The dynamical core is the heart of atmospheric weather and climate models, governing the “resolved” fluid flow and shaping crucial modeling choices, such as grid structure and staggering, vertical coordinates, treatment of topography, numerical methods, and the handling of dissipation. It also sets the framework for coupling with parameterizations of smaller-scale processes like clouds and turbulence. These design aspects can be explored through idealized dynamical core test cases as e.g. fostered by the Dynamical Core Model Intercomparison Project (DCMIP).
Christiane Jablonowski,
University of Michigan
This talk will address two main themes. First, it will provide a brief overview of the history and current state of dynamical cores, with a particular focus on recent developments in the Spectral Element (SE) dynamical core used in the U.S. Department of Energy’s E3SM and NCAR’s Community Earth System Model (CESM). Special attention will be given to the nonhydrostatic version of SE, which has recently been extended to accommodate the so-called “deep-atmosphere” equation set, including all Coriolis forces. Idealized test cases will be presented to illustrate the differences between the shallow- and deep-atmosphere SE configurations. Building on this, the second theme will survey the rationale behind idealized test cases for dynamical cores, showcasing highlights from past DCMIP events and presenting the design and key results of the latest DCMIP-2025 test suite from June 2025. The talk will conclude with perspectives on future directions for dynamical core development.
Christiane Jablonowski is a Professor in the Department of Climate and Space Sciences and Engineering at the University of Michigan. She previously held appointments at the National Center for Atmospheric Research (NCAR), NOAA’s Geophysical Fluid Dynamics Laboratory, and the European Centre for Medium-Range Weather Forecasts. Dr. Jablonowski’s work advances the weather and climate models from NOAA, the Department of Energy, and NCAR, and focuses on atmospheric fluid dynamics, high-resolution weather and climate modeling, scientific computing, and machine learning techniques. She is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE).