Analysis of Wave-Induced Stokes Transport Effects on Sea Surface Temperature Simulations in the Western Pacific Ocean

Sun, Zhanfeng; Shao, Weizeng; Wang, Weili; Zhou, Wei; Yu, Wupeng; Shen, Wei

doi:10.3390/jmse9080834

Cited by 11 publications

(14 citation statements)

References 49 publications

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“…It was found that the accuracy of the water temperature was improved by including the four wave-induced terms, especially for the high WW3-simulated SWH in July and September. As mentioned in [9], wave-induced Stokes transport in the China Seas gradually increased between 1987 and 2020. The term of wave should be included in the SST simulation by sbPOM at specific regions, e.g., the Arctic Ocean [5] and China Seas, and at extreme events, e.g., typhoon and hurricane.…”

Section: Discussionmentioning

confidence: 84%

“…The characteristics of ocean dynamics are complicated in the open sea areas of the Antarctic Ocean, e.g., the circumpolar current [7] and huge swells [8]. In fact, both the currents and waves play important roles in water temperature [9], especially under the high sea state in the Antarctic Ocean. However, regarding the oceanography research conducted on the Antarctic Ocean, the characteristics of the atmospheric-marine dynamics and climate change, i.e., wind [10], waves [11], and currents [12], have been individually studied.…”

Section: Introductionmentioning

confidence: 99%

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Short-Term Variations in Water Temperature of the Antarctic Surface Layer

Shao

Jun

et al. 2022

JMSE

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Against the background of global climate change, the warming water temperature of Antarctic surface water (<500 m) is worthy of study. Therefore, the purpose of this study was to investigate short-term variations in the water temperature of Antarctic surface water from January 2000 to December 2016, using offline coupled ocean models, i.e., the WAVEWATCH-III (WW3) model and the Stony Brook Parallel Ocean Model (sbPOM). The validation of the WW3-simulated significant wave height (SWH) against the measurements from the Jason-2 altimeter produced a root mean square error (RMSE) of 0.39 m with a 0.30-m bias. Moreover, the sbPOM-simulated water temperatures were compared with the collocated measurements from Argo buoys, yielding, and RMSE for water temperatures of less than 1.0 °C and a correlation coefficient (COR) of 0.92. The annual variation in the sbPOM-simulated water temperatures of the Antarctic surface water layer showed that the sea surface temperatures (SSTs) of the Pacific Ocean and Atlantic Ocean were greater than the SST of the Indian Ocean. Moreover, the SST rapidly increased to 2°C in the Pacific Ocean in specific years due to El Nino and La Nina events. The vertical profile of the water temperature showed that the depth of the cool water in the Indian Ocean has decreased to 100 m since 2008, while the depth in the Pacific Ocean and Atlantic Ocean is about 200 m. It was also found that the seasonal variations in the water temperature at depths of 0 m, 50 m, and 100 m in the Pacific Ocean were more sensitive to El Nino events between January and June. However, it was revealed in this work that the increasing intensity of El Nino and La Nina events could reduce water warming in the Pacific Ocean.

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Section: Discussionmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Short-Term Variations in Water Temperature of the Antarctic Surface Layer

Shao

Jun

et al. 2022

JMSE

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“…This obviously ignores potential interactions among different factors, such as wind speed, sea surface temperature, and salinity, which may have indirect effects on the CO 2 partial pressure at the surface. Several studies have also provided evidence that changes in SST, SSS, and wind speed can induce variations in the partial pressure of CO 2 via physicochemical processes that affect CO 2 exchange (Kashef-Haghighi and Ghoshal, 2013;Sun et al, 2021a;Sun et al, 2021b;Wanninkhof, 1992). We hope that necessary groundwork can be performed to address these problems in future research.…”

Section: Discussionmentioning

confidence: 95%

Spatiotemporal distributions of air-sea CO2 flux modulated by windseas in the Southern Indian Ocean

Sun

Zheng

et al. 2023

Front. Mar. Sci.

