The diurnal cycle of sea surface temperature (SST) plays an important role in the upper ocean and climate system. One way to represents the diurnal variation of SST in Ocean General Circulation Models (OGCMs) with coarse vertical resolution is to parameterize the diurnal cycle of SST without considering detailed dynamic processes within the mixed layer. In this study, a diagnostic sublayer parameterization scheme following Schiller and Godfrey (2005) was incorporated into a global OGCM (NEMO), for the first time, to simulate the diurnal cycle of SST. Moreover, three different sublayer temperature profiles (constant, linear, and exponential) were evaluated. Comparison with satellite SST and mooring temperature data indicated that the parameterization scheme with an exponential temperature profile showed the most reasonable diurnal warming simulation results, significantly improving the probability of (from 1.7% with constant temperature profile to 13.4%) diurnal SST amplitude larger than 1.0°C. Furthermore, the mean biases in each season were all reduced to less than 0.16°C, in good agreement with observations.

As a critical physical parameter in the sea–air interface, sea surface temperature (SST) plays a crucial role in the sea–air interaction process. The SST diurnal cycle is one of the most critical changes that occur in the various time scales of SST. Currently, accurate simulation and prediction of SST diurnal cycle amplitude remain challenging. The application of machine learning in marine environment research, simulation, and prediction has received increasing attention. In this study, a regression prediction model for SST diurnal cycle amplitude was constructed based on TOGA/COARE buoy-observed data and an extreme gradient boosting algorithm (XGBoost). The XGBoost algorithm was also optimized using label distribution smoothing (LDS) to respond to the problem of uneven cycle amplitude size distribution. The results showed that the LDS-XGB model outperformed various empirical models and other machine learning models in terms of prediction error and prediction accuracy while effectively improving the data imbalance problem without losing model accuracy and achieving accurate and efficient predictions of the SST diurnal cycle amplitude. This work is a good demonstration of the integration of marine science and machine learning, which indicates that machine learning plays an important role in the model parametrizations and understanding the mechanisms.

Three tiers of experiments in the Global Monsoons Model Intercomparison Project (GMMIP), one of the endorsed model intercomparison projects of phase 6 of the Coupled Model Intercomparison Project (CMIP6), are implemented by the First Institute of Oceanography Earth System Model version 2 (FIO-ESM v2.0), following the GMMIP protocols. Evaluation of global mean surface air temperature from 1870 to 2014 and climatological precipitation (1979–2014) in tier-1 shows that the atmosphere model of FIO-ESM v2.0 can reproduce the basic observed atmospheric features. In tier-2, the internal variability is captured by the coupled model, with the SST restoring to the model climatology plus the observed anomalies in the tropical Pacific and North Atlantic. Simulation of the Northern Hemisphere summer monsoon circulation is significantly improved by the SST restoration in the North Atlantic. In tier-3, five orographic perturbation experiments are conducted covering the period 1979–2014 by modifying the surface elevation or vertical heating in the prescribed region. In particular, the strength of the South Asian summer monsoon is reduced by removing the topography or thermal forcing above 500 m over the Asian continent. Monthly and daily simulated outputs of FIO-ESM v2.0 are provided through the Earth System Grid Federation (ESGF) node to contribute to a better understanding of the global monsoon system.

The earliest Asian summer monsoon onset (SMO) occurs in the Bay of Bengal (BoB), heralding the coming of the rainy season. In late April or early May, the strong sea surface temperature (SST) diurnal variation accompanied by ocean surface warming triggers the SMO. However, this observed diurnal cycle intensity cannot be reasonably simulated by state‐of‐the‐art climate models, resulting in a spurious delayed SMO. To address this issue, the SST diurnal cycle parameterized by a diagnostic sublayer scheme was incorporated into a climate model named FIO‐ESM v2.0. The large diurnal amplitude of SST contributes to surface warming and changes atmospheric circulation. Consequently, the high‐pressure anomaly at high levels and an inverted trough at low levels promote more convective activity, triggering an earlier SMO. Our findings improve the ability of climate models in simulating the evolution of the Asian monsoon system.

High-resolution global ocean general circulation models (OGCMs) play a key role in accurate ocean forecasting. However, the models of the operational forecasting systems are still not in high resolution due to the subsequent high demand for large computation, as well as the low parallel efficiency barrier. Good scalability is an important index of parallel efficiency and is still a challenge for OGCMs. We found that the communication cost in a barotropic solver, namely, the preconditioned conjugate gradient (PCG) method, is the key bottleneck for scalability due to the high frequency of the global reductions. In this work, we developed a new algorithm—a communication-avoiding Krylov subspace method with a PCG (CA-PCG)—to improve scalability and then applied it to the Nucleus for European Modelling of the Ocean (NEMO) as an example. For PCG, inner product operations with global communication were needed in every iteration, while for CA-PCG, inner product operations were only needed every eight iterations. Therefore, the global communication cost decreased from more than 94.5% of the total execution time with PCG to less than 63.4% with CA-PCG. As a result, the execution time of the barotropic modes decreased from more than 17,000 s with PCG to less than 6000 s with CA-PCG, and the total execution time decreased from more than 18,000 s with PCG to less than 6200 s with CA-PCG. Besides, the ratio of the speedup can also be increased from 3.7 to 4.6. In summary, the high process count scalability when using CA-PCG was effectively improved from that using the PCG method, providing a highly effective solution for accurate ocean simulation.

El Niño‐Southern Oscillation (ENSO) asymmetry, characterized by stronger warm anomalies during El Niño than cold anomalies during La Niña, remains underestimated in climate models. Although nonlinear atmospheric and oceanic feedbacks are known to contribute to this asymmetry, the small‐scale processes modulating their strength remain unclear. Here, we show that interannual variations in the diurnal amplitude (DA) of sea surface temperature (SST) act to enhance ENSO asymmetry. Analyses of 35 Coupled Model Intercomparison Project Phase 6 (CMIP6) models reveal that those with larger SST DAs capture this asymmetry more realistically. In targeted model experiments, enhanced DA strengthens asymmetric zonal SST anomalies across the equatorial Pacific during ENSO events, reinforcing nonlinear air‐sea feedback. The resulting positive SSTA modification, caused by overall wind speed weakening, improves ENSO asymmetry by 38.5% in the Niño 3 region. Our finding indicates large DA as an important role in shaping ENSO nonlinearity and its simulation in climate models.