Variance-Preserving Power Spectral Analysis of Current, Wind and 20º Isothermal Depth of RAMA Project from the Equatorial Indian Ocean Vivek Kumar Pandey K. Banerjee Centre of Atmospheric and Ocean Studies (KBCAOS) Institute of Interdisciplinary Studies (IIDS), Nehru Science Centre University of Allahabad, Allahabad-211002, India E-mail: vivekbhuoa@gmail.com Abstract Variance-preserving Power Spectra (VPS) from Research Moored Array for African- Asian-Australian Monsoon Analysis and Prediction (RAMA) observational platform depicting ocean parameters for the periods 2002-2006 at mooring location 90ºE, 1.5 ºS was investigated to understand the variability of ocean parameters. The VPS of 20º isothermal depth (d20) reveals fact that it has intra-seasonal, seasonal and semiannual oscillational trends. Zonal current s VPS show the intra-seasonal, biweekly and weekly characteristics. Meridional current s VPS gives biweekly and weekly oscillation and while the sea surface Zonal wind s VPS reveals fact that it has intra-seasonal, biweekly and weekly oscillation. These scientific findings are consistent with the results obtained from similar analysis of Triangle Trans Ocean Buoys Network (TRITON) data by other investigations. Therefore the use of RAMA in assimilation process in similar way to TRITON will increase the efficiency of Climate Forecast System (CFS) based on Global Ocean Data Assimilation (GODAS). Keywords: VPS, RAMA, Indian Ocean, CFS, GODAS, TRITON Introduction The in-situ oceanic parameter s measurements available are important for the study of oceanic response to the seasonal varying wind, current and temperature. Knowledge of high frequency variability of wind, current and temperature permits determination of forced oceanic oscillations (Farneti and Vallis, 2009), so the Indian Ocean observation becomes important for the predictability studies. An assimilation process, which uses the high resolution (less than 100 Km) and high frequency (daily or hourly) data in the Indian Ocean, may give the better scenario of the region, and oceanic oscillation in the region will be visible from reanalysis as well as from the observations. With the high-resolution (below the 100 Km) SST data from satellite and mooring buoys in the Bay of Bengal (Sengupta and Ravichandran, 2001), it is clear that the Intraseasonal Variability (ISV) of SST over the north Indian Ocean has large amplitude and in large spatial scale similar to that of the atmospheric ISV (Sengupta et al., 2001). The annual cycle of the SST is very important for the annual evolution of the monsoon precipitation (Fennessy and Shukla, 1994). The SST in the Indian Ocean is determined by the net heat flux at the surface and the wind stress forcing (Murtugudde et al., 1996; Murtugudde and Busalacchi, 1999). An annually reversing upper ocean meridional circulation is required for 181
maintaining the annual cycle of the SST (Laschingg and Webster, 2000). While SST has a strong control on the annual evolution of the (Tropical Convergence Zone) TCZ, the precipitation in the TCZ has a strong control on the evolution of the SST. A biweekly mode (10-20 days) in the equatorial India Ocean is observed in meridional current (Sengupta et al., 2004). Intra-seasonal variability of circulation in the tropical central Indian Ocean was found in winds and currents (Sengupta et al., 2001). Using ocean model forced by QuickSCAT large intra-seasonal (10-60 days) variability were observed in winds and upper ocean currents in equatorial Indian Ocean (Sengupta et al., 2007). Thus it is clear from the literature that the Ocean, the strong source of heat indeed influences the atmospheric circulation in a significant way and hence the variability of ocean parameter is needed to be studied for explaining the variability of Asian Summer Monsoon. The ocean is monitored at the near locations to study the local responses. The objectives of these observations are to determine the local forcing in the region, at