Comparative analyses of different biogenic CO2 emission accounting systems in life cycle assessment - Liu, Weiguo; Zhu, Qiuan; Zhou, Xiaolu; Peng, Changhui (Science of The Total Environment, 2019)

Quantification of provincial-level carbon emissions from energy consumption in China - Geng, Yuhuan; Tian, Mingzhong; Zhu, Qiuan; Zhang, Jianjun; Peng, Changhui (Renewable and Sustainable Energy Reviews, 2011)

From plant functional types to plant functional traits: A new paradigm in modelling global vegetation dynamics - Yang, Yanzheng; Zhu, Qiuan; Peng, Changhui; Wang, Han; Chen, Huai (Progress in Physical Geography: Earth and Environment, 2015)
Dynamic global vegetation models (DGVMs) typically track the material and energy cycles in ecosystems with finite plant functional types (PFTs). Increasingly, the community ecology and modelling studies recognize that current PFT scheme is not sufficient for simulating ecological processes. Recent advances in the study of plant functional traits (FTs) in community ecology provide a novel and feasible approach for the improvement of PFT-based DGVMs. This paper reviews the development of current DGVMs over recent decades. After characterizing the advantages and disadvantages of the PFT-based scheme, it summarizes trait-based theories and discusses the possibility of incorporating FTs into DGVMs. More importantly, this paper summarizes three strategies for constructing next-generation DGVMs with FTs. Finally, the method’s limitations, current challenges and future research directions for FT theory are discussed for FT theory. We strongly recommend the inclusion of several FTs, namely specific leaf area (SLA), leaf nitrogen content (LNC), carbon isotope composition of leaves (Leaf δ 13 C), the ratio between leaf-internal and ambient mole fractions of CO 2 (Leaf C i /C a ), seed mass and plant height. These are identified as the most important in constructing DGVMs based on FTs, which are also recognized as important ecological strategies for plants. The integration of FTs into dynamic vegetation models is a critical step towards improving the results of DGVM simulations; communication and cooperation among ecologists and modellers is equally important for the development of the next generation of DGVMs.

Stand carbon density drivers and changes under future climate scenarios across global forests - Guo, Yanrong; Peng, Changhui; Trancoso, Ralph; Zhu, Qiuan; Zhou, Xiaolu (Forest Ecology and Management, 2019)

Assessment of frozen ground organic carbon pool on the Qinghai-Tibet Plateau - Jiang, Lin; Chen, Huai; Zhu, Qiuan; Yang, Yanzheng; Li, Mingxu; Peng, Changhui; Zhu, Dan; He, Yixin (Journal of Soils and Sediments, 2019)

Delayed spring phenology on the Tibetan Plateau may also be attributable to other factors than winter and spring warming - Chen, Huai; Zhu, Qiuan; Wu, Ning; Wang, Yanfen; Peng, Chang-Hui (Proceedings of the National Academy of Sciences, 2011)

Large-scale detection of vegetation dynamics and their potential drivers using MODIS images and BFAST: A case study in Quebec, Canada - Fang, Xiuqin; Zhu, Qiuan; Ren, Liliang; Chen, Huai; Wang, Kai; Peng, Changhui (Remote Sensing of Environment, 2018)

Quantification of methane emissions from municipal solid waste landfills in China during the past decade - Du, Mingxi; Peng, Changhui; Wang, Xiaoge; Chen, Huai; Wang, Meng; Zhu, Qiuan (Renewable and Sustainable Energy Reviews, 2017)

Direct and Indirect Effects of UV-B Exposure on Litter Decomposition: A Meta-Analysis - Song, Xinzhang; Peng, Changhui; Jiang, Hong; Zhu, Qiuan; Wang, Weifeng (PLoS ONE, 2013)

Soil properties and species composition under different grazing intensity in an alpine meadow on the eastern Tibetan Plateau, China - Yang, Zhen’an; Xiong, Wan; Xu, Yingyi; Jiang, Lin; Zhu, Erxiong; Zhan, Wei; He, Yixin; Zhu, Dan; Zhu, Qiuan; Peng, Changhui; Chen, Huai (Environmental Monitoring and Assessment, 2016)

Effects of soil warming, rainfall reduction and water table level on CH 4 emissions from the Zoige peatland in China - Yang, Gang; Chen, Huai; Wu, Ning; Tian, Jianqing; Peng, Changhui; Zhu, Qiuan; Zhu, Dan; He, Yixin; Zheng, Qunying; Zhang, Changbing (Soil Biology and Biochemistry, 2014)

Assessing the spatio-temporal variation and uncertainty patterns of historical and future projected water resources in China - Zhu, Qiuan; Jiang, Hong; Peng, Changhui; Liu, Jinxun; Fang, Xiuqin; Chen, Huai; Liu, Shirong (Journal of Water and Climate Change, 2013)
The spatial and temporal variation and uncertainty of precipitation and runoff in China were compared and evaluated between historical and future periods under different climate change scenarios. The precipitation pattern is derived from observed and future projected precipitation data for historical and future periods, respectively. The runoff is derived from simulation results in historical and future periods using a dynamic global vegetation model (DGVM) forced with historical observed and global climate models (GCMs) future projected climate data, respectively. One GCM (CGCM3.1) under two emission scenarios (SRES A2 and SRES B1) was used for the future period simulations. The results indicated high uncertainties and variations in climate change effects on hydrological processes in China: precipitation and runoff showed a significant increasing trend in the future period but a decreasing trend in the historical period at the national level; the temporal variation and uncertainty of projected precipitation and runoff in the future period were predicted to be higher than those in the historical period; the levels of precipitation and runoff in the future period were higher than those in the historical period. The change in trends of precipitation and runoff are highly affected by different climate change scenarios. GCM structure and emission scenarios should be the major sources of uncertainty.

Modeling Global Soil Carbon and Soil Microbial Carbon by Integrating Microbial Processes into the Ecosystem Process Model TRIPLEX‐GHG - Wang, Kefeng; Peng, Changhui; Zhu, Qiuan; Zhou, Xiaolu; Wang, Meng; Zhang, Kerou; Wang, Gangsheng (Journal of Advances in Modeling Earth Systems, 2017)
Abstract Microbial physiology plays a critical role in the biogeochemical cycles of the Earth system. However, most traditional soil carbon models are lacking in terms of the representation of key microbial processes that control the soil carbon response to global climate change. In this study, the improved process‐based model TRIPLEX‐GHG was developed by coupling it with the new MEND (Microbial‐ENzyme‐mediated Decomposition) model to estimate total global soil organic carbon (SOC) and global soil microbial carbon. The new model (TRIPLEX‐MICROBE) shows considerable improvement over the previous version (TRIPLEX‐GHG) in simulating SOC. We estimated the global soil carbon stock to be approximately 1195 Pg C, with 348 Pg C located in the high northern latitudes, which is in good agreement with the well‐regarded Harmonized World Soil Database (HWSD) and the Northern Circumpolar Soil Carbon Database (NCSCD). We also estimated the global soil microbial carbon to be 21 Pg C, similar to the 23 Pg C estimated by Xu et al. (2014). We found that the microbial carbon quantity in the latitudinal direction showed reversions at approximately 30°N, near the equator and at 25°S. A sensitivity analysis suggested that the tundra ecosystem exhibited the highest sensitivity to a 1°C increase or decrease in temperature in terms of dissolved organic carbon (DOC), microbial biomass carbon (MBC), and mineral‐associated organic carbon (MOC). However, our work represents the first step toward a new generation of ecosystem process models capable of integrating key microbial processes into soil carbon cycles. , Key Points Traditional soil carbon models are lacking in their representation of key microbial processes that control the soil carbon response to global climate change A Ecosystem model (TRIPLEX‐MICROBE) offers considerable improvement over a previous version (TRIPLEX‐GHG) in simulating soil organic carbon Our work is the first step toward a new generation of ecosystem process models that integrate key microbial processes into soil carbon cycles

