Abstract
Abstract. One of the least understood temporal scales of global carbon cycle (C-cycle) dynamics is its interannual variability (IAV). This variability is mainly driven by variations in the local climatic drivers of terrestrial ecosystem activity, which in turn are controlled by large-scale modes of atmospheric variability. Here, we quantify the fraction of global C-cycle IAV that is explained by large-scale atmospheric circulation variability, which is quantified by spatiotemporal sea level pressure (SLP) fields. C-cycle variability is diagnosed from the global detrended atmospheric CO2 growth rate and the land CO2 sink from 16 dynamic global vegetation models and two atmospheric inversions in the Global Carbon Budget 2018. We use a regularized linear regression model, which represents a statistical learning technique apt to deal with the large number of atmospheric circulation predictors (p$≥$800, each representing one pixel-based time series of SLP anomalies) in a relatively short observed record (n$<$60 years). We show that boreal winter and spring SLP anomalies allow predicting IAV in the atmospheric CO2 growth rate and the global land sink, with Pearson correlations between reference and predicted values between 0.70 and 0.84 for boreal winter SLP anomalies. This is comparable to or higher than that of a similar model using 15 traditional teleconnection indices as predictors. The spatial patterns of regression coefficients of the model based on SLP fields show a predominant role of the tropical Pacific and over Southeast Asia extending to Australia, corresponding to the regions associated with the El Niño–Southern Oscillation variability. We also identify another important region in the western Pacific, roughly corresponding to the West Pacific pattern. We further evaluate the influence of the time series length on the predictability of IAV and find that reliable estimates of global C-cycle IAV can be obtained from records of 30–54 years. For shorter time series (n$<$30 years), however, our results show that conclusions about CO2 IAV patterns and drivers need to be evaluated with caution. Overall, our study illustrates a new data-driven and flexible approach to model the relationship between large-scale atmospheric circulation variations and C-cycle variability at global and regional scales, complementing the traditional use of teleconnection indices.
