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The joint exposure of plants to surface ozone, atmospheric aerosols, and heat stress can lead to considerable decreases in crop yields. Surface ozone negatively impacts plant photosynthesis while aerosols can have positive or negative effects from its dual impact on light and temperature. Here, using a statistical model, we show that in the United States, as a result of improvements in air quality, the damages caused by ozone and aerosols have decreased since 1980. Historically, relative yield losses due to ozone were 8.7% and 4.8%, and due to aerosols were 11.3% and 23.2% for maize and soybean, respectively. Maize yields are more sensitive to ozone pollution while soybean yields are more sensitive to aerosol pollution. In future RCP 8.5 scenario, absent significant reductions in emissions or improvements in air quality, maize and soybean would have on average, 58.5% and 36.9% additional yield reductions, respectively, mainly caused by warming. Future climate warming and fossil fuel combustion driven changes to air pollution may have differing impacts on crop yield and should be considered in any assessment of U.S. food security.
Xiang Liu; Ankur R. Desai. Significant Reductions in Crop Yields From Air Pollution and Heat Stress in the United States. Earth's Future 2021, 9, 1 .
AMA StyleXiang Liu, Ankur R. Desai. Significant Reductions in Crop Yields From Air Pollution and Heat Stress in the United States. Earth's Future. 2021; 9 (8):1.
Chicago/Turabian StyleXiang Liu; Ankur R. Desai. 2021. "Significant Reductions in Crop Yields From Air Pollution and Heat Stress in the United States." Earth's Future 9, no. 8: 1.
Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20∘ S to 20∘ N) the spring onset of elevated CH4 emissions starts 3 d earlier, and the CH4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.
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; Robert B. Jackson. FLUXNET-CH4: a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands. Earth System Science Data 2021, 13, 3607 -3689.
AMA StyleKyle 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, Robert B. Jackson. FLUXNET-CH4: a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands. Earth System Science Data. 2021; 13 (7):3607-3689.
Chicago/Turabian StyleKyle 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; Robert B. Jackson. 2021. "FLUXNET-CH4: a global, multi-ecosystem dataset and analysis of methane seasonality from freshwater wetlands." Earth System Science Data 13, no. 7: 3607-3689.
Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).
Jeremy Irvin; Sharon Zhou; Gavin McNicol; Fred Lu; Vincent Liu; Etienne Fluet-Chouinard; Zutao Ouyang; Sara Helen Knox; Antje Lucas-Moffat; Carlo Trotta; Dario Papale; Domenico Vitale; Ivan Mammarella; Pavel Alekseychik; Mika Aurela; Anand Avati; Dennis Baldocchi; Sheel Bansal; Gil Bohrer; David I Campbell; Jiquan Chen; Housen Chu; Higo J Dalmagro; Kyle B Delwiche; Ankur R Desai; Eugenie Euskirchen; Sarah Feron; Mathias Goeckede; Martin Heimann; Manuel Helbig; Carole Helfter; Kyle S Hemes; Takashi Hirano; Hiroki Iwata; Gerald Jurasinski; Aram Kalhori; Andrew Kondrich; Derrick Yf Lai; Annalea Lohila; Avni Malhotra; Lutz Merbold; Bhaskar Mitra; Andrew Ng; Mats B Nilsson; Asko Noormets; Matthias Peichl; A. Camilo Rey-Sanchez; Andrew D Richardson; Benjamin Rk Runkle; Karina Vr Schäfer; Oliver Sonnentag; Ellen Stuart-Haëntjens; Cove Sturtevant; Masahito Ueyama; Alex C Valach; Rodrigo Vargas; George L Vourlitis; Eric J Ward; Guan Xhuan Wong; Donatella Zona; Ma. Carmelita R Alberto; David P Billesbach; Gerardo Celis; Han Dolman; Thomas Friborg; Kathrin Fuchs; Sébastien Gogo; Mangaliso J Gondwe; Jordan P Goodrich; Pia Gottschalk; Lukas Hörtnagl; Adrien Jacotot; Franziska Koebsch; Kuno Kasak; Regine Maier; Timothy H Morin; Eiko Nemitz; Walter C Oechel; Patricia Y Oikawa; Keisuke Ono; Torsten Sachs; Ayaka Sakabe; Edward A Schuur; Robert Shortt; Ryan C Sullivan; Daphne J Szutu; Eeva-Stiina Tuittila; Andrej Varlagin; Joeseph G Verfaillie; Christian Wille; Lisamarie Windham-Myers; Benjamin Poulter; Robert B Jackson. Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands. Agricultural and Forest Meteorology 2021, 308-309, 108528 .
AMA StyleJeremy Irvin, Sharon Zhou, Gavin McNicol, Fred Lu, Vincent Liu, Etienne Fluet-Chouinard, Zutao Ouyang, Sara Helen Knox, Antje Lucas-Moffat, Carlo Trotta, Dario Papale, Domenico Vitale, Ivan Mammarella, Pavel Alekseychik, Mika Aurela, Anand Avati, Dennis Baldocchi, Sheel Bansal, Gil Bohrer, David I Campbell, Jiquan Chen, Housen Chu, Higo J Dalmagro, Kyle B Delwiche, Ankur R Desai, Eugenie Euskirchen, Sarah Feron, Mathias Goeckede, Martin Heimann, Manuel Helbig, Carole Helfter, Kyle S Hemes, Takashi Hirano, Hiroki Iwata, Gerald Jurasinski, Aram Kalhori, Andrew Kondrich, Derrick Yf Lai, Annalea Lohila, Avni Malhotra, Lutz Merbold, Bhaskar Mitra, Andrew Ng, Mats B Nilsson, Asko Noormets, Matthias Peichl, A. Camilo Rey-Sanchez, Andrew D Richardson, Benjamin Rk Runkle, Karina Vr Schäfer, Oliver Sonnentag, Ellen Stuart-Haëntjens, Cove Sturtevant, Masahito Ueyama, Alex C Valach, Rodrigo Vargas, George L Vourlitis, Eric J Ward, Guan Xhuan Wong, Donatella Zona, Ma. Carmelita R Alberto, David P Billesbach, Gerardo Celis, Han Dolman, Thomas Friborg, Kathrin Fuchs, Sébastien Gogo, Mangaliso J Gondwe, Jordan P Goodrich, Pia Gottschalk, Lukas Hörtnagl, Adrien Jacotot, Franziska Koebsch, Kuno Kasak, Regine Maier, Timothy H Morin, Eiko Nemitz, Walter C Oechel, Patricia Y Oikawa, Keisuke Ono, Torsten Sachs, Ayaka Sakabe, Edward A Schuur, Robert Shortt, Ryan C Sullivan, Daphne J Szutu, Eeva-Stiina Tuittila, Andrej Varlagin, Joeseph G Verfaillie, Christian Wille, Lisamarie Windham-Myers, Benjamin Poulter, Robert B Jackson. Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands. Agricultural and Forest Meteorology. 2021; 308-309 ():108528.
Chicago/Turabian StyleJeremy Irvin; Sharon Zhou; Gavin McNicol; Fred Lu; Vincent Liu; Etienne Fluet-Chouinard; Zutao Ouyang; Sara Helen Knox; Antje Lucas-Moffat; Carlo Trotta; Dario Papale; Domenico Vitale; Ivan Mammarella; Pavel Alekseychik; Mika Aurela; Anand Avati; Dennis Baldocchi; Sheel Bansal; Gil Bohrer; David I Campbell; Jiquan Chen; Housen Chu; Higo J Dalmagro; Kyle B Delwiche; Ankur R Desai; Eugenie Euskirchen; Sarah Feron; Mathias Goeckede; Martin Heimann; Manuel Helbig; Carole Helfter; Kyle S Hemes; Takashi Hirano; Hiroki Iwata; Gerald Jurasinski; Aram Kalhori; Andrew Kondrich; Derrick Yf Lai; Annalea Lohila; Avni Malhotra; Lutz Merbold; Bhaskar Mitra; Andrew Ng; Mats B Nilsson; Asko Noormets; Matthias Peichl; A. Camilo Rey-Sanchez; Andrew D Richardson; Benjamin Rk Runkle; Karina Vr Schäfer; Oliver Sonnentag; Ellen Stuart-Haëntjens; Cove Sturtevant; Masahito Ueyama; Alex C Valach; Rodrigo Vargas; George L Vourlitis; Eric J Ward; Guan Xhuan Wong; Donatella Zona; Ma. Carmelita R Alberto; David P Billesbach; Gerardo Celis; Han Dolman; Thomas Friborg; Kathrin Fuchs; Sébastien Gogo; Mangaliso J Gondwe; Jordan P Goodrich; Pia Gottschalk; Lukas Hörtnagl; Adrien Jacotot; Franziska Koebsch; Kuno Kasak; Regine Maier; Timothy H Morin; Eiko Nemitz; Walter C Oechel; Patricia Y Oikawa; Keisuke Ono; Torsten Sachs; Ayaka Sakabe; Edward A Schuur; Robert Shortt; Ryan C Sullivan; Daphne J Szutu; Eeva-Stiina Tuittila; Andrej Varlagin; Joeseph G Verfaillie; Christian Wille; Lisamarie Windham-Myers; Benjamin Poulter; Robert B Jackson. 2021. "Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands." Agricultural and Forest Meteorology 308-309, no. : 108528.