Integrating a model with remote sensing observations by a data assimilation approach to improve the model simulation accuracy of carbon flux and evapotranspiration at two flux sites - Zhang, TingLong; Sun, Rui; Peng, ChangHui; Zhou, GuoYi; Wang, ChunLing; Zhu, QiuAn; Yang, YanZheng (Science China Earth Sciences, 2016)

A novel pathway of direct methane production and emission by eukaryotes including plants, animals and fungi: An overview - Liu, Jiangong; Chen, Huai; Zhu, Qiuan; Shen, Yan; Wang, Xue; Wang, Meng; Peng, Changhui (Atmospheric Environment, 2015)

MODELING INDIVIDUAL TREE MORTALITY RATES USING MARGINAL AND RANDOM EFFECTS REGRESSION MODELS - Ma, Zhihai; Peng, Changhui; Li, Weizhong; Zhu, Qiuan; Wang, Weifeng; Song, Xinzhang; Liu, Jianwei (Natural Resource Modeling, 2013)
A bstract Developing models to predict tree mortality using data from long‐term repeated measurement data sets can be difficult and challenging due to the nature of mortality as well as the effects of dependence on observations. Marginal (population‐averaged) generalized estimating equations (GEE) and random effects (subject‐specific) models offer two possible ways to overcome these effects. For this study, standard logistic, marginal logistic based on the GEE approach, and random logistic regression models were fitted and compared. In addition, four model evaluation statistics were calculated by means of K ‐fold cross‐valuation. They include the mean prediction error, the mean absolute prediction error, the variance of prediction error, and the mean square error. Results from this study suggest that the random effects model produced the smallest evaluation statistics among the three models. Although marginal logistic regression accommodated for correlations between observations, it did not provide noticeable improvements of model performance compared to the standard logistic regression model that assumed impendence. This study indicates that the random effects model was able to increase the overall accuracy of mortality modeling. Moreover, it was able to ascertain correlation derived from the hierarchal data structure as well as serial correlation generated through repeated measurements.

Modeling Global Riverine DOC Flux Dynamics From 1951 to 2015 - Li, Mingxu; Peng, Changhui; Zhou, Xiaolu; Yang, Yanzheng; Guo, Yanrong; Shi, Guohua; Zhu, Qiuan (Journal of Advances in Modeling Earth Systems, 2019)
Abstract Climate change has a profound impact on the global carbon cycle, including effects on riverine carbon pools, which connect terrestrial, oceanic, and atmospheric carbon pools. Until now, terrestrial ecosystem models have rarely incorporated riverine carbon components into global carbon budgets. Here we developed a new process‐based model, TRIPLEX‐HYDRA (TRIPLEX‐hydrological routing algorithm), that considers the production, consumption, and transport processes of nonanthropogenic dissolved organic carbon (DOC) from soil to river ecosystems. After the parameter calibration, model results explained more than 50% of temporal variations in all but three rivers. Validation results suggested that DOC yield simulated by TRIPLEX‐HYDRA has a good fit ( R 2 = 0.61, n = 71, p < 0.001) with global river observations. And then, we applied this model for global rivers. We found that mean DOC yield of global river approximately 1.08 g C/m 2 year, where most high DOC yield appeared in the rivers from high northern or tropic regions. Furthermore, our results suggested that global riverine DOC flux appeared a significant decrease trend (average rate: 0.38 Pg C/year) from 1951 to 2015, although the variation patterns of DOC fluxes in global rivers are diverse. A decreasing trend in riverine DOC flux appeared in the middle and high northern latitude regions (30–90°N), which could be attributable to an increased flow path and DOC degradation during the transport process. Furthermore, increasing trend of DOC fluxes is found in rivers from tropical regions (30°S–30°N), which might be related to an increase in terrestrial organic carbon input. Many other rivers (e.g., Mississippi, Yangtze, and Lena rivers) experienced no significant changes under a changing environment. , Key Points Terrestrial ecosystem models rarely incorporate riverine DOC components into the global carbon cycle The TRIPLEX‐HYDRA model simulates the spatiotemporal variation in the DOC fluxes in global rivers The global riverine DOC flux simulated by the TRIPLEX‐HYDRA model has significantly decreased from 1951 to 2015

Modelling the impacts of climate and land use changes on soil water erosion: Model applications, limitations and future challenges - Guo, Yanrong; Peng, Changhui; Zhu, Qiuan; Wang, Meng; Wang, Han; Peng, Shushi; He, Honglin (Journal of Environmental Management, 2019)

The linkage between vegetation and soil nutrients and their variation under different grazing intensities in an alpine meadow on the eastern Qinghai-Tibetan Plateau - Yang, Zhenan; Zhu, Qiuan; Zhan, Wei; Xu, Yingyi; Zhu, Erxiong; Gao, Yongheng; Li, Shiqing; Zheng, Qunying; Zhu, Dan; He, Yixin; Peng, Changhui; Chen, Huai (Ecological Engineering, 2018)

The role of industrial nitrogen in the global nitrogen biogeochemical cycle - Gu, Baojing; Chang, Jie; Min, Yong; Ge, Ying; Zhu, Qiuan; Galloway, James N.; Peng, Changhui (Scientific Reports, 2013)

Allocation Mechanisms of Non-Structural Carbohydrates of Robinia pseudoacacia L. Seedlings in Response to Drought and Waterlogging - Yang, Bin; Peng, Changhui; Harrison, Sandy P.; Wei, Hua; Wang, Han; Zhu, Qiuan; Wang, Meng (Forests, 2018)
Climate change is likely to lead to an increased frequency of droughts and floods, both of which are implicated in large-scale carbon allocation and tree mortality worldwide. Non-structural carbohydrates (NSCs) play an important role in tree survival under stress, but how NSC allocation changes in response to drought or waterlogging is still unclear. We measured soluble sugars (SS) and starch in leaves, twigs, stems and roots of Robinia pseudoacacia L. seedlings that had been subjected to a gradient in soil water availability from extreme drought to waterlogged conditions for a period of 30 days. Starch concentrations decreased and SS concentrations increased in tissues of R. pseudoacacia seedlings, such that the ratio of SS to starch showed a progressive increase under both drought and waterlogging stress. The strength of the response is asymmetric, with the largest increase occurring under extreme drought. While the increase in SS concentration in response to extreme drought is the largest in roots, the increase in the ratio of SS to starch is the largest in leaves. Individual components of SS showed different responses to drought and waterlogging across tissues: glucose concentrations increased significantly with drought in all tissues but showed little response to waterlogging in twigs and stems; sucrose and fructose concentrations showed marked increases in leaves and roots in response to drought but a greater response to drought and waterlogging in stems and twigs. These changes are broadly compatible with the roles of individual SS under conditions of water stress. While it is important to consider the role of NSC in buffering trees against mortality under stress, modelling this behaviour is unlikely to be successful unless it accounts for different responses within organs and the type of stress involved.

