Machine Learning Driven Sensitivity Analysis of E3SM Land Model
  Parameters for Wetland Methane Emissions
Sandeep Chinta Xiang Gao Qing Zhu
Methane (CH4) is the second most critical greenhouse gas after carbon
dioxide, contributing to 16-25% of the observed atmospheric warming. Wetlands
are the primary natural source of methane emissions globally. However, wetland
methane emission estimates from biogeochemistry models contain considerable
uncertainty. One of the main sources of this uncertainty arises from the
numerous uncertain model parameters within various physical, biological, and
chemical processes that influence methane production, oxidation, and transport.
Sensitivity Analysis (SA) can help identify critical parameters for methane
emission and achieve reduced biases and uncertainties in future projections.
This study performs SA for 19 selected parameters responsible for critical
biogeochemical processes in the methane module of the Energy Exascale Earth
System Model (E3SM) land model (ELM). The impact of these parameters on various
CH4 fluxes is examined at 14 FLUXNET- CH4 sites with diverse vegetation types.
Given the extensive number of model simulations needed for global
variance-based SA, we employ a machine learning (ML) algorithm to emulate the
complex behavior of ELM methane biogeochemistry. ML enables the computational
time to be shortened significantly from 6 CPU hours to 0.72 milliseconds,
achieving reduced computational costs. We found that parameters linked to CH4
production and diffusion generally present the highest sensitivities despite
apparent seasonal variation. Comparing simulated emissions from perturbed
parameter sets against FLUXNET-CH4 observations revealed that better
performances can be achieved at each site compared to the default parameter
values. This presents a scope for further improving simulated emissions using
parameter calibration with advanced optimization techniques like Bayesian
optimization.