Spatial-temporal variations in the thermal growing degree-days and season under climate warming in China during 1960-2011.

Vegetation growth and phenology are largely regulated by base temperature (Tb) and thermal accumulation. Hence, the growing degree-days (GDD) and growing season (GS) calculated based on Tb have primary effects on terrestrial ecosystems, and could be changed by the significant warming during the last century. By choosing 0, 5, and 10 °C, three key Tb for vegetation growth, the GDD and GS in China during 1960-2011 were developed based on 536 meteorological stations with homogenized daily mean temperatures. Results show that both the GDD and GS showed positive sensitivity to the annual mean temperature. The start of the growing season (SOS) has advanced by 4.86-6.71 days, and the end of the growing season (EOS) has been delayed by 4.32-6.19 days, lengthening the GS by 10.76-11.02 days in China as a whole during 1960-2011, depending on the Tb chosen. Consistently, the GDD has totally increased 218.92-339.40 °C days during the 52 years, with trends more pronounced in those based on a lower Tb. The GDD increase was significant (Mann-Kendall test, p < 0.01) over China except for the north of Southwest China, while the significant GS extension only scattered over China. Whereas the extensions of GS0 and GS5 were dominated by the advance in SOS, the GS10 extension was closely linked to the delay in EOS. Regionally, the GS extension in the eastern monsoon zone and northwest arid/semi-arid zone was driven by the advance in SOS and delay in EOS, respectively. Moreover, each variation has a substantial acceleration mostly in 1987 or 1996, and a speed reduction or even a trend reversal in the early 2000s. Changes in the thermal growing degree-days and season are expected to have great implications for biological phenology, agricultural production, and terrestrial carbon cycle in the future.

Nonlinear variations of forest leaf area index over China during 1982-2010 based on EEMD method.

Variations in leaf area index (LAI) are critical to research on forest ecosystem structure and function, especially carbon and water cycle, and their responses to climate change. Using the ensemble empirical mode decomposition (EEMD) method and global inventory modeling and mapping studies (GIMMS) LAI3g dataset from 1982 to 2010, we analyzed the nonlinear feature and spatial difference of forest LAI variability over China for the past 29 years in this paper. Results indicated that the national-averaged forest LAI was characterized by quasi-3- and quasi-7-year oscillations, which generally exhibited a rising trend with an increasing rate. When compared with 1982, forest LAI change by 2010 was more evident than that by 1990 and 2000. The largest increment of forest LAI occurred in Central and South China, while along the southeastern coastal areas LAI increased at the fastest pace. During the study period, forest LAI experienced from decrease to increase or vice versa across much of China and varied monotonically for only a few areas. Focusing on regional-averaged trend processes, almost all eco-geographical regions showed continuously increasing trends in forest LAI with different magnitudes and speeds, other than tropical humid region and temperate humid/subhumid region, where LAI decreased initially and increased afterwards.

Complex effects of moisture conditions and temperature enhanced vegetation growth in the Arid/humid transition zone in Northern China.

Ecosystems in the arid/humid transition zone (AHTZ) of northern China are highly sensitive to climate change and human activities. Accurately assessing the impact of climate change on these ecosystems is important for effectively reducing the risks faced by them under future climate change. In this study, the leaf area index during the selected growing season (LAIGS) was used as an indicator for vegetation activity. After comparison different potential indicators, the growing season temperature (TGS) was used to indicate temperature, and the growing season aridity index (AIGS), which considers the regional water budget, was used to indicate moisture rather than precipitation, which is used more commonly. Correlation analysis and residual trends were used to study the influence of climatic and non-climatic factors on vegetation activity in the AHTZ from 1982 to 2016. The results for regions where LAIGS increased significantly (0.037/10 yr, 53.58% of the study area), the regions where LAIGS dominated by non-climatic factors (18.40%) was larger than areas dominated by climatic factors (9.61%). However, most (25.57%) of the regions in the selected study area were mainly driven by both climatic and non-climatic factors. In about half (49.73%) of the climate-affected regions, significant changes in LAIGS were driven jointly by TGS and AIGS. These regions were mainly in the northern and western Loess Plateau. The regions where changes were driven mainly by AIGS, and those where changes were driven mainly by TGS, each accounted for nearly a quarter of climate-affected regions (24.87% and 25.40%, respectively). The former regions were on the western Songliao Plain, the northern North China Plain, and the northern Loess Plateau, and the latter regions were in the northern Greater Khingan Mountains, on the southern North China Plain, in the western mountains of North China, and on the southern Loess Plateau.

