Articles | Volume 17, issue 23
https://doi.org/10.5194/bg-17-6185-2020
© Author(s) 2020. 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-17-6185-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Dec 2020
Research article |
| 09 Dec 2020
| 09 Dec 2020
Landsat near-infrared (NIR) band and ELM-FATES sensitivity to forest disturbances and regrowth in the Central Amazon
Robinson I. Negrón-Juárez
CORRESPONDING AUTHOR
Climate Sciences Department, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
Jennifer A. Holm
Climate Sciences Department, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
Boris Faybishenko
Climate Sciences Department, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
Daniel Magnabosco-Marra
Max Planck Institute for Biogeochemistry, Hans-Knoell Str. 10, 07745 Jena, Germany
National Institute of Amazonian Research (INPA), Av
André Araújo 2936, 690060-001, Manaus, Brazil
Rosie A. Fisher
National Center for Atmospheric Research (NCAR), 1850 Table Mesa Dr., Boulder, CO 80305, USA
Centre Européen de Recherche et de Formation Avencée en Calcul
Scientifique (CERFACS), Toulouse, France
Jacquelyn K. Shuman
National Center for Atmospheric Research (NCAR), 1850 Table Mesa Dr., Boulder, CO 80305, USA
Alessandro C. de Araujo
Embrapa Amazonia Oriental, Tv. Dr. Enéas Piheiro, s/n, Marco, CEP 66095-903, Caixa postal 48, Belem-Para, Brazil
William J. Riley
Climate Sciences Department, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
Jeffrey Q. Chambers
Climate Sciences Department, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
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Yitong Yao, Emilie Joetzjer, Philippe Ciais, Nicolas Viovy, Fabio Cresto Aleina, Jerome Chave, Lawren Sack, Megan Bartlett, Patrick Meir, Rosie Fisher, and Sebastiaan Luyssaert
Geosci. Model Dev., 15, 7809–7833, https://doi.org/10.5194/gmd-15-7809-2022, https://doi.org/10.5194/gmd-15-7809-2022, 2022
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Marco A. Franco, Florian Ditas, Leslie A. Kremper, Luiz A. T. Machado, Meinrat O. Andreae, Alessandro Araújo, Henrique M. J. Barbosa, Joel F. de Brito, Samara Carbone, Bruna A. Holanda, Fernando G. Morais, Janaína P. Nascimento, Mira L. Pöhlker, Luciana V. Rizzo, Marta Sá, Jorge Saturno, David Walter, Stefan Wolff, Ulrich Pöschl, Paulo Artaxo, and Christopher Pöhlker
Atmos. Chem. Phys., 22, 3469–3492, https://doi.org/10.5194/acp-22-3469-2022, https://doi.org/10.5194/acp-22-3469-2022, 2022
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Qing Zhu, Fa Li, William J. Riley, Li Xu, Lei Zhao, Kunxiaojia Yuan, Huayi Wu, Jianya Gong, and James Randerson
Geosci. Model Dev., 15, 1899–1911, https://doi.org/10.5194/gmd-15-1899-2022, https://doi.org/10.5194/gmd-15-1899-2022, 2022
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Wildfire is a devastating Earth system process that burns about 500 million hectares of land each year. It wipes out vegetation including trees, shrubs, and grasses and causes large losses of economic assets. However, modeling the spatial distribution and temporal changes of wildfire activities at a global scale is challenging. This study built a machine-learning-based wildfire surrogate model within an existing Earth system model and achieved high accuracy.
Jinyun Tang, William J. Riley, and Qing Zhu
Geosci. Model Dev., 15, 1619–1632, https://doi.org/10.5194/gmd-15-1619-2022, https://doi.org/10.5194/gmd-15-1619-2022, 2022
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We here describe version 2 of BeTR, a reactive transport model created to help ease the development of biogeochemical capability in Earth system models that are used for quantifying ecosystem–climate feedbacks. We then coupled BeTR-v2 to the Energy Exascale Earth System Model to quantify how different numerical couplings of plants and soils affect simulated ecosystem biogeochemistry. We found that different couplings lead to significant uncertainty that is not correctable by tuning parameters.
