Abstract

AbstractIn this study we aim to present the successful development of an energy conserving conceptual stochastic climate model based on the inviscid 2-layer Quasi-Geostrophic (QG) equations. The stochastic terms have been systematically derived and introduced in such away that the total energy is conserved. In this proof of concept studywe give particular emphasis to the numerical aspects of energy conservation in a highdimensional complex stochastic system andwe analyzewhat kind of assumptions regarding the noise should be considered in order to obtain physical meaningful results. Our results show that the stochastic model conserves energy to an accuracy of about 0.5% of the total energy; this level of accuracy is not affected by the introduction of the noise, but is mainly due to the level of accuracy of the deterministic discretization of the QG model. Furthermore, our results demonstrate that spatially correlated noise is necessary for the conservation of energy and the preservation of important statistical properties, while using spatially uncorrelated noise violates energy conservation and gives unphysical results. A dynamically consistent spatial covariance structure is determined through Empirical Orthogonal Functions (EOFs). We find that only a small number of EOFs is needed to get good results with respect to energy conservation, autocorrelation functions, PDFs and eddy length scale when comparing a deterministic control simulation on a 512 × 512 grid to a stochastic simulation on a 128 × 128 grid. Our stochastic approach has the potential to seamlessly be implemented in comprehensive weather and climate prediction models.

Highlights

  • The dynamics of the atmosphere and the oceans are by nature complex

  • In this study we aim to present the successful development of an energy conserving conceptual stochastic climate model based on the inviscid 2-layer Quasi-Geostrophic (QG) equations

  • We rst look at the conservation of energy and at other statistical properties like the autocorrelation function (ACF) and probability density function (PDF)

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Summary

Introduction

The dynamics of the atmosphere and the oceans are by nature complex. Processes with di erent time and length scales interact with each other a ecting the system as a whole. While climate and ocean models have considerably improved over the last few decades, we still cannot resolve all important physical scales and processes, see for instance [20, 6, 22]. The discretization of the continuous governing equations of motion is limited by the model resolution, which determines the size of the smallest resolvable scale. Despite the continued increase of computer power and, of resolution, there are still many important processes in the atmosphere and in the oceans that cannot be explicitly resolved. These include turbulent motions with scales ranging from a few centimeters to the size of the model grid box, as well as processes that occur at a molecular

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