结合Copula与适用于双峰数据的极值框架，分析最高气温与最小相对湿度的联合重现水平
Joint return levels of maximum temperature and minimum relative humidity by combining copulas with an extreme value framework for bimodal data
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中文总结 AI 辅助
本研究结合Copula与适用于双峰数据的极值框架，分析巴西利亚最高气温与最小相对湿度的联合重现水平，为当地气候风险应对提供关键依据。
中文摘要 AI 辅助
气候变化已成为日益突出的问题，尤其是在极端事件愈发频繁的地区。巴西首都巴西利亚的气候模式发生了显著变化，引发了广泛关注，其中包括酷热天气、异常寒冷天气以及持续干旱期，这些情况常伴随野火发生。这些现象直接影响当地居民和生态系统，因此需要开展详细分析以理解其成因与影响。本研究采用基于Copula的方法，确定巴西利亚最高气温与最小相对湿度联合分布的重现期。首先，通过Copula对变量间的依赖结构进行建模，同时采用复杂系统极值建模的新方法拟合边缘分布。基于信息准则对不同Copula族（包括旋转后的版本）进行评估，最终选定的模型能充分捕捉依赖结构，包括不对称性和尾部行为。该模型还能复现联合密度中观察到的多峰模式，这一特征与可能存在的多种气候 regime（气候态）相符。在气候数据建模背景下，采用条件方法确定双变量重现水平，以评估由高温与低相对湿度组合构成的极端场景的发生情况。该分析可量化此类事件的预期频率，为该地区的环境风险监测、预防措施规划以及气候变化适应策略制定提供重要信息。
英文摘要
Climate change has become a growing concern, particularly in regions experiencing increasingly frequent extreme events. In Brasília, the capital of Brazil, significant shifts in climate patterns have drawn attention, including episodes of intense heat, unusually cold weather, and prolonged dry periods, often accompanied by wildfires. These phenomena directly affect the population and local ecosystems, requiring detailed analyses to understand their causes and effects. In this work, the return period for the joint distribution of maximum air temperature and minimum relative humidity in Brasília is determined using a copula-based approach. First, the dependence structure between the variables is modeled through a copula, while the marginal distributions are fitted using the novel methodology for modelling extreme values in complex systems. Different copula families, including rotated versions, were evaluated using information criteria, leading to the selection of a model that adequately captured the dependence structure, including asymmetry and tail behavior. The selected model also reproduced the multimodal pattern observed in the joint density, a feature consistent with the possible presence of multiple climate regimes. In the context of climate data modelling, the bivariate return level was determined using a conditional approach to assess the occurrence of extreme scenarios characterized by high temperatures combined with low relative humidity. This analysis makes it possible to quantify the expected frequency of such events, providing valuable information for environmental risk monitoring, the planning of preventive measures, and the development of climate change adaptation strategies in the region.
