Our work on regional and global climate modeling focuses on developing advanced numerical frameworks and high-resolution datasets that translate large-scale climate information into actionable insights at local scales. We contribute to the design and evaluation of global climate models, including regionally-refined approaches that support finer detail over regions of interest while maintaining global consistency. We also work on the creation and assessment of downscaled climate datasets, ensuring that regional projections are physically consistent, statistically robust, and suitable for decision-making. This includes evaluating uncertainties across models and downscaling methods, and establishing best practices for producing credible, user-relevant climate information. Together, our efforts aim to bridge the gap between global climate simulations and the localized data needed for applications such as water management, infrastructure planning, and climate resilience.
Tropical cyclones and atmospheric rivers are among the most devestating storms around the world. Our work on tropical cyclones and atmospheric rivers focuses on developing the tools and modeling frameworks needed to study how these storms are represented in models and observations, and how they respond to large-scale forcing, including a warming climate. We have created objective detection and tracking algorithms (such as TempestExtremes) that track and characterizes cyclones and atmospheric rivers, and have developed metrics to evaluate how well models capture storm frequency, intensity, and structure. Our work also examines the contribution of tropical cyclones and atmospheric rivers to precipitation and flooding, linking storm behavior to real-world impacts. We use high-resolution and variable-resolution climate models to conduct storyline simulations, which include replays of past extreme events and how those events may play out in the future. Overall, our work provides the methodological foundation for analyzing tropical cyclones within the broader context of extreme weather and climate change.
Our work on heat waves and droughts focuses on understanding, detecting, and characterizing these extremes within large climate datasets, and improving climate models ability to represent these extremes. We develops objective, data-driven methods to identify heat and drought events, and accompanying blocking events, enabling consistent analysis across observations and simulations. Our research examines how these extremes are influenced by atmospheric dynamics, land–atmosphere interactions, and shifting climate conditions, with particular attention to changes in frequency, intensity, and duration in response to large-scale forcing. We also evaluate how well climate models capture these events and uses high-resolution and downscaled datasets to better resolve their regional characteristics. Our work aims to provide more reliable information on heat waves and droughts to support risk assessment, water resource management, and climate resilience planning.
We are working on developing scalable, data-driven methods to extract meaningful information from large and complex climate datasets. We emphasize computational efficiency and portability, allowing these algorithms to run on modern high-performance computing systems and across diverse (structured and unstructured) model grids. We are developing statistical and diagnostic frameworks to evaluate climate model performance, quantify uncertainty, and compare results across ensembles. Together, these efforts provide the analytical backbone needed to turn raw climate data into robust scientific insight and actionable information.