Interannual Variability Of Terrestrial Gross Primary Productivity And Underlying Driving Factors In China During 1982-2016

As an important part of the global carbon cycle process,the terrestrial ecosystem absorbs about 30%of the CO₂ emitted by human activities.The terrestrial ecosystem carbon cycle has an important impact on the mitigation of global warming and climate change,and is also one of the biggest uncertainties in the prediction of future climate change.Terrestrial ecosystem gross primary productivity（GPP）is not only one of the main determinants of carbon exchange between land and atmosphere,but also an important indicator of carbon cycle in terrestrial ecosystem.Due to natural factors such as climate change,atmospheric CO₂ concentration increase and nitrogen deposition,as well as human activities such as land use change,farmland management and forest disturbance,GPP in terrestrial ecosystem has great interannual variability.The interannual variability of GPP has an important impact on regional and global carbon balance and climate change.It is of great significance to study the interannual variability of GPP and its main controlling factors for understanding the carbon cycle of terrestrial ecosystem and predicting the future global climate change.In this study,two-leaf light use efficiency model（TL-LUE）was used as a tool,based on the global reanalysis meteorological data Climate Research Unit-National Centers for Environmental Prediction（CRUNCEP）and three commonly used satellite-derived LAI data（Global Inventory Monitoring and Modeling System（GIMMS）,Global Land Surface Satellite（GLASS）,Global Mapping（Glob Map）LAI）to simulate the GPP in terrestrial ecosystem of China during 1982 to 2016（Spatial resolution is0.05°,temporal resolution for the day）.The effects of different LAI input data on the simulated GPP in China were compared.The interannual variability of GPP in terrestrial ecosystem of China were analyzed.The response of the interannual variability of GPP to the main control factors such as temperature,precipitation and solar radiation were assessed.The main conclusions of this study are as follows:（1）Spatial and temporal differences of three satellite-derived LAI in ChinaThere were differences in spatial distribution of GIMMS,GLASS and Glob Map LAI in China during 1982 to 2016.GLASS LAI and Glob Map LAI were higher than GIMMS LAI in most of the forest area,GLASS LAI and GIMMS LAI were higher than Glob Map LAI in cropland and grassland.The interannual variability of GLASS LAI was larger than GIMMS LAI.For three LAI products,there was an increasing trend in the northern region.The areas with significant increasing trend of GIMMS LAI,GLASS LAI and Glob Map LAI（p<0.05）accounted for 56.9%,51.7%and 77.3%of vegetation area respectively,and the areas with significant decreasing trend（p<0.05）accounted for 7.2%,9.8%and 6.0%of vegetation area respectively.（2）Spatial and temporal differences of GPP simulated by different LAIThe GPP simulated by different LAI were quite different for evergreen broad-leaved forest,mixed forest and cropland.The GPP simulated by three LAI showed an obvious increasing trend in the areas of North China Plain,Huaihe River Basin,Loess Plateau and Qinghai Tibet Plateau.The areas with significant increasing trend of GPP-GIMMS,GPP-GLASS and GPP-Glob Map（p<0.05）accounted for 61.1%,49.4%and74.1%of vegetation area respectively.Mean annual total GPP of China simulated by GLASS LAI was biggest(8.44 Pg C a^-1),that simulated by Glob Map LAI was smallest(7.48 Pg C a^-1).Compared with Fluxcom GPP and SIF data,the GPP simulated by GIMMS LAI has a poor correlation with Fluxcom GPP and SIF in Yunnan,the GPP simulated by GLASS LAI has a poor correlation in Sichuan Basin,and the GPP simulated by Glob Map LAI has a poor correlation in North China Plain.（3）Interannual variability and trends of GPP in China during 1982 to 2016The interannual variability of GPP in the northern region was greater than that in the southern region.In 44.2%vegetation area,the interannual variability of GPP over100 g C m^-2 a^-1.The annual mean GPP increased significantly in China during 1982-2016（p<0.05）,and the rate was 4.30 g C m^-2 a^-1.In North China,the annual GPP increased the fastest(12.05 g C m^-2 a^-1).In Qinghai Tibet Plateau,the annual GPP increased the slowest(1.14 g C m^-2 a^-1).The annual GPP increased the fasetest for cropland(9.83 g C m^-2 a^-1).The annual GPP increased the slowest for grassland(3.5 g C m^-2 a^-1).In summer,GPP increased the fastest(1.80 g C m^-2 a^-1).In winter,GPP increased the slowest(0.23 g C m^-2 a^-1).（4）Effects of different meteorological factors on the interannual variability of GPP in ChinaThe increase of temperature would lead to an increase in GPP in 73.8%vegetation area.In the Qinghai Tibet Plateau and most of the southern regions,there was a significant positive correlation between annual GPP with temperature（p<0.05）.In Inner Mongolia,Loess Plateau and North China,there was a negative correlation between annual GPP with temperature（p<0.1）.The increase of precipitation would lead to an increase in GPP in 72.2%vegetation area.In Inner Mongolia and Loess Plateau,there was a significant positive correlation between annual GPP with precipitation（p<0.05）,and in Daxinganling,Western Sichuan and the middle and lower reaches of the Yangtze River,there was a negative correlation between annual GPP with precipitation（p<0.1）.The increase of solar radiation would lead to an increase in GPP in 82.1%vegetation area.In most parts of eastern China,there was a significant positive correlation between annual GPP with solar radiation（p<0.05）.The interannual variability of GPP controlled by temperature,precipitation,direct radiation and diffuse radiation accounted for 39.1%,31.9%,15.6%and 13.4%respectively.