Recent progress and review of issues related to Physics Dynamics Coupling in geophysical models

TL;DR

This review discusses the challenges and recent advances in physics-dynamics coupling in geophysical models, highlighting analysis techniques, open questions, and future directions for model development.

Contribution

It provides a comprehensive overview of the state-of-the-art in physics-dynamics coupling, analyzing phenomenology, methodologies, and open issues in geophysical modeling.

Findings

Identifies key challenges in physics-dynamics coupling.

Surveys analysis techniques used in the field.

Discusses future modeling strategies and infrastructure changes.

Abstract

Geophysical models of the atmosphere and ocean invariably involve parameterizations. These represent two distinct areas: Subgrid processes that the model cannot resolve, and diabatic sources in the equations, due to radiation for example. Hence, coupling between these physics parameterizations and the resolved fluid dynamics and also between the dynamics of the air and water, is necessary. In this paper weather and climate models are used to illustrate the problems. Nevertheless the same applies to other geophysical models. This coupling is an important aspect of geophysical models. However, often model development is strictly segregated into either physics or dynamics. As a consequence, this area has many unanswered questions. Recent developments in the design of dynamical cores, extended process physics and predicted future changes of the computational infrastructure are increasing complexity. This paper reviews the state-of-the-art of the physics-dynamics coupling in geophysical models, surveys the analysis techniques, and illustrates open questions in this field. This paper focuses on two objectives: To illustrate the phenomenology of the coupling problem with references to examples in the literature and to show how the problem can be analysed. Proposals are made on how to advance the understanding and upcoming challenges with emerging modeling strategies. This paper is of interest to model developers who aim to improve the models and have to make choices on and test new implementations, to users who have to understand choices presented to them and finally users of outputs, who have to distinguish physical features from numerical problems in the model data.