Mengtian Huang

Declining global warming effects on the phenology of spring leaf unfolding

Earlier spring leaf unfolding is a frequently observed response of plants to climate warming. Many deciduous tree species require chilling for dormancy release, and warming-related reductions in chilling may counteract the advance of leaf unfolding in response to warming. Empirical evidence for this, however, is limited to saplings or twigs in climate-controlled chambers. Using long-term in situ observations of leaf unfolding for seven dominant European tree species at 1,245 sites, here we show that the apparent response of leaf unfolding to climate warming (ST, expressed in days advance of leaf unfolding per °C warming) has significantly decreased from 1980 to 2013 in all monitored tree species. Averaged across all species and sites, ST decreased by 40% from 4.0 ± 1.8 days °C−1 during 1980–1994 to 2.3 ± 1.6 days °C−1 during 1999–2013. The declining ST was also simulated by chilling-based phenology models, albeit with a weaker decline (24–30%) than observed in situ. The reduction in ST is likely to be partly attributable to reduced chilling. Nonetheless, other mechanisms may also have a role, such as ‘photoperiod limitation’ mechanisms that may become ultimately limiting when leaf unfolding dates occur too early in the season. Our results provide empirical evidence for a declining ST, but also suggest that the predicted strong winter warming in the future may further reduce ST and therefore result in a slowdown in the advance of tree spring phenology.

Detection and attribution of vegetation greening trend in China over the last 30 years

The reliable detection and attribution of changes in vegetation growth is a prerequisite for the development of strategies for the sustainable management of ecosystems. This is an extraordinary challenge. To our knowledge, this study is the first to comprehensively detect and attribute a greening trend in China over the last three decades. We use three different satellite-derived Leaf Area Index (LAI) datasets for detection as well as five different process-based ecosystem models for attribution. Rising atmospheric CO2 concentration and nitrogen deposition are identified as the most likely causes of the greening trend in China, explaining 85% and 41% of the average growing-season LAI trend (LAIGS) estimated by satellite datasets (average trend of 0.0070 yr(-1), ranging from 0.0035 yr(-1) to 0.0127 yr(-1)), respectively. The contribution of nitrogen deposition is more clearly seen in southern China than in the north of the country. Models disagree about the contribution of climate change alone to the trend in LAIGS at the country scale (one model shows a significant increasing trend, whereas two others show significant decreasing trends). However, the models generally agree on the negative impacts of climate change in north China and Inner Mongolia and the positive impact in the Qinghai-Xizang plateau. Provincial forest area change tends to be significantly correlated with the trend of LAIGS (P < 0.05), and marginally significantly (P = 0.07) correlated with the residual of LAIGS trend, calculated as the trend observed by satellite minus that estimated by models through considering the effects of climate change, rising CO2 concentration and nitrogen deposition, across different provinces. This result highlights the important role of Chinas afforestation program in explaining the spatial patterns of trend in vegetation growth.

Evaporative cooling over the Tibetan Plateau induced by vegetation growth

Significance Understanding land-surface biophysical feedbacks to the atmosphere is needed if we are to simulate regional climate accurately. In the Arctic, previous studies have shown that enhanced vegetation growth decreases albedo and amplifies warming. In contrast, on the Tibetan Plateau, a statistical model based on in situ observations and decomposition of the surface energy budget suggests that increased vegetation activity may attenuate daytime warming by enhancing evapotranspiration (ET), a cooling process. A regional climate model also simulates daytime cooling when prescribed with increased vegetation activity, but with a magnitude smaller than observed, likely because this model simulates weaker ET enhancement in response to increased vegetation growth. In the Arctic, climate warming enhances vegetation activity by extending the length of the growing season and intensifying maximum rates of productivity. In turn, increased vegetation productivity reduces albedo, which causes a positive feedback on temperature. Over the Tibetan Plateau (TP), regional vegetation greening has also been observed in response to recent warming. Here, we show that in contrast to arctic regions, increased growing season vegetation activity over the TP may have attenuated surface warming. This negative feedback on growing season vegetation temperature is attributed to enhanced evapotranspiration (ET). The extra energy available at the surface, which results from lower albedo, is efficiently dissipated by evaporative cooling. The net effect is a decrease in daily maximum temperature and the diurnal temperature range, which is supported by statistical analyses of in situ observations and by decomposition of the surface energy budget. A daytime cooling effect from increased vegetation activity is also modeled from a set of regional weather research and forecasting (WRF) mesoscale model simulations, but with a magnitude smaller than observed, likely because the WRF model simulates a weaker ET enhancement. Our results suggest that actions to restore native grasslands in degraded areas, roughly one-third of the plateau, will both facilitate a sustainable ecological development in this region and have local climate cobenefits. More accurate simulations of the biophysical coupling between the land surface and the atmosphere are needed to help understand regional climate change over the TP, and possible larger scale feedbacks between climate in the TP and the Asian monsoon system.