Application of the ecosystem model and Markov Chain Monte Carlo for parameter estimation and productivity prediction - Li, Weizhong; Peng, Changhui; Zhou, Xiaolu; Sun, Jianfeng; Zhu, Qiuan; Wu, Haibin; St-Onge, Benoit (Ecosphere, 2015)
It is increasingly being recognized that global ecological research requires novel methods and strategies in which to combine process‐based ecological models and data in cohesive, systematic ways. In process‐based model applications, inherent spatial and temporal heterogeneities found within terrestrial ecosystems may lead to the uncertainties of model predictions. To reduce simulation uncertainties due to inaccurate model parameters, the Markov Chain Monte Carlo (MCMC) method was applied in this study to improve the estimations of four key parameters used in the process‐based ecosystem model of TRIPLEX‐FLUX. These four key parameters include a maximum photosynthetic carboxylation rate of 25°C (Vmax), an electron transport (Jmax) light‐saturated rate within the photosynthetic carbon reduction cycle of leaves, a coefficient of stomatal conductance (m), and a reference respiration rate of 10°C (R10). Seven forest flux tower sites located across North America were used to investigate and facilitate understanding of the daily variation in model parameters for three deciduous forests, three evergreen temperate forests, and one evergreen boreal forest. Eddy covariance CO 2 exchange measurements were assimilated to optimize the parameters in the year 2006. After parameter optimization and adjustment took place, net ecosystem production prediction significantly improved (by approximately 25%) compared to the CO 2 flux measurements taken at the seven forest ecosystem sites. Results suggest that greater seasonal variability occurs in broadleaf forests in respect to the selected parameters than in needleleaf forests. This study also demonstrated that the model‐data fusion approach by incorporating MCMC method is able to better estimate parameters and improve simulation accuracy for different ecosystems located across North America.

Structural and functional differentiation of the microbial community in the surface and subsurface peat of two minerotrophic fens in China - Wang, Meng; Tian, Jianqing; Bu, Zhaojun; Lamit, Louis J.; Chen, Huai; Zhu, Qiuan; Peng, Changhui (Plant and Soil, 2019)

Effects of future climate change, CO2 enrichment, and vegetation structure variation on hydrological processes in China - Zhu, Qiuan; Jiang, Hong; Peng, Changhui; Liu, Jinxun; Fang, Xiuqin; Wei, Xiaohua; Liu, Shirong; Zhou, Guomo (Global and Planetary Change, 2012)

Soil CH4 and CO2 dynamics and nitrogen transformations with incubation in mountain forest and meadow ecosystems - Zhang, Junjun; Peng, Changhui; Xue, Wei; Yang, Zhenan; Yang, Bin; Li, Peng; Zhu, Qiuan; Wang, Meng (CATENA, 2018)

The carbon flux of global rivers: A re-evaluation of amount and spatial patterns - Li, Mingxu; Peng, Changhui; Wang, Meng; Xue, Wei; Zhang, Kerou; Wang, Kefeng; Shi, Guohua; Zhu, Qiuan (Ecological Indicators, 2017)

Simulated effects of nitrogen saturation on the global carbon budget using the IBIS model - Lu, Xuehe; Jiang, Hong; Liu, Jinxun; Zhang, Xiuying; Jin, Jiaxin; Zhu, Qiuan; Zhang, Zhen; Peng, Changhui (Scientific Reports, 2016)
Abstract Over the past 100 years, human activity has greatly changed the rate of atmospheric N (nitrogen) deposition in terrestrial ecosystems, resulting in N saturation in some regions of the world. The contribution of N saturation to the global carbon budget remains uncertain due to the complicated nature of C-N (carbon-nitrogen) interactions and diverse geography. Although N deposition is included in most terrestrial ecosystem models, the effect of N saturation is frequently overlooked. In this study, the IBIS (Integrated BIosphere Simulator) was used to simulate the global-scale effects of N saturation during the period 1961–2009. The results of this model indicate that N saturation reduced global NPP (Net Primary Productivity) and NEP (Net Ecosystem Productivity) by 0.26 and 0.03 Pg C yr −1 , respectively. The negative effects of N saturation on carbon sequestration occurred primarily in temperate forests and grasslands. In response to elevated CO 2 levels, global N turnover slowed due to increased biomass growth, resulting in a decline in soil mineral N. These changes in N cycling reduced the impact of N saturation on the global carbon budget. However, elevated N deposition in certain regions may further alter N saturation and C-N coupling.

A global meta-analysis of changes in soil carbon, nitrogen, phosphorus and sulfur, and stoichiometric shifts after forestation - Shi, Shengwei; Peng, Changhui; Wang, Meng; Zhu, Qiuan; Yang, Gang; Yang, Yanzheng; Xi, Tingting; Zhang, Tinglong (Plant and Soil, 2016)

Estimating global natural wetland methane emissions using process modelling: spatio‐temporal patterns and contributions to atmospheric methane fluctuations - Zhu, Qiuan; Peng, Changhui; Chen, Huai; Fang, Xiuqin; Liu, Jinxun; Jiang, Hong; Yang, Yanzheng; Yang, Gang (Global Ecology and Biogeography, 2015)
Abstract Aim The fluctuations of atmospheric methane ( CH 4 ) that have occurred in recent decades are not fully understood, particularly with regard to the contribution from wetlands. The application of spatially explicit parameters has been suggested as an effective method for reducing uncertainties in bottom‐up approaches to wetland CH 4 emissions, but has not been included in recent studies. Our goal was to estimate spatio‐temporal patterns of global wetland CH 4 emissions using a process model and then to identify the contribution of wetland emissions to atmospheric CH 4 fluctuations. Location Global. Methods A process‐based model integrated with full descriptions of methanogenesis ( TRIPLEX‐GHG ) was used to simulate global wetland CH 4 emissions. Results Global annual wetland CH 4 emissions ranged from 209 to 245 T g CH 4 year −1 between 1901 and 2012, with peaks occurring in 1991 and 2012. There is a decreasing trend between 1990 and 2010 with a rate of approximately 0.48 T g CH 4 year −1 , which was largely caused by emissions from tropical wetlands showing a decreasing trend of 0.44 T g CH 4 year −1 since the 1970s. Emissions from tropical, temperate and high‐latitude wetlands comprised 59, 26 and 15% of global emissions, respectively. Main conclusion Global wetland CH 4 emissions, the interannual variability of which was primary controlled by tropical wetlands, partially drive the atmospheric CH 4 burden. The stable to decreasing trend in wetland CH 4 emissions, a result of a balance of emissions from tropical and extratropical wetlands, was a particular factor in slowing the atmospheric CH 4 growth rate during the 1990s. The rapid decrease in tropical wetland CH 4 emissions that began in 2000 was supposed to offset the increase in anthropogenic emissions and resulted in a relatively stable level of atmospheric CH 4 from 2000 to 2006. Increasing wetland CH 4 emissions, particularly after 2010, should be an important contributor to the growth in atmospheric CH 4 seen since 2007.

