Atmosphere-Vegetation Interaction Model Research Articles

Gravel parameterization scheme and verification using BCC_CSM

The soil in China contains an abundance of gravels, but it is poorly described in land surface models. To solve this problem, the Beijing Climate Center Atmosphere–Vegetation Interaction Model (BCC_AVIM), which is a land surface model with the gravel parameterization, is coupled to the Beijing Climate Center Climate System Model (BCC_CSM). The simulation ability of BCC_CSM for China using the gravel parameterization is evaluated by comparing the simulation results using default and new schemes with the observed data. The results show that the annual average surface temperature simulated with the new schemes is more consistent with the observation in terms of spatial distribution, and the simulation results are significantly improved, especially in summer. From the perspective of the area-averaged variables, more precipitation simulated using the default schemes is improved except for summer. The high-level and low-level wind fields simulated by BCC_CSM significantly improve the Qinghai-Tibet Plateau. In general, this gravel parameterization is more suitable for areas with high gravel content, and it improves the simulation performance of BCC_CSM in some areas of China.

Evaluating Hydrological Processes of the Atmosphere–Vegetation Interaction Model and MERRA-2 at Global Scale

Hydrological processes are a key component of land surface models and link to the energy budget and carbon cycle. This study assessed the global hydrological processes of the Atmosphere–Vegetation Interaction Model (AVIM) using multiple datasets, including the Global Land Data Assimilation System (GLDAS), the University of New Hampshire and Global Runoff Data Centre (UNH-GRDC), the European Space Agency (ESA) Climate Change Initiative (CCI), the Global Land Evaporation Amsterdam Model (GLEAM), and the Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) datasets. The comparisons showed that the AVIM gives a reasonable spatial pattern for surface soil moisture and surface runoff, but a less satisfactory spatial pattern for evapotranspiration. The AVIM clearly underestimates surface runoff worldwide and overestimates the surface soil moisture in the high latitudes of the Northern Hemisphere, while yielding moderately higher evapotranspiration in arid areas and lower evapotranspiration in low-latitude areas near the equator. The annual cycle of evapotranspiration in the AVIM shows good agreement with the GLEAM dataset, whereas the surface soil moisture in the AVIM has a poor annual cycle relative to the CCI dataset. The AVIM simulates a late start time for snowmelt, which leads to a two-month delay in the peak surface runoff. These results clearly point out the directions required for improvements in the AVIM, which will support future investigations of water–carbon–atmosphere interactions. In addition, the evapotranspiration in the MERRA-2 dataset had an overall good performance comparable with that of the GLEAM dataset, but its surface soil moisture did not perform well when validated against the CCI dataset.

Integration of nitrogen dynamics into the land surface model AVIM. Part 2: baseline data and variation of carbon and nitrogen fluxes in China

The spatiotemporal features of carbon and nitrogen fluxes over China between 1979 and 2015 were simulated by the Atmosphere–Vegetation Interaction Model (AVIM). The carbon fluxes of gross primary p...

Implications of diurnal variations in land surface temperature to data assimilation using MODIS LST data

Based on the Beijing Climate Center’s land surface model BCC_AVIM (Beijing Climate Center Atmosphere-Vegetation Interaction Model), the ensemble Kalman filter (EnKF) algorithm has been used to perform an assimilation experiment on the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) product to study the influence of satellite LST data frequencies on surface temperature data assimilations. The assimilation results have been independently tested and evaluated by Global Land Data Assimilation System (GLDAS) LST products. The results show that the assimilation scheme can effectively reduce the BCC_AVIM model simulation bias and the assimilation results reflect more reasonable spatial and temporal distributions. Diurnal variation information in the observation data has a significant effect on the assimilation results. Assimilating LST data that contain diurnal variation information can further improve the accuracy of the assimilation analysis. Overall, when assimilation is performed using observation data at 6-hour intervals, a relatively good assimilation result can be obtained, indicated by smaller bias (<2.2K) and root-mean-square-error (RMSE) (<3.7K) and correlation coefficients larger than 0.60. Conversely, the assimilation using 24-hour data generally showed larger bias (>2.2K) and RMSE (>4K). Further analysis showed that the sensitivity of assimilation effect to diurnal variations in LST varies with time and space. The assimilation using observations with a time interval of 3 hours has the smallest bias in Oceania and Africa (both<1K); the use of 24-hour interval observation data for assimilation produces the smallest bias (<2.2K) in March, April and July.