Chandra S. Deshmukh; Dony Julius; Ankur R. Desai; Adibtya Asyhari; Susan E. Page; Nardi Nardi; Ari P. Susanto; Nurholis Nurholis; M. Hendrizal; Sofyan Kurnianto; Yogi Suardiwerianto; Yuandanis W. Salam; Fahmuddin Agus; Dwi Astiani; Supiandi Sabiham; Vincent Gauci; Chris D. Evans. Conservation slows down emission increase from a tropical peatland in Indonesia. Nature Geoscience 2021, 14, 484 -490.
AMA StyleChandra S. Deshmukh, Dony Julius, Ankur R. Desai, Adibtya Asyhari, Susan E. Page, Nardi Nardi, Ari P. Susanto, Nurholis Nurholis, M. Hendrizal, Sofyan Kurnianto, Yogi Suardiwerianto, Yuandanis W. Salam, Fahmuddin Agus, Dwi Astiani, Supiandi Sabiham, Vincent Gauci, Chris D. Evans. Conservation slows down emission increase from a tropical peatland in Indonesia. Nature Geoscience. 2021; 14 (7):484-490.
Chicago/Turabian StyleChandra S. Deshmukh; Dony Julius; Ankur R. Desai; Adibtya Asyhari; Susan E. Page; Nardi Nardi; Ari P. Susanto; Nurholis Nurholis; M. Hendrizal; Sofyan Kurnianto; Yogi Suardiwerianto; Yuandanis W. Salam; Fahmuddin Agus; Dwi Astiani; Supiandi Sabiham; Vincent Gauci; Chris D. Evans. 2021. "Conservation slows down emission increase from a tropical peatland in Indonesia." Nature Geoscience 14, no. 7: 484-490.
The atmospheric boundary layer mediates the exchange of energy, matter, and momentum between the land surface and the free troposphere, integrating a range of physical, chemical, and biological processes and is defined as the lowest layer of the atmosphere (ranging from a few meters to 3 km). In this review, we investigate how continuous, automated observations of the atmospheric boundary layer can enhance the scientific value of co-located eddy covariance measurements of land-atmosphere fluxes of carbon, water, and energy, as are being made at FLUXNET sites worldwide. We highlight four key opportunities to integrate tower-based flux measurements with continuous, long-term atmospheric boundary layer measurements: (1) to interpret surface flux and atmospheric boundary layer exchange dynamics and feedbacks at flux tower sites, (2) to support flux footprint modelling, the interpretation of surface fluxes in heterogeneous and mountainous terrain, and quality control of eddy covariance flux measurements, (3) to support regional-scale modeling and upscaling of surface fluxes to continental scales, and (4) to quantify land-atmosphere coupling and validate its representation in Earth system models. Adding a suite of atmospheric boundary layer measurements to eddy covariance flux tower sites, and supporting the sharing of these data to tower networks, would allow the Earth science community to address new emerging research questions, better interpret ongoing flux tower measurements, and would present novel opportunities for collaborations between FLUXNET scientists and atmospheric and remote sensing scientists.
Manuel Helbig; Tobias Gerken; Eric R. Beamesderfer; Dennis D. Baldocchi; Tirtha Banerjee; Sébastien C. Biraud; William O.J. Brown; Nathaniel A. Brunsell; Elizabeth A Burakowski; Sean P. Burns; Brian J. Butterworth; W. Stephen Chan; Kenneth J. Davis; Ankur R. Desai; Jose D. Fuentes; David Y. Hollinger; Natascha Kljun; Matthias Mauder; Kimberly A. Novick; John M. Perkins; David A. Rahn; Camilo Rey-Sanchez; Joseph A. Santanello; Russell L. Scott; Bijan Seyednasrollah; Paul C. Stoy; Ryan C. Sullivan; Jordi Vilà-Guerau de Arellano; Sonia Wharton; Chuixiang Yi; Andrew D. Richardson. Integrating continuous atmospheric boundary layer and tower-based flux measurements to advance understanding of land-atmosphere interactions. Agricultural and Forest Meteorology 2021, 307, 108509 .
AMA StyleManuel Helbig, Tobias Gerken, Eric R. Beamesderfer, Dennis D. Baldocchi, Tirtha Banerjee, Sébastien C. Biraud, William O.J. Brown, Nathaniel A. Brunsell, Elizabeth A Burakowski, Sean P. Burns, Brian J. Butterworth, W. Stephen Chan, Kenneth J. Davis, Ankur R. Desai, Jose D. Fuentes, David Y. Hollinger, Natascha Kljun, Matthias Mauder, Kimberly A. Novick, John M. Perkins, David A. Rahn, Camilo Rey-Sanchez, Joseph A. Santanello, Russell L. Scott, Bijan Seyednasrollah, Paul C. Stoy, Ryan C. Sullivan, Jordi Vilà-Guerau de Arellano, Sonia Wharton, Chuixiang Yi, Andrew D. Richardson. Integrating continuous atmospheric boundary layer and tower-based flux measurements to advance understanding of land-atmosphere interactions. Agricultural and Forest Meteorology. 2021; 307 ():108509.
Chicago/Turabian StyleManuel Helbig; Tobias Gerken; Eric R. Beamesderfer; Dennis D. Baldocchi; Tirtha Banerjee; Sébastien C. Biraud; William O.J. Brown; Nathaniel A. Brunsell; Elizabeth A Burakowski; Sean P. Burns; Brian J. Butterworth; W. Stephen Chan; Kenneth J. Davis; Ankur R. Desai; Jose D. Fuentes; David Y. Hollinger; Natascha Kljun; Matthias Mauder; Kimberly A. Novick; John M. Perkins; David A. Rahn; Camilo Rey-Sanchez; Joseph A. Santanello; Russell L. Scott; Bijan Seyednasrollah; Paul C. Stoy; Ryan C. Sullivan; Jordi Vilà-Guerau de Arellano; Sonia Wharton; Chuixiang Yi; Andrew D. Richardson. 2021. "Integrating continuous atmospheric boundary layer and tower-based flux measurements to advance understanding of land-atmosphere interactions." Agricultural and Forest Meteorology 307, no. : 108509.
Accounting for temporal changes in carbon dioxide (CO2) emissions from freshwaters remains a challenge for global and regional carbon budgets. Here, we synthesize 171 site-months of eddy covariance flux measurements of CO2 from 13 lakes and reservoirs in the Northern Hemisphere (NH) and quantify the magnitude and dynamics at multiple temporal scales. We found pronounced diel and sub-monthly oscillatory variations in CO2 flux at all sites. Diel variation converted sites to daily net sinks of CO2 in only 11% of site-months. Upscaled annual emissions had an average of 25% (range 3-58%) interannual variation. Given temporal variation remains under-represented in inventories of CO2 emissions from lakes and reservoirs, revisions in CO2 flux are needed using a better representation of sub-daily to interannual variability. Constraining short- and long-term variability is necessary to improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers.
Malgorzata GolubiD; Ankur Rashmikant DesaiiD; Timo Vesala; Ivan MammarellaiD; Anne Ojala; Gil BohreriD; Gesa A WeyhenmeyeriD; Peter D. BlankeniD; Werner Eugster; Franziska Koebsch; Jiquan CheniD; Kevin P. Czajkowski; Chandrashekhar Deshmukh; Frédéric Guérin; Jouni Juhana Heiskanen; Elyn R Humphreys; Anders JonssoniD; Jan KarlssoniD; George W. KlingiD; Xuhui LeeiD; Heping Liu; Annalea Lohila; Erik Johannes LundiniD; Timothy Hector Morin; Eva Podgrajsek; Maria Provenzale; Anna RutgerssoniD; Torsten SachsiD; Erik Sahlée; Dominique Serçaid; Changliang ShaoiD; Christopher Spence; Ian B. StrachaniD; Wei XiaoiD. New insights into diel to interannual variation in carbon dioxide emissions from lakes and reservoirs. 2021, 1 .
