114 results on '"Griffis, Timothy J."'
Search Results
2. KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N2O emission using data from mesocosm experiments
- Author
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Liu, Licheng, Xu, Shaoming, Tang, Jinyun, Guan, Kaiyu, Griffis, Timothy J, Erickson, Matthew D, Frie, Alexander L, Jia, Xiaowei, Kim, Taegon, Miller, Lee T, Peng, Bin, Wu, Shaowei, Yang, Yufeng, Zhou, Wang, Kumar, Vipin, and Jin, Zhenong
- Subjects
Earth Sciences ,Climate Action ,Earth sciences - Abstract
Agricultural nitrous oxide (N2O) emission accounts for a non-trivial fraction of global greenhouse gas (GHG) budget. To date, estimating N2O fluxes from cropland remains a challenging task because the related microbial processes (e.g., nitrification and denitrification) are controlled by complex interactions among climate, soil, plant and human activities. Existing approaches such as process-based (PB) models have well-known limitations due to insufficient representations of the processes or uncertainties of model parameters, and due to leverage recent advances in machine learning (ML) a new method is needed to unlock the "black box"to overcome its limitations such as low interpretability, out-of-sample failure and massive data demand. In this study, we developed a first-of-its-kind knowledge-guided machine learning model for agroecosystems (KGML-ag) by incorporating biogeophysical and chemical domain knowledge from an advanced PB model, ecosys, and tested it by comparing simulating daily N2O fluxes with real observed data from mesocosm experiments. The gated recurrent unit (GRU) was used as the basis to build the model structure. To optimize the model performance, we have investigated a range of ideas, including (1) using initial values of intermediate variables (IMVs) instead of time series as model input to reduce data demand; (2) building hierarchical structures to explicitly estimate IMVs for further N2O prediction; (3) using multi-task learning to balance the simultaneous training on multiple variables; and (4) pre-training with millions of synthetic data generated from ecosys and fine-tuning with mesocosm observations. Six other pure ML models were developed using the same mesocosm data to serve as the benchmark for the KGML-ag model. Results show that KGML-ag did an excellent job in reproducing the mesocosm N2O fluxes (overall r2Combining double low line0.81, and RMSECombining double low line3.6g€¯mgNm-2d-1 from cross validation). Importantly, KGML-ag always outperforms the PB model and ML models in predicting N2O fluxes, especially for complex temporal dynamics and emission peaks. Besides, KGML-ag goes beyond the pure ML models by providing more interpretable predictions as well as pinpointing desired new knowledge and data to further empower the current KGML-ag. We believe the KGML-ag development in this study will stimulate a new body of research on interpretable ML for biogeochemistry and other related geoscience processes.
- Published
- 2022
3. Surface Resistance Controls Differences in Evapotranspiration Between Croplands and Prairies in U.S. Corn Belt Sites
- Author
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Schreiner‐McGraw, Adam P., primary, Baker, John M., additional, Wood, Jeffrey D., additional, Abraha, Michael, additional, Chen, Jiquan, additional, Griffis, Timothy J., additional, and Robertson, G. Phillip, additional
- Published
- 2024
- Full Text
- View/download PDF
4. Seasonal Variations of CH4 Emissions in the Yangtze River Delta Region of China Are Driven by Agricultural Activities
- Author
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Huang, Wenjing, Griffis, Timothy J., Hu, Cheng, Xiao, Wei, and Lee, Xuhui
- Published
- 2021
- Full Text
- View/download PDF
5. Warming temperatures lead to reduced summer carbon sequestration in the U.S. Corn Belt
- Author
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Yu, Zhongjie, Griffis, Timothy J., and Baker, John M.
- Published
- 2021
- Full Text
- View/download PDF
6. Nitrous oxide emissions are enhanced in a warmer and wetter world
- Author
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Griffis, Timothy J., Chen, Zichong, Baker, John M., Wood, Jeffrey D., Millet, Dylan B., Lee, Xuhui, Venterea, Rodney T., and Turner, Peter A.
- Published
- 2017
7. Indirect nitrous oxide emissions from streams within the US Corn Belt scale with stream order
- Author
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Turner, Peter A., Griffis, Timothy J., Lee, Xuhui, Baker, John M., Venterea, Rodney T., and Wood, Jeffrey D.
- Published
- 2015
8. Anthropogenic CO2 emission reduction during the COVID-19 pandemic in Nanchang City, China
- Author
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Hu, Cheng, primary, Griffis, Timothy J., additional, Xia, Lingjun, additional, Xiao, Wei, additional, Liu, Cheng, additional, Xiao, Qitao, additional, Huang, Xin, additional, Yang, Yanrong, additional, Zhang, Leying, additional, and Hou, Bo, additional
- Published
- 2022
- Full Text
- View/download PDF
9. Three Gorges Dam Operations Affect the Carbon Dioxide Budget of a Large Downstream Connected Lake.
- Author
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Zhao, Xiaosong, Fan, Xingwang, Griffis, Timothy J., Xiao, Ke, Li, Xiang, Liu, Yuanbo, Lai, Xijun, Wan, Rongrong, and Li, Tingting
- Subjects
SAN Xia Dam (China) ,DAM retirement ,ENDORHEIC lakes ,GORGES ,DAMS ,CARBON cycle ,LAKES ,CARBON dioxide - Abstract
The effects of dams on carbon dioxide (CO2) fluxes in downstream lakes remain elusive. Here we combined eddy covariance observations and random forest models to examine multi‐decadal variations in CO2 fluxes in the Poyang Lake, the largest freshwater lake in China, and quantified the contribution of the Three Gorges Dam (TGD), the world's largest hydraulic project. We found the lake fluctuated between CO2 source and sink in 1961–2016, and tended to be CO2 sink in the post‐TGD period (2003–2016) when vegetation expanded early and spatially due to declining water level. TGD can explain approximately 6% of the total differences in annual CO2 fluxes, with major contributions in the impoundment period (up to 22% in middle September to October). The results show a positive side of operational major hydraulic projects on lake carbon sink, and probably caution the negative side of carbon release after dam removal. Plain Language Summary: In the past century, dams have significantly altered the hydrological connectivity between rivers and lakes, which affect CO2 exchange in the downstream lake systems. As the largest freshwater lake in China, Poyang Lake has also undergone drastic hydrological changes, attributable largely to the operation of the Three Gorges Dam (TGD), the world's largest hydraulic project ever, in 2003. Based on flux observations and machine learning method, we show that annual lake CO2 exchange shifted toward carbon sink during 1961–2016. The TGD has a major impact on lake CO2 fluxes, especially during the impoundment stage in middle September–October, explaining 22% of the flux differences between the pre‐ and post‐TGD period. The results show a positive side of hydraulic projects albeit their adverse impact on ecological protection. Key Points: Poyang Lake as a CO2 source or sink significantly depends on water levelPoyang Lake became a CO2 sink since the Three Gorges Dam operation in 2003Dam explains 22% of differences in CO2 fluxes in autumn impoundment period [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
10. The global biogeography of soil priming effect intensity
- Author
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Ren, Chengjie, primary, Mo, Fei, additional, Zhou, Zhenghu, additional, Bastida, Felipe, additional, Delgado‐Baquerizo, Manuel, additional, Wang, Jieying, additional, Zhang, Xinyi, additional, Luo, Yiqi, additional, Griffis, Timothy J., additional, Han, Xinhui, additional, Wei, Gehong, additional, Wang, Jun, additional, Zhong, Zekun, additional, Feng, Yongzhong, additional, Ren, Guangxin, additional, Wang, Xiaojiao, additional, Yu, Kailiang, additional, Zhao, Fazhu, additional, Yang, Gaihe, additional, and Yuan, Fenghui, additional
- Published
- 2022
- Full Text
- View/download PDF
11. The global biogeography of soil priming effect intensity
- Author
