Articles | Volume 15, issue 19
https://doi.org/10.5194/bg-15-5801-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-15-5801-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Oct 2018
Research article |
| 04 Oct 2018
| 04 Oct 2018
Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation
Istem Fer
CORRESPONDING AUTHOR
Department of Earth and Environment, Boston University, Boston, MA 02215, USA
Ryan Kelly
RK Analytics, Durham, NC 27712, USA
Paul R. Moorcroft
Department Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
Andrew D. Richardson
School of Informatics, Computing and Cyber Systems, Northern Arizona University Flagstaff, AZ 86011, USA
Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ 86011, USA
Elizabeth M. Cowdery
Department of Earth and Environment, Boston University, Boston, MA 02215, USA
Michael C. Dietze
Department of Earth and Environment, Boston University, Boston, MA 02215, USA
Related authors
Istem Fer, Britta Tietjen, Florian Jeltsch, and Christian Wolff
Biogeosciences, 14, 4355–4374, https://doi.org/10.5194/bg-14-4355-2017, https://doi.org/10.5194/bg-14-4355-2017, 2017
Short summary
Short summary
El Niño–Southern Oscillation (ENSO) has been identified as one of the main drivers for the interannual variability in eastern African rainfall. But we know little about its direct impact on vegetation and how it might change in the future. In this study, we quantified this relationship and predict its future under certain climate change scenarios. Results suggest that we need to consider an increase in future ENSO intensity to cover the full range of potential changes in vegetation responses.
Guohua Liu, Mirco Migliavacca, Christian Reimers, Basil Kraft, Markus Reichstein, Andrew Richardson, Lisa Wingate, Nicolas Delpierre, Hui Yang, and Alexander Winkler
EGUsphere, https://doi.org/10.5194/egusphere-2024-464, https://doi.org/10.5194/egusphere-2024-464, 2024
Short summary
Short summary
Our study employs Long Short-Term Memory (LSTM) networks to model canopy greenness and phenology, integrating meteorological memory effects. 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 unlocking the secrets of vegetation phenology responses to climate change with deep learning techniques.
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
Short summary
Short summary
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.
Xin Huang, Dan Lu, Daniel M. Ricciuto, Paul J. Hanson, Andrew D. Richardson, Xuehe Lu, Ensheng Weng, Sheng Nie, Lifen Jiang, Enqing Hou, Igor F. Steinmacher, and Yiqi Luo
Geosci. Model Dev., 14, 5217–5238, https://doi.org/10.5194/gmd-14-5217-2021, https://doi.org/10.5194/gmd-14-5217-2021, 2021
Short summary
Short summary
In the data-rich era, data assimilation is widely used to integrate abundant observations into models to reduce uncertainty in ecological forecasting. However, applications of data assimilation are restricted by highly technical requirements. To alleviate this technical burden, we developed a model-independent data assimilation (MIDA) module which is friendly to ecologists with limited programming skills. MIDA also supports a flexible switch of different models or observations in DA analysis.
Kyle B. Delwiche, Sara Helen Knox, Avni Malhotra, Etienne Fluet-Chouinard, Gavin McNicol, Sarah Feron, Zutao Ouyang, Dario Papale, Carlo Trotta, Eleonora Canfora, You-Wei Cheah, Danielle Christianson, Ma. Carmelita R. Alberto, Pavel Alekseychik, Mika Aurela, Dennis Baldocchi, Sheel Bansal, David P. Billesbach, Gil Bohrer, Rosvel Bracho, Nina Buchmann, David I. Campbell, Gerardo Celis, Jiquan Chen, Weinan Chen, Housen Chu, Higo J. Dalmagro, Sigrid Dengel, Ankur R. Desai, Matteo Detto, Han Dolman, Elke Eichelmann, Eugenie Euskirchen, Daniela Famulari, Kathrin Fuchs, Mathias Goeckede, Sébastien Gogo, Mangaliso J. Gondwe, Jordan P. Goodrich, Pia Gottschalk, Scott L. Graham, Martin Heimann, Manuel Helbig, Carole Helfter, Kyle S. Hemes, Takashi Hirano, David Hollinger, Lukas Hörtnagl, Hiroki Iwata, Adrien Jacotot, Gerald Jurasinski, Minseok Kang, Kuno Kasak, John King, Janina Klatt, Franziska Koebsch, Ken W. Krauss, Derrick Y. F. Lai, Annalea Lohila, Ivan Mammarella, Luca Belelli Marchesini, Giovanni Manca, Jaclyn Hatala Matthes, Trofim Maximov, Lutz Merbold, Bhaskar Mitra, Timothy H. Morin, Eiko Nemitz, Mats B. Nilsson, Shuli Niu, Walter C. Oechel, Patricia Y. Oikawa, Keisuke Ono, Matthias Peichl, Olli Peltola, Michele L. Reba, Andrew D. Richardson, William Riley, Benjamin R. K. Runkle, Youngryel Ryu, Torsten Sachs, Ayaka Sakabe, Camilo Rey Sanchez, Edward A. Schuur, Karina V. R. Schäfer, Oliver Sonnentag, Jed P. Sparks, Ellen Stuart-Haëntjens, Cove Sturtevant, Ryan C. Sullivan, Daphne J. Szutu, Jonathan E. Thom, Margaret S. Torn, Eeva-Stiina Tuittila, Jessica Turner, Masahito Ueyama, Alex C. Valach, Rodrigo Vargas, Andrej Varlagin, Alma Vazquez-Lule, Joseph G. Verfaillie, Timo Vesala, George L. Vourlitis, Eric J. Ward, Christian Wille, Georg Wohlfahrt, Guan Xhuan Wong, Zhen Zhang, Donatella Zona, Lisamarie Windham-Myers, Benjamin Poulter, and Robert B. Jackson
Earth Syst. Sci. Data, 13, 3607–3689, https://doi.org/10.5194/essd-13-3607-2021, https://doi.org/10.5194/essd-13-3607-2021, 2021
Short summary
Short summary
Methane is an important greenhouse gas, yet we lack knowledge about its global emissions and drivers. We present FLUXNET-CH4, a new global collection of methane measurements and a critical resource for the research community. We use FLUXNET-CH4 data to quantify the seasonality of methane emissions from freshwater wetlands, finding that methane seasonality varies strongly with latitude. Our new database and analysis will improve wetland model accuracy and inform greenhouse gas budgets.
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
Short summary
Short summary
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
Short summary
Short summary
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.
Rui Cheng, Troy S. Magney, Debsunder Dutta, David R. Bowling, Barry A. Logan, Sean P. Burns, Peter D. Blanken, Katja Grossmann, Sophia Lopez, Andrew D. Richardson, Jochen Stutz, and Christian Frankenberg
Biogeosciences, 17, 4523–4544, https://doi.org/10.5194/bg-17-4523-2020, https://doi.org/10.5194/bg-17-4523-2020, 2020
Short summary
Short summary
We measured reflected sunlight from an evergreen canopy for a year to detect changes in pigments that play an important role in regulating the seasonality of photosynthesis. Results show a strong mechanistic link between spectral reflectance features and pigment content, which is validated using a biophysical model. Our results show spectrally where, why, and when spectral features change over the course of the season and show promise for estimating photosynthesis remotely.
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
Short summary
Short summary
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
Short summary
Short summary
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, David M. Medvigy, Naomi M. Levine, Michael C. Dietze, Yeonjoo Kim, Abigail L. S. Swann, Ke Zhang, Christine R. Rollinson, Rafael L. Bras, Steven C. Wofsy, and Paul R. Moorcroft
Geosci. Model Dev., 12, 4309–4346, https://doi.org/10.5194/gmd-12-4309-2019, https://doi.org/10.5194/gmd-12-4309-2019, 2019
Short summary
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.
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
Short summary
Short summary
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.
Verónika Ceballos-Núñez, Andrew D. Richardson, and Carlos A. Sierra
Biogeosciences, 15, 1607–1625, https://doi.org/10.5194/bg-15-1607-2018, https://doi.org/10.5194/bg-15-1607-2018, 2018
Short summary
Short summary
Will the terrestrial biosphere be a carbon source or sink in the future? Different model simulations cannot reach a consensus, so we need to diagnose the performance of these models. We implemented three models differing in their carbon allocation strategies and assessed their performance using three metrics. The most sensible metric was the distribution of carbon age and transit times. Thus, empirical measurements of these distributions could be key to reduce the model uncertainty.
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
Short summary
Short summary
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.
