Midwest US Croplands Determine Model Divergence in North American Carbon Fluxes
Abstract
Large uncertainties in North American terrestrial carbon fluxes hinder regional climate projections. Terrestrial biosphere models (TBMs), the essential tools for understanding continental-scale carbon cycle, diverge on whether temperate forests or croplands dominate carbon uptake in North America. Evidence from novel photosynthetic proxies, such as those based on chlorophyll fluorescence, has cast doubt on the “weak cropland, strong forest” carbon uptake patterns simulated by most TBMs. However, no systematic evaluation of TBMs has yet been attempted to pin down space-time patterns that are most consistent with regional CO2 observational constraints. Here, we leverage atmospheric CO2 observations and satellite-observed photosynthetic proxies to understand emergent space-time patterns in North American carbon fluxes from a large suite of TBMs and data-driven models. To do so, we evaluate how well the atmospheric signals resulting from carbon flux estimates reproduce the space-time variability in atmospheric CO2, as is observed by a network of continuous-monitoring towers over North America. Models with gross or net carbon fluxes that are consistent with the observed CO2 variability share a salient feature of growing-season carbon uptake in Midwest US croplands. Conversely, the remaining models place most growing-season uptake in boreal or temperate forests. Differences in model explanatory power depend mainly onmore »
- Authors:
-
- Carnegie Inst. of Science, Stanford, CA (United States)
- Carnegie Inst. of Science, Stanford, CA (United States); Bay Area Air Quality Management District, San Francisco, CA (United States)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States); National Univ. of Singapore (Singapore)
- Carnegie Inst. of Science, Stanford, CA (United States); Universities Space Research Association, Mountain View, CA (United States)
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States)
- National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States)
- California Institute of Technology (CalTech), Pasadena, CA (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); NASA Terrestrial Ecology Interdisciplinary Science (IDS); NASA Research Opportunities in Space and Earth Sciences (ROSES)
- OSTI Identifier:
- 1865076
- Grant/Contract Number:
- AC02-05CH11231; NNH17AE86I; NNX10AG01A; NNH10AN681
- Resource Type:
- Accepted Manuscript
- Journal Name:
- AGU Advances
- Additional Journal Information:
- Journal Volume: 2; Journal Issue: 2; Journal ID: ISSN 2576-604X
- Publisher:
- Wiley
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES
Citation Formats
Sun, Wu, Fang, Yuanyuan, Luo, Xiangzhong, Shiga, Yoichi P., Zhang, Yao, Andrews, Arlyn E., Thoning, Kirk W., Fisher, Joshua B., Keenan, Trevor F., and Michalak, Anna M. Midwest US Croplands Determine Model Divergence in North American Carbon Fluxes. United States: N. p., 2021.
Web. doi:10.1029/2020av000310.
Sun, Wu, Fang, Yuanyuan, Luo, Xiangzhong, Shiga, Yoichi P., Zhang, Yao, Andrews, Arlyn E., Thoning, Kirk W., Fisher, Joshua B., Keenan, Trevor F., & Michalak, Anna M. Midwest US Croplands Determine Model Divergence in North American Carbon Fluxes. United States. https://doi.org/10.1029/2020av000310
Sun, Wu, Fang, Yuanyuan, Luo, Xiangzhong, Shiga, Yoichi P., Zhang, Yao, Andrews, Arlyn E., Thoning, Kirk W., Fisher, Joshua B., Keenan, Trevor F., and Michalak, Anna M. Thu .
"Midwest US Croplands Determine Model Divergence in North American Carbon Fluxes". United States. https://doi.org/10.1029/2020av000310. https://www.osti.gov/servlets/purl/1865076.
@article{osti_1865076,
title = {Midwest US Croplands Determine Model Divergence in North American Carbon Fluxes},
author = {Sun, Wu and Fang, Yuanyuan and Luo, Xiangzhong and Shiga, Yoichi P. and Zhang, Yao and Andrews, Arlyn E. and Thoning, Kirk W. and Fisher, Joshua B. and Keenan, Trevor F. and Michalak, Anna M.},
abstractNote = {Large uncertainties in North American terrestrial carbon fluxes hinder regional climate projections. Terrestrial biosphere models (TBMs), the essential tools for understanding continental-scale carbon cycle, diverge on whether temperate forests or croplands dominate carbon uptake in North America. Evidence from novel photosynthetic proxies, such as those based on chlorophyll fluorescence, has cast doubt on the “weak cropland, strong forest” carbon uptake patterns simulated by most TBMs. However, no systematic evaluation of TBMs has yet been attempted to pin down space-time patterns that are most consistent with regional CO2 observational constraints. Here, we leverage atmospheric CO2 observations and satellite-observed photosynthetic proxies to understand emergent space-time patterns in North American carbon fluxes from a large suite of TBMs and data-driven models. To do so, we evaluate how well the atmospheric signals resulting from carbon flux estimates reproduce the space-time variability in atmospheric CO2, as is observed by a network of continuous-monitoring towers over North America. Models with gross or net carbon fluxes that are consistent with the observed CO2 variability share a salient feature of growing-season carbon uptake in Midwest US croplands. Conversely, the remaining models place most growing-season uptake in boreal or temperate forests. Differences in model explanatory power depend mainly on the simulated annual cycles of cropland uptake—especially, the timing of peak uptake—rather than the distribution of annual mean fluxes across biomes. Our results suggest that improved model representation of cropland phenology is crucial to robust, policy-relevant estimation of North American carbon exchange.},
doi = {10.1029/2020av000310},
journal = {AGU Advances},
number = 2,
volume = 2,
place = {United States},
year = {Thu May 20 00:00:00 EDT 2021},
month = {Thu May 20 00:00:00 EDT 2021}
}
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