Development of Land Surface Model BCC_AVIM2.0 and Its Preliminary Performance in LS3MIP/CMIP6

The improvements and validation of several parameterization schemes in the second version of the Beijing Climate Center Atmosphere–Vegetation Interaction Model (BCC_AVIM2.0) are introduced in this study. The main updates include a replacement of the water-only lake module by the common land model lake module (CoLM-lake) with a more realistic snow–ice–water–soil framework, a parameterization scheme for rice paddies added in the vegetation module, renewed parameterizations of snow cover fraction and snow surface albedo to accommodate the varied snow aging effect during different stages of a snow season, a revised parameterization to calculate the threshold temperature to initiate freeze (thaw) of soil water (ice) rather than being fixed at 0°C in BCC_AVIM1.0, a prognostic phenology scheme for vegetation growth instead of empirically prescribed dates for leaf onset/fall, and a renewed scheme to depict solar radiation transfer through the vegetation canopy. The above updates have been implemented in BCC_AVIM2.0 to serve as the land component of the BCC Climate System Model (BCC_CSM). Preliminary results of BCC_AVIM in the ongoing Land Surface, Snow, and Soil Moisture Model Intercomparison Project (LS3MIP) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) show that the overall performance of BCC_AVIM2.0 is better than that of BCC_AVIM1.0 in the simulation of surface energy budgets at the seasonal timescale. Comparing the simulations of annual global land average before and after the updates in BCC_AVIM2.0 reveals that the bias of net surface radiation is reduced from −12.0 to −11.7 W m−2 and the root mean square error (RMSE) is reduced from 20.6 to 19.0 W m−2; the bias and RMSE of latent heat flux are reduced from 2.3 to −0.1 W m−2 and from 15.4 to 14.3 W m−2, respectively; the bias of sensible heat flux is increased from 2.5 to 5.1 W m−2 but the RMSE is reduced from 18.4 to 17.0 W m−2.

The Improved Freeze–Thaw Process of a Climate‐Vegetation Model: Calibration and Validation Tests in the Source Region of the Yellow River

AbstractThe freeze–thaw process significantly impacts land surface processes in permafrost and influences the regional climate. In this study, the freeze–thaw process in the atmosphere‐vegetation interaction model (AVIM) was improved by utilizing the universal soil hydrothermal coupling equations, and by introducing the freezing depression point‐based freeze–thaw parameterization scheme to form a model known as the AVIM frozen soil model (AVIM_FSM). Then, the seasonal frozen soil observational data at Maqu station, located in the Yellow River source region, were used to calibrate the freeze–thaw‐related parameters with an artificial intelligence particle swarm optimization method, and the model was validated. The results indicated that by improving the freeze–thaw process, the temporal variations in soil temperature and liquid water content simulated by the AVIM_FSM model agreed well with the observations. By calibrating the parameters, the deviations between the observations and corresponding simulations were reduced compared with those in the AVIM. Based on the AVIM_FSM, the physical mechanism of the freeze–thaw process was discussed, the different freeze–thaw parameterization schemes were compared, the related freeze–thaw process parameters were quantitatively evaluated, and the following was indicated: (1) the freeze–thaw process was mainly determined by radiation, sensible heat flux, and ice change in frozen soil; (2) the freezing depression point‐based freeze–thaw parameterization scheme was superior to the empirical scheme, which can reasonably describe the freezing and thawing start dates with lower deviations; and (3) the particle swarm optimization algorithm can efficiently calibrate the freeze–thaw‐related parameters and improve the simulation accuracy.

Simulated Long-Term Vegetation–Climate Feedbacks in the Tibetan Plateau

The Tibetan Plateau (TP) is an important region of land–atmosphere interactions with high climate variability. In this study, an atmosphere–vegetation interaction model was applied to explore the possible responses of vegetation to climate warming, and to assess the impacts of land cover change on the land surface physical processes across the TP. Results showed that long-term warming over the TP could influence vegetation growth via different mechanisms. Most likely, increased temperature would enhance the physiological activity in most high cold areas on the TP, whereas high temperature would inhibit vegetation growth by increasing respiration in areas with favorable water and temperature conditions. In addition, for areas where the climate is warmer but not wetter, higher temperature could influence photosynthesis via the moisture condition of the vegetation rather than by modulating respiration. Numerical simulations demonstrated that vegetation could control the land surface–atmosphere energy balance effectively. The change of land cover from vegetated land to desert steppe decreased the net radiation absorbed by the surface, weakening the surface thermal effects, and reducing sensible and latent heat fluxes. Furthermore, sensitivity simulations also revealed that global warming would likely accelerate vegetation growth in most areas of the TP, resulting in increased surface heat flux.