Istem Fer, Britta Tietjen, Florian Jeltsch, and Christian Wolff
Biogeosciences, 14, 4355–4374, https://doi.org/10.5194/bg-14-4355-2017, https://doi.org/10.5194/bg-14-4355-2017, 2017
Short summary
Short summary
El Niño–Southern Oscillation (ENSO) has been identified as one of the main drivers for the interannual variability in eastern African rainfall. But we know little about its direct impact on vegetation and how it might change in the future. In this study, we quantified this relationship and predict its future under certain climate change scenarios. Results suggest that we need to consider an increase in future ENSO intensity to cover the full range of potential changes in vegetation responses.
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
Short summary
Short summary
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.
Paul J. Hanson, Jeffery S. Riggs, W. Robert Nettles, Jana R. Phillips, Misha B. Krassovski, Leslie A. Hook, Lianhong Gu, Andrew D. Richardson, Donald M. Aubrecht, Daniel M. Ricciuto, Jeffrey M. Warren, and Charlotte Barbier
Biogeosciences, 14, 861–883, https://doi.org/10.5194/bg-14-861-2017, https://doi.org/10.5194/bg-14-861-2017, 2017
Short summary
Short summary
This paper describes operational methods to achieve whole-ecosystem warming (WEW) for tall-stature, high-carbon, boreal forest peatlands. The methods enable scientists to study immediate and longer-term (1 decade) responses of organisms (microbes to trees) and ecosystem functions (carbon, water and nutrient cycles). The WEW technology allows researchers to have a plausible glimpse of future environmental conditions for study that are not available in the current observational record.
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
Short summary
Short summary
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.
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
Short summary
Short summary
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
S. T. Klosterman, K. Hufkens, J. M. Gray, E. Melaas, O. Sonnentag, I. Lavine, L. Mitchell, R. Norman, M. A. Friedl, and A. D. Richardson
Biogeosciences, 11, 4305–4320, https://doi.org/10.5194/bg-11-4305-2014, https://doi.org/10.5194/bg-11-4305-2014, 2014
M. Verma, M. A. Friedl, A. D. Richardson, G. Kiely, A. Cescatti, B. E. Law, G. Wohlfahrt, B. Gielen, O. Roupsard, E. J. Moors, P. Toscano, F. P. Vaccari, D. Gianelle, G. Bohrer, A. Varlagin, N. Buchmann, E. van Gorsel, L. Montagnani, and P. Propastin
Biogeosciences, 11, 2185–2200, https://doi.org/10.5194/bg-11-2185-2014, https://doi.org/10.5194/bg-11-2185-2014, 2014
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
Related subject area
Biogeochemistry: Modelling, Terrestrial
Non-steady-state stomatal conductance modeling and its implications: from leaf to ecosystem
Modelled forest ecosystem carbon–nitrogen dynamics with integrated mycorrhizal processes under elevated CO2
A chemical kinetics theory for interpreting the non-monotonic temperature dependence of enzymatic reactions
Using Free Air CO2 Enrichment data to constrain land surface model projections of the terrestrial carbon cycle
Multiscale assessment of North American terrestrial carbon balance
Simulating net ecosystem exchange under seasonal snow cover at an Arctic tundra site
Spatial biases reduce the ability of Earth system models to simulate soil heterotrophic respiration fluxes
Tropical dry forest response to nutrient fertilization: a model validation and sensitivity analysis
Connecting competitor, stress-tolerator and ruderal (CSR) theory and Lund Potsdam Jena managed Land 5 (LPJmL 5) to assess the role of environmental conditions, management and functional diversity for grassland ecosystem functions
A global fuel characteristic model and dataset for wildfire prediction
Can models adequately reflect how long-term nitrogen enrichment alters the forest soil carbon cycle?
Temporal variability of observed and simulated gross primary productivity, modulated by vegetation state and hydrometeorological drivers
Empirical upscaling of OzFlux eddy covariance for high-resolution monitoring of terrestrial carbon uptake in Australia
Temperature Acclimation of Photosystem II Efficiency across Plant Functional Types and Climate
A modeling approach to investigate drivers, variability and uncertainties in O2 fluxes and O2 : CO2 exchange ratios in a temperate forest
Modeling coupled nitrification–denitrification in soil with an organic hotspot
Elevated atmospheric CO2 and vegetation structural changes contributed to GPP increase more than climate and forest cover changes in subtropical forests of China
A new method for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland
Enabling a process-oriented hydro-biogeochemical model to simulate soil erosion and nutrient losses
Potassium limitation of forest productivity – Part 1: A mechanistic model simulating the effects of potassium availability on canopy carbon and water fluxes in tropical eucalypt stands
Potassium limitation of forest productivity – Part 2: CASTANEA-MAESPA-K shows a reduction in photosynthesis rather than a stoichiometric limitation of tissue formation
Global evaluation of terrestrial biogeochemistry in the Energy Exascale Earth System Model (E3SM) and the role of the phosphorus cycle in the historical terrestrial carbon balance
Assessing carbon storage capacity and saturation across six central US grasslands using data–model integration
Optimizing the carbonic anhydrase temperature response and stomatal conductance of carbonyl sulfide leaf uptake in the Simple Biosphere model (SiB4)
Exploring environmental and physiological drivers of the annual carbon budget of biocrusts from various climatic zones with a mechanistic data-driven model
Improved process representation of leaf phenology significantly shifts climate sensitivity of ecosystem carbon balance
Mapping of ESA's Climate Change Initiative land cover data to plant functional types for use in the CLASSIC land model
Exploring the impacts of unprecedented climate extremes on forest ecosystems: hypotheses to guide modeling and experimental studies
Effect of droughts and climate change on future soil weathering rates in Sweden
Information content in time series of litter decomposition studies and the transit time of litter in arid lands
Long-term changes of nitrogen leaching and the contributions of terrestrial nutrient sources to lake eutrophication dynamics on the Yangtze Plain of China
Towards an ensemble-based evaluation of land surface models in light of uncertain forcings and observations
Historical dynamics of terrestrial carbon during 1901–2016 as simulated by the CLM-Microbe model
Effect of land-use legacy on the future carbon sink for the conterminous US
Peatlands and their carbon dynamics in northern high latitudes from 1990 to 2300: a process-based biogeochemistry model analysis
Improved representation of phosphorus exchange on soil mineral surfaces reduces estimates of phosphorus limitation in temperate forest ecosystems
A coupled ground heat flux–surface energy balance model of evaporation using thermal remote sensing observations
Modeling nitrous oxide emissions from agricultural soil incubation experiments using CoupModel
Local-scale evaluation of the simulated interactions between energy, water and vegetation in ISBA, ORCHIDEE and a diagnostic model
Implementation and initial calibration of carbon-13 soil organic matter decomposition in the Yasso model
The carbon budget of the managed grasslands of Great Britain – informed by earth observations
Accounting for non-rainfall moisture and temperature improves litter decay model performance in a fog-dominated dryland system
Ideas and perspectives: Allocation of carbon from net primary production in models is inconsistent with observations of the age of respired carbon
Exploring the role of bedrock representation on plant transpiration response during dry periods at four forested sites in Europe
Effects of climate change in European croplands and grasslands: productivity, greenhouse gas balance and soil carbon storage
Assimilation of passive microwave vegetation optical depth in LDAS-Monde: a case study over the continental USA
Global modelling of soil carbonyl sulfide exchanges
Assessing the impacts of agricultural managements on soil carbon stocks, nitrogen loss, and crop production – a modelling study in eastern Africa
The effects of varying drought-heat signatures on terrestrial carbon dynamics and vegetation composition
Resolving temperature limitation on spring productivity in an evergreen conifer forest using a model–data fusion framework
Ke Liu, Yujie Wang, Troy S. Magney, and Christian Frankenberg
Biogeosciences, 21, 1501–1516, https://doi.org/10.5194/bg-21-1501-2024, https://doi.org/10.5194/bg-21-1501-2024, 2024
Short summary
Short summary
Stomata are pores on leaves that regulate gas exchange between plants and the atmosphere. Existing land models unrealistically assume stomata can jump between steady states when the environment changes. We implemented dynamic modeling to predict gradual stomatal responses at different scales. Results suggested that considering this effect on plant behavior patterns in diurnal cycles was important. Our framework also simplified simulations and can contribute to further efficiency improvements.
Melanie A. Thurner, Silvia Caldararu, Jan Engel, Anja Rammig, and Sönke Zaehle
Biogeosciences, 21, 1391–1410, https://doi.org/10.5194/bg-21-1391-2024, https://doi.org/10.5194/bg-21-1391-2024, 2024
Short summary
Short summary
Due to their crucial role in terrestrial ecosystems, we implemented mycorrhizal fungi into the QUINCY terrestrial biosphere model. Fungi interact with mineral and organic soil to support plant N uptake and, thus, plant growth. Our results suggest that the effect of mycorrhizal interactions on simulated ecosystem dynamics is minor under constant environmental conditions but necessary to reproduce and understand observed patterns under changing conditions, such as rising atmospheric CO2.
