Session AS5.1

[Programme]

AS5.1 | Advances in Numerical Earth System Modeling: Identifying Systematic Errors and Charting Innovative Approaches to Enhance Numerical Weather and Climate Prediction

Advances in Numerical Earth System Modeling: Identifying Systematic Errors and Charting Innovative Approaches to Enhance Numerical Weather and Climate Prediction

Convener: Werner Bauer | Co-conveners: Ariane FrassoniECSECS, Jemma Shipton, Nils Wedi, Hiroe YamazakiECSECS, Tim Graham, Fanglin Yang

Orals

| Tue, 16 Apr, 14:00–15:45 (CEST)

Room M1

Posters on site

| Attendance Wed, 17 Apr, 10:45–12:30 (CEST) | Display Wed, 17 Apr, 08:30–12:30

Hall X5

In the domain of weather prediction and climate modeling, Earth System Models (ESMs) play a pivotal role. EMSs are built with advanced numerical techniques to simulate the dynamics, physics, and biogeochemistry of the Earth's components. These models integrate a fluid dynamics solver (dynamical core) coupled with physical parameterizations to represent sub-grid processes. Over time these models have become capable of sophisticated simulations, thanks to the growth of computing power and the development of new techniques, like Machine Learning (ML). This technological surge allows for more sophisticated modeling systems and larger ensembles, setting the frame for a new era of operational weather and climate prediction systems.

Despite significant progress, challenges persist in identifying and correcting errors within individual ESM components, exacerbated by complex inter-component interactions. Moreover, limitations in the observational network hinder the full constraint of ESMs. Efforts to assess, test, and enhance models have reduced major systematic errors, yet challenges persist while new ones have emerged. Ongoing research and development efforts focus on enhancing the accuracy, seeking a better representation of different processes in ESMs by identifying and correcting systematic errors, but also considering the efficiency, and scalability of ESM components, including the dynamical core, physics, and their coupling.

This session invites contributions related to the development, testing, and application of innovative techniques for ESMs, as well as contributions that provide a deep understanding of the nature and causes of systematic errors in ESMs. Topics encompass governing equations, horizontal and vertical discretizations, structure-preserving methods, time-stepping schemes (including parallel in time schemes), advection schemes, adaptive multi-scale models, model errors across space and time scales, hierarchies of models, physics-dynamics and physics-physics cross-component coupling, initialized predictions, climatology of weather prediction models, data assimilation methodologies, the use of ML to identify errors and detect causal connections, stochastic parameterization for uncertainty representation, and verification diagnostics and metrics for characterizing systematic errors and process understanding across various modeling communities (regional and global km-scale modeling).