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Title: The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model

We conduct a global sensitivity analysis (GSA) of the Energy Exascale Earth System Model (E3SM), land model (ELM) to calculate the sensitivity of five key carbon cycle outputs to 68 model parameters. This GSA is conducted by first constructing a Polynomial Chaos (PC) surrogate via new Weighted Iterative Bayesian Compressive Sensing (WIBCS) algorithm for adaptive basis growth leading to a sparse, high-dimensional PC surrogate with 3,000 model evaluations. The PC surrogate allows efficient extraction of GSA information leading to further dimensionality reduction. The GSA is performed at 96 FLUXNET sites covering multiple plant functional types (PFTs) and climate conditions. About 20 of the model parameters are identified as sensitive with the rest being relatively insensitive across all outputs and PFTs. These sensitivities are dependent on PFT, and are relatively consistent among sites within the same PFT. The five model outputs have a majority of their highly sensitive parameters in common. A common subset of sensitive parameters is also shared among PFTs, but some parameters are specific to certain types (e.g., deciduous phenology). In conclusion, the relative importance of these parameters shifts significantly among PFTs and with climatic variables such as mean annual temperature.
Authors:
ORCiD logo [1] ;  [2] ; ORCiD logo [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  2. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Publication Date:
Report Number(s):
SAND2017-1661J
Journal ID: ISSN 1942-2466; TRN: US1802305
Grant/Contract Number:
AC05-00OR22725; NA0003525; AC04-94AL85000
Type:
Published Article
Journal Name:
Journal of Advances in Modeling Earth Systems
Additional Journal Information:
Journal Volume: 10; Journal Issue: 2; Journal ID: ISSN 1942-2466
Publisher:
American Geophysical Union (AGU)
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; 54 ENVIRONMENTAL SCIENCES; 59 BASIC BIOLOGICAL SCIENCES
OSTI Identifier:
1419691
Alternate Identifier(s):
OSTI ID: 1419692; OSTI ID: 1426568; OSTI ID: 1429735

Ricciuto, Daniel, Sargsyan, Khachik, and Thornton, Peter. The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model. United States: N. p., Web. doi:10.1002/2017MS000962.
Ricciuto, Daniel, Sargsyan, Khachik, & Thornton, Peter. The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model. United States. doi:10.1002/2017MS000962.
Ricciuto, Daniel, Sargsyan, Khachik, and Thornton, Peter. 2018. "The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model". United States. doi:10.1002/2017MS000962.
@article{osti_1419691,
title = {The Impact of Parametric Uncertainties on Biogeochemistry in the E3SM Land Model},
author = {Ricciuto, Daniel and Sargsyan, Khachik and Thornton, Peter},
abstractNote = {We conduct a global sensitivity analysis (GSA) of the Energy Exascale Earth System Model (E3SM), land model (ELM) to calculate the sensitivity of five key carbon cycle outputs to 68 model parameters. This GSA is conducted by first constructing a Polynomial Chaos (PC) surrogate via new Weighted Iterative Bayesian Compressive Sensing (WIBCS) algorithm for adaptive basis growth leading to a sparse, high-dimensional PC surrogate with 3,000 model evaluations. The PC surrogate allows efficient extraction of GSA information leading to further dimensionality reduction. The GSA is performed at 96 FLUXNET sites covering multiple plant functional types (PFTs) and climate conditions. About 20 of the model parameters are identified as sensitive with the rest being relatively insensitive across all outputs and PFTs. These sensitivities are dependent on PFT, and are relatively consistent among sites within the same PFT. The five model outputs have a majority of their highly sensitive parameters in common. A common subset of sensitive parameters is also shared among PFTs, but some parameters are specific to certain types (e.g., deciduous phenology). In conclusion, the relative importance of these parameters shifts significantly among PFTs and with climatic variables such as mean annual temperature.},
doi = {10.1002/2017MS000962},
journal = {Journal of Advances in Modeling Earth Systems},
number = 2,
volume = 10,
place = {United States},
year = {2018},
month = {2}
}

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