Prediction CH4 Emissions from the Wetlands in the Sanjiang Plain of Northeastern China in the 21st Century

The Sanjiang Plain has been experienced significant wetland loss due to expanded agricultural activities, and will be potentially restored by the China National Wetland Conservation Action Plan (NWCP) in future. The objective of this study is to evaluate the impact of future climate warming and wetland restoration on wetland CH4 emissions in northeast China. We used an atmosphere-vegetation interaction model (AVIM2) to drive a modified biogeophysical model (CH4MODwetland), and projected CH4 flux variations from the Sanjiang Plain wetlands under different Representative Concentration Pathway scenarios throughout the 21st century. Model validation showed that the regressions between the observed and simulated CH4 fluxes by the modified model produced an R2 of 0.49 with a slope of 0.87 (p<0.001, n = 237). According to the AVIM2 simulation, the net primary productivity of the Sanjiang Plain wetlands will increase by 38.2 g m-2 yr-1, 116.6 g m-2 yr-1 and 250.4 g m-2 yr-1 under RCP 2.6, RCP 4.5 and RCP 8.5, respectively, by the end of this century. For RCP 2.6, 4.5 and 8.5 scenarios, the CH4 fluxes will increase by 5.7 g m-2 yr-1, 57.5 g m-2 yr-1 and 112.2 g m-2 yr-1. Combined with the wetland restoration, the regional emissions will increase by 0.18‒1.52 Tg. The CH4 emissions will be stimulated by climate change and wetland restoration. Regional wetland restoration planning should be directed against different climate scenarios in order to suppress methane emissions.

Uncertainty of forest biomass carbon patterns simulation on provincial scale: A case study in Jiangxi Province, China

Forest vegetation carbon patterns are significant for evaluating carbon emission and accumulation. Many methods were used to simulate patterns of forest vegetation carbon stock in previous studies, however, uncertainty apparently existed between results of different methods, even estimates of same method in different studies. Three previous methods, including Atmosphere-vegetation interaction model 2 (AVIM2), Kriging, Satellite-data Based Approach (SBA), and a new method, High Accuracy Surface Modeling (HASM), were used to simulate forest vegetation carbon stock patterns in Jiangxi Province in China. Cross-validation was used to evaluate methods. The uncertainty and applicability of the four methods on provincial scale were analyzed and discussed. The results showed that HASM had the highest accuracy, which improved by 50.66%, 33.37% and 28.58%, compared with AVIM2, Kriging and SBA, respectively. Uncertainty of simulation of forest biomass carbon stock was mainly derived from modeling error, sampling error and statistical error of forest area. Total forest carbon stock, carbon density and forest area of Jiangxi were 288.62 Tg, 3.06 kg/m2 and 94.32×109 m2 simulated by HASM, respectively.

Climate change risks for net primary production of ecosystems in China

ABSTRACTFew studies have investigated ecosystem risk under climate change from the perspective of critical thresholds. We presented a framework to assess the climate change risk on ecosystems based on the definition of critical thresholds. Combined with climate scenario, vegetation, and soil data, the Atmosphere Vegetation Interaction Model version 2 was used to simulate net primary productivity in the period of 1961–2080. The thresholds of dangerous and unacceptable impacts were then defined, and climate change risks on ecosystems in China were assessed. Results showed that risk areas will be closely associated with future climate change and will mainly occur in the southwest and northwest areas, Inner Mongolia, the southern part of the northeast areas, and South China. The risk regions will expand to 343.66 Mha in the long term (2051–2080), accounting for 35.80% of China. The risk levels on all ecosystems (eco-regions) are likely to increase continually. The ecosystems of wooded savanna, temperate grassland, and desert grassland, which typically exhibit strong water stress, will have the maximum risk indices in the future. The Northwest Region is likely to be the most vulnerable because of precipitation restrictions and obvious warming. By contrast, Qinghai–Tibet Region will not be so vulnerable to future climate change.

