TY - JOUR
T1 - Divergence in land surface modeling: linking spread to structure
AU - Schwalm, Christopher R.
AU - Schaefer, Kevin
AU - Fisher, Joshua B.
AU - Huntzinger, Deborah
AU - Elshorbany, Yasin
N1 - Schwalm, Christopher R., S., Kevin, Fisher, Joshua B., H., Deborah, Elshorbany, Yasin, F., Yuanyuan, Hayes, Daniel, J., Elchin, Michalak, Anna M., P., M. (2019). Divergence in land surface modeling: linking spread to structure. Environmental Research Communications, 1(11), 111004. https://doi.org/10.1088/2515-7620/ab4a8a
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - global change ecology, carbon cycle modeling, data-driven discovery, inter-model spread
UR - https://digitalcommons.usf.edu/fac_publications/3880
UR - https://doi.org/10.1088/2515-7620/ab4a8a
M3 - Article
JO - Default journal
JF - Default journal
ER -