Jinyun Tang and William J. Riley
Biogeosciences, 21, 1061–1070, https://doi.org/10.5194/bg-21-1061-2024, https://doi.org/10.5194/bg-21-1061-2024, 2024
Short summary
Short summary
A chemical kinetics theory is proposed to explain the non-monotonic relationship between temperature and biochemical rates. It incorporates the observed thermally reversible enzyme denaturation that is ensured by the ceaseless thermal motion of molecules and ions in an enzyme solution and three well-established theories: (1) law of mass action, (2) diffusion-limited chemical reaction theory, and (3) transition state theory.
Nina Raoult, Louis-Axel Edouard-Rambaut, Nicolas Vuichard, Vladislav Bastrikov, Anne Sofie Lansø, Bertrand Guenet, and Philippe Peylin
Biogeosciences, 21, 1017–1036, https://doi.org/10.5194/bg-21-1017-2024, https://doi.org/10.5194/bg-21-1017-2024, 2024
Short summary
Short summary
Observations are used to reduce uncertainty in land surface models (LSMs) by optimising poorly constraining parameters. However, optimising against current conditions does not necessarily ensure that the parameters treated as invariant will be robust in a changing climate. Manipulation experiments offer us a unique chance to optimise our models under different (here atmospheric CO2) conditions. By using these data in optimisations, we gain confidence in the future projections of LSMs.
Kelsey T. Foster, Wu Sun, Yoichi P. Shiga, Jiafu Mao, and Anna M. Michalak
Biogeosciences, 21, 869–891, https://doi.org/10.5194/bg-21-869-2024, https://doi.org/10.5194/bg-21-869-2024, 2024
Short summary
Short summary
Assessing agreement between bottom-up and top-down methods across spatial scales can provide insights into the relationship between ensemble spread (difference across models) and model accuracy (difference between model estimates and reality). We find that ensemble spread is unlikely to be a good indicator of actual uncertainty in the North American carbon balance. However, models that are consistent with atmospheric constraints show stronger agreement between top-down and bottom-up estimates.
Victoria R. Dutch, Nick Rutter, Leanne Wake, Oliver Sonnentag, Gabriel Hould Gosselin, Melody Sandells, Chris Derksen, Branden Walker, Gesa Meyer, Richard Essery, Richard Kelly, Phillip Marsh, Julia Boike, and Matteo Detto
Biogeosciences, 21, 825–841, https://doi.org/10.5194/bg-21-825-2024, https://doi.org/10.5194/bg-21-825-2024, 2024
Short summary
Short summary
We undertake a sensitivity study of three different parameters on the simulation of net ecosystem exchange (NEE) during the snow-covered non-growing season at an Arctic tundra site. Simulations are compared to eddy covariance measurements, with near-zero NEE simulated despite observed CO2 release. We then consider how to parameterise the model better in Arctic tundra environments on both sub-seasonal timescales and cumulatively throughout the snow-covered non-growing season.
Bertrand Guenet, Jérémie Orliac, Lauric Cécillon, Olivier Torres, Laura Sereni, Philip A. Martin, Pierre Barré, and Laurent Bopp
Biogeosciences, 21, 657–669, https://doi.org/10.5194/bg-21-657-2024, https://doi.org/10.5194/bg-21-657-2024, 2024
Short summary
Short summary
Heterotrophic respiration fluxes are a major flux between surfaces and the atmosphere, but Earth system models do not yet represent them correctly. Here we benchmarked Earth system models against observation-based products, and we identified the important mechanisms that need to be improved in the next-generation Earth system models.
Shuyue Li, Bonnie Waring, Jennifer Powers, and David Medvigy
Biogeosciences, 21, 455–471, https://doi.org/10.5194/bg-21-455-2024, https://doi.org/10.5194/bg-21-455-2024, 2024
Short summary
Short summary
We used an ecosystem model to simulate primary production of a tropical forest subjected to 3 years of nutrient fertilization. Simulations parameterized such that relative allocation to fine roots increased with increasing soil phosphorus had leaf, wood, and fine root production consistent with observations. However, these simulations seemed to over-allocate to fine roots on multidecadal timescales, affecting aboveground biomass. Additional observations across timescales would benefit models.
Stephen Björn Wirth, Arne Poyda, Friedhelm Taube, Britta Tietjen, Christoph Müller, Kirsten Thonicke, Anja Linstädter, Kai Behn, Sibyll Schaphoff, Werner von Bloh, and Susanne Rolinski
Biogeosciences, 21, 381–410, https://doi.org/10.5194/bg-21-381-2024, https://doi.org/10.5194/bg-21-381-2024, 2024
Short summary
Short summary
In dynamic global vegetation models (DGVMs), the role of functional diversity in forage supply and soil organic carbon storage of grasslands is not explicitly taken into account. We introduced functional diversity into the Lund Potsdam Jena managed Land (LPJmL) DGVM using CSR theory. The new model reproduced well-known trade-offs between plant traits and can be used to quantify the role of functional diversity in climate change mitigation using different functional diversity scenarios.
Joe R. McNorton and Francesca Di Giuseppe
Biogeosciences, 21, 279–300, https://doi.org/10.5194/bg-21-279-2024, https://doi.org/10.5194/bg-21-279-2024, 2024
Short summary
Short summary
Wildfires have wide-ranging consequences for local communities, air quality and ecosystems. Vegetation amount and moisture state are key components to forecast wildfires. We developed a combined model and satellite framework to characterise vegetation, including the type of fuel, whether it is alive or dead, and its moisture content. The daily data is at high resolution globally (~9 km). Our characteristics correlate with active fire data and can inform fire danger and spread modelling efforts.
Brooke A. Eastman, William R. Wieder, Melannie D. Hartman, Edward R. Brzostek, and William T. Peterjohn
Biogeosciences, 21, 201–221, https://doi.org/10.5194/bg-21-201-2024, https://doi.org/10.5194/bg-21-201-2024, 2024
Short summary
Short summary
We compared soil model performance to data from a long-term nitrogen addition experiment in a forested ecosystem. We found that in order for soil carbon models to accurately predict future forest carbon sequestration, two key processes must respond dynamically to nitrogen availability: (1) plant allocation of carbon to wood versus roots and (2) rates of soil organic matter decomposition. Long-term experiments can help improve our predictions of the land carbon sink and its climate impact.
Jan De Pue, Sebastian Wieneke, Ana Bastos, José Miguel Barrios, Liyang Liu, Philippe Ciais, Alirio Arboleda, Rafiq Hamdi, Maral Maleki, Fabienne Maignan, Françoise Gellens-Meulenberghs, Ivan Janssens, and Manuela Balzarolo
Biogeosciences, 20, 4795–4818, https://doi.org/10.5194/bg-20-4795-2023, https://doi.org/10.5194/bg-20-4795-2023, 2023
Short summary
Short summary
The gross primary production (GPP) of the terrestrial biosphere is a key source of variability in the global carbon cycle. To estimate this flux, models can rely on remote sensing data (RS-driven), meteorological data (meteo-driven) or a combination of both (hybrid). An intercomparison of 11 models demonstrated that RS-driven models lack the sensitivity to short-term anomalies. Conversely, the simulation of soil moisture dynamics and stress response remains a challenge in meteo-driven models.
Chad A. Burton, Luigi J. Renzullo, Sami W. Rifai, and Albert I. J. M. Van Dijk
Biogeosciences, 20, 4109–4134, https://doi.org/10.5194/bg-20-4109-2023, https://doi.org/10.5194/bg-20-4109-2023, 2023
Short summary
Short summary
Australia's land-based ecosystems play a critical role in controlling the variability in the global land carbon sink. However, uncertainties in the methods used for quantifying carbon fluxes limit our understanding. We develop high-resolution estimates of Australia's land carbon fluxes using machine learning methods and find that Australia is, on average, a stronger carbon sink than previously thought and that the seasonal dynamics of the fluxes differ from those described by other methods.
Patrick Neri, Lianhong Gu, and Yang Song
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-163, https://doi.org/10.5194/bg-2023-163, 2023
Revised manuscript accepted for BG
Short summary
Short summary
We made the first global-scale effort that modeled how plant functional types and habitat climatology regulated the temperature responses of the photosynthetic efficiency of adsorbed lights. We found that the more temperature-resilient plants were less temperature-tolerant and vice versa. Habitat climatology is more critical than plant types for regulating the temperature responses of plants with broad distributions or experiences of large temperature variability.
