Inter–annual global carbon cycle variations linked to atmospheric circulation variability

Abstract. One of the least understood temporal–scales of global carbon cycle (C–cycle) dynamics is its inter–annual 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 C–cycle IAV that is explained by large–scale atmospheric circulation variability, which is quantified by spatio–temporal sea level pressure (SLP) fields. CO₂ variability is diagnosed from the detrended atmospheric CO₂ growth rate and the land CO₂ sink from different datasets in the global carbon budget. We use a regularized linear regression model, 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 atmospheric CO₂ growth rate and of the global land sink, with Pearson correlations between reference and predicted values as high as 0.70–0.84 with boreal winter SLP anomalies. This is comparable or higher than that of a similar model using 15 traditional teleconnection indices as predictors. The coefficient patterns 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 one 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 C–cycle IAV can be obtained from records of ~30–60 years. For shorter time–series (n < 30 years), however, 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.