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Summary For decades, researchers have thought it was difficult to remove the uncertainty from the estimates of forest carbon storage and its changes on national sales. This is not only because of stochasticity in the data but also the bias to overcome in the computations. Most studies of the estimation, however, ignore quantitative analyses for the latter uncertainty. This bias primarily results from the widely used volume‐biomass method via scaling up forest biomass from limited sample plots to large areas. This paper addresses (i) the mechanism of scaling‐up error occurrence, and (ii) the quantitative effects of the statistical factors on the error. The error compensators were derived, and expressed by ternary functions with three variables: expectation, variance and the power in the volume‐biomass equation. This is based on analysing the effect of power‐law function convexity on scaling‐up error by solving the difference of both sides of the weighted Jensen inequality. The simulated data and the national forest inventory of China were used for algorithm testing and application, respectively. Scaling‐up error occurrence stems primarily from an effect of the distribution heterogeneity of volume density on the total biomass amount, and secondarily from the extent of function nonlinearities. In our experiments, on average 94·2% of scaling‐up error can be reduced for the statistical populations of forest stands in a region. China's forest biomass carbon was estimated as approximately 6·0 PgC or less at the beginning of the 2010s after on average 1·1% error compensation. The results of both the simulated data experiment and national‐scale estimation suggest that the biomass is overestimated for young forests more than others. It implies a necessity to compensate scaling‐up error, especially for the areas going through extensive afforestation and reforestation in past decades. This study highlights the importance of understanding how both the function nonlinearity and the statistics of the variables quantitatively affect the scaling‐up error. Generally, the presented methods will help to translate fine‐scale ecological relationships to estimate coarser scale ecosystem properties by correcting aggregation errors.

Abstract Precipitation changes modify C, N, and P cycles, which regulate the functions and structure of terrestrial ecosystems. Although altered precipitation affects above‐ and belowground C:N:P stoichiometry, considerable uncertainties remain regarding plant–microbial nutrient allocation strategies under increased (IPPT) and decreased (DPPT) precipitation. We meta‐analyzed 827 observations from 235 field studies to investigate the effects of IPPT and DPPT on the C:N:P stoichiometry of plants, soils, and microorganisms. DPPT reduced leaf C:N ratio, but increased the leaf and root N:P ratios reflecting stronger decrease of P compared with N mobility in soil under drought. IPPT increased microbial biomass C (+13%), N (+15%), P (26%), and the C:N ratio, whereas DPPT decreased microbial biomass N (−12%) and the N:P ratio. The C:N and N:P ratios of plant leaves were more sensitive to medium DPPT than to IPPT because drought increased plant N content, particularly in humid areas. The responses of plant and soil C:N:P stoichiometry to altered precipitation did not fit the double asymmetry model with a positive asymmetry under IPPT and a negative asymmetry under extreme DPPT. Soil microorganisms were more sensitive to IPPT than to DPPT, but they were more sensitive to extreme DPPT than extreme IPPT, consistent with the double asymmetry model. Soil microorganisms maintained stoichiometric homeostasis, whereas N:P ratios of plants follow that of the soils under altered precipitation. In conclusion, specific N allocation strategies of plants and microbial communities as well as N and P availability in soil critically mediate C:N:P stoichiometry by altered precipitation that need to be considered by prediction of ecosystem functions and C cycling under future climate change scenarios.

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.

Summary For decades, researchers have thought it was difficult to remove the uncertainty from the estimates of forest carbon storage and its changes on national sales. This is not only because of stochasticity in the data but also the bias to overcome in the computations. Most studies of the estimation, however, ignore quantitative analyses for the latter uncertainty. This bias primarily results from the widely used volume‐biomass method via scaling up forest biomass from limited sample plots to large areas. This paper addresses (i) the mechanism of scaling‐up error occurrence, and (ii) the quantitative effects of the statistical factors on the error. The error compensators were derived, and expressed by ternary functions with three variables: expectation, variance and the power in the volume‐biomass equation. This is based on analysing the effect of power‐law function convexity on scaling‐up error by solving the difference of both sides of the weighted Jensen inequality. The simulated data and the national forest inventory of China were used for algorithm testing and application, respectively. Scaling‐up error occurrence stems primarily from an effect of the distribution heterogeneity of volume density on the total biomass amount, and secondarily from the extent of function nonlinearities. In our experiments, on average 94·2% of scaling‐up error can be reduced for the statistical populations of forest stands in a region. China's forest biomass carbon was estimated as approximately 6·0 PgC or less at the beginning of the 2010s after on average 1·1% error compensation. The results of both the simulated data experiment and national‐scale estimation suggest that the biomass is overestimated for young forests more than others. It implies a necessity to compensate scaling‐up error, especially for the areas going through extensive afforestation and reforestation in past decades. This study highlights the importance of understanding how both the function nonlinearity and the statistics of the variables quantitatively affect the scaling‐up error. Generally, the presented methods will help to translate fine‐scale ecological relationships to estimate coarser scale ecosystem properties by correcting aggregation errors.

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.

Significance Understanding the location of carbon sources and sinks is essential for accurately predicting future changes in atmospheric carbon dioxide and climate. Mid- to high-latitude terrestrial ecosystems are well known to be the principal carbon sink regions, yet less attention has been paid to the mid- to low-latitude ecosystems. In this study, long-term eddy covariance observations demonstrate that there is a high carbon dioxide uptake (net ecosystem productivity) by the mid- to low-latitude East Asian monsoon subtropical forests that were shaped by the uplift of the Tibetan Plateau. Increasing nitrogen deposition, a young forest age structure, and sufficient water and heat availability combined to contribute to this large carbon dioxide uptake. , Temperate- and high-latitude forests have been shown to contribute a carbon sink in the Northern Hemisphere, but fewer studies have addressed the carbon balance of the subtropical forests. In the present study, we integrated eddy covariance observations established in the 1990s and 2000s to show that East Asian monsoon subtropical forests between 20°N and 40°N represent an average net ecosystem productivity (NEP) of 362 ± 39 g C m −2 yr −1 (mean ± 1 SE). This average forest NEP value is higher than that of Asian tropical and temperate forests and is also higher than that of forests at the same latitudes in Europe–Africa and North America. East Asian monsoon subtropical forests have comparable NEP to that of subtropical forests of the southeastern United States and intensively managed Western European forests. The total NEP of East Asian monsoon subtropical forests was estimated to be 0.72 ± 0.08 Pg C yr −1 , which accounts for 8% of the global forest NEP. This result indicates that the role of subtropical forests in the current global carbon cycle cannot be ignored and that the regional distributions of the Northern Hemisphere's terrestrial carbon sinks are needed to be reevaluated. The young stand ages and high nitrogen deposition, coupled with sufficient and synchronous water and heat availability, may be the primary reasons for the high NEP of this region, and further studies are needed to quantify the contribution of each underlying factor.

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.