Monitoring the impact of aerosol contamination on the drought-induced decline of gross primary productivity - Zhang, Yao; Li, Weizhong; Zhu, Qiuan; Chen, Huai; Fang, Xiuqin; Zhang, Tinglong; Zhao, Pengxiang; Peng, Changhui (International Journal of Applied Earth Observation and Geoinformation, 2015)

Analysis of vegetation dynamics and climatic variability impacts on greenness across Canada using remotely sensed data from 2000 to 2009 - Fang, Xiuqin; Zhu, Qiuan; Chen, Huai; Ma, Zhihai; Wang, Weifeng; Song, Xinzhang; Zhao, Pengxiang; Peng, Changhui (Journal of Applied Remote Sensing, 2014)

Quantification of soil respiration in forest ecosystems across China - Song, Xinzhang; Peng, Changhui; Zhao, Zhengyong; Zhang, Zhiting; Guo, Baohua; Wang, Weifeng; Jiang, Hong; Zhu, Qiuan (Atmospheric Environment, 2014)

Monitoring and estimating drought-induced impacts on forest structure, growth, function, and ecosystem services using remote-sensing data: recent progress and future challenges - Zhang, Yao; Peng, Changhui; Li, Weizhong; Fang, Xiuqin; Zhang, Tinglong; Zhu, Qiuan; Chen, Huai; Zhao, Pengxiang (Environmental Reviews, 2013)
Alongside global warming, droughts are expected to increase in frequency, severity, and extent in the near future, which will likely result in significant impacts on forest growth, production, structure, composition, and ecosystem services. However, due to spatial and temporal characteristics, it is difficult to monitor and assess the potential effects of droughts. Remote sensing can provide an effective way to obtain real-time conditions of forests affected by drought and offer a range of spatial and temporal insights into drought-induced changes to forest ecosystem structure, function, and services. Remote sensing is rapidly developing as more satellites are launched. In situ and remotely sensed data fusion techniques have achieved notable success in assessing drought-induced damage to forests and carbon cycles. Even so, constraints still exist when using satellite data. The objectives of this review are to (1) briefly review existing data sources and methods of remote sensing; (2) synthesize current applications and contributions of remote sensing in monitoring and estimating impacts of droughts on forest ecosystems; and (3) highlight research gaps and future challenges.

Weakening of the ‘enzymatic latch’ mechanism following long-term fertilization in a minerotrophic peatland - Li, Tong; Bu, Zhaojun; Liu, Wenyan; Zhang, Mingying; Peng, Changhui; Zhu, Qiuan; Shi, Shengwei; Wang, Meng (Soil Biology and Biochemistry, 2019)

Peatland degradation reduces methanogens and methane emissions from surface to deep soils - Yang, Gang; Tian, Jianqing; Chen, Huai; Jiang, Lin; Zhan, Wei; Hu, Ji; Zhu, Erxiong; Peng, Changhui; Zhu, Qiuan; Zhu, Dan; He, Yixin; Li, Mingxu; et al. (Ecological Indicators, 2019)

Carbon dynamics of peatlands in China during the Holocene - Wang, Mei; Chen, Huai; Wu, Ning; Peng, Changhui; Zhu, Qiuan; Zhu, Dan; Yang, Gang; Wu, Jianghua; He, Yixin; Gao, Yongheng; Tian, Jianqing; Zhao, Xinquan (Quaternary Science Reviews, 2014)

Regional drought-induced reduction in the biomass carbon sink of Canada's boreal forests - Ma, Zhihai; Peng, Changhui; Zhu, Qiuan; Chen, Huai; Yu, Guirui; Li, Weizhong; Zhou, Xiaolu; Wang, Weifeng; Zhang, Wenhua (Proceedings of the National Academy of Sciences, 2012)
The boreal forests, identified as a critical “tipping element” of the Earth's climate system, play a critical role in the global carbon budget. Recent findings have suggested that terrestrial carbon sinks in northern high-latitude regions are weakening, but there has been little observational evidence to support the idea of a reduction of carbon sinks in northern terrestrial ecosystems. Here, we estimated changes in the biomass carbon sink of natural stands throughout Canada's boreal forests using data from long-term forest permanent sampling plots. We found that in recent decades, the rate of biomass change decreased significantly in western Canada (Alberta, Saskatchewan, and Manitoba), but there was no significant trend for eastern Canada (Ontario and Quebec). Our results revealed that recent climate change, and especially drought-induced water stress, is the dominant cause of the observed reduction in the biomass carbon sink, suggesting that western Canada's boreal forests may become net carbon sources if the climate change–induced droughts continue to intensify.

Evaluating the effects of future climate change and elevated CO2 on the water use efficiency in terrestrial ecosystems of China - Zhu, Qiuan; Jiang, Hong; Peng, Changhui; Liu, Jinxun; Wei, Xiaohua; Fang, Xiuqin; Liu, Shirong; Zhou, Guomo; Yu, Shuquan (Ecological Modelling, 2011)

Estimates and Predictions of Methane Emissions from Wastewater in China from 2000 to 2020 - Du, Mingxi; Zhu, Qiuan; Wang, Xiaoge; Li, Peng; Yang, Bin; Chen, Huai; Wang, Meng; Zhou, Xiaolu; Peng, Changhui (Earth's Future, 2018)
Methane accounts for 20% of the global warming caused by greenhouse gases, and wastewater is a major anthropogenic source of methane. Based on the Intergovernmental Panel on Climate Change greenhouse gas inventory guidelines and current research findings, we calculated the amount of methane emissions from 2000 to 2014 that originated from wastewater from different provinces in China. Methane emissions from wastewater increased from 1349.01 to 3430.03 Gg from 2000 to 2014, and the mean annual increase was 167.69 Gg. The methane emissions from industrial wastewater treated by wastewater treatment plants ( E It ) accounted for the highest proportion of emissions. We also estimated the future trend of industrial wastewater methane emissions using the artificial neural network model. A comparison of the emissions for the years 2020, 2010, and 2000 showed an increasing trend in methane emissions in China and a spatial transition of industrial wastewater emissions from eastern and southern regions to central and southwestern regions and from coastal regions to inland regions. These changes were caused by changes in economics, demographics, and relevant policies. , Key Points Methane emission from wastewater from 2000 to 2014 was calculated to increase from 1349.01 Gg to 3430.03 Gg. Methane emission from wastewater from 2015 to 2020 was estimated to increase from 3875.30 Gg to 5212.75 Gg. A spatial transition of methane emission from wastewater was found and discussed in the present study.

