A Study of Wave-Induced Effects on Sea Surface Temperature Simulations during Typhoon Events

In this work, we investigate sea surface temperature (SST) cooling under binary typhoon conditions. We particularly focus on parallel- and cross-type typhoon paths during four typhoon events: Tembin and Bolaven in 2012, and Typhoon Chan-hom and Linfa in 2015. Wave-induced effects were simulated using a third-generation numeric model, WAVEWATCH III (WW3), and were subsequently included in SST simulations using the Stony Brook Parallel Ocean Model (sbPOM). Four wave-induced effects were analyzed: breaking waves, nonbreaking waves, radiation stress, and Stokes drift. Comparison of WW3-simulated significant wave height (SWH) data with measurements from the Jason-2 altimeter showed that the root mean square error (RMSE) was less than 0.6 m with a correlation (COR) of 0.9. When the four typhoon-wave-induced effects were included in sbPOM simulations, the simulated SSTs had an RMSE of 1 °C with a COR of 0.99 as compared to the Argos data. This was better than the RMSE and COR recovered between the measured and simulated SSTs, which were 1.4 °C and 0.96, respectively, when the four terms were not included. In particular, our results show that the effects of Stokes drift, as well as of nonbreaking waves, were an important factor in SST reduction during binary typhoons. The horizontal profile of the sbPOM-simulated SST for parallel-type typhoon paths (Typhoons Tembin and Bolaven) suggested that the observed finger pattern of SST cooling (up to 2 °C) was probably caused by drag from typhoon Tembin. SST was reduced by up to 4 °C for cross-type typhoon paths (Typhoons Chan-hom and Linfa). In general, mixing significantly increased when the four wave-induced effects were included. The vertical profile of SST indicated that disturbance depth increased (up to 100 m) for cross-type typhoon paths because the mixing intensity was greater for cross-type typhoons than for parallel-type typhoons.

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Numerical simulation of the wave-induced Stokes drift effect on sea surface temperature in the North Pacific

Oceanological and Hydrobiological Studies ◽

10.2478/ohs-2019-0034 ◽

2019 ◽

Vol 48 (4) ◽

pp. 381-403

Author(s):

Jingdong Liu ◽

Jian Shi ◽

Wenjing Zhang

Keyword(s):

Numerical Simulation ◽

Surface Temperature ◽

Sea Surface Temperature ◽

North Pacific ◽

Sea Surface ◽

Stokes Drift ◽

Parameterization Scheme ◽

The North ◽

Wave Induced ◽

The North Pacific

Abstract The effect of the wave-induced Stokes drift is not taken into account in traditional ocean circulation models used for SST simulations. The spectral parameterization scheme is considered to be the most accurate of the wave-induced Stokes drift calculation schemes. The numerical simulation results of sea surface temperature (SST) with the Stokes drift and SST without the Stokes drift in the North Pacific in 2014 were analyzed. The Stokes drift plays a cooling role in the North Pacific, and the most affected areas are high-latitude sea areas. The following factors are responsible for cooling: the seawater divergence caused by Stokes transport, changes in the sea surface current field caused by the Coriolis-Stokes force and the effects of turbulence caused by Langmuir circulation. The simulation of the vertical temperature profile in the mixed layer is improved when the Stokes drift is accounted for. The simulated results of SST using the Stokes drift approximate parameterization schemes and the spectral parameterization scheme are compared. The results confirm that the spectral parameterization scheme can be used for accurate SST simulation, and the Phillips spectrum approximate parameterization scheme is the best among the approximate parameterization schemes.

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

Journal of Marine Science and Engineering ◽

10.3390/jmse9080834 ◽

2021 ◽

Vol 9 (8) ◽

pp. 834

Author(s):

Zhanfeng Sun ◽

Weizeng Shao ◽

Weili Wang ◽

Wei Zhou ◽

Wupeng Yu ◽

...

