Assessment and Visualization of Regional Climate Risks through High-Resolution Ensemble Modeling and Big Data Analytics

最近的模拟工作表明，温室气体(GHG)的持续排放将导致气候系统所有组成部分进一步变暖和长期变化，增加对人类社会和自然系统造成严重、普遍和不可逆转影响的可能性。虽然缓解气候变化需要通过全球共识和合作大幅和持续地减少温室气体排放，但适应气候变化已成为当地政策制定者和发展实践者的主要关注点。在加拿大尤其如此，近年来，极端天气事件频繁袭击其主要城市，造成了巨大的破坏。制定合理的气候适应决策在很大程度上依赖于基于影响的模型，这就需要在空间和时间尺度上进行更精细的分辨率预测。此外，对温室气体排放情景、全球气候模型和物理参数方案的建模选择可能会导致与未来气候预测相关的各种不确定性。这些挑战已成为在各种空间和时间尺度上评估未来气候风险的主要障碍。 为了在未来气候变化下支持明智的决策和有弹性的工程规划，制定可靠和高分辨率的气候预测极其重要，以促进探索区域气候的所有可能变化，并量化当地社区和生态系统面临的潜在气候风险。因此，本研究的目标是开发一种基于最新的多学科方法和技术的系统方法，用于高分辨率气候建模、不确定性量化和风险评估、大数据分析和高维数据可视化，以促进在不同空间和时间尺度上对未来气候风险的评估和可视化。拟议的办法将使我们能够更有效和全面地考虑区域气候建模和风险评估中的复杂性和不确定性。将开发更有效、更可靠的方法和平台，以支持国家、区域和地方各级在不断变化的气候条件下基于风险的决策。拟议研究的结果将为政策制定者、利益攸关方、资源管理者和发展实践者提供科学依据，为在未来气候变化下建立有弹性和可持续的社区做出明智的决定。

Recent modeling efforts suggest that the continued emission of greenhouse gases (GHG) will cause further warming and long-lasting changes in all components of the climate system, increasing the likelihood of severe, pervasive, and irreversible impacts on human society and natural systems. While mitigating climate change would require substantial and sustained reductions in GHG emissions through worldwide consensus and collaborations, adapting to climate change has become a major focus of local policymakers and development practitioners. This is especially true in Canada where extreme weather events have frequently struck its major cities in recent years and caused tremendous amounts of damage. Development of sound decisions for climate adaptation relies heavily on impact-based models which requires finer resolution projections at both spatial and temporal scales. Furthermore, modeling choices on GHG emissions scenarios, global climate models, and physical parameterization schemes can result in various uncertainties associated with future climate projections. Such challenges have become major barriers for assessing future climate risks at various spatial and temporal scales. In order to support informed decision making and resilient engineering planning under future climate change, it is extremely important to develop reliable and high-resolution climate projections to facilitate the exploration of all possible changes to regional climatology and to quantify potential climate risks to local communities and ecosystems. Therefore, the objective of this research is to develop a systematic approach based on the state-of-the-art multidisciplinary methods and techniques for high-resolution climate modeling, uncertainty quantification and risk assessment, big data analytics, and high-dimensional data visualization to facilitate the assessment and visualization of future climate risks at various spatial and temporal scales. The proposed approach will allow more efficient and comprehensive consideration of complexities and uncertainties in regional climate modeling and risk assessment. More effective and reliable methods and platforms will be developed to support risk-based decision making at national, regional, and local scales under changing climatic conditions. The outcomes of the proposed research will provide a scientific basis for policymakers, stakeholders, resources managers, and development practitioners to make informed decisions for building resilient and sustainable communities under future climate change.