U.S. Department of EnergyOffice of Scientific and Technical Information
How initial conditions-, structural-, and parameter-based model uncertainty interact and influence predictions in permafrost ecosystems: Modeling Archive
Abstract
This dataset contains model output and input data, as well as source code examples for the Terrestrial Ecosystem Model with the Dynamic Vegetation Model and Dynamic Organic Soil (DVM-DOS-TEM) for the field sites Imnavait creek and the Bonanza creek Long Term Ecological Research Network (LTER). The data covers simulations from the last glacial maximum (LGM) until 2100 for a selection of paleo scenarios, setting the mean temperature of the LGM up to 10°C lower than pre-industrial conditions. The model structure was modulated to represent various model versions, and this dataset contains the relevant changes in the source code. The raw output data, the processed statistical data, the setup and processing scripts as well as parameter value distribution files from a parameter sensitivity analysis are included as well. Model outputs include active layer depth, organic soil carbon, soil layer depths, gross primary productivity (GPP) with and without nitrogen limitation, net primary productivity (NPP), soil liquid water content, heterotrophic, maintenance, and growth respiration, soil temperature, and vegetation carbon (*.nc files). The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictivemore »
- Publication Date:
- Other Number(s):
- NGA704
- DOE Contract Number:
- AC02-05CH11231
- Research Org.:
- Next-Generation Ecosystem Experiments (NGEE) Arctic
- Sponsoring Org.:
- U.S. DOE > Office of Science > Biological and Environmental Research (BER)
- Subject:
- 54 ENVIRONMENTAL SCIENCES; EARH SCIENCE > LAND SURFACE > FROZEN GROUND; EARTH SCIENCE > BIOSPHERE > ECOLOGICAL DYNAMICS > ECOSYSTEM FUNCTIONS > RESPIRATION RATE; EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > TERRESTRIAL ECOSYSTEMS > ALPINE/TUNDRA; EARTH SCIENCE > BIOSPHERE > ECOSYSTEMS > TERRESTRIAL ECOSYSTEMS > FORESTS > BOREAL FOREST/TAIGA; EARTH SCIENCE > BIOSPHERE > VEGETATION > CARBON; EARTH SCIENCE > LAND SURFACE > FROZEN GROUND > ACTIVE LAYER; EARTH SCIENCE > LAND SURFACE > SOILS > CARBON; EARTH SCIENCE > LAND SURFACE > SOILS > SOIL MOISTURE/WATER CONTENT; EARTH SCIENCE > LAND SURFACE > SOILS > SOIL RESPIRATION; EARTH SCIENCE > LAND SURFACE > SOILS > SOIL TEMPERATURE; ESS-DIVE CSV File Formatting Guidelines Reporting Format; ESS-DIVE File Level Metadata Reporting Format; ESS-DIVE Model Data Archiving Guidelines
- OSTI Identifier:
- 3003050
- DOI:
- https://doi.org/10.15485/3003050
Citation Formats
Mevenkamp, Hannah, Maglio, Benjamin, Carman, Tobey, Genet, Helene, Rutter, Ruth, Serbin, Shawn, and Euskirchen, Eugenie. How initial conditions-, structural-, and parameter-based model uncertainty interact and influence predictions in permafrost ecosystems: Modeling Archive. United States: N. p., 2025.
Web. doi:10.15485/3003050.
Mevenkamp, Hannah, Maglio, Benjamin, Carman, Tobey, Genet, Helene, Rutter, Ruth, Serbin, Shawn, & Euskirchen, Eugenie. How initial conditions-, structural-, and parameter-based model uncertainty interact and influence predictions in permafrost ecosystems: Modeling Archive. United States. doi:https://doi.org/10.15485/3003050
Mevenkamp, Hannah, Maglio, Benjamin, Carman, Tobey, Genet, Helene, Rutter, Ruth, Serbin, Shawn, and Euskirchen, Eugenie. 2025.
"How initial conditions-, structural-, and parameter-based model uncertainty interact and influence predictions in permafrost ecosystems: Modeling Archive". United States. doi:https://doi.org/10.15485/3003050. https://www.osti.gov/servlets/purl/3003050. Pub date:Wed Jan 01 04:00:00 UTC 2025
@article{osti_3003050,
title = {How initial conditions-, structural-, and parameter-based model uncertainty interact and influence predictions in permafrost ecosystems: Modeling Archive},
author = {Mevenkamp, Hannah and Maglio, Benjamin and Carman, Tobey and Genet, Helene and Rutter, Ruth and Serbin, Shawn and Euskirchen, Eugenie},
abstractNote = {This dataset contains model output and input data, as well as source code examples for the Terrestrial Ecosystem Model with the Dynamic Vegetation Model and Dynamic Organic Soil (DVM-DOS-TEM) for the field sites Imnavait creek and the Bonanza creek Long Term Ecological Research Network (LTER). The data covers simulations from the last glacial maximum (LGM) until 2100 for a selection of paleo scenarios, setting the mean temperature of the LGM up to 10°C lower than pre-industrial conditions. The model structure was modulated to represent various model versions, and this dataset contains the relevant changes in the source code. The raw output data, the processed statistical data, the setup and processing scripts as well as parameter value distribution files from a parameter sensitivity analysis are included as well. Model outputs include active layer depth, organic soil carbon, soil layer depths, gross primary productivity (GPP) with and without nitrogen limitation, net primary productivity (NPP), soil liquid water content, heterotrophic, maintenance, and growth respiration, soil temperature, and vegetation carbon (*.nc files). The Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) project is a research effort to reduce uncertainty in the Department of Energy’s Energy Exascale Earth System Model (E3SM) by developing a predictive understanding of Arctic tundra ecosystems underlain by permafrost and to quantify feedbacks from the Arctic tundra to the Earth system. NGEE Arctic is supported by the Department of Energy's Office of Biological and Environmental Research.Over Phases 1–3, observations made by the NGEE Arctic team across a gradient of permafrost landscapes in Arctic Alaska improved the representation of tundra processes in the land surface component of E3SM (the E3SM Land Model, ELM). Model improvements emphasized unique aspects of permafrost environments and explored reductions in model complexity while retaining predictive power. The Arctic-informed ELM developed by NGEE Arctic has been used to make novel predictions on processes ranging from permafrost thaw to soil biogeochemical cycling to Earth system feedbacks associated with the unique characteristics of tundra plants. In Phase 4, the NGEE Arctic team is evaluating our new predictive understanding under novel conditions across the Arctic domain. In collaboration with partners at long-term pan-Arctic research sites we are examining whether an Arctic-informed ELM can faithfully simulate interactions among surface and subsurface processes at site, regional, and pan-Arctic scales. In turn, we are using variety of tools to dynamically extend and evaluate ELM inference, with an emphasis on data synthesis and pan-Arctic model evaluation, reintegration of code with an evolving E3SM, scaling across heterogeneous Arctic landscapes, and the appropriate representation of the impacts of increasingly frequent Arctic disturbances.},
doi = {10.15485/3003050},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Jan 01 04:00:00 UTC 2025},
month = {Wed Jan 01 04:00:00 UTC 2025}
}
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