Yuan Yan, Anne Klosterhalfen, Fernando Moyano, Matthias Cuntz, Andrew C. Manning, and Alexander Knohl
Biogeosciences, 20, 4087–4107, https://doi.org/10.5194/bg-20-4087-2023, https://doi.org/10.5194/bg-20-4087-2023, 2023
Short summary
Short summary
A better understanding of O2 fluxes, their exchange ratios with CO2 and their interrelations with environmental conditions would provide further insights into biogeochemical ecosystem processes. We, therefore, used the multilayer canopy model CANVEG to simulate and analyze the flux exchange for our forest study site for 2012–2016. Based on these simulations, we further successfully tested the application of various micrometeorological methods and the prospects of real O2 flux measurements.
Jie Zhang, Elisabeth Larsen Kolstad, Wenxin Zhang, Iris Vogeler, and Søren O. Petersen
Biogeosciences, 20, 3895–3917, https://doi.org/10.5194/bg-20-3895-2023, https://doi.org/10.5194/bg-20-3895-2023, 2023
Short summary
Short summary
Manure application to agricultural land often results in large and variable N2O emissions. We propose a model with a parsimonious structure to investigate N transformations around such N2O hotspots. The model allows for new detailed insights into the interactions between transport and microbial activities regarding N2O emissions in heterogeneous soil environments. It highlights the importance of solute diffusion to N2O emissions from such hotspots which are often ignored by process-based models.
Tao Chen, Félicien Meunier, Marc Peaucelle, Guoping Tang, Ye Yuan, and Hans Verbeeck
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-140, https://doi.org/10.5194/bg-2023-140, 2023
Revised manuscript accepted for BG
Short summary
Short summary
The Chinese subtropical forest ecosystems are an extremely important component of those global forest ecosystems and are hence crucial for the global carbon cycle and regional climate change. However, there is still a great uncertainty in the relationship between subtropical forest carbon sequestration and its drivers. Here, we provided the first quantitative estimates of the individual and interactive effects of different drivers on the GPP of various subtropical forest types in China.
Jukka Alm, Antti Wall, Jukka-Pekka Myllykangas, Paavo Ojanen, Juha Heikkinen, Helena M. Henttonen, Raija Laiho, Kari Minkkinen, Tarja Tuomainen, and Juha Mikola
Biogeosciences, 20, 3827–3855, https://doi.org/10.5194/bg-20-3827-2023, https://doi.org/10.5194/bg-20-3827-2023, 2023
Short summary
Short summary
In Finland peatlands cover one-third of land area. For half of those, with 4.3 Mha being drained for forestry, Finland reports sinks and sources of greenhouse gases in forest lands on organic soils following its UNFCCC commitment. We describe a new method for compiling soil CO2 balance that follows changes in tree volume, tree harvests and temperature. An increasing trend of emissions from 1.4 to 7.9 Mt CO2 was calculated for drained peatland forest soils in Finland for 1990–2021.
Siqi Li, Bo Zhu, Xunhua Zheng, Pengcheng Hu, Shenghui Han, Jihui Fan, Tao Wang, Rui Wang, Kai Wang, Zhisheng Yao, Chunyan Liu, Wei Zhang, and Yong Li
Biogeosciences, 20, 3555–3572, https://doi.org/10.5194/bg-20-3555-2023, https://doi.org/10.5194/bg-20-3555-2023, 2023
Short summary
Short summary
Physical soil erosion and particulate carbon, nitrogen and phosphorus loss modules were incorporated into the process-oriented hydro-biogeochemical model CNMM-DNDC to realize the accurate simulation of water-induced erosion and subsequent particulate nutrient losses at high spatiotemporal resolution.
Ivan Cornut, Nicolas Delpierre, Jean-Paul Laclau, Joannès Guillemot, Yann Nouvellon, Otavio Campoe, Jose Luiz Stape, Vitoria Fernanda Santos, and Guerric le Maire
Biogeosciences, 20, 3093–3117, https://doi.org/10.5194/bg-20-3093-2023, https://doi.org/10.5194/bg-20-3093-2023, 2023
Short summary
Short summary
Potassium is an essential element for living organisms. Trees are dependent upon this element for certain functions that allow them to build their trunks using carbon dioxide. Using data from experiments in eucalypt plantations in Brazil and a simplified computer model of the plantations, we were able to investigate the effect that a lack of potassium can have on the production of wood. Understanding nutrient cycles is useful to understand the response of forests to environmental change.
Ivan Cornut, Guerric le Maire, Jean-Paul Laclau, Joannès Guillemot, Yann Nouvellon, and Nicolas Delpierre
Biogeosciences, 20, 3119–3135, https://doi.org/10.5194/bg-20-3119-2023, https://doi.org/10.5194/bg-20-3119-2023, 2023
Short summary
Short summary
After simulating the effects of low levels of potassium on the canopy of trees and the uptake of carbon dioxide from the atmosphere by leaves in Part 1, here we tried to simulate the way the trees use the carbon they have acquired and the interaction with the potassium cycle in the tree. We show that the effect of low potassium on the efficiency of the trees in acquiring carbon is enough to explain why they produce less wood when they are in soils with low levels of potassium.
Xiaojuan Yang, Peter Thornton, Daniel Ricciuto, Yilong Wang, and Forrest Hoffman
Biogeosciences, 20, 2813–2836, https://doi.org/10.5194/bg-20-2813-2023, https://doi.org/10.5194/bg-20-2813-2023, 2023
Short summary
Short summary
We evaluated the performance of a land surface model (ELMv1-CNP) that includes both nitrogen (N) and phosphorus (P) limitation on carbon cycle processes. We show that ELMv1-CNP produces realistic estimates of present-day carbon pools and fluxes. We show that global C sources and sinks are significantly affected by P limitation. Our study suggests that introduction of P limitation in land surface models is likely to have substantial consequences for projections of future carbon uptake.
Kevin R. Wilcox, Scott L. Collins, Alan K. Knapp, William Pockman, Zheng Shi, Melinda D. Smith, and Yiqi Luo
Biogeosciences, 20, 2707–2725, https://doi.org/10.5194/bg-20-2707-2023, https://doi.org/10.5194/bg-20-2707-2023, 2023
Short summary
Short summary
The capacity for carbon storage (C capacity) is an attribute that determines how ecosystems store carbon in the future. Here, we employ novel data–model integration techniques to identify the carbon capacity of six grassland sites spanning the US Great Plains. Hot and dry sites had low C capacity due to less plant growth and high turnover of soil C, so they may be a C source in the future. Alternately, cooler and wetter ecosystems had high C capacity, so these systems may be a future C sink.
Ara Cho, Linda M. J. Kooijmans, Kukka-Maaria Kohonen, Richard Wehr, and Maarten C. Krol
Biogeosciences, 20, 2573–2594, https://doi.org/10.5194/bg-20-2573-2023, https://doi.org/10.5194/bg-20-2573-2023, 2023
Short summary
Short summary
Carbonyl sulfide (COS) is a useful constraint for estimating photosynthesis. To simulate COS leaf flux better in the SiB4 model, we propose a novel temperature function for enzyme carbonic anhydrase (CA) activity and optimize conductances using observations. The optimal activity of CA occurs below 40 °C, and Ball–Woodrow–Berry parameters are slightly changed. These reduce/increase uptakes in the tropics/higher latitudes and contribute to resolving discrepancies in the COS global budget.
Yunyao Ma, Bettina Weber, Alexandra Kratz, José Raggio, Claudia Colesie, Maik Veste, Maaike Y. Bader, and Philipp Porada
Biogeosciences, 20, 2553–2572, https://doi.org/10.5194/bg-20-2553-2023, https://doi.org/10.5194/bg-20-2553-2023, 2023
Short summary
Short summary
We found that the modelled annual carbon balance of biocrusts is strongly affected by both the environment (mostly air temperature and CO2 concentration) and physiology, such as temperature response of respiration. However, the relative impacts of these drivers vary across regions with different climates. Uncertainty in driving factors may lead to unrealistic carbon balance estimates, particularly in temperate climates, and may be explained by seasonal variation of physiology due to acclimation.
Alexander J. Norton, A. Anthony Bloom, Nicholas C. Parazoo, Paul A. Levine, Shuang Ma, Renato K. Braghiere, and T. Luke Smallman
Biogeosciences, 20, 2455–2484, https://doi.org/10.5194/bg-20-2455-2023, https://doi.org/10.5194/bg-20-2455-2023, 2023
Short summary
Short summary
This study explores how the representation of leaf phenology affects our ability to predict changes to the carbon balance of land ecosystems. We calibrate a new leaf phenology model against a diverse range of observations at six forest sites, showing that it improves the predictive capability of the processes underlying the ecosystem carbon balance. We then show how changes in temperature and rainfall affect the ecosystem carbon balance with this new model.
