ARTICLE IN PRESS. Dynamics of Atmospheres and Oceans xxx (2010) xxx xxx. Contents lists available at ScienceDirect. Dynamics of Atmospheres and Oceans

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1 Dynamics of Atmospheres and Oceans xxx (2010) xxx xxx Contents lists available at ScienceDirect Dynamics of Atmospheres and Oceans journal homepage: Validation of a regional Indonesian Seas model based on a comparison between model and INSTANT transports D. Rosenfield a,, V. Kamenkovich a, K. O Driscoll b, J. Sprintall c a Department of Marine Science, The University of Southern Mississippi, 1020 Balch Boulevard, Stennis Space Center, MS 39529, USA b Institute of Oceanography, University of Hamburg, Bundesstraße 53, Hamburg 20146, Germany c Scripps Institution of Oceanography, University of California San Diego, Nierenberg Hall, La Jolla, CA 92093, USA article info abstract Available online xxx Keywords: Modeling Volume transport Oceanography Water currents Indonesian Throughflow Princeton Ocean Model The International Nusantara Stratification and Transport (INSTANT) program measured currents through multiple Indonesian Seas passages simultaneously over a three-year period (from January 2004 to December 2006). The Indonesian Seas region has presented numerous challenges for numerical modelers the Indonesian Throughflow (ITF) must pass over shallow sills, into deep basins, and through narrow constrictions on its way from the Pacific to the Indian Ocean. As an important region in the global climate puzzle, a number of models have been used to try and best simulate this throughflow. In an attempt to validate our model, we present a comparison between the transports calculated from our model and those calculated from the INSTANT in situ measurements at five passages within the Indonesian Seas (Labani Channel, Lifamatola Passage, Lombok Strait, Ombai Strait, and Timor Passage). Our Princeton Ocean Model (POM) based regional Indonesian Seas model was originally developed to analyze the influence of bottom topography on the temperature and salinity distributions in the Indonesian seas region, to disclose the path of the South Pacific Water from the continuation of the New Guinea Coastal Current entering the region of interest up to the Lifamatola Passage, and to assess the role of the pressure head in driving the ITF and in determining its total transport. Previous studies found that this Corresponding author. Tel.: ; fax: addresses: david.rosenfield@eagles.usm.edu (D. Rosenfield), vladimir.kamenkovich@usm.edu (V. Kamenkovich), kieran.odriscoll@zmaw.de (K. O Driscoll), jsprintall@ucsd.edu (J. Sprintall) /$ see front matter 2010 Elsevier B.V. All rights reserved.

2 2 D. Rosenfield et al. / Dynamics of Atmospheres and Oceans xxx (2010) xxx xxx model reasonably represents the general long-term flow (seasons) through this region. The INSTANT transports were compared to the results of this regional model over multiple timescales. Overall trends are somewhat represented but changes on timescales shorter than seasonal (three months) and longer than annual were not considered in our model. Normal velocities through each passage during every season are plotted. Daily volume transports and transport-weighted temperature and salinity are plotted and seasonal averages are tabulated Elsevier B.V. All rights reserved. 1. Introduction 1.1. Indonesian Throughflow (ITF) The Indonesian Throughflow from the Pacific to the Indian Ocean has been studied for decades (Gordon, 1986; Wyrtki, 1987). As this water passes through the Indonesian Seas, it is mixed, heated by the sun, and diluted by runoff and precipitation resulting in a significantly altered profile (Ffield and Gordon, 1992; Gordon and Susanto, 2001). The Indonesian Seas, are an integral part of the Great Ocean Conveyer Belt, and as such play an important role in helping maintain a stable global climate (Gordon, 1986). While several ocean modeling efforts were valuable in studying this complex flow, their individual differences (e.g. spatial resolution, forcing mechanisms, etc.) result in better (or worse) simulations in different locations of the Indonesian Seas region. In order to determine whether these simulations adequately represent the real world, a comparison of the output of multiple simulations with in situ data must be made. This paper is a comparison that will help validate our regional model against the most reliable dataset available the INSTANT dataset The INSTANT dataset The International Nusantara Stratification and Transport (INSTANT) program has been instrumental in acquiring the crucial oceanic measurements needed for this type of comparison (Sprintall et al., 2004, 2009; Gordon et al., 2008; Van Aken et al., 2009). Their simultaneous deployment of buoys at five passages within the Indonesian Seas (Labani Channel, Lifamatola Passage, Lombok Strait, Ombai Strait, and Timor Passage) resulted in an unprecedented look at the complex flow through this region. Previous large-scale measurements of this flow (e.g. ARLINDO) were not simultaneous, and for ocean modelers could only serve as guidelines for the local flow at particular time periods (Gordon, 2005). The INSTANT dataset established the most representative baseline against which numerical simulations can be compared. Modelers can now compare model data to in situ data from the five main Indonesian passages simultaneously for a three-year period. Transport data from the five passages were used to validate our regional model. We used three years (January 2004 to December 2006) of monthly averaged transport data from the Lombok Strait, Ombai Strait, and Timor Passage, provided by J. Sprintall. A version of this data with only annual and semi-annual variations can be found in Table 3 of Sprintall et al. (2009). Nearly three years of data with maximum and minimum values (uncertainty ranges) obtained from different interpolations/extrapolations across each passage and with depth from the Labani Channel (January 2004 to November 2006) were used in the comparison (from Fig. 3 of Gordon et al., 2008). This 30-day low pass filtered product is consistent with the other monthly averaged INSTANT transports. Transports through the Lifamatola Passage (February 2004 to November 2006) were from below 1250 m depth (from Fig. 11 of Van Aken et al., 2009). In order to compare all in situ data to model output in a similar fashion, and because this is a 15-day averaged product, this data was averaged over an additional 15 days to get monthly averaged transports. We chose to compare our model to the monthly average value from the middle of each month for the entire 36-month period.

