Physical Oceanography - Science topic

This is not a programming question. It's a time-series question. Imagine measuring temperature in your back yard every hour for a day. You could use that to make predictions, but they might not be very useful, because weather changes from day to day. So, you need more than a day. Maybe you measure for 3 days. That would be better. But maybe those were 3 warm days, which are followed by 3 cool days. Etc.

Depending on your background, you might start by reading books on time-series analysis. Then move on to books about ocean physics. And then climate physics. You will soon see that statistical prediction is a weak approach, and that dynamical models are required. That takes you from the domain of reading and plotting with python to the domain of building PhD-level scientific and computing skills. The latter go way beyond plotting with python; you'll need to deploy supercomputers to run models that were took many person-decades to develop and take person-years to learn to run. Oh, and the end result will be a model prediction that will not agree with other model predictions to within the error bars we want for climate prediction.

Forecasting the quantity of chlorophyll-a based on time series (historical data) is not considered scientifically reliable due to the variable nature of chlorophyll-a, as it serves as the sole reference source for predicting the past behavior of the study area. Instead, it is recommended to estimate the amount of chlorophyll-a based on the variability of SST and SSS. To initiate this process, it is appropriate to utilize the simple mlp artificial network algorithm, which can be tailored to suit your specific requirements. Please refer to this article for further information: https://doi.org/10.1016/j.jenvman.2022.115636

Helpful and thanks!

Sebastian Enrique Cornejo Guzmán AI is becoming an increasingly significant tool in oceanography because of its ability to handle and analyze vast volumes of data rapidly and efficiently. It is crucial to highlight, however, that the application of AI in oceanography may result in research inequities, since certain subfields may have more resources and skills to deploy AI-based methodologies than others. Furthermore, it is critical to be open about the limitations of AI in each sector and to utilize the technology with caution, ensuring that the findings are accurate and dependable.

There are several papers that explore the application of AI in oceanography, some of which are as follows:

These papers can give more thorough information on the application of AI in oceanography, including possible advantages and problems.

Dear colleagues

I think will definitely be able to find information in the famous and huge Lenin State Library in Moscow…

In particular, the works on line.

I have friends who have active library cards - I will ask.

I will contact you soon

Hello, We are now making a second attempt at the Cape Verde. I will keep you informed.

Vest regards

A very complete review on Ertel potential vorticity theorem, thank you for suggesting its reading, Prof. Aref Wazwaz .

Best Regards.

may this article will help

Above mentioned pl66tn function is available in https://github.com/sea-mat/bobstuff/blob/master/pl66tn.m

Dear Jagadeesan,

You may check sentinel 3 data provided by ESA.

Best of luck!

Have a look at our paper, "Resolving the Euphotic Zone," Deep-Sea Research I, vol. 83, pp. 45-50. We propose that the biological compensation depth is roughly equivalent to the depth of the base of the fluorescence maximum.

If your result is SARIMA(3,1,1)(3,1,1) it means that you have both seasonal and nonseasonal autoregressive orders of 3, moving average of orders 1 and orders of difference of 1.

If you are looking for a reputable Journals in Physical Oceanography you should first look to the Journal impact factor. (IR) .

So from my experience if you wish to publish in peer reviewed Journals,

I recommend the following Journals

1- Journal of Progress in Oceanography (Elsevier)

2- Journal of Dynamics of Atmospheres and Oceans (Elsevier)

3- Journal of Marine Science and Engineering (MDPI)

There are others such as Journal of Physical Oceanography , Ocean modelling ...

Dear Alamgir,

There are too many interesting topics, these some remote sensing of oceanography applications could help you!

- coastal erosion by GIS & RS

- Coral reef change detection

- water quality variables obtained by satellites

- Sea level rise

Best of luck!

Justin -

Could you be using logs to reduce heteroscedasticity?