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National Natural Science Foundation of China, China Postdoctoral Science Foundation, Chinese Academy of Sciences, Shaanxi Province, National Forestry and Grassland Administration (China), Ren, Chengjie [0000-0003-4959-3129], Zhou, Zhenghu [0000-0001-9958-7099], Bastida, F. [0000-0001-9958-7099], Delgado-Baquerizo, Manuel [0000-0002-6499-576X], Zhang, Xinyi [0000-0002-7124-4278], Han, Xinhui [0000-0002-7124-4278], Wang, Jun [0000-0002-8011-3149], Yu, Kailiang [0000-0003-4223-5169], Zhao, Fazhu [0000-0003-4758-3277], Yang, Gaihe [0000-0002-6076-4104], Yuan, Fenghui [0000-0003-1004-873X], Ren, Chengjie, Mo, Fei, Zhou, Zhenghu, Bastida, F., Delgado-Baquerizo, Manuel, Wang, Jieying, Zhang, Xinyi, Luo, Yiqi, Griffis, Timothy J., Han, Xinhui, Wei, Gehong, Wang, Jun, Zhong, Zekun, Feng, Yongzhong, Ren, Guangxin, Wang, Xiaojiao, Yu, Kailiang, Zhao, Fazhu, Yang, Gaihe, Yuan, Fenghui, National Natural Science Foundation of China, China Postdoctoral Science Foundation, Chinese Academy of Sciences, Shaanxi Province, National Forestry and Grassland Administration (China), Ren, Chengjie [0000-0003-4959-3129], Zhou, Zhenghu [0000-0001-9958-7099], Bastida, F. [0000-0001-9958-7099], Delgado-Baquerizo, Manuel [0000-0002-6499-576X], Zhang, Xinyi [0000-0002-7124-4278], Han, Xinhui [0000-0002-7124-4278], Wang, Jun [0000-0002-8011-3149], Yu, Kailiang [0000-0003-4223-5169], Zhao, Fazhu [0000-0003-4758-3277], Yang, Gaihe [0000-0002-6076-4104], Yuan, Fenghui [0000-0003-1004-873X], Ren, Chengjie, Mo, Fei, Zhou, Zhenghu, Bastida, F., Delgado-Baquerizo, Manuel, Wang, Jieying, Zhang, Xinyi, Luo, Yiqi, Griffis, Timothy J., Han, Xinhui, Wei, Gehong, Wang, Jun, Zhong, Zekun, Feng, Yongzhong, Ren, Guangxin, Wang, Xiaojiao, Yu, Kailiang, Zhao, Fazhu, Yang, Gaihe, and Yuan, Fenghui
- Abstract
Aim Fresh carbon (C) inputs to the soil can have important consequences for the decomposition rates of soil organic matter (priming effect), thereby impacting the delicate global C balance at the soil-atmosphere interface. Yet, the environmental factors that control soil priming effect intensity remain poorly understood at a global scale. Location Global. Time period 1980-2020. Major taxa studied Soil priming effect intensity. Methods We conducted a global dataset of CO2 effluxes in 711 pairwise soils with C-13 or C-14 simple C sources inputs and without C inputs from incubation experiments in which isotope-labelled C was used to quantify fresh C-induced rather than exudate-induced priming. Results Soil priming effect intensity is predominantly positive. Soil texture and C content were identified as the most important factors associated with priming effects, with sandy soils from tropical and mid-latitudes supporting the highest soil priming effect intensity, and soils with greater C content and fine textures from high latitudes maintaining the lowest soil priming effects. The negative association between C content and soil priming effect intensity was also indirectly driven by changing mean annual temperature, net primary productivity, and fungi : bacteria ratio. Using this information, we generated a global map of soil priming effect intensity, and found that the priming was lower at high latitudes and higher at lower latitudes. Main conclusions Global patterns of soil priming effect intensity can be predicted using environmental data, with soil texture and C content playing a predominant role in explaining in priming effects. These effects were also indirectly driven by climate, vegetation and soil microbial properties. We present the first global atlas of soil priming effect intensity and advance our knowledge on the potential mechanisms underlying soil priming effect intensity, which are integral to improving the climate change and soil C dynamics components of
- Published
- 2022
12. Quantifying nitrous oxide fluxes on multiple spatial scales in the Upper Midwest, USA
- Author
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Zhang, Xin, Lee, Xuhui, Griffis, Timothy J., Andrews, Arlyn E., Baker, John M., Erickson, Matt D., Hu, Ning, and Xiao, Wei
- Published
- 2015
- Full Text
- View/download PDF
13. KGML-ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: A Case Study of Estimating N2O Emission using Data from Mesocosm Experiments
- Author
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Liu, Licheng, Xu, Shaoming, Tang, Jinyun, Guan, Kaiyu, Griffis, Timothy J, Erickson, Matthew D, Frie, Alexander L, Jia, Xiaowei, Kim, Taegon, Miller, Lee T, Peng, Bin, Wu, Shaowei, Yang, Yufeng, Zhou, Wang, Kumar, Vipin, and Jin, Zhenong
- Subjects
Climate Action ,Earth Sciences - Abstract
Agricultural nitrous oxide (N2O) emission accounts for a non-trivial fraction of global greenhouse gas (GHG) budget. To date, estimating N2O fluxes from cropland remains a challenging task because the related microbial processes (e.g., nitrification and denitrification) are controlled by complex interactions among climate, soil, plant and human activities. Existing approaches such as process-based (PB) models have well-known limitations due to insufficient representations of the processes or uncertainties of model parameters, and due to leverage recent advances in machine learning (ML) a new method is needed to unlock the “black box” to overcome its limitations such as low interpretability, out-of-sample failure and massive data demand. In this study, we developed a first-of-its-kind knowledge-guided machine learning model for agroecosystems (KGML-ag) by incorporating biogeophysical and chemical domain knowledge from an advanced PB model, ecosys, and tested it by comparing simulating daily N2O fluxes with real observed data from mesocosm experiments. The gated recurrent unit (GRU) was used as the basis to build the model structure. To optimize the model performance, we have investigated a range of ideas, including (1) using initial values of intermediate variables (IMVs) instead of time series as model input to reduce data demand; (2) building hierarchical structures to explicitly estimate IMVs for further N2O prediction; (3) using multi-task learning to balance the simultaneous training on multiple variables; and (4) pre-training with millions of synthetic data generated from ecosys and fine-tuning with mesocosm observations. Six other pure ML models were developed using the same mesocosm data to serve as the benchmark for the KGML-ag model. Results show that KGML-ag did an excellent job in reproducing the mesocosm N2O fluxes (overall r2=0.81, and RMSE=3.6 mgNm-2d-1 from cross validation). Importantly, KGML-ag always outperforms the PB model and ML models in predicting N2O fluxes, especially for complex temporal dynamics and emission peaks. Besides, KGML-ag goes beyond the pure ML models by providing more interpretable predictions as well as pinpointing desired new knowledge and data to further empower the current KGML-ag. We believe the KGML-ag development in this study will stimulate a new body of research on interpretable ML for biogeochemistry and other related geoscience processes.
- Published
- 2021
14. The influence of plants on atmospheric methane in an agriculture-dominated landscape
- Author
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Zhang, Xin, Lee, Xuhui, Griffis, Timothy J., Baker, John M., Erickson, Matt D., Hu, Ning, and Xiao, Wei
- Published
- 2014
- Full Text
- View/download PDF
15. Seasonality in the Surface Energy Balance of Tundra in the Lower Mackenzie River Basin
- Author
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Rouse, Wayne R., Eaton, Andrea K., Petrone, Richard M., Boudreau, L. Dale, Marsh, Philip, and Griffis, Timothy J.
- Published
- 2003
16. Anthropogenic and natural controls on atmospheric δ13C-CO2 variations in the Yangtze River delta: insights from a carbon isotope modeling framework
- Author
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Hu, Cheng, Xu, Jiaping, Liu, Cheng, Chen, Yan, Yang, Dong, Huang, Wenjing, Deng, Lichen, Liu, Shoudong, Griffis, Timothy J., and Lee, Xuhui
- Subjects
TheoryofComputation_MATHEMATICALLOGICANDFORMALLANGUAGES - Abstract
The atmospheric carbon dioxide (CO2) mixing ratio and its carbon isotope (δ13C-CO2) composition contain important CO2 sink and source information spanning from ecosystem to global scales. The observation and simulation for both CO2 and δ13C-CO2 can be used to constrain regional emissions and better understand the anthropogenic and natural mechanisms that control δ13C-CO2 variations. Such work remains rare for urban environments, especially megacities. Here, we used near-continuous CO2 and δ13C-CO2 measurements, from September 2013 to August 2015, and inverse modeling to constrain the CO2 budget and investigate the main factors that dominated δ13C-CO2 variations for the Yangtze River delta (YRD) region, one of the largest anthropogenic CO2 hotspots and densely populated regions in China. We used the WRF-STILT model framework with category-specified EDGAR v4.3.2 CO2 inventories to simulate hourly CO2 mixing ratios and δ13C-CO2, evaluated these simulations with observations, and constrained the total anthropogenic CO2 emission. We show that (1) top-down and bottom-up estimates of anthropogenic CO2 emissions agreed well (bias < 6 %) on an annual basis, (2) the WRF-STILT model can generally reproduce the observed diel and seasonal atmospheric δ13C-CO2 variations, and (3) anthropogenic CO2 emissions played a much larger role than ecosystems in controlling the δ13C-CO2 seasonality. When excluding ecosystem respiration and photosynthetic discrimination in the YRD area, δ13C-CO2 seasonality increased from 1.53 ‰ to 1.66 ‰. (4) Atmospheric transport processes in summer amplified the cement CO2 enhancement proportions in the YRD area, which dominated monthly δs (the mixture of δ13C-CO2 from all regional end-members) variations. These findings show that the combination of long-term atmospheric carbon isotope observations and inverse modeling can provide a powerful constraint on the carbon cycle of these complex megacities.