AMA StyleMalgorzata GolubiD, Ankur Rashmikant DesaiiD, Timo Vesala, Ivan MammarellaiD, Anne Ojala, Gil BohreriD, Gesa A WeyhenmeyeriD, Peter D. BlankeniD, Werner Eugster, Franziska Koebsch, Jiquan CheniD, Kevin P. Czajkowski, Chandrashekhar Deshmukh, Frédéric Guérin, Jouni Juhana Heiskanen, Elyn R Humphreys, Anders JonssoniD, Jan KarlssoniD, George W. KlingiD, Xuhui LeeiD, Heping Liu, Annalea Lohila, Erik Johannes LundiniD, Timothy Hector Morin, Eva Podgrajsek, Maria Provenzale, Anna RutgerssoniD, Torsten SachsiD, Erik Sahlée, Dominique Serçaid, Changliang ShaoiD, Christopher Spence, Ian B. StrachaniD, Wei XiaoiD. New insights into diel to interannual variation in carbon dioxide emissions from lakes and reservoirs. . 2021; ():1.
Chicago/Turabian StyleMalgorzata GolubiD; Ankur Rashmikant DesaiiD; Timo Vesala; Ivan MammarellaiD; Anne Ojala; Gil BohreriD; Gesa A WeyhenmeyeriD; Peter D. BlankeniD; Werner Eugster; Franziska Koebsch; Jiquan CheniD; Kevin P. Czajkowski; Chandrashekhar Deshmukh; Frédéric Guérin; Jouni Juhana Heiskanen; Elyn R Humphreys; Anders JonssoniD; Jan KarlssoniD; George W. KlingiD; Xuhui LeeiD; Heping Liu; Annalea Lohila; Erik Johannes LundiniD; Timothy Hector Morin; Eva Podgrajsek; Maria Provenzale; Anna RutgerssoniD; Torsten SachsiD; Erik Sahlée; Dominique Serçaid; Changliang ShaoiD; Christopher Spence; Ian B. StrachaniD; Wei XiaoiD. 2021. "New insights into diel to interannual variation in carbon dioxide emissions from lakes and reservoirs." , no. : 1.
Sexual harassment in field settings brings unique challenges for prevention and response, as field research occurs outside “typical” workplaces, often in remote locations that create additional safety concerns and new team dynamics. We report on a project that has 1) trained field project participants to recognize, report, and confront sexual harassment, and 2) investigated the perceptions, attitudes, and experiences of field researchers regarding sexual harassment. Pre-campaign surveys from four major, multi-institutional, domestic and international field projects indicate that the majority of sexual harassment reported prior to the field campaigns was hostile work environment harassment, and women were more likely to be the recipients, on average reporting 2-3 incidents each. The majority of those disclosing harassment indicated that they coped with past experiences by avoiding their harasser or downplaying incidents. Of the incidences reported (47) in post-campaign surveys of the four field teams, all fell under the category of hostile work environment and included incidents of verbal, visual, and physical harassment. Women’s harassment experiences were perpetrated by men 100% of the time, and the majority of the perpetrators were in more senior positions than the victims. Men’s harassment experiences were perpetrated by a mix of women and men, and the majority came from those at the same position of seniority. Post-project surveys indicate that the training programs (taking place before the field projects) helped participants come away with more positive than negative emotions and perceptions of the training, the leadership, and their overall experiences on the field campaign.
Emily V. Fischer; Brittany Bloodhart; Kristen Rasmussen; Ilana B. Pollack; Meredith G. Hastings; Erika Marin-Spiotta; Ankur R. Desai; Joshua P. Schwarz; Stephen Nesbitt; Deanna Hence. Leveraging Field-Campaign Networks to Identify Sexual Harassment in Atmospheric Science and Pilot Promising Interventions. Bulletin of the American Meteorological Society 2021, -1, 1 -32.
AMA StyleEmily V. Fischer, Brittany Bloodhart, Kristen Rasmussen, Ilana B. Pollack, Meredith G. Hastings, Erika Marin-Spiotta, Ankur R. Desai, Joshua P. Schwarz, Stephen Nesbitt, Deanna Hence. Leveraging Field-Campaign Networks to Identify Sexual Harassment in Atmospheric Science and Pilot Promising Interventions. Bulletin of the American Meteorological Society. 2021; -1 (aop):1-32.
Chicago/Turabian StyleEmily V. Fischer; Brittany Bloodhart; Kristen Rasmussen; Ilana B. Pollack; Meredith G. Hastings; Erika Marin-Spiotta; Ankur R. Desai; Joshua P. Schwarz; Stephen Nesbitt; Deanna Hence. 2021. "Leveraging Field-Campaign Networks to Identify Sexual Harassment in Atmospheric Science and Pilot Promising Interventions." Bulletin of the American Meteorological Society -1, no. aop: 1-32.
Plant transpiration links physiological responses of vegetation to water supply and demand with hydrological, energy, and carbon budgets at the land–atmosphere interface. However, despite being the main land evaporative flux at the global scale, transpiration and its response to environmental drivers are currently not well constrained by observations. Here we introduce the first global compilation of whole-plant transpiration data from sap flow measurements (SAPFLUXNET, https://sapfluxnet.creaf.cat/, last access: 8 June 2021). We harmonized and quality-controlled individual datasets supplied by contributors worldwide in a semi-automatic data workflow implemented in the R programming language. Datasets include sub-daily time series of sap flow and hydrometeorological drivers for one or more growing seasons, as well as metadata on the stand characteristics, plant attributes, and technical details of the measurements. SAPFLUXNET contains 202 globally distributed datasets with sap flow time series for 2714 plants, mostly trees, of 174 species. SAPFLUXNET has a broad bioclimatic coverage, with woodland/shrubland and temperate forest biomes especially well represented (80 % of the datasets). The measurements cover a wide variety of stand structural characteristics and plant sizes. The datasets encompass the period between 1995 and 2018, with 50 % of the datasets being at least 3 years long. Accompanying radiation and vapour pressure deficit data are available for most of the datasets, while on-site soil water content is available for 56 % of the datasets. Many datasets contain data for species that make up 90 % or more of the total stand basal area, allowing the estimation of stand transpiration in diverse ecological settings. SAPFLUXNET adds to existing plant trait datasets, ecosystem flux networks, and remote sensing products to help increase our understanding of plant water use, plant responses to drought, and ecohydrological processes. SAPFLUXNET version 0.1.5 is freely available from the Zenodo repository (https://doi.org/10.5281/zenodo.3971689; Poyatos et al., 2020a). The “sapfluxnetr” R package – designed to access, visualize, and process SAPFLUXNET data – is available from CRAN.