Libo Wang, Vivek K. Arora, Paul Bartlett, Ed Chan, and Salvatore R. Curasi
Biogeosciences, 20, 2265–2282, https://doi.org/10.5194/bg-20-2265-2023, https://doi.org/10.5194/bg-20-2265-2023, 2023
Short summary
Short summary
Plant functional types (PFTs) are groups of plant species used to represent vegetation distribution in land surface models. There are large uncertainties associated with existing methods for mapping land cover datasets to PFTs. This study demonstrates how fine-resolution tree cover fraction and land cover datasets can be used to inform the PFT mapping process and reduce the uncertainties. The proposed largely objective method makes it easier to implement new land cover products in models.
Jennifer A. Holm, David M. Medvigy, Benjamin Smith, Jeffrey S. Dukes, Claus Beier, Mikhail Mishurov, Xiangtao Xu, Jeremy W. Lichstein, Craig D. Allen, Klaus S. Larsen, Yiqi Luo, Cari Ficken, William T. Pockman, William R. L. Anderegg, and Anja Rammig
Biogeosciences, 20, 2117–2142, https://doi.org/10.5194/bg-20-2117-2023, https://doi.org/10.5194/bg-20-2117-2023, 2023
Short summary
Short summary
Unprecedented climate extremes (UCEs) are expected to have dramatic impacts on ecosystems. We present a road map of how dynamic vegetation models can explore extreme drought and climate change and assess ecological processes to measure and reduce model uncertainties. The models predict strong nonlinear responses to UCEs. Due to different model representations, the models differ in magnitude and trajectory of forest loss. Therefore, we explore specific plant responses that reflect knowledge gaps.
Veronika Kronnäs, Klas Lucander, Giuliana Zanchi, Nadja Stadlinger, Salim Belyazid, and Cecilia Akselsson
Biogeosciences, 20, 1879–1899, https://doi.org/10.5194/bg-20-1879-2023, https://doi.org/10.5194/bg-20-1879-2023, 2023
Short summary
Short summary
In a future climate, extreme droughts might become more common. Climate change and droughts can have negative effects on soil weathering and plant health.
In this study, climate change effects on weathering were studied on sites in Sweden using the model ForSAFE, a climate change scenario and an extreme drought scenario. The modelling shows that weathering is higher during summer and increases with global warming but that weathering during drought summers can become as low as winter weathering.
Agustín Sarquis and Carlos A. Sierra
Biogeosciences, 20, 1759–1771, https://doi.org/10.5194/bg-20-1759-2023, https://doi.org/10.5194/bg-20-1759-2023, 2023
Short summary
Short summary
Although plant litter is chemically and physically heterogenous and undergoes multiple transformations, models that represent litter dynamics often ignore this complexity. We used a multi-model inference framework to include information content in litter decomposition datasets and studied the time it takes for litter to decompose as measured by the transit time. In arid lands, the median transit time of litter is about 3 years and has a negative correlation with mean annual temperature.
Qi Guan, Jing Tang, Lian Feng, Stefan Olin, and Guy Schurgers
Biogeosciences, 20, 1635–1648, https://doi.org/10.5194/bg-20-1635-2023, https://doi.org/10.5194/bg-20-1635-2023, 2023
Short summary
Short summary
Understanding terrestrial sources of nitrogen is vital to examine lake eutrophication changes. Combining process-based ecosystem modeling and satellite observations, we found that land-leached nitrogen in the Yangtze Plain significantly increased from 1979 to 2018, and terrestrial nutrient sources were positively correlated with eutrophication trends observed in most lakes, demonstrating the necessity of sustainable nitrogen management to control eutrophication.
Vivek K. Arora, Christian Seiler, Libo Wang, and Sian Kou-Giesbrecht
Biogeosciences, 20, 1313–1355, https://doi.org/10.5194/bg-20-1313-2023, https://doi.org/10.5194/bg-20-1313-2023, 2023
Short summary
Short summary
The behaviour of natural systems is now very often represented through mathematical models. These models represent our understanding of how nature works. Of course, nature does not care about our understanding. Since our understanding is not perfect, evaluating models is challenging, and there are uncertainties. This paper illustrates this uncertainty for land models and argues that evaluating models in light of the uncertainty in various components provides useful information.
Liyuan He, Jorge L. Mazza Rodrigues, Melanie A. Mayes, Chun-Ta Lai, David A. Lipson, and Xiaofeng Xu
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-15, https://doi.org/10.5194/bg-2023-15, 2023
Revised manuscript accepted for BG
Short summary
Short summary
The microbial-explicit model – CLM-Microbe – was first applied to investigate the carbon cycle in terrestrial ecosystems. The simulated carbon storages and fluxes are consistent with previous estimates. The bacterial and fungal biomass carbon showed increasing trends from 1901 to 2016, with large spatial variations. The long-term global estimation of microbial dynamics provides a quantitive understanding of microbial contributions to the global carbon cycle.
Benjamin S. Felzer
Biogeosciences, 20, 573–587, https://doi.org/10.5194/bg-20-573-2023, https://doi.org/10.5194/bg-20-573-2023, 2023
Short summary
Short summary
The future of the terrestrial carbon sink depends upon the legacy of past land use, which determines the stand age of the forest and nutrient levels in the soil, both of which affect vegetation growth. This study uses a modeling approach to determine the effects of land-use legacy in the conterminous US from 1750 to 2099. Not accounting for land legacy results in a low carbon sink and high biomass, while water variables are not as highly affected.
Bailu Zhao and Qianlai Zhuang
Biogeosciences, 20, 251–270, https://doi.org/10.5194/bg-20-251-2023, https://doi.org/10.5194/bg-20-251-2023, 2023
Short summary
Short summary
In this study, we use a process-based model to simulate the northern peatland's C dynamics in response to future climate change during 1990–2300. Northern peatlands are projected to be a C source under all climate scenarios except for the mildest one before 2100 and C sources under all scenarios afterwards.
We find northern peatlands are a C sink until pan-Arctic annual temperature reaches −2.09 to −2.89 °C. This study emphasizes the vulnerability of northern peatlands to climate change.
Lin Yu, Silvia Caldararu, Bernhard Ahrens, Thomas Wutzler, Marion Schrumpf, Julian Helfenstein, Chiara Pistocchi, and Sönke Zaehle
Biogeosciences, 20, 57–73, https://doi.org/10.5194/bg-20-57-2023, https://doi.org/10.5194/bg-20-57-2023, 2023
Short summary
Short summary
In this study, we addressed a key weakness in current ecosystem models regarding the phosphorus exchange in the soil and developed a new scheme to describe this process. We showed that the new scheme improved the model performance for plant productivity, soil organic carbon, and soil phosphorus content at five beech forest sites in Germany. We claim that this new model could be used as a better tool to study ecosystems under future climate change, particularly phosphorus-limited systems.
Bimal K. Bhattacharya, Kaniska Mallick, Devansh Desai, Ganapati S. Bhat, Ross Morrison, Jamie R. Clevery, William Woodgate, Jason Beringer, Kerry Cawse-Nicholson, Siyan Ma, Joseph Verfaillie, and Dennis Baldocchi
Biogeosciences, 19, 5521–5551, https://doi.org/10.5194/bg-19-5521-2022, https://doi.org/10.5194/bg-19-5521-2022, 2022
Short summary
Short summary
Evaporation retrieval in heterogeneous ecosystems is challenging due to empirical estimation of ground heat flux and complex parameterizations of conductances. We developed a parameter-sparse coupled ground heat flux-evaporation model and tested it across different limits of water stress and vegetation fraction in the Northern/Southern Hemisphere. The model performed particularly well in the savannas and showed good potential for evaporative stress monitoring from thermal infrared satellites.
Jie Zhang, Wenxin Zhang, Per-Erik Jansson, and Søren O. Petersen
Biogeosciences, 19, 4811–4832, https://doi.org/10.5194/bg-19-4811-2022, https://doi.org/10.5194/bg-19-4811-2022, 2022
Short summary
Short summary
In this study, we relied on a properly controlled laboratory experiment to test the model’s capability of simulating the dominant microbial processes and the emissions of one greenhouse gas (nitrous oxide, N2O) from agricultural soils. This study reveals important processes and parameters that regulate N2O emissions in the investigated model framework and also suggests future steps of model development, which have implications on the broader communities of ecosystem modelers.
Jan De Pue, José Miguel Barrios, Liyang Liu, Philippe Ciais, Alirio Arboleda, Rafiq Hamdi, Manuela Balzarolo, Fabienne Maignan, and Françoise Gellens-Meulenberghs
Biogeosciences, 19, 4361–4386, https://doi.org/10.5194/bg-19-4361-2022, https://doi.org/10.5194/bg-19-4361-2022, 2022
Short summary
Short summary
The functioning of ecosystems involves numerous biophysical processes which interact with each other. Land surface models (LSMs) are used to describe these processes and form an essential component of climate models. In this paper, we evaluate the performance of three LSMs and their interactions with soil moisture and vegetation. Though we found room for improvement in the simulation of soil moisture and drought stress, the main cause of errors was related to the simulated growth of vegetation.
