The Synergism between Methanogens and Methanotrophs and the Nature of their Contributions to the Seasonal Variation of Methane Fluxes in a Wetland: The Case of Dajiuhu Subalpine Peatland

Wetland ecosystems are the most important natural methane (CH₄) sources, whose fluxes periodically fluctuate. Methanogens (methane producers) and methanotrophs (methane consumers) are considered key factors affecting CH₄ fluxes in wetlands. However, the symbiotic relationship between methanogens and methanotrophs remains unclear. To help close this research gap, we collected and analyzed samples from four soil depths in the Dajiuhu subalpine peatland in January, April, July, and October 2019 and acquired seasonal methane flux data from an eddy covariance (EC) system, and investigated relationships. A phylogenetic molecular ecological networks (pMENs) analysis was used to identify keystone species and the seasonal variations of the co-occurrence patterns of methanogenic and methanotrophic communities. The results indicate that the seasonal variations of the interactions between methanogenic and methanotrophic communities contributed to CH₄ emissions in wetlands. The keystone species discerned by the network analysis also showed their importance in mediating CH₄ fluxes. Methane (CH₄) emissions in wetlands were lowest in spring; during this period, the most complex interactions between microbes were observed, with intense competition among methanogens while methanotrophs demonstrated better cooperation. Reverse patterns manifested themselves in summer when the highest CH₄ flux was observed. Methanoregula formicica was negatively correlated with CH₄ fluxes and occupied the largest ecological niches in the spring network. In contrast, both Methanocella arvoryzae and Methylocystaceae demonstrated positive correlations with CH₄ fluxes and were better adapted to the microbial community in the summer. In addition, soil temperature and nitrogen were regarded as significant environmental factors to CH₄ fluxes. This study was successful in explaining the seasonal patterns and microbial driving mechanisms of CH₄ emissions in wetlands.     

拉萨-林芝铁路沿线主要气象灾害风险研究

基于1980~2010年拉萨-林芝铁路沿线17个地面站的气象资料、2019年西藏统计年鉴和西藏自治区地理信息数据资料,运用自然灾害风险综合指数法、层次分析法以及GIS空间处理技术,分析了孕灾环境脆弱性、灾害因子危害性和承灾体脆弱性,建立了拉萨-林芝铁路沿线主要气象灾害的风险研究模型,完成了拉萨-林芝铁路沿线主要气象灾害风险等级区划。结果表明：拉萨-林芝铁路沿线闪电高发季节是夏季和早秋,占91.23%;暴雨高发季节是盛夏和秋季,占93.60%;暴雪主要发生在冬季,占87.06%;大风主要发生在春季,占74.59%。拉萨-林芝铁路沿线暴雪灾害高风险区主要分布在林芝东南部和米林以东,大风灾害高风险区主要分布在加查和朗县附近,闪电和暴雨灾害高风险区主要分布在林芝市和山南市附近。     

1981—2018年中国东北地区秋冬季霾日变化及其与北极海冰的关系

利用中国东北地区1981—2018年166个地面气象观测站资料,定义了中国东北地区秋冬季霾日指数,分析了年际尺度上该地区霾日数与同期大气环流异常的内在关系。结果表明：中国东北地区秋冬季霾日指数存在显著的年际变化特征,欧亚—太平洋遥相关型(Eurasia-Pacific Teleconnection Pattern, EUP)负位相、东亚大槽偏弱等大气环流异常配置导致中国东北地区秋冬季霾的发生频次增加。巴伦支海与喀拉海北部海域是影响中国东北地区秋冬季霾日年际变化的海冰关键区,该区域海冰面积与霾日数呈显著负相关,北极海冰通过改变大气环流间接影响中国东北地区秋冬季霾日发生频次,当北极海冰异常偏少时,东亚冬季风偏弱,近地面风速偏低,环境湿度偏高,中国东北地区受东北亚异常反气旋西侧的异常偏南风控制,且受“EUP”负位相模态影响,东亚大槽减弱,有利于大气污染物和水汽向中国东北地区输送,该地区秋冬季霾的发生频次增加。     

大连地区阵风系数特征分析

利用2015年5月至2020年4月辽宁省大连地区9个国家气象站、2017年165个区域气象站逐10 min测风资料,从风向、风级、月际变化、日变化、空间分布和天气影响系统等对大连地区最大、平均、最小阵风系数进行统计分析。结果表明：1—12月平均阵风系数的变化范围为1.66～1.77,秋末冬初平均阵风系数偏大,春夏季节偏小;与冷空气相对应风向的平均阵风系数大于与暖空气相对应的风向;随着风级的增大,最大、最小阵风系数向平均阵风系数收敛;不同风级下阵风系数的频率分布均呈单峰型分布,风级越大,分布范围越窄。除西南风外,其他风向的阵风系数均表现出白天大、夜间小的特点。大连地区阵风系数具有明显的地域特点,东南和西北部沿海区域的阵风系数比内陆和西南沿海偏小,风向基本不影响阵风系数的空间分布。大连的大风过程多受海上气旋和高压前部双系统共同影响,气旋、台风以及雷暴大风的平均阵风系数大于同风级的平均值。     