It sounds like you want to use the overlap in data sets to see how one predicts for the other, and then assume that relationship holds up outside of that set? Hopefully you have reason to think that. If you have enough "overlap" data, perhaps you can see if you get compatible results when you do this for the first half of these "overlap" data, and then for the second half, considering the estimated variances of the prediction errors, or at least the standard errors of the slopes.

At any rate, to consider heteroscedasticity, I would avoid any transformations and use weighted least squares regression, considering heteroscedasticity in the error structure. The first link below provides some examples, and the next is for a tool you can use to do this.

https://www.researchgate.net/publication/333642828_Estimating_the_Coefficient_of_Heteroscedasticity

https://www.researchgate.net/publication/333659087_Tool_for_estimating_coefficient_of_heteroscedasticityxlsx

However, it sounds like you should also look into serial correlation. That isn't an area I can help you with though. I've worked more with finite populations than time series. I suggest you might want to research "generalized least squares" (GLS regression).

Best wishes - Jim

Hi Volney Junior Borges de Bitencourt, thanks for your reply. Just to check I've understood this method, the suggestion is that I carry out a weighted interpolation based on a composite of the DEM and measured values? I will give this a try but my (possibly naive) concern is that this would result in obvious bumps around my measured points since in many locations, the elevation difference between these points and the DEM is really extreme, and the spacing of observed points is much higher than the DEM resolution.

Dear Auad,

Thank a lot for your answer. The paper you shared did helped a lot on distinguish and verify the effect of daily wind. But the paper also use the bulk formula estimating the wind stress, which is not capable to exclude the effect of surface wave. But the paper did open an new pathway for me on how to verify the effect of different scale of motions. Thank you very much for your help! @Auad

Experimenting with different criteria is the way to go as L. J. Hamilton suggested. Plot number of profiles with the UML depth and visually check, how the criteria works.

One might consider also vertical density gradient criteria instead of density difference from surface. I tried and played a lot with this and ended up gradient criteria depending on background stratification:

You can also check the paper discussing the topic:

using the temperature and salinity profile, you can compute the potential density at each points and draw the isopycnal, the downward movement of isopycnals indicate the downwelling phenomina

Working with time-of-day as a factor will be a challenge because I assume your sampling times are random randomly spaced. That means you might be better off fitting for a trend plus some model of diurnal variation. The problem is that latter can take many functional forms, depending on solar forcing, whether there is nighttime convection, and so on.

I would advise starting by regressing to a formula that has a trend, plus both a sine() and a cosine() with period one day. (Using both sin and cos lets you find the phase, but still in a linear regression, which is easier to deal with than a nonlinear one, unless you are experienced.) Then add a twice-daily harmonic. Then add a thrice-daily harmonic. By comparing the results you may start to learn how the diurnal variation is affecting your estimate of the trend.

I would start with artificial data, so you can test the accuracy. AIC or other methods should be used to avoid overfitting. (If you don't know what AIC means, you ought to give yourself some time to learn elementary stats, before trying anything complicated with your data.)

Whether you will see a linear trend is questionable. I've never seen such a simple pattern in ocean data. So you might want to start by fitting this sort of sequence of diurnal models, then removing the diurnal signal, and then simply plotting the results, rather than expecting a trend.

Regardless of the method, an important thing is to consider the methods uncertainty in your results. Indicators such as Success of Detection Rate (SDR, identifying those eddies that exist), and Excess of Detection Rate (EDR, identifying as an eddy something that is not) should be part of your results. Some methods publish those (or others) indicators in their description, others don't. For an example, see Fig. 2 of Chaigneau et al (2008), where their method has a similar SDR as using Okubo-Weiss method (~90%), but much lower (but still ~20%) EDR.

My suggestion is to use a method of your choice and then do a manual identification of 10-20 sample images, to validate the code in your results by visually identifying the eddies. The more colleagues that can help your with this, the better.

