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The most extreme environments are the most vulnerable to transformation under a rapidly changing climate. These ecosystems harbor some of the most specialized species, which will likely suffer the highest extinction rates. We document the steepest temperature increase (2010-2021) on record at altitudes of above 4,000 m, triggering a decline of the relictual and highly adapted moss Takakia lepidozioides. Its de-novo-sequenced genome with 27,467 protein-coding genes includes distinct adaptations to abiotic stresses and comprises the largest number of fast-evolving genes under positive selection. The uplift of the study site in the last 65 million years has resulted in life-threatening UV-B radiation and drastically reduced temperatures, and we detected several of the molecular adaptations of Takakia to these environmental changes. Surprisingly, specific morphological features likely occurred earlier than 165 mya in much warmer environments. Following nearly 400 million years of evolution and resilience, this species is now facing extinction.

China contributes 23 % of global carbon emissions, of which 26 % originate from the household sector. Due to vast variations in both climatic conditions and the affordability and accessibility of fuels, household carbon emissions (HCEs) differ significantly across China. This study compares HCEs (per person) from urban and rural regions in northern China with their counterparts in southern China. Annual macroeconomic data for the study period 2005 to 2012 were obtained from Chinese government sources, whereas the direct HCEs for different types of fossil fuels were obtained using the IPCC reference approach, and indirect HCEs were calculated by input-output analysis. Results suggest that HCEs from urban areas are higher than those from rural areas. Regardless of the regions, there is a similarity in per person HCEs in urban areas, but the rural areas of northern China had significantly higher HCEs than those from southern China. The reasons for the similarity between urban areas and differences between rural areas and the percentage share of direct and indirect HCEs from different sources are discussed. Similarly, the reasons and solutions to why decarbonising policies are working in urban areas but not in rural areas are discussed.

AbstractThe recent rise in atmospheric methane (CH4) concentrations accelerates climate change and offsets mitigation efforts. Although wetlands are the largest natural CH4 source, estimates of global wetland CH4 emissions vary widely among approaches taken by bottom‐up (BU) process‐based biogeochemical models and top‐down (TD) atmospheric inversion methods. Here, we integrate in situ measurements, multi‐model ensembles, and a machine learning upscaling product into the International Land Model Benchmarking system to examine the relationship between wetland CH4 emission estimates and model performance. We find that using better‐performing models identified by observational constraints reduces the spread of wetland CH4 emission estimates by 62% and 39% for BU‐ and TD‐based approaches, respectively. However, global BU and TD CH4 emission estimate discrepancies increased by about 15% (from 31 to 36 TgCH4 year−1) when the top 20% models were used, although we consider this result moderately uncertain given the unevenly distributed global observations. Our analyses demonstrate that model performance ranking is subject to benchmark selection due to large inter‐site variability, highlighting the importance of expanding coverage of benchmark sites to diverse environmental conditions. We encourage future development of wetland CH4 models to move beyond static benchmarking and focus on evaluating site‐specific and ecosystem‐specific variabilities inferred from observations.