Rafael Poyatos; Víctor Granda; Víctor Flo; Mark A. Adams; Balázs Adorján; David Aguadé; Marcos P. M. Aidar; Scott Allen; M. Susana Alvarado-Barrientos; Kristina J. Anderson-Teixeira; Luiza Maria Aparecido; M. Altaf Arain; Ismael Aranda; Heidi Asbjornsen; Robert Baxter; Eric Beamesderfer; Z. Carter Berry; Daniel Berveiller; Bethany Blakely; Johnny Boggs; Gil Bohrer; Paul V. Bolstad; Damien Bonal; Rosvel Bracho; Patricia Brito; Jason Brodeur; Fernando Casanoves; Jérôme Chave; Hui Chen; Cesar Cisneros; Kenneth Clark; Edoardo Cremonese; Hongzhong Dang; Jorge S. David; Teresa S. David; Nicolas Delpierre; Ankur R. Desai; Frederic C. Do; Michal Dohnal; Jean-Christophe Domec; Sebinasi Dzikiti; Colin Edgar; Rebekka Eichstaedt; Tarek S. El-Madany; Jan Elbers; Cleiton B. Eller; Eugénie S. Euskirchen; Brent Ewers; Patrick Fonti; Alicia Forner; David I. Forrester; Helber C. Freitas; Marta Galvagno; Omar Garcia-Tejera; Chandra Prasad Ghimire; Teresa E. Gimeno; John Grace; André Granier; Anne Griebel; Yan Guangyu; Mark B. Gush; Paul J. Hanson; Niles J. Hasselquist; Ingo Heinrich; Virginia Hernandez-Santana; Valentine Herrmann; Teemu Hölttä; Friso Holwerda; James Irvine; Supat Isarangkool Na Ayutthaya; Paul G. Jarvis; Hubert Jochheim; Carlos A. Joly; Julia Kaplick; Hyun Seok Kim; Leif Klemedtsson; Heather Kropp; Fredrik Lagergren; Patrick Lane; Petra Lang; Andrei Lapenas; Víctor Lechuga; Minsu Lee; Christoph Leuschner; Jean-Marc Limousin; Juan Carlos Linares; Maj-Lena Linderson; Anders Lindroth; Pilar Llorens; Álvaro López-Bernal; Michael M. Loranty; Dietmar Lüttschwager; Cate Macinnis-Ng; Isabelle Maréchaux; Timothy A. Martin; Ashley Matheny; Nate McDowell; Sean McMahon; Patrick Meir; Ilona Mészáros; Mirco Migliavacca; Patrick Mitchell; Meelis Mölder; Leonardo Montagnani; Georgianne W. Moore; Ryogo Nakada; Furong Niu; Rachael H. Nolan; Richard Norby; Kimberly Novick; Walter Oberhuber; Nikolaus Obojes; A. Christopher Oishi; Rafael S. Oliveira; Ram Oren; Jean-Marc Ourcival; Teemu Paljakka; Oscar Perez-Priego; Pablo L. Peri; Richard L. Peters; Sebastian Pfautsch; William T. Pockman; Yakir Preisler; Katherine Rascher; George Robinson; Humberto Rocha; Alain Rocheteau; Alexander Röll; Bruno H. P. Rosado; Lucy Rowland; Alexey V. Rubtsov; Santiago Sabaté; Yann Salmon; Roberto L. Salomón; Elisenda Sánchez-Costa; Karina V. R. Schäfer; Bernhard Schuldt; Alexandr Shashkin; Clément Stahl; Marko Stojanović; Juan Carlos Suárez; Ge Sun; Justyna Szatniewska; Fyodor Tatarinov; Miroslav Tesař; Frank M. Thomas; Pantana Tor-Ngern; Josef Urban; Fernando Valladares; Christiaan van der Tol; Ilja van Meerveld; Andrej Varlagin; Holm Voigt; Jeffrey Warren; Christiane Werner; Willy Werner; Gerhard Wieser; Lisa Wingate; Stan Wullschleger; Koong Yi; Roman Zweifel; Kathy Steppe; Maurizio Mencuccini; Jordi Martínez-Vilalta. Global transpiration data from sap flow measurements: the SAPFLUXNET database. Earth System Science Data 2021, 13, 2607 -2649.
AMA StyleRafael Poyatos, Víctor Granda, Víctor Flo, Mark A. Adams, Balázs Adorján, David Aguadé, Marcos P. M. Aidar, Scott Allen, M. Susana Alvarado-Barrientos, Kristina J. Anderson-Teixeira, Luiza Maria Aparecido, M. Altaf Arain, Ismael Aranda, Heidi Asbjornsen, Robert Baxter, Eric Beamesderfer, Z. Carter Berry, Daniel Berveiller, Bethany Blakely, Johnny Boggs, Gil Bohrer, Paul V. Bolstad, Damien Bonal, Rosvel Bracho, Patricia Brito, Jason Brodeur, Fernando Casanoves, Jérôme Chave, Hui Chen, Cesar Cisneros, Kenneth Clark, Edoardo Cremonese, Hongzhong Dang, Jorge S. David, Teresa S. David, Nicolas Delpierre, Ankur R. Desai, Frederic C. Do, Michal Dohnal, Jean-Christophe Domec, Sebinasi Dzikiti, Colin Edgar, Rebekka Eichstaedt, Tarek S. El-Madany, Jan Elbers, Cleiton B. Eller, Eugénie S. Euskirchen, Brent Ewers, Patrick Fonti, Alicia Forner, David I. Forrester, Helber C. Freitas, Marta Galvagno, Omar Garcia-Tejera, Chandra Prasad Ghimire, Teresa E. Gimeno, John Grace, André Granier, Anne Griebel, Yan Guangyu, Mark B. Gush, Paul J. Hanson, Niles J. Hasselquist, Ingo Heinrich, Virginia Hernandez-Santana, Valentine Herrmann, Teemu Hölttä, Friso Holwerda, James Irvine, Supat Isarangkool Na Ayutthaya, Paul G. Jarvis, Hubert Jochheim, Carlos A. Joly, Julia Kaplick, Hyun Seok Kim, Leif Klemedtsson, Heather Kropp, Fredrik Lagergren, Patrick Lane, Petra Lang, Andrei Lapenas, Víctor Lechuga, Minsu Lee, Christoph Leuschner, Jean-Marc Limousin, Juan Carlos Linares, Maj-Lena Linderson, Anders Lindroth, Pilar Llorens, Álvaro López-Bernal, Michael M. Loranty, Dietmar Lüttschwager, Cate Macinnis-Ng, Isabelle Maréchaux, Timothy A. Martin, Ashley Matheny, Nate McDowell, Sean McMahon, Patrick Meir, Ilona Mészáros, Mirco Migliavacca, Patrick Mitchell, Meelis Mölder, Leonardo Montagnani, Georgianne W. Moore, Ryogo Nakada, Furong Niu, Rachael H. Nolan, Richard Norby, Kimberly Novick, Walter Oberhuber, Nikolaus Obojes, A. Christopher Oishi, Rafael S. Oliveira, Ram Oren, Jean-Marc Ourcival, Teemu Paljakka, Oscar Perez-Priego, Pablo L. Peri, Richard L. Peters, Sebastian Pfautsch, William T. Pockman, Yakir Preisler, Katherine Rascher, George Robinson, Humberto Rocha, Alain Rocheteau, Alexander Röll, Bruno H. P. Rosado, Lucy Rowland, Alexey V. Rubtsov, Santiago Sabaté, Yann Salmon, Roberto L. Salomón, Elisenda Sánchez-Costa, Karina V. R. Schäfer, Bernhard Schuldt, Alexandr Shashkin, Clément Stahl, Marko Stojanović, Juan Carlos Suárez, Ge Sun, Justyna Szatniewska, Fyodor Tatarinov, Miroslav Tesař, Frank M. Thomas, Pantana Tor-Ngern, Josef Urban, Fernando Valladares, Christiaan van der Tol, Ilja van Meerveld, Andrej Varlagin, Holm Voigt, Jeffrey Warren, Christiane Werner, Willy Werner, Gerhard Wieser, Lisa Wingate, Stan Wullschleger, Koong Yi, Roman Zweifel, Kathy Steppe, Maurizio Mencuccini, Jordi Martínez-Vilalta. Global transpiration data from sap flow measurements: the SAPFLUXNET database. Earth System Science Data. 2021; 13 (6):2607-2649.