- Published
- 2021
17. Anthropogenic and natural controls on atmospheric <i>δ</i><sup>13</sup>C-CO<sub>2</sub> variations in the Yangtze River delta: insights from a carbon isotope modeling framework
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Hu, Cheng, primary, Xu, Jiaping, additional, Liu, Cheng, additional, Chen, Yan, additional, Yang, Dong, additional, Huang, Wenjing, additional, Deng, Lichen, additional, Liu, Shoudong, additional, Griffis, Timothy J., additional, and Lee, Xuhui, additional
- Published
- 2021
- Full Text
- View/download PDF
18. Fossil Versus Nonfossil CO Sources in the US: New Airborne Constraints From ACT‐America and GEM
- Author
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Gonzalez, Andres, primary, Millet, Dylan B., additional, Yu, Xueying, additional, Wells, Kelley C., additional, Griffis, Timothy J., additional, Baier, Bianca C., additional, Campbell, Patrick C., additional, Choi, Yonghoon, additional, DiGangi, Joshua P., additional, Gvakharia, Alexander, additional, Halliday, Hannah S., additional, Kort, Eric A., additional, McKain, Kathryn, additional, Nowak, John B., additional, and Plant, Genevieve, additional
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- 2021
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19. A Multiyear Constraint on Ammonia Emissions and Deposition Within the US Corn Belt
- Author
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Hu, Cheng, primary, Griffis, Timothy J., additional, Frie, Alexander, additional, Baker, John M., additional, Wood, Jeffrey D., additional, Millet, Dylan B., additional, Yu, Zhongjie, additional, Yu, Xueying, additional, and Czarnetzki, Alan C., additional
- Published
- 2021
- Full Text
- View/download PDF
20. Anthropogenic and natural controls on atmospheric δ13C-CO2 variations in the Yangtze River Delta: Insights from a carbon isotope modeling framework
- Author
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Hu, Cheng, Xu, Jiaping, Liu, Cheng, Chen, Yan, Yang, Dong, Huang, Wenjing, Deng, Lichen, Liu, Shoudong, Griffis, Timothy J., and Lee, Xuhui
- Abstract
The atmospheric CO2 mixing ratio and its δ13C-CO2 composition contain important CO2 sink and source information spanning from ecosystem to global scales. The observation and simulation for both CO2 and its carbon isotope ratio (δ13C-CO2) can be used to constrain regional emissions and better understand the anthropogenic and natural mechanisms that control δ13C-CO2 variations. Such work remains rare for urban environments, especially megacities. Here, we used near-continuous CO2 and δ13C-CO2 measurements, from September 2013 to August 2015, and inverse modeling to constrain the CO2 budget and investigate the main factors that dominated δ13C-CO2 variations for the Yangtze River Delta (YRD) region, one of the largest anthropogenic CO2 hotspots and densely populated regions in China. We used the WRF-STILT model framework with category-specified EDGAR v432 CO2 inventories to simulate hourly CO2 mixing ratios and δ13C-CO2, evaluated these simulations with observations, and constrained the anthropogenic CO2 emission categories. Our study shows that: (1) Top-down and bottom-up estimates of anthropogenic CO2 emissions agreed well (bias δ13C-CO2 variations; (3) Anthropogenic CO2 emissions played a much larger role than ecosystems in controlling the δ13C-CO2 seasonality. When excluding ecosystem respiration and photosynthetic discrimination in the YRD area, δ13C-CO2 seasonality increased from 1.53 ‰ to 1.66 ‰; (4) Atmospheric transport processes in summer amplified the cement CO2 enhancement proportions in the YRD area, which dominated monthly δs variations. These findings support that the combination of long-term atmospheric carbon isotope observations and inverse modeling can provide a powerful constraint on the carbon cycle of these complex megacities.
- Published
- 2020
21. ECOSTRESS: NASA's next generation mission to measure evapotranspiration from the International Space Station
- Author
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Fisher, Joshua B., Lee, Brian, Purdy, Adam J., Halverson, Gregory H., Dohlen, Matthew B., Cawse‐Nicholson, Kerry, Wang, Audrey, Anderson, Ray G., Aragon, Bruno, Arain, M. Altaf, Baldocchi, Dennis D., Baker, John M., Barral, Hélène, Bernacchi, Carl J., Christian, Bernhofer, Biraud, Sébastien C., Bohrer, Gil, Brunsell, Nathaniel, Cappelaere, Bernard, Castro‐Contreras, Saulo, Chun, Junghwa, Conrad, Bryan J., Cremonese, Edoardo, Demarty, Jérôme, Desai, Ankur R., De Ligne, Anne, Foltýnová, Lenka, Goulden, Michael L., Griffis, Timothy J., Grünwald, Thomas, Johnson, Mark S., Kang, Minseok, Kelbe, Dave, Kowalska, Natalia, Lim, Jong‐Hwan, Maïnassara, Ibrahim, McCabe, Matthew F., Missik, Justine E.C., Mohanty, Binayak P., Moore, Caitlin E., Morillas, Laura, Morrison, Ross, Munger, J. William, Posse, Gabriela, Richardson, Andrew D., Russell, Eric S., Ryu, Youngryel, Sanchez‐Azofeifa, Arturo, Schmidt, Marius, Schwartz, Efrat, Sharp, Iain, Šigut, Ladislav, Tang, Yao, Hulley, Glynn, Anderson, Martha, Hain, Christopher, French, Andrew, Wood, Eric, Hook, Simon, Fisher, Joshua B., Lee, Brian, Purdy, Adam J., Halverson, Gregory H., Dohlen, Matthew B., Cawse‐Nicholson, Kerry, Wang, Audrey, Anderson, Ray G., Aragon, Bruno, Arain, M. Altaf, Baldocchi, Dennis D., Baker, John M., Barral, Hélène, Bernacchi, Carl J., Christian, Bernhofer, Biraud, Sébastien C., Bohrer, Gil, Brunsell, Nathaniel, Cappelaere, Bernard, Castro‐Contreras, Saulo, Chun, Junghwa, Conrad, Bryan J., Cremonese, Edoardo, Demarty, Jérôme, Desai, Ankur R., De Ligne, Anne, Foltýnová, Lenka, Goulden, Michael L., Griffis, Timothy J., Grünwald, Thomas, Johnson, Mark S., Kang, Minseok, Kelbe, Dave, Kowalska, Natalia, Lim, Jong‐Hwan, Maïnassara, Ibrahim, McCabe, Matthew F., Missik, Justine E.C., Mohanty, Binayak P., Moore, Caitlin E., Morillas, Laura, Morrison, Ross, Munger, J. William, Posse, Gabriela, Richardson, Andrew D., Russell, Eric S., Ryu, Youngryel, Sanchez‐Azofeifa, Arturo, Schmidt, Marius, Schwartz, Efrat, Sharp, Iain, Šigut, Ladislav, Tang, Yao, Hulley, Glynn, Anderson, Martha, Hain, Christopher, French, Andrew, Wood, Eric, and Hook, Simon
- Abstract
The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station ECOSTRESS) was launched to the International Space Station on June 29, 2018. The primary science focus of ECOSTRESS is centered on evapotranspiration (ET), which is produced as level‐3 (L3) latent heat flux (LE) data products. These data are generated from the level‐2 land surface temperature and emissivity product (L2_LSTE), in conjunction with ancillary surface and atmospheric data. Here, we provide the first validation (Stage 1, preliminary) of the global ECOSTRESS clear‐sky ET product (L3_ET_PT‐JPL, version 6.0) against LE measurements at 82 eddy covariance sites around the world. Overall, the ECOSTRESS ET product performs well against the site measurements (clear‐sky instantaneous/time of overpass: r2 = 0.88; overall bias = 8%; normalized RMSE = 6%). ET uncertainty was generally consistent across climate zones, biome types, and times of day (ECOSTRESS samples the diurnal cycle), though temperate sites are over‐represented. The 70 m high spatial resolution of ECOSTRESS improved correlations by 85%, and RMSE by 62%, relative to 1 km pixels. This paper serves as a reference for the ECOSTRESS L3 ET accuracy and Stage 1 validation status for subsequent science that follows using these data.
- Published
- 2020
22. Aircraft-based inversions quantify the importance of wetlands and livestock for Upper Midwest methane emissions
- Author
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Yu, Xueying, primary, Millet, Dylan B., additional, Wells, Kelley C., additional, Henze, Daven K., additional, Cao, Hansen, additional, Griffis, Timothy J., additional, Kort, Eric A., additional, Plant, Genevieve, additional, Deventer, Malte J., additional, Kolka, Randall K., additional, Roman, D. Tyler, additional, Davis, Kenneth J., additional, Desai, Ankur R., additional, Baier, Bianca C., additional, McKain, Kathryn, additional, Czarnetzki, Alan C., additional, and Bloom, A. Anthony, additional
- Published
- 2021
- Full Text
- View/download PDF
23. Evaluation of a CONUS-Wide ECOSTRESS DisALEXI Evapotranspiration Product
- Author
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Cawse-Nicholson, Kerry, primary, Anderson, Martha C., additional, Yang, Yang, additional, Yang, Yun, additional, Hook, Simon J., additional, Fisher, Joshua B., additional, Halverson, Gregory, additional, Hulley, Glynn C., additional, Hain, Christopher, additional, Baldocchi, Dennis D., additional, Brunsell, Nathaniel A., additional, Desai, Ankur R., additional, Griffis, Timothy J., additional, and Novick, Kimberly A., additional
- Published
- 2021
- Full Text
- View/download PDF
24. The annual carbon budget for fen and forest in a wetland at Arctic treeline
- Author
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Rouse, Wayne R., Bello, Richard L., D'Souza, Alberta, Griffis, Timothy J., and Lafleur, Peter M.
