Evaluating Vegetation Modeling in Earth System Models with Machine Learning Approaches

Ranjini Swaminathan, Tristan L. Quaife, Richard Philip Allan

Preprint · 20 November 2023 · University of Reading

In one sentence. Interpretable machine learning attributes differences in Earth system model gross primary productivity to atmospheric drivers versus land process representation across models.

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).

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