Chicago/Turabian StyleRafael Poyatos; Víctor Granda; Víctor Flo; Mark A. Adams; Balázs Adorján; David Aguadé; Marcos P. M. Aidar; Scott Allen; M. Susana Alvarado-Barrientos; Kristina J. Anderson-Teixeira; Luiza Maria Aparecido; M. Altaf Arain; Ismael Aranda; Heidi Asbjornsen; Robert Baxter; Eric Beamesderfer; Z. Carter Berry; Daniel Berveiller; Bethany Blakely; Johnny Boggs; Gil Bohrer; Paul V. Bolstad; Damien Bonal; Rosvel Bracho; Patricia Brito; Jason Brodeur; Fernando Casanoves; Jérôme Chave; Hui Chen; Cesar Cisneros; Kenneth Clark; Edoardo Cremonese; Hongzhong Dang; Jorge S. David; Teresa S. David; Nicolas Delpierre; Ankur R. Desai; Frederic C. Do; Michal Dohnal; Jean-Christophe Domec; Sebinasi Dzikiti; Colin Edgar; Rebekka Eichstaedt; Tarek S. El-Madany; Jan Elbers; Cleiton B. Eller; Eugénie S. Euskirchen; Brent Ewers; Patrick Fonti; Alicia Forner; David I. Forrester; Helber C. Freitas; Marta Galvagno; Omar Garcia-Tejera; Chandra Prasad Ghimire; Teresa E. Gimeno; John Grace; André Granier; Anne Griebel; Yan Guangyu; Mark B. Gush; Paul J. Hanson; Niles J. Hasselquist; Ingo Heinrich; Virginia Hernandez-Santana; Valentine Herrmann; Teemu Hölttä; Friso Holwerda; James Irvine; Supat Isarangkool Na Ayutthaya; Paul G. Jarvis; Hubert Jochheim; Carlos A. Joly; Julia Kaplick; Hyun Seok Kim; Leif Klemedtsson; Heather Kropp; Fredrik Lagergren; Patrick Lane; Petra Lang; Andrei Lapenas; Víctor Lechuga; Minsu Lee; Christoph Leuschner; Jean-Marc Limousin; Juan Carlos Linares; Maj-Lena Linderson; Anders Lindroth; Pilar Llorens; Álvaro López-Bernal; Michael M. Loranty; Dietmar Lüttschwager; Cate Macinnis-Ng; Isabelle Maréchaux; Timothy A. Martin; Ashley Matheny; Nate McDowell; Sean McMahon; Patrick Meir; Ilona Mészáros; Mirco Migliavacca; Patrick Mitchell; Meelis Mölder; Leonardo Montagnani; Georgianne W. Moore; Ryogo Nakada; Furong Niu; Rachael H. Nolan; Richard Norby; Kimberly Novick; Walter Oberhuber; Nikolaus Obojes; A. Christopher Oishi; Rafael S. Oliveira; Ram Oren; Jean-Marc Ourcival; Teemu Paljakka; Oscar Perez-Priego; Pablo L. Peri; Richard L. Peters; Sebastian Pfautsch; William T. Pockman; Yakir Preisler; Katherine Rascher; George Robinson; Humberto Rocha; Alain Rocheteau; Alexander Röll; Bruno H. P. Rosado; Lucy Rowland; Alexey V. Rubtsov; Santiago Sabaté; Yann Salmon; Roberto L. Salomón; Elisenda Sánchez-Costa; Karina V. R. Schäfer; Bernhard Schuldt; Alexandr Shashkin; Clément Stahl; Marko Stojanović; Juan Carlos Suárez; Ge Sun; Justyna Szatniewska; Fyodor Tatarinov; Miroslav Tesař; Frank M. Thomas; Pantana Tor-Ngern; Josef Urban; Fernando Valladares; Christiaan van der Tol; Ilja van Meerveld; Andrej Varlagin; Holm Voigt; Jeffrey Warren; Christiane Werner; Willy Werner; Gerhard Wieser; Lisa Wingate; Stan Wullschleger; Koong Yi; Roman Zweifel; Kathy Steppe; Maurizio Mencuccini; Jordi Martínez-Vilalta. 2021. "Global transpiration data from sap flow measurements: the SAPFLUXNET database." Earth System Science Data 13, no. 6: 2607-2649.
This is the paper code for "Significant reductions in crop yields from air pollution and heat stress in the United States". Please read the README for more information, contact the authors if you have any issues, email: [email protected] or [email protected]
Xiang Liu; Ankur Desai. Second release code for paper entitled "Significant reductions in crop yields from air pollution and heat stress in the United States". 2021, 1 .
AMA StyleXiang Liu, Ankur Desai. Second release code for paper entitled "Significant reductions in crop yields from air pollution and heat stress in the United States". . 2021; ():1.
Chicago/Turabian StyleXiang Liu; Ankur Desai. 2021. "Second release code for paper entitled "Significant reductions in crop yields from air pollution and heat stress in the United States"." , no. : 1.
This is the paper code for "Significant reductions in crop yields from air pollution and heat stress in the United States". Please read the README for more information, contact the authors if you have any issues, email: [email protected] or [email protected]
Xiang Liu; Ankur Desai. Second release code for paper entitled "Significant reductions in crop yields from air pollution and heat stress in the United States". 2021, 1 .
AMA StyleXiang Liu, Ankur Desai. Second release code for paper entitled "Significant reductions in crop yields from air pollution and heat stress in the United States". . 2021; ():1.
Chicago/Turabian StyleXiang Liu; Ankur Desai. 2021. "Second release code for paper entitled "Significant reductions in crop yields from air pollution and heat stress in the United States"." , no. : 1.
Surface-atmosphere fluxes and their drivers vary across space and time. A growing area of interest is in downscaling, localizing, and/or resolving sub-grid scale energy, water, and carbon fluxes and drivers. Existing downscaling methods require inputs of land surface properties at relatively high spatial (e.g., sub-kilometer) and temporal (e.g., hourly) resolutions, but many observed land surface drivers are not available at these resolutions. We evaluate an approach to overcome this challenge for land surface temperature (LST), a World Meteorological Organization Essential Climate Variable and a key driver for surface heat fluxes. The Chequamegon Heterogenous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors (CHEESEHEAD19) field experiment provided a scalable testbed. We downscaled LST from satellites (GOES-16 and ECOSTRESS) with further refinement using airborne hyperspectral imagery. Temporally and spatially downscaled LST compared well to observations from a network of 20 micrometeorological towers and airborne in addition to Landsat-based LST retrieval and drone-based LST observed at one tower site. The downscaled 50-meter hourly LST showed good relationships with tower (r2=0.79, precision=3.5 K) and airborne (r2=0.75, precision=2.4 K) observations over space and time, with precision lower over wetlands and lakes, and some improvement for capturing spatio-temporal variation compared to geostationary satellite. Further downscaling to 10 m using hyperspectral imagery resolved hotspots and cool spots on the landscape detected in drone LST, with significant improvement in precision by 1.3 K. These results demonstrate a simple pathway for multi-sensor retrieval of high space and time resolution LST.
Ankur Rashmikant DesaiiD; Anam Munir Khan; Ting Zheng; Sreenath Paleri; Brian J. ButterworthiD; Temple R. LeeiD; Joshua B FisheriD; Glynn Hulley; Tania KleynhansiD; Aaron Gerace; Philip A Townsend; Paul Christopher Stoy; Stefan MetzgeriD. Multi-sensor approach for high space and time resolution land surface temperature. 2021, 1 .
AMA StyleAnkur Rashmikant DesaiiD, Anam Munir Khan, Ting Zheng, Sreenath Paleri, Brian J. ButterworthiD, Temple R. LeeiD, Joshua B FisheriD, Glynn Hulley, Tania KleynhansiD, Aaron Gerace, Philip A Townsend, Paul Christopher Stoy, Stefan MetzgeriD. Multi-sensor approach for high space and time resolution land surface temperature. . 2021; ():1.
Chicago/Turabian StyleAnkur Rashmikant DesaiiD; Anam Munir Khan; Ting Zheng; Sreenath Paleri; Brian J. ButterworthiD; Temple R. LeeiD; Joshua B FisheriD; Glynn Hulley; Tania KleynhansiD; Aaron Gerace; Philip A Townsend; Paul Christopher Stoy; Stefan MetzgeriD. 2021. "Multi-sensor approach for high space and time resolution land surface temperature." , no. : 1.
Process-based ecosystem models help us understand and predict ecosystem processes, but using them has long involved a difficult choice between performing data- and labor-intensive site-level calibrations or relying on general parameters that may not reflect local conditions. Hierarchical Bayesian (HB) calibration provides a third option that frees modelers from assuming model parameters to be completely generic or completely site-specific and allows a formal distinction between prediction at known calibration sites and “out-of-sample” prediction to new sites. Here, we compare calibrations of a process-based dynamic vegetation model to eddy-covariance data across 12 temperate deciduous Ameriflux sites fit using either site-specific, joint cross-site, or HB approaches. To be able to apply HB to computationally demanding process-based models we introduce a novel emulator-based HB calibration tool, which we make available through the PEcAn community cyberinfrastructure. Using these calibrations to make predictions at held-out tower sites, we show that the joint cross-site calibration is falsely over-confident because it neglects parameter variability across sites and therefore underestimates variance in parameter distributions. By showing which parameters show high site-to-site variability, HB calibration also formally gives us a structure that can detect which process representations are missing from the models and prioritize errors based on the magnitude of the associated uncertainty. For example, in our case-study, we were able to identify large site-to-site variability in the parameters related to the temperature responses of respiration and photosynthesis, associated with a lack of thermal acclimation and adaptation in the model. Moving forward, HB approaches present important new opportunities for statistical modeling of the spatiotemporal variability in modeled parameters and processes that yields both new insights and improved predictions.
Istem Fer; Alexey Shiklomanov; Kimberly A. Novick; Christopher M. Gough; M. Altaf Arain; Jiquan Chen; Bailey Murphy; Ankur R. Desai; Michael C. Dietze. Capturing site-to-site variability through Hierarchical Bayesian calibration of a process-based dynamic vegetation model. 2021, 1 .
