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Abstract Here, we use a coupled atmospheric‐ocean‐aerosol model to investigate the plume development and climate effects of the smoke generated by fires following a regional nuclear war between emerging third‐world nuclear powers. We simulate a standard scenario where 5 Tg of black carbon ( BC ) is emitted over 1 day in the upper troposphere–lower stratosphere. However, it is likely that the emissions from the fires ignited by bomb detonations include a substantial amount of particulate organic matter ( POM ) and that they last more than 1 day. We therefore test the sensitivity of the aerosol plume and climate system to the BC / POM ratio (1:3, 1:9) and to the emission length (1 day, 1 week, 1 month). We find that in general, an emission length of 1 month substantially reduces the cooling compared to the 1‐day case, whereas taking into account POM emissions notably increases the cooling and the reduction of precipitation associated with the nuclear war during the first year following the detonation. Accounting for POM emissions increases the particle size in the short‐emission‐length scenarios (1 day/1 week), reducing the residence time of the injected particle. While the initial cooling is more intense when including POM emission, the long‐lasting effects, while still large, may be less extreme compared to the BC ‐only case. Our study highlights that the emission altitude reached by the plume is sensitive to both the particle type emitted by the fires and the emission duration. Consequently, the climate effects of a nuclear war are strongly dependent on these parameters. , Key Points Importance of including OC when simulating nuclear wars Importance of the fire emission length when simulating nuclear wars

Abstract Despite the importance of net primary productivity (NPP) and net biome productivity (NBP), estimates of NPP and NBP for China are highly uncertain. To investigate the main sources of uncertainty, we synthesized model estimates of NPP and NBP for China from published literature and the Multi‐scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). The literature‐based results showed that total NPP and NBP in China were 3.35 ± 1.25 and 0.14 ± 0.094 Pg C yr −1 , respectively. Classification and regression tree analysis based on literature data showed that model type was the primary source of the uncertainty, explaining 36% and 64% of the variance in NPP and NBP, respectively. Spatiotemporal scales, land cover conditions, inclusion of the N cycle, and effects of N addition also contributed to the overall uncertainty. Results based on the MsTMIP data suggested that model structures were overwhelmingly important (>90%) for the overall uncertainty compared to simulations with different combinations of time‐varying global change factors. The interannual pattern of NPP was similar among diverse studies and increased by 0.012 Pg C yr −1 during 1981–2000. In addition, high uncertainty in China's NPP occurred in areas with high productivity, whereas NBP showed the opposite pattern. Our results suggest that to significantly reduce uncertainty in estimated NPP and NBP, model structures should be substantially tested on the basis of empirical results. To this end, coordinated distributed experiments with multiple global change factors might be a practical approach that can validate specific structures of different models. , Key Points Large uncertainty exists in estimates of terrestrial NPP and NBP in China Methodological differences greatly contribute to the uncertainty in NPP and NBP Uncertainty in the interannual pattern of NBP is greater than that of NPP

Abstract Lignin and cellulose are thought to be critical factors that affect the rate of litter decomposition; however, few data are available on their degradation dynamics during litter decomposition in lotic ecosystems, such as forest rivers, where litter can decompose much more rapidly than in terrestrial ecosystems. We studied the degradation of lignin and cellulose in the foliar litter of four dominant riparian species (willow: Salix paraplesia ; azalea: Rhododendron lapponicum ; cypress: Sabina saltuaria ; and larch: Larix mastersiana ) in an alpine forest river. Over an entire year's incubation, litter lignin and cellulose degraded by 14.7–100% and 57.7–100% of their initial masses, respectively, depending on litter species. Strong degradations of lignin and cellulose occurred in the prefreezing period (i.e., the first 41 d) during litter decomposition, and the degradation rate was the highest among all the decomposition periods regardless of litter species. Litter species, decomposition period, and environmental factors such as temperature and nutrient availability showed significant influences on lignin and cellulose degradation rates. Compared with previously reported data regarding the dynamics of lignin and cellulose during litter decomposition in terrestrial ecosystems, our results suggest that lignin and cellulose can be degraded much more rapidly in lotic ecosystems, indicating that the traditionally used two‐phased model for the dynamics of lignin in decomposing litter may not be suitable in lotic ecosystems.

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.

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.