A comprehensive data set of extreme hydrological events (EHEs)—floods and droughts, consisting of 2,171 occurrences worldwide, during 1960‐2014 was compiled, and then their economic losses were normalized using a price index in U.S. dollar. The data set showed a significant increasing trend of EHEs before 2000, while a slight post‐2000 decline. Correspondingly, the EHE‐caused economic losses increased obviously before 2000 followed by a slight decrease; the post‐2000 decline could be partially attributed to the decreases in drought and flood‐prone area or climate adaptation practices. Spatially, Asia experienced most EHEs (969), corresponding to the largest share of economic losses (approximately $868 billion for floods and $50 billion for droughts, respectively), while Oceania had the least EHEs (102) and the least economic losses (approximately $19 billion for floods and $45 billion for droughts). The five countries with the highest EHE‐caused economic losses were China, United States, Canada, Australia, and India. Countries that suffered the highest flood‐caused economic losses were China, United States, and Canada. This data set provides a quantitative linkage between climate science and economic losses at a global scale, and it is beneficial for the regional climatic impact assessments and strategical development for mitigating climate change impacts.

Spatial heterogeneities in soil hydrology have been confirmed as a key control on CO₂ and CH₄ fluxes in the Arctic tundra ecosystem. In this study, we applied a mechanistic ecosystem model, CLM‐Microbe, to examine the microtopographic impacts on CO₂ and CH₄ fluxes across seven landscape types in Utqiaġvik, Alaska: trough, low‐centered polygon (LCP) center, LCP transition, LCP rim, high‐centered polygon (HCP) center, HCP transition, and HCP rim. We first validated the CLM‐Microbe model against static‐chamber measured CO₂ and CH₄ fluxes in 2013 for three landscape types: trough, LCP center, and LCP rim. Model application showed that low‐elevation and thus wetter landscape types (i.e., trough, transitions, and LCP center) had larger CH₄ emissions rates with greater seasonal variations than high‐elevation and drier landscape types (rims and HCP center). Sensitivity analysis indicated that substrate availability for methanogenesis (acetate, CO₂ + H₂) is the most important factor determining CH₄ emission, and vegetation physiological properties largely affect the net ecosystem carbon exchange and ecosystem respiration in Arctic tundra ecosystems. Modeled CH₄ emissions for different microtopographic features were upscaled to the eddy covariance (EC) domain with an area‐weighted approach before validation against EC‐measured CH₄ fluxes. The model underestimated the EC‐measured CH₄ flux by 20% and 25% at daily and hourly time steps, suggesting the importance of the time step in reporting CH₄ flux. The strong microtopographic impacts on CO₂ and CH₄ fluxes call for a model‐data integration framework for better understanding and predicting carbon flux in the highly heterogeneous Arctic landscape.

Explicitly representing microbial processes has been recognized as a key improvement to Earth system models for the realistic projections of soil carbon (C) and climate dynamics. The CLM‐Microbe model builds upon the CLM4.5 and explicitly represents two major soil microbial groups, fungi and bacteria. Based on the compiled time‐series data of fungal (FBC) and bacterial (BBC) biomass C from nine biomes, we parameterized and validated the CLM‐Microbe model, and further conducted sensitivity and uncertainty analysis for simulating C cycling. The model performance was evaluated with mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R²) for relative change in FBC and BBC. The CLM‐Microbe model is able to reasonably capture the seasonal dynamics of FBC and BBC across biomes, particularly for tropical/subtropical forest, temperate broadleaf forest, and grassland, with MAE <0.49 for FBC and <0.36 for BBC and RMSE <0.52 FBC and <0.39 for BBC, while R² values are relatively smaller in some biomes (e.g., shrub) due to small sample sizes. We found good consistencies between simulated and observed FBC (R² = 0.70, P < 0.001) and BBC (R² = 0.26, P < 0.05) on average across biomes, but the model is not able to fully capture the large variation in observed FBC and BBC. Sensitivity analysis shows that the most critical parameters are turnover rate and carbon‐to‐nitrogen ratio of fungi and bacteria and microbial assimilation efficiency. This study confirms that the explicit representation of soil microbial mechanisms enhances model performance in simulating C variables such as heterotrophic respiration and soil organic C density. The further application of the CLM‐Microbe model would deepen our understanding of microbial contributions to global C cycle.