the low and high frequency alike, and to validate the wind, temperature and current field products deduced from the model reanalysis. The uses of local observational data in the ocean reanalysis process increase the efficiency of predictability of the forecast system based on it. This study represents the work for tropical India Ocean whose representation was not in the National Centre for Environmental Prediction/Global Ocean Data Assimilation (NCEP/GODAS) (Behringer and Xue, 2004) in assimilation process due to unavailability of observations in this area (at present no real time data available other than RAMA). As a part of fieldwork, simultaneous and continuous in-situ high frequency observations are now available for tropical Indian Ocean from RAMA. In this paper our objective is to describe, analyze the low and high frequency variability of observational field of RAMA using Variance-Preserving-Spectra (VPS) and to point the possibility of using RAMA data in assimilation process in GODAS in similar way to TRITON. Data Used The Indian Ocean is very data sparse region among the world oceans. There is some recent mooring buoys network representing the ocean field in the region on very high frequency with respect to time (daily). Improved definition of the time dependent variability in the temperature and salinity distribution in the global ocean is essential, including the airsea fluxes of heat and fresh water. This will require full implementation of a system with the capabilities of the current and planned ocean observation systems, satellites or in-situ observations. In the present study we have used the RAMA in-situ observational data for VPS of the temperature, surface current and surface wind at 90ºE, 1.5 ºS. Pacific Marine Environmental Laboratory (PMEL) is implementing the Indian Ocean RAMA in cooperation with Japan, France, India, and Indonesia. Near-real-time RAMA data are available from PMEL data delivery webpage: http://www.pmel.noaa.gov/tao/data_deliv/deliv-nojavarama.html. Results and Discussions The technique of VPS for the meteorological parameter is used which measures the ratio of log of squared magnitude of the continuous Fourier transform of the signal to that of log of frequency (Emery and Thomson, 2001). On this basis we can identify the strength of 182
the signals and their respective frequencies i.e. weekly, biweekly, intra-seasonal, seasonal and inter-annual. The d20 has been calculated using ferret script for subsurface temperature. The depth levels at 90ºE, 1.5 ºS (in meter) are: 1.5, 10.0, 20.0, 25.0, 40.0, 50.0, 60.0, 75.0, 80.0, 100.0, 120.0, 125.0, 140.0, 150.0, 200.0, 250.0, 300.0, 500.0, 750.0. Temperature (d20) and current data are available for period 2002-2006 but wind data is available with small gap. The gaps were filled using ferret script of interpolation. The VPS has been plotted for RAMA at the position 90ºE, 1.5 ºS in the Indian Ocean (Fig. 1). The VPS of temperature for RAMA has been plotted, in which the strength of signals of seasonal and intra-seasonal oscillation were obtained. The VPS of RAMA d20, surface zonal and meridional current and wind are shown here in the figures. Among these four parameters, wind produced interesting high frequency intra-seasonal oscillations, which are very relevant to the region. Fig. 1: Location of the RAMA mooring buoys. The filled symbols represent locations already occupied (as of 2008). Fig. 2: VPS of RAMA d20 at the location 90ºE, 1.5 ºS. In the VPS spectra of the parameters, the X-axis represents logarithmic of frequency and Y-axis represents the intensity of the signal. VPS are presented and observation of intensity of signals and their particular frequency band are described. In Fig. 2, there are three significant peak of energetic oscillation of d20 signal found within the time band 60-90, 120-180 and 180-365 days i.e. signals are intra-seasonal, seasonal and semiannual in nature at the mooring position (Tropical India Ocean). This implies that the temperature signal shows high 183