3 D. Rosenfield et al. / Dynamics of Atmospheres and Oceans xxx (2010) xxx xxx The regional model Both regional and global models of the Indonesian seas circulation exist. Regional models are aimed basically at process oriented studies. Requiring limited computer resources, they allow us to run many experiments with proper resolution to study the physical processes of interest. However, regional models have some disadvantages: they require formulation of open boundary conditions, which, in turn, require the specification of some data (either simulated within the global models or taken from observations; e.g. specifying the total transport of the Indonesian Throughflow). Global models require massive computer resources, which may be unavailable at some research centers. Global models also simulate various sub-regions of the World Ocean using approximately the same resolution. This approach is not adequate in some specific areas (e.g. where the New Guinea Coastal Current and Undercurrent are formed). But global models need less observational data and can provide an analysis of the impact of governing parameters on the overall circulation. In particular, they can help determine the total transport through the Indonesian Seas generated by the global wind system and heat and freshwater fluxes. A regional model has been developed (O Driscoll and Kamenkovich, 2009; Kamenkovich et al., 2009) to: (1) analyze the influence of bottom topography on the temperature and salinity distributions in the Indonesian Seas region, (2) to investigate the processes causing the transformation of South Pacific Water and North Pacific Water, (3) to disclose the path of the South Pacific Water from the continuation of the New Guinea Coastal Current entering the region of interest up to the Lifamatola Passage, and (4) to assess the role of the pressure head in driving the ITF. Should we have trusted the model s results? The preliminary validation of the model was performed by using historical data. With the existence of the INSTANT database, now considered the most comprehensive database for the Indonesian seas region, the main objective of this paper is to show that the developed regional model is capable of adequately describing properties of the Indonesian seas circulation by comparing some of the simulated characteristics (e.g. transports through some important passages in the region) with observed ones. The regional model of the Indonesian seas circulation outlined in O Driscoll and Kamenkovich (2009) was based on the Princeton Ocean Model (POM). It is known that the POM provides an adequate resolution of the bottom boundary layer, which is extremely important in the vicinity of deep sills in the region. The specifics of the developed regional model are in the model configuration and boundary conditions, at both the open boundaries and the sea surface. The model domain is shown in Fig. 1. It has grid cells in the horizontal with a resolution of approximately 10 km. This resolution was chosen so that all important bottom topography features were adequately resolved and all important passages were properly represented. In the vertical, 29 -levels were chosen such that salinity maxima and minima and surface and bottom boundary layers in the region were reasonably resolved. To simulate the impact of major currents, such as the Mindanao Current (MC), the New Guinea Coastal Current and Undercurrent (NGCC), and the North Equatorial Counter Current (NECC), on the Indonesian seas circulation, we introduced three open ports on the Pacific boundary of the model domain. They are the MC, NECC, and NGCC ports, respectively (see Fig. 1). To balance these inflows and outflows, a fourth port, the IO port, was introduced, through which ITF water exits the region toward the Indian Ocean. The total transport through the IO port is clearly equal to the total transport of the ITF. The boundary segments connecting these four ports are assumed to be closed. Therefore, we do not include flows from the Sulu Sea, Java Sea, and through the Torres Strait (see discussion of this assumption in O Driscoll and Kamenkovich, 2009). In the Lesser Sunda Island Chain, only the Lombok and Ombai Straits were retained. The open boundary conditions consist of the specification of total water transports Q MC, Q NECC, Q NGCC, and Q IO through the open ports, Orlanski s type of conditions for the normal 3D velocity u and temperature and salinity at the ports, and setting to zero the turbulence energy and length scale there. Following the recommendation of Marchesiello et al. (2001) nudging of u to a typical normal port velocity u e (z), and nudging of temperature and salinity to the corresponding climatological values was introduced in the formulation of Orlanski s conditions. Thus, to run the model, seasonally varying total water transports through the open ports Q MC, Q NECC, Q NGCC, and Q IO, as well as typical velocities

