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.

Increasing heatwave and drought events can potentially alter the carbon cycle. Few studies have investigated the impacts of hundred-year return heatwaves and droughts, as those events are rare. In the summer of 2013, southern China experienced its strongest drought and heatwave on record for the past 113 years. We show that the record-breaking heatwave and drought lasted two months (from July to August), significantly reduced the satellite-based vegetation index and gross primary production, substantially altered the regional carbon cycle, and produced the largest negative crop yield anomaly since 1960. The event resulted in a net reduction of 101.54 Tg C in carbon sequestration in the region during these two months, which was 39–53% of the annual net carbon sink of China’s terrestrial ecosystems (190–260 Tg C yr−1). Moreover, model experiments showed that heatwaves and droughts consistently decreased ecosystem vegetation primary production but had opposite impacts on ecosystem respiration (TER), with increased TER by 6.78 ± 2.15% and decreased TER by 15.34 ± 3.57% assuming only changed temperature and precipitation, respectively. In light of increasing frequency and severity of future heatwaves and droughts, our study highlights the importance of accounting for the impacts of heatwaves and droughts in assessing the carbon sequestration in terrestrial ecosystems.

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The lateral movement of soil carbon has a profound effect on the carbon budget of terrestrial ecosystems; however, it has never been quantified in China, which is one of the strongest soil erosion areas in the world. In this study, we estimated that the overall soil erosion in China varies from 11.27 to 18.17 Pg yr−1 from 1982 to 2011, accounting for 7–21% of total soil erosion globally. Soil erosion induces a substantial lateral redistribution of soil organic carbon ranging from 0.64 to 1.04 Pg C yr−1. The erosion-induced carbon flux ranges from a 0.19 Pg C yr−1 carbon source to a 0.24 Pg C yr−1 carbon sink in the terrestrial ecosystem, which is potentially comparable in magnitude to previously estimated total carbon budget of China (0.19 to 0.26 Pg yr−1). Our results showed that the lateral movement of soil carbon strongly alters the carbon budget in China, and highlighted the urgent need to integrate the processes of soil erosion into the regional or global carbon cycle estimates.

Vegetation phenology models are important for examining the impact of climate change on the length of the growing season and carbon cycles in terrestrial ecosystems. However, large uncertainties in present phenology models make accurate assessment of the beginning of the growing season (BGS) a challenge. In this study, based on the satellite-based phenology product (i.e. the V005 MODIS Land Cover Dynamics (MCD12Q2) product), we calibrated four phenology models, compared their relative strength to predict vegetation phenology; and assessed the spatial pattern and interannual variability of BGS in the Northern Hemisphere. The results indicated that parameter calibration significantly influences the models' accuracy. All models showed good performance in cool regions but poor performance in warm regions. On average, they explained about 67% (the Growing Degree Day model), 79% (the Biome-BGC phenology model), 73% (the Number of Growing Days model) and 68% (the Number of Chilling Days-Growing Degree Day model) of the BGS variations over the Northern Hemisphere. There were substantial differences in BGS simulations among the four phenology models. Overall, the Biome-BGC phenology model performed best in predicting the BGS, and showed low biases in most boreal and cool regions. Compared with the other three models, the two-phase phenology model (NCD-GDD) showed the lowest correlation and largest biases with the MODIS phenology product, although it could catch the interannual variations well for some vegetation types. Our study highlights the need for further improvements by integrating the effects of water availability, especially for plants growing in low latitudes, and the physiological adaptation of plants into phenology models.

Terrestrial gross primary production (GPP) is the largest global CO 2 flux and determines other ecosystem carbon cycle variables. Light use efficiency (LUE) models may have the most potential to adequately address the spatial and temporal dynamics of GPP, but recent studies have shown large model differences in GPP simulations. In this study, we investigated the GPP differences in the spatial and temporal patterns derived from seven widely used LUE models at the global scale. The result shows that the global annual GPP estimates over the period 2000-2010 varied from 95.10 to 139.71 Pg C·yr −1 among models. The spatial and temporal variation of global GPP differs substantially between models, due to different model structures and dominant environmental drivers. In almost all models, water availability dominates the interannual variability of GPP over large vegetated areas. Solar radiation and air temperature are not the primary controlling factors for interannual variability of global GPP estimates for most models. The disagreement among the current LUE models highlights the need for further model improvement to quantify the global carbon cycle.

Plant phenology is one of the preferred indicators of climate change, and its variation potentially impacts community dynamics and ecosystem functions. To better understand the responses of plants' flowering phenology to rising temperatures, we investigated the temperature sensitivity (expressed as the date of changes in phenology per change in temperature in degree Celsius, d°C À1 ) of flowering phenology for more than 220 plant species at 59 sites in China during the period 1963-1988. Our results indicated that most flowerings in China were significantly sensitive to the temperature in the 2 months (60 days) prior to the flowering dates. Plants in warmer regions showed larger sensitivities to increased temperatures. Species flowering in the late spring and early summer were generally less sensitive to changing temperature than species flowering at other times of the year. For plants flowering in the spring, species that flower earlier showed higher temperature sensitivity; however, for plants flowering in the summer and autumn, species that flower earlier showed lower temperature sensitivity. The responses of the first and last flowering times to changing temperature were mostly consistent, so flowering durations were rarely (6.1%) sensitive to changing temperature. We hypothesize that plants in cold regions may have adapted to the more variable temperatures and thus showed lower temperature sensitivities than plants in warm regions. Overall, the responses of flowering phenology to temperature varied significantly among temperature zones and plant species, so it should be considered carefully when estimating the impacts of climate warming on the terrestrial biosphere.

Shifts in plants' phenophases caused by climate change can strongly affect the ecosystem structure and function. Temperature sensitivity, which is expressed as the date of phenological event change for per degree Celsius change of temperature (days °C−1), has been widely used to characterize the plants' responses to changed temperature. In this study, we analyzed the temperature sensitivity of leaf phenology (leaf unfolding date, LUD; leaf falling date, LFD) for more than 700 plant species based on the phenological observations at 56 sites in China from 1963 to 1988. Our results suggested significant spatial and interspecific variations in the responses of leaf phenology to changing temperature. Approximately 48.8% cases of LUD advanced significantly (p < 0.1) with the increasing spring temperature and 33.9% cases of LFD delayed significantly with increasing autumn temperature. The spring events were overall more sensitive to temperature than autumn events. Although the LUD of plants in warmer regions was more sensitive to changing temperature, the temperature sensitivities of LFD for plants in warmer regions were not significantly different from that for plants in cooler regions. Moreover, for spring phenology, many sites suggested higher temperature sensitivity for species that unfold their leaves earlier. For autumn phenology, species with later leaf‐falling dates were generally more sensitive to temperature. Differences in species' responses to temperature that caused the discrepant changes in species' growing season can strongly impact vegetation dynamic and ecosystem function, and thus should be considered more when predicting the effects of climate warming on the biosphere.