Auditing the Global Carbon Budget: Exploring the 2024--2025 Vintage Shift

2026-08-12

AI Summary

This study addresses a critical inconsistency introduced in the 2025 Global Carbon Budget (GCB) update, which exhibits a pronounced imbalance shift that jeopardizes the long-term coherence of carbon cycle analyses. Employing a model-free attribution framework, a dynamic statistical GCB model, and regression with climate covariates, we systematically evaluate the impacts of data revisions across GCB versions from 2017 to 2025. Our analysis reveals, for the first time, that while the 2025 release improves the fit to recent decadal observations, it concurrently exacerbates historical imbalances: the land carbon sink is revised downward by 0.40 GtC/yr, the ocean sink slightly upward, and the global budget imbalance rises to a mean of +0.61 GtC/yr—statistically significant as its 95% confidence interval excludes zero. This trade-off between short-term fidelity and long-term consistency offers a crucial methodological caution for future carbon budget assessments.

Abstract

The Global Carbon Budget (GCB), the community reference dataset for the carbon cycle, is reissued annually. The 2025 release introduces several adjustments to the published series that we compare with prior releases starting in 2017. On a common $1959$--$2016$ sample, the mean of the GCB budget imbalance jumps from within $\pm 0.17$~GtC/yr of zero for every vintage $2017$--$2024$ to $+0.61$~GtC/yr in 2025, the only vintage whose $95\%$ confidence interval for the imbalance mean excludes zero. We document and explore this shift in two ways. First, we conduct a model-free analysis, where we attribute the shift to a new adjustment that places the published land sink $0.40$~GtC/yr below its ensemble mean (the average of the underlying models), a smaller adjustment in the ocean sink in the opposite direction, and the removal of one model from the bookkeeping ensemble. Second, we consider the dynamic statistical GCB model of \cite{BHK2023}, augmented with climate covariates. Its parameters are estimated for every GCB vintage 2017--2025. The coefficients of atmospheric concentrations in the sink equations shift sharply on the 2025 issue in opposite directions, mirroring the model-free findings. There is a persistent drifting imbalance across the entire sample in the budget equation. In case the intended effect of the adjustments to the 2025 vintage is to reduce the mean of the budget imbalance on the window of the last ten years, our results show that this comes at the cost of increased budget imbalance over the whole sample and inconsistency of the data record. We argue that the costs of the adjustments outweigh the benefits of a narrow view on the last ten years and are detrimental to statistical analysis of the full GCB sample.

Problem

• Global Carbon Budget
• budget imbalance
• vintage shift
• data consistency
• carbon cycle

Innovation

• Global Carbon Budget
• budget imbalance
• model-free analysis
• dynamic statistical model
• carbon sink adjustment