitahara,
F., Searle, E. B., Neldner, V. J., Ngugi, M. R., Baraloto, C., Frizzera, L.,
Bałazy, R., Oleksyn, J., Zawiła-Niedźwiecki, T., Bouriaud, O.,
Bussotti, F., Finér, L., Jaroszewicz, B., Jucker, T., Valladares, F.,
Jagodzinski, A. M., Peri, P. L., Gonmadje, C., Marthy, W., O'Brien, T.,
Martin, E. H., Marshall, A. R., Rovero, F., Bitariho, R., Niklaus, P. A.,
Alvarez-Loayza, P., Chamuya, N., Valencia, R., Mortier, F., Wortel, V.,
Engone-Obiang, N. L., Ferreira, L. V., Odeke, D. E., Vasquez, R. M., Lewis,
S. L., and Reich, P. B.: Positive biodiversity-productivity relationship
predominant in global forests, Science, 354, aaf8957,
https://doi.org/10.1126/science.aaf8957, 2016. a
Lindeskog, M., Arneth, A., Bondeau, A., Waha, K., Seaquist, J., Olin, S., and
Smith, B.: Implications of accounting for land use in simulations of
ecosystem carbon cycling in Africa, Earth Syst. Dynam., 4, 385–407,
https://doi.org/10.5194/esd-4-385-2013, 2013. a
Lloyd, J., Patiño, S., Paiva, R. Q., Nardoto, G. B., Quesada, C. A.,
Santos, A. J. B., Baker, T. R., Brand, W. A., Hilke, I., Gielmann, H.,
Raessler, M., Luizão, F. J., Martinelli, L. A., and Mercado, L. M.:
Optimisation of photosynthetic carbon gain and within-canopy gradients of
associated foliar traits for Amazon forest trees, Biogeosciences, 7,
1833–1859, https://doi.org/10.5194/bg-7-1833-2010, 2010. a
Lombardozzi, D. L., Smith, N. G., Cheng, S. J., Dukes, J. S., Sharkey, T. D.,
Rogers, A., Fisher, R., and Bonan, G. B.: Triose phosphate limitation in
photosynthesis models reduces leaf photosynthesis and global terrestrial
carbon storage, Environ. Res. Lett., 13, 074025,
https://doi.org/10.1088/1748-9326/aacf68, 2018. a, b
Longo, M. and Keller, M.: Not the same old(-growth) forest, New Phytol., 221,
1672–1675, https://doi.org/10.1111/nph.15636, 2019. a
Longo, M., Knox, R. G., Levine, N. M., Alves, L. F., Bonal, D., Camargo, P. B.,
Fitzjarrald, D. R., Hayek, M. N., Restrepo-Coupe, N., Saleska, S. R.,
da Silva, R., Stark, S. C., Tapajós, R. P., Wiedemann, K. T., Zhang,
K., Wofsy, S. C., and Moorcroft, P. R.: Ecosystem heterogeneity and diversity
mitigate Amazon forest resilience to frequent extreme droughts, New
Phytol., 219, 914–931, https://doi.org/10.1111/nph.15185, 2018. a, b
Longo, M., Knox, R. G., Levine, N. M., Swann, A. L. S., Medvigy, D. M., Dietze, M. C., Kim, Y., Zhang, K., Bonal, D., Burban, B., Camargo, P. B., Hayek, M. N., Saleska, S. R., da Silva, R., Bras, R. L., Wofsy, S. C., and Moorcroft, P. R.: The biophysics, ecology, and biogeochemistry of functionally
diverse, vertically and horizontally heterogeneous ecosystems:
the Ecosystem Demography model, version 2.2 – Part 2: Model
evaluation for tropical South America, Geosci. Model Dev., 12, 4347–4374, https://doi.org/10.5194/gmd-12-4347-2019, 2019a. a, b, c
Longo, M., Knox, R. G., Medvigy, D. M., Levine, N. M., Dietze, M. C., Swann, A. L. S., Zhang, K., Rollinson, C. R., di Porcia e Brugnera, M., Scott, D., Serbin, S. P., Kooper, R., Pourmokhtarian, A., Shiklomanov, A., Viskari, T., and Moorcroft, P.: Ecosystem Demography Model, version 2.2 (ED-2.2) (Version rev-86), https://doi.org/10.5281/zenodo.3365659, Zenodo, 2019b. a