REF: Chaigneau, A., Gizolme, A., & Grados, C. (2008). Mesoscale eddies off Peru in altimeter records: Identification algorithms and eddy spatio-temporal patterns. Progress in Oceanography, 79(2-4), 106-119.

How can I plot U and V wind vectors in ArcGIS 9.3

This paper may help in your research Direct Formula to Compute Wave Height and Angle at Incipient Breaking, JWPCE, ASCE

It is a very big limitiation for using SWAN model. In wavewatch3 all features for shallow water was added and ice module working good.

 Thanks a lot.

Hi, you can use Ocean Data View to plot/view time series data. 

Use "View->Derived Variables" and request the "Time (station date&time)" derived

variable.

or search online by keywords"Ocean Data View Instructions to generate time series plots"

Thanks Guillermo. Yes, I do have wind data and estimated the Ekman upwelling strength, which I think is acceptable (5-10 m/day).  The problem is that I don't have the evidence for the local wind effect on upwelling to accept the theoretically calculated Ekman upwelling strength. Hence, I am searching for a sound reason to include only a fraction of that upwelling in a model.  The current meter data comes in hourly time resolution but I low pass filter them to get rid of tidal components.  I am in a coastal region (southern Vancouver Island) and yes there are wind speed and direction gradients  along the west coast of North America with less upwelling favorable winds near my region. It appears now the poor correlation between current (from depths shallower than mine) and local wind in my study region has been documented in another work . I know some upwelling regions where observed water properties were satisfactorily explained without Ekman upwelling (Best example is a paper by Allen J Clarke, http://onlinelibrary.wiley.com/doi/10.1029/JC084iC07p03743/abstract).  When it comes to my region, the issue of whether Ekman upwelling is negligible is not settled. Therefore, including at least a fraction of it in a model is what I am convinced to do.

ps. I use reanalysis wind with 32 km grid spacing and wind location is not a problem. Some grid points are right next to the mooring.

I guess, Liu means the paper from Goda 1988?! Its probably not the paper, Jan has mentioned?!

Yoshimi Goda, STATISTICAL VARIABILITY OF SEA STATE PARAMETERS

AS A FUNCTION OF WAVE SPECTRUM, Coastal Engineering in Japan, Vol. 31, No. 1, 1988

thank you(it would be definitely do-able using GrADS maxloc() function. You may want to further polish with some maskout() function to provide any criterion of your liking)

it is very good. thank you very much

I think your interest to examine these temperature and salinity density/stability ratio on the coastline rather on the open water. If so, I suggest you look in other factors i.e., tidal chandes, local current circulation and bathymetry. 

These elements are affected local dwillers in many ways i.e., induce spawning

Thank you Guillermo.

 

In this paper they assimilate data from an on-board ADCP. The data is "averaged" to remove small scale features not represented by the model.

My case, however, is a little different since the ADCP is moored.

The model is able to reproduce the high frequency I am interested on. Given the processed data is provided hourly, there should not be any "mandatory" a priori filtering for the assimilation... 

I had generated some sketches for the air-entrainment, bubble formation and buoyant degassing processes occurring during plunging breaking for my review article.

Here's a link to the paper. I hope this helps. Best regards. 

Hello Sri Hari,

You can check for SST data from the following websites. https://climexp.knmi.nl/select.cgi?id=someone@somewhere&field=coads_sst

Check the source of the data in the beginning of the page. Go to the end of the page and download the NetCDF file. 

Cheers :-) 

Dear Nicolas,

You can also use a Bland Altman diagram by ploting the series of differences as a function of the mean of the series and compute the limit you admit for the differences (e.g. +/- one sigma). One limit of the method is the gaussian distribution of the difference series

Hoping it can help you, Regards, G.C.

you can try to find it in marine copernicus

it's free

Abbas,

It would be better to ask the relevant institution that concerns about the records of that region at least they might guide you to your goal.

M. Basel

Hello Tom,

Apologies for my delayed response. Many thanks for letting know about the paper.

