A Fast and Generic Method to Identify Parameters in Complex and Embedded Geophysical Models: The Example of Turbulent Mixing in the Ocean.

Geophysical models make predictions relying on parameter values to be estimated from data. However, existing methods are costly because they require either many runs of the complex geophysical model or to implement an adjoint model. Here, we propose an alternative approach based on optimal control theory which is the simultaneous perturbations stochastic approximation (SPSA). This gradient‐descent method is generic and easy to implement, and its computational cost does not increase with the number of parameters to optimize. This study aims at highlighting the potential of SPSA for parameter identification in geophysical models. Through the example of vertical turbulent mixing in the upper ocean, we show with twin experiments that the method could successfully identify parameter values that minimize model‐data discrepancy. The efficient and easy‐to‐get results provided by SPSA in this study should pave the way for a broader use of parameter identification in the complex and embedded models commonly used in geophysical sciences. Plain Language Summary: Predictions on the past and future state of geophysical systems are made through mathematical models, which rely on numerous constant values (parameters) to be calibrated from prior knowledge and available data. Fine‐tuning those parameter values is one of the major means of improving the accuracy of model predictions. To achieve that goal for complex geophysical models in which multiple scales and processes are nested, existing methods are limited by either (a) an important computational cost or (b) an important cost in terms of development and implementation of an adjoint model. Here, we highlight a method from optimal control theory, called simultaneous perturbations stochastic approximation (SPSA). This generic method is easy to implement, and its computational cost is comparatively low. To show the potential of SPSA for parameter identification in geophysical science, we apply it to the example of wind‐induced turbulent mixing near the ocean surface. Using the approach of twin experiments, we show that the method can successfully tune the parameter values to minimize the discrepancy between model predictions and empirical data. The efficient and easy‐to‐get results provided by SPSA in this study pave the way for a broader use of parameter identification in the complex models commonly used in geophysical sciences. Key Points: Parameters identification is a key to reduce model uncertainty in geophysical modelsWe present a method from optimal control called simultaneous perturbations stochastic approximation (SPSA) that is easy to implement and fast to runWe illustrate the potential of SPSA with a simplified example of turbulent mixing in the ocean [ABSTRACT FROM AUTHOR]