The Respondence of Wave on Sea Surface Temperature in the Context of Global Change
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 s...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/7/1948 |
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| author | Ru Yao Weizeng Shao Mengyu Hao Juncheng Zuo Song Hu |
| author_facet | Ru Yao Weizeng Shao Mengyu Hao Juncheng Zuo Song Hu |
| author_sort | Ru Yao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T05:25:44Z |
| format | Article |
| id | doaj.art-5a3bc0d22011422ab6ae0572393ad420 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T05:25:44Z |
| publishDate | 2023-04-01 |
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| spelling | doaj.art-5a3bc0d22011422ab6ae0572393ad4202023-11-17T17:31:19ZengMDPI AGRemote Sensing2072-42922023-04-01157194810.3390/rs15071948The Respondence of Wave on Sea Surface Temperature in the Context of Global ChangeRu Yao0Weizeng Shao1Mengyu Hao2Juncheng Zuo3Song Hu4College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Marine Sciences, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Marine Sciences, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Marine Sciences, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Marine Sciences, Shanghai Ocean University, Shanghai 201306, ChinaSeveral 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.https://www.mdpi.com/2072-4292/15/7/1948sea surface wavesea surface temperatureocean modelling |
| spellingShingle | Ru Yao Weizeng Shao Mengyu Hao Juncheng Zuo Song Hu The Respondence of Wave on Sea Surface Temperature in the Context of Global Change Remote Sensing sea surface wave sea surface temperature ocean modelling |
| title | The Respondence of Wave on Sea Surface Temperature in the Context of Global Change |
| title_full | The Respondence of Wave on Sea Surface Temperature in the Context of Global Change |
| title_fullStr | The Respondence of Wave on Sea Surface Temperature in the Context of Global Change |
| title_full_unstemmed | The Respondence of Wave on Sea Surface Temperature in the Context of Global Change |
| title_short | The Respondence of Wave on Sea Surface Temperature in the Context of Global Change |
| title_sort | respondence of wave on sea surface temperature in the context of global change |
| topic | sea surface wave sea surface temperature ocean modelling |
| url | https://www.mdpi.com/2072-4292/15/7/1948 |
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