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

Huang, Yuanyuan; Jiang, Jiang; Ma, Shuang; Ricciuto, Daniel; Hanson, Paul J.; Luo, Yiqi

doi:10.1002/2016JG003725

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

Journal Article · Fri Aug 18 00:00:00 UTC 2017 · Journal of Geophysical Research. Biogeosciences

DOI: https://doi.org/10.1002/2016JG003725 · OSTI ID:1399947

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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

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.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)

Grant/Contract Number:: AC05-00OR22725; SC0008270; SC0014085

OSTI ID:: 1399947

Journal Information:: Journal of Geophysical Research. Biogeosciences, Journal Name: Journal of Geophysical Research. Biogeosciences Journal Issue: 8 Vol. 122; ISSN 2169-8953

Publisher:: American Geophysical UnionCopyright Statement

Country of Publication:: United States

Language:: English

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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

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