Spatial patterns of leaf δ^13 C and its relationship with plant functional groups and environmental factors in China: Factors Dominate Plant Traits - Li, Mingxu; Peng, Changhui; Wang, Meng; Yang, Yanzheng; Zhang, Kerou; Li, Peng; Yang, Yan; Ni, Jian; Zhu, Qiuan (Journal of Geophysical Research: Biogeosciences, 2017)

Temperature sensitivity of soil carbon dioxide and nitrous oxide emissions in mountain forest and meadow ecosystems in China - Zhang, Junjun; Peng, Changhui; Zhu, Qiuan; Xue, Wei; Shen, Yan; Yang, Yanzheng; Shi, Guohua; Shi, Shengwei; Wang, Meng (Atmospheric Environment, 2016)

Spatial and temporal variations of N2O emissions from global forest and grassland ecosystems - Zhang, Kerou; Zhu, Qiuan; Liu, Jinxun; Wang, Meng; Zhou, Xiaolu; Li, Mingxu; Wang, Kefeng; Ding, Juhua; Peng, Changhui (Agricultural and Forest Meteorology, 2019)

Changes in soil organic carbon and microbial carbon storage projected during the 21st century using TRIPLEX-MICROBE - Wang, Kefeng; Peng, Changhui; Zhu, Qiuan; Wang, Meng; Wang, Gangsheng; Zhou, Xiaolu; Yang, Yanzheng; Ding, Juhua; Wei, Hua (Ecological Indicators, 2019)

Simulation of dissolved organic carbon concentrations and fluxes in Chinese monsoon forest ecosystems using a modified TRIPLEX-DOC model - Shi, Shengwei; Yang, Mingxia; Hou, Yue; Peng, Changhui; Wu, Haibin; Zhu, Qiuan; Liang, Qiong; Xie, Junfei; Wang, Meng (Science of The Total Environment, 2019)

Higher recent peat C accumulation than that during the Holocene on the Zoige Plateau - Wang, Mei; Yang, Gang; Gao, Yongheng; Chen, Huai; Wu, Ning; Peng, Changhui; Zhu, Qiuan; Zhu, Dan; Wu, Jianghua; He, Yixin; Tian, Jianqing; Zhao, Xinquan; et al. (Quaternary Science Reviews, 2015)

Soil methane uptake by grasslands and forests in China - Wang, Yanfen; Chen, Huai; Zhu, Qiuan; Peng, Changhui; Wu, Ning; Yang, Gang; Zhu, Dan; Tian, Jianqing; Tian, Liuxi; Kang, Xiaoming; He, Yixin; Gao, Yongheng; et al. (Soil Biology and Biochemistry, 2014)

Model prediction of biome‐specific global soil respiration from 1960 to 2012 - Zhao, Zhengyong; Peng, Changhui; Yang, Qi; Meng, Fan‐Rui; Song, Xinzhang; Chen, Shutao; Epule, Terence Epule; Li, Peng; Zhu, Qiuan (Earth's Future, 2017)
Abstract Biome‐specific soil respiration (Rs) has important yet different roles in both the carbon cycle and climate change from regional to global scales. To date, no comparable studies related to global biome‐specific Rs have been conducted applying comprehensive global Rs databases. The goal of this study was to develop artificial neural network ( ANN ) models capable of spatially estimating global Rs and to evaluate the effects of interannual climate variations on 10 major biomes. We used 1976 annual Rs field records extracted from global Rs literature to train and test the ANN models. We determined that the best ANN model for predicting biome‐specific global annual Rs was the one that applied mean annual temperature ( MAT ), mean annual precipitation ( MAP ), and biome type as inputs ( r 2 = 0.60). The ANN models reported an average global Rs of 93.3 ± 6.1 Pg C yr −1 from 1960 to 2012 and an increasing trend in average global annual Rs of 0.04 Pg C yr −1 . Estimated annual Rs increased with increases in MAT and MAP in cropland, boreal forest, grassland, shrubland, and wetland biomes. Additionally, estimated annual Rs decreased with increases in MAT and increased with increases in MAP in desert and tundra biomes, and only significantly decreased with increases in MAT ( r 2 = 0.87) in the savannah biome. The developed biome‐specific global Rs database for global land and soil carbon models will aid in understanding the mechanisms underlying variations in soil carbon dynamics and in quantifying uncertainty in the global soil carbon cycle. , Key Points Predict biome‐specific global soil respiration from 1960 to 2012 using an artificial neural network model Prediction determined an average global soil respiration of 93.3 ± 6.1 Pg C yr −1 and an increasing trend of 0.04 Pg C yr −1 The 10 biome‐specific soil respiration estimates made it possible to trace different responses to global climate change in each biome

A drought-induced pervasive increase in tree mortality across Canada's boreal forests - Peng, Changhui; Ma, Zhihai; Lei, Xiangdong; Zhu, Qiuan; Chen, Huai; Wang, Weifeng; Liu, Shirong; Li, Weizhong; Fang, Xiuqin; Zhou, Xiaolu (Nature Climate Change, 2011)

Evaluating the spatiotemporal variations of water budget across China over 1951–2006 using IBIS model - Zhu, Qiuan; Jiang, Hong; Liu, Jinxun; Wei, Xiaohua; Peng, Changhui; Fang, Xiuqing; Liu, Shirong; Zhou, Guomo; Yu, Shuquan; Ju, Weiming (Hydrological Processes, 2010)
Abstract The Integrated Biosphere Simulator is used to evaluate the spatial and temporal patterns of the crucial hydrological variables [run‐off and actual evapotranspiration (AET)] of the water balance across China for the period 1951–2006 including a precipitation analysis. Results suggest three major findings. First, simulated run‐off captured 85% of the spatial variability and 80% of the temporal variability for 85 hydrological gauges across China. The mean relative errors were within 20% for 66% of the studied stations and within 30% for 86% of the stations. The Nash–Sutcliffe coefficients indicated that the quantity pattern of run‐off was also captured acceptably except for some watersheds in southwestern and northwestern China. The possible reasons for underestimation of run‐off in the Tibetan plateau include underestimation of precipitation and uncertainties in other meteorological data due to complex topography, and simplified representations of the soil depth attribute and snow processes in the model. Second, simulated AET matched reasonably with estimated values calculated as the residual of precipitation and run‐off for watersheds controlled by the hydrological gauges. Finally, trend analysis based on the Mann–Kendall method indicated that significant increasing and decreasing patterns in precipitation appeared in the northwest part of China and the Yellow River region, respectively. Significant increasing and decreasing trends in AET were detected in the Southwest region and the Yangtze River region, respectively. In addition, the Southwest region, northern China (including the Heilongjiang, Liaohe, and Haihe Basins), and the Yellow River Basin showed significant decreasing trends in run‐off, and the Zhemin hydrological region showed a significant increasing trend. Copyright © 2009 John Wiley & Sons, Ltd.

