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Title: Quantifying peat carbon accumulation in Alaska using a process-based biogeochemistry model: Simulating Peat Carbon Accumulation

Authors:
 [1];  [2];  [3];  [4];  [5]
  1. Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette Indiana USA
  2. Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette Indiana USA, Department of Agronomy, Purdue University, West Lafayette Indiana USA
  3. Department of Earth and Environmental Sciences, Lehigh University, Bethlehem Pennsylvania USA
  4. The Institute of Ecology and Evolution, University of Oregon, Eugene Oregon USA
  5. Schmid College of Science and Technology, Chapman University, Orange California USA
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1402324
Grant/Contract Number:
SC0008092
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
Journal of Geophysical Research. Biogeosciences
Additional Journal Information:
Journal Volume: 121; Journal Issue: 8; Related Information: CHORUS Timestamp: 2017-10-23 17:43:33; Journal ID: ISSN 2169-8953
Publisher:
Wiley Blackwell (John Wiley & Sons)
Country of Publication:
United States
Language:
English

Citation Formats

Wang, Sirui, Zhuang, Qianlai, Yu, Zicheng, Bridgham, Scott, and Keller, Jason K. Quantifying peat carbon accumulation in Alaska using a process-based biogeochemistry model: Simulating Peat Carbon Accumulation. United States: N. p., 2016. Web. doi:10.1002/2016JG003452.
Wang, Sirui, Zhuang, Qianlai, Yu, Zicheng, Bridgham, Scott, & Keller, Jason K. Quantifying peat carbon accumulation in Alaska using a process-based biogeochemistry model: Simulating Peat Carbon Accumulation. United States. doi:10.1002/2016JG003452.
Wang, Sirui, Zhuang, Qianlai, Yu, Zicheng, Bridgham, Scott, and Keller, Jason K. 2016. "Quantifying peat carbon accumulation in Alaska using a process-based biogeochemistry model: Simulating Peat Carbon Accumulation". United States. doi:10.1002/2016JG003452.
@article{osti_1402324,
title = {Quantifying peat carbon accumulation in Alaska using a process-based biogeochemistry model: Simulating Peat Carbon Accumulation},
author = {Wang, Sirui and Zhuang, Qianlai and Yu, Zicheng and Bridgham, Scott and Keller, Jason K.},
abstractNote = {},
doi = {10.1002/2016JG003452},
journal = {Journal of Geophysical Research. Biogeosciences},
number = 8,
volume = 121,
place = {United States},
year = 2016,
month = 8
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1002/2016JG003452

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  • The Community Atmosphere Model (CAM5), equipped with a technique to tag black carbon (BC) emissions by source regions and types, has been employed to establish source–receptor relationships for atmospheric BC and its deposition to snow over western North America. The CAM5 simulation was conducted with meteorological fields constrained by reanalysis for year 2013 when measurements of BC in both near-surface air and snow are available for model evaluation. We find that CAM5 has a significant low bias in predicted mixing ratios of BC in snow but only a small low bias in predicted atmospheric concentrations over northwestern USA and westernmore » Canada. Even with a strong low bias in snow mixing ratios, radiative transfer calculations show that the BC-in-snow darkening effect is substantially larger than the BC dimming effect at the surface by atmospheric BC. Local sources contribute more to near-surface atmospheric BC and to deposition than distant sources, while the latter are more important in the middle and upper troposphere where wet removal is relatively weak. Fossil fuel (FF) is the dominant source type for total column BC burden over the two regions. FF is also the dominant local source type for BC column burden, deposition, and near-surface BC, while for all distant source regions combined the contribution of biomass/biofuel (BB) is larger than FF. An observationally based positive matrix factorization (PMF) analysis of the snow-impurity chemistry is conducted to quantitatively evaluate the CAM5 BC source-type attribution. Furthermore, while CAM5 is qualitatively consistent with the PMF analysis with respect to partitioning of BC originating from BB and FF emissions, it significantly underestimates the relative contribution of BB. In addition to a possible low bias in BB emissions used in the simulation, the model is likely missing a significant source of snow darkening from local soil found in the observations.« less
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  • The Community Atmosphere Model (CAM5), equipped with a technique to tag black carbon (BC) emissions by source regions and types, has been employed to establish source–receptor relationships for atmospheric BC and its deposition to snow over western North America. The CAM5 simulation was conducted with meteorological fields constrained by reanalysis for year 2013 when measurements of BC in both near-surface air and snow are available for model evaluation. We find that CAM5 has a significant low bias in predicted mixing ratios of BC in snow but only a small low bias in predicted atmospheric concentrations over northwestern USA and westernmore » Canada. Even with a strong low bias in snow mixing ratios, radiative transfer calculations show that the BC-in-snow darkening effect is substantially larger than the BC dimming effect at the surface by atmospheric BC. Local sources contribute more to near-surface atmospheric BC and to deposition than distant sources, while the latter are more important in the middle and upper troposphere where wet removal is relatively weak. Fossil fuel (FF) is the dominant source type for total column BC burden over the two regions. FF is also the dominant local source type for BC column burden, deposition, and near-surface BC, while for all distant source regions combined the contribution of biomass/biofuel (BB) is larger than FF. An observationally based positive matrix factorization (PMF) analysis of the snow-impurity chemistry is conducted to quantitatively evaluate the CAM5 BC source-type attribution. Furthermore, while CAM5 is qualitatively consistent with the PMF analysis with respect to partitioning of BC originating from BB and FF emissions, it significantly underestimates the relative contribution of BB. In addition to a possible low bias in BB emissions used in the simulation, the model is likely missing a significant source of snow darkening from local soil found in the observations.« less
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