Best regards,

Netra

Very interesting question. The bottom current has an effect on detrital input which in turn, may influence the feeding activities of macrobenthic polychaete community.

The following references may help you more.

Regards.

The problem that I'm investigating is related to topics in atmospheric science, specifically the planetary boundary layer. A lot of overlap can be found between the two branches, but I'm looking at a specific problem that might require a detour in this direction. I have found laboratory experiments discussing the interaction of two jets, but these are narrow jets, and I'm not sure the same would result if the jets were much broader O~100km.

From the great webpage of Colorado Sea level Group: "the ocean basins are getting slightly larger since the end of the last glacial cycle. GIA is not caused by current glacier melt, but by the rebound of the Earth from the several kilometer thick ice sheets that covered much of North America and Europe around 20,000 years ago. Mantle material is still moving from under the oceans into previously glaciated regions on land. The effect is that currently some land surfaces are rising and some ocean bottoms are falling relative to the center of the Earth (the center of the reference frame of the satellite altimeter). Averaged over the global ocean surface, the mean rate of sea level change due to GIA is independently estimated from models at -0.3 mm/yr (Peltier, 2001, 2002, 2009; Peltier & Luthcke, 2009)." Check it, ypu will probably find a good answer: http://sealevel.colorado.edu/content/what-glacial-isostatic-adjustment-gia-and-why-do-you-correct-it

To go deeper, my suggestions is to read Peltier, as Arvind suggested you: Peltier, W. R. (2004). Global glacial isostasy and the surface of the ice-age Earth: the ICE-5G (VM2) model and GRACE. Annu. Rev. Earth Planet. Sci., 32, 111-14, although he is more phocused in the modelling on every paper he publishes, and I do not know if you are so interested on theses details.

Nevertheless, do not only take into account these isostasic models. If you are dealing with local data, check tide gauges time series, and especially, compare them with global measn sea level rise time series (Church and White, 2012) and altimetry satellite data, to check what is going on in your area. Peltier model is clear for higher lattitudes but it is still unclear about mid latitudes.

Hi Thomas,

What is the directional resolution you use in your SWAN discretization for swell?

If you scroll down to the last three paragraphs on directional space:

You will see that for swell, a directional resolution of 2 degrees or less may be required, for swell with a directional spreading of less than 10 degrees.

Heng

@Alastair Bain McDonald,,, thank you sir

Upwelling, such as that typical in eastern boundary current regions (California Current, Peru-Chile Current, Benguela Current), brings relatively cooler water to the surface. This creates cool, moist air over the ocean surface. If this air moves over the relatively warmer land mass, the moisture condenses and creates fog.

There is a natural onshore flow that develops in response to the warm thermal low that typically sets up over the land mass adjacent to the eastern boundary, such as in the SW US. This pattern draws cooler marine air over the land, resulting in a persistent foggy condition during the local summer (high upwelling and warm continent). Local vegetation can adapted to this condition, and agriculture interests take advantage of it.

One interesting area of study is how anthropogenic warming will affect this pattern. The hypothesis is that global warming will lead to a warmer continent AND greater upwelling-favorable winds, which may result in greater fog. I am not aware of any substantive study to confirm this. One problem with looking at historical records of fog and SST is that other factors contribute to fog in coastal regions, and the fog is spatially very heterogeneous and localized.

Thanks to all. I finally found an answer in Cole et al. (JGR 1998): "Cyclic loading and creep response of aligned first-year sea ice" which is generally consistent with Wadhams (JGR 1973), in proposing a reduced B coefficient for creep. This is now in version 5.10 of WAVEWATCH III (to be released in a few months) ... and was used for this paper:

But yes it is in general a function of ice temperature and thickness and floe sizes are very important parameters too. 