Nitrous oxide fluxes from three forest types of the tropical mountain rainforests on Hainan Island, China - Bai, Zhenzhi; Yang, Gang; Chen, Huai; Zhu, Qiuan; Chen, Dexiang; Li, Yide; Wang, Xu; Wu, Zhongmin; Zhou, Guangyi; Peng, Changhui (Atmospheric Environment, 2014)

The effects of persistent drought and waterlogging on the dynamics of nonstructural carbohydrates of Robinia pseudoacacia L. seedlings in Northwest China - Yang, Bin; Peng, Changhui; Zhu, Qiuan; Zhou, Xiaolu; Liu, Weiguo; Duan, Min; Wang, Hui; Liu, Zhihao; Guo, Xinyi; Wang, Meng (Forest Ecosystems, 2019)

A novel approach for modelling vegetation distributions and analysing vegetation sensitivity through trait-climate relationships in China - Yang, Yanzheng; Zhu, Qiuan; Peng, Changhui; Wang, Han; Xue, Wei; Lin, Guanghui; Wen, Zhongming; Chang, Jie; Wang, Meng; Liu, Guobin; Li, Shiqing (Scientific Reports, 2016)
Abstract Increasing evidence indicates that current dynamic global vegetation models (DGVMs) have suffered from insufficient realism and are difficult to improve, particularly because they are built on plant functional type (PFT) schemes. Therefore, new approaches, such as plant trait-based methods, are urgently needed to replace PFT schemes when predicting the distribution of vegetation and investigating vegetation sensitivity. As an important direction towards constructing next-generation DGVMs based on plant functional traits, we propose a novel approach for modelling vegetation distributions and analysing vegetation sensitivity through trait-climate relationships in China. The results demonstrated that a Gaussian mixture model (GMM) trained with a LMA-N mass -LAI data combination yielded an accuracy of 72.82% in simulating vegetation distribution, providing more detailed parameter information regarding community structures and ecosystem functions. The new approach also performed well in analyses of vegetation sensitivity to different climatic scenarios. Although the trait-climate relationship is not the only candidate useful for predicting vegetation distributions and analysing climatic sensitivity, it sheds new light on the development of next-generation trait-based DGVMs.

The Spatial and Temporal Distribution of Dissolved Organic Carbon Exported from Three Chinese Rivers to the China Sea - Shi, Guohua; Peng, Changhui; Wang, Meng; Shi, Shengwei; Yang, Yanzheng; Chu, Junyao; Zhang, Junjun; Lin, Guanghui; Shen, Yan; Zhu, Qiuan (PLOS ONE, 2016)

Trait-Based Climate Change Predictions of Vegetation Sensitivity and Distribution in China - Yang, Yanzheng; Zhao, Jun; Zhao, Pengxiang; Wang, Hui; Wang, Boheng; Su, Shaofeng; Li, Mingxu; Wang, Liming; Zhu, Qiuan; Pang, Zhiyong; Peng, Changhui (Frontiers in Plant Science, 2019)

Detecting One-Hundred-Year Environmental Changes in Western China Using Seven-Year Repeat Photography - Chen, Huai; Yin, Kaipu; Wang, Haiyan; Zhong, Shenxian; Wu, Ning; Shi, Fusun; Zhu, Dan; Zhu, Qiuan; Wang, Weifeng; Ma, Zhihai; Fang, Xiuqin; Li, Weizhong; et al. (PLoS ONE, 2011)

The carbon stock of alpine peatlands on the Qinghai–Tibetan Plateau during the Holocene and their future fate - Chen, Huai; Yang, Gang; Peng, Changhui; Zhang, Yao; Zhu, Dan; Zhu, Qiuan; Hu, Ji; Wang, Mei; Zhan, Wei; Zhu, Erxiong; Bai, Zengzhi; Li, Wei; et al. (Quaternary Science Reviews, 2014)

The impacts of climate change and human activities on biogeochemical cycles on the Q inghai‐ T ibetan P lateau - Chen, Huai; Zhu, Qiuan; Peng, Changhui; Wu, Ning; Wang, Yanfen; Fang, Xiuqing; Gao, Yongheng; Zhu, Dan; Yang, Gang; Tian, Jianqing; Kang, Xiaoming; Piao, Shilong; et al. (Global Change Biology, 2013)
Abstract With a pace of about twice the observed rate of global warming, the temperature on the Qinghai‐Tibetan Plateau (Earth's ‘third pole’) has increased by 0.2 °C per decade over the past 50 years, which results in significant permafrost thawing and glacier retreat. Our review suggested that warming enhanced net primary production and soil respiration, decreased methane ( CH 4 ) emissions from wetlands and increased CH 4 consumption of meadows, but might increase CH 4 emissions from lakes. Warming‐induced permafrost thawing and glaciers melting would also result in substantial emission of old carbon dioxide ( CO 2 ) and CH 4 . Nitrous oxide ( N 2 O ) emission was not stimulated by warming itself, but might be slightly enhanced by wetting. However, there are many uncertainties in such biogeochemical cycles under climate change. Human activities (e.g. grazing, land cover changes) further modified the biogeochemical cycles and amplified such uncertainties on the plateau. If the projected warming and wetting continues, the future biogeochemical cycles will be more complicated. So facing research in this field is an ongoing challenge of integrating field observations with process‐based ecosystem models to predict the impacts of future climate change and human activities at various temporal and spatial scales. To reduce the uncertainties and to improve the precision of the predictions of the impacts of climate change and human activities on biogeochemical cycles, efforts should focus on conducting more field observation studies, integrating data within improved models, and developing new knowledge about coupling among carbon, nitrogen, and phosphorus biogeochemical cycles as well as about the role of microbes in these cycles.

