Response of Growing Season Gross Primary Production to El Niño in Different Phases of the Pacific Decadal Oscillation over Eastern China Based on Bayesian Model Averaging

Gross primary production (GPP) plays a crucial part in the carbon cycle of terrestrial ecosystems. A set of validated monthly GPP data from 1957 to 2010 in 0.5° × 0.5° grids of China was weighted from the Multi-scale Terrestrial Model Intercomparison Project using Bayesian model averaging (BMA). The spatial anomalies of detrended BMA GPP during the growing seasons of typical El Niño years indicated that GPP response to El Niño varies with Pacific Decadal Oscillation (PDO) phases: when the PDO was in the cool phase, it was likely that GPP was greater in northern China (32°–38°N, 111°–122°E) and less in the Yangtze River valley (28°–32°N, 111°–122°E); in contrast, when PDO was in the warm phase, the GPP anomalies were usually reversed in these two regions. The consistent spatiotemporal pattern and high partial correlation revealed that rainfall dominated this phenomenon. The previously published findings on how El Niño during different phases of PDO affecting rainfall in eastern China make the statistical relationship between GPP and El Niño in this study theoretically credible. This paper not only introduces an effective way to use BMA in grids that have mixed plant function types, but also makes it possible to evaluate the carbon cycle in eastern China based on the prediction of El Niño and PDO