- Subjects
Soils -- Carbon content ,Soil chemistry -- Research -- Statistics -- Environmental aspects ,Tundra ecology -- Statistics -- Research -- Environmental aspects ,Wetlands -- Environmental aspects -- Research -- Statistics ,Earth sciences ,Regional focus/area studies ,Statistics ,Composition ,Research ,Environmental aspects - Abstract
ABSTRACT. Three separate research efforts conducted in the same wetland-peatland system in the northern Hudson Bay Lowland near the town of Churchill, Manitoba, allow a comparison of two carbon budget [...]
- Published
- 2002
25. Climate Sensitivity of Peatland Methane Emissions Mediated by Seasonal Hydrologic Dynamics
- Author
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Feng, Xue, primary, Deventer, M. Julian, additional, Lonchar, Rachel, additional, Ng, G. H. Crystal, additional, Sebestyen, Stephen D., additional, Roman, D. Tyler, additional, Griffis, Timothy J., additional, Millet, Dylan B., additional, and Kolka, Randall K., additional
- Published
- 2020
- Full Text
- View/download PDF
26. KGML-ag: A Modeling Framework of Knowledge-Guided Machine Learning to Simulate Agroecosystems: A Case Study of Estimating N2O Emission using Data from Mesocosm Experiments.
- Author
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Licheng Liu, Shaoming Xu, Zhenong Jin, Jinyun Tang, Kaiyu Guan, Griffis, Timothy J., Erickson, Matthew D., Frie, Alexander L., Xiaowei Jia, Taegon Kim, Miller, Lee T., Bin Peng, Shaowei Wu, Yufeng Yang, Wang Zhou, and Vipin Kumar
- Subjects
MACHINE learning ,AGRICULTURAL ecology ,NITROUS oxide ,GREENHOUSE gases ,TIME series analysis - Abstract
Agricultural nitrous oxide (N
2 O) emission accounts for a non-trivial fraction of global greenhouse gases (GHGs) budget. To date, estimating N2 O fluxes from cropland remains a challenging task because the related microbial processes (e.g., nitrification and denitrification) are controlled by complex interactions among climate, soil, plant and human activities. Existing approaches such as process-based (PB) models have well-known limitations due to insufficient representations of the processes or constraints of model parameters, and to leverage recent advances in machine learning (ML) new method is needed to unlock the 'black box' to overcome its limitations due to low interpretability, out-of- sample failure and massive data demand. In this study, we developed a first of its kind knowledge-guided machine learning model for agroecosystems (KGML-ag), by incorporating biogeophysical/chemical domain knowledge from an advanced PB model, ecosys, and tested it by simulating daily N2 O fluxes with real observed data from mesocosm experiments. The Gated Recurrent Unit (GRU) was used as the basis to build the model structure. To optimize the model performance, we have investigated a range of ideas, including: 1) Using initials of intermediate variables (IMVs) instead of time series as model input to reduce data demand; 2) Building hierarchical structures to explicitly estimate IMVs for further N2 O prediction; 3) Using multitask learning to balance the simultaneous training on multiple variables; and 4) Pretraining with millions of synthetic data generated from ecosys and fine tuning with mesocosm observations. Six other pure ML models were developed using the same mesocosm data to serve as the benchmark for the KGML-ag model. Results show that KGML-ag did an excellent job in reproducing the mesocosm N2 O fluxes (overall r2 = 0.81, and RMSE = 3.6 mg N m-2 day-1 from cross-validation). Importantly KGML-ag always outperforms the PB model and ML models in predicting N2 O fluxes, especially for complex temporal dynamics and emission peaks. Besides, KGML-ag goes beyond the pure ML models by providing more interpretable predictions as well as pinpointing desired new knowledge and data to further empower the current KGML- ag. We believe the KGML-ag development in this study will stimulate a new body of research on interpretable ML for biogeochemistry and other related geoscience processes. [ABSTRACT FROM AUTHOR]- Published
- 2021
- Full Text
- View/download PDF
27. Bidirectional Ecosystem-Atmosphere Fluxes of Volatile Organic Compounds Across the Mass Spectrum: How Many Matter?
- Author
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Millet, Dylan B., Alwe, Hariprasad D., Chen, Xin, Deventer, Malte Julian, Griffis, Timothy J., Holzinger, Rupert, Bertman, Steven B., Rickly, Pamela S., Stevens, Philip S., Léonardis, Thierry, Locoge, Nadine, Dusanter, Sébastien, Tyndall, Geoffrey S., Alvarez, Sergio L., Erickson, Matthew H., Flynn, James H., Sub Plant Ecology and Biodiversity begr., Sub Atmospheric physics and chemistry, Marine and Atmospheric Research, Sub Plant Ecology and Biodiversity begr., Sub Atmospheric physics and chemistry, Marine and Atmospheric Research, University of Minnesota, St Paul, MN United States, Utrecht University [Utrecht], Western Michigan University [Kalamazoo], Indiana University [Bloomington], Indiana University System, Centre for Energy and Environment (CERI EE), Ecole nationale supérieure Mines-Télécom Lille Douai (IMT Lille Douai), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Institut Mines-Télécom [Paris] (IMT), National Center for Atmospheric Research [Boulder] (NCAR), and University of Houston
- Subjects
Atmospheric Science ,010504 meteorology & atmospheric sciences ,Chemical transport model ,Eddy covariance ,010501 environmental sciences ,Atmospheric sciences ,Mass spectrometry ,01 natural sciences ,deposition ,Article ,Sink (geography) ,Flux (metallurgy) ,Geochemistry and Petrology ,volatile organic compounds ,eddy covariance ,chemical transport model ,ComputingMilieux_MISCELLANEOUS ,0105 earth and related environmental sciences ,geography ,geography.geographical_feature_category ,emissions ,Temperate forest ,15. Life on land ,flux ,reactivity ,13. Climate action ,Space and Planetary Science ,[SDE]Environmental Sciences ,Mass spectrum ,Environmental science ,Terrestrial ecosystem - Abstract
Terrestrial ecosystems are simultaneously the largest source and a major sink of volatile organic compounds (VOCs) to the global atmosphere, and these two-way fluxes are an important source of uncertainty in current models. Here, we apply high-resolution mass spectrometry (proton transfer reaction-quadrupole interface time-of-flight; PTR-QiTOF) to measure ecosystem-atmosphere VOC fluxes across the entire detected mass range (m/z 0-335) over a mixed temperate forest and use the results to test how well a state-of-science chemical transport model (GEOS-Chem CTM) is able to represent the observed reactive carbon exchange. We show that ambient humidity fluctuations can give rise to spurious VOC fluxes with PTR-based techniques and present a method to screen for such effects. After doing so, 377 of the 636 detected ions exhibited detectable gross fluxes during the study, implying a large number of species with active ecosystem-atmosphere exchange. We introduce the reactivity flux as a measure of how Earth-atmosphere fluxes influence ambient OH reactivity and show that the upward total VOC (-VOC) carbon and reactivity fluxes are carried by a far smaller number of species than the downward fluxes. The model underpredicts the -VOC carbon and reactivity fluxes by 40-60% on average. However, the observed net fluxes are dominated (90% on a carbon basis, 95% on a reactivity basis) by known VOCs explicitly included in the CTM. As a result, the largest CTM uncertainties in simulating VOC carbon and reactivity exchange for this environment are associated with known rather than unrepresented species. This conclusion pertains to the set of species detectable by PTR-TOF techniques, which likely represents the majority in terms of carbon mass and OH reactivity, but not necessarily in terms of aerosol formation potential. In the case of oxygenated VOCs, the model severely underpredicts the gross fluxes and the net exchange. Here, unrepresented VOCs play a larger role, accounting for ∼30% of the carbon flux and ∼50% of the reactivity flux. The resulting CTM biases, however, are still smaller than those that arise from uncertainties for known and represented compounds.
- Published
- 2018
28. Top-down constraints on global N2O emissions at optimal resolution:Application of a new dimension reduction technique
- Author
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Wells, Kelley C., Millet, Dylan B., Bousserez, Nicolas, Henze, Daven K., Griffis, Timothy J., Chaliyakunnel, Sreelekha, Dlugokencky, Edward J., Saikawa, Eri, Xiang, Gao, Prinn, Ronald G., O'Doherty, Simon, Young, Dickon, Weiss, Ray F., Dutton, Geoff S., Elkins, James W., Krummel, Paul B., Langenfelds, Ray, and Paul Steele, L.