Climate-driven increase of natural wetland methane emissions offset by human-induced wetland reduction in China over the past three decades - Zhu, Qiuan; Peng, Changhui; Liu, Jinxun; Jiang, Hong; Fang, Xiuqin; Chen, Huai; Niu, Zhenguo; Gong, Peng; Lin, Guanghui; Wang, Meng; Wang, Han; Yang, Yanzheng; et al. (Scientific Reports, 2016)
Abstract Both anthropogenic activities and climate change can affect the biogeochemical processes of natural wetland methanogenesis. Quantifying possible impacts of changing climate and wetland area on wetland methane (CH 4 ) emissions in China is important for improving our knowledge on CH 4 budgets locally and globally. However, their respective and combined effects are uncertain. We incorporated changes in wetland area derived from remote sensing into a dynamic CH 4 model to quantify the human and climate change induced contributions to natural wetland CH 4 emissions in China over the past three decades. Here we found that human-induced wetland loss contributed 34.3% to the CH 4 emissions reduction (0.92 TgCH 4 ), and climate change contributed 20.4% to the CH 4 emissions increase (0.31 TgCH 4 ), suggesting that decreasing CH 4 emissions due to human-induced wetland reductions has offset the increasing climate-driven CH 4 emissions. With climate change only, temperature was a dominant controlling factor for wetland CH 4 emissions in the northeast (high latitude) and Qinghai-Tibet Plateau (high altitude) regions, whereas precipitation had a considerable influence in relative arid north China. The inevitable uncertainties caused by the asynchronous for different regions or periods due to inter-annual or seasonal variations among remote sensing images should be considered in the wetland CH 4 emissions estimation.

Interannual variation in methane emissions from tropical wetlands triggered by repeated El Niño Southern Oscillation - Zhu, Qiuan; Peng, Changhui; Ciais, Philippe; Jiang, Hong; Liu, Jinxun; Bousquet, Philippe; Li, Shiqin; Chang, Jie; Fang, Xiuqin; Zhou, Xiaolu; Chen, Huai; Liu, Shirong; et al. (Global Change Biology, 2017)
Abstract Methane (CH 4 ) emissions from tropical wetlands contribute 60%–80% of global natural wetland CH 4 emissions. Decreased wetland CH 4 emissions can act as a negative feedback mechanism for future climate warming and vice versa. The impact of the El Niño–Southern Oscillation (ENSO) on CH 4 emissions from wetlands remains poorly quantified at both regional and global scales, and El Niño events are expected to become more severe based on climate models’ projections. We use a process‐based model of global wetland CH 4 emissions to investigate the impacts of the ENSO on CH 4 emissions in tropical wetlands for the period from 1950 to 2012. The results show that CH 4 emissions from tropical wetlands respond strongly to repeated ENSO events, with negative anomalies occurring during El Niño periods and with positive anomalies occurring during La Niña periods. An approximately 8‐month time lag was detected between tropical wetland CH 4 emissions and ENSO events, which was caused by the combined time lag effects of ENSO events on precipitation and temperature over tropical wetlands. The ENSO can explain 49% of interannual variations for tropical wetland CH 4 emissions. Furthermore, relative to neutral years, changes in temperature have much stronger effects on tropical wetland CH 4 emissions than the changes in precipitation during ENSO periods. The occurrence of several El Niño events contributed to a lower decadal mean growth rate in atmospheric CH 4 concentrations throughout the 1980s and 1990s and to stable atmospheric CH 4 concentrations from 1999 to 2006, resulting in negative feedback to global warming.

Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901–2005 - Shao, Junjiong; Zhou, Xuhui; Luo, Yiqi; Zhang, Guodong; Yan, Wei; Li, Jiaxuan; Li, Bo; Dan, Li; Fisher, Joshua B.; Gao, Zhiqiang; He, Yong; Huntzinger, Deborah; et al. (Journal of Geophysical Research: Biogeosciences, 2016)
Abstract Despite the importance of net primary productivity (NPP) and net biome productivity (NBP), estimates of NPP and NBP for China are highly uncertain. To investigate the main sources of uncertainty, we synthesized model estimates of NPP and NBP for China from published literature and the Multi‐scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). The literature‐based results showed that total NPP and NBP in China were 3.35 ± 1.25 and 0.14 ± 0.094 Pg C yr −1 , respectively. Classification and regression tree analysis based on literature data showed that model type was the primary source of the uncertainty, explaining 36% and 64% of the variance in NPP and NBP, respectively. Spatiotemporal scales, land cover conditions, inclusion of the N cycle, and effects of N addition also contributed to the overall uncertainty. Results based on the MsTMIP data suggested that model structures were overwhelmingly important (>90%) for the overall uncertainty compared to simulations with different combinations of time‐varying global change factors. The interannual pattern of NPP was similar among diverse studies and increased by 0.012 Pg C yr −1 during 1981–2000. In addition, high uncertainty in China's NPP occurred in areas with high productivity, whereas NBP showed the opposite pattern. Our results suggest that to significantly reduce uncertainty in estimated NPP and NBP, model structures should be substantially tested on the basis of empirical results. To this end, coordinated distributed experiments with multiple global change factors might be a practical approach that can validate specific structures of different models. , Key Points Large uncertainty exists in estimates of terrestrial NPP and NBP in China Methodological differences greatly contribute to the uncertainty in NPP and NBP Uncertainty in the interannual pattern of NBP is greater than that of NPP

The Global N2O Model Intercomparison Project - Tian, Hanqin; Yang, Jia; Lu, Chaoqun; Xu, Rongting; Canadell, Josep G.; Jackson, Robert B.; Arneth, Almut; Chang, Jinfeng; Chen, Guangsheng; Ciais, Philippe; Gerber, Stefan; Ito, Akihiko; et al. (Bulletin of the American Meteorological Society, 2018)
Abstract Nitrous oxide (N 2 O) is an important greenhouse gas and also an ozone-depleting substance that has both natural and anthropogenic sources. Large estimation uncertainty remains on the magnitude and spatiotemporal patterns of N 2 O fluxes and the key drivers of N 2 O production in the terrestrial biosphere. Some terrestrial biosphere models have been evolved to account for nitrogen processes and to show the capability to simulate N 2 O emissions from land ecosystems at the global scale, but large discrepancies exist among their estimates primarily because of inconsistent input datasets, simulation protocol, and model structure and parameterization schemes. Based on the consistent model input data and simulation protocol, the global N 2 O Model Intercomparison Project (NMIP) was initialized with 10 state-of-the-art terrestrial biosphere models that include nitrogen (N) cycling. Specific objectives of NMIP are to 1) unravel the major N cycling processes controlling N 2 O fluxes in each model and identify the uncertainty sources from model structure, input data, and parameters; 2) quantify the magnitude and spatial and temporal patterns of global and regional N 2 O fluxes from the preindustrial period (1860) to present and attribute the relative contributions of multiple environmental factors to N 2 O dynamics; and 3) provide a benchmarking estimate of N 2 O fluxes through synthesizing the multimodel simulation results and existing estimates from ground-based observations, inventories, and statistical and empirical extrapolations. This study provides detailed descriptions for the NMIP protocol, input data, model structure, and key parameters, along with preliminary simulation results. The global and regional N 2 O estimation derived from the NMIP is a key component of the global N 2 O budget synthesis activity jointly led by the Global Carbon Project and the International Nitrogen Initiative.

