The sea surface drag coefficient plays an important role in momentum transmission between the atmosphere and the ocean, which is affected by ocean waves. The total air–sea momentum flux consists of effective momentum flux and sea spray momentum flux. Sea spray momentum flux involves sea surface drag, which is largely affected by the ocean wave state. Under strong winds, the sea surface drag coefficient ($C_D$) does not increase linearly with the increasing wind speed, namely, the increase of $C_D$ is inhibited by strong winds. In this study, a sea surface drag coefficient is constructed that can be applied to the calculation of the air–sea momentum flux under high wind speed. The sea surface drag coefficient also considers the influence of wave state and sea spray droplets generated by wave breaking. Specially, the wave-dependent sea spray generation function is employed to calculate sea spray momentum flux. This facilitates the analysis not only on the sensitivity of the sea spray momentum flux to wave age, but also on the effect of wave state on the effective $C_D$ ($C_{D, eff}$) under strong winds. Our results indicate that wave age plays an important role in determining $C_D$. When the wave age is >0.4, $C_D$ decreases with the wave age. However, when the wave age is ≤0.4, $C_D$ increases with the wave age at low and moderate wind speeds but tends to decrease with the wave age at high wind speeds.

Tropical cyclones (TCs) seriously threaten the safety of human life and property especially when approaching a coast or making landfall. Robust, long-lead predictions are valuable for managing policy responses. However, despite decades of efforts, seasonal prediction of TCs remains a challenge. Here, we introduce a deep-learning prediction model to make skillful seasonal prediction of TC track density in the Western North Pacific (WNP) during the typhoon season, with a lead time of up to four months. To overcome the limited availability of observational data, we use TC tracks from CMIP5 and CMIP6 climate models as the training data, followed by a transfer-learning method to train a fully convolutional neural network named SeaUnet. Through the deep-learning process (i.e., heat map analysis), SeaUnet identifies physically based precursors. We show that SeaUnet has a good performance for typhoon distribution, outperforming state-of-the-art dynamic systems. The success of SeaUnet indicates its potential for operational use.

Despite numerous studies, the impact of global warming on the tropical cyclone (TC) track and intensity by reasons of data inhomogeneity in remote sensing and large natural variability over a relatively short period of observation is still controversial. Three carbon-emission sensitivity experiments are conducted to investigate how TC track and intensity respond to changes in the oceanic and atmospheric environment under global warming. The results show a high sensitivity of the simulated TC track and intensity to global warming. On one hand, with increase in carbon emissions, the western Pacific subtropical high expands notably, increasing the poleward steering flow and eventually leading to a poleward shift of TC. On the other hand, the underlying sea-surface temperature and surface-entropy flux increase and, thus, favor the convections near the eyewall. Moreover, the TC structure becomes more upright, which is closely related to the larger pressure gradient near the eyewall. As a result, TC intensity increases with carbon emissions. However, this increase is notably smaller than the maximum potential intensity theory as the TC intensity can reach a threshold if carbon emission still increases in the future. The involved mechanisms on the changes of TC track and intensity are also revealed.

Due to limited in-situ ocean observations, reanalysis data are often considered as an important source for studying tropical cyclone (TC)-induced ocean waves. Here, we introduced a method to quantitatively evaluate the errors of TC-induced ocean waves in reanalysis datasets. The TC data are from the IBTrACS dataset. We compared TC-induced ocean waves in two reanalysis datasets (i.e., ERA5 and WAVERYS) with those in buoy data when TCs are near the buoy stations. We showed that the errors of TC-induced ocean waves in WAVERYS and ERA5 are similar, because the surface winds in these two datasets are the same. We noticed that the errors in the North Pacific are remarkably smaller than those in the North Atlantic due to more realistic probability density of TC-induced ocean waves in the North Pacific compared with those in the North Atlantic. Moreover, the errors are strongly related to significant wave height (SWH) and the distance from TC center. The larger the SWH and the shorter the distance, the larger the error. Furthermore, the errors in TC-induced ocean waves decreased significantly over the past decades.

Clouds critically influence Earth’s radiation balance and climate, making accurate cloud detection essential for improving climate models. This study develops the TSAR model to improve the cloud detection accuracy of the FY-4A CLM product by incorporating physical features. The input features include FY-4A brightness temperature (BT) data from channels 8–14, geometric parameters (satellite zenith angle (SAZ), satellite azimuth angle (SAA), solar zenith angle (SOZ), solar azimuth angle (SOA), and latitude), and four ERA5 meteorological factors (2 m air temperature (T2m), skin temperature (SKT), air temperature profiles (ATP), and relative humidity profiles (RH)). Using the CALIPSO cloud detection product as labels, the model outputs cloud/clear-sky classification results. Additionally, four machine learning (ML) algorithms—RF, LightGBM, XGBoost, and MLP—achieved overall accuracies of 91.5%, 92.2%, 92.5%, and 92.8%, respectively, considerably outperforming the FY-4A L2 CLM product (83.1%). The results demonstrate that incorporating physical factors significantly improves cloud detection performance regardless of the algorithm employed. Incorporating meteorological factors notably improved nighttime and water–cloud detection, narrowing day–night accuracy gaps. Shapley additive explanation (SHAP) analysis indicated feature contributions of 15.8%, 50.8%, and 33.3% from geometric, BT, and meteorological variables, respectively, with stronger meteorological effects at mid- to high-latitudes. These findings demonstrate that integrating meteorological factors significantly improves FY-4A cloud detection accuracy and consistency, highlighting the MLP-TSAR model’s effectiveness for reliable all-day operational applications.

Following a request from Xiangbo Feng, together with input from the University of Reading regarding the accuracy of the authorship list, the original author list has been amended