Jarmo Mäkelä, Laura Arppe, Hannu Fritze, Jussi Heinonsalo, Kristiina Karhu, Jari Liski, Markku Oinonen, Petra Straková, and Toni Viskari
Biogeosciences, 19, 4305–4313, https://doi.org/10.5194/bg-19-4305-2022, https://doi.org/10.5194/bg-19-4305-2022, 2022
Short summary
Short summary
Soils account for the largest share of carbon found in terrestrial ecosystems, and accurate depiction of soil carbon decomposition is essential in understanding how permanent these carbon storages are. We present a straightforward way to include carbon isotope concentrations into soil decomposition and carbon storages for the Yasso model, which enables the model to use 13C as a natural tracer to track changes in the underlying soil organic matter decomposition.
Vasileios Myrgiotis, Thomas Luke Smallman, and Mathew Williams
Biogeosciences, 19, 4147–4170, https://doi.org/10.5194/bg-19-4147-2022, https://doi.org/10.5194/bg-19-4147-2022, 2022
Short summary
Short summary
This study shows that livestock grazing and grass cutting can determine whether a grassland is adding (source) or removing (sink) carbon (C) to/from the atmosphere. The annual C balance of 1855 managed grassland fields in Great Britain was quantified for 2017–2018 using process modelling and earth observation data. The examined fields were, on average, small C sinks, but the summer drought of 2018 led to a 9-fold increase in the number of fields that became C sources in 2018 compared to 2017.
J. Robert Logan, Kathe E. Todd-Brown, Kathryn M. Jacobson, Peter J. Jacobson, Roland Vogt, and Sarah E. Evans
Biogeosciences, 19, 4129–4146, https://doi.org/10.5194/bg-19-4129-2022, https://doi.org/10.5194/bg-19-4129-2022, 2022
Short summary
Short summary
Understanding how plants decompose is important for understanding where the atmospheric CO2 they absorb ends up after they die. In forests, decomposition is controlled by rain but not in deserts. We performed a 2.5-year study in one of the driest places on earth (the Namib desert in southern Africa) and found that fog and dew, not rainfall, closely controlled how quickly plants decompose. We also created a model to help predict decomposition in drylands with lots of fog and/or dew.
Carlos A. Sierra, Verónika Ceballos-Núñez, Henrik Hartmann, David Herrera-Ramírez, and Holger Metzler
Biogeosciences, 19, 3727–3738, https://doi.org/10.5194/bg-19-3727-2022, https://doi.org/10.5194/bg-19-3727-2022, 2022
Short summary
Short summary
Empirical work that estimates the age of respired CO2 from vegetation tissue shows that it may take from years to decades to respire previously produced photosynthates. However, many ecosystem models represent respiration processes in a form that cannot reproduce these observations. In this contribution, we attempt to provide compelling evidence, based on recent research, with the aim to promote a change in the predominant paradigm implemented in ecosystem models.
César Dionisio Jiménez-Rodríguez, Mauro Sulis, and Stanislaus Schymanski
Biogeosciences, 19, 3395–3423, https://doi.org/10.5194/bg-19-3395-2022, https://doi.org/10.5194/bg-19-3395-2022, 2022
Short summary
Short summary
Vegetation relies on soil water reservoirs during dry periods. However, when this source is depleted, the plants may access water stored deeper in the rocks. This rock moisture contribution is usually omitted in large-scale models, which affects modeled plant water use during dry periods. Our study illustrates that including this additional source of water in the Community Land Model improves the model's ability to reproduce observed plant water use at seasonally dry sites.
Marco Carozzi, Raphaël Martin, Katja Klumpp, and Raia Silvia Massad
Biogeosciences, 19, 3021–3050, https://doi.org/10.5194/bg-19-3021-2022, https://doi.org/10.5194/bg-19-3021-2022, 2022
Short summary
Short summary
Crop and grassland production indicates a strong reduction due to the shortening of the length of the growing cycle associated with rising temperatures. Greenhouse gas emissions will increase exponentially over the century, often exceeding the CO2 accumulation of agro-ecosystems. Water demand will double in the next few decades, whereas the benefits in terms of yield will not fill the gap of C losses due to climate perturbation. Climate change will have a regionally distributed effect in the EU.
Anthony Mucia, Bertrand Bonan, Clément Albergel, Yongjun Zheng, and Jean-Christophe Calvet
Biogeosciences, 19, 2557–2581, https://doi.org/10.5194/bg-19-2557-2022, https://doi.org/10.5194/bg-19-2557-2022, 2022
Short summary
Short summary
For the first time, microwave vegetation optical depth data are assimilated in a land surface model in order to analyze leaf area index and root zone soil moisture. The advantage of microwave products is the higher observation frequency. A large variety of independent datasets are used to verify the added value of the assimilation. It is shown that the assimilation is able to improve the representation of soil moisture, vegetation conditions, and terrestrial water and carbon fluxes.
Camille Abadie, Fabienne Maignan, Marine Remaud, Jérôme Ogée, J. Elliott Campbell, Mary E. Whelan, Florian Kitz, Felix M. Spielmann, Georg Wohlfahrt, Richard Wehr, Wu Sun, Nina Raoult, Ulli Seibt, Didier Hauglustaine, Sinikka T. Lennartz, Sauveur Belviso, David Montagne, and Philippe Peylin
Biogeosciences, 19, 2427–2463, https://doi.org/10.5194/bg-19-2427-2022, https://doi.org/10.5194/bg-19-2427-2022, 2022
Short summary
Short summary
A better constraint of the components of the carbonyl sulfide (COS) global budget is needed to exploit its potential as a proxy of gross primary productivity. In this study, we compare two representations of oxic soil COS fluxes, and we develop an approach to represent anoxic soil COS fluxes in a land surface model. We show the importance of atmospheric COS concentration variations on oxic soil COS fluxes and provide new estimates for oxic and anoxic soil contributions to the COS global budget.
Jianyong Ma, Sam S. Rabin, Peter Anthoni, Anita D. Bayer, Sylvia S. Nyawira, Stefan Olin, Longlong Xia, and Almut Arneth
Biogeosciences, 19, 2145–2169, https://doi.org/10.5194/bg-19-2145-2022, https://doi.org/10.5194/bg-19-2145-2022, 2022
Short summary
Short summary
Improved agricultural management plays a vital role in protecting soils from degradation in eastern Africa. We simulated the impacts of seven management practices on soil carbon pools, nitrogen loss, and crop yield under different climate scenarios in this region. This study highlights the possibilities of conservation agriculture when targeting long-term environmental sustainability and food security in crop ecosystems, particularly for those with poor soil conditions in tropical climates.
Elisabeth Tschumi, Sebastian Lienert, Karin van der Wiel, Fortunat Joos, and Jakob Zscheischler
Biogeosciences, 19, 1979–1993, https://doi.org/10.5194/bg-19-1979-2022, https://doi.org/10.5194/bg-19-1979-2022, 2022
Short summary
Short summary
Droughts and heatwaves are expected to occur more often in the future, but their effects on land vegetation and the carbon cycle are poorly understood. We use six climate scenarios with differing extreme occurrences and a vegetation model to analyse these effects. Tree coverage and associated plant productivity increase under a climate with no extremes. Frequent co-occurring droughts and heatwaves decrease plant productivity more than the combined effects of single droughts or heatwaves.
Stephanie G. Stettz, Nicholas C. Parazoo, A. Anthony Bloom, Peter D. Blanken, David R. Bowling, Sean P. Burns, Cédric Bacour, Fabienne Maignan, Brett Raczka, Alexander J. Norton, Ian Baker, Mathew Williams, Mingjie Shi, Yongguang Zhang, and Bo Qiu
Biogeosciences, 19, 541–558, https://doi.org/10.5194/bg-19-541-2022, https://doi.org/10.5194/bg-19-541-2022, 2022
Short summary
Short summary
Uncertainty in the response of photosynthesis to temperature poses a major challenge to predicting the response of forests to climate change. In this paper, we study how photosynthesis in a mountainous evergreen forest is limited by temperature. This study highlights that cold temperature is a key factor that controls spring photosynthesis. Including the cold-temperature limitation in an ecosystem model improved its ability to simulate spring photosynthesis.
Cited articles
Arulampalam, M., Maskell, S., Gordon, N., and Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE T. Signal. Process., 50, 174–188, https://doi.org/10.1109/78.978374, 2002.
Aslanyan, G., Easther, R., and Price, L. C.: Learn-as-you-go acceleration of cosmological parameter estimates, J. Cosmol. Astropart. P., 2015, 005, 2015.
