ESCO 2022

ESCO 2024

ESCO 2024 is the 9th event in a successful series of interdisciplinary international conferences. It promotes modern technologies and practices in scientific computing and visualization, and strengthens the interaction between researchers and practitioners in various areas of computational engineering, sciences and education. The conference will take place in person, but online participation is possible.

Minisymposium "Advanced Computational Methods for Climate Modeling and Analysis"

The development and application of global climate models for understanding and predicting the effects of global climate change and sea-level rise is critical, since it can direct energy and infrastructure planning, as well as inform public policy. Earth System Models (ESMs), which are global climate models including biogeochemistry, integrate the interactions between atmosphere, ocean, land, ice, and biosphere to enable the simulation of the state of regional and global climate under a wide variety of conditions. In recent years, there has been a push to develop “next generation” ESMs, models which: (1) are able to perform realistic, high-resolution, continental scale simulations, (2) are robust, efficient and scalable on next-generation hybrid systems (multi-core, many-core, GPU) towards achieving exascale performance, and (3) possess built-in advanced analysis capabilities (e.g., sensitivity analysis, optimization, uncertainty quantification).

This minisymposium will consist of talks describing new and ongoing research in the development of accurate and tractable “next-generation” models for stand-alone climate components (e.g., atmosphere, land-ice, sea-ice, ocean, land, biogeochemistry), as well as talks addressing the challenges in coupling climate components for integration into ESMs. Of particular interest are:

1. efficient computational strategies and software for tackling the complex, nonlinear, multi- scale, multi-physics problems arising in climate modeling, with an eye towards next- generation hybrid platforms,
2. advanced ensemble methods and analysis techniques that can inform/enhance existing models through the incorporation of observational data, e.g., approaches for model initialization/calibration, uncertainty quantification (UQ) and data assimilation, and
3. approaches involving the integration and application of data-driven methods, including artificial intelligence (AI) and machine learning (ML), into climate modeling and analysis.

Additionally, we encourage submissions on the emerging area of using climate models/data to study the impacts of climate intervention/geoengineering strategies.