- Abstract
We present top-down constraints on global monthly N2O emissions for 2011 from a multi-inversion approach and an ensemble of surface observations. The inversions employ the GEOS-Chem adjoint and an array of aggregation strategies to test how well current observations can constrain the spatial distribution of global N2O emissions. The strategies include (1) a standard 4D-Var inversion at native model resolution (4° × 5°), (2) an inversion for six continental and three ocean regions, and (3) a fast 4D-Var inversion based on a novel dimension reduction technique employing randomized singular value decomposition (SVD). The optimized global flux ranges from 15.9 TgNyr-1 (SVD-based inversion) to 17.5-17.7 TgNyr-1 (continental-scale, standard 4D-Var inversions), with the former better capturing the extratropical N2O background measured during the HIAPER Pole-to-Pole Observations (HIPPO) airborne campaigns. We find that the tropics provide a greater contribution to the global N2O flux than is predicted by the prior bottom-up inventories, likely due to underestimated agricultural and oceanic emissions. We infer an overestimate of natural soil emissions in the extratropics and find that predicted emissions are seasonally biased in northern midlatitudes. Here, optimized fluxes exhibit a springtime peak consistent with the timing of spring fertilizer and manure application, soil thawing, and elevated soil moisture. Finally, the inversions reveal a major emission underestimate in the US Corn Belt in the bottom-up inventory used here. We extensively test the impact of initial conditions on the analysis and recommend formally optimizing the initial N2O distribution to avoid biasing the inferred fluxes. We find that the SVD-based approach provides a powerful framework for deriving emission information from N2O observations: by defining the optimal resolution of the solution based on the information content of the inversion, it provides spatial information that is lost when aggregating to political or geographic regions, while also providing more temporal information than a standard 4D-Var inversion.
- Published
- 2018
29. Top-down constraints on global N[subscript 2]O emissions at optimal resolution: application of a new dimension reduction technique
- Author
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Wells, Kelley C., Millet, Dylan B., Bousserez, Nicolas, Henze, Daven K., Griffis, Timothy J., Chaliyakunnel, Sreelekha, Dlugokencky, Edward J., Saikawa, Eri, Prinn, Ronald G., Young, Dickon, Weiss, Ray F., Dutton, Geoff S., Elkins, James W., Krummel, Paul B., Langenfelds, Ray, Steele, L. Paul, O'Doherty, Simon, Xiang, Gao, Massachusetts Institute of Technology. Joint Program on the Science & Policy of Global Change, and Xiang, Gao
- Abstract
We present top-down constraints on global monthly N[subscript 2]O emissions for 2011 from a multi-inversion approach and an ensemble of surface observations. The inversions employ the GEOS-Chem adjoint and an array of aggregation strategies to test how well current observations can constrain the spatial distribution of global N[subscript 2]O emissions. The strategies include (1) a standard 4D-Var inversion at native model resolution (4° × 5°), (2) an inversion for six continental and three ocean regions, and (3) a fast 4D-Var inversion based on a novel dimension reduction technique employing randomized singular value decomposition (SVD). The optimized global flux ranges from 15.9 Tg N yr[superscript −1] (SVD-based inversion) to 17.5–17.7 Tg N yr[superscript −1] (continental-scale, standard 4D-Var inversions), with the former better capturing the extratropical N[subscript 2]O background measured during the HIAPER Pole-to-Pole Observations (HIPPO) airborne campaigns. We find that the tropics provide a greater contribution to the global N[superscript 2]O flux than is predicted by the prior bottom-up inventories, likely due to underestimated agricultural and oceanic emissions. We infer an overestimate of natural soil emissions in the extratropics and find that predicted emissions are seasonally biased in northern midlatitudes. Here, optimized fluxes exhibit a springtime peak consistent with the timing of spring fertilizer and manure application, soil thawing, and elevated soil moisture. Finally, the inversions reveal a major emission underestimate in the US Corn Belt in the bottom-up inventory used here. We extensively test the impact of initial conditions on the analysis and recommend formally optimizing the initial N[superscript 2]O distribution to avoid biasing the inferred fluxes. We find that the SVD-based approach provides a powerful framework for deriving emission information from N2O observations: by defining the optimal resolution of the solution based on the information content of the inversion, it provides spatial information that is lost when aggregating to political or geographic regions, while also providing more temporal information than a standard 4D-Var inversion., United States. National Oceanic and Atmospheric Administration (Grant NA13OAR4310086), United States. National Oceanic and Atmospheric Administration (Grant NA13OAR4310081)
- Published
- 2017
30. Bidirectional Ecosystem-Atmosphere Fluxes of Volatile Organic Compounds Across the Mass Spectrum: How Many Matter?
- Author
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Sub Plant Ecology and Biodiversity begr., Sub Atmospheric physics and chemistry, Marine and Atmospheric Research, Millet, Dylan B., Alwe, Hariprasad D., Chen, Xin, Deventer, Malte Julian, Griffis, Timothy J., Holzinger, Rupert, Bertman, Steven B., Rickly, Pamela S., Stevens, Philip S., Léonardis, Thierry, Locoge, Nadine, Dusanter, Sébastien, Tyndall, Geoffrey S., Alvarez, Sergio L., Erickson, Matthew H., Flynn, James H., Sub Plant Ecology and Biodiversity begr., Sub Atmospheric physics and chemistry, Marine and Atmospheric Research, Millet, Dylan B., Alwe, Hariprasad D., Chen, Xin, Deventer, Malte Julian, Griffis, Timothy J., Holzinger, Rupert, Bertman, Steven B., Rickly, Pamela S., Stevens, Philip S., Léonardis, Thierry, Locoge, Nadine, Dusanter, Sébastien, Tyndall, Geoffrey S., Alvarez, Sergio L., Erickson, Matthew H., and Flynn, James H.
- Published
- 2018
31. Comparing crop growth and carbon budgets simulated across AmeriFlux agricultural sites using the Community Land Model (CLM)
- Author
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Chen, Ming, Griffis, Timothy J., Baker, John M., Wood, Jeffrey D., Meyers, Tilden, Suyker, Andrew E., Chen, Ming, Griffis, Timothy J., Baker, John M., Wood, Jeffrey D., Meyers, Tilden, and Suyker, Andrew E.
- Abstract
Improvement of process-based crop models is needed to achieve high fidelity forecasts of regional energy, water, and carbon exchanges. However, most state-of-the-art Land Surface Models (LSMs) assessed in the fifth phase of the Coupled Model Inter-comparison project (CMIP5) simulated crops as unmanaged C3 or C4 grasses. This study evaluated the crop-enabled version of one of the most widely used LSMs, the Community Land Model (CLM4- Crop), for simulating corn and soybean agro-ecosystems at relatively long-time scales (up to 11 years) using 54 site-years of data. We found that CLM4-Crop had a biased phenology during the early growing season and that carbon emissions from corn and soybean were underestimated. The model adopts universal physiological parameters for all crop types neglecting the fact that different crops have different specific leaf area, leaf nitrogen content and vcmax25, etc. As a result, model performance varied considerably according to crop type. Overall, the energy and carbon exchange of corn systems were better simulated than soybean systems. Long-term simulations at multiple sites showed that gross primary production (GPP) was consistently over-estimated at soybean sites leading to very large short and long-term biases. A modified model, CLM4-CropM’, with optimized phenology and calibrated crop physiological parameters yielded significantly better simulations of gross primary production (GPP), ecosystem respiration (ER) and leaf area index (LAI) at both short (hourly) and long-term (annual to decadal) timescales for both soybean and corn.
