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Abstract Using the data compiled from China's second national soil survey and an improved method of soil carbon bulk density, we have estimated the changes of soil organic carbon due to land use, and compared the spatial distribution and storage of soil organic carbon (SOC) in cultivated soils and noncultivated soils in China. The results reveal that ∼  57% of the cultivated soil subgroups ( ∼  31% of the total soil surface) have experienced a significant carbon loss, ranging from 40% to 10% relative to their noncultivated counterparts. The most significant carbon loss is observed for the non‐irrigated soils (dry farmland) within a semiarid/semihumid belt from northeastern to southwestern China, with the maximum loss occurring in northeast China. On the contrary, SOC has increased in the paddy and irrigated soils in northwest China. No significant change is observed for forest soils in southern China, grassland and desert soils in northwest China, as well as irrigated soils in eastern China. The SOC storage and density under noncultivated conditions in China are estimated to ∼  77.4 Pg (10 15  g) and ∼  8.8 kg C m −2 , respectively, compared to a SOC storage of ∼  70.3 Pg and an average SOC density of ∼  8.0 kg C m −2 under the present‐day conditions. This suggests a loss of ∼  7.1 Pg SOC and a decrease of ∼  0.8 kg C m −2 SOC density due to increasing human activities, in which the loss in organic horizons has contributed to ∼  77%. This total loss of SOC in China induced by land use represents ∼  9.5% of the world's SOC decrease. This amount is equivalent to ∼  3.5 ppmv of the atmospheric CO 2 increase. Since ∼  78% of the currently cultivated soils in China have been degraded to a low/medium productivities and are responsible for most of the SOC loss, an improved land management, such as the development of irrigated and paddy land uses, would have a considerable potential in restoring the SOC storage. Assuming a restoration of ∼  50% of the lost SOC during the next 20–50 years, the soils in China would absorb ∼  3.5 Pg of carbon from the atmosphere.

Surface soils hold the largest terrestrial organic carbon pool, although estimates of the world's soil organic carbon storage remain controversial, largely due to spatial data gaps or insufficient data density. In this study, spatial distribution and storage of soil organic carbon in China are estimated using the published data from 34,411 soil profiles investigated during China's second national soil survey. Results show that organic carbon density in soils varies from 0.73 to 70.79 kg C/m 2 with the majority ranging between 4.00 and 11.00 kg C/m 2 . Carbon density decreases from east to west. A general southward increase is obvious for western China, while carbon density decreases from north to south in eastern China. Highest values are observed in forest soils in northeast China and in subalpine soils in the southeastern part of the Tibetan Plateau. The average density of ∼8.01 kg C/m 2 in China is lower than the world's mean organic carbon density in soil (∼10.60 kg C/m 2 ), mainly due to the extended arid and semi‐arid regions. Total organic carbon storage in soils in China is estimated to be ∼70.31 Pg C, representing ∼4.7% of the world storage. Carbon storage in the surface organic horizons which is most sensitive to interactions with the atmosphere and environmental change is ∼32.54 Pg C.

Abstract The knowledge of potential impacts of climate change on terrestrial vegetation is crucial to understand long‐term global carbon cycle development. Discrepancy in data has long existed between past carbon storage reconstructions since the Last Glacial Maximum by way of pollen, carbon isotopes, and general circulation model (GCM) analysis. This may be due to the fact that these methods do not synthetically take into account significant differences in climate distribution between modern and past conditions, as well as the effects of atmospheric CO 2 concentrations on vegetation. In this study, a new method to estimate past biospheric carbon stocks is reported, utilizing a new integrated ecosystem model (PCM) built on a physiological process vegetation model (BIOME4) coupled with a process‐based biospheric carbon model (DEMETER). The PCM was constrained to fit pollen data to obtain realistic estimates. It was estimated that the probability distribution of climatic parameters, as simulated by BIOME4 in an inverse process, was compatible with pollen data while DEMETER successfully simulated carbon storage values with corresponding outputs of BIOME4. The carbon model was validated with present‐day observations of vegetation biomes and soil carbon, and the inversion scheme was tested against 1491 surface pollen spectra sample sites procured in Africa and Eurasia. Results show that this method can successfully simulate biomes and related climates at most selected pollen sites, providing a coefficient of determination ( R ) of 0.83–0.97 between the observed and reconstructed climates, while also showing a consensus with an R ‐value of 0.90–0.96 between the simulated biome average terrestrial carbon variables and the available observations. The results demonstrate the reliability and feasibility of the climate reconstruction method and its potential efficiency in reconstructing past terrestrial carbon storage.

The knowledge of tropical palaeoclimates is crucial for understanding global climate change, because it is a test bench for general circulation models that are ultimately used to predict future global warming. A longstanding issue concerning the last glacial maximum in the tropics is the discrepancy between the decrease in sea-surface temperatures reconstructed from marine proxies and the high-elevation decrease in land temperatures estimated from indicators of treeline elevation. In this study, an improved inverse vegetation modeling approach is used to quantitatively reconstruct palaeoclimate and to estimate the effects of different factors (temperature, precipitation, and atmospheric CO 2 concentration) on changes in treeline elevation based on a set of pollen data covering an altitudinal range from 100 to 3,140 m above sea level in Africa. We show that lowering of the African treeline during the last glacial maximum was primarily triggered by regional drying, especially at upper elevations, and was amplified by decreases in atmospheric CO 2 concentration and perhaps temperature. This contrasts with scenarios for the Holocene and future climates, in which the increase in treeline elevation will be dominated by temperature. Our results suggest that previous temperature changes inferred from tropical treeline shifts may have been overestimated for low-CO 2 glacial periods, because the limiting factors that control changes in treeline elevation differ between glacial and interglacial periods.

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.

Abstract During the late Miocene, a dramatic global expansion of C 4 plant distribution occurred with broad spatial and temporal variations. Although the event is well documented, whether subsequent expansions were caused by a decreased atmospheric CO 2 concentration or climate change is a contentious issue. In this study, we used an improved inverse vegetation modeling approach that accounts for the physiological responses of C 3 and C 4 plants to quantitatively reconstruct the paleoclimate in the Siwalik of Nepal based on pollen and carbon isotope data. We also studied the sensitivity of the C 3 and C 4 plants to changes in the climate and the atmospheric CO 2 concentration. We suggest that the expansion of the C 4 plant distribution during the late Miocene may have been primarily triggered by regional aridification and temperature increases. The expansion was unlikely caused by reduced CO 2 levels alone. Our findings suggest that this abrupt ecological shift mainly resulted from climate changes related to the decreased elevation of the Himalayan foreland.