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Based on the 30-year global hourly sea surface temperature (SST) dataset (MLSST) produced by the National Marine Environmental Forecasting Center, Ministry of Natural Resources of China, we analyzed the variability of diurnal sea surface temperature amplitude (DSST) of the tropical Indian Ocean at multiple time scales, as well as its influencing factors. The results show that the DSST in the Arabian Sea, Bay of Bengal, and equatorial Indian Ocean exhibits a bimodal seasonal variation with a semi-annual cycle, while the DSST in the southern Indian Ocean shows an annual cycle. The seasonal variation of DSST is mainly influenced by factors such as sea surface wind speed, shortwave solar radiation, and precipitation. The DSST in the equatorial Indian Ocean is generally higher during El Niño years compared to La Niña years. At the intraseasonal scale, the large standard deviation of DSST in boreal winter is mainly distributed in the southern hemisphere, while the large standard deviation of DSST in boreal summer shifts northward. The intraseasonal variation amplitude of DSST in boreal winter of the tropical Indian Ocean is greater than that in boreal summer. The DSST in the tropical Indian Ocean exhibits significant variation characteristics at multi-time scales. This study provides reference for numerical simulation of air-sea interaction patterns in the tropical Indian Ocean, as well as improvement of short-term climate prediction. [ABSTRACT FROM AUTHOR]

This article describes the implementation of a coupling between a global forecast model (CFSv2.0) and a wave model (WW3) and investigates the effects of ocean surface waves on the air–sea interface in the new framework. Several major wave-related processes, including the Langmuir mixing, the Stokes–Coriolis force with entrainment, air–sea fluxes modified by the Stokes drift, and momentum roughness length, are evaluated in two groups of 56 d experiments, one for boreal winter and the other for boreal summer. Comparisons are made against in situ buoys, satellite measurements, and reanalysis data to evaluate the influence of waves on intraseasonal prediction of sea surface temperature (SST), 2 m air temperature (T02), mixed layer depth (MLD), 10 m wind speed (WSP10), and significant wave height (SWH). The wave-coupled experiments show that overestimated SSTs and T02s, as well as underestimated MLDs at mid-to-high latitudes in summer from original CFSv2.0, are significantly improved due to enhanced vertical mixing generated by the Stokes drift. For WSP10s and SWHs, the wave-related processes generally reduce biases in regions where WSP10s and SWHs are overestimated. On the one hand, the decreased SSTs stabilize the marine atmospheric boundary layer and weaken WSP10s and then SWHs. On the other hand, the increased roughness length due to waves reduces the originally overestimated WSP10s and SWHs. In addition, the effects of the Stokes drift and current on air–sea fluxes also rectify WSP10s and SWHs. These cases are helpful for the future development of the two-way CFSv2.0–wave coupled system. [ABSTRACT FROM AUTHOR]

The South China Sea Operational Oceanography Forecasting System (SCSOFS), constructed and operated by the National Marine Environmental Forecasting Center of China, has been providing daily updated hydrodynamic forecasting in the South China Sea (SCS) for the next 5 d since 2013. This paper presents recent comprehensive updates to the configurations of the physical model and data assimilation scheme in order to improve the forecasting skill of the SCSOFS. This paper highlights three of the most sensitive updates: the sea surface atmospheric forcing method, the discrete tracer advection scheme, and a modification of the data assimilation scheme. Intercomparison and accuracy assessment among the five sub-versions were performed during the entire upgrading process using the OceanPredict Intercomparison and Validation Task Team Class 4 metrics. The results indicate that remarkable improvements have been made to the SCSOFSv2 with respect to the original version (known as SCSOFSv1). The domain-averaged monthly mean root-mean-square errors of the sea surface temperature and sea level anomaly have decreased from 1.21 to 0.52 ∘ C and from 21.6 to 8.5 cm, respectively. [ABSTRACT FROM AUTHOR]

