Changes in snow cover over the Qinghai–Tibetan Plateau have attracted much attention in recent years owing to climate change. Because of the limitations of in situ observations, only a few studies have analyzed the dynamics of snow cover. Using observations from 103 meteorological stations across the Qinghai–Tibetan Plateau, this study investigated the spatial and temporal variability of snow depth and the number of snow-cover days. The results show a very weak negative trend for the snow depth and the number of snow-cover days in spring and winter from 1961 to 2010, but two different trends were found: an initial increase followed by a decrease. In summer and autumn, snow depth and the number of snow-cover days show a significant decreasing trend for most sites. The duration of snow cover exhibits a significant decreasing trend (−3.5 ± 1.2 days decade−1), which was jointly controlled by a later snow starting time (1.6 ± 0.8 days decade−1) and an earlier snow ending time (−1.9 ± 0.8 days decade−1) consistent with a response to climate change. This study highlights the competing effects of rising temperatures and changing precipitation, which remain an important challenge in understanding and interpreting the observed changes in snow depth and the number of snow-cover days for the Qinghai–Tibetan Plateau.

Drought has been a concern in global and regional water, carbon, and energy cycles. From 1999 to 2011, northern China experienced a multiyear precipitation reduction that significantly decreased water availability as indicated by the Palmer Drought Severity Index and soil moisture measurements. In this study, a light use efficiency model (EC‐LUE) and an ecosystem physiological model (IBIS) were used to characterize the impacts of long‐term drought on terrestrial carbon fluxes in northern China. EC‐LUE and IBIS models showed the reduction of averaged GPP of 0.09 and 0.05 Pg C yr⁻¹ during 1999–2011 compared with 1982–1998. Based on the IBIS model, simulated ecosystem respiration experienced an insignificant decrease from 1999 to 2011. The multiyear precipitation reduction changed the regional carbon uptake of 0.011 Pg C yr⁻¹ from 1982 to 1998 to a net source of 0.018 Pg C yr⁻¹ from 1999 to 2011. Moreover, a pronounced decrease in maize yield in almost all provinces in the study region was found from 1999 to 2011 versus the average of yield from1978 to 2011. The largest maize yield reduction occurred in Beijing (2499 kg ha⁻¹ yr⁻¹), Jilin (2180 kg ha⁻¹ yr⁻¹), Tianjing (1923 kg ha⁻¹ yr⁻¹), and Heilongjiang (1791 kg ha⁻¹ yr⁻¹), and the maize yield anomaly was significantly correlated with the annual precipitation over the entire study area. Our results revealed that recent climate change, especially drought‐induced water stress, is the dominant cause of the reduction in the terrestrial carbon sink over northern China.

Grassland ecosystems play an important role in subsistence agriculture and the global carbon cycle. However, the global spatio-temporal patterns and environmental controls of grassland biomass are not well quantified and understood. The goal of this study was to estimate the spatial and temporal patterns of the global grassland biomass and analyze their driving forces using field measurements, Normalized Difference Vegetation Index (NDVI) time series from satellite data, climate reanalysis data, and a satellite-based statistical model. Results showed that the NDVI-based biomass carbon model developed from this study explained 60% of the variance across 38 sites globally. The global carbon stock in grassland aboveground live biomass was 1.05 Pg·C, averaged from 1982 to 2006, and increased at a rate of 2.43 Tg·C·y⁻¹ during this period. Temporal change of the global biomass was significantly and positively correlated with temperature and precipitation. The distribution of biomass carbon density followed the precipitation gradient. The dynamics of regional grassland biomass showed various trends largely determined by regional climate variability, disturbances, and management practices (such as grazing for meat production). The methods and results from this study can be used to monitor the dynamics of grassland aboveground biomass and evaluate grassland susceptibility to climate variability and change, disturbances, and management.