Raquel Fernandes Araujo, Samuel Grubinger, Carlos Henrique Souza Celes, Robinson I. Negrón-Juárez, Milton Garcia, Jonathan P. Dandois, and Helene C. Muller-Landau
Biogeosciences, 18, 6517–6531, https://doi.org/10.5194/bg-18-6517-2021, https://doi.org/10.5194/bg-18-6517-2021, 2021
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Our study contributed to improving the understanding of temporal variation and climate correlates of canopy disturbances mainly caused by treefalls and branchfalls. We used a unique dataset of 5 years of approximately monthly drone-acquired RGB (red–green–blue) imagery for 50 ha of mature tropical forest on Barro Colorado Island, Panama. We found that canopy disturbance rates were highly temporally variable, were higher in the wet season, and were related to extreme rainfall events.
Jing Tao, Qing Zhu, William J. Riley, and Rebecca B. Neumann
The Cryosphere, 15, 5281–5307, https://doi.org/10.5194/tc-15-5281-2021, https://doi.org/10.5194/tc-15-5281-2021, 2021
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We improved the DOE's E3SM land model (ELMv1-ECA) simulations of soil temperature, zero-curtain period durations, cold-season CH4, and CO2 emissions at several Alaskan Arctic tundra sites. We demonstrated that simulated CH4 emissions during zero-curtain periods accounted for more than 50 % of total emissions throughout the entire cold season (Sep to May). We also found that cold-season CO2 emissions largely offset warm-season net uptake currently and showed increasing trends from 1950 to 2017.
Maria Prass, Meinrat O. Andreae, Alessandro C. de Araùjo, Paulo Artaxo, Florian Ditas, Wolfgang Elbert, Jan-David Förster, Marco Aurélio Franco, Isabella Hrabe de Angelis, Jürgen Kesselmeier, Thomas Klimach, Leslie Ann Kremper, Eckhard Thines, David Walter, Jens Weber, Bettina Weber, Bernhard M. Fuchs, Ulrich Pöschl, and Christopher Pöhlker
Biogeosciences, 18, 4873–4887, https://doi.org/10.5194/bg-18-4873-2021, https://doi.org/10.5194/bg-18-4873-2021, 2021
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Bioaerosols in the atmosphere over the Amazon rain forest were analyzed by molecular biological staining and microscopy. Eukaryotic, bacterial, and archaeal aerosols were quantified in time series and altitude profiles which exhibited clear differences in number concentrations and vertical distributions. Our results provide insights into the sources and dispersion of different Amazonian bioaerosol types as a basis for a better understanding of biosphere–atmosphere interactions.
Benjamin M. Sanderson, Angeline G. Pendergrass, Charles D. Koven, Florent Brient, Ben B. B. Booth, Rosie A. Fisher, and Reto Knutti
Earth Syst. Dynam., 12, 899–918, https://doi.org/10.5194/esd-12-899-2021, https://doi.org/10.5194/esd-12-899-2021, 2021
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Emergent constraints promise a pathway to the reduction in climate projection uncertainties by exploiting ensemble relationships between observable quantities and unknown climate response parameters. This study considers the robustness of these relationships in light of biases and common simplifications that may be present in the original ensemble of climate simulations. We propose a classification scheme for constraints and a number of practical case studies.
Polly C. Buotte, Charles D. Koven, Chonggang Xu, Jacquelyn K. Shuman, Michael L. Goulden, Samuel Levis, Jessica Katz, Junyan Ding, Wu Ma, Zachary Robbins, and Lara M. Kueppers
Biogeosciences, 18, 4473–4490, https://doi.org/10.5194/bg-18-4473-2021, https://doi.org/10.5194/bg-18-4473-2021, 2021
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We present an approach for ensuring the definitions of plant types in dynamic vegetation models are connected to the underlying ecological processes controlling community composition. Our approach can be applied regionally or globally. Robust resolution of community composition will allow us to use these models to address important questions related to future climate and management effects on plant community composition, structure, carbon storage, and feedbacks within the Earth system.
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
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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.
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.
Jessica C. A. Baker, Luis Garcia-Carreras, Manuel Gloor, John H. Marsham, Wolfgang Buermann, Humberto R. da Rocha, Antonio D. Nobre, Alessandro Carioca de Araujo, and Dominick V. Spracklen
Hydrol. Earth Syst. Sci., 25, 2279–2300, https://doi.org/10.5194/hess-25-2279-2021, https://doi.org/10.5194/hess-25-2279-2021, 2021
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Evapotranspiration (ET) is a vital part of the Amazon water cycle, but it is difficult to measure over large areas. In this study, we compare spatial patterns, seasonality, and recent trends in Amazon ET from a water-budget analysis with estimates from satellites, reanalysis, and global climate models. We find large differences between products, showing that many widely used datasets and climate models may not provide a reliable representation of this crucial variable over the Amazon.
