CarbonBench: A Global Benchmark for Upscaling of Carbon Fluxes Using Zero-Shot Learning

TL;DR

CarbonBench is a comprehensive benchmark designed to evaluate zero-shot spatial transfer learning methods for upscaling terrestrial carbon fluxes across diverse ecosystems and climate regimes globally.

Contribution

It introduces the first standardized benchmark for zero-shot transfer in carbon flux modeling, with evaluation protocols, datasets, and baseline methods.

Findings

Benchmark includes over 1.3 million observations from 567 sites.

Provides evaluation protocols for unseen vegetation and climate regimes.

Includes baseline models from tree-based to domain-generalization architectures.

Abstract

Accurately quantifying terrestrial carbon exchange is essential for climate policy and carbon accounting, yet models must generalize to ecosystems underrepresented in sparse eddy covariance observations. Despite this challenge being a natural instance of zero-shot spatial transfer learning for time series regression, no standardized benchmark exists to rigorously evaluate model performance across geographically distinct locations with different climate regimes and vegetation types. We introduce CarbonBench, the first benchmark for zero-shot spatial transfer in carbon flux upscaling. CarbonBench comprises over 1.3 million daily observations from 567 flux tower sites globally (2000-2024). It provides: (1) stratified evaluation protocols that explicitly test generalization across unseen vegetation types and climate regimes, separating spatial transfer from temporal autocorrelation; (2) a harmonized set of remote sensing and meteorological features to enable flexible architecture design; and (3) baselines ranging from tree-based methods to domain-generalization architectures. By bridging machine learning methodologies and Earth system science, CarbonBench aims to enable systematic comparison of transfer learning methods, serves as a testbed for regression under distribution shift, and contributes to the next-generation climate modeling efforts.