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Surface Wind ◽

Japan Sea ◽

Ocean Model ◽

Sea Surface ◽

Stokes Drift ◽

Wind Data ◽

Wave Measurements ◽

Stokes Transport

This study investigated the performance of two ocean wave models, that is, Simulation Wave Nearshore (SWAN) and WAVEWATCH-III (WW3), and the interannual and seasonal variability of transport induced by Stokes drift during the period from 1989 to 2019. Three types of sea surface wind products were used for wave simulation: the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim, the Cross Calibrated Multi-Platform Version 2.0 (CCMP V2.0) from Remote Sensing Systems (RSS), and the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The modeling was validated against wave measurements from the Jason-2 altimeter in 2015. The analysis found that the root mean square error (RMSE) of significant wave height (SWH) from the WW3 model using CCMP wind data was 0.17 m, which is less than the ~0.6-m RMSE of SWH from the SWAN model using the other types of wind data. The simulations from the WW3 model using CCMP wind data indicated that the Stokes transport is up to 2 m2/s higher in the South China Sea and Japan Sea than that at other ocean regions in January. The interannual variation showed that the Stokes transport generally increased from 0.25 m2/s in 1989 to 0.35 m2/s in 2018. We also found that the accuracy of the sea surface temperature (SST) simulation using the Stony Brook Parallel Ocean Model (sbPOM) is improved by as much as 0.5 °C when Stokes transport is considered to validate the sbPOM-simulated SST against the measurements from Argo in 2012-2015. In particular, the Stokes transport has a negative effect on Summer (March to June) and has a positive effect in Autumn (July to September), which is probably caused by the tropical cyclones.

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Controlling atmospheric forcing parameters of global ocean models: sequential assimilation of sea surface Mercator-Ocean reanalysis data

Ocean Science ◽

10.5194/os-5-403-2009 ◽

2009 ◽

Vol 5 (4) ◽

pp. 403-419 ◽

Cited By ~ 11

Author(s):

C. Skandrani ◽

J.-M. Brankart ◽

N. Ferry ◽

J. Verron ◽

P. Brasseur ◽

...

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Reanalysis Data ◽

Ocean Model ◽

Sea Surface ◽

Global Ocean ◽

Atmospheric Forcing ◽

Ocean Reanalysis ◽

Ocean Reanalysis Data

Abstract. In the context of stand alone ocean models, the atmospheric forcing is generally computed using atmospheric parameters that are derived from atmospheric reanalysis data and/or satellite products. With such a forcing, the sea surface temperature that is simulated by the ocean model is usually significantly less accurate than the synoptic maps that can be obtained from the satellite observations. This not only penalizes the realism of the ocean long-term simulations, but also the accuracy of the reanalyses or the usefulness of the short-term operational forecasts (which are key GODAE and MERSEA objectives). In order to improve the situation, partly resulting from inaccuracies in the atmospheric forcing parameters, the purpose of this paper is to investigate a way of further adjusting the state of the atmosphere (within appropriate error bars), so that an explicit ocean model can produce a sea surface temperature that better fits the available observations. This is done by performing idealized assimilation experiments in which Mercator-Ocean reanalysis data are considered as a reference simulation describing the true state of the ocean. Synthetic observation datasets for sea surface temperature and salinity are extracted from the reanalysis to be assimilated in a low resolution global ocean model. The results of these experiments show that it is possible to compute piecewise constant parameter corrections, with predefined amplitude limitations, so that long-term free model simulations become much closer to the reanalysis data, with misfit variance typically divided by a factor 3. These results are obtained by applying a Monte Carlo method to simulate the joint parameter/state prior probability distribution. A truncated Gaussian assumption is used to avoid the most extreme and non-physical parameter corrections. The general lesson of our experiments is indeed that a careful specification of the prior information on the parameters and on their associated uncertainties is a key element in the computation of realistic parameter estimates, especially if the system is affected by other potential sources of model errors.

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Satellite-based time-series of sea-surface temperature since 1981 for climate applications

Scientific Data ◽

10.1038/s41597-019-0236-x ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 27

Author(s):

Christopher J. Merchant ◽

Owen Embury ◽

Claire E. Bulgin ◽

Thomas Block ◽

Gary K. Corlett ◽

...

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Ocean Model ◽

Sea Surface ◽

Climate Data ◽

Satellite Measurements ◽

Data Record ◽

The Mean ◽

Climate Data Record

Abstract A climate data record of global sea surface temperature (SST) spanning 1981–2016 has been developed from 4 × 1012 satellite measurements of thermal infra-red radiance. The spatial area represented by pixel SST estimates is between 1 km2 and 45 km2. The mean density of good-quality observations is 13 km−2 yr−1. SST uncertainty is evaluated per datum, the median uncertainty for pixel SSTs being 0.18 K. Multi-annual observational stability relative to drifting buoy measurements is within 0.003 K yr−1 of zero with high confidence, despite maximal independence from in situ SSTs over the latter two decades of the record. Data are provided at native resolution, gridded at 0.05° latitude-longitude resolution (individual sensors), and aggregated and gap-filled on a daily 0.05° grid. Skin SSTs, depth-adjusted SSTs de-aliased with respect to the diurnal cycle, and SST anomalies are provided. Target applications of the dataset include: climate and ocean model evaluation; quantification of marine change and variability (including marine heatwaves); climate and ocean-atmosphere processes; and specific applications in ocean ecology, oceanography and geophysics.