variability at these frequencies, which were also reported in model study by Sengupta and Ravichandran (2001). Fig. 3: VPS of RAMA surface zonal current at the 90ºE, 1.5 ºS. In Fig. 3, there are also two significant peaks of the signal of zonal current at the time bands at ~60-90 and 90-120 days, implying that the currents are intra-seasonal in nature at the mooring sites. There are several other high frequency spikes are present, suggesting that the signal of the zonal currents may be weekly or biweekly in nature. This finding was also supported through model study by Sengupta et al. (2004). Fig 4: VPS of RAMA surface meridional current at the 90ºE, 1.5 ºS. The Fig. 4 shows significant energetic oscillations present in the meridional current at the time band 10-20 and 20-30 days with strong peaks. Thus, we can say that the meridional current is highly intra-seasonal in nature. Presently there are several spikes at high frequency time bands of weekly and biweekly period which govern the ocean-atmospheric circulation and heat flux of the region at large (Sengupta et al., 2007). 184
The Zonal Wind of RAMA mooring is also showing intra-seasonal, weekly and diurnal behavior as obvious by the VPS spectra in Fig. 5. The similar signals of ocean parameters are observed in RAMA as in the VPS study of Serra et al. (2008) of the Tropical Atmosphere Ocean (TAO)/TRITON which used in the Global Ocean data assimilation system (GODAS) (McPhaden et al., 2001; Behringer and Xue, 2004). Also in case of the RAMA mooring we observed that the wind has more accurate intra-seasonal oscillation starting from diurnal. In Fig. 5, there are high amplitude peaks in 10-20 days time band and spikes present at high frequency time bands which implying that there are high intraseasonal oscillations present in the zonal wind signal. Fig. 5: VPS of RAMA surface zonal wind at the 90ºE, 1.5 ºS. Model produced intra-seasonal and biweekly variability of circulation in the tropical Indian Ocean in currents and winds (Sengupta et al., 2001, Sengupta et al., 2004 and Sengupta et al., 2007) and similar results are also observed in the TRITON and RAMA also produces the similar behavior as obvious from the spectrum. Spectral analysis of wind observation showing the presence of a 10-20 days oscillation in very similar way to the 15 days oscillation associated with westward propagation mode (Parker, 1973). Krishnamurti and Krishnamurti (1980) described the characteristics of the 15 days oscillation and associated it with westward propagation mode. Energetic oscillation period from 4-6 days also appear. Weisberg et al., 1987 also found large peak of energy centered around 35, 14 and 7.5 day period at 0ºN and 20ºW. Conclusions VPS of the ocean parameters for the period of 2002-2006 of the RAMA mooring at location 90ºE, 1.5ºS reveals fact that the d20 have strong intra-seasonal, seasonal and semiannual oscillation. The ocean surface currents and wind have highly weekly, biweekly and intra-seasonal oscillation. Forecast depends on ocean reanalysis, because of non availability of the observation data at all grid point of the ocean. The standard GODAS used in CFS which assimilates temperature profiles from XBTs, TAO, TRITON, PIRATA moorings (McPhaden et al., 2001) and Argo profiling floats but it have some biases in lack of more observational data points and appropriate forcing (Pandey and Singh, 2010a; Pandey and Singh, 2010b). Similar VPS observed in ocean parameters from RAMA as in 185