Ocean surface gravity waves have enormous effects on physical processes at the atmosphere-ocean interface. The effects of wave-related processes on global intraseasonal prediction were evaluated after we incorporated the WAVEWATCH III model into the Climate Forecast System model version 2.0 (CFSv2.0), with the Chinese Community Coupler version 2.0. Several major wave-related processes, including the Langmuir mixing, Stokes-Coriolis force with entrainment, air-sea fluxes modified by Stokes drift and momentum roughness length, were evaluated in two groups of 56-day experiments, one for boreal winter and the other for boreal summer. Comparisons were performed against in-situ buoys, satellite measurements and reanalysis data, to evaluate the influence of waves on intraseasonal prediction of sea surface temperature (SST), 2-m air temperature (T02), mixed layer depth (MLD), 10-m wind speed (WSP10) and significant wave height (SWH) in CFSv2.0. Overestimated SST and T02, as well as underestimated MLD in mid and high latitudes in summer from original CFSv2.0 are clearly improved, mainly due to enhanced vertical mixing generated by Stokes drift. The largest regional mean SST improvement reaches 35.89 % in the Southern Ocean. For WSP10 and SWH, the wave-related processes generally lead to reduction of biases in regions where wind speed and SWH are overestimated. The decreased SST caused by Stokes drift-related mixing stabilizes marine atmospheric boundary layer, weakens wind speed and then SWH. Compared with the NDBC buoy data, the overestimated WSP10 is improved by up to 13.52 % in boreal summer. The increased roughness length due to waves leads to some reduction in the originally overestimated wind speed and SWH, with the largest SWH improvement of 11.93 % and 20.05 % in boreal winter and summer respectively. The effects of Stokes drift and current on air-sea fluxes are investigated separately. Their overall effects on air-sea fluxes reduce the overestimated WSP10 by up to 17.31 % and 23.21 % in boreal winter and summer respectively. These cases are helpful for the future development of the two-way CFS-wave coupled system. [ABSTRACT FROM AUTHOR]

The Arctic regional coupled sea-ice–ocean–atmosphere model (ArcIOAM) has been developed to provide reliable Arctic sea ice prediction on seasonal timescales. The description and implementation of ArcIOAM and its preliminary results for the year of 2012 are presented in this paper. In the ArcIOAM configuration, the Community Coupler 2 (C-Coupler2) is used to couple the Arctic sea-ice–oceanic configuration of the MITgcm (Massachusetts Institute of Technology general circulation model) with the Arctic atmospheric configuration of the Polar WRF (Weather Research and Forecasting) model. A scalability test is performed to investigate the parallelization of the coupled model. As the first step toward reliable Arctic seasonal sea ice prediction, ArcIOAM, implemented with two-way coupling strategy along with one-way coupling strategy, is evaluated with respect to available observational data and reanalysis products for the year of 2012. A stand-alone MITgcm run with prescribed atmospheric forcing is performed for reference. From the comparison, all the experiments simulate reasonable evolution of sea ice and ocean states in the Arctic region over a 1-year simulation period. The two-way coupling has better performance in terms of sea ice extent, concentration, thickness and sea surface temperature (SST), especially in summer. This result indicates that sea-ice–ocean–atmosphere interaction plays a crucial role in controlling Arctic summertime sea ice distribution. [ABSTRACT FROM AUTHOR]

A dataset of hourly sea surface temperature (SST) from the period 1 January 1982 to 31 December 2012, and covering the global ocean at a resolution of 0.3° × 0.3°, was created using a validated ocean mixed-layer model (MLSST). The model inputs were heat flux and surface wind speed obtained from the Coupled Forecast System Reanalysis dataset. Comparisons with in-situ data from the Tropical Atmosphere Ocean array and the National Data Buoy Center showed that the MLSST fitted very well with observations, with a mean bias of 0.07°C, and a root-mean-square error (RMSE) and correlation coefficient of 0.37°C and 0.98, respectively. Also, the MLSST fields successfully reproduced the diurnal cycle of SST in the in-situ data, with a mean bias of −0.005°C and RMSE of 0.26°C. The 31-year climatology revealed that the diurnal range was small across most regions, with higher values in the eastern and western equatorial Pacific, northern Indian Ocean, western Central America, northwestern Australia, and several coastal regions. Significant seasonal variation of diurnal SST existed in all basins. In the Atlantic and Pacific basins, this seasonal pattern was oriented north-south, following the variation in solar insolation, whereas in the Indian basin it was dominated by monsoonal variability. At the interannual scale, the results highlighted the relationship between diurnal and interannual variations of SST, and revealed that the diurnal warming in the central equatorial Pacific could be a potential climatic indicator for ENSO prediction. [ABSTRACT FROM AUTHOR]