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Plant phenology under changing climate is a critical factor controlling terrestrial vegetation productivity. However, large uncertainties exist due to different data sources and phenological parameter extraction methods. In this study, we took advantage of a suite of long-term field observational data in northern grassland of China to investigate the drivers of phenological shifts and their effect on the maximum aboveground net primary productivity (ANPP max ) across four representative grassland types during 1984-2017. Results showed that drivers of phenological events (i.e., start (SOS), end (EOS), and length (GSL) of the growing season) with warming influence dramatically differed among grassland types, indicating that the synergistic effect of temperature and precipitation should be highlighted. For temperate desert steppe and alpine meadow, GSL of dominant species was both significantly lengthened with temperature rising with averaged 0.94 days year -1 (P < 0.001) and 1.15 days year -1 (P < 0.001), respectively, while for typical temperate grassland, GSL was considerably shortened by an average of 0.58 days year -1 (P < 0.01) as a result of water deficit caused by sharp warming and precipitation decreasing in summer and autumn. For most grassland types in our study, both SOS and GSL were significantly correlated with ANPP max under different precipitation gradients with SOS advanced and GSL extended leading to higher ANPP max . Only the typical temperate grassland presents a relatively poor correlation between phenological events and productivity. Furthermore, compared with GSL, ANPP max was more sensitive to the advancement of SOS for every 1-day phenological change. However, the effect of EOS on ANPP max across the four grassland types was much weaker and unstable. There were spatial response differences between ANPP max and phenological transition events, with the temperate meadow grassland tending to be more sensitive compared with three other grassland types.

Differences, arising from differences in gross primary production (GPP) model structures and driving forces, have fuelled arguments concerning interannual changes of GPP in China since 2000. To better investigate the interannual variability of GPP and its covariance with climate factors in China, this study adopted a multi‐model analysis based on three GPP models (i.e., Terrestrial Ecosystem Carbon flux model [TEC], Breathing Earth System Simulator model [BESS], and MOD17 GPP model). The results show that annual GPP in China increased by 0.021–0.057 Pg C year−1 from 2000 to 2015 attributable to atmospheric‐CO2 fertilization effects and favourable climate change, that is, increasing precipitation (Pr) and temperature (Ta). However, northern China and southern China had a large difference in the amplitude of these GPP changes; annual GPP increased by 0.017–0.039 Pg C year−1 in northern China but only 0.001–0.018 Pg C year−1 in southern China. Northern China and southern China occupy contrasting climate zones and this contrast produced different interannual variability of GPP through different mechanisms. Northern China has a dry climate with GPP changes sensitive to Pr. As a result, more Pr along with higher Ta in northern China produced the strong uptrend of GPP from 2000 to 2015. In contrast, southern China has a wet climate with its GPP sensitive to solar radiation and Ta. For the interval of 2000–2015, decreasing radiation plus drought exerted a negative influence on GPP in southern China. This study highlights the diverse mechanisms in which climate change affects GPP in dry and wet climate zones. A robust multi‐model analysis is preferred to reduce uncertainties arising from a single GPP model and its driving data. [ABSTRACT FROM AUTHOR]

The carbon budgets in terrestrial ecosystems in China are strongly coupled with climate changes. Over the past decade, China has experienced dramatic climate changes characterized by enhanced summer monsoon and decelerated warming. However, the changes in the trends of terrestrial net ecosystem production (NEP) in China under climate changes are not well documented. Here, we used three ecosystem models to simulate the spatiotemporal variations in China's NEP during 1982–2010 and quantify the contribution of the strengthened summer monsoon and warming hiatus to the NEP variations in four distinct climatic regions of the country. Our results revealed a decadal-scale shift in NEP from a downtrend of –5.95 Tg C/yr2 (reduced sink) during 1982–2000 to an uptrend of 14.22 Tg C/yr2 (enhanced sink) during 2000–10. This shift was essentially induced by the strengthened summer monsoon, which stimulated carbon uptake, and the warming hiatus, which lessened the decrease in the NEP trend. Compared to the contribution of 56.3% by the climate effect, atmospheric CO2 concentration and nitrogen deposition had relatively small contributions (8.6 and 11.3%, respectively) to the shift. In conclusion, within the context of the global-warming hiatus, the strengthening of the summer monsoon is a critical climate factor that enhances carbon uptake in China due to the asymmetric response of photosynthesis and respiration. Our study not only revealed the shift in ecosystem carbon sequestration in China in recent decades, but also provides some insight for understanding ecosystem carbon dynamics in other monsoonal areas. [ABSTRACT FROM AUTHOR]

Seagrass is an important foundational species in marine ecosystems that plays an important role in providing ecosystem services. However, seagrass distribution and suitable habitat have rapidly declined in recent decades because of climate change and human-mediated disturbances. To achieved better conservation and management of the seagrass Zostera marina , an important representative seagrass in northern China, studies must investigate how this species responds to climate change. Here, we used an ensemble species distribution model to forecast the potential distribution and future suitable habitat change for the eelgrass Z. marina in coastal areas of the Bohai Sea and Shandong Peninsula, China. Two future climate scenarios, RCP 4.5 and RCP 8.5, were considered, which represent stable and high levels of greenhouse gas emissions, respectively. The ensemble model accurately predicted the current Z. marina distribution and performed better than the single-algorithm methods of random forest, generalized boosting model and maximum entropy according to performance matrices (AUC and TSS). The distance to land (78.50 ± 3.51%) and the maximum sea surface temperature (11.90 ± 0.25%) were the most important variables determining the distribution of Z. marina , while the contributions of other variables, maximum sea surface current velocity (3.80 ± 0.33%), minimum surface salinity (2.15 ± 0.22%) and water depth (1.74 ± 0.24%) were relatively low. Under future climate scenarios, the suitable habitat of Z. marina was predicted to decrease by more than half by the 2050s, whereas under the pessimistic greenhouse gas emission scenario (RCP 8.5), suitable Z. marina habitat was predicted to decrease by nearly 80% by the end of this century. The loss of Z. marina seagrass beds will have important ecological effects on the marine ecosystem. Our results not only provide valuable information to assist in identifying potential Z. marina meadows, but also have important implications for guiding conservation and management of Z. marina in the northern coastal region of China. • The distribution and suitable habitat of Zostera marina was predicted under current conditions and future climate scenarios. • Ensemble model could accurately predict the present distribution of Zostera marina compare with single-algorithm models. • Climate change may lead to over 50% loss of the suitable habitat for Zostera marina in northern coastal areas of China. [ABSTRACT FROM AUTHOR]