Predictability Horizons in the Global Carbon Cycle Inferred From a Perfect‐Model Framework

Abstract

On interannual timescales the growth rate of atmospheric CO 2 is largely controlled by the response of the land and ocean carbon sinks to climate variability. Yet, it is unknown to what extent this variability limits the predictability of atmospheric CO 2 variations. Using perfect-model Earth System Model simulations, we show that variations in atmospheric CO 2 are potentially predictable for 3 years. We find a 2-year predictability horizon for global oceanic CO 2 flux with longer regional predictability of up to 7 years. The 2-year predictability horizon of terrestrial CO 2 flux originates in the tropics and midlatitudes. With the predictability of the isolated effects of land and ocean carbon sink on atmospheric CO 2 of 5 and 12 years respectively, land dampens the overall predictability of atmospheric CO 2 variations. Our research shows the potential of Earth System Model-based predictions to forecast multiyear variations in atmospheric CO 2 . Plain Language SummaryThe amount of anthropogenic carbon emissions absorbed by land and ocean from the atmosphere varies annually due to their sensitivity to climate. Therefore, the atmospheric CO 2 growth rate does not strictly follow the emissions signal. Whether decadal prediction systems can also predict variations of atmospheric CO 2 has not been shown yet but is crucial to inform policy makers about the efficiency of the implementation of the Paris Agreement. Using numerical Earth System simulations in an idealized prediction framework, we show that global atmospheric CO 2 is predictable up to 3 years in advance. The global ocean and land CO 2 fluxes are predictable for 2 years. The isolated effects of the land and ocean carbon sink on atmospheric CO 2 are predictable for 5 and 12 years, respectively. Therefore, the land carbon cycle limits atmospheric CO 2 predictability. Our study demonstrates that simulation-based multiyear forecasts have the potential to predict natural atmospheric CO 2 variations.

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

“…Given the longer predictive horizons of the ocean carbon sink (in the models which provided output from both the ocean and the land biogeochemistry components), our results indicate that predictability of the atmospheric CO 2 growth in these models is limited by the land carbon sink predictability. Analogously, a previous study, based on a perfect model framework (Spring & Ilyina, 2020), demonstrates that the predictive skill of atmospheric CO 2 concentration of 3 years is dampened by land.…”

“…On the land side, a potential prediction skill of 2 years was established for terrestrial net ecosystem production (Lovenduski, Bonan, et al., 2019), but only of 9 months for tropical land‐atmosphere carbon flux (Zeng et al., 2008). Perfect‐model frameworks based on idealized simulations suggest analogous predictability horizons for the carbon sinks (Séférian et al., 2018; Spring & Ilyina, 2020). However, previous studies were either limited to internally consistent model environments of perfect models (Frölicher et al., 2020; Séférian et al., 2018; Spring & Ilyina, 2020) or single initialized models (Fransner et al., 2020; Li et al., 2019, 2016; Lovenduski, Yeager, et al., 2019; Krumhardt et al., 2020; Yeager et al., 2018).…”

The Climate Response to Emissions Reductions Due to COVID‐19: Initial Results From CovidMIP

“…Multiple studies have found increases in ozone in populated urban areas during lockdown (e.g., Keller et al., 2020), in contrast to a global decrease in tropospheric ozone (Weber et al., 2020). This MIP provides an opportunity to shed process‐level understanding on these changes in a range of models of varying degrees of complexity with regards to atmospheric chemistry. Impact on the global carbon cycle : There is increasing interest in the ability to make predictions from one year to the next of changes in atmospheric CO 2 (Betts et al., 2016; Fransner et al., 2020; Lovenduski et al., 2019; Séférian et al., 2018; Spring & Ilyina, 2020). These studies require knowledge of natural causes of interannual variability, notable from ENSO (Watanabe et al., 2020), but they also require knowledge of up to date estimates of anthropogenic CO 2 emissions.…”

Large ensemble climate model simulations: introduction, overview, and future prospects for utilising multiple types of large ensemble

“…Over the last decade SMILEs have been increasingly utilised in climate science (e.g. Zelle et al, 2005;Branstator and Selten, 2009;Kay et al, 2015;Frankignoul et al, 2017;Kirchmeier-Young et al, 2017;Sanderson et al, 2018;Stolpe et al, 2018;Maher et al, 2019;Deser et al, 2020). The value of SMILEs comes from the ability to quantify and separate the internal variability of the climate system and the forced response to changes in external forcing (e.g.…”