Loreau, M. and Hector, A.: Partitioning selection and complementarity in
biodiversity experiments, Nature, 412, 72–76, https://doi.org/10.1038/35083573, 2001. a
Manabe, S., Smagorinsky, J., and Strickler, R. F.: Simulated climatology of a
general circulation model with a hydrologic cycle, Mon. Weather Rev., 93,
769–798, https://doi.org/10.1175/1520-0493(1965)093<0769:SCOAGC>2.3.CO;2, 1965. a
Mangeon, S., Voulgarakis, A., Gilham, R., Harper, A., Sitch, S., and Folberth,
G.: INFERNO: a fire and emissions scheme for the UK Met Office's
Unified Model, Geosci. Model Dev., 9, 2685–2700,
https://doi.org/10.5194/gmd-9-2685-2016, 2016. a
Maréchaux, I. and Chave, J.: An individual-based forest model to jointly
simulate carbon and tree diversity in Amazonia: description and
applications, Ecol. Monogr., 87, 632–664, https://doi.org/10.1002/ecm.1271, 2017. a
Medvigy, D. and Moorcroft, P. R.: Predicting ecosystem dynamics at regional
scales: an evaluation of a terrestrial biosphere model for the forests of
northeastern North America, Philos. Trans. R. Soc. B-Biol. Sci., 367,
222–235, https://doi.org/10.1098/rstb.2011.0253, 2012. a
Medvigy, D., Wang, G., Zhu, Q., Riley, W. J., Trierweiler, A. M., Waring,
B. G., Xu, X., and Powers, J. S.: Observed variation in soil properties can
drive large variation in modeled forest functioning and composition during
tropical forest secondary succession, New Phytol., 223, 1820–1833,
https://doi.org/10.1111/nph.15848, 2019. a
Medvigy, D. M., Wofsy, S. C., Munger, J. W., Hollinger, D. Y., and Moorcroft,
P. R.: Mechanistic scaling of ecosystem function and dynamics in space and
time: Ecosystem Demography model version 2, J. Geophys. Res.-Biogeosci., 114,
G01002, https://doi.org/10.1029/2008JG000812, 2009. a, b, c, d, e, f, g
Monin, A. S. and Obukhov, A. M.: Osnovnye zakonomernosti turbulentnogo pere-
meshivanija v prizemnom sloe atmosfery (Basic laws of turbulent mixing in the
atmosphere near the ground), Trudy Geofiz. Inst. AN SSSR, 24, 163–187, 1954 (in Russian). a
Monteith, J. L. and Unsworth, M. H.: Principles of environmental physics,
Academic Press, London, 3rd Edn., 418 pp., 2008. a
Moorcroft, P. R.: Recent advances in ecosystem-atmosphere interactions: an
ecological perspective, Proc. R. Soc. Lond. B-Biol. Sci., 270, 1215–1227,
https://doi.org/10.1098/rspb.2002.2251, 2003. a
Moorcroft, P. R.: How close are we to a predictive science of the biosphere?,
Trends Ecol. Evol., 21, 400–407, https://doi.org/10.1016/j.tree.2006.04.009, 2006. a, b, c
Naeem, S. and Li, S.: Biodiversity enhances ecosystem reliability, Nature, 390,
507–509, https://doi.org/10.1038/37348, 1997. a
Neilson, R. P.: A Model for Predicting Continental-Scale Vegetation
Distribution and Water Balance, Ecol. Appl., 5, 362–385,
https://doi.org/10.2307/1942028, 1995. a
Noilhan, J. and Planton, S.: A Simple Parameterization of Land Surface
Processes for Meteorological Models, Mon. Weather Rev., 117, 536–549,
https://doi.org/10.1175/1520-0493(1989)117<0536:ASPOLS>2.0.CO;2, 1989. a