Eva Y. Pfannerstill, Nina G. Reijrink, Achim Edtbauer, Akima Ringsdorf, Nora Zannoni, Alessandro Araújo, Florian Ditas, Bruna A. Holanda, Marta O. Sá, Anywhere Tsokankunku, David Walter, Stefan Wolff, Jošt V. Lavrič, Christopher Pöhlker, Matthias Sörgel, and Jonathan Williams
Atmos. Chem. Phys., 21, 6231–6256, https://doi.org/10.5194/acp-21-6231-2021, https://doi.org/10.5194/acp-21-6231-2021, 2021
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Tropical forests are globally significant for atmospheric chemistry. However, the mixture of reactive organic gases emitted by these ecosystems is poorly understood. By comprehensive observations at an Amazon forest site, we show that oxygenated species were previously underestimated in their contribution to the tropical-forest reactant mix. Our results show rain and temperature effects and have implications for models and the understanding of ozone and particle formation above tropical forests.
Hella van Asperen, João Rafael Alves-Oliveira, Thorsten Warneke, Bruce Forsberg, Alessandro Carioca de Araújo, and Justus Notholt
Biogeosciences, 18, 2609–2625, https://doi.org/10.5194/bg-18-2609-2021, https://doi.org/10.5194/bg-18-2609-2021, 2021
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Termites are insects that are highly abundant in tropical ecosystems. It is known that termites emit CH4, an important greenhouse gas, but their absolute emission remains uncertain. In the Amazon rainforest, we measured CH4 emissions from termite nests and groups of termites. In addition, we tested a fast and non-destructive field method to estimate termite nest colony size. We found that termites play a significant role in an ecosystem's CH4 budget and probably emit more than currently assumed.
Jaan Pärn, Kaido Soosaar, Thomas Schindler, Katerina Machacova, Waldemar Alegría Muñoz, Lizardo Fachín, José Luis Jibaja Aspajo, Robinson I. Negron-Juarez, Martin Maddison, Jhon Rengifo, Danika Journeth Garay Dinis, Adriana Gabriela Arista Oversluijs, Manuel Calixto Ávila Fucos, Rafael Chávez Vásquez, Ronal Huaje Wampuch, Edgar Peas García, Kristina Sohar, Segundo Cordova Horna, Tedi Pacheco Gómez, Jose David Urquiza Muñoz, Rodil Tello Espinoza, and Ülo Mander
Biogeosciences Discuss., https://doi.org/10.5194/bg-2021-46, https://doi.org/10.5194/bg-2021-46, 2021
Manuscript not accepted for further review
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Despite alarming forecasts for the Amazonian peat swamp forests, greenhouse gas emissions from the different peat environments have rarely been compared. We measured CO2, CH4 and N2O emissions from the soil in 3 sites around Iquitos, Peru: a pristine swamp forest, a young forest and a slash-and-burn manioc field. We saw a devastating effect on global climate from a slight water-table drawdown in the peat swamp forests, while the manioc field emitted moderate amounts of the greenhouse gases.
Guilherme F. Camarinha-Neto, Julia C. P. Cohen, Cléo Q. Dias-Júnior, Matthias Sörgel, José Henrique Cattanio, Alessandro Araújo, Stefan Wolff, Paulo A. F. Kuhn, Rodrigo A. F. Souza, Luciana V. Rizzo, and Paulo Artaxo
Atmos. Chem. Phys., 21, 339–356, https://doi.org/10.5194/acp-21-339-2021, https://doi.org/10.5194/acp-21-339-2021, 2021
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It was observed that friagem phenomena (incursion of cold waves from the high latitudes of the Southern Hemisphere to the Amazon region), very common in the dry season of the Amazon region, produced significant changes in microclimate and atmospheric chemistry. Moreover, the effects of the friagem change the surface O3 and CO2 mixing ratios and therefore interfere deeply in the microclimatic conditions and the chemical composition of the atmosphere above the rainforest.
