Gross primary productivity (GPP) quantifies the photosynthetic uptake of carbon by terrestrial ecosystems. High‐temperature extremes and associated droughts significantly reduce terrestrial ecosystem carbon uptake, but there is uncertainty as to whether climatic extremes can be beneficial to ecosystem carbon uptake. In this study, we used three ecological models: the Boreal Ecosystem Productivity Simulator, the Terrestrial Ecosystem Carbon flux model, and the Global Production Efficiency Model coupled with the Carbon Exchange between Vegetation, Soil, and the Atmosphere model, to simulate China's terrestrial ecosystem GPP during the study period of 1982–2015. Positive GPP extremes were identified on yearly scales and analyzed for their temperature, precipitation, and solar radiation attributions. We found that the 3 years of positive GPP extremes occurred in 1990, 1998, and 2013, with the detrended GPP anomalies of 0.4 PgC/year, 0.2 PgC/year, and 0.3 PgC/year, respectively. Maximum GPP years were associated with an increased carbon uptake in response to increasing temperature and solar radiation under adequate precipitation for plant growth. China's subtropical‐tropical monsoonal region, a region dominated by managed forests and agricultural lands, contributed the largest in GPP extremes and accounted for 46%, 50%, and 46% of the total detrended GPP anomalies in 1990, 1998, and 2013, respectively. Positive GPP extremes were associated with increasing temperature and solar radiation, indicating favorable positive climate extremes would be beneficial to carbon uptake in China's terrestrial ecosystem. This study provides a method to assess positive GPP extremes in response to global climate change.

Leaf area index (LAI) is a vegetation structural parameter that modulates the interaction between the land surface and the atmosphere and therefore is used in many terrestrial biosphere models. However, there are still large uncertainties in simulating the global LAI in Earth system models. In this study, we used climate and soil variables and the Farquhar's biochemical model to reconstruct global LAI, explore the mechanisms controlling global LAI seasonality, and analyze the feasibility of Farquhar's biochemical model in estimating the effect of CO₂ fertilization on global LAI. The results show that the reconstructed LAI (RLAI) based on climate and soil variables can explain 93% of the seasonal dynamics of global LAI. However, RLAI only explained 27.3% of the global LAI trend and captured 29.8% of the land area with a significant trend. RLAI after incorporating the CO₂ fertilization effect, which is estimated by Farquhar's biochemical model, can explain 68.8% (41.5% improvement from RLAI) of the global LAI trend and capture 63.3% (33.5% improvement from RLAI) of the area with a significant LAI trend. These results suggest that it is feasible to use Farquhar's biochemical model to estimate the effect of CO₂ fertilization on the global trend in LAI. The statistical model for reconstructing the seasonal dynamics of LAI and the Farquhar model‐based method for estimating the LAI temporal trend developed in this study would be useful for improving or evaluating the performance of prognostic models for future global carbon cycle research. Furthermore, this study may provide a new way to simulate global LAI for prognostic modeling.

Global climate change has significantly affected terrestrial carbon sinks. Net ecosystem production (NEP) plays a critical role in the global carbon cycle. However, interannual variability (IAV) of the NEP and its regional contributions and climate attributions are not well-understood on a global scale. This study used a diagnostic model driven by remote sensing leaf area index (LAI) to investigate the NEP IAV and analyze regional and climate contributions on a global scale from 1982 to 2016. We found large NEP IAV during the study period, with the NEP detrended anomaly ranging from −2.3 Pg C in 1998 to 1.6 Pg C in 2013 at a global scale. Furthermore, 63.7% and 34.1% of the areas showed positive and negative contributions to NEP IAVs globally, respectively. Evergreen broadleaf forest (EBF) contributed the most (31.1%) to NEP IAV, followed by cropland (21.7%) and grassland (20.8%). Temperature played the most critical roles in the global NEP IAV, with a contribution of 45.5%. However, the partial correlation between NEP and temperature was negative, and the correlation with precipitation was positive in most areas of the globe, indicating that global warming is not conducive to the global carbon sink, but abundant rainfall is important for the global carbon cycle. This study suggests that, to increase the global carbon sink, we should pay more attention to tropical forests (EBFs) and highlight the importance of water availability.

Optimizing soil carbon content is essential for mitigating climate change. Understanding the soil organic carbon (SOC) contents and their relationship with plantation age is important for enhancing SOC in tea plantations. However, there is still a lack of studies in quantifying the SOC–age curve of the whole life cycle in tea plantations. Thus, in this study, we collected 140 soil samples aged 3 to 60 years in the four representative regions (Anxi, Datian, Qingliu, and Fuzhou) in Fujian Province to quantify the SOC contents and their relationship with plantation age. We found that the average SOC was 14.6 ± 6.1 g/kg in the four sampling regions. Nitrogen (N) emerged as having a highly significant positive correlation with SOC (R² = 0.9). We also found a significant negative correlation between SOC and mean annual temperature (MAT) (R² = 0.6), and a significant positive correlation with mean annual precipitation (MAP) (R² = 0.6). The SOC increased with plantation age at 3–20 years old and peaked at 16–20 years old. After 35 years, the SOC decreased gradually with the aging of the plantation. The results indicated that tea plantations could be renewed after the age of 35. These results showed that optimizing age structure is important in enhancing SOC in tea plantations and is meaningful in achieving carbon neutrality.

Carbon use efficiency (CUE) represents the proficiency of plants in transforming carbon dioxide (CO₂) into carbon stock in terrestrial ecosystems. CUE extremes represent ecosystems’ extreme proficiency in carbon transformation. Studying CUE extremes and their forming climate conditions is critical for enhancing ecosystem carbon storage. However, the study of CUE extremes and their forming climate conditions on the global scale is still lacking. In this study, we used the results from the daily Boreal Ecosystem Productivity Simulator (BEPS) model to detect the positive and negative CUE extremes and analyze their forming climatic conditions on a global scale. We found grasslands have the largest potential in changing global CUE, with the contribution being approximately 32.4% to positive extremes and 30.2% to negative extremes. Spring in the Northern Hemisphere (MAM) contributed the most (30.5%) to positive CUE extremes, and summer (JJA) contributed the most (29.7%) to negative CUE extremes. The probabilities of gross primary production (GPP) extremes resulted in CUE extremes (>25.0%) being larger than autotrophic respiration (Ra), indicating CUE extremes were mainly controlled by GPP rather than Ra extremes. Positive temperature anomalies (0~1.0 °C) often accompanied negative CUE extreme events, and positive CUE extreme events attended negative temperature anomalies (−1.0~0 °C). Moreover, positive (0~20.0 mm) and negative precipitation (−20.0~0 mm) anomalies often accompanied positive and negative CUE extremes, respectively. These results suggest that cooler and wetter climate conditions could be beneficial to enhance carbon absorptions of terrestrial ecosystems. The study provides new knowledge on proficiency in carbon transformation by terrestrial ecosystems.