IPCC AR6解读：全球和区域海平面变化的监测和预估

IPCC第六次评估报告第一工作组报告第九章综合评估了与海平面相关的最新监测和数值模拟结果,指出目前（2006—2018年）的海平面上升速率处于加速状态（3.7 mm/a),并会在未来持续上升,且呈现不可逆的趋势。其中低排放情景（SSP1-1.9）和高排放情景（SSP5-8.5）下,到2050年,预估全球平均海平面（GMSL）分别上升0.15~0.23 m和0.20~0.30 m;到2100年,预估GMSL分别上升0.28~0.55 m和0.63~1.02 m。南极冰盖不稳定性是影响未来海平面上升预估的最大不确定性来源之一。区域海平面变化是影响沿海极端静水位的重要因素。     

山区小流域坡面和沟道侵蚀的数学模式探讨

小流域土壤侵蚀预报和模拟一直是世界土壤侵蚀研究的前沿领域,其中,坡面侵蚀和沟道侵蚀的计算方法、侵蚀能力的不均匀问题以及泥沙在塘库河道的沉积问题是研究的关键.对此,重点从侵蚀机理和计算方法上进行探讨,提出考虑到坡面土壤抗侵蚀能力和塘库沉积作用的计算方法,研讨了影响流域侵蚀和泥沙输送的主要环节和关键影响因子,分析了流域土壤抗蚀能力的空间差异及其对模拟结果的影响,提出了小流域侵蚀泥沙数学模型的基本结构框架,为进一步完善现有侵蚀泥沙过程模型提供了新的计算方法.     

人工调控措施对玄武湖水质的影响

对玄武湖实施清淤引水、藻华治理、种群恢复等措施前后的水体数据进行分析,结果表明：清淤引水工程可短时间内降低沉积物中污染物浓度,缓解水体富营养化程度,但从长期效果分析,水体中的营养盐含量并未显著改善.藻华治理能在短期内有效抑制水体中的蓝藻水华,治理后水体各项指标均有提升,水生植物种群得以恢复,是短期改善水体水质的有效方式.在种群恢复阶段与往年相比,水体各项指标均有所改善,持续时间更长,是一种理想的湖泊治理方法.     

A Positivity-preserving Conservative Semi-Lagrangian Multi-moment Global Transport Model on the Cubed Sphere

A positivity-preserving conservative semi-Lagrangian transport model by multi-moment finite volume method has been developed on the cubed-sphere grid. Two kinds of moments(i.e., point values(PV moment) at cell interfaces and volume integrated average(VIA moment) value) are defined within a single cell. The PV moment is updated by a conventional semi-Lagrangian method, while the VIA moment is cast by the flux form formulation to assure the exact numerical conservation. Different from the spatial approximation used in the CSL2(conservative semi-Lagrangian scheme with second order polynomial function) scheme, a monotonic rational function which can effectively remove non-physical oscillations is reconstructed within a single cell by the PV moments and VIA moment. To achieve exactly positive-definite preserving, two kinds of corrections are made on the original conservative semi-Lagrangian with rational function(CSLR)scheme. The resulting scheme is inherently conservative, non-negative, and allows a Courant number larger than one.Moreover, the spatial reconstruction can be performed within a single cell, which is very efficient and economical for practical implementation. In addition, a dimension-splitting approach coupled with multi-moment finite volume scheme is adopted on cubed-sphere geometry, which benefitsthe implementation of the 1 D CSLR solver with large Courant number.The proposed model is evaluated by several widely used benchmark tests on cubed-sphere geometry. Numerical results show that the proposed transport model can effectively remove nonphysical oscillations and preserve the numerical nonnegativity, and it has the potential to transport the tracers accurately in a real atmospheric model.     

Seasonal Variations of CH_4 Emissions in the Yangtze River Delta Region of China Are Driven by Agricultural Activities

Developed regions of the world represent a major atmospheric methane(CH_4) source, but these regional emissions remain poorly constrained. The Yangtze River Delta(YRD) region of China is densely populated(about 16% of China's total population) and consists of large anthropogenic and natural CH_4 sources. Here, atmospheric CH_4 concentrations measured at a 70-m tall tower in the YRD are combined with a scale factor Bayesian inverse(SFBI) modeling approach to constrain seasonal variations in CH_4 emissions. Results indicate that in 2018 agricultural soils(AGS, rice production) were the main driver of seasonal variability in atmospheric CH_4 concentration. There was an underestimation of emissions from AGS in the a priori inventories(EDGAR—Emissions Database for Global Atmospheric Research v432 or v50), especially during the growing seasons. Posteriori CH_4 emissions from AGS accounted for 39%(4.58 Tg, EDGAR v432) to 47%(5.21 Tg, EDGAR v50) of the total CH_4 emissions. The posteriori natural emissions(including wetlands and water bodies) were1.21 Tg and 1.06 Tg, accounting for 10.1%(EDGAR v432) and 9.5%(EDGAR v50) of total emissions in the YRD in2018. Results show that the dominant factor for seasonal variations in atmospheric concentration in the YRD was AGS,followed by natural sources. In summer, AGS contributed 42%(EDGAR v432) to 64%(EDGAR v50) of the CH_4 concentration enhancement while natural sources only contributed about 10%(EDGAR v50) to 15%(EDGAR v432). In addition, the newer version of the EDGAR product(EDGAR v50) provided more reasonable seasonal distribution of CH_4 emissions from rice cultivation than the old version(EDGAR v432).