TY - JOUR
T1 - Soil thermal dynamics, snow cover, and frozen depth under five temperature treatments in an ombrotrophic bog
T2 - Constrained forecast with data assimilation
AU - Huang, Yuanyuan
AU - Jiang, Jiang
AU - Ma, Shuang
AU - Ricciuto, Daniel
AU - Hanson, Paul J.
AU - Luo, Yiqi
N1 - Funding Information:
This work is primarily supported by subcontract 4000144122 from Oak Ridge National Laboratory (ORNL) to the University of Oklahoma. ORNL's work is supported by the U.S. Department of Energy (DOE), Office of Science, Office of Biological, and Environmental Research. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the U.S. Department of Energy under contract DE-AC05-00OR22725. Research in Yiqi Luo's EcoLab was also financially supported by U.S. DOE DE-SC0008270 and DE-SC00114085 and U.S. National Science Foundation (NSF) grant EF 1137293 and OIA-1301789. Relevant measurements were obtained from the SPRUCE webpage (http://mnspruce.ornl.gov/) or the archived ftp site (ftp://sprucedata.ornl.gov). Source code of the TECO thermal model is available at http://ecolab.ou.edu/download/Huang2017JGR.php. Additional data sets are available in the supporting information.
Publisher Copyright:
©2017. American Geophysical Union. All Rights Reserved.
PY - 2017/8
Y1 - 2017/8
N2 - 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. 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.
AB - 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. 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.
KW - climate
KW - data assimilation
KW - ecological forecasting
KW - freeze-thaw
KW - uncertainty
KW - warming
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U2 - 10.1002/2016JG003725
DO - 10.1002/2016JG003725
M3 - Article
AN - SCOPUS:85027505547
SN - 2169-8953
VL - 122
SP - 2046
EP - 2063
JO - Journal of Geophysical Research: Biogeosciences
JF - Journal of Geophysical Research: Biogeosciences
IS - 8
ER -