Indonesian Throughflow Transport Variability

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Indonesian Throughflow Transport Variability or collective redistirbution of any portion article of any by of this or collective redistirbution Th THE INDONESIAN SEAS articleis has been in published Oceanography INDONESIAN THROUGHFLOW 18, Number journal of Th 4, a quarterly , Volume TRANSPORT VARIABILITY ESTIMATED permitted only w is photocopy machine, reposting, means or other FROM Satellite 2005 by Th e Oceanography Copyright Society. AltimetryBY JAMES T. POTEMRA ith the approval of Th approval the ith gran e Oceanography is Society. All rights reserved. Permission or Th e Oceanography [email protected] Society. Send to: all correspondence Figure 1. Th e Pacifi c to Indian Ocean exchange, known as the Indonesian throughfl ow (ITF), has been sampled with a repeat expendable bathythermograph (XBT) hydrographic section from Australia to Indonesia (IX-1, red stars). Sea-level estimates from the TOPEX/Poseidon (T/P) satel- lite (ground tracks given with the black lines) are used to estimate ITF transport. Th e major straits through which the ITF passes are indicated on the map. ted to copy this article Repu for use copy this and research. to in teaching ted e Oceanography Society, PO Box 1931, Rockville, MD 20849-1931, USA. blication, systemmatic reproduction, reproduction, systemmatic blication, 98 Oceanography Vol. 18, No. 4, Dec. 2005 The relatively intense boundary cur- ment of sea level that may be used to prise the Indonesian seas (Figure 1). rents of the western equatorial Pacifi c, index ITF fl ow. Velocities through some of the narrow as well as the western Pacifi c warm pool, Wyrtki (1987) was the fi rst to suggest straits, Lombok Strait for example, can have long been recognized as key com- that ITF transport could be estimated be quite extreme, reaching more than 1 ponents in the global climate system. from sea-level differences between the m s-1 over a shallow sill at the southern More recently, the eastern Indian Ocean Pacifi c and Indian Oceans. The reason- side (Hautala et al., 2001). In addition, has been identifi ed as a potential source ing was based on the hypothesis that the tides in the Indonesian seas are large and for climate variability over a larger area large-scale pressure difference between can sometimes contribute to swift fl ows (Saji and Yamagata, 2003). These systems the two basins provided the forcing for in the straits. Upper-ocean instruments are not isolated, however, and Pacifi c- the ITF. Wyrtki used sea-level varia- on a bottom-anchored mooring in the to-Indian Ocean exchange occurs via tions from coastal tide-gauge stations at Makassar Strait, for example, were driven the Indonesian throughfl ow (ITF) (see Davao, Mindanao Island (Philippines), more than 100 m vertically from moor- Gordon, this issue). Understanding this and at Darwin, Australia. The latter was ing blow-over due to semi-diurnal tides exchange, and the ability to estimate it, to be representative of Indian Ocean within the strait (Gordon et al., 1999). are therefore essential for understand- conditions. Wyrtki pointed out, however, Although fl ow through these individ- ing the global climate system. Directly that a more suitable location for sea-level ual straits is of interest, the net exchange measuring this fl ow has been challeng- measurements would have been along between the Pacifi c and Indian Oceans ing. This paper outlines a method to es- the Indonesian archipelago, but data is also important, as it represents a key timate the large-scale oceanic transport were not available at that time. Clarke component in the heat and freshwater in the ITF by using satellite-measured and Liu (1994) further explained that the budgets of the two ocean basins. The sea level from the TOPEX/Poseidon (T/P) sea-level patterns associated with the an- variability of ITF transport, as it affects altimeter. The satellite measurements do nual cycle and those associated with in- upper-ocean heat content, may also play not directly measure transport, but they terannual variations were quite different a role in climate variability in the region. comprise a long, almost global, measure- at these locations; the simple sea-level The forcing, therefore, as well as the difference between Davao and Darwin potential effects of ITF transport vari- cannot adequately represent both time ability, is large scale. To estimate ITF scales. Today, however, satellite estimates variations, simultaneous large-scale of sea level can be used to produce a measurements are needed. Satellite mea- more robust index, while long-term di- surements of sea level provide one such rect measurements of ITF transport have measurement. Sea level from the T/P not yet become available. altimeter provides