4 4 D. Rosenfield et al. / Dynamics of Atmospheres and Oceans xxx (2010) xxx xxx Fig. 1. Domain of regional model where color indicates depth in meters and white lines denote cross-sections of passages used in model-data comparison (clockwise from top: Labani Channel (LAB), Lifamatola Passage (LIF), Ombai Strait (OMB), Timor Passage (TIM), and Lombok Strait (LOM)). Four open ports are shown (interruptions in the black border at the extent of the model domain), with three on the Pacific Ocean side of the Indonesian Seas (clockwise from top: Mindanao Current (MC) Port, North Equatorial Counter Current (NECC) Port, and New Guinea Coastal Current (NGCC) Port) and Indian Ocean (IO) Port on the Indian Ocean side. Model domain corner locations (latitude/longitude; clockwise from NW): 4 N/111 E; 11 N/135 E; 13 S/142 E; and 20 S/118 E. u e (z) and climatological values of temperature and salinity at the ports are required. For transports Q through the ports we assume that Q MC + Q NECC + Q NGCC + Q IO = 0, (1) where the outflow transport is considered positive while the inflow transport is considered negative. The smallness of the effect of seasonal variation of the sea-surface height was checked a posteriori. It was supposed that the Indonesian seas are located sufficiently far away from the open boundaries that the effect of some arbitrariness in open boundary conditions is minimized. For the calculation of Q IO we utilized three years of data, provided by J. Sprintall, and averaged them e.g. as follows: Q JAN (averaged) = 1 3 [Q JAN(year 1 ) + Q JAN (year 2 ) + Q JAN (year 3 )]. (2) These monthly data were interpolated by using the Fourier polynomial with 12 components. The result is given in Fig. 2. The maximum and minimum values of Q IO are 18.1 and 11.8 Sv which were reached in June and September, while the annual mean value equaled 14.9 Sv. The typical port velocity profile u e (z) was assumed to be the same for all seasons and was taken from geostrophic calculations by Fieux et al. (1994; see their Figs. 12 and 13). For the seasonally varying total volume transport through the NGCC port, Q NGCC, we chose 22.5 Sv as an annual mean transport, 28.0 Sv in August and 17.0 Sv in February. These values were based on calculations provided by Ueki et al. (2003; see their Figs. 12 and 17). From this paper we also took u e (z) for February and August (see their Figs. 3, 8 and 9). The annual variation of this transport and u e (z) was approximated by the sum of the mean value plus the cosine of the annual period with the corresponding amplitude. For the specification of the seasonally varying total volume transport through the North Equatorial Counter Current port, Q NECC, we chose 40.0 Sv as an annual mean transport, 46.0 Sv in August and 35.0 Sv in February. These data were based on calculations provided by Qiu and Joyce (1992) and Gouriou and Toole (1993). The annual variation

5 D. Rosenfield et al. / Dynamics of Atmospheres and Oceans xxx (2010) xxx xxx 5 Fig. 2. Transports (in Sv) through model s four open ports. Simple Fourier harmonics based on seasonal maxima and minima used to calculate Q NECC and Q NGCC (see Section 1.3 for data sources). Q IO values are calculated by applying a 12 harmonic Fourier polynomial to a representative year of the sum of Lombok, Timor and Ombai transports. Representative year created by averaging similar months of transport values (see Section 2.3 for data source). Remaining transport calculated from balance equation: Q IO + Q NGCC + Q NECC + Q MC = 0. Note that here we consider outward, and inward, transports positive, and negative, respectively. of this transport was approximated similarly to Q NGCC (Fig. 2). The typical port velocity profile u e (z) was the same for all seasons and was taken from geostrophic calculations by Qiu and Joyce (1992). Finally, the seasonally varying total volume transport through the MC port, Q MC, was calculated from the balance equation (Eq. (1); see Fig. 2). We obtained 34.4 Sv for an annual mean value with a seasonal variation of approximately 5 Sv. By and large these values do not contradict calculations by Wijffels et al. (1995), Qu et al. (1998) and Qiu and Lukas (1996). The typical port velocity profile u e (z) was assumed the same for all seasons and was taken from geostrophic calculations by Lukas et al. (1991; Figs. 6 and 7). Note that we did not use global model outputs at the open boundary because we were not sure whether global simulations could reasonably reproduce all important currents, for example, the NGCC, which is formed in an area with extremely complicated coastline and bottom topography requiring very fine resolution. At the sea surface, the seasonally varying heat flux was specified using the Adjusted Southampton Oceanography Centre surface flux climatology (Grist and Josey, 2003). Shortwave solar radiation was added to the heat equation following recommendation of POM Users Guide (Mellor, 2004). The seasonally varying salinity at the sea surface was specified using Levitus climatology (Conkright et al., 2002). Monthly climatological winds were taken from the Comprehensive Ocean-Atmosphere Data Set (COADS) analyzed by da Silva et al. (1994). All external characteristics were represented by Fourier polynomials in time to make it possible to calculate them at every internal time step. A weak nudging to the corresponding climatological values was introduced in the temperature and salinity equations to overcome some underestimate of mixing processes in the POM at the surface and bottom boundary layers and for the acceleration of the establishment of temperature and salinity in the deep ocean (see the discussion in O Driscoll and Kamenkovich, 2009). To get reasonable simulated transports through the above mentioned passages we were forced to incorporate into the model within these passages some additional (relative to the standard POM) friction of the type negative r(u, v), where u and v are horizontal velocities and r is specified parameter. We consider this friction as a rather crude parameterization of tidal friction, which plays a substantial role in the Indonesian seas region (Ffield and Gordon, 1996; Koch-Larrouy et al., 2008a,b). Without such a friction, practically all models, due to western intensification, for example, provide unrealistically high volume transport through the Lombok Strait. A similar approach to the parameterization of tidal friction was used by Schiller et al. (1998). The values of friction coefficient r were strongly localized,

