A Regression-Based Approach to the CO2 Airborne Fraction: Enhancing Statistical Precision and Tackling Zero Emissions

TL;DR

This paper introduces a regression-based estimator for the CO2 airborne fraction, improving statistical properties and providing more accurate, constrained estimates over 1959-2022, with applicability to future low-emission scenarios.

Contribution

It proposes a novel regression-based method for estimating the airborne fraction, addressing statistical issues of traditional ratio estimators and enhancing estimation accuracy.

Findings

Estimated airborne fraction over 1959-2022 is 47.0% with 1.1% uncertainty.

Regression estimator has a Gaussian limiting distribution, reducing uncertainty.

Method performs well in climate model scenarios with near-zero emissions.

Abstract

The global fraction of anthropogenically emitted carbon dioxide (CO$_2$) that stays in the atmosphere, the CO$_2$ airborne fraction, has been fluctuating around a constant value over the period 1959 to 2022. The consensus estimate of the airborne fraction is around $44\%$; the remaining $56\%$ is absorbed by the oceanic and terrestrials biospheres. In this study, we show that the conventional estimator of the airborne fraction, based on a ratio of changes in atmospheric CO$_2$ concentrations and CO$_2$ emissions, suffers from a number of statistical deficiencies, such as non-existence of moments and a non-Gaussian limiting distribution. We propose an alternative regression-based estimator of the airborne fraction that does not suffer from these deficiencies. We show that the regression-based estimator has a Gaussian limiting distribution and reduces estimation uncertainty substantially. Our empirical analysis leads to an estimate of the airborne fraction over 1959--2022 of $47.0\%$ ($\pm 1.1\%$; $1 \sigma$), implying a higher, and better constrained, estimate than the current consensus. Using climate model output, we show that a regression-based approach provides sensible estimates of the airborne fraction, also in future scenarios where emissions are at or near zero.