The Respondence of Wave on Sea Surface Temperature in the Context of Global Change

Abstract

Several aspects of global climate change, e.g., the rise of sea level and water temperature anomalies, suggest the advantages of studying wave distributions. In this study, WAVEWATCH-III (WW3) (version 6.07), which is a well-known numerical wave model, was employed for simulating waves over global seas from 1993–2020. The European Centre for Medium-Range Weather Forecasts (ECMWF), Copernicus Marine Environment Monitoring Service (CMEMS), current and sea level were used as the forcing fields in the WW3 model. The validation of modelling simulations against the measurements from the National Data Buoy Center (NDBC) buoys and Haiyang-2B (HY-2B) altimeter yielded a root mean square error (RMSE) of 0.49 m and 0.63 m, with a correlation (COR) of 0.89 and 0.90, respectively. The terms calculated by WW3-simulated waves, i.e., breaking waves, nonbreaking waves, radiation stress, and Stokes drift, were included in the water temperature simulation by a numerical circulation model named the Stony Brook Parallel Ocean Model (sbPOM). The water temperature was simulated in 2005–2015 using the high-quality Simple Ocean Data Assimilation (SODA) data. The validation of sbPOM-simulated results against the measurements obtained from the Array for Real-time Geostrophic Oceanography (Argo) buoys yielded a RMSE of 1.12 °C and a COR of 0.99. By the seasonal variation, the interrelation of the currents, sea level anomaly, and significant wave heights (SWHs) were strong in the Indian Ocean. In the strong current areas, the distribution of the sea level was consistent with the SWHs. The monthly variation of SWHs, currents, sea surface elevation, and sea level anomalies revealed that the upward trends of SWHs and sea level anomalies were consistent from 1993–2015 over the global ocean. In the Indian Ocean, the SWHs were obviously influenced by the SST and sea surface wind stress. The rise of wind stress intensity and sea level enlarges the growth of waves, and the wave-induced terms strengthen the heat exchange at the air–sea layer. It was assumed that the SST oscillation had a negative response to the SWHs in the global ocean from 2005–2015. This feedback indicates that the growth of waves could slow down the amplitude of water warming.

Validation of Surface Waves Investigation and Monitoring Data against Simulation by Simulating Waves Nearshore and Wave Retrieval from Gaofen-3 Synthetic Aperture Radar Image

“…However, the bias has no significant correlation with current and sea level. It is reasonable that the wave is mainly determined by wind stress [49].…”

Can Sea Surface Waves Be Simulated by Numerical Wave Models Using the Fusion Data from Remote-Sensed Winds?

“…According to [30], it has been found that sea surface currents and sea levels have an influence on wave simulations. Here, 0.125 • gridded fusion wind at intervals of 12 h and daily average Copernicus Marine Environment Monitoring Service (CMEMS) sea currents and sea levels, with a spatial resolution of a 0.08 • grid, were treated as the forcing fields.…”

“…As a case study, Figure 4a,b depict the daily average CMEMS sea currents and sea levels map for 7 September 2020. The parameterization scheme for the WW3 model [30,50] included the switch ST6 package for the input/dissipation source terms, the switch TR1 package, the switch GMD2 package for the wave−wave interactions, and the switch FLD2 and BT1 packages for the wave breaking and bottom friction, respectively. The parameterization scheme for the SWAN model [32] included WESTHuysen, that is, the propagation scheme that combines the wind and non-linear dissipation, and the switches QUADrupl, TRIad, and BREakinge for the wave−wave interactions and the wave bottom friction.…”

“…As mentioned in the Introduction, WW3 has good efficiency for wave simulations over global seas, and the influence of wave hindcasting by the WW3 model on sea surface temperatures (SSTs) was assessed in our recent study [30]. Although there were some gaps in the fusion wind field data, the simulations by WW3 for the gaps were filled using linear interpolation.…”

“…Several types of wind data derived from meteorological numerical models are convenient to use, i.e., the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim (ERA-Interim) data [27], the Cross-Calibrated Multi-Platform (CCMP) from Remote Sensing Systems (RSS) data [28], and the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) data [29]. In the above data, winds are implemented in two wave numerical models, that is, WAVEWATCH-III (WW3) [30], which was developed by the National Centers for Environmental Prediction (NCEP) of the National Oceanic and Atmospheric Administration (NOAA), and Simulation Wave Nearshore (SWAN) [31], which was developed by the Delft University of Technology. It was revealed in [32] that WW3, using 1 • gridded CCMP winds that are assimilated by ERA-Interim reanalysis winds and observations, had the best performance in wave simulation for the Western Pacific Ocean.…”