Boundary-Induced Biases in Climate Networks of Extreme Precipitation and Temperature
University of Texas at Arlington（德克萨斯大学阿灵顿分校）；University of Connecticut（康涅狄格大学）；University of Maryland at Baltimore County（马里兰大学巴尔的摩县分校）；Broad Institute of MIT and Harvard（麻省理工学院和哈佛大学的布罗德研究所）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Humboldt University of Berlin（柏林洪堡大学）
Behzad Ghanbarian, Victor Oladoja, Kehinde Bosikun, Tayeb Jamali, Jürgen Kurths
To address spatial boundary effects in climate networks, two surrogate-based correction methods, (1) subtraction and (2) division, have been widely applied in the literature. In the subtraction method, an original network measure is adjusted by subtracting the expected value obtained from a surrogate ensemble, whereas in the division method, it is normalized by dividing by this expected value. However, to the best of our knowledge, no prior study has assessed whether these two correction approaches yield statistically different results. In this study, we constructed complex networks of extreme precipitation and temperature events (EPEs and ETEs) across the CONUS for both summer (June-August, JJA) and winter (December-February, DJF) seasons. We computed key network metrics degree centrality (DC), clustering coefficient (CC), mean geographic distance (MGD), and betweenness centrality (BC) and applied both correction methods. Although the corrected spatial patterns generally appeared visually similar, statistical analyses revealed that the network measures derived from the subtraction and division methods were significantly different at the 95 percent confidence level. Across the CONUS, network hubs of EPEs were primarily concentrated in the northwestern United States during summer and shifted toward the east during winter, reflecting seasonal differences in the dominant atmospheric drivers. In contrast, the ETE networks showed strong spatial coherence and pronounced regional teleconnections in both seasons, with higher connectivity and longer synchronization distances in winter, consistent with large-scale circulation patterns such as the Pacific-North American and North Atlantic Oscillation modes. Our results indicated that the network metrics CC and MGD were more sensitive to the correction methods than the DC and BC, particularly in the EPE networks.
Evaluation of the performance of Euro-CORDEX RCMs for assessing hydrological climate change impacts in Great Britain: a comparison of different spatial resolutions and quantile mapping bias correction methods
Diagnosing added value of convection-permitting regional models using precipitation event identification and tracking
高分辨率区域气候模式的动态降尺度可能提供在气候模拟中真实再现降水和天气事件的可能性。当分辨率降到千米级时，使用显式对流而不是参数化对流可以提供更高的保真度。然而，这些增加的模型分辨率允许并且需要越来越复杂的诊断来评估模型保真度。在这项研究中，我们使用了一套动态缩小规模的模拟夏季美国（WRF驱动的NCEP）与系统的变化参数和治疗对流作为一个测试案例模型降水的评估。特别是，我们使用了一种新的暴雨识别和跟踪算法，该算法基本上将所有降雨分配给单个降水事件（Chang et al. 2016）。这种方法允许多方面的见解，包括，至少在这些运行中，模型湿偏差是由过度的面积范围的降水事件。偏差是时间依赖性的，产生过度的昼夜周期振幅。我们发现，这种效果是由新的生产的事件，但在白天的长期降水事件的过度放大，并在域平均，降水偏差出现最好的表示为添加剂的偏移。在所有评估的模型配置中，对流允许模拟最一致地减少了降水事件特征的偏差。
Assessment of Future Changes in Intensity-Duration-Frequency Curves for Southern Ontario using North American (NA)-CORDEX Models with Nonstationary Methods
Modelling the combined effects of land use and climatic changes: coupling bioclimatic modelling with markov-chain cellular automata in a case study in Cyprus
