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Title: Divergence in land surface modeling: linking spread to structure

Abstract

Divergence in land carbon cycle simulation is persistent and widespread. Regardless of model intercomparison project, results from individual models diverge significantly from each other and, in consequence, from reference datasets. Here we link model spread to structure using a 15-member ensemble of land surface models from the Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP) as a test case. Our analysis uses functional benchmarks and model structure as predicted by model skill in a machine learning framework to isolate discrete aspects of model structure associated with divergence. We also quantify how initial conditions prejudice present-day model outcomes after centennial-scale transient simulations. Overall, the functional benchmark and machine learning exercises emphasize the importance of ecosystem structure in correctly simulating carbon and water cycling, highlight uncertainties in the structure of carbon pools, and advise against hard parametric limits on ecosystem function. We also find that initial conditions explain 90% of variation in global satellite-era values—initial conditions largely predetermine transient endpoints, historical environmental change notwithstanding. As MsTMIP prescribes forcing data and spin-up protocol, the range in initial conditions and high levels of predetermination are also structural. Our results suggest that methodological tools linking divergence to discrete aspects of model structure would complement currentmore » community best practices in model development.« less

Authors:
ORCiD logo [1];  [2];  [3];  [4];  [5];  [6];  [7]; ORCiD logo [8];  [6];  [9]; ORCiD logo [10]; ORCiD logo [9]; ORCiD logo [11]
  1. Woods Hole Research Center, Falmouth, MA (United States)
  2. National Snow and Ice Data Center, Boulder, CO (United States)
  3. California Inst. of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Lab. (JPL)
  4. Northern Arizona Univ., Flagstaff, AZ (United States)
  5. Univ. of South Florida, St. Petersburg, FL (United States)
  6. Carnegie Inst. of Science, Stanford, CA (United States)
  7. Univ. of Maine, Orono, ME (United States)
  8. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  9. Univ. of Colorado, Boulder, CO (United States)
  10. Conservation Science Partners, Truckee, CA (United States)
  11. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA); National Aeronautic and Space Administration (NASA); National Science Foundation (NSF); USDOE Office of Science (SC)
OSTI Identifier:
1597354
Alternate Identifier(s):
OSTI ID: 1651279
Report Number(s):
LA-UR-19-29265
Journal ID: ISSN 2515-7620
Grant/Contract Number:  
89233218CNA000001; 14-CMAC14-NNX16AB19G; N4-TE14-0047-NNH14ZDA001N-TE; 13-CARBON13_2-0036-NNH13ZDA001N-CARBON; NSF-OPP-1503559; 1900795; AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Environmental Research Communications
Additional Journal Information:
Journal Volume: 1; Journal Issue: 11; Journal ID: ISSN 2515-7620
Publisher:
IOP Science
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; global change ecology; carbon cycle modeling; data-driven discovery; inter-model spread

Citation Formats

Schwalm, Christopher R., Schaefer, Kevin, Fisher, Joshua B., Huntzinger, Deborah, Elshorbany, Yasin, Fang, Yuanyuan, Hayes, Daniel, Jafarov, Elchin, Michalak, Anna M., Piper, Mark, Stofferahn, Eric, Wang, Kang, and Wei, Yaxing. Divergence in land surface modeling: linking spread to structure. United States: N. p., 2019. Web. doi:10.1088/2515-7620/ab4a8a.
Schwalm, Christopher R., Schaefer, Kevin, Fisher, Joshua B., Huntzinger, Deborah, Elshorbany, Yasin, Fang, Yuanyuan, Hayes, Daniel, Jafarov, Elchin, Michalak, Anna M., Piper, Mark, Stofferahn, Eric, Wang, Kang, & Wei, Yaxing. Divergence in land surface modeling: linking spread to structure. United States. doi:10.1088/2515-7620/ab4a8a.
Schwalm, Christopher R., Schaefer, Kevin, Fisher, Joshua B., Huntzinger, Deborah, Elshorbany, Yasin, Fang, Yuanyuan, Hayes, Daniel, Jafarov, Elchin, Michalak, Anna M., Piper, Mark, Stofferahn, Eric, Wang, Kang, and Wei, Yaxing. Mon . "Divergence in land surface modeling: linking spread to structure". United States. doi:10.1088/2515-7620/ab4a8a. https://www.osti.gov/servlets/purl/1597354.
@article{osti_1597354,
title = {Divergence in land surface modeling: linking spread to structure},
author = {Schwalm, Christopher R. and Schaefer, Kevin and Fisher, Joshua B. and Huntzinger, Deborah and Elshorbany, Yasin and Fang, Yuanyuan and Hayes, Daniel and Jafarov, Elchin and Michalak, Anna M. and Piper, Mark and Stofferahn, Eric and Wang, Kang and Wei, Yaxing},
abstractNote = {Divergence in land carbon cycle simulation is persistent and widespread. Regardless of model intercomparison project, results from individual models diverge significantly from each other and, in consequence, from reference datasets. Here we link model spread to structure using a 15-member ensemble of land surface models from the Multi-scale synthesis and Terrestrial Model Intercomparison Project (MsTMIP) as a test case. Our analysis uses functional benchmarks and model structure as predicted by model skill in a machine learning framework to isolate discrete aspects of model structure associated with divergence. We also quantify how initial conditions prejudice present-day model outcomes after centennial-scale transient simulations. Overall, the functional benchmark and machine learning exercises emphasize the importance of ecosystem structure in correctly simulating carbon and water cycling, highlight uncertainties in the structure of carbon pools, and advise against hard parametric limits on ecosystem function. We also find that initial conditions explain 90% of variation in global satellite-era values—initial conditions largely predetermine transient endpoints, historical environmental change notwithstanding. As MsTMIP prescribes forcing data and spin-up protocol, the range in initial conditions and high levels of predetermination are also structural. Our results suggest that methodological tools linking divergence to discrete aspects of model structure would complement current community best practices in model development.},
doi = {10.1088/2515-7620/ab4a8a},
journal = {Environmental Research Communications},
number = 11,
volume = 1,
place = {United States},
year = {2019},
month = {10}
}

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