Particularmente en el Golfo de California ocurren demasiados procesos de diferente índole que resultan en frentes superficiales de temperatura. En el norte la mezcla de marea genera frentes térmicos de hasta 3 grados en el archipielago central, y frentes más difusos en el alto golfo. En el sur del golfo los procesos predominantes que generan frentes son la surgencia en invierno/primavera y los remolinos en verano, En la boca por otro lado la interacción con la corriente de California y el agua tropical generan frentes de masas de agua.

Entonces, todo el golfo es susceptible a los procesos de mesoescala, pero hablar de un frente superficial, sin tomar en cuenta su origen, dificulta evaluar en que medida promueve el aumento de la productividad.

Particularly in the Gulf of California too many different kinds processes resulting in surface temperature fronts. In the north, tidal mixing generates thermal fronts up to 3 degrees in the central archipelago, and more diffuse fronts in the upper gulf. In the southern Gulf, predominant processes that generate fronts are upwelling in winter / spring and eddys in summer. In the mouth, interaction with the California current and tropical water, and gulf water generate fronts throughout the year.

Then, the entire Gulf is susceptible to mesoscale processes, but talk of a surface front, regardless of its origin, difficult to assess to what extent promotes increased productivity.

The 'Pactical Salinity Scale' (PSS) was defined in 1978 and later promulgated by the UNESCO/ICES/SCOR/IAPSO Joint Panel on Oceanographic Tables and Standards in Sidney, BC, Canada, 1-5 September 1980. Because it makes no sense to say the salinity is , for example, 35 PSS, the term PSU was introduced, as the scale is composed of units. However, the use of PSU is discouraged and, because salinity is by definition a dimensionless parameter, it should simply be represented by a number. As practical salinity has been given the symbol S (cf. Unesco Technical Papers in Marine Science 45, 1985, or IAPSO Publication Scientifique No. 32, 1985), one should speak of a salinity of, for example, S = 35.

Burg Flemming

Dear Prof. Stigter, Thank you very much! 

Many thanks for all the answers!

@Hui @ Auad

Please see the location of Transect A given in the attached Figures. The T, S, and Density profiles are also attached. This feature is just a snapshot of one cruise. Is the slope break sharp? There is a relative strong density gradient at 123.5 °E (with high turbidity). Can the density front lead to upwelling? I remember that the wintertime upwelling off the Zhe-Min Coast is due to the strong density front (Qiao et al., 2006JGR). Are they similar or different?

Basic information can be found in McWilliam note:

in addition I suggest:

Large, W G., J.C. Mc Williams, and S.C. Doney, 1994: Ocean vertical mixing: A review and a model with a nonlocal boundary layer parameterization. Rev. Geophys.,32, 363-403.

Dear Peter,

You may find other indications in Paulson and Simpson (1977) JPO 7, 952-956 and in Prieur and Legendre (1988) in "Toward a theory on biological-Physical interactions of the world ocean" (Elsevier, New-York). But the best for you is the climatology of mean optuical properties of Simonot and Letreut (1986), JGR, 91, 6642-6646 that will provide you the Jerlov's coefficients for the North Atlantic basin

Best

G.C.

Our article published in peer-reviewed Journal "Communicative & Integrative Biology". A few major points discussed in the paper:

(1) Brain is not the source of consciousness.

(2) Consciousness is ubiquitous in all living organisms, starting from bacteria to human beings.

(3) The individual cells in the multicellular organisms are also individually cognitive entities.

(4) Proposals like “artificial life”, “artificial intelligence”, “sentient machines” and so on are only fairytales because no designer can produce an artifact with the properties like internal teleology (Naturzweck) and formative force (bildende Kraft).

(5) The material origin of life and objective evolution are only misconceptions that biologists must overcome.

Hi  Abbas,

The Froude Number is broadly defined as a ratio of some current speeds over some wave phase speeds. Specifically in the estuaries, there are three forms of Froude Number.