6 6 D. Rosenfield et al. / Dynamics of Atmospheres and Oceans xxx (2010) xxx xxx Fig. 3. The total energy (sum of kinetic and available potential energies) of simulated flow through the Indonesian seas regional model. After a few model years (spin-up period), the simulated flow is established and no net slope is visible. with maximum values on the order of /s. Notice that the boundary conditions for the model described in O Driscoll and Kamenkovich (2009) and the model considered here differ basically by values of Q IO and Q MC only. To validate the model against the INSTANT data, we selected those transports which are not directly determined by the specification of Q NECC, Q NGCC, and Q IO. Our interest lies in the correct simulation of the partition of the ITF between Lifamatola and Makassar transports and Lombok and Ombai Straits and the Timor Passage. These transports are determined not only by the specified open boundary conditions but by the internal dynamics as well. Spin-up of the model generally requires a few years and steady-state is reached when the total energy (sum of kinetic and available potential energies) stabilizes (see Fig. 3). After several model years, when stabilization is reached, the total energy changes seasonally but no inter-annual trend is evident. After spin-up, when the system was established, we ran the model for an additional three years and the output was used in our comparison. While the transports from all three of these years were identical, they were chosen for a better comparison with the INSTANT dataset Model output treatment The model normal velocity, temperature, and salinity across each passage were integrated to calculate the transports through all five INSTANT passages as well as the transport-weighted temperature and salinity (TrWT and TrWS) (Fig. 4) for a three-year period. TrWT and TrWS are calculated using the following formulas: TrWT = T u da u da and TrWS = S u da u da, (3, 4) where subscripts denote parameters where velocities are downstream ( ), in the direction of the ITF. Seasonally averaged transports, TrWT, and TrWS are given in Tables 1 3. It should be noted that on average, in all five passages in the regional model, the downstream component of velocity (u ) makes up 65% or more of the daily total volume transport. However, during the months of May and October at Lifamatola, u only makes up 40 50% of the total volume transport. Normal velocities across all five INSTANT passages are plotted for the middle day of each season (15th day of February, May, August and November; Fig. 5a e). Plotted on these cross-sections are

7 D. Rosenfield et al. / Dynamics of Atmospheres and Oceans xxx (2010) xxx xxx 7 Fig. 4. (a) The seasonally varying model transport (thin line) and transport-weighted temperature (TrWT; thick line) through all five INSTANT passages for the three-year period. Temperatures and velocities used in Lifamatola TrWT calculations are from model depths below 1250 m, thus temporal changes are small and the scale is exaggerated by a factor of 2. TrWT calculated using the following formula: TrWT = T u da / u da, where subscripts denote velocity, temperature, and area at model grid locations where velocities are downstream ( ), in the direction of the ITF. (b) The seasonally varying model transport and transport-weighted salinity (TrWS) through all five INSTANT passages for the three-year period. Salinities and velocities used in Lifamatola TrWT calculations are from model depths at or below 1250 m, thus temporal changes are small and the scale is exaggerated by a factor of 10. TrWS calculated using the following formula: TrWS = S u da / u da, where subscript denotes velocity, salinity, and area at model grid locations where velocities are downstream ( ), in the direction of the ITF.

8 8 D. Rosenfield et al. / Dynamics of Atmospheres and Oceans xxx (2010) xxx xxx Fig. 5. (a) Normal velocities across the Labani Channel in the regional model from the middle of each season. Depth is plotted in km and velocities in m/s (negative: South or West). Vertical lines denote approximate position of INSTANT buoy with associated profile from model simulation on right. (b) Lifamatola Passage. See a for description. (c) Lombok Strait. See a for description. (d) Ombai Strait. See a for description. (e) Timor Passage. See a for description.

9 D. Rosenfield et al. / Dynamics of Atmospheres and Oceans xxx (2010) xxx xxx 9 Fig. 5. (Continued)

10 10 D. Rosenfield et al. / Dynamics of Atmospheres and Oceans xxx (2010) xxx xxx Fig. 5. (Continued). vertical lines which denote approximate buoy locations. The plotted buoy position is estimated relative to their actual position within their respective passages latitude and longitude are approximate in the model domain. Along with each season s normal velocity section, the normal velocity profile is plotted for the associated buoy location(s) within the model domain. Monthly averaged transports from the middle of each month were selected for model-data comparison. Because the only INSTANT data available from Lifamatola were those from below 1250 m, this depth is the upper limit in our transport integrations at this location. Table 1 Seasonally and full-dataset averaged INSTANT and model (in bold) transports (in Sv). Standard deviations for Labani are taken from Table 2 of Gordon et al. (2008). All other standard deviations have been calculated from the monthly transports and are simply a measure of the variability of the monthly transports within a given season. Model transports that fall outside of this variability range are denoted by asterisks. Model and INSTANT transports Winter Spring Summer Fall Labani Channel Lifamatola Passage Lombok Strait Ombai Strait Timor Passage * ± ± ± ± ± * ± ± ± ± ± * ± ± ± ± ± ± ± ± ± ± * ± ± ± ± ± 2.5