Temperature increase reduces global yields of major crops in four independent estimates

Significance Agricultural production is vulnerable to climate change. Understanding climate change, especially the temperature impacts, is critical if policymakers, agriculturalists, and crop breeders are to ensure global food security. Our study, by compiling extensive published results from four analytical methods, shows that independent methods consistently estimated negative temperature impacts on yields of four major crops at the global scale, generally underpinned by similar impacts at country and site scales. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops, with important implications for developing crop- and region-specific adaptation strategies to ensure future food supply of an increasing world population. Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop- and region-specific adaptation strategies to ensure food security for an increasing world population.

The contribution of China's emissions to global climate forcing.

Knowledge of the contribution that individual countries have made to global radiative forcing is important to the implementation of the agreement on “common but differentiated responsibilities” reached by the United Nations Framework Convention on Climate Change. Over the past three decades, China has experienced rapid economic development, accompanied by increased emission of greenhouse gases, ozone precursors and aerosols, but the magnitude of the associated radiative forcing has remained unclear. Here we use a global coupled biogeochemistry–climate model and a chemistry and transport model to quantify China’s present-day contribution to global radiative forcing due to well-mixed greenhouse gases, short-lived atmospheric climate forcers and land-use-induced regional surface albedo changes. We find that China contributes 10% ± 4% of the current global radiative forcing. China’s relative contribution to the positive (warming) component of global radiative forcing, mainly induced by well-mixed greenhouse gases and black carbon aerosols, is 12% ± 2%. Its relative contribution to the negative (cooling) component is 15% ± 6%, dominated by the effect of sulfate and nitrate aerosols. China’s strongest contributions are 0.16 ± 0.02 watts per square metre for CO2 from fossil fuel burning, 0.13 ± 0.05 watts per square metre for CH4, −0.11 ± 0.05 watts per square metre for sulfate aerosols, and 0.09 ± 0.06 watts per square metre for black carbon aerosols. China’s eventual goal of improving air quality will result in changes in radiative forcing in the coming years: a reduction of sulfur dioxide emissions would drive a faster future warming, unless offset by larger reductions of radiative forcing from well-mixed greenhouse gases and black carbon.

Temperature, precipitation, and insolation effects on autumn vegetation phenology in temperate China.

Autumn phenology plays a critical role in regulating climate-biosphere interactions. However, the climatic drivers of autumn phenology remain unclear. In this study, we applied four methods to estimate the date of the end of the growing season (EOS) across Chinas temperate biomes based on a 30-year normalized difference vegetation index (NDVI) dataset from Global Inventory Modeling and Mapping Studies (GIMMS). We investigated the relationships of EOS with temperature, precipitation sum, and insolation sum over the preseason periods by computing temporal partial correlation coefficients. The results showed that the EOS date was delayed in temperate China by an average rate at 0.12 ± 0.01 days per year over the time period of 1982-2011. EOS of dry grassland in Inner Mongolia was advanced. Temporal trends of EOS determined across the four methods were similar in sign, but different in magnitude. Consistent with previous studies, we observed positive correlations between temperature and EOS. Interestingly, the sum of precipitation and insolation during the preseason was also associated with EOS, but their effects were biome dependent. For the forest biomes, except for evergreen needle-leaf forests, the EOS dates were positively associated with insolation sum over the preseason, whereas for dry grassland, the precipitation over the preseason was more dominant. Our results confirmed the importance of temperature on phenological processes in autumn, and further suggested that both precipitation and insolation should be considered to improve the performance of autumn phenology models.

Lateral transport of soil carbon and land−atmosphere CO2 flux induced by water erosion in China