Impacts of extreme precipitation on tree plantation carbon cycle

Extreme precipitation events are expected to increase in frequency and magnitude in future due to global warming, but relevant impacts on tree plantation ecosystem carbon cycle are unknown. In this study, we use an atmosphere–vegetation interaction model (AVIM2) to estimate the likely impacts of extreme precipitation events on carbon fluxes and carbon stocks of a tree plantation in south China. Our results indicate that shifting from moderate precipitation events to extreme precipitation events whilst keeping monthly precipitation unchanged could decrease the tree plantation carbon accumulation. Tree plantation net primary productivity, net ecosystem productivity, soil carbon stock and vegetation carbon stock could decrease by 4.2, 28, 4.3 and 1.4 % during the studying period of 1962–2004, respectively. Though reductions in net primary productivity and net ecosystem productivity are relatively smaller than their annual variations, our sensitivity test shows that the tree plantation carbon stock could decrease by 3.3 % if the assumed extreme precipitation regime lasts for 500 years. Observed and simulated gross primary productivity, ecosystem respiration and net ecosystem productivity have significant positive correlation with soil water content (SWC), especially the deep SWC. The mechanism for the extreme precipitation effect is that the increase in extreme precipitation events will cause SWC to decrease, consequently, reducing carbon fluxes and stocks.

Simulating seasonal and inter-annual variations in energy and carbon exchanges and forest dynamics using a process-based atmosphere–vegetation dynamics model

The present paper shows simulated results of seasonal and inter-annual variations in energy and carbon exchanges and forest dynamics in a sub-boreal deciduous forest using a fully coupled atmosphere–vegetation interaction model [multilayered integrated numerical model of surface physics-growing plants interaction (MINoSGI)]. With careful adjustment of site-specific eco-physiological parameters, MINoSGI reproduced successfully stand biomass–tree density relationship based on the forest inventory data for 7 years (1999–2005) and seasonal and inter-annual variations in energy and CO2 fluxes measured by means of eddy covariance technique for 3 years (2003–2005) in the sub-boreal forest, northern Japan. In addition, MINoSGI estimated annual evapotranspiration (Evt) at 328.6 ± 25.8 mm year−1, net primary production (NPP) at 372.1 ± 31.5 gC m−2 year−1 and net ecosystem exchange (NEE) at −224.2 ± 32.2 gC m−2 year−1. We found the estimate of annual NEE in our site lies among the estimates at other forest stands with the almost same climatic conditions in northern Japan, although the tree species and stand age of these forests are different from those of our site. Overall, MINoSGI was found useful to present simultaneous simulations of forest dynamics, surface energy, and carbon exchanges of a forest stand in the future from micro-meteorological and ecophysiological points of view.

Climate-vegetation interannual variability in a coupled atmosphere-ocean-land model

The coupled models of both the Global Ocean-Atmosphere-Land System (GOALS) and the Atmosphere-Vegetation Interaction Model (GOALS-AVIM) are used to study the main characteristics of interannual variations. The simulated results are also used to investigate some significant interannual variability and correlation analysis of the atmospheric circulation and terrestrial ecosystem. By comparing the simulations of the climate model GOALS-AVIM and GOALS, it is known that the simulated results of the interannual variations of the spatial and temporal distributions of the surface air temperatures and precipitation are generally improved by using AVIM in GOALS-AVIM. The interannual variation displays some distinct characteristics of the geographical distribution. Both the Net Primary Production (NPP) and the Leap Area Index (LAI) have quasi 1–2-year cycles. Meanwhile, precipitation and the surface temperatures have 2–4-year cycles. Conditions when the spectrum density values of GOALS are less than those of GOALS-AVIM, tell us that the model coupled with AVIM enhances the simulative capability for interannual variability and makes the annual cycle variability more apparent. Using Singular Value Decomposition (SVD) analysis, the relationship between the ecosystem and the atmospheric circulation in East Asia is explored. The result shows that the strengthening and weakening of the East Asian monsoon, characterized by the geopotential heights at 500 hPa and the wind fields at 850 hPa, correspond to the spatiotemporal pattern of the NPP. The correlation between NPP and the air temperature, precipitation and solar radiation are different in interannual variability because of the variation in vegetation types.