The detailed descriptions about estuarine dimensionless numbers could be found in the book: "Estuaries, a physical introduction, 2nd Edition" by Keith R. Dyer, published in 1997.

Give a lool to the Copernicus Marine Environmental Monitoring Service (CMEMS), formerly MyOcean.

There are various bio-physical parameters - including chlorophyll -  obtained both from Earth Observation data and models which are provided at different resolutions. In some cases the data series start from earlier than 2000.

A simple version of TC and ETC results,  based on :

McColl, K. A., J. Vogelzang, A. G. Konings,

D. Entekhabi, M. Piles, and A. Stoffelen

(2014), Extended triple collocation:

Estimating errors and correlation

coefficients with respect to an unknown

target, Geophys. Res. Lett., 41,

6229–6236, doi:10.1002/2014GL061322.

c=cov(x);

q=[];

q(1)=sqrt(c(1,1)-c(1,2).*c(1,3)/c(2,3));

q(2)=sqrt(c(2,2)-c(1,2).*c(2,3)/c(1,3));

q(3)=sqrt(c(3,3)-c(1,3).*c(2,3)/c(1,2));

p=[];

p(1)=sqrt((c(1,2)*c(1,3))/(c(1,1)*c(2,3)));

p(2)=sqrt((c(1,2)*c(2,3))/(c(2,2)*c(1,3)));

p(3)=sqrt((c(1,3)*c(2,3))/(c(3,3)*c(1,2)));

Having 'integral' properties of the wave field, as Hs & Tz it is IMPOSSIBLE to make a separation; a sum of say two contributions , far & local fields. The reason is that the 'Significant Wave' and 'wave period' are  properties derived from the wave spectra in ALL its contributions (if you only know the sum, it is just out of the question to separate 'contributions'). Only if you have the time series from which such integral properties were estimated, then it is (if the local and far fields have 'non-overlaping' spectra) plausible a separation.....via computing spectra & figuring out which peaks /bands to add as separate contributions.

The most straight forward way is to use harmonic analysis to create a tide time series and then subtract that from the data to create a residual. However, in our experience, there will always be some "tidal frequency" left overs for various reasons. A better way is to Fourier transform your time series and patch over the tidal bands in frequency space with random phase oscillations that match the adjacent spectrum. Then re-transform to time domain. This approach was successful for the long San Francisco tide gauge record. See: Bromirski, Flick, and Cayan, 2003. Storminess Variability along the California Coast: 1858 - 2000, J. Climate 16(6), 982-993. Your record is short, so that may create difficulty narrowing the spectral peaks, but on the other hand noise form errors and glitches will be easier to deal with.

Please see the report arXiv:1503.04313v1.pdf

 You may wish to check:

Robert H. Cole: "Underwater Explosions" , Princeton University Press (1948), reprinted in 1965 by Dover Publications Inc, in particular chapters 6, 7 and 8. 

Thanks Carlo,

I think Collins coefficient is a good solution to this issue.

V.V.T

I have to respectfully disagree with Alastair McDonald's reply.  The upper air away from the surface does of course follow the cylindrical circulation (so long as you are still low in the atmosphere).  But the question is about wind close to the surface, which is governed not only by the pressure gradient and coriolis effects of the upper air, but also frictional effects from the surface.

The Eckman calculation is single column, and is indifferent to the large scale circulation.  In a low pressure system you see the effects as air near the ground cuts across the pressure contours at nearly 45 degrees, but upper air follows the pressure contours.  A hurricane is just a massive low pressure system.  The Eckman profile holds at each location, so that the wind near the ground inclines towards the local low pressure (not directly towards it) while higher  air will circulate perpendicular to the pressure.  Higher still the wind becomes vertical, then eventually switches to circulating the opposite direction.  But all this higher stuff is not Eckman circulation, and does not pertain to the question about surface winds.  About a km high the air is moving around the local low pressure; below it begins to turn so that near the ground it has a component towards the low pressure.