11 D. Rosenfield et al. / Dynamics of Atmospheres and Oceans xxx (2010) xxx xxx 11 Table 2 Seasonally and full-dataset averaged INSTANT and model (in bold) transport-weighted temperature (TrWT; in C). See Section 1.4 for equation. Labani TrWT and standard deviations are taken from Table 2 of Gordon et al. (2008). Lifamatola TrWT is not provided in Van Aken et al. (2009). Overall means of TrWT taken from Fig. 8 of Sprintall et al. (2009). Seasonal mean TrWT approximated from Fig. 8 as well. The standard deviations for the overall (and seasonal) mean of Lombok, Ombai, and Timor, were calculated from the approximated mean seasonal (and monthly) TrWT and are simply a measure of the variability of the seasonal (and monthly) INSTANT TrWT. Model TrWT that fall outside of this variability range are denoted by asterisks. Model and INSTANT TrWT Winter Spring Summer Fall Labani Channel Lifamatola Passage Lombok Strait Ombai Strait Timor Passage ± ± ± ± ± * 17.4* 16.2* 16.8* 16.9* 19.6 ± ± ± ± ± * 12.7* 13.3* 10.5* 12.0* 14.3 ± ± ± ± ± ± ± ± ± ± 2.3 Table 3 Seasonally and full-dataset averaged INSTANT and model (in bold) transport-weighted salinities (TrWS; unitless). See Section 1.4 for equation. Lifamatola TrWS comes from Van Aken et al. (2009). Model and INSTANT TrWS Winter Spring Summer Fall Labani Channel Lifamatola Passage Lombok Strait Ombai Strait Timor Passage This type of comparison might lead to the question of whether the buoys used in transport calculations can really represent the normal velocity across the entire section. Fig. 5a e show that there is considerable variation in the normal velocity with both time and space for all five INSTANT passages. We admit that our model is a simplified representation of the flow through this region. The fact that our model shows such spatial variation in the normal velocity is indicative of the difficulty observationalists have in adequately capturing the subtleties of this complex flow. Next to each season s section, we plotted the profile of the location within the model that best represents the normal velocity profile captured by each buoy in that section. While one can be more confident with the results calculated for Timor, which has four buoys, for Lifamatola, with only one buoy across the entire section, it is not as straightforward. 2. Model-data comparison 2.1. Preliminary month-to-month comparison By its construction (mainly due to open boundary conditions chosen) the model can provide us with time-averaged characteristics only (monthly or seasonally). Thus, we should not compare the simulated characteristics with instantaneous but with time-averaged observed characteristics. In a preliminary comparison between the monthly averaged model transports and those calculated from the in situ data, large differences and subtle similarities were immediately apparent. The left portion of Fig. 6 is a plot of both monthly INSTANT transports (dots) and transport values from the regional model (diamonds). Vertical dashed lines separate consecutive years. Model transports that coincide with (and stray far from) buoy transports occur sporadically throughout the time series,

12 12 D. Rosenfield et al. / Dynamics of Atmospheres and Oceans xxx (2010) xxx xxx Fig. 6. Left figure: Monthly averaged three-year INSTANT transports (dots) overlain by monthly regional model transports (diamonds). Consecutive years are separated by vertical dashed line. See Section 1.2 for data sources. Right figure: Monthly INSTANT and model transports (from left portion) plotted against one another (overlain by dashed 1:1 line). The ranges of model values are clearly smaller than those for in situ data. although some general trends are evident. For example, in Labani, Lifamatola, and Ombai, model transport magnitudes decrease by varying amounts over the course of a year. This trend is visible in the INSTANT dataset as well, for example, the magnitude of monthly transports through Labani and Lifamatola decreased over the course of all three years (from every January to the following December). Model-data differences (visible in this plot as gaps between the dots and diamonds) seem to occur during short-term (two to four months) events. These model-data deviations occur during most years at all locations. The events which could have caused these deviations (e.g. Kelvin waves penetrating the region) are not considered in our regional model. One of the most obvious differences between the monthly averaged model transports and the corresponding INSTANT transports is clear from scatter plots (Fig. 6). For a perfect fit, dots would cluster near the 1:1 diagonal line. At all locations (with Lombok being the most severe), the range of the model data is smaller than the range of the INSTANT data. At Lombok and Ombai, the range of model transports was the least similar to that of the INSTANT dataset. The range of model transports at Labani ( 6 Sv; from 7 Sv and peaking at 13 Sv) was, in contrast, much more comparable to the range of INSTANT transports ( 9 Sv; 15 Sv to 6 Sv). Range differences reflect the fact that our regional model does not attempt to simulate short-term events which are most likely to produce large deviations from the longer-term trends. Notice that we did not provide correlation coefficients for the scatter plots considered. The correlation coefficient is an appropriate characteristic of closeness of two random variables, but not for two averaged variables, which are certainly not random Timescales of comparison In order to compare our model s simulation, which does not change from year-to-year, to a threeyear dataset of observations, where changes exist on both an inter-annual scale as well as timescales

13 D. Rosenfield et al. / Dynamics of Atmospheres and Oceans xxx (2010) xxx xxx 13 Fig. 7. Representative year of monthly INSTANT transports (dots) with associated measures of uncertainty. Lombok, Ombai, and Timor values (with uncertainty intervals) from Tables 1 and 3 of Sprintall et al. (2009). Labani and Lifamatola monthly averages created by averaging all available similar months (e.g. May avg =(May 04 + May 05 + May 06)/3). Labani error bars from annual transport of Table 2 of Gordon et al. (2008). Monthly model transports are denoted by diamonds. shorter than a season, it is important to consider model-data differences on a variety of timescales. We chose to make this comparison on four timescales: Annual, Seasonal, Quarterly, and Monthly. A Quarter is the average of consecutive monthly transports (e.g. Spring 04 =(April 04 + May 04 + June 04)/3) and a Season is the average of similar quarters (e.g. Summer 04 =(Summer 04 + Summer 05 + Summer 06)/3) Monthly comparison Along with comparing INSTANT transports to model transports month-to-month (Fig. 6), the three years of INSTANT data were reduced to a representative year and plotted along with their respective measures of uncertainty (Fig. 7). Lombok, Ombai and Timor values (as well as their uncertainty intervals) are based on data provided by J. Sprintall. A similar representative year for Labani and Lifamatola was created by averaging all available similar monthly transport values (e.g. May avg =(May 04 + May 05 + May 06)/3). Labani error bars were taken from annual transport of Table 2 of Gordon et al. (2008). At this timescale, a number of matches and mismatches for periods of several months are evident. For example, from July to September, the model underestimated Labani and Lombok transport magnitudes by approximately 4 and 2 Sv each month. This underestimation accounts for nearly 35% (and 60%) of the mean flow through Labani (and Lombok) during the representative year. However, for almost all other months, the model predicted the INSTANT transports through these two passages within 1 Sv. Nearly all of the model transport magnitudes through Labani, Lifamatola, and Timor were underestimates of the INSTANT values (by approximately 1.5 Sv on average; or 20 60% of this location s mean throughflow). Transports through Ombai during most months were either underestimated or overestimated by the model (by up to 2 Sv in either direction; or 40% of Ombai s mean throughflow).