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Change in the Recent Warming Trend of Sea Surface Temperature in the East Sea (Sea of Japan) over Decades (1982–2018)

Remote Sensing ◽

10.3390/rs11222613 ◽

2019 ◽

Vol 11 (22) ◽

pp. 2613 ◽

Cited By ~ 4

Author(s):

Eun-Young Lee ◽

Kyung-Ae Park

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Sea Of Japan ◽

Warming Trend ◽

Frequent Occurrence ◽

Sea Surface ◽

East Sea ◽

The Arctic ◽

Arctic Oscillation Index ◽

Sst Cooling

Long-term trends of sea surface temperature (SST) of the East Sea (Sea of Japan, EJS) were estimated by using 37-year-long satellite data, for the observation period from 1982 to 2018. Overall, the SST tended to increase with time, for all analyzed regions. However, the warming trend was steeper in the earlier decades since the 1980s and slowed down during the recent two decades. Based on the analysis of the occurrence of events with extreme SST (high in the summertime and low in the wintertime), a shift toward the more frequent occurrence of events with extremely high SST and the less frequent occurrence of events with extremely low SST has been observed. This supports the observations of the consistent warming of the EJS. However, seasonal trends revealed continuous SST warming in the summertime, but frequent extreme SST cooling in the wintertime, in recent decades. The observed reduction in the warming rates occurred more frequently in specific regions of the EJS, where the occurrence frequency of events with extremely low SST was unusually high in the recent decade. The recent tendency toward the SST cooling was distinctively connected with variations in the Arctic Oscillation index. This suggests that changes in the Arctic Ocean environment likely affect the recently observed SST changes in the EJS, as one of the marginal seas in the mid-latitude region far from the polar region.

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Intraseasonal Sea Surface Temperature Variability across the Indonesian Seas*

Journal of Climate ◽

10.1175/jcli-d-14-00758.1 ◽

2015 ◽

Vol 28 (22) ◽

pp. 8710-8727 ◽

Cited By ~ 14

Author(s):

Asmi M. Napitu ◽

Arnold L. Gordon ◽

Kandaga Pujiana

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Intraseasonal Variability ◽

Longwave Radiation ◽

Active Phase ◽

Ocean Model ◽

Sea Surface ◽

Indonesian Seas ◽

Sst Variability ◽

Slab Ocean

Abstract Sea surface temperature (SST) variability at intraseasonal time scales across the Indonesian Seas during January 1998–mid-2012 is examined. The intraseasonal variability is most energetic in the Banda and Timor Seas, with a standard deviation of 0.4°–0.5°C, representing 55%–60% of total nonseasonal SST variance. A slab ocean model demonstrates that intraseasonal air–sea heat flux variability, largely attributed to the Madden–Julian oscillation (MJO), accounts for 69%–78% intraseasonal SST variability in the Banda and Timor Seas. While the slab ocean model accurately reproduces the observed intraseasonal SST variations during the northern winter months, it underestimates the summer variability. The authors posit that this is a consequence of a more vigorous cooling effect induced by ocean processes during the summer. Two strong MJO cycles occurred in late 2007–early 2008, and their imprints were clearly evident in the SST of the Banda and Timor Seas. The passive phase of the MJO [enhanced outgoing longwave radiation (OLR) and weak zonal wind stress) projects on SST as a warming period, while the active phase (suppressed OLR and westerly wind bursts) projects on SST as a cooling phase. SST also displays significant intraseasonal variations in the Sulawesi Sea, but these differ in characteristics from those of the Banda and Timor Seas and are attributed to ocean eddies and atmospheric processes independent from the MJO.

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Optimizing COSMO_REA6 reanalysis radiation flux for a high resolution coastal ocean model

10.5194/egusphere-egu2020-3305 ◽

2020 ◽

Author(s):

Martin Vodopivec ◽

Matjaž Ličer

Keyword(s):

High Resolution ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Atmospheric Model ◽

