中国碳收支:1980~2021年

As one of the world's largest emitters of greenhouse gases,China has set itself the ambitious goal of achieving carbon peaking and carbon neutrality.Therefore,it is crucial to quantify the magnitude and trend of sources and sinks of atmospheric carbon dioxide(CO_(2)),and to monitor China's progress toward these goals.Using state-of-the-art datasets and models,this study comprehensively estimated the anthropogenic CO_(2)emissions from energy,industrial processes and product use,and waste along with natural sources and sinks of CO_(2)for all of China during 1980-2021.To recognize the differences among various methods of estimating greenhouse emissions,the estimates are compared with China's National Greenhouse Gas Inventories(NGHGIs)for 1994,2005,2010,2012,and 2014.Anthropogenic CO_(2)emissions in China have increased by 7.39 times from 1980 to 12.77 Gt CO_(2)a^(-1)in 2021.While benefiting from ecological projects(e.g.,Three Norths Shelter Forest System Project),the land carbon sink in China has reached 1.65 Gt CO_(2)a^(-1)averaged through 2010-2021,which is almost 15.81 times that of the carbon sink in the 1980s.On average,China's terrestrial ecosystems offset 14.69%±2.49%of anthropogenic CO_(2)emissions through 2010-2021.Two provincial-level administrative regions of China,Xizang and Qinghai,have achieved carbon neutrality according to our estimates,but nearly half of the administrative regions of China have terrestrial carbon sink offsets of less than 10%of anthropogenic CO_(2)emissions.This study indicated a high level of consistency between NGHGIs and various datasets used for estimating fossil CO_(2)emissions,but found notable differences for land carbon sinks.Future estimates of the terrestrial carbon sinks of NGHGIs urgently need to be verified with process-based models which integrate the comprehensive carbon cycle processes.

Estimating Aboveground Carbon Dynamic of China Using Optical and Microwave Remote-Sensing Datasets from 2013 to 2019

Over the past 2 to 3 decades,Chinese forests are estimated to act as a large carbon sink,yet the magnitude and spatial patterns of this sink differ considerably among studies.Using 3 microwave(L-and X-band vegetation optical depth[VOD])and 3 optical(normalized difference vegetation index,leaf area index,and tree cover)remote-sensing vegetation products,this study compared the estimated live woody aboveground biomass carbon(AGC)dynamics over China between 2013 and 2019.Our results showed that tree cover has the highest spatial consistency with 3 published AGC maps(mean correlation value R=0.84),followed by L-VOD(R=0.83),which outperform the other VODs.An AGC estimation model was proposed to combine all indices to estimate the annual AGC dynamics in China during 2013 to 2019.The performance of the AGC estimation model was good(root mean square error=0.05 Pg C and R^(2)=0.90 with a mean relative uncertainty of 9.8% at pixel scale[0.25°]).Results of the AGC estimation model showed that carbon uptake by the forests in China was about+0.17 Pg C year^(-1) from 2013 to 2019.At the regional level,provinces in southwest China including Guizhou(+22.35 Tg C year^(-1)),Sichuan(+14.49 Tg C year^(-1)),and Hunan(+11.42 Tg C year^(-1))provinces had the highest carbon sink rates during 2013 to 2019.Most of the carbon-sink regions have been afforested recently,implying that afforestation and ecological engineering projects have been effective means for carbon sequestration in these regions.

Litterfall seasonality and adaptive strategies of tropical and subtropical evergreen forests in China

Tropical and subtropical evergreen broad-leaved forests(EBFs)and needle-leaved forests(ENFs)in China exhibit complex leaf shedding strategies in responses to soil water availability,vapor pressure deficits(VPDs)and sunlight availability.However,the seasonal variations and triggers of litterfall differ significantly in tropical/subtropical forests,and there are still many uncertainties.Herein,we aim to explore the distinct climatic factors of seasonal litterfall in a climate–phenology correlation framework.We collected seasonal litterfall data from 85 sites across tropical/subtropical China and used linear correlation coefficients between sunlight and rainfall to partition synchronous/asynchronous climates.Additional phase analysis and structural equation model analysis were conducted to model the climatic triggers of tropical phenology.Results indicated two types of tropical litterfall phenology under two types of climates.In synchronous climates,where seasonal sunlight and rainfall are positively correlated,the litterfall peak of the unimodal phenology and the first litterfall peak of the bimodal phenology both happen at the end of dry season.The second litterfall peak of the bimodal phenology occurs at the end of rainy season due to water stress.In asynchronous climates,where seasonal sunlight and rainfall are negatively correlated,VPD shows consistent seasonal variations with incoming sunlight.The leaf senescence is accelerated at the end of dry season by higher VPD;while soil water deficit is in anti-phase with sunlight and mainly controls the second litterfall peak of the bimodal phenology in EBF.Our findings provide an important reference for modeling tropical phenology in Earth system models.

