Inherent uncertainty disguises attribution of reduced atmospheric CO₂ growth to CO₂ emission reductions for up to a decade

Abstract

The growth rate of atmospheric CO2 on inter-annual time scales is largely controlled by the response of the land and ocean carbon sinks to climate variability. Therefore, the effect of CO2 emission reductions to achieve the Paris Agreement on atmospheric CO2 concentrations may be disguised by internal variability, and the attribution of a reduction in atmospheric CO2 growth rate to CO2 emission reductions induced by a policy change is unclear for the near term. We use 100 single-model simulations and interpret CO2 emission reductions starting in 2020 as a policy change from scenario Representative Concentration Pathway (RCP) 4.5 to 2.6 in a comprehensive causal theory framework. Five-year CO2 concentration trends grow stronger in 2021–2025 after CO2 emission reductions than over 2016–2020 in 30% of all realizations in RCP2.6 compared to 52% in RCP4.5 without CO2 emission reductions. This implies that CO2 emission reductions are sufficient by 42%, necessary by 31% and both necessary and sufficient by 22% to cause reduced atmospheric CO2 trends. In the near term, these probabilities are far from certain. Certainty implying sufficient or necessary causation is only reached after, respectively, ten and sixteen years. Assessments of the efficacy of CO2 emission reductions in the near term are incomplete without quantitatively considering internal variability.

Reconstructions and predictions of the global carbon budget with an emission-driven Earth system model

“…The CO 2 fluxes between the atmosphere and the underlying surface, and therefore the atmospheric carbon growth rate, vary substantially on interannual to decadal time scales (Peters et al, 2017;Friedlingstein et al, 2019;Landschützer et al, 2019;Friedlingstein et al, 2020). These variations reflect the combined effects of the internal variability of the global carbon cycle (Li and Ilyina, 2018;Séférian et al, 2018;Spring et al, 2020;Fransner et al, 2020) and its responses to external forcings (McKinley et al, 2020).…”

Predictable Variations of the Carbon Sinks and Atmospheric CO2Growth in a Multi‐Model Framework

“…On interannual to decadal time‐scales, atmospheric CO 2 growth rates exhibit pronounced anomalies driven by varying strengths of the land and ocean carbon sinks; these anomalies are linked to climate variability on decadal and interannual time scales (Bacastow, 1976; Friedlingstein et al., 2019; Keeling et al., 1976; Landschützer et al., 2019; Peters et al., 2017; Spring et al., 2020). Variability in ocean carbon uptake is associated with major carbon uptake regions such as the Southern Ocean and the North Atlantic (Hauck et al., 2020; Landschützer et al., 2019).…”

Process-based analysis of terrestrial carbon flux predictability

“…As a net sink for atmospheric CO 2 , terrestrial ecosystems absorb around one-third of the anthropogenic emissions (Friedlingstein et al, 2020). Carbon fluxes between the landatmosphere interface have a high interannual variability with a standard deviation (SD) of 0.7 PgC yr −1 (Sitch et al, 2015) and cause the majority of the atmospheric CO 2 fluctuations (Ciais et al, 2013;Spring et al, 2020). The high variability of terrestrial carbon fluxes can be attributed to the sensitivity of land surface processes to climatic drivers; however the relative importance of temperature and precipitation are still debated (Jones et al, 2001;Beer et al, 2010;Bloom et al, 2016;Fang et al, 2017;Jung et al, 2017;Bastos et al, 2018).…”

“…This is reflected in the poor representation of soil organic carbon (SOC) in Earth system models (ESMs), the inability to adequately model gross primary production (GPP) from eddy covariance flux tower sites (Luo et al, 2015), and the dif-I. Dunkl et al: Process-based analysis of terrestrial carbon flux predictability ficulty to detect the efforts taken in emission reduction due to internal variability of atmospheric CO 2 variability (Spring et al, 2020). In order to produce more realistic predictions, efforts in model development have been directed towards using observations to constrain model parameters (Zeng et al, 2014;Bloom et al, 2016;Mystakidis et al, 2016;Chadburn et al, 2017;Tziolas et al, 2020) and to refine model structure to incorporate more processes and interactions (Krull et al, 2003;Stockmann et al, 2013;Xu et al, 2014;Luo et al, 2016).…”

“…Their statistical model uses the annual average SST in the Niño 3.4 region to successfully predict the CO 2 rise with a precision of 0.53 ppm yr −1 . Furthermore, Spring and Ilyina (2020) showed that ESM-based initialized predictions can predict atmospheric CO 2 variations up to 3 years in advance.…”