This study assesses the capability of 12 models in the Phase 6 of the Coupled Models Intercomparison Project (CMIP6) in simulating the Southeast Indian Subantarctic mode water (SEISAMW) by comparing to Argo observations. The results show that all of the analyzed CMIP6 coupled models can reproduce the SEISAMW and its seasonal cycle, albeit with discrepancies of formation region and properties among the models. The SEISAMW subduction rate shows significant interannual variability, which is primarily induced by lateral induction in these models, in agreement with observations. Furthermore, the longterm trends of the SEISAMW show volume loss in accordance with the descending trend of the subduction rate, which are co‐occurred with the ascending trend of the Southern Annular Mode (SAM) indices in most of the analyzed CMIP6 models, similar to those in the CMIP3 and CMIP5 coupled models. Meanwhile, the potential density of the SEISAMWs show decreasing trends in these volume loss models, which could be explained by the warming (SAM0‐UNICON, CESM2‐WACCM, CAS‐ESM2‐0 and CIESM), the freshening (IPSL‐CM6A‐LR, CAMS‐CSM1‐0, MRI‐ESM2‐0, and FGOALS‐f3‐L) or the both (FIO‐ESM‐2‐0, E3SM‐1‐0, and CanESM5) trends of the SEISAMWs in different CMIP6 coupled models. Decreases in the projected subduction rate and volume of SEISAMW imply a slowdown of Southern Indian Ocean circulation in the future, reducing the heat and carbon transport from atmosphere to ocean interior contributed by SEISAMW.
Hubei Province, located in the middle reaches of the Yangtze River, is a complex area of fragile ecological environment and traditional agricultural production in China. With the further intensification of the impact of global warming, flood disasters have brought a more severe threat to the sustainable development of farmers’ livelihoods. This paper therefore examines the livelihood resilience of farmers with different livelihood strategies in the region by constructing a livelihood resilience evaluation system based on three target levels: buffering capacity, Adaptation and restoration, and using a contribution model to identify the main contributing factors affecting the livelihood resilience of fa rmers. The following three conclusions were found: (1). The overall level of livelihood resilience of farmers in flood-affected areas in Hubei Province is not high, and the difference in livelihood resilience indices between farmers with different livelihood strategies is large; (2). Farming-led farmers and part-time balanced farmers can better adapt to external shocks brought about by floods; (3). The main contributing factors affecting the livelihood resilience of various types of farmers have Convergence.
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