AMA StyleIstem Fer, Alexey Shiklomanov, Kimberly A. Novick, Christopher M. Gough, M. Altaf Arain, Jiquan Chen, Bailey Murphy, Ankur R. Desai, Michael C. Dietze. Capturing site-to-site variability through Hierarchical Bayesian calibration of a process-based dynamic vegetation model. . 2021; ():1.
Chicago/Turabian StyleIstem Fer; Alexey Shiklomanov; Kimberly A. Novick; Christopher M. Gough; M. Altaf Arain; Jiquan Chen; Bailey Murphy; Ankur R. Desai; Michael C. Dietze. 2021. "Capturing site-to-site variability through Hierarchical Bayesian calibration of a process-based dynamic vegetation model." , no. : 1.
While wetlands are the largest natural source of methane (CH4) to the atmosphere, they represent a large source of uncertainty in the global CH4 budget due to the complex biogeochemical controls on CH4 dynamics. Here we present, to our knowledge, the first multi‐site synthesis of how predictors of freshwater wetland CH4 fluxes (FCH4) vary across wetland types at diel, multiday (synoptic), and seasonal time scales. We used several statistical approaches (correlation analysis, generalized additive modeling, mutual information, random forests) in a wavelet‐based multiresolution framework to assess the importance of environmental predictors, nonlinearities and lags on FCH4 across 23 eddy covariance sites. Seasonally, soil and air temperature were dominant predictors of FCH4 at sites with smaller seasonal variation in water table depth (WTD). In contrast, WTD was the dominant predictor for wetlands with smaller variations in temperature (e.g., seasonal tropical/subtropical wetlands). Changes in seasonal FCH4 lagged fluctuations in WTD by ~17 ± 11 days, and lagged air and soil temperature by median values of 8 ± 16 and 5 ± 15 days, respectively. Temperature and WTD were also dominant predictors at the multiday scale. Atmospheric pressure (PA) was another important multiday scale predictor for peat dominated sites, with drops in PA coinciding with synchronous releases of CH4. At the diel scale, synchronous relationships with latent heat flux and vapor pressure deficit suggest that physical processes controlling evaporation and boundary layer mixing exert similar controls on CH4 volatilization, and suggest the influence of pressurized ventilation in aerenchymatous vegetation. In addition, 1‐4 hour lagged relationships with ecosystem photosynthesis indicate recent carbon substrates, such as root exudates, may also control FCH4. By addressing issues of scale, asynchrony, and nonlinearity, this work improves understanding of the predictors and timing of wetland FCH4 that can inform future studies and models, and help constrain wetland CH4 emissions.
Sara Helen Knox; Sheel Bansal; Gavin McNicol; Karina Schafer; Cove Sturtevant; Masahito Ueyama; Alex C. Valach; Dennis Baldocchi; Kyle Delwiche; Ankur R. Desai; Eugenie Euskirchen; Jinxun Liu; Annalea Lohila; Avni Malhotra; Lulie Melling; William Riley; Benjamin R. K. Runkle; Jessica Turner; Rodrigo Vargas; Qing Zhu; Tuula Alto; Etienne Fluet‐Chouinard; Mathias Goeckede; Joe R. Melton; Oliver Sonnentag; Timo Vesala; Eric Ward; Zhen Zhang; Sarah Feron; Zutao Ouyang; Pavel Alekseychik; Mika Aurela; Gil Bohrer; David I. Campbell; Jiquan Chen; Housen Chu; Higo J. Dalmagro; Jordan P. Goodrich; Pia Gottschalk; Takashi Hirano; Hiroki Iwata; Gerald Jurasinski; Minseok Kang; Franziska Koebsch; Ivan Mammarella; Mats B. Nilsson; Keisuke Ono; Matthias Peichl; Olli Peltola; Youngryel Ryu; Torsten Sachs; Ayaka Sakabe; Jed P. Sparks; Eeva‐Stiina Tuittila; George L. Vourlitis; Guan Xhuan Wong; Lisamarie Windham‐Myers; Benjamin Poulter; Robert B. Jackson. Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales. Global Change Biology 2021, 27, 3582 -3604.
AMA StyleSara Helen Knox, Sheel Bansal, Gavin McNicol, Karina Schafer, Cove Sturtevant, Masahito Ueyama, Alex C. Valach, Dennis Baldocchi, Kyle Delwiche, Ankur R. Desai, Eugenie Euskirchen, Jinxun Liu, Annalea Lohila, Avni Malhotra, Lulie Melling, William Riley, Benjamin R. K. Runkle, Jessica Turner, Rodrigo Vargas, Qing Zhu, Tuula Alto, Etienne Fluet‐Chouinard, Mathias Goeckede, Joe R. Melton, Oliver Sonnentag, Timo Vesala, Eric Ward, Zhen Zhang, Sarah Feron, Zutao Ouyang, Pavel Alekseychik, Mika Aurela, Gil Bohrer, David I. Campbell, Jiquan Chen, Housen Chu, Higo J. Dalmagro, Jordan P. Goodrich, Pia Gottschalk, Takashi Hirano, Hiroki Iwata, Gerald Jurasinski, Minseok Kang, Franziska Koebsch, Ivan Mammarella, Mats B. Nilsson, Keisuke Ono, Matthias Peichl, Olli Peltola, Youngryel Ryu, Torsten Sachs, Ayaka Sakabe, Jed P. Sparks, Eeva‐Stiina Tuittila, George L. Vourlitis, Guan Xhuan Wong, Lisamarie Windham‐Myers, Benjamin Poulter, Robert B. Jackson. Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales. Global Change Biology. 2021; 27 (15):3582-3604.
Chicago/Turabian StyleSara Helen Knox; Sheel Bansal; Gavin McNicol; Karina Schafer; Cove Sturtevant; Masahito Ueyama; Alex C. Valach; Dennis Baldocchi; Kyle Delwiche; Ankur R. Desai; Eugenie Euskirchen; Jinxun Liu; Annalea Lohila; Avni Malhotra; Lulie Melling; William Riley; Benjamin R. K. Runkle; Jessica Turner; Rodrigo Vargas; Qing Zhu; Tuula Alto; Etienne Fluet‐Chouinard; Mathias Goeckede; Joe R. Melton; Oliver Sonnentag; Timo Vesala; Eric Ward; Zhen Zhang; Sarah Feron; Zutao Ouyang; Pavel Alekseychik; Mika Aurela; Gil Bohrer; David I. Campbell; Jiquan Chen; Housen Chu; Higo J. Dalmagro; Jordan P. Goodrich; Pia Gottschalk; Takashi Hirano; Hiroki Iwata; Gerald Jurasinski; Minseok Kang; Franziska Koebsch; Ivan Mammarella; Mats B. Nilsson; Keisuke Ono; Matthias Peichl; Olli Peltola; Youngryel Ryu; Torsten Sachs; Ayaka Sakabe; Jed P. Sparks; Eeva‐Stiina Tuittila; George L. Vourlitis; Guan Xhuan Wong; Lisamarie Windham‐Myers; Benjamin Poulter; Robert B. Jackson. 2021. "Identifying dominant environmental predictors of freshwater wetland methane fluxes across diurnal to seasonal time scales." Global Change Biology 27, no. 15: 3582-3604.
Current in situ soil moisture monitoring networks are sparsely distributed while remote sensing satellite soil moisture maps have a very coarse spatial resolution. In this study, an empirical global surface soil moisture (SSM) model was established via fusion of in situ continental and regional scale soil moisture networks, remote sensing data (SMAP and Sentinel-1) and high-resolution land surface parameters (e.g., soil texture, terrain) using a quantile random forest (QRF) algorithm. The model had a spatial resolution of 100m and performed moderately well under cultivated, herbaceous, forest, and shrub soils (R2 = 0.524, RMSE = 0.07 m3 m−3). It has a relatively good transferability at the regional scale among different continental and regional networks (mean RMSE = 0.08–0.10 m3 m−3). The global model was then applied to map SSM dynamics at 30–100m across a field-scale network (TERENO-Wüstebach) in Germany and an 80-ha irrigated cropland in Wisconsin, USA. Without local training data, the model was able to delineate the variations in SSM at the field scale but contained large bias. With the addition of 10% local training datasets (“spiking”), the bias of the model was significantly reduced. The QRF model was also affected by the resolution and accuracy of soil maps. It was concluded that the empirical model has the potential to be applied elsewhere across the globe to map SSM at the regional to field scales for research and applications. Future research is required to improve the performance of the model by incorporating more field-scale soil moisture sensor networks and high-resolution soil maps as well as assimilation with process-based water flow models.