14 14 D. Rosenfield et al. / Dynamics of Atmospheres and Oceans xxx (2010) xxx xxx Fig. 8. Seasonally (a; left figure) and annually (b; right figure) averaged three-year INSTANT transports (dots) with seasonally (and annually) averaged model transports (diamonds). Labani data and error bars from Table 2 of Gordon et al. (2008). Lifamatola transports were digitized from Fig. 11 of Van Aken et al. (2009). Monthly Lombok, Ombai, and Timor transports were provided by J. Sprintall. Seasonal uncertainty estimates taken from Table 1. Annual uncertainty estimates calculated by averaging appropriate maximum and minimum transport uncertainty estimates Seasonal comparison Averaging over similar seasons for all three years, reveals that both winter and fall were best represented by the model. Fig. 8a shows seasonally averaged INSTANT data plotted with available uncertainty intervals. Labani data and error bars come from Table 2 of Gordon et al. (2008), while Lifamatola transports were digitized from Fig. 11 of Van Aken et al. (2009). Monthly Lombok, Ombai, and Timor transports for three consecutive years were provided by J. Sprintall. The model underestimates the magnitude of summer transports through Lombok ( 2 Sv; or 50% of the mean transport through this passage), Lifamatola ( 1 Sv or 30% of the mean), and Labani ( 4Svor 30% of the mean). The model underestimated the magnitude of spring transports through Lifamatola ( 2 Sv or 60% of the mean), Labani ( 1 Sv or 8% of the mean), and Timor ( 3 Sv or 30% of the mean). Seasonal transports through Lombok and Ombai were best represented by the model as there were no seasonal model averages that deviated from the averaged INSTANT transports by more than the maximum and minimum of uncertainty values. Table 1 shows the seasonal comparison of the volume transports (along with standard deviations) through the five INSTANT passages. Standard deviations for Labani are taken from Table 2 of Gordon et al. (2008). All other standard deviations have been calculated from the monthly transports and are simply a measure of the variability of the monthly transports within the three consecutive months that make up a given season. Only four model values (those with asterisks) lie outside of the INSTANT transport ranges, two during the summer and another two during the spring. Not only were none of these transport values through Ombai, model seasonal Ombai transport values did not deviate from the INSTANT values for all four seasons. A seasonal comparison for transport-weighted temperature (in C) is shown in Table 2. Labani TrWT and standard deviations come from Table 2 of Gordon et al. (2008). Lifamatola TrWT is taken from Van Aken et al. (2009). Overall means of TrWT come from Fig. 8 of Sprintall et al. (2009). Seasonal mean

15 D. Rosenfield et al. / Dynamics of Atmospheres and Oceans xxx (2010) xxx xxx 15 TrWT approximated from Fig. 8 as well. The standard deviations for the overall (and seasonal) means of Lombok, Ombai, and Timor, were calculated from the approximated mean seasonal (and monthly) TrWT and are simply a measure of the variability of the seasonal TrWT. The model TrWT seems to fit the INSTANT data in Labani and Timor, but shares little resemblance to the Lombok and Ombai TrWT. Model TrWT underestimated considerably the INSTANT values in these two locations (by 3 and 5 C for Ombai and Lombok, respectively). The model values overestimated TrWT in the case of Lifamatola (by 1.5 C). A seasonal comparison of TrWS was not possible as there were no seasonal INSTANT measurements of this property. We present, in Table 3, our seasonal averages for TrWS, along with an overall average value. The lone INSTANT TrWS value for Lifamatola comes from Van Aken et al. (2009). It shows somewhat of a small difference (of 0.2) between the model s calculated overall mean TrWS at Lifamatola and that of the INSTANT dataset. For both TrWT and TrWS, aside from the fact that the model does not simulate the actual environment exactly, these differences may arise from the application of different methodologies in the calculation of TrWS. We chose our method (see Section 1.4) primarily to account for total transport (the denominator in TrWS) values nearing zero, which occurs at few points in both the model and INSTANT time series. Note that at all times in the model time series, some component (between 5% and 55%; mean 35%) of transport is directed in the non-itf direction (toward or away from the Indian Ocean; see Fig. 4b for seasonal sections). Thus in Fig. 4a and b and Tables 2 and 3, the temperatures and salinities that result are the values most likely to pass through each INSTANT passage in the ITF direction Annual comparison Averaging over each year of the time series, as well as the entire three-year dataset, reveals that the model generally reproduces the data well (Fig. 8b and Table 1, 04 06), although at all locations but Ombai, it underestimates the transports. The annual means for Labani in Fig. 8b are plotted with error bars taken from annual transport in Table 2 of Gordon et al. (2008). Just as in the seasonal average, for Lombok, Ombai, and Timor the data were taken from Sprintall s dataset. Most notably, model transport magnitudes at Labani and Timor were smaller than INSTANT values by over 1 Sv. This represents an underestimation of between 9% and 15% of the mean. For the other locations, annual model transports deviated from the annual INSTANT transport values by an average of less than 0.5 Sv (or less than 10 20% of the mean) Quarterly comparison Fig. 9 shows the transports averaged quarterly from monthly averaged INSTANT data (Winter 04 to Fall 06; where e.g. Summer 04 =(Summer 04 + Summer 05 + Summer 06)/3). Labani error bars are from seasonal transport of Table 2 of Gordon et al. (2008). For Lombok, Ombai, and Timor, the data were taken from Sprintall s dataset. At this timescale, it is clear what, if any, inter-annual variation exists. During the three years of INSTANT measurements, the pattern of flow through Labani seemed remarkably consistent in year-to-year transports, while at all other locations, there existed no consistent year-to-year pattern. Of all locations, Timor seemed to have the most different yearly pattern. Fig. 9 shows that the model best represents the transports through the Ombai Strait (model values in 11 of 12 quarters lie within 1.5 Sv of the average in situ quarterly data). However, of all locations, the transport through this constriction varies the least inter-annually. Large inter-annual variation is likely the reason the model seems to poorly represent INSTANT Timor transports. 3. Discussion The INSTANT program simultaneously measured transports (from January 2004 to December 2006) through five key passages in the Indonesian Seas (Labani Channel, Lifamatola Passage, Lombok Strait, Ombai Strait, and Timor Passage) along the path water travels from the Pacific to the Indian Ocean. In this paper, we compared the averaged transports simulated by a regional Indonesian Seas model