Simulation and analysis of China climate using two-way interactive atmosphere-vegetation model (RIEMS-AVIM)

In this paper, an Atmosphere-Vegetation Interaction Model (AVIM) is coupled to the Regional Integrated Environment Model System (RIEMS), and a 10-year integration for China is performed using the RIEMS-AVIM. The analysis of the results of the 10-year integration shows that the characters of the spatial distributions of temperature and precipitation over China are well simulated. The patterns of simulated surface sensible and latent heat fluxes match well with the spatial climatological atlas: the values of winter surface sensible and latent heat fluxes are both lower than climatological values over the whole country. Summer surface sensible heat flux is higher than climatological values in western China and lower in eastern China, while summer surface latent heat flux is higher than climatological values in the eastern and lower in the western. Seasonal variations of simulated temperature and precipitation of RIMES-AVIM agree with those of the observed. Simulated temperature is lower than the observed in the Tibetan Plateau and Northwest China for the whole year, slightly lower in the remaining regions in winter, but consistent with the observed in summer. The simulated temperature of RIEMS-AVIM is higher in winter and lower in summer than that of RIEMS, which shows that the simulated temperature of RIEMS-AVIM is closer to the observed value. Simulated precipitation is excessive in the first half of the year, but consistent with the observed in the second half of the year. The simulated summer precipitation of RIEMS-AVIM has significant improvement compared to that of RIEMS, which is less and closer to the observed value. The interannual variations of temperature and precipitation are also fairly well simulated, with temperature simulation being superior to precipitation simulation. The interannual variation of simulated temperature is significantly correlated with the observed in Northeast China, the Transition Region, South China, and the Tibetan Plateau, but the correlation between precipitation simulation and observation is only significant in Northwest China.

An updated coupled model for land-atmosphere interaction. Part II: Simulations of biological processes

In Part I, the authors succeeded in coupling the spectral atmospheric model (SAMIL_R42L9) developed at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences (LASG/IAP/CAS) with the land surface model, Atmosphere-Vegetation-Interaction-Model (AVIM) and analyzed the climate basic state and land surface physical fluxes simulated by R42_AVIM. In this Part II, we further evaluate the simulated results of the biological processes, including leaf area index (LAI), biomass and net primary productivity (NPP) etc. Results indicate that R42_AVIM can simulate the global distribution of LAI and has good consistency with the monthly mean LAI provided by Max Planck Institute for Meteorology. The simulated biomass corresponds reasonably to the vegetation classifications. In addition, the simulated annual mean NPP has a consistent distribution with the data provided by IGBP and MODIS, and compares well with the work in literature. This land-atmosphere coupled model will offer a new experiment tool for the research on the two-way interaction between climate and biosphere, and the global terrestrial ecosystem carbon cycle.

Prediction of carbon exchanges between China terrestrial ecosystem and atmosphere in 21st century

The projected changes in carbon exchange between China terrestrial ecosystem and the atmosphere and vegetation and soil carbon storage during the 21st century were investigated using an atmosphere-vegetation interaction model (AVIM2). The results show that in the coming 100 a, for SRES B2 scenario and constant atmospheric CO(2) concentration, the net primary productivity (NPP) of terrestrial ecosystem in China will be decreased slowly, and vegetation and soil carbon storage as well as net ecosystem productivity (NEP) will also be decreased. The carbon sink for China terrestrial ecosystem in the beginning of the 20th century will become totally a carbon source by the year of 2020, while for B2 scenario and changing atmospheric CO(2) concentration, NPP for China will increase continuously from 2.94 GtC center dot a(-1) by the end of the 20th century to 3.99 GtC center dot a(-1) by the end of the 21st century, and vegetation and soil carbon storage will increase to 110.3 GtC. NEP in China will keep rising during the first and middle periods of the 21st century, and reach the peak around 2050s, then will decrease gradually and approach to zero by the end of the 21st century.