Norby, R. J., Gu, L., Haworth, I. C., Jensen, A. M., Turner, B. L., Walker,
A. P., Warren, J. M., Weston, D. J., Xu, C., and Winter, K.: Informing models
through empirical relationships between foliar phosphorus, nitrogen and
photosynthesis across diverse woody species in tropical forests of Panama,
New Phytol., 215, 1425–1437, https://doi.org/10.1111/nph.14319, 2017. a
Oleson, K. W., Lawrence, D. M., Bonan, G. B., Drewniak, B., Huang, M., Koven,
C. D., Levis, S., Li, F., Riley, W. J., Subin, Z. M., Swenson, S. C.,
Thornton, P. E., Bozbiyik, A., Fisher, R., Heald, C. L., Kluzek, E.,
Lamarque, J.-F., Lawrence, P. J., Leung, L. R., Lipscomb, W., Muszala, S.,
Ricciuto, D. M., Sacks, W., Sun, Y., Tang, J., and Yang, Z.-L.: Technical
description of version 4.5 of the Community Land Model (CLM), Technical
Report NCAR/TN-503+STR, NCAR, Boulder, CO, https://doi.org/10.5065/D6RR1W7M,
420pp., 2013. a, b, c, d
Pandit, K., Dashti, H., Glenn, N. F., Flores, A. N., Maguire, K. C., Shinneman, D. J., Flerchinger, G. N., and Fellows, A. W.: Optimizing shrub parameters to estimate gross primary production of the sagebrush ecosystem using the Ecosystem Demography (EDv2.2) model, Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2018-264, in review, 2018. a
Pereira, F. F., Farinosi, F., Arias, M. E., Lee, E., Briscoe, J., and Moorcroft, P. R.: Technical note: A hydrological routing scheme for the Ecosystem Demography model (ED2+R) tested in the Tapajós River basin in the Brazilian Amazon, Hydrol. Earth Syst. Sci., 21, 4629–4648, https://doi.org/10.5194/hess-21-4629-2017, 2017. a
Pereira Jr., R., Zweede, J., Asner, G. P., and Keller, M.: Forest canopy damage
and recovery in reduced-impact and conventional selective logging in eastern
Para, Brazil, Forest Ecol. Manag., 168, 77–89,
https://doi.org/10.1016/S0378-1127(01)00732-0, 2002. a
Philip, J. R.: Evaporation, and moisture and heat fields in the soil, J.
Meteor., 14, 354–366, https://doi.org/10.1175/1520-0469(1957)014<0354:EAMAHF>2.0.CO;2,
1957. a
Phillips, O. L., van der Heijden, G., Lewis, S. L., López-González, G.,
Aragão, L. E. O. C., Lloyd, J., Malhi, Y., Monteagudo, A., Almeida, S.,
Alvarez Dávila, E., Amaral, I., Andelman, S., Andrade, A., Arroyo, L.,
Aymard, G., Baker, T. R., Blanc, L., Bonal, D., Alves de Oliveira, A. C.,
Chao, K.-J., Dávila Cardozo, N., da Costa, L., Feldpausch, T. R., Fisher,
J. B., Fyllas, N. M., Freitas, M. A., Galbraith, D., Gloor, E., Higuchi, N.,
Honorio, E., Jiménez, E., Keeling, H., Killeen, T. J., Lovett, J. C.,
Meir, P., Mendoza, C., Morel, A., Núñez Vargas, P., Patiño, S.,
Peh, K. S.-H., Peña Cruz, A., Prieto, A., Quesada, C. A., Ramírez,
F., Ramírez, H., Rudas, A., Salamão, R., Schwarz, M., Silva, J.,
Silveira, M., Slik, J. W. F., Sonké, B., Thomas, A. S., Stropp, J.,
Taplin, J. R. D., Vásquez, R., and Vilanova, E.: Drought-mortality
relationships for tropical forests, New Phytol., 187, 631–646,
https://doi.org/10.1111/j.1469-8137.2010.03359.x, 2010. a
Piao, S., Sitch, S., Ciais, P., Friedlingstein, P., Peylin, P., Wang, X.,