Short Wave ◽

Ocean Model ◽

Sea Surface ◽

Atmospheric Models ◽

Fine Grid ◽

Wave Flux

<p>When modelling coastal areas in high spatial resolution, it is also essential to obtain atmospheric forcing with suitably fine grid. The complex coastline and coastal orography exert strong influence on atmospheric fields, wind in particular, and the east Adriatic coast with numerous islands and coastal mountain ridges is a fine example. We decided to use a high resolution COSMO atmospheric reanalysis for our long term ROMS_AGRIF hindcasts, but in our initial experiments we found out that the atmospheric model significantly underestimates the short wave flux over the Mediterranean Sea, probably due to overestimation of high clouds formation and erroneous sea surface temperature used as a boundary condition. We explore different atmospheric models and different combinations of fluxes - direct, diffuse and clear sky solar radiation and combinations of fluxes from different atmospheric models (eg. ERA5). We compare them with solar irradiance observations at a coastal meteorological station and run year-long simulations to compare model sea surface temperature (SST) with satellite observations obtained from Coprenicus Marine Environment Monitoring Service.</p>

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Including Uncertainties of Sea Surface Temperature in an Ensemble Kalman Filter: A Case Study of Typhoon Sinlaku (2008)

Weather and Forecasting ◽

10.1175/waf-d-11-00136.1 ◽

2012 ◽

Vol 27 (6) ◽

pp. 1586-1597 ◽

Cited By ~ 16

Author(s):

Masaru Kunii ◽

Takemasa Miyoshi

Keyword(s):

Kalman Filter ◽

Surface Temperature ◽

Sea Surface Temperature ◽

Ensemble Kalman Filter ◽

Forecast Error ◽

Ocean Model ◽

Sea Surface ◽

Ensemble Spread ◽

Error Covariance ◽

The Impact

Abstract Sea surface temperature (SST) plays an important role in tropical cyclone (TC) life cycle evolution, but often the uncertainties in SST estimates are not considered in the ensemble Kalman filter (EnKF). The lack of uncertainties in SST generally results in the lack of ensemble spread in the atmospheric states near the sea surface, particularly for temperature and moisture. In this study, the uncertainties of SST are included by adding ensemble perturbations to the SST field, and the impact of the SST perturbations is investigated using the local ensemble transform Kalman filter (LETKF) with the Weather Research and Forecasting Model (WRF) in the case of Typhoon Sinlaku (2008). In addition to the experiment with the perturbed SST, another experiment with manually inflated ensemble perturbations near the sea surface is performed for comparison. The results indicate that the SST perturbations within EnKF generally improve analyses and their subsequent forecasts, although manually inflating the ensemble spread instead of perturbing SST does not help. Investigations of the ensemble-based forecast error covariance indicate larger scales for low-level temperature and moisture from the SST perturbations, although manual inflation of ensemble spread does not produce such structural effects on the forecast error covariance. This study suggests the importance of considering SST perturbations within ensemble-based data assimilation and promotes further studies with more sophisticated methods of perturbing SST fields such as using a fully coupled atmosphere–ocean model.

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An Analytical Framework for Understanding Tropical Meridional Modes

Journal of Climate ◽

10.1175/jcli-d-16-0450.1 ◽

2017 ◽

Vol 30 (9) ◽

pp. 3303-3323 ◽

Cited By ~ 5

Author(s):

Cristian Martinez-Villalobos ◽

Daniel J. Vimont

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Ocean Model ◽

Sea Surface ◽

Transient Growth ◽

Kelvin Wave ◽

Wes Feedback ◽

Meridional Mode ◽

The Tropics

A theoretical framework is developed for understanding the transient growth and propagation characteristics of thermodynamically coupled, meridional mode–like structures in the tropics. The model consists of a Gill–Matsuno-type steady atmosphere under the long-wave approximation coupled via a wind–evaporation–sea surface temperature (WES) feedback to a “slab” ocean model. When projected onto meridional basis functions for the atmosphere the system simplifies to a nonnormal set of equations that describes the evolution of individual sea surface temperature (SST) modes, with clean separation between equatorially symmetric and antisymmetric modes. The following major findings result from analysis of the system: 1) a transient growth process exists whereby specific SST modes propagate toward lower-order modes at the expense of the higher-order modes; 2) the same dynamical mechanisms govern the evolution of symmetric and antisymmetric SST modes except for the lowest-order wavenumber, where for symmetric structures the atmospheric Kelvin wave plays a critically different role in enhancing decay; and 3) the WES feedback is positive for all modes (with a maximum for the most equatorially confined antisymmetric structure) except for the most equatorially confined symmetric mode where the Kelvin wave generates a negative WES feedback. Taken together, these findings explain why equatorially antisymmetric “dipole”-like structures may dominate thermodynamically coupled ocean–atmosphere variability in the tropics. The role of nonnormality and the role of realistic mean states in meridional mode variability are discussed.

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