Efficient surrogate modeling methods for large-scale Earth system models based on machine-learning techniques
Improving predictive understanding of Earth system variability and change
requires data–model integration. Efficient data–model integration for
complex models requires surrogate modeling to reduce model evaluation time.
However, building a surrogate of a large-scale Earth system model (ESM) with
many output variables is computationally intensive because it involves a
large number of expensive ESM simulations. In this effort, we propose an
efficient surrogate method capable of using a few ESM runs to build an
accurate and fast-to-evaluate surrogate system of model outputs over large
spatial and temporal domains. We first use singular value decomposition to
reduce the output dimensions and then use Bayesian optimization techniques to
generate an accurate neural network surrogate model based on limited ESM
simulation samples. Our machine-learning-based surrogate methods can build
and evaluate a large surrogate system of many variables quickly. Thus,
whenever the quantities of interest change, such as a different objective
function, a new site, and a longer simulation time, we can simply extract the
information of interest from the surrogate system without rebuilding new
surrogates, which significantly reduces computational efforts. We apply the
proposed method to a regional ecosystem model to approximate the relationship
between eight model parameters and 42 660 carbon flux outputs. Results
indicate that using only 20 model simulations, we can build an accurate
surrogate system of the 42 660 variables, wherein the consistency between
the surrogate prediction and actual model simulation is 0.93 and the mean
squared error is 0.02. This highly accurate and fast-to-evaluate surrogate
system will greatly enhance the computational efficiency of data–model
integration to improve predictions and advance our understanding of the Earth
system.
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