Vegetation Functional Properties Determine Uncertainty of Simulated Ecosystem Productivity: A Traceability Analysis in the East Asian Monsoon Region - Cui, Erqian; Huang, Kun; Arain, Muhammad Altaf; Fisher, Joshua B.; Huntzinger, Deborah N.; Ito, Akihiko; Luo, Yiqi; Jain, Atul K.; Mao, Jiafu; Michalak, Anna M.; Niu, Shuli; Parazoo, Nicholas C.; et al. (Global Biogeochemical Cycles, 2019)
Abstract Global and regional projections of climate change by Earth system models are limited by their uncertain estimates of terrestrial ecosystem productivity. At the middle to low latitudes, the East Asian monsoon region has higher productivity than forests in Europe‐Africa and North America, but its estimate by current generation of terrestrial biosphere models (TBMs) has seldom been systematically evaluated. Here, we developed a traceability framework to evaluate the simulated gross primary productivity (GPP) by 15 TBMs in the East Asian monsoon region. The framework links GPP to net primary productivity, biomass, leaf area and back to GPP via incorporating multiple vegetation functional properties of carbon‐use efficiency (CUE), vegetation C turnover time ( τ veg ), leaf C fraction (F leaf ), specific leaf area (SLA), and leaf area index (LAI)‐level photosynthesis (P LAI ), respectively. We then applied a relative importance algorithm to attribute intermodel variation at each node. The results showed that large intermodel variation in GPP over 1901–2010 were mainly propagated from their different representation of vegetation functional properties. For example, SLA explained 77% of the intermodel difference in leaf area, which contributed 90% to the simulated GPP differences. In addition, the models simulated higher CUE (18.1 ± 21.3%), τ veg (18.2 ± 26.9%), and SLA (27.4±36.5%) than observations, leading to the overestimation of simulated GPP across the East Asian monsoon region. These results suggest the large uncertainty of current TBMs in simulating GPP is largely propagated from their poor representation of the vegetation functional properties and call for a better understanding of the covariations between plant functional properties in terrestrial ecosystems. , Key Points A GPP‐traceability framework is established to diagnose the uncertainty sources of modeled GPP Large intermodel differences of modeled GPP result from their different representation of vegetation functional properties Positive bias in simulated GPP over the East Asian monsoon region could be attributed to the higher simulated CUE and SLA comparing with observations

Disentangling climatic and anthropogenic controls on global terrestrial evapotranspiration trends - Mao, Jiafu; Fu, Wenting; Shi, Xiaoying; Ricciuto, Daniel M; Fisher, Joshua B; Dickinson, Robert E; Wei, Yaxing; Shem, Willis; Piao, Shilong; Wang, Kaicun; Schwalm, Christopher R; Tian, Hanqin; et al. (Environmental Research Letters, 2015)

Global wetland contribution to 2000–2012 atmospheric methane growth rate dynamics - Poulter, Benjamin; Bousquet, Philippe; Canadell, Josep G; Ciais, Philippe; Peregon, Anna; Saunois, Marielle; Arora, Vivek K; Beerling, David J; Brovkin, Victor; Jones, Chris D; Joos, Fortunat; Gedney, Nicola; et al. (Environmental Research Letters, 2017)
Abstract Increasing atmospheric methane (CH 4 ) concentrations have contributed to approximately 20% of anthropogenic climate change. Despite the importance of CH 4 as a greenhouse gas, its atmospheric growth rate and dynamics over the past two decades, which include a stabilization period (1999–2006), followed by renewed growth starting in 2007, remain poorly understood. We provide an updated estimate of CH 4 emissions from wetlands, the largest natural global CH 4 source, for 2000–2012 using an ensemble of biogeochemical models constrained with remote sensing surface inundation and inventory-based wetland area data. Between 2000–2012, boreal wetland CH 4 emissions increased by 1.2 Tg yr −1 (−0.2–3.5 Tg yr −1 ), tropical emissions decreased by 0.9 Tg yr −1 (−3.2−1.1 Tg yr −1 ), yet globally, emissions remained unchanged at 184 ± 22 Tg yr −1 . Changing air temperature was responsible for increasing high-latitude emissions whereas declines in low-latitude wetland area decreased tropical emissions; both dynamics are consistent with features of predicted centennial-scale climate change impacts on wetland CH 4 emissions. Despite uncertainties in wetland area mapping, our study shows that global wetland CH 4 emissions have not contributed significantly to the period of renewed atmospheric CH 4 growth, and is consistent with findings from studies that indicate some combination of increasing fossil fuel and agriculture-related CH 4 emissions, and a decrease in the atmospheric oxidative sink.

Field-experiment constraints on the enhancement of the terrestrial carbon sink by CO2 fertilization - Liu, Yongwen; Piao, Shilong; Gasser, Thomas; Ciais, Philippe; Yang, Hui; Wang, Han; Keenan, Trevor F.; Huang, Mengtian; Wan, Shiqiang; Song, Jian; Wang, Kai; Janssens, Ivan A.; et al. (Nature Geoscience, 2019)

Asymmetric responses of primary productivity to altered precipitation simulated by ecosystem models across three long-term grassland sites - Wu, Donghai; Ciais, Philippe; Viovy, Nicolas; Knapp, Alan K.; Wilcox, Kevin; Bahn, Michael; Smith, Melinda D.; Vicca, Sara; Fatichi, Simone; Zscheischler, Jakob; He, Yue; Li, Xiangyi; et al. (Biogeosciences, 2018)
Abstract. Field measurements of aboveground net primary productivity (ANPP) in temperate grasslands suggest that both positive and negative asymmetric responses to changes in precipitation (P) may occur. Under normal range of precipitation variability, wet years typically result in ANPP gains being larger than ANPP declines in dry years (positive asymmetry), whereas increases in ANPP are lower in magnitude in extreme wet years compared to reductions during extreme drought (negative asymmetry). Whether the current generation of ecosystem models with a coupled carbon–water system in grasslands are capable of simulating these asymmetric ANPP responses is an unresolved question. In this study, we evaluated the simulated responses of temperate grassland primary productivity to scenarios of altered precipitation with 14 ecosystem models at three sites: Shortgrass steppe (SGS), Konza Prairie (KNZ) and Stubai Valley meadow (STU), spanning a rainfall gradient from dry to moist. We found that (1) the spatial slopes derived from modeled primary productivity and precipitation across sites were steeper than the temporal slopes obtained from inter-annual variations, which was consistent with empirical data; (2) the asymmetry of the responses of modeled primary productivity under normal inter-annual precipitation variability differed among models, and the mean of the model ensemble suggested a negative asymmetry across the three sites, which was contrary to empirical evidence based on filed observations; (3) the mean sensitivity of modeled productivity to rainfall suggested greater negative response with reduced precipitation than positive response to an increased precipitation under extreme conditions at the three sites; and (4) gross primary productivity (GPP), net primary productivity (NPP), aboveground NPP (ANPP) and belowground NPP (BNPP) all showed concave-down nonlinear responses to altered precipitation in all the models, but with different curvatures and mean values. Our results indicated that most models overestimate the negative drought effects and/or underestimate the positive effects of increased precipitation on primary productivity under normal climate conditions, highlighting the need for improving eco-hydrological processes in those models in the future.