Soil thermal dynamics, snow cover, and frozen depth under five temperature treatments in an ombrotrophic bog: Constrained forecast with data assimilation: Forecast With Data Assimilation
Abstract
We report that accurate simulation of soil thermal dynamics is essential for realistic prediction of soil biogeochemical responses to climate change. To facilitate ecological forecasting at the Spruce and Peatland Responses Under Climatic and Environmental change site, we incorporated a soil temperature module into a Terrestrial ECOsystem (TECO) model by accounting for surface energy budget, snow dynamics, and heat transfer among soil layers and during freeze-thaw events. We conditioned TECO with detailed soil temperature and snow depth observations through data assimilation before the model was used for forecasting. The constrained model reproduced variations in observed temperature from different soil layers, the magnitude of snow depth, the timing of snowfall and snowmelt, and the range of frozen depth. The conditioned TECO forecasted probabilistic distributions of soil temperature dynamics in six soil layers, snow, and frozen depths under temperature treatments of +0.0, +2.25, +4.5, +6.75, and +9.0°C. Air warming caused stronger elevation in soil temperature during summer than winter due to winter snow and ice. And soil temperature increased more in shallow soil layers in summer in response to air warming. Whole ecosystem warming (peat + air warmings) generally reduced snow and frozen depths. The accuracy of forecasted snow and frozen depthsmore »
- Authors:
-
[1];
[1];
[3];
[3];
[4]
- Univ. of Oklahoma, Norman, OK (United States). Department of Microbiology and Plant Biology
- Univ. of Oklahoma, Norman, OK (United States). Department of Microbiology and Plant Biology ; Nanjing Forestry University (China). Key Laboratory of Soil and Water Conservation and Ecological Restoration in Jiangsu Province, Collaborative Innovation Center of Sustainable Forestry in Southern China of Jiangsu Province
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Environmental Sciences Division and Climate Change Science Institute
- Univ. of Oklahoma, Norman, OK (United States). Department of Microbiology and Plant Biology ; Tsinghua University, Beijing (China). Department of Earth System Science
- Publication Date:
- Research Org.:
- Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1399947
- Alternate Identifier(s):
- OSTI ID: 1375536
- Grant/Contract Number:
- AC05-00OR22725; SC0008270; SC00114085; 4000144122; SC0014085; EF 1137293; OIA-1301789
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Geophysical Research. Biogeosciences
- Additional Journal Information:
- Journal Volume: 122; Journal Issue: 8; Journal ID: ISSN 2169-8953
- Publisher:
- American Geophysical Union
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES; 54 ENVIRONMENTAL SCIENCES
Citation Formats
Huang, Yuanyuan, Jiang, Jiang, Ma, Shuang, Ricciuto, Daniel, Hanson, Paul J., and Luo, Yiqi. Soil thermal dynamics, snow cover, and frozen depth under five temperature treatments in an ombrotrophic bog: Constrained forecast with data assimilation: Forecast With Data Assimilation. United States: N. p., 2017.
Web. doi:10.1002/2016JG003725.
Huang, Yuanyuan, Jiang, Jiang, Ma, Shuang, Ricciuto, Daniel, Hanson, Paul J., & Luo, Yiqi. Soil thermal dynamics, snow cover, and frozen depth under five temperature treatments in an ombrotrophic bog: Constrained forecast with data assimilation: Forecast With Data Assimilation. United States. https://doi.org/10.1002/2016JG003725
Huang, Yuanyuan, Jiang, Jiang, Ma, Shuang, Ricciuto, Daniel, Hanson, Paul J., and Luo, Yiqi. Fri .
"Soil thermal dynamics, snow cover, and frozen depth under five temperature treatments in an ombrotrophic bog: Constrained forecast with data assimilation: Forecast With Data Assimilation". United States. https://doi.org/10.1002/2016JG003725. https://www.osti.gov/servlets/purl/1399947.
@article{osti_1399947,
title = {Soil thermal dynamics, snow cover, and frozen depth under five temperature treatments in an ombrotrophic bog: Constrained forecast with data assimilation: Forecast With Data Assimilation},
author = {Huang, Yuanyuan and Jiang, Jiang and Ma, Shuang and Ricciuto, Daniel and Hanson, Paul J. and Luo, Yiqi},
abstractNote = {We report that accurate simulation of soil thermal dynamics is essential for realistic prediction of soil biogeochemical responses to climate change. To facilitate ecological forecasting at the Spruce and Peatland Responses Under Climatic and Environmental change site, we incorporated a soil temperature module into a Terrestrial ECOsystem (TECO) model by accounting for surface energy budget, snow dynamics, and heat transfer among soil layers and during freeze-thaw events. We conditioned TECO with detailed soil temperature and snow depth observations through data assimilation before the model was used for forecasting. The constrained model reproduced variations in observed temperature from different soil layers, the magnitude of snow depth, the timing of snowfall and snowmelt, and the range of frozen depth. The conditioned TECO forecasted probabilistic distributions of soil temperature dynamics in six soil layers, snow, and frozen depths under temperature treatments of +0.0, +2.25, +4.5, +6.75, and +9.0°C. Air warming caused stronger elevation in soil temperature during summer than winter due to winter snow and ice. And soil temperature increased more in shallow soil layers in summer in response to air warming. Whole ecosystem warming (peat + air warmings) generally reduced snow and frozen depths. The accuracy of forecasted snow and frozen depths relied on the precision of weather forcing. Uncertainty is smaller for forecasting soil temperature but large for snow and frozen depths. Lastly, timely and effective soil thermal forecast, constrained through data assimilation that combines process-based understanding and detailed observations, provides boundary conditions for better predictions of future biogeochemical cycles.},
doi = {10.1002/2016JG003725},
journal = {Journal of Geophysical Research. Biogeosciences},
number = 8,
volume = 122,
place = {United States},
year = {Fri Aug 18 00:00:00 EDT 2017},
month = {Fri Aug 18 00:00:00 EDT 2017}
}
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