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The Southern Indian Ocean is a major reservoir for rapid carbon exchange with the atmosphere, plays a key role in the world’s carbon cycle. To understand the importance of anthropogenic CO2 uptake in the Southern Indian Ocean, a variety of methods have been used to quantify the magnitude of the CO2 flux between air and sea. The basic approach is based on the bulk formula—the air-sea CO2 flux is commonly calculated by the difference in the CO2 partial pressure between the ocean and the atmosphere, the gas transfer velocity, the surface wind speed, and the CO2 solubility in seawater. However, relying solely on wind speed to measure the gas transfer velocity at the sea surface increases the uncertainty of CO2 flux estimation. Recent studies have shown that the generation and breaking of ocean waves also significantly affect the gas transfer process at the air-sea interface. In this study, we highlight the impact of windseas on the process of air-sea CO2 exchange and address its important role in CO2 uptake in the Southern Indian Ocean. We run the WAVEWATCH III model to simulate surface waves in this region over the period from January 1st 2002 to December 31st 2021. Then, we use the spectral partitioning method to isolate windseas and swells from total wave fields. Finally, we calculate the CO2 flux based on the new semiempirical equation for gas transfer velocity considering only windseas. We found that after considering windseas’ impact, the seasonal mean zonal flux (mmol/m2·d) increased approximately 10%-20% compared with that calculated solely on wind speed in all seasons. Evolution of air-sea net carbon flux (PgC) increased around 5.87%-32.12% in the latest 5 years with the most significant seasonal improvement appeared in summer. Long-term trend analysis also indicated that the CO2 absorption capacity of the whole Southern Indian Ocean gradually increased during the past 20 years. These findings extend the understanding of the roles of the Southern Indian Ocean in the global carbon cycle and are useful for making management policies associated with marine environmental protection and global climatic change mitigation.

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“…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. However, two aspects of employing those winds must be noted: (1) the inevitable error in the meteorological numerical model and (2) the under-estimation in the TCs [33].…”

Section: Introductionmentioning

confidence: 99%

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

Shi,

Shao,

Shi

et al. 2023

Remote Sensing

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The purpose of our work is to investigate the performance of fusion wind from multiple remote-sensed data in forcing numeric wave models, and the experiment is described herein. In this study, 0.125° gridded wind fields at 12 h intervals were fused by using swath products from an advanced scatterometer (ASCAT) (a Haiyang-2B (HY-2B) scatterometer) and a spaceborne polarimetric microwave radiometer (WindSAT) during the period November 2019 to October 2020. The daily average wind speeds were compared with observations from National Data Buoy Center (NDBC) buoys from the National Oceanic and Atmospheric Administration (NOAA), yielding a 1.66 m/s root mean squared error (RMSE) with a 0.81 correlation (COR). This suggests that fusion wind was reliable for our work. The fusion winds were used for hindcasting sea surface waves by using two third-generation numeric wave models, denoted as WAVEWATCH-III (WW3) and Simulation Wave Nearshore (SWAN). The WW3-simulated waves in the North Pacific Ocean and the SWAN-simulated waves in the Gulf of Mexico were validated against the measurements from the NDBC buoys and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5) for the period June−September 2020. The analysis of significant wave heights (SWHs) up to 9 m yielded a < 0.5 m RMSE with a > 0.8 COR for the WW3 and SWAN models. Therefore, it was believed that the accuracy of the simulation using the two numeric models was comparable with that forced by a numeric atmospheric model. An error analysis was systematically conducted by comparing the modeled WW3-simulated SWHs with the monthly average products from the HY-2B and a Jason-3 altimeter over global seas. The seasonal analysis showed that the differences in the SWHs (i.e., altimeter minus the WW3) were within ±1.5 m in March and June; however, the difference was quite significant in December. It was concluded that remote-sensed fusion wind can serve as a driving force for hindcasting waves using numeric wave models.

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Analysis of Wave-Induced Stokes Transport Effects on Sea Surface Temperature Simulations in the Western Pacific Ocean

Cited by 11 publications

References 49 publications

Short-Term Variations in Water Temperature of the Antarctic Surface Layer

Short-Term Variations in Water Temperature of the Antarctic Surface Layer

Spatiotemporal distributions of air-sea CO2 flux modulated by windseas in the Southern Indian Ocean

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

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