- Published
- 2018
32. Top-down constraints on global N[subscript 2]O emissions at optimal resolution: application of a new dimension reduction technique
- Author
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Massachusetts Institute of Technology. Joint Program on the Science & Policy of Global Change, Xiang, Gao, Wells, Kelley C., Millet, Dylan B., Bousserez, Nicolas, Henze, Daven K., Griffis, Timothy J., Chaliyakunnel, Sreelekha, Dlugokencky, Edward J., Saikawa, Eri, Prinn, Ronald G., Young, Dickon, Weiss, Ray F., Dutton, Geoff S., Elkins, James W., Krummel, Paul B., Langenfelds, Ray, Steele, L. Paul, O'Doherty, Simon, Massachusetts Institute of Technology. Joint Program on the Science & Policy of Global Change, Xiang, Gao, Wells, Kelley C., Millet, Dylan B., Bousserez, Nicolas, Henze, Daven K., Griffis, Timothy J., Chaliyakunnel, Sreelekha, Dlugokencky, Edward J., Saikawa, Eri, Prinn, Ronald G., Young, Dickon, Weiss, Ray F., Dutton, Geoff S., Elkins, James W., Krummel, Paul B., Langenfelds, Ray, Steele, L. Paul, and O'Doherty, Simon
- Abstract
We present top-down constraints on global monthly N[subscript 2]O emissions for 2011 from a multi-inversion approach and an ensemble of surface observations. The inversions employ the GEOS-Chem adjoint and an array of aggregation strategies to test how well current observations can constrain the spatial distribution of global N[subscript 2]O emissions. The strategies include (1) a standard 4D-Var inversion at native model resolution (4° × 5°), (2) an inversion for six continental and three ocean regions, and (3) a fast 4D-Var inversion based on a novel dimension reduction technique employing randomized singular value decomposition (SVD). The optimized global flux ranges from 15.9 Tg N yr[superscript −1] (SVD-based inversion) to 17.5–17.7 Tg N yr[superscript −1] (continental-scale, standard 4D-Var inversions), with the former better capturing the extratropical N[subscript 2]O background measured during the HIAPER Pole-to-Pole Observations (HIPPO) airborne campaigns. We find that the tropics provide a greater contribution to the global N[superscript 2]O flux than is predicted by the prior bottom-up inventories, likely due to underestimated agricultural and oceanic emissions. We infer an overestimate of natural soil emissions in the extratropics and find that predicted emissions are seasonally biased in northern midlatitudes. Here, optimized fluxes exhibit a springtime peak consistent with the timing of spring fertilizer and manure application, soil thawing, and elevated soil moisture. Finally, the inversions reveal a major emission underestimate in the US Corn Belt in the bottom-up inventory used here. We extensively test the impact of initial conditions on the analysis and recommend formally optimizing the initial N[superscript 2]O distribution to avoid biasing the inferred fluxes. We find that the SVD-based approach provides a powerful framework for deriving emission information from N2O observations: by defining the optimal resolution of the solution based on the i, United States. National Oceanic and Atmospheric Administration (Grant NA13OAR4310086), United States. National Oceanic and Atmospheric Administration (Grant NA13OAR4310081)
- Published
- 2018
33. Seasonal Variations of CH4Emissions in the Yangtze River Delta Region of China Are Driven by Agricultural Activities
- Author
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Huang, Wenjing, Griffis, Timothy J., Hu, Cheng, Xiao, Wei, and Lee, Xuhui
- Abstract
Developed regions of the world represent a major atmospheric methane (CH4) source, but these regional emissions remain poorly constrained. The Yangtze River Delta (YRD) region of China is densely populated (about 16% of China’s total population) and consists of large anthropogenic and natural CH4sources. Here, atmospheric CH4concentrations measured at a 70-m tall tower in the YRD are combined with a scale factor Bayesian inverse (SFBI) modeling approach to constrain seasonal variations in CH4 emissions. Results indicate that in 2018 agricultural soils (AGS, rice production) were the main driver of seasonal variability in atmospheric CH4concentration. There was an underestimation of emissions from AGS in the a priori inventories (EDGAR—Emissions Database for Global Atmospheric Research v432 or v50), especially during the growing seasons. Posteriori CH4emissions from AGS accounted for 39% (4.58 Tg, EDGAR v432) to 47% (5.21 Tg, EDGAR v50) of the total CH4emissions. The posteriori natural emissions (including wetlands and water bodies) were 1.21 Tg and 1.06 Tg, accounting for 10.1% (EDGAR v432) and 9.5% (EDGAR v50) of total emissions in the YRD in 2018. Results show that the dominant factor for seasonal variations in atmospheric concentration in the YRD was AGS, followed by natural sources. In summer, AGS contributed 42% (EDGAR v432) to 64% (EDGAR v50) of the CH4concentration enhancement while natural sources only contributed about 10% (EDGAR v50) to 15% (EDGAR v432). In addition, the newer version of the EDGAR product (EDGAR v50) provided more reasonable seasonal distribution of CH4emissions from rice cultivation than the old version (EDGAR v432).
- Published
- 2021
- Full Text
- View/download PDF
34. Prediction of Evapotranspiration and Yields of Maize: An Inter-comparison among 29 Maize Models
- Author
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Kimball, Bruce A., Boote, Kenneth J., Hatfield, Jerry L., Ahuja, Laj R., Stöckle, Claudio O., Archontoulis, Sotiris V., Christian Baron, Bruno Basso, Patrick Bertuzzi, Julie Constantin, Delphine Deryng, Benjamin Dumont, Franck Ewert, Thomas Gaiser, Griffis, Timothy J., Hoffmann, Munir P., Qianjing Jiang, Soo-Hyung Kim, Jon Lizaso, Sophie Moulin, Philip Parker, Taru Palusuo, Zhiming Qi Z., Amit Srivastava, Tao, F., Thorp, K., Dennis Timlin, Heidi Webber, Magali Willaume, Williams, K., Ming Chen, Jean-Louis Durand, Sebastian Gayler, Eckart Priesack, Tracy Twine, USDA-ARS : Agricultural Research Service, Agronomy Department, University of Florida [Gainesville] (UF), Agricultural Systems Research Unit, USDA, Biological Systems Engineering, University of Wisconsin-Madison, Department of Agronomy, Purdue University [West Lafayette], Territoires, Environnement, Télédétection et Information Spatiale (UMR TETIS), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS), Department of Geological Sciences and W. K. Kellogg Biological Station, Michigan State University [East Lansing], Michigan State University System-Michigan State University System, Agroclim (AGROCLIM), Institut National de la Recherche Agronomique (INRA), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Computation Institute, Loyola University of Chicago, Dpt. Agronomy, Bio- Engineering and Chemistry, Crop Science Unit, Université de Liège, Gembloux Agro-Bio Tech [Gembloux], Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF), Leibniz Association, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Department of Soil, Water, and Climate, University of Minnesota System, Crop Production Systems in the Tropics, Georg-August-University [Göttingen], Department of Bioresource Engineering [Montréal] (BIOENG), McGill University = Université McGill [Montréal, Canada], Center for Urban Horticulture, University of Washington, Dept. Producción Agraria-CEIGRAM, Universidad Politécnica de Madrid (UPM), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Natural resources institute Finland, Institute of Geographic Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences [Beijing] (CAS), Crop Systems and Global Change Research Unit, Climate Adaptation Scientist Meteorological Office, Unité de Recherche Pluridisciplinaire Prairies et Plantes Fourragères (P3F), Institute of Soil Science and Land Evaluation, Biogeophysics, University of Hohenheim, Institute of Biochemical Plant Pathology, German Research Center for Environmental Health - Helmholtz Center München (GmbH), Department of Soil, Water and Climate, University of Florida [Gainesville], Agricultural Research Service, Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), UE Agroclim (UE AGROCLIM), UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Institute of Crop Science and Resource Conservation, University of Bonn-Division of Plant Nutrition, Georg-August-Universität Göttingen, McGill University, Natural Resources Institute Finland, and United States Department of Agriculture - Agricultural Research Service
- Subjects
consommation en eau ,comparaison de modèles ,[SDV]Life Sciences [q-bio] ,evapotranspiration ,évapotranspiration ,culture de mais ,croissance des cultures ,analyse de rendement ,modèle de croissance ,caracteristique variétale - Abstract
An important aspect that determines the ability of crop growth models to predict growth and yield is their ability to predict the rate of water consumption or evapotranspiration (ET) of the crop, especially for rain-fed crops. If, for example, the predicted ET rate is too high, the simulated crop may exhaust its soil water supply before the next rain event, thereby causing growth and yield predictions that are too low. In a prior inter-comparison among maize growth models, ET predictions varied widely, but no observations of actual ET were available for comparison. Therefore, another study has been initiated under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). This time observations of ET using the eddy covariance technique from an 8-year-long experiment conducted at Ames, IA are being used as the standard. Simulation results from 29 models have been completed. In the first “blind” phase for which only weather, soils, and management information were furnished to the modelers, estimates of seasonal ET varied from about 200 to about 700 mm. A detailed statistical analysis of the daily ET data from 2011, a “typical” rainfall year, showed that, as expected, the median of all the models was more accurate across several criteria (correlation, root mean square error, average difference, regression slope) than any particular model. However, some individual models were better than the median for a particular criteria. Predictions improved somewhat in later stages when the modelers were provided additional leaf area and growth information that allowed them to “calibrate” some of the parameters in their models to account for varietal characteristics, etc.
- Published
- 2016
35. Anthropogenic and natural controls on atmospheric δ13C-CO2 variations in the Yangtze River Delta: Insights from a carbon isotope modeling framework.
