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

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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.