Coupled climate models integrate atmospheric, oceanic, and land submodels, while the uncertainty of model parameters from different parameterization schemes or empirically derived parameters inevitably introduces systematic biases. Coupled parameter optimization (CPO) can reduce these biases to improve weather forecast and climate prediction, but must address strong nonlinearities inherent in coupled models. The analytical fou...

Strongly coupled data assimilation (SCDA) is a critical tool for improving Earth system predictions by directly integrating observational data into coupled numerical models that simulate interactions among atmospheric, oceanic, and terrestrial components. However, SCDA faces significant challenges, including high sensitivity to hyperparameters such as localization and difficulties in diagnosing cross-component interactions. Th...

The spatially varying geographic-parameters introduce significant uncertainty into the ocean model. Due to the impracticality of manually tuning spatial varying parameters, data assimilation methods are widely used for geographic-parameter optimization (GPO). Practically, the limited observations do not contain enough information to perform GPO directly on the entire grid. Therefore, techniques are required to reduce the compl...

Introduction Salinity is a key variable in the dynamic and thermal balance of the entire climate system. To address the complexities of diverse terrains and fluctuating ocean waves, we commonly use free-surface models with quasi-stationary (e.g. height, pressure, or terrain following) coordinates for simulating salinity. In such models, the vertical grid dynamically adjusts with the undulation of seawater. However, this adjust...

The uncertainty in the initial condition seriously affects the forecasting skill of numerical models. Targeted observations play an important role in reducing uncertainty in numerical prediction. The conditional nonlinear optimal perturbation (CNOP) method is a useful tool for studying adaptive observation. However, the traditional CNOP method highly relies on the adjoint model, and it is difficult to find the global optimal s...

Data-driven predictions of marine environmental variables are typically focused on single variables. However, in real marine environments, there are correlations among different oceanic variables. Additionally, sea–air interactions play a significant role in influencing the evolution of the marine environment. Both internal dynamics and external drivers contribute to these changes. In this study, a data-driven model is propose...

At present, many prediction models based on deep learning methods have been widely used in ocean prediction with satisfactory results. However, few deep learning models are used to predict the Kuroshio path south of Japan. In this study, a hybrid deep learning prediction model is constructed based on the long short-term memory (LSTM) neural network, combined with the complex empirical orthogonal function (CEOF) and bivariate e...