16 16 D. Rosenfield et al. / Dynamics of Atmospheres and Oceans xxx (2010) xxx xxx Fig. 9. Quarterly averaged three-year INSTANT transports (dots) plotted with associated uncertainty intervals, overlain by quarterly averaged model values (diamonds). See Section 1.2 for data sources. Labani error bars from seasonal transport of Table 2 of Gordon et al. (2008). based on the Princeton Ocean Model with corresponding observed INSTANT values. In earlier studies, this model was used as a basis for the dynamical analysis of the Indonesian Seas circulation and was preliminarily validated against some historical data. We can consider the comparison with the unique INSTANT observations as a crucial validation of the model. An effort was made to determine the spatial variability of normal velocity at the INSTANT passages during each season. Plotting sections of normal velocity across each passage shows some time variation in the distribution of normal velocities at both the surface and in deeper waters. While Lifamatola, Lombok, and Timor stayed fairly unchanged throughout the model year, Labani and Ombai did not. These sections help to show how difficult it could be to compute a reasonable transport value from sparse measurements provided by buoy stations used in the INSTANT program. We used different time-averaging of observations for the comparison. By its construction (mainly due to open boundary conditions chosen) the model can provide us with time-averaged characteristics only (monthly or seasonally). Thus, we should compare the simulated characteristics not with instantaneous but with time-averaged observed ones. For monthly averaged data, a number of mismatches for periods of several months occurred. For example, from July to September, the model underestimated Labani (and Lombok) transport magnitudes by approximately 4 Sv (and 2 Sv) each month. This underestimation accounts for nearly 35% (and 60%) of the mean flow through Labani (and Lombok) during the representative year. However, for almost all other months, the model predicted the INSTANT transports through these two passages within 1 Sv. Nearly all of the model transport magnitudes through Labani, Lifamatola, and Timor were underestimates of the INSTANT values (by approximately 1.5 Sv on average; or 20 60% of this location s mean throughflow). Transports through Ombai during most months were either underestimated or overestimated by the model (by up to 2 Sv in either direction; or 40% of Ombai s mean throughflow). Averaging over similar seasons for all three years, reveals that both winter and fall were best represented by the model. The model underestimates the magnitude of summer transports through