Bradford, J. B., Weishampel, P., Smith, M.-L., Kolka, R., Birdsey, R. A., Ollinger, S. V., and Ryan, M. G.: Carbon pools and fluxes in small temperate forest landscapes: Variability and implications for sampling design, Forest Ecol. Manag., 259, 1245–1254, https://doi.org/10.1016/j.foreco.2009.04.009, 2010.
Braswell, B. H., Sacks, W. J., Linder, E., and Schimel, D. S.: Estimating diurnal to annual ecosystem parameters by synthesis of a carbon flux model with eddy covariance net ecosystem exchange observations, Global Change Biol., 11, 335–355, https://doi.org/10.1111/j.1365-2486.2005.00897.x, 2005.
Brynjarsdóttir, J. and O'Hagan, A.: Learning about physical parameters: the importance of model discrepancy, Inverse Problems, 30, 114007, 2014.
Clark, J. S.: Why environmental scientists are becoming Bayesians, Ecol. Lett., 8, 2–14, https://doi.org/10.1111/j.1461-0248.2004.00702.x, 2005.
Dancik, G. M.: mlegp: Maximum Likelihood Estimates of Gaussian Processes, R package version 3.1.4, available at: https://CRAN.R-project.org/package=mlegp (last access: 30 September 2018), 2013.
Datta, A., Banerjee, S., Finley, A. O., and Gelfand, A. E.: On nearest-neighbor Gaussian process models for massive spatial data, WIRES Comput. Statistics, 8, 162–171, https://doi.org/10.1002/wics.1383, 2016.
Dietze, M. C.: Ecological Forecasting, https://doi.org/10.1002/eap.1589, 2017a.
Dietze, M. C.: Prediction in ecology: a first-principles framework, Ecol. Appl., 27, 2048–2060, https://doi.org/10.1002/eap.1589, 2017b.
Dietze, M. C. and Moorcroft, P. R.: Tree mortality in the eastern and central United States: patterns and drivers, Global Change Biol., 17, 3312–3326, https://doi.org/10.1111/j.1365-2486.2011.02477.x, 2011.
Dietze, M. C., Shawn, 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.
Erickson, C. B., Ankenman, B. E., and Sanchez, S. M.: Comparison of Gaussian process modeling software, Eur. J. Oper. Res., 266, 179–192, https://doi.org/10.1016/j.ejor.2017.10.002, 2018.
Fisher, J., Huntzinger, D., Schwalm, C., and Sitch, S.: Modeling the terrestrial biosphere, Annu. Rev. Env. Resour., 39, 91–123, https://doi.org/10.1146/annurev-environ-012913-093456, 2014.
Fisher, R. A.: On the mathematical foundations of theoretical statistics, Phil. T. R. Soc. A, 222, https://doi.org/10.1098/rsta.1922.0009, 1922.
Forrester, A. I. and Keane, A. J.: Recent advances in surrogate-based optimization, Prog. Aerosp. Sci., 45, 50–79, https://doi.org/10.1016/j.paerosci.2008.11.001, 2009.
Fox, A., Williams, M., Richardson, A. D., Cameron, D., Gove, J. H., Quaife, T., Ricciuto, D., Reichstein, M., Tomelleri, E., Trudinger, C. M., and Wijk, M. T. V.: The REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data, Agr. Forest Meteorol., 149, 1597–1615, https://doi.org/10.1016/j.agrformet.2009.05.002, 2009.
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.
Gong, W. and Duan, Q.: An adaptive surrogate modeling-based sampling strategy for parameter optimization and distribution estimation (ASMO-PODE), Environ. Modell. Softw., 95, 61–75, https://doi.org/10.1016/j.envsoft.2017.05.005, 2017.
Gupta, H. V., Clark, M. P., Jasper, J. A. V., Abramowitz, G., and Ye, M.: Towards a comprehensive assessment of model structural adequacy, Water Resour. Res., 48, W08301, https://doi.org/10.1029/2011WR011044, 2012.
Gutmann, M. U. and Corander, J.: Bayesian Optimization for Likelihood-Free Inference of Simulator-Based Statistical Models, J. Mach. Learn. Res., 17, 1–47, 2016.
Haario, H., Saksman, E., and Tamminen, J.: An adaptive Metropolis algorithm, Bernoulli, 7, 223–242, 2001.
Hartig, F., Dyke, J., Hickler, T., Higgins, S. I., O'Hara, R. B., Scheiter, S., and Huth, A.: Connecting dynamic vegetation models to data – an inverse perspective, J. Biogeogr., 39, 2240–2252, https://doi.org/10.1111/j.1365-2699.2012.02745.x, 2012.
Hartig, F., Minuno, F., and Paul, S.: BayesianTools: General-Purpose MCMC and SMC Samplers and Tools for Bayesian Statistics, R package version 0.1.3, available at: https://CRAN.R-project.org/package=BayesianTools (last access: 30 September 2018, 2017.
Huang, M., Ray, J., Hou, Z., Ren, H., Liu, Y., and Swiler, L.: On the applicability of surrogate-based Markov chain Monte Carlo-Bayesian inversion to the Community Land Model: Case studies at flux tower sites, J. Geophys. Res.-Atmos., 121, 7548–7563, https://doi.org/10.1002/2015JD024339, 2016.
Jandarov, R., Haran, M., Bjørnstad, O., and Grenfell, B.: Emulating a gravity model to infer the spatiotemporal dynamics of an infectious disease, J. R. Stat. Soc. C-Appl., 63, 423–444, https://doi.org/10.1111/rssc.12042, 2014.
Jenkins, J. C., Chojnacky, D. C., Heath, L. S., and Birdsey, R. A.: Comprehensive database of diameter-based biomass regressions for North American tree species, United States Department of Agriculture, available at: https://www.fs.fed.us/ne/durham/4104/papers/ne_gtr319_jenkins_and_others.pdf (last access: 30 September 2018), 2004.
Joyce, P. and Marjoram, P.: Approximately sufficient statistics and Bayesian computation, Stat. Appl. Genet. Mo. B., 7, https://doi.org/10.2202/1544-6115.1389, 2008.
Kandasamy, K., Schneider, J., and Póczos, B.: Bayesian Active Learning for Posterior Estimation, AAAI Publications, Twenty-Fourth International Joint Conference on Artificial Intelligence, Buenos Aires, Argentina, 2015.
Keenan, T. F., Carbone, M. S., Reichstein, M., and Richardson, A. D.: The model-data fusion pitfall: assuming certainty in an uncertain world, Oecologia, 167, 587, https://doi.org/10.1007/s00442-011-2106-x, 2011.
Keenan, T. F., Davidson, E. A., Munger, J. W., and Richardson, A. D.: Rate my data: quantifying the value of ecological data for the development of models of the terrestrial carbon cycle, Ecol. Appl., 23, 273–286, https://doi.org/10.1890/12-0747.1, 2013.
Kennedy, M., Anderson, C., O'Hagan, A., Lomas, M., Woodward, I., Gosling, J. P., and Heinemeyer, A.: Quantifying uncertainty in the biospheric carbon flux for England and Wales, J. R. Stat. Soc. A Stat., 171, 109–135, https://doi.org/10.1111/j.1467-985X.2007.00489.x, 2008.
Kennedy, M. C. and O'Hagan, A.: Bayesian calibration of computer models, J. R. Stat. Soc. B Stat., 63, 425–464, https://doi.org/10.1111/1467-9868.00294, 2001.
Laloy, E. and Vrugt, J. A.: High-dimensional posterior exploration of hydrologic models using multiple-try DREAM(ZS) and high-performance computing, Water Resour. Res., 48, W01526, https://doi.org/10.1029/2011WR010608, 2012.
Lasslop, G., Reichstein, M., Kattge, J., and Papale, D.: Influences of observation errors in eddy flux data on inverse model parameter estimation, Biogeosciences, 5, 1311–1324, https://doi.org/10.5194/bg-5-1311-2008, 2008.
LeBauer, D., Kooper, R., Mulrooney, P., Rohde, S., Wang, D., Long, S. P., and Dietze, M. C.: BETYdb: a yield, trait, and ecosystem service database applied to second-generation bioenergy feedstock production, GCB Bioenergy, 10, 61–71, https://doi.org/10.1111/gcbb.12420, 2017.
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.
Lee, M. S., Hollinger, D. Y., Keenan, T. F., Ouimette, A. P., Ollinger, S. V., and Richardson, A. D.: Model-based analysis of the impact of diffuse radiation on CO2 exchange in a temperate deciduous forest, Agr. Forest Meteorol., 249, 377–389, https://doi.org/10.1016/j.agrformet.2017.11.016, 2018.
Li, J., Duan, Q., Wang, Y.-P., Gong, W., Gan, Y., and Wang, C.: Parameter optimization for carbon and water fluxes in two global land surface models based on surrogate modelling, Int. J. Climatol., 38, e1016–e1031, https://doi.org/10.1002/joc.5428, 2018.