The ecosystem carbon accumulation after conversion of grasslands to pine plantations in subtropical red soil of South China

Since 1980s, afforestation in China has led to the establishment of over 0.53 × 108 ha of new plantation forests. While this leads to rapid accumulation of carbon (C) in vegetation, the effects of afforestation on soil C are poorly understood. In this study, a newversion of the Atmosphere-Vegetation Interaction Model (AVIM2)was used to examine howchanges in plant C inputs following afforestation might lead to changes in soil C at one of the Chinaflux sites and to estimate the effect of afforestation on ex-grassland. The potential total C accumulation of tree plantation was also predicted. The model was calibrated by net ecosystem exchange (NEE), ecosystem respiration (RE) and gross primary production (GPP) based on eddy-covariance measurements. The simulated vegetation C and soil C stocks were compared with the filed observations.The simulates indicate that after 22 yr of conversion of grassland to needle leaf forests (Pinus massoniana and Pinus elliottii), the net carbon accumulation in tree ecosystem was 1.96 times more than that in grassland. The soil C in the initial 7 yr of planting decreased at a rate of 0.1871 kg C m-2 yr-1, and after that it increased at a rate of 0.090 kg C m-2 yr-1. The C accumulation in the studied plantation ecosystem is estimated to be 76–81% of that value in equilibrium state (the net ecosystem productivity approaches to zero).Sensitivity analyses show that conversion from grassland to plantation caused an initial (7 or 8 yr) periods of decrease in soil C stocks in wider red soil area of southern China. The soil C stocks were reduced between 19.2 and 20.4% in the initial decreasing period. After 7 or 8 yr C loss, the increased in soil C stocks was predicted to be between 0.073 and 0.074 kg C m-2 yr-1.

The terrestrial NPP simulations in China since Last Glacial Maximum

Based on Atmosphere-Vegetation Interaction Model (AVIM), the magnitude and spatial distribution of terrestrial net primary productivity (NPP) in China is simulated during three different geological eras, Last Glacial Maximum (LGM), Mid-Holocene (MH) and the present. The simulation shows that the glacial-interglacial variation of East Asian summer monsoon in China is the key factor affecting the NPP change. During the three eras, mean NPPs are 208 g/m2.a, 409 g/m2.a, and 355 g/m2.a. The total NPPs are 2.05 Pg/a, 3.89 Pg/a and 3.33 Pg/a, respectively. The terrestrial NPP in China during warm-humid climate is larger than that during cold-arid eras, and the correlation analysis between NPP and climate factors suggests that temperature is the primary factor affecting the terrestrial NPP during 21 kaBP (LGM), and for 6 kaBP (MH) and the present the primary factor is precipitation.

The soil moisture of China in a high resolution climate-vegetation model

The spatial distribution of soil moisture, especially the temporal variation at seasonal and interannual scales, is difficult for many land surface models (LSMs) to capture partly due to the deficiencies of the LSMs and the highly spatial variability of soil moisture, which makes it problematic to simulate the moisture for climate studies. However the soil moisture plays an important role in influencing the energy and hydrological cycles between the land and air, so it should be considered in land surface models. In this paper, a soil moisture simulation in China with a T213 resolution (about 0.5625δ × 0.5625°) is compared to the observational data, and its relationship to precipitation is explored. The soil moisture distribution agrees roughly with the observations, and the soil moisture pattern reflects the variation and intensity of the precipitation. In particular, for the 1998 summer catastrophic floods along the Yangtze River, the soil moisture remains high in this region from July to August and represents the flood well. The seasonal cycle of soil moisture is roughly consistent with the observed data, which is a good calibration for the ground simulation capacity of the Atmosphere-Vegetation Interaction Model (AVIM) with respect to this tough problem for land surface models.

Climatic and biological simulations in a two-way coupled atmosphere–biosphere model (CABM)

Today, most land surface process models have prescribed seasonal change of vegetation with regard to the exchange processes between land and the atmosphere. However, in order to consider the real interaction between vegetation and atmosphere and represent it best in a climate model, the vegetation growth process should be included. In other words, “life” should be brought into climate models. In this study, we have coupled the physical and biological components of AVIM (Atmosphere–Vegetation Interaction Model), a land surface model including plant ecophysiological processes, into the IAP/LASG L9 R15 GOALS GCM. To exhibit terrestrial vegetation information, the vegetation is given a high resolution of 1.5° by 1.5° to nest and couple the fine grid cells of land with the coarse grid cells of atmosphere, which is 7.5° longitude and 4.5° latitude. The simulated monthly mean surface air temperature and precipitation is close to the observations. The monthly mean Leaf Area Index (LAI) is consistent with the observed data. The global annual mean net primary production (NPP) simulation is also reasonable. The coupled model is stable, providing a good platform for research on two-way interaction between land and atmosphere, and the global terrestrial ecosystem carbon cycle.