Over ocean, the friction is low ccmpared to land, so near the surface the wind will be closer to circulating around the low pressure than over land, but will still have a component towards the low pressure.

Dear Nathan,

Thanks for your reply. In fact I just need the best possible estimation of the horizontal current at a fixed point. Current speed module and direction in N-S, E-W coordinates, that's all. My interest is to obtain time series of the current itself at the depth of deployment of the meter, and also to compute (horizontal) suspended sediment flux by means of a coupled turbidimeter.

Best regards, Jacobo

Thanks for the clarification, Felix. It is fascinating, and we should study these possibilities. If not the algae, we will get something else I am sure! We have CAIPEEX cloud experiments, but I am not sure if they can detect organic components.. let us see. Meanwhile, are there other instances of cloud drafts carrying algae (or algae-like) particles??

This link may help you.

Dear Francisco, satellite ground truth and weather stations provide readings that have in common the name "temperature" but are essentially different and need a calibration to be related to each other. This calibration should be made over a relatively long period of common readings, in wich both sources were active. Missin this calibration, the uncertainty may be large to very large. Yoiu may try to rely on a third station, that was common to the weather station and later to the satellite data. This triangulation increases uncertiainty but in better than nothing and your homogenization becomes more reasonable, being based on some objective reference attempt.

Good luck and good work! 

Dario

Hi,

There are a few oceanography and climate libraries for python. You should check a list like this:

1) http://oceanpython.org/links/

2) http://www.imr.no/~bjorn/python/links.html

As for extracting the exact parameters you're requesting I would advise checking scientific literature (since I don't know how to do it). A quick google search for "RISAT-1 ocean waves" revealed this article:

You can request the full article here in research gate. I'm sure a more extensive search would reveal a lot more info on the subject.

Best of luck.

I do not agree with what Alastair has said, pCO2 depends on pH of the water, it is other way around, pH of the sea water is dependent on pCO2. moreover, blooms can be also limited by CO2 in the oceans if not having constitute CCM. an increase in pH has been observed many times with enhancement in phytoplankton biomass. However, I agree with the last part of your answer. and also what Koji has mentioned. This is possible that for warming or upwelling, significant amount of CO2 is added up to the surface water and there could be simultaneous uptake also by phytoplankton and enhancement of fluorescence. However, the net increase in CO2 could be more and hence positively correlating with the fluorescence. In the Oligotrophic condition under high solar light there can be photo-inhibition leading to net heterotrophy and can may not uptake considerable amount of pCO2. As Koji said, phytoplankton stock is also important.

If you want nice explanations about the difference between model and their numerical formalism you can check S. Griffies books especially:

Griffies, Stephen M., 2005: Some ocean model fundamentals In Ocean Weather Forecasting: An Integrated View of Oceanography, Berlin, Germany, Springer, 19-74.

cheers

Hi you can proceed by analyzing hydrochemical composition, ionic ratios (Na+/Cl–, Ca2+/Cl–, Mg2+/Cl–, SO42-/Cl- and especially Br–/Cl– molar ratios; Ca2+/Mg2+ weight ratio; and chloride concentrations) and calcite-saturation index (SIcalcite) of seawater and groundwater from selected wells. Also, isotope analysis of (2H/18O, 3H, 14C, 34S).

Chloride/Br ratios are used to determine the causes of groundwater salinisation for water.

You could also look at the WOCE Pacific Atlas - see http://www-pord.ucsd.edu/whp_atlas/pacific_index.html   This is a graphical representataion of the data referred to by Loic in the previous answer.

Hi Archana... while this is not my area of expertise, I do know that the Mediterranean Sea is closing between the north moving African plate and the European plate.  The Atlantic Ocean is opening wider at the mid-Atlantic ridge and the Pacific plate is subducting beneath the Asian and Australian Plates. The Australian and African plates are spreading making the Indian Ocean larger.

oce: Analysis of Oceanographic data

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