Coupling of internal waves on the main thermocline to the diurnal surface layer and sea surface temperature during the Tropical Ocean-Global Atmosphere Coupled Ocean-Atmosphere Response Experiment

1998 ◽  
Vol 103 (C6) ◽  
pp. 12613-12628 ◽  
Author(s):  
E. J. Walsh ◽  
R. Pinkel ◽  
D. E. Hagan ◽  
R. A. Weller ◽  
C. W. Fairall ◽  
...  
1991 ◽  
Vol 96 (C5) ◽  
pp. 8593 ◽  
Author(s):  
C. A. Friehe ◽  
W. J. Shaw ◽  
D. P. Rogers ◽  
K. L. Davidson ◽  
W. G. Large ◽  
...  

2020 ◽  
Author(s):  
Olga Lavrova ◽  
Andrey Kostianoy

<p>Internal waves (IWs) are an intrinsic feature of all density stratified water bodies: oceans, seas, lakes and reservoirs. IWs occur due to various causes. Among them are tides and inertial motions, variations in atmospheric pressure and wind, underwater earthquakes, water flows over bottom topography, anthropogenic factors, etc. In coastal areas of oceans and tidal seas,  IWs induced by tidal currents over shelf edge predominate. Such IWs are well-studied in multiple field, laboratory and numerical experiments. However, the data on IWs in non-tidal seas, such as the Black, Baltic and Caspian Seas, are scarce. Meanwhile, our multi-year satellite observations prove IWs to be quite a characteristic hydrophysical phenomenon of the Caspian Sea. The sea is considered non-tidal because tide height does not exceed 12 cm at the coastline. And yet surface manifestations of IWs are regularly observed in satellite data, both radar and visible. The goal of our study was to reveal spatial, seasonal and interannual variability of IW surface manifestations in the Caspian Sea in the periods of 1999-2012 and 2018-2019 from the analysis of satellite data. All available satellite radar and visible data were used, that is data from ERS1/2 SAR; Envisat ASAR; Sentinel-1A,1B SAR-C; Landsat-4,5 TM; Landsat-7 ETM+; Landsat-8 OLI; Sentinel-2A,2B MSI sensors. During the year, IWs were observed from the beginning of May to mid-September. In certain years, depending on hydrometeorological conditions, such as water heating, wind field, etc., no IWs could be seen in May or September. IWs regularly occur in the east of Middle Caspian and in the northeast of South Caspian. In North Caspian, due to its shallowness and absence of pronounced stratification, IWs are not generated, at least their surface signatures cannot be found in satellite data. In the west of the sea, IWs are scarcely observed, primarily at the beginning of the summer season. IW trains propagate toward the coast, their generation sites are mainly over the depths of 50-200 m.</p><p>According to the available data for the studied periods, the time of the first appearance of IW signatures differs significantly from year to year. For example, in 1999 and 2000 it happened only in July.</p><p>Since no in situ measurements were conducted in the sites of regular IW manifestations, an attempt  was made to establish the dependence of IW occurrence frequency  on seasonal and interannual variations of sea surface temperature, an indirect indicator of the depth of the diurnal or seasonal thermocline, that is where IW were generated. Sea surface temperature was also estimated from satellite data.</p><p>Another issue addressed in the work was the differentiation between the sea surface signatures of IWs in the atmosphere and the sea. The Caspian Sea is known for their close similarity in spatial characteristics.</p><p>The work was carried out with financial support of the Russian Science Foundation grant #19-77-20060.  Processing of satellite data was carried out by Center for Collective Use “IKI-Monitoring” with the use of “See The Sea” system, that was implemented in frame of Theme “Monitoring”, State register No. 01.20.0.2.00164.</p>


2008 ◽  
Vol 21 (20) ◽  
pp. 5304-5317 ◽  
Author(s):  
Hye-Mi Kim ◽  
Carlos D. Hoyos ◽  
Peter J. Webster ◽  
In-Sik Kang

Abstract The influence of sea surface temperature (SST) on the simulation and predictability of the Madden–Julian oscillation (MJO) is examined using the Seoul National University atmospheric general circulation model (SNU AGCM). Forecast skill was examined using serial climate simulations spanning eight different winter seasons with 30-day forecasts commencing every 5 days, giving a total of 184 thirty-day simulations. The serial runs were repeated using prescribing observed SST with monthly, weekly, and daily temporal resolutions. The mean SST was the same for all cases so that differences between experiments result from the different temporal resolutions of the SST boundary forcing. It is shown that high temporal SST frequency acts to improve 1) the MJO activity of 200-hPa velocity potential field over the entire Asian monsoon region at all lead times; 2) the percentage of filtered variance of the two leading EOF modes that explain the eastward propagation of MJO; 3) the power of the wavenumber 1 eastward propagating mode; and 4) the forecast skill of MJO, maintaining it for longer periods. However, the MJO phase relationship between MJO convection and SST, as is often the case with many atmosphere-only models, although well simulated at the beginning of forecast period becomes distorted rapidly as the forecast lead time increases, even with the daily SST forcing case. Comparison of AGCM simulations with coupled GCM (CGCM) integrations shows that ocean–atmosphere coupling improves considerably the phase relationship between SST and convection. The CGCM results reinforce that the MJO is a coupled phenomenon and suggest strongly the need of the ocean–atmosphere coupled processes to extend predictability.


Author(s):  
J. Thomas Farrar ◽  
Christopher J. Zappa ◽  
Robert A. Weller ◽  
Andrew T. Jessup

2008 ◽  
Vol 21 (21) ◽  
pp. 5501-5523 ◽  
Author(s):  
Susan C. Bates

Abstract Many previous studies point to a connection between the annual cycle and interannual variability in the tropical Atlantic Ocean. To investigate the importance of the annual cycle in the generation of tropical Atlantic variability (TAV) as well as its associated coupled feedback mechanisms, a set of controlled experiments is conducted using a global coupled ocean–atmosphere general circulation model (GCM) in which the climatological annual cycle is modified. An anomaly coupling strategy was developed to improve the model-simulated annual cycle and mean sea surface temperature (SST), which is critical to the experiments. Experiments include a control simulation in which the annual cycle is present and a fixed annual cycle simulation in which the coupled model is forced to remain in a perpetual annual mean state. Results reveal that the patterns of TAV, defined as the leading three rotated EOFs, and their relationship to coupled feedback mechanisms are present even in the absence of the annual cycle, suggesting that the generation of TAV is not dependent on the annual cycle. Each pattern of variability arises from an alteration of the easterly trade winds. Results suggest that it is the presence of these winds in the mean state that is the determining factor for the structure of the coupled ocean–atmosphere variability. Additionally, the patterns of variability persist longer in the simulation with no annual cycle. Most remarkable is the doubling of the decay phase related to the north tropical Atlantic variability, which is attributed to the persistence of the local wind–evaporation–sea surface temperature (WES) feedback mechanism. The author concludes that the annual cycle acts to cut off or interrupt conditions favorable for feedback mechanisms to operate, therefore putting a limit on the length of the event life cycle.


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