Ahlström, A., Anav, A., Canadell, J. G., Cong, N., Huntingford, C., Jung,
M., Levis, S., Levy, P. E., Li, J., Lin, X., Lomas, M. R., Lu, M., Luo, Y.,
Ma, Y., Myneni, R. B., Poulter, B., Sun, Z., Wang, T., Viovy, N., Zaehle, S.,
and Zeng, N.: Evaluation of terrestrial carbon cycle models for their
response to climate variability and to CO2 trends, Global Change Biol.,
19, 2117–2132, https://doi.org/10.1111/gcb.12187, 2013. a
Poorter, L., van der Sande, M. T., Thompson, J., Arets, E. J. M. M.,
Alarcón, A., Álvarez-Sánchez, J., Ascarrunz, N., Balvanera,
P., Barajas-Guzmán, G., Boit, A., Bongers, F., Carvalho, F. A.,
Casanoves, F., Cornejo-Tenorio, G., Costa, F. R. C., de Castilho, C. V.,
Duivenvoorden, J. F., Dutrieux, L. P., Enquist, B. J.,
Fernández-Méndez, F., Finegan, B., Gormley, L. H. L., Healey,
J. R., Hoosbeek, M. R., Ibarra-Manríquez, G., Junqueira, A. B., Levis,
C., Licona, J. C., Lisboa, L. S., Magnusson, W. E., Martínez-Ramos, M.,
Martínez-Yrizar, A., Martorano, L. G., Maskell, L. C., Mazzei, L.,
Meave, J. A., Mora, F., Muñoz, R., Nytch, C., Pansonato, M. P., Parr,
T. W., Paz, H., Pérez-García, E. A., Rentería, L. Y.,
Rodríguez-Velázquez, J., Rozendaal, D. M. A., Ruschel, A. R.,
Sakschewski, B., Salgado-Negret, B., Schietti, J., Simões, M.,
Sinclair, F. L., Souza, P. F., Souza, F. C., Stropp, J., ter Steege, H.,
Swenson, N. G., Thonicke, K., Toledo, M., Uriarte, M., van der Hout, P.,
Walker, P., Zamora, N., and Peña-Claros, M.: Diversity enhances
carbon storage in tropical forests, Global Ecol. Biogeogr., 24, 1314–1328,
https://doi.org/10.1111/geb.12364, 2015. a
Powell, T. L., Galbraith, D. R., Christoffersen, B. O., Harper, A., Imbuzeiro,
H. M. A., Rowland, L., Almeida, S., Brando, P. M., da Costa, A. C. L., Costa,
M. H., Levine, N. M., Malhi, Y., Saleska, S. R., Sotta, E., Williams, M.,
Meir, P., and Moorcroft, P. R.: Confronting model predictions of carbon
fluxes with measurements of Amazon forests subjected to experimental
drought, New Phytol., 200, 350–365, https://doi.org/10.1111/nph.12390, 2013. a
Prentice, I. C., Webb, R. S., Ter-Mikhaelian, M. T., Solomon, A. M., Smith,
T. M., Pitovranov, S. E., Nikolov, N. T., Minin, A. A., Leemans, R., Lavorel,
S., Korzukhin, M. D., Hrabovszky, J. P., Helmisaari, H. O., Harrison, S. P.,
Emanuel, W. R., and Bonan, G. B.: Developing a global vegetation dynamics
model: Results of an IIASA summer workshop, Research Report RR-89-7,
International Institute for Applied Systems Analysis, Laxenburg, Austria,
available at: http://pure.iiasa.ac.at/3223 (last access: 25 September 2019), 1989. a
Prentice, I. C., Cramer, W., Harrison, S. P., Leemans, R., Monserud, R. A., and
Solomon, A. M.: A Global Biome Model Based on Plant Physiology and Dominance,
Soil Properties and Climate, J. Biogeogr., 19, 117–134,
https://doi.org/10.2307/2845499, 1992. a
Purves, D. and Pacala, S.: Predictive Models of Forest Dynamics, Science, 320,
1452–1453, https://doi.org/10.1126/science.1155359, 2008. a, b
Purves, D. W., Lichstein, J. W., Strigul, N., and Pacala, S. W.: Predicting and
understanding forest dynamics using a simple tractable model, P. Natl.