Jingyi Huang; Ankur Desai; Jun Zhu; Alfred Hartemink; Paul Stoy; Steven Loheide Ii; Heye Bogena; Yakun Zhang; Zhou Zhang; Francisco Arriaga. A data-driven approach for mapping global surface soil moisture at 100 m using high-resolution remote sensing data and land surface parameters. 2021, 1 .
AMA StyleJingyi Huang, Ankur Desai, Jun Zhu, Alfred Hartemink, Paul Stoy, Steven Loheide Ii, Heye Bogena, Yakun Zhang, Zhou Zhang, Francisco Arriaga. A data-driven approach for mapping global surface soil moisture at 100 m using high-resolution remote sensing data and land surface parameters. . 2021; ():1.
Chicago/Turabian StyleJingyi Huang; Ankur Desai; Jun Zhu; Alfred Hartemink; Paul Stoy; Steven Loheide Ii; Heye Bogena; Yakun Zhang; Zhou Zhang; Francisco Arriaga. 2021. "A data-driven approach for mapping global surface soil moisture at 100 m using high-resolution remote sensing data and land surface parameters." , no. : 1.
Previous studies of long-term soil change have been focusing on the impacts of climate and land-use change, while neglecting the impacts of soil taxonomy on soil’s response to vegetational and human disturbance. In this study, a spatial-temporal framework was used to study the change in soil organic carbon (SOC) across National Ecological Observatory Network (NEON), USA over 30 years. We hypothesize that: 1) on the continental scale, the hot-spots and cold-spots of SOC change vary with soil orders across different eco-climatic domains, controlled by all soil forming factors that affect carbon input and output; 2) within the same eco-climatic regime, the effects of disturbance on SOC change are controlled by physical and biogeochemical processes, represented by varying soil properties including clay, bulk density, pH, and CEC. To separate the effects of disturbance under different land-use scenarios on SOC change, space-for-time substitution was used in combination with the Continuous Change Detection and Classification algorithm and structural equation models. Results suggested that 1) under natural vegetation, Ultisols, Spodosols, and Inceptisols showed a large SOC accumulation especially in the eastern coast, while Inceptisols, Andisols, and Aridisols in the western US showed a large SOC loss; 2) compared with the same reference soils under natural vegetation, Mollisols and Alfisols showed a large SOC decrease due to human disturbance (e.g., farming and grazing); 3) Inceptisols (+6.2 g/kg) and Gelisols (+27.5 g/kg) in Alaska presented the largest SOC increase among all the soil orders within the subsoil (B horizon); 4) clay content and pH were the most dominant factors that affected SOC content across the NEON sites. This empirical analysis of the 30-years SOC change across eco-climatic regimes could be used for ecosystem modelers to benchmark the models across biomes and study the physical and biogeochemical controls on SOC change under different land management scenarios.
Jie Hu; Jingyi Huang; Alfred Hartemink; Ankur Desai. A climate-soil-vegetation-human interaction analysis for SOC change monitoring over 30 years across the National Ecological Observatory Network, USA. 2021, 1 .
AMA StyleJie Hu, Jingyi Huang, Alfred Hartemink, Ankur Desai. A climate-soil-vegetation-human interaction analysis for SOC change monitoring over 30 years across the National Ecological Observatory Network, USA. . 2021; ():1.
Chicago/Turabian StyleJie Hu; Jingyi Huang; Alfred Hartemink; Ankur Desai. 2021. "A climate-soil-vegetation-human interaction analysis for SOC change monitoring over 30 years across the National Ecological Observatory Network, USA." , no. : 1.
Wetland methane (CH4) emissions ( $${F}_{{{CH}}_{4}}$$ F C H 4 ) are important in global carbon budgets and climate change assessments. Currently, $${F}_{{{CH}}_{4}}$$ F C H 4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent $${F}_{{{CH}}_{4}}$$ F C H 4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that $${F}_{{{CH}}_{4}}$$ F C H 4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between $${F}_{{{CH}}_{4}}$$ F C H 4 and temperature using observations from the FLUXNET-CH4 database. Measurements collected across the globe show substantial seasonal hysteresis between $${F}_{{{CH}}_{4}}$$ F C H 4 and temperature, suggesting larger $${F}_{{{CH}}_{4}}$$ F C H 4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH4 production are thus needed to improve global CH4 budget assessments.
Kuang-Yu Chang; William J. Riley; Sara H. Knox; Robert B. Jackson; Gavin McNicol; Benjamin Poulter; Mika Aurela; Dennis Baldocchi; Sheel Bansal; Gil Bohrer; David I. Campbell; Alessandro Cescatti; Housen Chu; Kyle B. Delwiche; Ankur R. Desai; Eugenie Euskirchen; Thomas Friborg; Mathias Goeckede; Manuel Helbig; Kyle S. Hemes; Takashi Hirano; Hiroki Iwata; Minseok Kang; Trevor Keenan; Ken W. Krauss; Annalea Lohila; Ivan Mammarella; Bhaskar Mitra; Akira Miyata; Mats B. Nilsson; Asko Noormets; Walter C. Oechel; Dario Papale; Matthias Peichl; Michele L. Reba; Janne Rinne; Benjamin R. K. Runkle; Youngryel Ryu; Torsten Sachs; Karina V. R. Schäfer; Hans Peter Schmid; Narasinha Shurpali; Oliver Sonnentag; Angela C. I. Tang; Margaret S. Torn; Carlo Trotta; Eeva-Stiina Tuittila; Masahito Ueyama; Rodrigo Vargas; Timo Vesala; Lisamarie Windham-Myers; Zhen Zhang; Donatella Zona. Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions. Nature Communications 2021, 12, 1 -10.
AMA StyleKuang-Yu Chang, William J. Riley, Sara H. Knox, Robert B. Jackson, Gavin McNicol, Benjamin Poulter, Mika Aurela, Dennis Baldocchi, Sheel Bansal, Gil Bohrer, David I. Campbell, Alessandro Cescatti, Housen Chu, Kyle B. Delwiche, Ankur R. Desai, Eugenie Euskirchen, Thomas Friborg, Mathias Goeckede, Manuel Helbig, Kyle S. Hemes, Takashi Hirano, Hiroki Iwata, Minseok Kang, Trevor Keenan, Ken W. Krauss, Annalea Lohila, Ivan Mammarella, Bhaskar Mitra, Akira Miyata, Mats B. Nilsson, Asko Noormets, Walter C. Oechel, Dario Papale, Matthias Peichl, Michele L. Reba, Janne Rinne, Benjamin R. K. Runkle, Youngryel Ryu, Torsten Sachs, Karina V. R. Schäfer, Hans Peter Schmid, Narasinha Shurpali, Oliver Sonnentag, Angela C. I. Tang, Margaret S. Torn, Carlo Trotta, Eeva-Stiina Tuittila, Masahito Ueyama, Rodrigo Vargas, Timo Vesala, Lisamarie Windham-Myers, Zhen Zhang, Donatella Zona. Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions. Nature Communications. 2021; 12 (1):1-10.
Chicago/Turabian StyleKuang-Yu Chang; William J. Riley; Sara H. Knox; Robert B. Jackson; Gavin McNicol; Benjamin Poulter; Mika Aurela; Dennis Baldocchi; Sheel Bansal; Gil Bohrer; David I. Campbell; Alessandro Cescatti; Housen Chu; Kyle B. Delwiche; Ankur R. Desai; Eugenie Euskirchen; Thomas Friborg; Mathias Goeckede; Manuel Helbig; Kyle S. Hemes; Takashi Hirano; Hiroki Iwata; Minseok Kang; Trevor Keenan; Ken W. Krauss; Annalea Lohila; Ivan Mammarella; Bhaskar Mitra; Akira Miyata; Mats B. Nilsson; Asko Noormets; Walter C. Oechel; Dario Papale; Matthias Peichl; Michele L. Reba; Janne Rinne; Benjamin R. K. Runkle; Youngryel Ryu; Torsten Sachs; Karina V. R. Schäfer; Hans Peter Schmid; Narasinha Shurpali; Oliver Sonnentag; Angela C. I. Tang; Margaret S. Torn; Carlo Trotta; Eeva-Stiina Tuittila; Masahito Ueyama; Rodrigo Vargas; Timo Vesala; Lisamarie Windham-Myers; Zhen Zhang; Donatella Zona. 2021. "Substantial hysteresis in emergent temperature sensitivity of global wetland CH4 emissions." Nature Communications 12, no. 1: 1-10.