nearly simultane- The ITF is diffi cult to measure for ous, global coverage and may be used a variety of reasons. As Pacifi c waters to produce an estimate of net ITF ex- move from the western equatorial Pa- change. The challenge is to determine cifi c to the eastern Indian Ocean, they the relationship between sea level and travel through a complex array of nar- ITF transport. In this study a numerical row straits and deep basins that com- model is used. To estimate ITF variations, simultaneous large-scale measurements are needed. Oceanography Vol. 18, No. 4, Dec. 2005 99 Potemra et al. (1997) (P97 model) altimeter data to estimate ITF transport. multivariate sequential data assimilation showed that sea level in certain key re- First, the variability of the ITF transport scheme (Carton et al., 2000) that updates gions, as measured by T/P, could be com- is described in order to judiciously select the ocean model with observations of bined to produce an estimate of net ITF sea-level locations that will best refl ect temperature and salinity every ten days. transport. A simple, linear model was ITF forcing. Next, the model results are It should be noted that satellite altimeter constructed as follows: described, and fi nally the index is ap- data were not assimilated into the model plied to the satellite data. run used in this study. T V ITF(t) = Σ αi ηi(t), [1] The annual cycle of ITF transport ESTIMATES OF ITF from SODA accurately shows peak T where V ITF(t) is the total ITF volume TRANSPORT BASED ON transport during the southeast monsoon transport as a function of time (mean OBSERVATIONS AND MODELS (roughly July–October) and minimum removed), ηi(t) are sea-level variations at Long-term, net ITF transport has been during the northwest monsoon, as pre- location i (mean removed and normal- estimated from in situ observations in dicted by Wyrtki (1961). The annual ized to unit variance), and αi are weights some of the key Indonesian passages. cycle is most evident in the upper 200 determined from a least squares fi t. Understanding that these measurements m in the SODA results (similar to other Since that study, advancements in nu- have been acquired in different years, the models), and represents about 60 per- merical models and new in situ measure- mean ITF is estimated to be between 6.0 cent of the variance in this layer (9 Sv2 ments have lead to a better understand- and 10.0 Sv (Hautala et al., 2001); see compared to 15 Sv2). A deeper layer, 200 ing of the temporal and spatial variability Gordon (this issue) for a full review. to 500 m, has less overall variance (6.9 of ITF transport, particularly the vertical Model estimates of ITF transport Sv2), but only 6 percent is due to sea- variability of this fl ow (Potemra et al., are typically higher than the observed sonal variability. 2003). In addition, there are now more estimates. Perhaps due to insuffi cient This model result is consistent with than ten years of T/P measurements that resolution of the complex bathymetry, the hypothesis that lower-frequency forc- can be used to make a more robust ITF or uncertainties in wind forcing, most ing of the ITF, for example, that associ- index. For this study, version 1.0 NASA/ large-scale models, particularly coarse ated with El Niño-Southern Oscillation GSFC Ocean Pathfi nder gridded sea-level resolution models, give net ITF trans- (ENSO) variations, is evident in a deeper variations (obtained from ftp://iliad.gsfc. ports in excess of 15 Sv. layer of the ITF (Potemra et al., 2003). nasa.gov) were used. The model used in this study, the Interannual variability in ITF transport The P97 study is, therefore, revis- Simple Ocean Data Assimilation-Paral- is less well known than the seasonal cycle, ited, using both a longer satellite record lel Ocean Program (SODA POP 1.2) mainly due to a lack of suffi ciently long of sea level and a more sophisticated model (henceforth referred to as SODA) observations. One notable exception is ocean model to determine the best fi t produces a realistic ITF transport with a the nearly twenty-year repeat expend- (the αi’s in equation 1). As in P97, the net mean of 12.7 Sv. This model has 40 able bathythermograph (XBT) section relationship between ITF and sea level vertical levels that vary from 10 m near from Shark Bay, Australia to the Sunda was determined using an ocean general the surface to 250-m thick in the deep Strait known as IX-1 (red stars in Figure circulation model (OGCM), and then ocean. The original SODA grid uses a 1). Meyers et al. (1995) computed upper- this relationship was applied to the T/P “displaced pole,” and is 0.4° in longitude ocean transport (0 to approximately 400 by 0.28° latitude, but the model results m) based on these XBT data for the early James T.
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