Acad. Sci. USA, 105, 17018–17022, https://doi.org/10.1073/pnas.0807754105,
2008. a
Raczka, B., Dietze, M. C., Serbin, S. P., and Davis, K. J.: What limits
predictive certainty of long-term carbon uptake?, J. Geophys.
Res.-Biogeosci., 123, 3570–3588, https://doi.org/10.1029/2018JG004504, 2018. a
Reich, P. B., Walters, M. B., and Ellsworth, D. S.: From tropics to tundra:
Global convergence in plant functioning, P. Natl. Acad. Sci. USA, 94,
13730–13734, https://doi.org/10.1073/pnas.94.25.13730, 1997. a
Rogers, A., Medlyn, B. E., Dukes, J. S., Bonan, G., von Caemmerer, S.,
Dietze, M. C., Kattge, J., Leakey, A. D. B., Mercado, L. M., Niinemets, U.,
Prentice, I. C., Serbin, S. P., Sitch, S., Way, D. A., and Zaehle, S.: A
roadmap for improving the representation of photosynthesis in Earth system
models, New Phytol., 213, 22–42, https://doi.org/10.1111/nph.14283, 2017. a
Santanello Jr, J. A., Dirmeyer, P. A., Ferguson, C. R., Findell, K. L., Tawfik,
A. B., Berg, A., Ek, M., Gentine, P., Guillod, B. P., van Heerwaarden, C.,
Roundy, J., and Wulfmeyer, V.: Land–atmosphere interactions: the LoCo
perspective, B. Am. Meteorol. Soc., 99, 1253–1272,
https://doi.org/10.1175/BAMS-D-17-0001.1, 2018. a
Sato, H., Itoh, A., and Kohyama, T.: SEIB–DGVM: A new Dynamic Global
Vegetation Model using a spatially explicit individual-based approach, Ecol.
Model., 200, 279–307, https://doi.org/10.1016/j.ecolmodel.2006.09.006, 2007. a, b
Sellers, P. J.: Canopy reflectance, photosynthesis and transpiration, Int. J.
Remote Sens., 6, 1335–1372, https://doi.org/10.1080/01431168508948283, 1985. a, b
Sellers, P. J., Mintz, Y., Sud, Y. C., and Dalcher, A.: A Simple Biosphere
model (SIB) for use within general circulation models, J. Atmos. Sci., 43,
505–531, https://doi.org/10.1175/1520-0469(1986)043<0505:ASBMFU>2.0.CO;2, 1986. a
Sellers, P. J., Randall, D. A., Collatz, G. J., Berry, J. A., Field, C. B.,
Dazlich, D. A., Zhang, C., Collelo, G. D., and Bounoua, L.: A revised land
surface parameterization (SiB2) for atmospheric GCMs. Part I: model
formulation, J. Climate, 9, 676–705,
https://doi.org/10.1175/1520-0442(1996)009<0676:ARLSPF>2.0.CO;2, 1996. a, b
Sellers, P. J., Dickinson, R. E., Randall, D. A., Betts, A. K., Hall, F. G.,
Berry, J. A., Collatz, G. J., Denning, A. S., Mooney, H. A., Nobre, C. A.,
Sato, N., Field, C. B., and Henderson-Sellers, A.: Modeling the exchanges of
energy, water, and carbon between continents and the atmosphere, Science,
275, 502–509, https://doi.org/10.1126/science.275.5299.502, 1997. a, b, c
Sheffield, J., Goteti, G., and Wood, E. F.: Development of a 50-year
high-resolution global dataset of meteorological forcings for land surface
modeling, J. Climate, 19, 3088–3111, https://doi.org/10.1175/JCLI3790.1, 2006. a, b
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
Sitch, S., Huntingford, C., Gedney, N., Levy, P. E., Lomas, M., Piao, S. L.,
Betts, R., Ciais, P., Cox, P., Friedlingstein, P., Jones, C. D., Prentice,
I. C., and Woodward, F. I.: Evaluation of the terrestrial carbon cycle,
future plant geography and climate-carbon cycle feedbacks using five Dynamic
Global Vegetation Models (DGVMs), Global Change Biol., 14, 2015–2039,
https://doi.org/10.1111/j.1365-2486.2008.01626.x, 2008. a
Smith, B., Prentice, I. C., and Sykes, M. T.: Representation of vegetation
dynamics in the modelling of terrestrial ecosystems: comparing two
contrasting approaches within European climate space, Global Ecol.
