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Abstract This paper investigates the potential of reanalyses as proxies of observed surface precipitation and temperature to force hydrological models. Three global atmospheric reanalyses (ERA-Interim, CFSR, and MERRA), one regional reanalysis (NARR), and one global meteorological forcing dataset obtained by bias-correcting ERA-Interim [Water and Global Change (WATCH) Forcing Data ERA-Interim (WFDEI)] were compared to one gridded observation database over the contiguous United States. Results showed that all temperature datasets were similar to the gridded observation over most of the United States. On the other hand, precipitation from all three global reanalyses was biased, especially in summer and winter in the southeastern United States. The regional reanalysis precipitation was closer to observations since it indirectly assimilates surface precipitation. The WFDEI dataset was generally less biased than the reanalysis datasets. All datasets were then used to force a global conceptual hydrological model on 370 watersheds of the Model Parameter Estimation Experiment (MOPEX) database. River flows were computed for each watershed, and results showed that the flows simulated using NARR and gridded observations forcings were very similar to the observed flows. The simulated flows forced by the global reanalysis datasets were also similar to the observations, except in the humid continental and subtropical climatic regions, where precipitation seasonality biases degraded river flow simulations. The WFDEI dataset led to better river flows than reanalysis in the humid continental and subtropical climatic regions but was no better than reanalysis—and sometimes worse—in other climatic zones. Overall, the results indicate that global reanalyses have good potential to be used as proxies to observations to force hydrological models, especially in regions with few weather stations.
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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
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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