Non-growing Season Plant Nutrient Uptake Controls Arctic Tundra Vegetation Composition: Modeling Archive

Riley, William; Mekonnen, Zelalem; Tang, Jinyun; Zhu, Qing; Bouskill, Nicholas; Grant, Robert

doi:10.5440/1785957

Title: Non-growing Season Plant Nutrient Uptake Controls Arctic Tundra Vegetation Composition: Modeling Archive

Dataset
Other Related Research

Abstract

This Modeling Archive is in support of a NGEE-Arctic publication: Riley et al. (2021) “Non-growing season plant nutrient uptake controls Arctic tundra vegetation composition under future climate” (Riley et al., 2021). The dataset contains “ecosys” model outputs reported in Riley et al. (2021) that examines the role of non-growing season (NGS) nutrient dynamics on 21st century vegetation composition. The study highlighted the importance of these nutrient dynamics on the emergent plant functional type distributions, focusing on their role in shrub expansion. The study showed that ignoring NGS nutrient dynamics led to a dramatic under-prediction of shrub expansion by year 2100, motivating the need to include these processes in Earth System Models. Included are modeled net primary production (NPP) and nitrogen (N) uptake for two scenarios: (1) baseline and (2) no NGS N uptake. The output is provided (1) at 25 km resolution across the North American tundra; (2) for years 2012 through 2100; and (3) PFT-specific. Dataset contains 20 NetCDF files in a zip file and one pdf.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arcticmore »« less

Authors:

;

; Tang, Jinyun; Zhu, Qing;

; Grant, Robert

Lawrence Berkeley National Laboratory

Publication Date:: Wed Jan 19 04:00:00 UTC 2022

Other Number(s):: https://doi.org/10.5440/1785957; NGA263

DOE Contract Number:: AC05-00OR22725

Research Org.:: Next Generation Ecosystems Experiment - Arctic, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (US)

Sponsoring Org.:: U.S. DOE > Office of Science > Biological and Environmental Research (BER)

Collaborations:: ORNL

Subject:: 54 ENVIRONMENTAL SCIENCES; Barrow Environmental Observatory (BEO); EARTH SCIENCE > BIOLOGICAL CLASSIFICATION > PLANTS; EARTH SCIENCE > BIOSPHERE > ECOLOGICAL DYNAMICS; EARTH SCIENCE > LAND SURFACE > SOILS; North Slope, Alaska; Seward Peninsula, Alaska; Utqiagvik, Alaska; ecosys model predictions

OSTI Identifier:: 1785957

DOI:: https://doi.org/10.5440/1785957

Citation Formats


                    Riley, William, Mekonnen, Zelalem, Tang, Jinyun, Zhu, Qing, Bouskill, Nicholas, and Grant, Robert. Non-growing Season Plant Nutrient Uptake Controls Arctic Tundra Vegetation Composition: Modeling Archive.  United States: N. p., 2022. 
        Web.  doi:10.5440/1785957.

Copy to clipboard


                    Riley, William, Mekonnen, Zelalem, Tang, Jinyun, Zhu, Qing, Bouskill, Nicholas, & Grant, Robert. Non-growing Season Plant Nutrient Uptake Controls Arctic Tundra Vegetation Composition: Modeling Archive.  United States.  doi:https://doi.org/10.5440/1785957

Copy to clipboard


                    Riley, William, Mekonnen, Zelalem, Tang, Jinyun, Zhu, Qing, Bouskill, Nicholas, and Grant, Robert. 2022.  
"Non-growing Season Plant Nutrient Uptake Controls Arctic Tundra Vegetation Composition: Modeling Archive".  United States.  doi:https://doi.org/10.5440/1785957.  https://www.osti.gov/servlets/purl/1785957. Pub date:Wed Jan 19 04:00:00 UTC 2022

Copy to clipboard


                    
@article{osti_1785957,

  title        = {Non-growing Season Plant Nutrient Uptake Controls Arctic Tundra Vegetation Composition: Modeling Archive},

  author       = {Riley, William and Mekonnen, Zelalem and Tang, Jinyun and Zhu, Qing and Bouskill, Nicholas and Grant, Robert},

  abstractNote = {This Modeling Archive is in support of a NGEE-Arctic publication: Riley et al. (2021) “Non-growing season plant nutrient uptake controls Arctic tundra vegetation composition under future climate” (Riley et al., 2021). The dataset contains “ecosys” model outputs reported in Riley et al. (2021) that examines the role of non-growing season (NGS) nutrient dynamics on 21st century vegetation composition. The study highlighted the importance of these nutrient dynamics on the emergent plant functional type distributions, focusing on their role in shrub expansion. The study showed that ignoring NGS nutrient dynamics led to a dramatic under-prediction of shrub expansion by year 2100, motivating the need to include these processes in Earth System Models. Included are modeled net primary production (NPP) and nitrogen (N) uptake for two scenarios: (1) baseline and (2) no NGS N uptake. The output is provided (1) at 25 km resolution across the North American tundra; (2) for years 2012 through 2100; and (3) PFT-specific. Dataset contains 20 NetCDF files in a zip file and one pdf.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).},

  doi          = {10.5440/1785957},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Wed Jan 19 04:00:00 UTC 2022},

  month        = {Wed Jan 19 04:00:00 UTC 2022}

}

Copy to clipboard

Dataset:

View Dataset

DOI: https://doi.org/10.5440/1785957

Save / Share:

Export Metadata

Save to My Library

Similar records in DOE Data Explorer and OSTI.GOV collections:

Non-growing season plant nutrient uptake controls Arctic tundra vegetation composition under future climate

Journal Article Riley, William J. ; Mekonnen, Zelalem A. ; Tang, Jinyun ; ... - Environmental Research Letters

Plant growth and distribution in high-latitude tundra ecosystems is strongly limited by nutrient availability and is critical for quantifying centennial-scale carbon-climate interactions. However, land model representations of plant-nutrient interactions are uncertain, leading to poor comparisons with high-latitude observations. Although it has been recognized for decades in the observational community that plants continue to acquire nutrients well past when aboveground activity has ceased, most large-scale land models ignore this process. Here we address the role tundra plant nutrient acquisition during the non-growing season (NGS) has on centennial-scale vegetation growth and dynamics, with a focus on shrub expansion. We apply a well-testedmore »« less
Non-growing season plant nutrient uptake controls Arctic tundra vegetation composition under future climate

Journal Article Riley, William J. ; Mekonnen, Zelalem A. ; Tang, Jinyun ; ... - Environmental Research Letters

Abstract Plant growth and distribution in high-latitude tundra ecosystems is strongly limited by nutrient availability and is critical for quantifying centennial-scale carbon-climate interactions. However, land model representations of plant–nutrient interactions are uncertain, leading to poor comparisons with high-latitude observations. Although it has been recognized for decades in the observational community that plants continue to acquire nutrients well past when aboveground activity has ceased, most large-scale land models ignore this process. Here we address the role tundra plant nutrient acquisition during the non-growing season (NGS) has on centennial-scale vegetation growth and dynamics, with a focus on shrub expansion. We apply amore »« less
Integrating Characteristic Arctic Vegetation in a Land Surface Model Improves Representation of Carbon Dynamics Across a Tundra Landscape: Modeling Archive

Dataset Murphy, Bailey ; Yuan, Fengming ; Sulman, Benjamin

This modeling archive is in support of the Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) publication "Integrating Characteristic Arctic Vegetation in a Land Surface Model Improves Representation of Carbon Dynamics Across a Tundra Landscape", by Murphy et al. (2025). This archive contains model input files and outputs from landscape-scale simulations conducted using ELM, the land model component of the Department of Energy’s Energy Exascale Earth System Model (E3SM), at the Council NGEE Arctic field site (Council Road mile marker 71) on Alaska’s Seward Peninsula. Input data and model output from two sets of ELM simulations are provided. The firstmore »« less
A Multi-Sensor Unoccupied Aerial System Improves Characterization of Vegetation Composition and Canopy Properties in the Arctic Tundra: Supporting Data

Dataset Serbin, Shawn ; Yang, Dedi ; McMahon, Andrew

Remote sensing data collected from Brookhaven National Laboratory's (BNL) heavy-lift unoccupied aerial system (UAS) octocopter platform - the Osprey - operated by the Terrestrial Ecosystem Science and Technology (TEST) group. Data was collected from a single flight over the Kougarok hillslope site on 26 July, 2018. The Osprey is a multi-sensor UAS platform that simultaneously measures very high spatial resolution optical red/green/blue (RGB) and thermal infrared (TIR) surface "skin" temperature imagery, as well as surface reflectance at 1nm intervals in the visible to near-infrared spectral range from ~350-1000 nm measured at regular intervals along each flight path. Derived image productsmore »« less
Modeling Anaerobic Soil Organic Carbon Decomposition in Arctic Polygon Tundra: Insights into Soil Geochemical Influences on Carbon Mineralization: Modeling Archive

Dataset Zheng, Jianqiu ; Thornton, Peter ; Painter, Scott ; ...

This anaerobic carbon decomposition model framework was developed with explicit representation of fermentation, methanogenesis and iron reduction by combining three well-known modeling approaches developed in different disciplines. A pool-based model to represent upstream carbon transformations and replenishment of dissolved organic carbon (DOC) pool, a thermodynamically-based model to calculate rate kinetics and biomass growth for methanogenesis and Fe(III) reduction, and a humic ion-binding model for aqueous phase speciation and pH calculations. The combined models are implemented in the open source geochemical model PHREEQC (V3.0). Installation of PHREEQC is required to run this model. This dataset contains one zipped file and onemore »« less

Similar Records