Climate change risk to forests in China associated with warming.

Variations in forest net primary productivity (NPP) reflects the combined effects of key climate variables on ecosystem structure and function, especially on the carbon cycle. We performed risk analysis indicated by the magnitude of future negative anomalies in NPP in comparison with the natural interannual variability to investigate the impact of future climatic projections on forests in China. Results from the multi-model ensemble showed that climate change risk of decreases in forest NPP would be more significant in higher emission scenario in China. Under relatively low emission scenarios, the total area of risk was predicted to decline, while for RCP8.5, it was predicted to first decrease and then increase after the middle of 21st century. The rapid temperature increases predicted under the RCP8.5 scenario would be probably unfavorable for forest vegetation growth in the long term. High-level risk area was likely to increase except RCP2.6. The percentage area at high risk was predicted to increase from 5.39% (2021-2050) to 27.62% (2071-2099) under RCP8.5. Climate change risk to forests was mostly concentrated in southern subtropical and tropical regions, generally significant under high emission scenario of RCP8.5, which was mainly attributed to the intensified dryness in south China.

Responses of terrestrial ecosystems' net primary productivity to future regional climate change in China.

The impact of regional climate change on net primary productivity (NPP) is an important aspect in the study of ecosystems' response to global climate change. China's ecosystems are very sensitive to climate change owing to the influence of the East Asian monsoon. The Lund-Potsdam-Jena Dynamic Global Vegetation Model for China (LPJ-CN), a global dynamical vegetation model developed for China's terrestrial ecosystems, was applied in this study to simulate the NPP changes affected by future climate change. As the LPJ-CN model is based on natural vegetation, the simulation in this study did not consider the influence of anthropogenic activities. Results suggest that future climate change would have adverse effects on natural ecosystems, with NPP tending to decrease in eastern China, particularly in the temperate and warm temperate regions. NPP would increase in western China, with a concentration in the Tibetan Plateau and the northwest arid regions. The increasing trend in NPP in western China and the decreasing trend in eastern China would be further enhanced by the warming climate. The spatial distribution of NPP, which declines from the southeast coast to the northwest inland, would have minimal variation under scenarios of climate change.

[Variation trends of natural vegetation net primary productivity in China under climate change scenario].

Based on the widely used Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ) for climate change study, and according to the features of natural environment in China, the operation mechanism of the model was adjusted, and the parameters were modified. With the modified LPJ model and taking 1961-1990 as baseline period, the responses of natural vegetation net primary productivity (NPP) in China to climate change in 1991-2080 were simulated under the Special Report on Emissions Scenarios (SRES) B2 scenario. In 1961-1990, the total NPP of natural vegetation in China was about 3.06 Pg C a(-1); in 1961-2080, the total NPP showed a fluctuant decreasing trend, with an accelerated decreasing rate. Under the condition of slight precipitation change, the increase of mean air temperature would have definite adverse impact on the NPP. Spatially, the NPP decreased from southeast coast to northwest inland, and this pattern would have less variation under climate change. In eastern China with higher NPP, especially in Northeast China, east of North China, and Loess Plateau, the NPP would mainly have a decreasing trend; while in western China with lower NPP, especially in the Tibetan Plateau and Tarim Basin, the NPP would be increased. With the intensive climate change, such a variation trend of NPP would be more obvious.

[Spatial pattern of vegetation landscape diversity in longitudinal Range-Gorge Region, southwestern China].

Based on the China 1:1000000 vegetation type map, and by using GIS spatial analysis, the spatial pattern of major vegetation landscape diversity indices and its relationships with environmental factors in Longitudinal Range-Gorge Region (LRGR) were analyzed. The proper scale for studying the vegetation landscape diversity in LRGR was 2000 m. In the study region, an obvious regional difference was observed in the vegetation landscape diversity indices, exhibiting typical longitudinal "corridor" and latitudinal "barrier" characteristics. The correlations between the vegetation landscape diversity indices and environmental elements were significant, and the regional difference in the environmental elements was the main factor controlling the spatial pattern of vegetation landscape diversity indices. The "corridor-barrier" function of the longitudinal range-gorge terrain made a spatial redistribution of hydro-thermal conditions, being the main cause of the special pattern of the vegetation landscape diversity in LRGR.