- Author
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Cheng Hu, Jiaping Xu, Cheng Liu, Yan Chen, Dong Yang, Wenjing Huang, Lichen Deng, Shoudong Liu, Griffis, Timothy J., and Xuhui Lee
- Abstract
The atmospheric CO
2 mixing ratio and its δ13 C-CO2 composition contain important CO2 sink and source information spanning from ecosystem to global scales. The observation and simulation for both CO2 and its carbon isotope ratio (δ13 C-CO2 ) can be used to constrain regional emissions and better understand the anthropogenic and natural mechanisms that control δ13 C-CO2 variations. Such work remains rare for urban environments, especially megacities. Here, we used near-continuous CO2 and δ13 C-CO2 measurements, from September 2013 to August 2015, and inverse modeling to constrain the CO2 budget and investigate the main factors that dominated δ13 C-CO2 variations for the Yangtze River Delta (YRD) region, one of the largest anthropogenic CO2 hotspots and densely populated regions in China. We used the WRF-STILT model framework with category-specified EDGAR v432 CO2 inventories to simulate hourly CO2 mixing ratios and δ13 C-CO2 , evaluated these simulations with observations, and constrained the anthropogenic CO2 emission categories. Our study shows that: (1) Top-down and bottom-up estimates of anthropogenic CO2 emissions agreed well (bias < 6 %) on an annual basis; (2) The WRF-STILT model performed well in reproducing the observed diel and seasonal atmospheric δ13 C-CO2 variations; (3) Anthropogenic CO2 emissions played a much larger role than ecosystems in controlling the δ13 C-CO2 seasonality. When excluding ecosystem respiration and photosynthetic discrimination in the YRD area, δ13 C-CO2 seasonality increased from 1.53 ‰ to 1.66 ‰; (4) Atmospheric transport processes in summer amplified the cement CO2 enhancement proportions in the YRD area, which dominated monthly δs variations. These findings support that the combination of long-term atmospheric carbon isotope observations and inverse modeling can provide a powerful constraint on the carbon cycle of these complex megacities. [ABSTRACT FROM AUTHOR]- Published
- 2020
- Full Text
- View/download PDF
36. Influences of Root Hydraulic Redistribution on N 2 O Emissions at AmeriFlux Sites
- Author
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Fu, Congsheng, primary, Lee, Xuhui, additional, Griffis, Timothy J., additional, Wang, Guiling, additional, and Wei, Zhongwang, additional
- Published
- 2018
- Full Text
- View/download PDF
37. Top-down constraints on global N<sub>2</sub>O emissions at optimal resolution: application of a new dimension reduction technique
- Author
-
Wells, Kelley C., primary, Millet, Dylan B., additional, Bousserez, Nicolas, additional, Henze, Daven K., additional, Griffis, Timothy J., additional, Chaliyakunnel, Sreelekha, additional, Dlugokencky, Edward J., additional, Saikawa, Eri, additional, Xiang, Gao, additional, Prinn, Ronald G., additional, O'Doherty, Simon, additional, Young, Dickon, additional, Weiss, Ray F., additional, Dutton, Geoff S., additional, Elkins, James W., additional, Krummel, Paul B., additional, Langenfelds, Ray, additional, and Steele, L. Paul, additional
- Published
- 2018
- Full Text
- View/download PDF
38. Investigation of the N2O emission strength in the U. S. Corn Belt
- Author
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Fu, Congsheng, primary, Lee, Xuhui, additional, Griffis, Timothy J., additional, Dlugokencky, Edward J., additional, and Andrews, Arlyn E., additional
- Published
- 2017
- Full Text
- View/download PDF
39. Multiscale analyses of solar‐induced florescence and gross primary production
- Author
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Wood, Jeffrey D., primary, Griffis, Timothy J., additional, Baker, John M., additional, Frankenberg, Christian, additional, Verma, Manish, additional, and Yuen, Karen, additional
- Published
- 2017
- Full Text
- View/download PDF
40. Investigating the source, transport, and isotope composition of water vapor in the planetary boundary layer
- Author
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Griffis, Timothy J., primary, Wood, Jeffrey D., additional, Baker, John M., additional, Lee, Xuhui, additional, Xiao, Ke, additional, Chen, Zichong, additional, Welp, Lisa R., additional, Schultz, Natalie M., additional, Gorski, Galen, additional, Chen, Ming, additional, and Nieber, John, additional
- Published
- 2016
- Full Text
- View/download PDF
41. Influences of Root Hydraulic Redistribution on N2O Emissions at AmeriFlux Sites.
- Author
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Fu, Congsheng, Lee, Xuhui, Griffis, Timothy J., Wang, Guiling, and Wei, Zhongwang
- Abstract
Abstract: It has long been suspected that root hydraulic redistribution (HR) affects the carbon and nitrogen cycles. Nitrous oxide (N
2 O) is an important greenhouse gas and is the primary stratospheric ozone‐depleting substance. To our knowledge, the influences of HR on N2 O emissions have not been investigated. Here we use the HR schemes of Ryel et al. and Amenu and Kumar incorporated into CLM4.5 to examine N2 O emissions at five AmeriFlux sites. The results show that HR reduced N2 O emissions by 28–92% in the four natural ecosystems experiencing a dry season, whereas it had a very limited effect on the Corn Belt site that has strong emissions but with no distinct dry season. We hypothesize that N2 O emissions in ecosystems with a distinct dry season are likely overestimated by CENTURY‐based Earth system models. [ABSTRACT FROM AUTHOR]- Published
- 2018
- Full Text
- View/download PDF
42. Top-down constraints on global N2O emissions at optimal resolution: application of a new dimension reduction technique.
- Author
-
Wells, Kelley C., Millet, Dylan B., Bousserez, Nicolas, Henze, Daven K., Griffis, Timothy J., Chaliyakunnel, Sreelekha, Dlugokencky, Edward J., Saikawa, Eri, Xiang, Gao, Prinn, Ronald G., O'Doherty, Simon, Young, Dickon, Weiss, Ray F., Dutton, Geoff S., Elkins, James W., Krummel, Paul B., Langenfelds, Ray, and Steele, L. Paul
- Subjects
NITROGEN oxides emission control ,DIMENSION reduction (Statistics) ,INVERSION (Geophysics) ,SINGULAR value decomposition ,SOIL moisture - Abstract
We present top-down constraints on global monthly N
2 O emissions for 2011 from a multi-inversion approach and an ensemble of surface observations. The inversions employ the GEOS-Chem adjoint and an array of aggregation strategies to test how well current observations can constrain the spatial distribution of global N2 O emissions. The strategies include (1) a standard 4D-Var inversion at native model resolution (4° 5°), (2) an inversion for six continental and three ocean regions, and (3) a fast 4D-Var inversion based on a novel dimension reduction technique employing randomized singular value decomposition (SVD). The optimized global flux ranges from 15.9 TgNyr-1 (SVDbased inversion) to 17.5-17.7 TgNyr-1 (continental-scale, standard 4D-Var inversions), with the former better capturing the extratropical N2 O background measured during the HIAPER Pole-to-Pole Observations (HIPPO) airborne campaigns. We find that the tropics provide a greater contribution to the global N2 O flux than is predicted by the prior bottomup inventories, likely due to underestimated agricultural and oceanic emissions. We infer an overestimate of natural soil emissions in the extratropics and find that predicted emissions are seasonally biased in northern midlatitudes. Here, optimized fluxes exhibit a springtime peak consistent with the timing of spring fertilizer and manure application, soil thawing, and elevated soil moisture. Finally, the inversions reveal a major emission underestimate in the US Corn Belt in the bottom-up inventory used here.We extensively test the impact of initial conditions on the analysis and recommend formally optimizing the initial N2 O distribution to avoid biasing the inferred fluxes. We find that the SVD-based approach provides a powerful framework for deriving emission information from N2 O observations: by defining the optimal resolution of the solution based on the information content of the inversion, it provides spatial information that is lost when aggregating to political or geographic regions, while also providing more temporal information than a standard 4D-Var inversion. [ABSTRACT FROM AUTHOR]- Published
- 2018
- Full Text
- View/download PDF
43. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence
- Author
-
Guanter, Luis, Zhang, Yongguang, Jung, Martin, Joiner, Joanna, Voigt, Maximilian, Berry, Joseph A., Frankenberg, Christian, Huete, Alfredo R., Zarco-Tejada, Pablo, Lee, Jung-Eun, Moran, M. Susan, Ponce-Campos, Guillermo, Beer, Christian, Camps-Valls, Gustavo, Buchmann, Nina, Gianelle, Damiano, Klumpp, Katja, Cescatti, Alessandro, Baker, John M., Griffis, Timothy J., Guanter, Luis, Zhang, Yongguang, Jung, Martin, Joiner, Joanna, Voigt, Maximilian, Berry, Joseph A., Frankenberg, Christian, Huete, Alfredo R., Zarco-Tejada, Pablo, Lee, Jung-Eun, Moran, M. Susan, Ponce-Campos, Guillermo, Beer, Christian, Camps-Valls, Gustavo, Buchmann, Nina, Gianelle, Damiano, Klumpp, Katja, Cescatti, Alessandro, Baker, John M., and Griffis, Timothy J.
- Abstract
Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. It is the primary source of energy for all life on Earth; hence it is important to understand how this process responds to climate change and human impact. However, model-based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. Recent advances in spectroscopy enable the space-based monitoring of sun-induced chlorophyll fluorescence (SIF) from terrestrial plants. Here we demonstrate that spaceborne SIF retrievals provide a direct measure of the GPP of cropland and grassland ecosystems. Such a strong link with crop photosynthesis is not evident for traditional remotely sensed vegetation indices, nor for more complex carbon cycle models. We use SIF observations to provide a global perspective on agricultural productivity. Our SIF-based crop GPP estimates are 50-75% higher than results from state-of-the-art carbon cycle models over, for example, the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. Our results indicate that SIF data can help us improve our global models for more accurate projections of agricultural productivity and climate impact on crop yields. Extension of our approach to other ecosystems, along with increased observational capabilities for SIF in the near future, holds the prospect of reducing uncertainties in the modeling of the current and future carbon cycle., AuthorCount:20
- Published
- 2014
- Full Text
- View/download PDF
44. Productivity and Carbon Dioxide Exchange of Leguminous Crops: Estimates from Flux Tower Measurements
- Author
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Gilmanov, Tagir, Baker, John M., Bernacchi, Carl J., Billesbach, David P., Burba, George G., Castro, Saulo, Chen, Jiquan, Eugster, Werner, Fischer, Marc L., Gamon, John A., Gebremedhin, Maheteme T., Glenn, Aaron J., Griffis, Timothy J., Hatfield, Jerry L., Heuer, Mark W., Howard, Daniel M., Leclerc, Monique Y., Loescher, Henry W., Marloie, Oliver, Meyers, Tilden P., Olioso, Albert, Phillips, Rebecca L., Prueger, John H., Skinner, R. Howard, Suyker, Andrew E., Tenuta, Mario, Wylie, Bruce K., Gilmanov, Tagir, Baker, John M., Bernacchi, Carl J., Billesbach, David P., Burba, George G., Castro, Saulo, Chen, Jiquan, Eugster, Werner, Fischer, Marc L., Gamon, John A., Gebremedhin, Maheteme T., Glenn, Aaron J., Griffis, Timothy J., Hatfield, Jerry L., Heuer, Mark W., Howard, Daniel M., Leclerc, Monique Y., Loescher, Henry W., Marloie, Oliver, Meyers, Tilden P., Olioso, Albert, Phillips, Rebecca L., Prueger, John H., Skinner, R. Howard, Suyker, Andrew E., Tenuta, Mario, and Wylie, Bruce K.
