Evaluating Vegetation Modeling in Earth System Models with Machine
Learning Approaches
Abstract
Vegetation Gross Primary Productivity (GPP) is the single largest carbon
flux of the terrestrial biosphere which, in turn, is responsible for
sequestering $25-30\%$ of anthropogenic carbon dioxide
emissions. The ability to model GPP is therefore critical for
calculating carbon budgets as well as understanding climate feedbacks.
Earth System Models (ESMs) have the capability to simulate GPP but vary
greatly in their individual estimates, resulting in large uncertainties.
We describe a Machine Learning (ML) approach to investigate two key
factors responsible for differences in simulated GPP quantities from
ESMs: the relative importance of different atmospheric drivers and
differences in the representation of land surface processes. We describe
the different steps in the development of our interpretable Machine
Learning (ML) framework including the choice of algorithms, parameter
tuning, training and evaluation. Our results show that ESMs largely
agree on the physical climate drivers responsible for GPP as seen in the
literature, for instance drought variables in the Mediterranean region
or radiation and temperature in the Arctic region. However differences
do exist since models don’t necessarily agree on which individual
variable is most relevant for GPP. We also explore a distance measure to
attribute GPP differences to climate influences versus process
differences and provide examples for where our methods work (South Asia,
Mediterranean)and where they are inconclusive (Eastern North America).