Katherine Dagon, Benjamin M. Sanderson, Rosie A. Fisher, and David M. Lawrence
Adv. Stat. Clim. Meteorol. Oceanogr., 6, 223–244, https://doi.org/10.5194/ascmo-6-223-2020, https://doi.org/10.5194/ascmo-6-223-2020, 2020
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Uncertainties in land model projections are important to understand in order to build confidence in Earth system modeling. In this paper, we introduce a framework for estimating uncertain land model parameters with machine learning. This method increases the computational efficiency of this process relative to traditional hand tuning approaches and provides objective methods to assess the results. We further identify key processes and parameters that are important for accurate land modeling.
Robbie Ramsay, Chiara F. Di Marco, Matthias Sörgel, Mathew R. Heal, Samara Carbone, Paulo Artaxo, Alessandro C. de Araùjo, Marta Sá, Christopher Pöhlker, Jost Lavric, Meinrat O. Andreae, and Eiko Nemitz
Atmos. Chem. Phys., 20, 15551–15584, https://doi.org/10.5194/acp-20-15551-2020, https://doi.org/10.5194/acp-20-15551-2020, 2020
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The Amazon rainforest is a unique
laboratoryto study the processes which govern the exchange of gases and aerosols to and from the atmosphere. This study investigated these processes by measuring the atmospheric concentrations of trace gases and particles at the Amazon Tall Tower Observatory. We found that the long-range transport of pollutants can affect the atmospheric composition above the Amazon rainforest and that the gases ammonia and nitrous acid can be emitted from the rainforest.
Kuang-Yu Chang, William J. Riley, Patrick M. Crill, Robert F. Grant, and Scott R. Saleska
Biogeosciences, 17, 5849–5860, https://doi.org/10.5194/bg-17-5849-2020, https://doi.org/10.5194/bg-17-5849-2020, 2020
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Methane (CH4) is a strong greenhouse gas that can accelerate climate change and offset mitigation efforts. A key assumption embedded in many large-scale climate models is that ecosystem CH4 emissions can be estimated by fixed temperature relations. Here, we demonstrate that CH4 emissions cannot be parameterized by emergent temperature response alone due to variability driven by microbial and abiotic interactions. We also provide mechanistic understanding for observed CH4 emission hysteresis.
Nina Löbs, David Walter, Cybelli G. G. Barbosa, Sebastian Brill, Rodrigo P. Alves, Gabriela R. Cerqueira, Marta de Oliveira Sá, Alessandro C. de Araújo, Leonardo R. de Oliveira, Florian Ditas, Daniel Moran-Zuloaga, Ana Paula Pires Florentino, Stefan Wolff, Ricardo H. M. Godoi, Jürgen Kesselmeier, Sylvia Mota de Oliveira, Meinrat O. Andreae, Christopher Pöhlker, and Bettina Weber
Biogeosciences, 17, 5399–5416, https://doi.org/10.5194/bg-17-5399-2020, https://doi.org/10.5194/bg-17-5399-2020, 2020
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Cryptogamic organisms, such as bryophytes, lichens, and algae, cover major parts of vegetation in the Amazonian rain forest, but their relevance in biosphere–atmosphere exchange, climate processes, and nutrient cycling is largely unknown.
Over the duration of 2 years we measured their water content, temperature, and light conditions to get better insights into their physiological activity patterns and thus their potential impact on local, regional, and even global biogeochemical processes.
Haifan Liu, Heng Dai, Jie Niu, Bill X. Hu, Dongwei Gui, Han Qiu, Ming Ye, Xingyuan Chen, Chuanhao Wu, Jin Zhang, and William Riley
Hydrol. Earth Syst. Sci., 24, 4971–4996, https://doi.org/10.5194/hess-24-4971-2020, https://doi.org/10.5194/hess-24-4971-2020, 2020
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It is still challenging to apply the quantitative and comprehensive global sensitivity analysis method to complex large-scale process-based hydrological models because of variant uncertainty sources and high computational cost. This work developed a new tool and demonstrate its implementation to a pilot example for comprehensive global sensitivity analysis of large-scale hydrological modelling. This method is mathematically rigorous and can be applied to other large-scale hydrological models.
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Short summary
The temporal variability in the Landsat satellite near-infrared (NIR) band captured the dynamics of forest regrowth after disturbances in Central Amazon. This variability was represented by the dynamics of forest regrowth after disturbances were properly represented by the ELM-FATES model (Functionally Assembled Terrestrial Ecosystem Simulator (FATES) in the Energy Exascale Earth System Model (E3SM) Land Model (ELM)).
The temporal variability in the Landsat satellite near-infrared (NIR) band captured the dynamics...
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