The Coupled Ocean-Atmosphere-Wave-Sediment Transport model has been used to simulate Super Typhoon Yutu (2018). The impacts of four momentum transfer parameterization schemes (COARE, TY, OT, and DN) and three heat transfer parameterization schemes (COARE, GR, and ZK) on typhoon modelling have been studied by means of the track, intensity, and radial structure of typhoon. The results show that the track of Yutu is not sensitive to the choice of parameterization scheme, while the combinations of different parameterization schemes affect the intensity of Yutu. Among the four momentum flux parameterization schemes, three wave-state-based schemes (TY, OT, and DN) provide better intensity results than the wind-speed-based COARE scheme, but the differences between the three wave-state-based schemes are not obvious. Among the three heat flux parameterization schemes, the results of the GR scheme are slightly better than those of the COARE scheme, and both the GR and COARE schemes are significantly better than the ZK scheme, from which the intensity of Yutu is underpredicted obviously. The influence of the combination of different parameterization schemes on the intensity of the typhoon is also reflected in the radial structure of the typhoon, and the radial structure of the typhoon simulated by experiments with stronger typhoon intensity also develops faster. Differences of intensity between experiments are due mainly to the differences in sea surface heat flux, the enthalpy transferred from sea surface to the atmosphere has a significant impact on the bottom atmosphere wind field, and there is a strong correspondence between the distribution of enthalpy flux and the bottom wind field.

A wave state related sea surface roughness parameterization scheme that takes into account the impact of sea foam is proposed in this study. Using eight observational datasets, the performances of two most widely used wave state related parameterizations are examined under various wave conditions. Based on the different performances of two wave state related parameterizations under different wave state, and by introducing the effect of sea foam, a new sea surface roughness parameterization suitable for low to extreme wind conditions is proposed. The behaviors of drag coefficient predicted by the proposed parameterization match the field and laboratory measurements well. It is shown that the drag coefficient increases with the increasing wind speed under low and moderate wind speed conditions, and then decreases with increasing wind speed, due to the effect of sea foam under high wind speed conditions. The maximum values of the drag coefficient are reached when the 10 m wind speeds are in the range of 30–35 m/s.

The intensity simulation of tropical cyclones (TCs) has been a long‐standing challenge for numerical models, and an accurate sea surface roughness (z0 ${z}_{0}$) parameterization scheme is the key to enhance the intensity prediction. In our study, a new z0 ${z}_{0}$ parameterization scheme (SD21) is proposed and applied in the Coupled Ocean‐Atmosphere‐Wave‐Sediment Transport model to simulate two super typhoons. The SD21 takes into account both the wave state and sea foam, and it is suitable for low to extreme wind conditions. The results of the eight numerical experiments show that the TC intensity and structure are sensitive to the choice of z0 ${z}_{0}$ parameterization schemes. Compared with the widely used z0 ${z}_{0}$ parameterization schemes, the SD21 scheme presents much better results in the simulation of the intensity and intensification speed of strong TCs. Notably, the simulation of the wind speeds generated by the SD21 is more compatible with the best track data and significantly better than that of the other schemes. Furthermore, we find that the wave state and sea foam remarkably affect the magnitude and spatial distribution of z0 ${z}_{0}$, the following two conclusions are obtained: (a) The z0 ${z}_{0}$ parameterization that takes into account the wave state can reduce the excessive roughness at the TC periphery and restrict the high‐value area of the roughness to the TC‐core region. (b) The sea foam significantly decreases the roughness value in areas with 10 m wind speeds above 40 m/s.

The Atmospheric General Circulation Model (AGCM) as one of the most important components of Climate System Model (CSM), has been proved to be an effective way for weather forecasting and climate prediction. Although lots of efforts have been conducted to improve the computing efficiency of AGCMs, such as exploit parallel algorithms, migrating codes, and even redesigning systems to adapt to the emerging computer architectures, it is not enough to match the real requirement, due to the limited scalability of the parallel algorithms themselves. Therefore, we design and implement a scalable parallel spectral-based atmospheric circulation mode called PAGCM in this paper. Specifically, we first analyze the data dependencies of the dimensions in different spaces according to the calculation characteristics of spectral models, and based on which we propose a two-dimensional decomposition algorithm in PAGCM to effectively increase the involving cores for the parallel computing, and thus reduce the overall computing time.Furthermore, to adapt to the novel data decomposition in each computing stage of dynamic framework, we propose three-dimensional data transposition algorithms and data collection algorithms correspondingly, by considering of load balancing and communication optimization.Extensive experiments are conducted on Tianhe-2 to validate the effectiveness and scalability of our proposals. KEYWORDS atmospheric general circulation model, parallel computing, scalability, spectral model INTRODUCTIONGlobal climate and environmental changes under global warming, have been one of the great challenges facing human societies in recent decades.There are various factors lead to these changes, which comprehensively reflect the complex interactions among atmosphere, hydrosphere, cryosphere, lithosphere, and biosphere within the climate system. The Climate System Model (CSM) as an effective tool for establishing numerical models for simulating the interactions and exploring the nature for the climate change, has received increasing attention with the rapid development of computers in recent decades. 1,2 After many years of efforts by countless researchers, now there exist many CSMs and their variations for climate prediction. Particulary, most existing CSMs are the loosely coupled systems consisting of several components. The Atmospheric General Circulation Model (AGCM) as one of the most important components, has been proved to be an effective way for weather forecasting and climate prediction 3-5 and has been a major focus of research in Meteorology and Climatology community.Generally, AGCMs usually require integration of mass data over several years or even decades. 3,6,7 Such large-scale calculations needs powerful computing capability, which forces us to enhance the capability of high performance computing, as well as to exploit more efficient algorithms to improve the computing efficiency. As far as we know, the trend of research on AGCMs mainly lies in improving the accuracy and computing efficiency. To improve the accura...

The relationship between ocean subsurface temperature and tropical cyclone (TC) over the western North Pacific (WNP) is studied based on the TC best-track data and global reanalysis data during the period of 1948–2012. Here the TC frequency (TCF), lifespan, and genesis position of TCs are analysed. A distinctive negative correlation between subsurface water temperature and TCF is observed, especially the TCF in the southeastern quadrant of the WNP (0–15°N, 150–180°E). According to the detrended subsurface temperature anomalies of the 125 m depth layer in the main TC genesis area (0–30°N, 100–180°E), we selected the subsurface cold and warm years. During the subsurface cold years, TCs tend to have a longer mean lifespan and a more southeastern genesis position than the subsurface warm years in general. To further investigate the causes of this characteristic, the TC genesis potential indexes (GPI) are used to analyse the contributions of environmental factors to TC activities. The results indicate that the negative correlation between subsurface water temperature and TCF is mainly caused by the variation of TCF in the southeastern quadrant of the WNP, where the oceanic and atmospheric environments are related to ocean subsurface conditions. Specifically, compared with the subsurface warm years, there are larger relative vorticity, higher relative humidity, smaller vertical wind shear, weaker net longwave radiation, and higher ocean mixed layer temperature in the southeastern quadrant during cold years, which are all favorable for genesis and development of TC.