Biogeogr., 10, 621–637, https://doi.org/10.1046/j.1466-822X.2001.t01-1-00256.x, 2001. a
Soares-Filho, B. S., Nepstad, D. C., Curran, L. M., Cerqueira, G. C., Garcia,
R. A., Ramos, C. A., Voll, E., McDonald, A., Lefebvre, P., and Schlesinger,
P.: Modelling conservation in the Amazon basin, Nature, 440, 520–523,
https://doi.org/10.1038/nature04389, 2006. a
Somerville, R., Stone, P., Halem, M., Hansen, J., Hogan, J., Druyan, L.,
Russell, G., Lacis, A., Quirk, W., and Tenenbaum, J.: The GISS model of the
global atmosphere, J. Atmos. Sci., 31, 84–117,
https://doi.org/10.1175/1520-0469(1974)031<0084:TGMOTG>2.0.CO;2, 1974. a
Swann, A. L. S., Longo, M., Knox, R. G., Lee, E., and Moorcroft, P. R.: Future
deforestation in the Amazon and consequences for South American
climate, Agr. Forest Meteorol., 214–215, 12–24,
https://doi.org/10.1016/j.agrformet.2015.07.006, 2015. a, b, c, d
The ED-2 model development team: Ecosystem Demography model (ED-2) code repository, available at: https://github.com/EDmodel/ED2 (last access: 25 September 2019), 2014.
The HDF Group: Hierarchical data format, version 5,
available at: http://www.hdfgroup.org/HDF5/ (last access: 25 September 2019), 2016. a
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
Tilman, D. and Downing, John, A.: Biodiversity and stability in grasslands,
Nature, 367, 363–365, https://doi.org/10.1038/367363a0, 1994. a
Tilman, D., Isbell, F., and Cowles, J. M.: Biodiversity and ecosystem
functioning, Ann. Rev. Ecol. Evol. Syst., 45, 471–493,
https://doi.org/10.1146/annurev-ecolsys-120213-091917, 2014. a
Trugman, A. T., Medvigy, D., Hoffmann, W. A., and Pellegrini, A. F. A.:
Sensitivity of woody carbon stocks to bark investment strategy in
Neotropical savannas and forests, Biogeosciences, 15, 233–243,
https://doi.org/10.5194/bg-15-233-2018, 2018. a, b
Vidale, P. L. and Stöckli, R.: Prognostic canopy air space solutions for
land surface exchanges, Theor. Appl. Climatol., 80, 245–257,
https://doi.org/10.1007/s00704-004-0103-2, 2005. a
von Caemmerer, S.: Biochemical models of leaf photosynthesis, no. 2 in
Techniques in Plant Sciences, CSIRO Publishing, Collingwood, VIC,
Australia, https://doi.org/10.1006/anbo.2000.1296, 2000. a, b
Walko, R. L., Band, L. E., Baron, J., Kittel, T. G. F., Lammers, R., Lee,
T. J., Ojima, D., Pielke, R. A., Taylor, C., Tague, C., Tremback, C. J., and
Vidale, P. L.: Coupled atmosphere–biophysics–hydrology models for
environmental modeling, J. Appl. Meteor., 39, 931–944,
https://doi.org/10.1175/1520-0450(2000)039<0931:CABHMF>2.0.CO;2, 2000. a, b, c, d, e, f, g
Wang, J.-W., Denning, A. S., Lu, L., Baker, I. T., Corbin, K. D., and Davis,
K. J.: Observations and simulations of synoptic, regional, and local
variations in atmospheric CO2, J. Geophys. Res.-Atmos., 112, D04108,
https://doi.org/10.1029/2006JD007410, 2007. a
Weedon, G. P., Balsamo, G., Bellouin, N., Gomes, S., Best, M. J., and Viterbo,
P.: The WFDEI meteorological forcing data set: WATCH forcing data
methodology applied to ERA-Interim reanalysis data, Water Resour. Res.,
