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Skillful subseasonal forecasts are crucial for various sectors of society but pose a grand scientific challenge. Recently, machine learning-based weather forecasting models outperform the most successful numerical weather predictions generated by the European Centre for Medium-Range Weather Forecasts (ECMWF), but have not yet surpassed conventional models at subseasonal timescales. This paper introduces FuXi Subseasonal-to-Seasonal (FuXi-S2S), a machine learning model that provides global daily mean forecasts up to 42 days, encompassing five upper-air atmospheric variables at 13 pressure levels and 11 surface variables. FuXi-S2S, trained on 72 years of daily statistics from ECMWF ERA5 reanalysis data, outperforms the ECMWF's state-of-the-art Subseasonal-to-Seasonal model in ensemble mean and ensemble forecasts for total precipitation and outgoing longwave radiation, notably enhancing global precipitation forecast. The improved performance of FuXi-S2S can be primarily attributed to its superior capability to capture forecast uncertainty and accurately predict the Madden-Julian Oscillation (MJO), extending the skillful MJO prediction from 30 days to 36 days. Moreover, FuXi-S2S not only captures realistic teleconnections associated with the MJO but also emerges as a valuable tool for discovering precursor signals, offering researchers insights and potentially establishing a new paradigm in Earth system science research., (© 2024. The Author(s).)

Background: Studies on the associations between temperature and osteoporotic fractures (OF) hospitalizations are limited. This study aimed to assess the short-term effect of apparent temperature (AT) on the risk of OF hospitalizations., Methods: This retrospective observational study was conducted in Beijing Jishuitan Hospital from 2004 to 2021. Daily OF hospitalizations, meteorological variables and fine particulate matter were collected. A Poisson generalized linear regression model combined with a distributed lag non-linear model was applied to analyze the lag-exposure-response relationship between AT and the number of OF hospitalizations. Subgroup analysis by gender, age and fracture type was also conducted., Results: Total daily hospitalization visits for OF during the studied period were 35,595. The exposure-response curve of AT and OF presented a non-linear relationship, with optimum apparent temperature (OAT) at 28.40 °C. Taking OAT as the reference, the cold effect (-10.58 °C, 2.5th percentage) on single lag day had statistical significance from the current day of exposure (RR = 1.18, 95 % CI: 1.08-1.28) to lag 4 day (RR = 1.04, 95 % CI: 1.01-1.08), while the cumulative cold effect increased the risk of OF hospitalization visits from lag 0 to 14 days, with the maximum RR over lag 0-14 days (RR = 1.84, 95 % CI: 1.21-2.79). There were no significant risks of OF hospitalizations for warm effects (32.53 °C, 97.5th percentage) on single or cumulative lag days. The cold effect might be more evident among females, patients aged 80 years or older, and patients with hip fractures., Conclusion: Exposure to cold temperatures is associated with an increased risk of OF hospitalizations. Females, patients aged 80 years or older and patients with hip fractures might be more vulnerable to the cold effect of AT., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Wetlands are large sinks of carbon dioxide (CO 2 ) and sources of methane (CH 4 ). Both fluxes can be altered by wetland management (e.g., restoration), leading to changes in the climate system. Here, we use multiple models to assess CH 4 emissions and CO 2 sequestration from the wetlands in China and the impacts on climate under three climate scenarios and four wetland management scenarios with various levels of wetland restoration in the 21st century. We find that wetland restoration leads to increased CH 4 emissions with a national total of 0.32-11.31 Tg yr -1 . These emissions induce an additional radiative forcing of 0.0005-0.0075 W m -2 yr -1 and global annual mean air temperature rise of 0.0003-0.0053 °C yr -1 , across all future climate and management scenarios. However, wetland restoration also resulted in net CO 2 sequestration, leading to a combined net greenhouse gas sink in all climate management scenarios, except in the highest restoration level combined with the hottest climate scenario. The highest climate cooling was achieved under medium restoration, with the climate scenario consistent with the Paris agreement target of below 2 °C, with a cumulative global warming potential of -3.2 Pg CO 2 -eq (2020-2100). Wetland restoration in the Qinghai-Tibet Plateau offers the greatest cooling effect.

Risks brought by climate change are inevitable obstacles to global development. Clarifying the features of climate change risks helps us to further understand and cope with climate change. There lacks a systematic review of climate change risks in terms of feature extraction and classification. The bibliometric analysis can be used to analyze and extract climate change risk features. The literature in the field of climate change was searched in the Web of Science database. Coauthors, citations, bibliographic coupling, co-citations, and keyword co-occurrence were analyzed. From five dimensions including nature, politics, economy, society, and culture, the risk features of climate change were extracted and summarized. Through text mining and cluster analysis, the climate change risk feature system was established, which is embodied in five different aspects: ecosystem and sustainability; uncertainty, vulnerability, and efficiency; behavior and decision-making; governance and management; and adaptation and mitigation. The feature system reflects that the current climate change risk presents strong variability and that the risk boundary is gradually blurred. The areas affected by risk are expanding and deepening. The strategies and governance for addressing risks are gradually diversified. This research contributes to the domain of climate change risk identification and assessment. The features of climate change indicate that we need to adjust policymaking and managerial practices for climate change in the future. Interdisciplinary cooperation, human cognition and preferences, public participation in global governance, and other unnatural factors related to climate change should be strengthened with a more positive attitude., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The evidence for the effects of environmental factors on COVID-19 case fatality remains controversial, and it is crucial to understand the role of preventable environmental factors in driving COVID-19 fatality. We thus conducted a nationwide cohort study to estimate the effects of environmental factors (temperature, particulate matter [PM 2.5 , PM 10 ], sulfur dioxide [SO 2 ], nitrogen dioxide [NO 2 ], and ozone [O 3 ]) on COVID-19 case fatality. A total of 71,808 confirmed COVID-19 cases were identified and followed up for their vital status through April 25, 2020. Exposures to ambient air pollution and temperature were estimated by linking the city- and county-level monitoring data to the residential community of each participant. For each participant, two windows were defined: the period from symptom onset to diagnosis (exposure window I) and the period from diagnosis date to date of death/recovery or end of the study period (exposure window II). Cox proportional hazards models were used to estimate the associations between these environmental factors and COVID-19 case fatality. COVID-19 case fatality increased in association with environmental factors for the two exposure windows. For example, each 10 μg/m 3 increase in PM 2.5 , PM 10 , O 3 , and NO 2 in window I was associated with a hazard ratio of 1.11 (95% CI 1.09, 1.13), 1.10 (95% CI 1.08, 1.13), 1.09 (95 CI 1.03, 1.14), and 1.27 (95% CI 1.19, 1.35) for COVID-19 fatality, respectively. A significant effect was also observed for low temperature, with a hazard ratio of 1.03 (95% CI 1.01, 1.04) for COVID-19 case fatality per 1°C decrease. Subgroup analysis indicated that these effects were stronger in the elderly, as well as in those with mild symptoms and living in Wuhan or Hubei. Overall, the sensitivity analyses also yielded consistent estimates. Short-term exposure to ambient air pollution and low temperature during the illness would play a nonnegligible part in causing case fatality due to COVID-19. Reduced exposures to high concentrations of PM 2.5 , PM 10 , O 3 , SO 2 , and NO 2 and low temperature would help improve the prognosis and reduce public health burden., Competing Interests: The authors declare they have no competing interests., (© 2021 The Authors.)