- Abstract
Net CO2 exchange data of legume crops at 17 flux tower sites in North America and three sites in Europe representing 29 site-years of measurements were partitioned into gross photosynthesis and ecosystem respiration by using the nonrectangular hyperbolic lightresponse function method. The analyses produced net CO2 exchange data and new ecosystem-scale ecophysiological parameter estimates for legume crops determined at diurnal and weekly time steps. Dynamics and annual totals of gross photosynthesis, ecosystem respiration, and net ecosystem production were calculated by gap filling with multivariate nonlinear regression. Comparison with the data from grain crops obtained with the same method demonstrated that CO2 exchange rates and ecophysiological parameters of legumes were lower than those of maize (Zea mays L.) but higher than for wheat (Triticum aestivum L.) crops. Year-round annual legume crops demonstrated a broad range of net ecosystem production, from sinks of 760 g CO2 m–2 yr–1 to sources of –2100 g CO2 m–2 yr–1, with an average of –330 g CO2 m–2 yr–1, indicating overall moderate CO2–source activity related to a shorter period of photosynthetic uptake and metabolic costs of N2 fixation. Perennial legumes (alfalfa, Medicago sativa L.) were strong sinks for atmospheric CO2, with an average net ecosystem production of 980 (range 550–1200) g CO2 m–2 yr–1.
- Published
- 2014
45. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence
- Author
-
German Research Foundation, National Aeronautics and Space Administration (US), Keck Institute for Space Studies, Guanter, Luis, Zarco-Tejada, Pablo J., Griffis, Timothy J., German Research Foundation, National Aeronautics and Space Administration (US), Keck Institute for Space Studies, Guanter, Luis, Zarco-Tejada, Pablo J., and Griffis, Timothy J.
- Abstract
Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. It is the primary source of energy for all life on Earth; hence it is important to understand how this process responds to climate change and human impact. However, model-based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. Recent advances in spectroscopy enable the space-based monitoring of sun-induced chlorophyll fluorescence (SIF) from terrestrial plants. Here we demonstrate that spaceborne SIF retrievals provide a direct measure of the GPP of cropland and grassland ecosystems. Such a strong link with crop photosynthesis is not evident for traditional remotely sensed vegetation indices, nor for more complex carbon cycle models. We use SIF observations to provide a global perspective on agricultural productivity. Our SIF-based crop GPP estimates are 50-75% higher than results from state-ofthe- art carbon cycle models over, for example, the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. Our results indicate that SIF data can help us improve our global models for more accurate projections of agricultural productivity and climate impact on crop yields. Extension of our approach to other ecosystems, along with increased observational capabilities for SIF in the near future, holds the prospect of reducing uncertainties in the modeling of the current and future carbon cycle.
- Published
- 2014
46. Reply to Magnani et al.: Linking large-scale chlorophyll fluorescence observations with cropland gross primary production
- Author
-
Guanter, Luis, Zarco-Tejada, Pablo J., Griffis, Timothy J., Guanter, Luis, Zarco-Tejada, Pablo J., and Griffis, Timothy J.
- Abstract
The derivation of the first global maps of sun-induced chlorophyll fluorescence (SIF) from Greenhouse Gases Observing Satellite (GOSAT) data in 2011 (1, 2), and later from Global Ozone Monitoring Experiment-2 (GOME-2) (3), was perceived as a milestone in the fields of vegetation remote sensing and carbon modeling.
- Published
- 2014
47. Top-down constraints on global N2O emissions at optimal resolution: application of a new dimension reduction technique.
- Author
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Wells, Kelley C., Millet, Dylan B., Bousserez, Nicolas, Henze, Daven K., Griffis, Timothy J., Chaliyakunnel, Sreelekha, Dlugokencky, Edward J., Saikawa, Eri, Gao Xiang, Prinn, Ronald G., O'Doherty, Simon, Young, Dickon, Weiss, Ray F., Dutton, Geoff S., Elkins, James W., Krummel, Paul B., Langenfelds, Ray, and Steele, L. Paul
- Abstract
We present top-down constraints on global, monthly N
2 O emissions for 2011 from a multi-inversion approach and an ensemble of surface observations. The inversions employ the GEOS-Chem adjoint and an array of aggregation strategies to test how well current observations can constrain the spatial distribution of global N2 O emissions. The strategies include: (1) a standard 4D-Var inversion at native model resolution (4° × 5°), (2) an inversion for six continental and three ocean regions, and (3) a fast 4D-Var inversion based on a novel dimension reduction technique employing randomized singular value decomposition (SVD). The optimized global flux ranges from 15.9 Tg N yr-1 (SVD-based inversion) to 17.5-17.7 Tg N yr-1 (continental-scale, standard 4D-Var inversions), with the former better capturing the N2 O background measured during the HIAPER Pole-to-Pole Observations (HIPPO) airborne campaigns. We find that the tropics provide a greater contribution to the global N2 O flux than is predicted by the prior bottom-up inventories, likely due to underestimated agricultural and oceanic emissions. We infer an overestimate of natural soil emissions in the extratropics, and find that predicted emissions are seasonally biased in northern midlatitudes. Here, optimized fluxes exhibit a springtime versus summertime peak more consistent with the timing of fertilizer application, soil thawing, and elevated soil moisture. Finally, the inversions reveal a major emission underestimate in the US Corn Belt (which may extend to other intensive agricultural regions), likely from underrepresentation of indirect N2 O emissions from leaching and runoff. We extensively test the impact of initial conditions on the analysis and recommend formally optimizing the initial N2 O distribution to avoid aliasing the inferred fluxes. We find that the SVD-based approach provides a powerful framework for deriving emission information from N2 O observations: by defining the state vector based on the information content of the inversion, it provides useful spatial information that is lost when aggregating to ad-hoc regions, while also better resolving temporal features than a standard 4D-Var inversion. [ABSTRACT FROM AUTHOR]- Published
- 2017
- Full Text
- View/download PDF
48. Reply to Magnani et al.: Linking large-scale chlorophyll fluorescence observations with cropland gross primary production
- Author
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Guanter, Luis, primary, Zhang, Yongguang, additional, Jung, Martin, additional, Joiner, Joanna, additional, Voigt, Maximilian, additional, Berry, Joseph A., additional, Frankenberg, Christian, additional, Huete, Alfredo R., additional, Zarco-Tejada, Pablo, additional, Lee, Jung-Eun, additional, Moran, M. Susan, additional, Ponce-Campos, Guillermo, additional, Beer, Christian, additional, Camps-Valls, Gustavo, additional, Buchmann, Nina, additional, Gianelle, Damiano, additional, Klumpp, Katja, additional, Cescatti, Alessandro, additional, Baker, John M., additional, and Griffis, Timothy J., additional
- Published
- 2014
- Full Text
- View/download PDF
49. Quantifying nitrous oxide fluxes on multiple spatial scales in the Upper Midwest, USA
- Author
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Zhang, Xin, primary, Lee, Xuhui, additional, Griffis, Timothy J., additional, Andrews, Arlyn E., additional, Baker, John M., additional, Erickson, Matt D., additional, Hu, Ning, additional, and Xiao, Wei, additional
- Published
- 2014
- Full Text
- View/download PDF
50. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence
- Author
-
Guanter, Luis, primary, Zhang, Yongguang, additional, Jung, Martin, additional, Joiner, Joanna, additional, Voigt, Maximilian, additional, Berry, Joseph A., additional, Frankenberg, Christian, additional, Huete, Alfredo R., additional, Zarco-Tejada, Pablo, additional, Lee, Jung-Eun, additional, Moran, M. Susan, additional, Ponce-Campos, Guillermo, additional, Beer, Christian, additional, Camps-Valls, Gustavo, additional, Buchmann, Nina, additional, Gianelle, Damiano, additional, Klumpp, Katja, additional, Cescatti, Alessandro, additional, Baker, John M., additional, and Griffis, Timothy J., additional
- Published
- 2014
- Full Text
- View/download PDF
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