Warming-induced carbon loss through terrestrial ecosystem respiration (Re) is likely getting stronger in high latitudes and cold regions because of the more rapid warming and higher temperature sensitivity of Re (Q 10). However, it is not known whether the spatial relationship between Q 10 and temperature also holds temporally under a future warmer climate. Here, we analyzed apparent Q 10 values derived from multiyear observations at 74 FLUXNET sites spanning diverse climates and biomes. We found warming-induced decline in Q 10 is stronger at colder regions than other locations, which is consistent with a meta-analysis of 54 field warming experiments across the globe. We predict future warming will shrink the global variability of Q 10 values to an average of 1.44 across the globe under a high emission trajectory (RCP 8.5) by the end of the century. Therefore, warming-induced carbon loss may be less than previously assumed because of Q 10 homogenization in a warming world.
Ben Niu; Xianzhou Zhang; Shilong Piao; Ivan A. Janssens; Gang Fu; Yongtao He; Yangjian Zhang; Peili Shi; Erfu Dai; Chengqun Yu; Jing Zhang; Guirui Yu; Ming Xu; Jianshuang Wu; Liping Zhu; Ankur R. Desai; Jiquan Chen; Gil Bohrer; Christopher M. Gough; Ivan Mammarella; Andrej Varlagin; Silvano Fares; Xinquan Zhao; Yingnian Li; Huiming Wang; Zhu Ouyang. Warming homogenizes apparent temperature sensitivity of ecosystem respiration. Science Advances 2021, 7, eabc7358 .
AMA StyleBen Niu, Xianzhou Zhang, Shilong Piao, Ivan A. Janssens, Gang Fu, Yongtao He, Yangjian Zhang, Peili Shi, Erfu Dai, Chengqun Yu, Jing Zhang, Guirui Yu, Ming Xu, Jianshuang Wu, Liping Zhu, Ankur R. Desai, Jiquan Chen, Gil Bohrer, Christopher M. Gough, Ivan Mammarella, Andrej Varlagin, Silvano Fares, Xinquan Zhao, Yingnian Li, Huiming Wang, Zhu Ouyang. Warming homogenizes apparent temperature sensitivity of ecosystem respiration. Science Advances. 2021; 7 (15):eabc7358.
Chicago/Turabian StyleBen Niu; Xianzhou Zhang; Shilong Piao; Ivan A. Janssens; Gang Fu; Yongtao He; Yangjian Zhang; Peili Shi; Erfu Dai; Chengqun Yu; Jing Zhang; Guirui Yu; Ming Xu; Jianshuang Wu; Liping Zhu; Ankur R. Desai; Jiquan Chen; Gil Bohrer; Christopher M. Gough; Ivan Mammarella; Andrej Varlagin; Silvano Fares; Xinquan Zhao; Yingnian Li; Huiming Wang; Zhu Ouyang. 2021. "Warming homogenizes apparent temperature sensitivity of ecosystem respiration." Science Advances 7, no. 15: eabc7358.
We thank all 2020 reviewers for their contributions.
Marguerite A. Xenopoulos; Miguel Goñi; Ankur Desai; Deborah Huntzinger. Letter of Appreciation to Our 2020 Reviewers in the Time of COVID‐19. Journal of Geophysical Research: Biogeosciences 2021, 126, 1 .
AMA StyleMarguerite A. Xenopoulos, Miguel Goñi, Ankur Desai, Deborah Huntzinger. Letter of Appreciation to Our 2020 Reviewers in the Time of COVID‐19. Journal of Geophysical Research: Biogeosciences. 2021; 126 (4):1.
Chicago/Turabian StyleMarguerite A. Xenopoulos; Miguel Goñi; Ankur Desai; Deborah Huntzinger. 2021. "Letter of Appreciation to Our 2020 Reviewers in the Time of COVID‐19." Journal of Geophysical Research: Biogeosciences 126, no. 4: 1.
The observing system design of multi-disciplinary field measurements involves a variety of considerations on logistics, safety, and science objectives. Typically, this is done based on investigator intuition and designs of prior field measurements. However, there is potential for considerable increase in efficiency, safety, and scientific success by integrating numerical simulations in the design process. Here, we present a novel approach to observing system simulation experiments that aids surface-atmosphere synthesis at the interface of meso- and microscale meteorology. We used this approach to optimize the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19). During pre-field simulation experiments, we considered the placement of 20 eddy-covariance flux towers, operations for 72 hours of low-altitude flux aircraft measurements, and integration of various remote sensing data products. High-resolution Large Eddy Simulations generated a super-sample of virtual ground, airborne, and satellite observations to explore two specific design hypotheses. We then analyzed these virtual observations through Environmental Response Functions to yield an optimal aircraft flight strategy for augmenting a stratified random flux tower network in combination with satellite retrievals. We demonstrate how this novel approach doubled CHEESEHEAD19’s ability to explore energy balance closure and spatial patterning science objectives while substantially simplifying logistics. Owing to its extensibility, the approach lends itself to optimize observing system designs also for natural climate solutions, emission inventory validation, urban air quality, industry leak detection and multi-species applications, among other use cases.
Stefan Metzger; David Durden; Sreenath Paleri; Matthias Sühring; Brian J. Butterworth; Christopher R Florian; Matthias R. Mauder; David M. Plummer; Luise Wanner; Ke Xu; Ankur Rashmikant Desai. Observing System Simulation Experiments double scientific return of surface-atmosphere synthesis. 2021, 1 .
AMA StyleStefan Metzger, David Durden, Sreenath Paleri, Matthias Sühring, Brian J. Butterworth, Christopher R Florian, Matthias R. Mauder, David M. Plummer, Luise Wanner, Ke Xu, Ankur Rashmikant Desai. Observing System Simulation Experiments double scientific return of surface-atmosphere synthesis. . 2021; ():1.
Chicago/Turabian StyleStefan Metzger; David Durden; Sreenath Paleri; Matthias Sühring; Brian J. Butterworth; Christopher R Florian; Matthias R. Mauder; David M. Plummer; Luise Wanner; Ke Xu; Ankur Rashmikant Desai. 2021. "Observing System Simulation Experiments double scientific return of surface-atmosphere synthesis." , no. : 1.
The observing system design of multi-disciplinary field measurements involves a variety of considerations on logistics, safety, and science objectives. Typically, this is done based on investigator intuition and designs of prior field measurements. However, there is potential for considerable increase in efficiency, safety, and scientific success by integrating numerical simulations in the design process. Here, we present a novel approach to observing system simulation experiments that aids surface-atmosphere synthesis at the interface of meso- and microscale meteorology. We used this approach to optimize the Chequamegon Heterogeneous Ecosystem Energy-balance Study Enabled by a High-density Extensive Array of Detectors 2019 (CHEESEHEAD19).During pre-field simulation experiments, we considered the placement of 20 eddy-covariance flux towers, operations for 72 hours of low-altitude flux aircraft measurements, and integration of various remote sensing data products. High-resolution Large Eddy Simulations generated a super-sample of virtual ground, airborne, and satellite observations to explore two specific design hypotheses. We then analyzed these virtual observations through Environmental Response Functions to yield an optimal aircraft flight strategy for augmenting a stratified random flux tower network in combination with satellite retrievals.We demonstrate how this novel approach doubled CHEESEHEAD19’s ability to explore energy balance closure and spatial patterning science objectives while substantially simplifying logistics. Owing to its extensibility, the approach lends itself to optimize observing system designs also for natural climate solutions, emission inventory validation, urban air quality, industry leak detection and multi-species applications, among other use cases.
Stefan Metzger; David Durden; Sreenath Paleri; Matthias Sühring; Brian Butterworth; Christopher Florian; Matthias Mauder; David M. Plummer; Luise Wanner; Ke Xu; Ankur R. Desai. Observing system simulation experiments double scientific return of surface-atmosphere synthesis. 2021, 2021, 1 -39.
AMA StyleStefan Metzger, David Durden, Sreenath Paleri, Matthias Sühring, Brian Butterworth, Christopher Florian, Matthias Mauder, David M. Plummer, Luise Wanner, Ke Xu, Ankur R. Desai. Observing system simulation experiments double scientific return of surface-atmosphere synthesis. . 2021; 2021 ():1-39.
Chicago/Turabian StyleStefan Metzger; David Durden; Sreenath Paleri; Matthias Sühring; Brian Butterworth; Christopher Florian; Matthias Mauder; David M. Plummer; Luise Wanner; Ke Xu; Ankur R. Desai. 2021. "Observing system simulation experiments double scientific return of surface-atmosphere synthesis." 2021, no. : 1-39.