TAO/TRITON (Serra et al., 2008) and these two observational data also produce similar magnitude of biases with GODAS (Pandey and Singh, 2010a) in tropical India Ocean. Therefore, exhaustive use of RAMA observational may be helpful to improve the prediction and forecast based on GODAS in tropical Indian Ocean in near future. RAMA mooring and other such processes may improve the resolution and frequency of data availability and thus improve the forecast based reanalysis in equatorial and tropical Ocean. Acknowledgement: Authors thank to PMEL/NOAA for providing RAMA moored buoys data. Special thanks to Prof. D. Sengupta, CAOS, IISc Bangalore and Prof. Avinash C. Pandey, HOD of KBCAOS for advice and encouragement during the work. References Behringer, D. W. and Y. Xue (2004) Evaluation of the global ocean data assimilation system at NCEP: The Pacific Ocean. Eighth Symposium on Integrated Observing and Assimilation System for Atmosphere, Ocean, and Land Surface, AMS 84 th Annual Meeting, Seattle, Washington, pp. 11-15. Emery, W. J. and R. E. Thomson (1998) Data analysis methods in physical oceanography. Elsevier Science, Amsterdam, 650 pp. Fennessy, M. and J. Shukla (1994) Simulation and predictability of monsoons. Proceedings of the international conference on monsoon variability and prediction, WMO/TD 619, Trieste, pp. 567-575. Farneti, R. and Vallis, G. K. (2009) Mechanisms of interdecadal climate variability and the role of ocean atmosphere coupling, Clim. Dyn., v. 36, No. 1, pp. 289 308. Krishnamurti, T.N. and Krishnamurti, R. (1980) Surface meteorology over the GATE A- scale, Deep-Sea Res., GATE, Suppl. v. 11, No. 26, pp. 26-61. Loschnigg, J. and P. J. Webster, 2000: A coupled ocean-atmosphere system of SST regulation for the Indian Ocean. J. Clim., 13, pp. 3342-3360. Madden, R.A. and Julian, P. R. (1972) Description of the global scale circulation in the tropics with a 40-50 day period, J. Atmos. Sci, v. 29, pp. 1109-1123. McPhaden, M. J., Delcroix, T., Hanawa, K., Kuroda, Y., Meyers, G., Picaut, J. and Swenson, M. (2001) The El Niño/Southern Oscillation (ENSO) Observing System. In: C. J. Koblinsky and N. R. Smith, (eds.) Observing the Ocean in the 21st Century, Australian Bureau of Meteorology, pp. 231 246. Murtugudde, R. G., Seager, R. and Busalacchi, A. (1996) Simulation of Tropical ocean with an Ocean GCM coupled to an Atmospheric mixed layer model. J. Climate, v. 9, pp. 1795-1815. Murtugudde, R. G. and Busalacchi, A. (1999) Interannual Variability of the dynamics and thermodynamics of the tropical Indian Ocean. J. Climate, v. 12, pp. 2300-2326. Parker D.E. (1973) On the variance spectra and spatial coherences of equatorial winds. Quart. J. Meteorol. Soc., v. 99, pp. 48-55. Pandey, Vivek Kumar and Singh, Sudhir Kumar (2010a) Comparison study of ECCO2 and NCEP reanalysis using TRITON and RAMA data at the Indian Ocean Mooring Buoy point. e-j Earth Science India, v. 3, No. 4, pp. 226-241. Pandey, Vivek Kumar and Singh, Sudhir Kumar (2010b) Validation of Temperature Field within Ocean Data Assimilation System with the Mooring Buoy's Data in the Indian Ocean. VSRD-Technical and Non- Technical Journal, v. 1, No. 4, pp. 222-234. Sengupta, D. and Ravichandran, M. (2001) Oscillations of Bay of Bengal sea surface temperature during the 1998 summer monsoon. Geophys. Res. Lett., v. 28, pp. 2033-2036. Sengupta, D., Goswami, B. N. and Senan, R. (2001) Coherent intraseasonal oscillations of ocean and atmosphere during the Asian summer monsoon. Geophys. Res. Lett, v. 28, pp. 4127-4130. Sengupta, D., Senan, R., Murty, V.S.N. and Fernando, V. (2004) A biweekly mode in the equatorial Indian Ocean. J. Geophys. Res., v. 109, pp. 1-12. doi: 10.1029/2004JC0023291 Sengupta, D., Senan, R. and Goswami, B. N. (2001) Origin of intraseasonal variability of circulation in the tropical central Indian Ocean. J. Geophys. Res., v. 28, No. 7, pp. 1267-1270. Sengupta, D., Senan, R., Goswami, B.N. and Vialard, J. (2007) Intraseasonal variability of equatorial Indian Ocean zonal currents. J. Climate, v. 20, pp. 3036-3055. 186
Serra, Y. L., Georgen K. and Meghan, F. C (2008) Horizontal and vertical structure of easterly waves in the Pacific ITCZ. J. Atmos. Sci., v. 65, pp. 1266-1284. Weiserg R.H., Hickman J.H., Tang T.Y. and Weingartner T.J. (1987) Velocity and temperature observations during the Seasonal Response of the Equatorial Atlantic Experiment at 0º, 28 º W. J. Geophys. Res., v. 92, No. 5, pp. 5061-5075. 187