Detection and attribution of positive net ecosystem productivity extremes in China's terrestrial ecosystems during 2000-2016 - Miaomiao Wang (Elsevier, 2021)
Positive net ecosystem productivity (NEP) extreme represents an extreme carbon sink in the terrestrial ecosystem. Studying positive NEP extremes and their control mechanisms is vital for a better understanding of the carbon cycle and mitigation of global warming. However, there are still challenges and difficulties in quantifying positive NEP extremes and finding optimal climate conditions to form them in China's terrestrial ecosystems. In this study, we used the Boreal Ecosystem Productivity Simulator (BEPS) to detect and reveal the control mechanisms of NEP extremes in China's terrestrial ecosystems. We found the positive NEP extremes occurred in 2002, 2012, and 2015, with the detrended NEP anomalies of 0.15 Pg C yr−1, 0.11 Pg C yr−1, and 0.09 Pg C yr−1, respectively. Also, the three positive NEP extremes contributed 70% of the NEP uptrend during 2000–2016. Evergreen needle-leaf forests and croplands in the subtropical-tropical monsoonal region played dominant roles in the positive NEP extremes, accounting for 34.2% and 32.6% in the positive NEP extremes on average, respectively. The relationship between the anomalies of Gross primary production (GPP) and NEP was significant (P < 0.001), with the R2 ranging from 0.32 to 0.37 in evergreen needle-leaf forests and ranging from 0.38 to 0.50 in croplands. However, the relationship between the anomalies of ecosystem respiration (Re) and NEP was not significant (P > 0.001). These results mean that GPP (rather than Re) played a decisive role in the positive NEP extremes. Also, the best climate conditions for carbon uptake in the subtropical-tropical region have temperature anomalies ranging from −1.0 to 0.5 °C, and precipitation anomalies ranging from 0 to 600 mm. This study provides new knowledge on the potential carbon sink scientific and technological support for mitigating climate warming in China's terrestrial ecosystem.