50, 7505–7514, https://doi.org/10.1002/2014WR015638, 2014. a
Weng, E. S., Malyshev, S., Lichstein, J. W., Farrior, C. E., Dybzinski, R.,
Zhang, T., Shevliakova, E., and Pacala, S. W.: Scaling from individual trees
to forests in an Earth system modeling framework using a mathematically
tractable model of height-structured competition, Biogeosciences, 12,
2655–2694, https://doi.org/10.5194/bg-12-2655-2015, 2015. a, b
Wohlfahrt, G., Bianchi, K., and Cernusca, A.: Leaf and stem maximum water
storage capacity of herbaceous plants in a mountain meadow, J. Hydrol., 319,
383–390, https://doi.org/10.1016/j.jhydrol.2005.06.036, 2006. a
Wright, I. J., Reich, P. B., Westoby, M., Ackerly, D. D., Baruch, Z., Bongers,
F., Cavender-Bares, J., Chapin, T., Cornelissen, J. H. C., Diemer, M.,
Flexas, J., Garnier, E., Groom, P. K., Gulias, J., Hikosaka, K., Lamont,
B. B., Lee, T., Lee, W., Lusk, C., Midgley, J. J., Navas, M.-L., Niinemets,
U., Oleksyn, J., Osada, N., Poorter, H., Poot, P., Prior, L., Pyankov, V. I.,
Roumet, C., Thomas, S. C., Tjoelker, M. G., Veneklaas, E. J., and Villar, R.:
The worldwide leaf economics spectrum, Nature, 428, 821–827,
https://doi.org/10.1038/nature02403, 2004. a
Xu, X., Medvigy, D., Powers, J. S., Becknell, J. M., and Guan, K.: Diversity in
plant hydraulic traits explains seasonal and inter-annual variations of
vegetation dynamics in seasonally dry tropical forests, New Phytol., 212,
80–95, https://doi.org/10.1111/nph.14009, 2016. a, b, c
Xu, X., Medvigy, D., Wright, S. J., Kitajima, K., Wu, J., Albert, L. P.,
Martins, G. A., Saleska, S. R., and Pacala, S. W.: Variations of leaf
longevity in tropical moist forests predicted by a trait-driven carbon
optimality model, Ecol. Lett., 20, 1097–1106, https://doi.org/10.1111/ele.12804, 2017. a
Yang, Y., Saatchi, S. S., Xu, L., Yu, Y., Choi, S., Phillips, N., Kennedy, R.,
Keller, M., Knyazikhin, Y., and Myneni, R. B.: Post-drought decline of the
Amazon carbon sink, Nat. Comm., 9, 3172,
https://doi.org/10.1038/s41467-018-05668-6, 2018. a
Zhang, K., Castanho, A. D. D. A., Galbraith, D. R., Moghim, S., Levine, N.,
Bras, R. L., Coe, M., Costa, M. H., Malhi, Y., Longo, M., Knox, R. G.,
McKnight, S., Wang, J., and Moorcroft, P. R.: The fate of Amazonian
ecosystems over the coming century arising from changes in climate,
atmospheric CO2 and land-use, Global Change Biol., 21, 2569–2587,
https://doi.org/10.1111/gcb.12903, 2015. a, b
Short summary
Our paper describes the Ecosystem Demography model. This computer program calculates how plants and ground exchange heat, water, and carbon with the air, and how plants grow, reproduce and die in different climates. Most models simplify forests to an average big tree. We consider that tall, deep-rooted trees get more light and water than small plants, and that some plants can with shade and drought. This diversity helps us to better explain how plants live and interact with the atmosphere.
Our paper describes the Ecosystem Demography model. This computer program calculates how plants...
