美国中南部地区的气温-降水联合模式：多变量功能推断与高斯混合建模
Joint Temperature-Precipitation Patterns in the U.S. South Central Region: Multivariate Functional Inference and Gaussian Mixture Modeling
AI总结：
本研究以美国中南部为对象，采用FMANOVA与高斯混合模型两种方法，揭示该区域冬夏两季气温-降水联合模式的显著差异，明确了冬夏气候 regime 的空间一致性及降水作用的差异。
AI中文摘要：
气温与降水的联合变异性是表征季节性气候结构及相关环境过程的核心，但许多区域分析依赖于边际或单变量汇总。我们采用两种互补的多变量统计方法，分析美国南部的季节性气温-降水模式：首先，使用功能多变量方差分析（FMANOVA），结合基于置换的Wilks' lambda和Pillai's trace统计量，检验州级双变量均值函数的相等性；其次，将高斯混合模型应用于站点级季节性汇总，以基于气温和降水的联合分布识别潜在气候 regime。FMANOVA结果显示，冬季和夏季各州的双变量均值轨迹均存在统计学显著差异，季节对比反映了气温和降水的不同贡献。聚类分析表明，冬季气候 regime 比夏季更清晰且空间一致性更强，夏季气候 regime 表现出更大变异性，且降水的作用更显著。
英文摘要：
Joint variability in temperature and precipitation is central in characterizing seasonal climate structure and associated environmental processes, yet many regional analyses rely on marginal or univariate summaries. We analyze seasonal temperature-precipitation patterns across the Southern United States using two complementary multivariate statistical approaches. First, functional multivariate analysis of variance (FMANOVA) is employed to test the equality of state-level bivariate mean functions, with the permutation-based Wilks' lambda and Pillai's trace statistics. Second, Gaussian mixture models are applied to station-level seasonal summaries to identify latent climate regimes based on the joint distribution of temperature and precipitation. The FMANOVA results indicate statistically significant differences in bivariate mean trajectories between states in both winter and summer, with seasonal contrasts reflecting differing contributions of temperature and precipitation. Clustering analysis indicates more clearly defined and spatially coherent winter regimes than summer regimes, with summer regimes exhibiting greater variability and a stronger role for precipitation.