Significance The role of soil erosion as a net sink or source of atmospheric CO2 remains highly debated. This work quantifies national-scale land−atmosphere CO2 fluxes induced by soil erosion. Severe water erosion in China has caused displacement of 180 ± 80 Mt C⋅y-1 of soil organic carbon during the last two decades, and the consequent land−atmosphere CO2 flux from water erosion is a net CO2 sink of 45 ± 25 Mt C⋅y-1, equivalent to 8–37% of the terrestrial carbon sink previously assessed in China. This closes an important gap concerning large-scale estimation of lateral and vertical CO2 fluxes from water erosion and highlights the importance of reducing uncertainty in assessing terrestrial carbon balance. Soil erosion by water impacts soil organic carbon stocks and alters CO2 fluxes exchanged with the atmosphere. The role of erosion as a net sink or source of atmospheric CO2 remains highly debated, and little information is available at scales larger than small catchments or regions. This study attempts to quantify the lateral transport of soil carbon and consequent land−atmosphere CO2 fluxes at the scale of China, where severe erosion has occurred for several decades. Based on the distribution of soil erosion rates derived from detailed national surveys and soil carbon inventories, here we show that water erosion in China displaced 180 ± 80 Mt C⋅y−1 of soil organic carbon during the last two decades, and this resulted a net land sink for atmospheric CO2 of 45 ± 25 Mt C⋅y−1, equivalent to 8–37% of the terrestrial carbon sink previously assessed in China. Interestingly, the “hotspots,” largely distributed in mountainous regions in the most intensive sink areas (>40 g C⋅m−2⋅y−1), occupy only 1.5% of the total area suffering water erosion, but contribute 19.3% to the national erosion-induced CO2 sink. The erosion-induced CO2 sink underwent a remarkable reduction of about 16% from the middle 1990s to the early 2010s, due to diminishing erosion after the implementation of large-scale soil conservation programs. These findings demonstrate the necessity of including erosion-induced CO2 in the terrestrial budget, hence reducing the level of uncertainty.

Plausible rice yield losses under future climate warming

Rice is the staple food for more than 50% of the worlds population1–3. Reliable prediction of changes in rice yield is thus central for maintaining global food security. This is an extraordinary challenge. Here, we compare the sensitivity of rice yield to temperature increase derived from field warming experiments and three modelling approaches: statistical models, local crop models and global gridded crop models. Field warming experiments produce a substantial rice yield loss under warming, with an average temperature sensitivity of −5.2 ± 1.4% K−1. Local crop models give a similar sensitivity (−6.3 ± 0.4% K−1), but statistical and global gridded crop models both suggest less negative impacts of warming on yields (−0.8 ± 0.3% and −2.4 ± 3.7% K−1, respectively). Using data from field warming experiments, we further propose a conditional probability approach to constrain the large range of global gridded crop model results for the future yield changes in response to warming by the end of the century (from −1.3% to −9.3% K−1). The constraint implies a more negative response to warming (−8.3 ± 1.4% K−1) and reduces the spread of the model ensemble by 33%. This yield reduction exceeds that estimated by the International Food Policy Research Institute assessment (−4.2 to −6.4% K−1) (ref. 4). Our study suggests that without CO2 fertilization, effective adaptation and genetic improvement, severe rice yield losses are plausible under intensive climate warming scenarios.

Multicriteria evaluation of discharge simulation in Dynamic Global Vegetation Models

In this study, we assessed the performance of discharge simulations by coupling the runoff from seven Dynamic Global Vegetation Models (DGVMs; LPJ, ORCHIDEE, Sheffield-DGVM, TRIFFID, LPJ-GUESS, CLM4CN, and OCN) to one river routing model for 16 large river basins. The results show that the seasonal cycle of river discharge is generally modeled well in the low and middle latitudes but not in the high latitudes, where the peak discharge (due to snow and ice melting) is underestimated. For the annual mean discharge, the DGVMs chained with the routing model show an underestimation. Furthermore, the 30 year trend of discharge is also underestimated. For the interannual variability of discharge, a skill score based on overlapping of probability density functions (PDFs) suggests that most models correctly reproduce the observed variability (correlation coefficient higher than 0.5; i.e., models account for 50% of observed interannual variability) except for the Lena, Yenisei, Yukon, and the Congo river basins. In addition, we compared the simulated runoff from different simulations where models were forced with either fixed or varying land use. This suggests that both seasonal and annual mean runoff has been little affected by land use change but that the trend itself of runoff is sensitive to land use change. None of the models when considered individually show significantly better performances than any other and in all basins. This suggests that based on current modeling capability, a regional-weighted average of multimodel ensemble projections might be appropriate to reduce the bias in future projection of global river discharge.

Field warming experiments shed light on the wheat yield response to temperature in China

Wheat growth is sensitive to temperature, but the effect of future warming on yield is uncertain. Here, focusing on China, we compiled 46 observations of the sensitivity of wheat yield to temperature change (SY,T, yield change per °C) from field warming experiments and 102 SY,T estimates from local process-based and statistical models. The average SY,T from field warming experiments, local process-based models and statistical models is −0.7±7.8(±s.d.)% per °C, −5.7±6.5% per °C and 0.4±4.4% per °C, respectively. Moreover, SY,T is different across regions and warming experiments indicate positive SY,T values in regions where growing-season mean temperature is low, and water supply is not limiting, and negative values elsewhere. Gridded crop model simulations from the Inter-Sectoral Impact Model Intercomparison Project appear to capture the spatial pattern of SY,T deduced from warming observations. These results from local manipulative experiments could be used to improve crop models in the future.