A comprehensive framework for seasonal controls of leaf abscission and productivity in evergreen broadleaved tropical and subtropical forests

Relationships among productivity,leaf phenology,and seasonal variation in moisture and light availability are poorly understood for evergreen broadleaved tropical/subtropical forests,which contribute 25% of terrestrial productivity.On the one hand,as moisture availability declines,trees shed leaves to reduce transpiration and the risk of hydraulic failure.On the other hand,increases in light availability promote the replacement of senescent leaves to increase productivity.Here,we provide a comprehensive framework that relates the seasonality of climate,leaf abscission,and leaf productivity across the evergreen broadleaved tropical/subtropical forest biome.The seasonal correlation between rainfall and light availability varies from strongly negative to strongly positive across the tropics and maps onto the seasonal correlation between litterfall mass and productivity for 68 forests.Where rainfall and light covary positively,litterfall and productivity also covary positively and are always greater in the wetter sunnier season.Where rainfall and light covary negatively,litterfall and productivity are always greater in the drier and sunnier season if moisture supplies remain adequate;otherwise productivity is smaller in the drier sunnier season.This framework will improve the representation of tropical/subtropical forests in Earth system models and suggests how phenology and productivity will change as climate change alters the seasonality of cloud cover and rainfall across tropical/subtropical forests.

Higher plant photosynthetic capability in autumn responding to low atmospheric vapor pressure deficit

It has been long established that the terrestrial vegetation in spring has stronger photosynthetic capability than in autumn.However,this study challenges this consensus by comparing photosynthetic capability of terrestrial vegetation between the spring and autumn seasons based on measurements of 100 in situ eddy covariance towers over global extratropical ecosystems.At the majority of these sites,photosynthetic capability,indicated by light use efficiency(LUE)and apparent quantum efficiency,is significantly higher in autumn than in spring,due to lower atmosphere vapor pressure deficit(VPD)at the same air temperature.Seasonal VPD differences also substantially explain the interannual variability of the differences in photosynthetic capability between spring and autumn.We further reveal that VPD in autumn is significantly lower than in spring over 74.14% of extratropical areas,based on a global climate dataset.In contrast,LUE derived from a data-driven vegetation production dataset is significantly higher in autumn in over 61.02% of extratropical vegetated areas.Six Earth system models consistently projected continuous larger VPD values in spring compared with autumn,which implies that the impacts on vegetation growth will long exist and should be adequately considered when assessing the seasonal responses of terrestrial ecosystems to future climate conditions.

Digitizing the thermal and hydrological parameters of land surface in subtropical China using AMSR-E brightness temperatures

Digitizing the land surface temperature(T_(s))and surface soil moisture(m _(v))is essential for developing the intelligent Digital Earth.Here,we developed a two parameter physical-based passive microwave remote sensing model for jointly retrieving T_(s) and m_(v) using the dual-polarized T_(b) of Aqua satellite advanced microwave scanning radiometer(AMSR-E)C-band(6.9 GHz)based on the simplified radiative transfer equation.Validation using in situ T_(s) and m_(v) in southern China showed the average root mean square errors(RMSE)of T s and m_(v) retrievals reach 2.42 K(R^(2)=0.61,n=351)and 0.025 g cm^(−3)(R^(2)=0.68,n=663),respectively.The results were also validated using global in situ T_(s)(n=2362)and m_(v)(n=1657)of International Soil Moisture Network.The corresponding RMSE are 3.44 k(R 2=0.86)and 0.039 g cm^(−3)(R^(2)=0.83),respectively.The monthly variations of model-derived Ts and mv are highly consistent with those of the Moderate Resolution Imaging Spectroradiometer T_(s)(R^(2)=0.57;RMSE=2.91 k)and ECV_SM m_(v)(R^(2)=0.51;RMSE=0.045 g cm^(−3)),respectively.Overall,this paper indicates an effective way to jointly modeling T_(s) and m_(v) using passive microwave remote sensing.