Loeppky, J. L., Sacks, J., and Welch, W. J.: Choosing the Sample Size of a Computer Experiment: A Practical Guide, Technometrics, 51, 366–376, https://doi.org/10.1198/TECH.2009.08040, 2009.
Lu, D., Ricciuto, D., Walker, A., Safta, C., and Munger, W.: Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods, Biogeosciences, 14, 4295–4314, https://doi.org/10.5194/bg-14-4295-2017, 2017.
MacBean, N., Peylin, P., Chevallier, F., Scholze, M., and Schürmann, G.: Consistent assimilation of multiple data streams in a carbon cycle data assimilation system, Geosci. Model Dev., 9, 3569–3588, https://doi.org/10.5194/gmd-9-3569-2016, 2016.
McMahon, S. M., Dietze, M. C., Hersh, M. H., Moran, E. V., and Clark, J. S.: A Predictive Framework to Understand Forest Responses to Global Change, Ann. NY Acad. Sci., 1162, 221–236, https://doi.org/10.1111/j.1749-6632.2009.04495.x, 2009.
Medlyn, B. E., Zaehle, S., De Kauwe, M. G., Walker, A. P., Dietze, M. C., Hanson, P. J., Hickler, T., Jain, A. K., Luo, Y., Parton, W., Prentice, I. C., Thornton, P. E., Wang, S., Wang, Y.-P., Weng, E., Iversen, C. M., McCarthy, H. R., Warren, J. M., Oren, R., and Norby, R. J.: Using ecosystem experiments to improve vegetation models, Nat. Clim. Change, 5, 528–534, https://doi.org/10.1038/nclimate2621, 2015.
Medvigy, D., 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.
Moorcroft, P. R., Hurtt, G. C., and Pacala, S. W.: A method for scaling vegetation dynamics: The Ecosystem Demography model (ED), Ecol. Monogr., 71, 557–586, https://doi.org/10.1890/0012-9615(2001)071[0557:AMFSVD]2.0.CO;2, 2001.
Oakley, J. E. and Youngman, B. D.: Calibration of Stochastic Computer Simulators Using Likelihood Emulation, Technometrics, 59, 80–92, https://doi.org/10.1080/00401706.2015.1125391, 2017.
Phillips, C. L., Bond-Lamberty, B., Desai, A. R., Lavoie, M., Risk, D., Tang, J., Todd-Brown, K., and Vargas, R.: The value of soil respiration measurements for interpreting and modeling terrestrial carbon cycling, Plant Soil, 413, 1–25, https://doi.org/10.1007/s11104-016-3084-x, 2017.
Post, H., Vrugt, J. A., Fox, A., Vereecken, H., and Hendricks, F. H.: Estimation of Community Land Model parameters for an improved assessment of net carbon fluxes at European sites, J. Geophys. Res.-Biogeosci., 122, 661–689, https://doi.org/10.1002/2015JG003297, 2017.
Rasmussen, C. E. and Williams, C. K. I.: Gaussian processes for machine learning, available at: http://www.gaussianprocess.org/gpml/chapters/RW.pdf (last access: 30 September 2018), 2006.
Raupach, M. R., Rayner, P. J., Barrett, D. J., DeFries, R. S., Heimann, M., Ojima, D. S., Quegan, S., and Schmullius, C. C.: Model–data synthesis in terrestrial carbon observation: methods, data requirements and data uncertainty specifications, Global Change Biol., 11, 378–397, https://doi.org/10.1111/j.1365-2486.2005.00917.x, 2005.
Ray, J., Hou, Z., Huang, M., Sargsyan, K., and Swiler, L.: Bayesian calibration of the Community Land Model using surrogates, SIAM/ASA J. Uncertain. Quantif., 3, 199–233, https://doi.org/10.1137/140957998, 2015.
Ricciuto, D. M., Davis, K. J., and Keller, K.: A Bayesian calibration of a simple carbon cycle model: The role of observations in estimating and reducing uncertainty, Global Biogeochem. Cy., 22, https://doi.org/10.1029/2006GB002908, 2008.
Richardson, A. D., Hollinger, D. Y., Burba, G. G., Davis, K. J., Flanagan, L. B., Katul, G. G., Munger, J. W., Ricciuto, D. M., Stoy, P. C., Suyker, A. E., Verma, S. B., and Wofsy, S. C.: A multi-site analysis of random error in tower-based measurements of carbon and energy fluxes, Agr. Forest Meteorol., 136, 1–18, https://doi.org/10.1016/j.agrformet.2006.01.007, 2006.
Richardson, A. D., Williams, M., Hollinger, D. Y., Moore, D. J. P., Dail, D. B., Davidson, E. A., Scott, N. A., Evans, R. S., Hughes, H., Lee, J. T., Rodrigues, C., and Savage, K.: Estimating parameters of a forest ecosystem C model with measurements of stocks and fluxes as joint constraints, Oecologia, 164, 25–40, https://doi.org/10.1007/s00442-010-1628-y, 2010.
Sacks, J., Welch, W. J., Mitchell, T. J., and Wynn, H. P.: Design and Analysis of Computer Experiments, Stat. Sci., 4, 409–423, https://doi.org/10.1214/ss/1177012413, 1989.
Sacks, W. J., Schimel, D. S., Monson, R. K., and Braswell, B. H.: Model-data synthesis of diurnal and seasonal CO2 fluxes at Niwot Ridge, Colorado, Global Change Biol., 12, 240–259, https://doi.org/10.1111/j.1365-2486.2005.01059.x, 2006.
Thomas, R. Q., Brooks, E. B., Jersild, A. L., Ward, E. J., Wynne, R. H., Albaugh, T. J., Dinon-Aldridge, H., Burkhart, H. E., Domec, J.-C., Fox, T. R., Gonzalez-Benecke, C. A., Martin, T. A., Noormets, A., Sampson, D. A., and Teskey, R. O.: Leveraging 35 years of Pinus taeda research in the southeastern US to constrain forest carbon cycle predictions: regional data assimilation using ecosystem experiments, Biogeosciences, 14, 3525–3547, https://doi.org/10.5194/bg-14-3525-2017, 2017.
Trudinger, C. M., Raupach, M. R., Rayner, P. J., Kattge, J., Liu, Q., Pak, B., Reichstein, M., Renzullo, L., Richardson, A. D., Roxburgh, S. H., Styles, J., Wang, Y. Y., Briggs, P., Barrett, D., and Nikolova, S.: OptIC project: An intercomparison of optimization techniques for parameter estimation in terrestrial biogeochemical models, J. Geophys. Res.-Biogeosci., 112, G02027, https://doi.org/10.1029/2006JG000367, 2007.
van Oijen, M.: Bayesian Methods for Quantifying and Reducing Uncertainty and Error in Forest Models, Current Forestry Reports, 3, 269–280, https://doi.org/10.1007/s40725-017-0069-9, 2017.
van Oijen, M., Cameron, D. R., Butterbach-Bahl, K., Farahbakhshazad, N., Jansson, P.-E., Kiese, R., Rahn, K.-H., Werner, C., and Yeluripati, J. B.: A Bayesian framework for model calibration, comparison and analysis: Application to four models for the biogeochemistry of a Norway spruce forest, Agr. Forest Meteorol., 151, 1609–1621, https://doi.org/10.1016/j.agrformet.2011.06.017, 2011.
Walker, A. P., Ye, M., Lu, D., De Kauwe, M. G., Gu, L., Medlyn, B. E., Rogers, A., and Serbin, S. P.: The multi-assumption architecture and testbed (MAAT v1.0): R code for generating ensembles with dynamic model structure and analysis of epistemic uncertainty from multiple sources, Geosci. Model Dev., 11, 3159–3185, https://doi.org/10.5194/gmd-11-3159-2018, 2018.
Wang, C., Duan, Q., Gong, W., Ye, A., Di, Z., and Miao, C.: An evaluation of adaptive surrogate modeling based optimization with two benchmark problems, Environ. Modell. Softw., 60, 167–179, https://doi.org/10.1016/j.envsoft.2014.05.026, 2014.
Williams, M., Richardson, A. D., Reichstein, M., Stoy, P. C., Peylin, P., Verbeeck, H., Carvalhais, N., Jung, M., Hollinger, D. Y., Kattge, J., Leuning, R., Luo, Y., Tomelleri, E., Trudinger, C. M., and Wang, Y.-P.: Improving land surface models with FLUXNET data, Biogeosciences, 6, 1341–1359, https://doi.org/10.5194/bg-6-1341-2009, 2009.
Short summary
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.
The computer models we use to understand and forecast the ecosystem changes have multiple...
Altmetrics
Final-revised paper
Preprint