17 D. Rosenfield et al. / Dynamics of Atmospheres and Oceans xxx (2010) xxx xxx 17 Lombok ( 2 Sv; or 50% of the mean transport through this passage), Lifamatola ( 1 Sv or 30% of the mean), and Labani ( 4 Sv or 30% of the mean). The model underestimated spring transport magnitudes through Lifamatola ( 2 Sv or 60% of the mean), Labani ( 1 Sv or 8% of the mean), and Timor ( 3Sv or 30% of the mean). Seasonal transports through Lombok and Ombai were best represented by the model as there were no seasonal model averages that deviated from the averaged INSTANT transports by more than 1.5 Sv. The model Transport-Weighted Temperature seems to fit the INSTANT data in Labani and Timor, but shares little resemblance to the Lombok and Ombai TrWT. Model TrWT underestimated considerably the INSTANT values in these two locations (by 3 and 5 C for Ombai and Lombok, respectively). The model values overestimated TrWT in the case of Lifamatola (by 1.5 C). The model calculated overall mean Transport-Weighted Salinity reasonably agrees with Lifamatola INSTANT calculation. For other passages this characteristic was not calculated from INSTANT data. For quarterly averaging it is clear what, if any, inter-annual variation exists. During the three years of INSTANT measurements, the pattern of flow through Labani seemed remarkably consistent in yearto-year transports, while at all other locations, there existed no consistent year-to-year pattern. Of all locations, Timor seemed to have the most different yearly pattern. The model best represents the transports through the Ombai Strait. However, of all locations, the transport through this passage varies the least inter-annually. Large inter-annual variation is likely the reason the model seems to poorly represent INSTANT Timor transports. Finally, averaging over each year of the time series, as well as the entire three year dataset, reveals that the model generally reproduces the data well although at all locations but Ombai, it underestimates the transport magnitudes. But this underestimation is between 10% and 20% of the mean. In conclusion, taking into account a rather approximate presentation of seasonally varying transports through NGCC and NECC ports we argue that the regional model can be considered validated against INSTANT data. Acknowledgments This comparison would not be possible if it were not for the hard working INSTANT team, specifically all the contributors to the following INSTANT papers used in the comparison: Gordon et al. (2008), Sprintall et al. (2009), and Van Aken et al. (2009). V.M. Kamenkovich gratefully acknowledges the NSF support through the grant OCE We are also thankful to Dr. Dmitri Nechaev for his helpful discussions on this topic. References Conkright, M., Locarmini, R., Garcia, H., O Brien, T., Boyer, T., Stephens, C., Antonov, J., World Ocean Atlas 2001: Objective Analyses, Data Statistics, and Figures, CD-ROM Documentation. National Oceanographic Data Center, Silver Spring, MD, 17 pp. Ffield, A., Gordon, A., Vertical mixing in the Indonesian thermocline. J. Phys. Oceanogr. 22, Ffield, A., Gordon, A., Tidal mixing signatures in the Indonesian Seas. J. Phys. Oceanogr. 26 (9), Fieux, M., Andrié, C., Delecluse, P., Ilahude, A.G., Kartavtseff, A., Mantisi, F., Molcard, R., Swallow, J., Measurements within the Pacific Indian Oceans throughflow region. Deep-sea Res. 41, Gordon, A., Interocean exchange of thermocline water. J. Geophys. Res. 91, Gordon, A., Oceanography of the Indonesian Seas and their throughflow. Oceanography 18 (4), Gordon, A., Susanto, R.D., Banda Sea surface-layer divergence. Ocean Dyn. 52, Gordon, A., Susanto, R., Ffield, A., Huber, B., Pranowo, W., Wirasantosa, S., Makassar Strait throughflow, Geophys. Res. Lett. 35, L Gouriou, Y., Toole, J., Mean circulation of the upper layers of the western equatorial Pacific Ocean. J. Geophys. Res. 98, Grist, J., Josey, S., Inverse analysis adjustment of the SOC air sea flux climatology using ocean heat transport constraints. J. Climate 20, Kamenkovich, V., O Driscoll, K., Nechaev, D., Dynamics of the Indonesian seas circulation. Part II. The role of pressure head. J. Mar. Res. 67, Koch-Larrouy, A., Madec, G., Blanke, B., Molcard, R., 2008a. Water mass transformation along the Indonesian throughflow in an OGCM. Ocean Dyn. 58 (3 4), , doi: /s Koch-Larrouy, A., Madec, G., Iudicone, D., Atmadipoera, A., Molcard, R., 2008b. Physical processes contributing to the water mass transformation of the Indonesian Throughflow. Ocean Dyn. 58 (3 4), , doi: /s Lukas, R., Firing, E., Hacker, P., Richardson, P., Collins, C., Fine, R., Gammon, R., Observations of the Mindanao current during the Western Equatorial Pacific Ocean Circulation Study. J. Geophys. Res. 96,

18 18 D. Rosenfield et al. / Dynamics of Atmospheres and Oceans xxx (2010) xxx xxx Marchesiello, P., McWilliams, J., Shchepetkin, A., Open boundary conditions for long-term integration of regional ocean models. Ocean Model. 3, Mellor, G., User s Guide for a Three-dimensional, Primitive Equation, Numerical Ocean Model. Program in Atmospheric and Ocean Sciences. Princeton University, Princeton, NJ, p. 56. O Driscoll, K., Kamenkovich, V., Dynamics of the Indonesian Seas circulation. Part I. The influence of bottom topography on temperature and salinity distributions. J. Mar. Res. 67, Qiu, B., Joyce, T.M., Interannual variability in the mid- and low-latitude western North Pacific. J. Phys. Oceanogr. 22, Qiu, B., Lukas, R., Seasonal and interannual variability of the North Equatorial Current, the Mindanao Current, and the Kuroshio along the Pacific western boundary. J. Geophys. Res. 101 (C5), 12,315 12,330. Qu, T., Mitsudera, H., Yamagata, T., On the western boundary currents in the Philippine Sea. J. Geophys. Res. 103, Schiller, A., Godfrey, J.S., McIntosh, P.C., Meyers, G., Wijffels, S.E., Seasonal near-surface dynamics and thermodynamics of the Indian Ocean and Indonesian Throughflow, in a global ocean general circulation model. J. Phys. Oceanogr. 28, da Silva, A., Young, A., and Levitus, S., Atlas of surface marine data 1994, volume 1: algorithms and procedures. Tech. Rep. 6, U.S. Department of Commerce, NOAA, NESDIS. Sprintall, J., Wijffels, S., Gordon, A., Ffield, A., Molcard, R., Susanto, R., Soesilo, I., Sopaheluwakan, J., Surachman, Y., van Aken, H., A New International Array to Measure the Indonesian Throughflow: INSTANT. EOS Trans. 85 (39), Sprintall, J., Wijffels, S., Molcard, R., Jaya, I., Direct estimates of the Indonesian Throughflow entering the Indian Ocean: J. Geophys. Res. 114, C07001, doi: /2008jc Ueki, I., Kashino, Y., Kuroda, Y., Observation of current variations off the New Guinea coast including the El Niño period and their relationship with Sverdrup transport. J. Geophys. Res. 108 (C7), Van Aken, H., Brodjonegoro, I., Jaya, I., The deep-water motion through the Lifamatola Passage and its contribution to the Indonesian throughflow. Deep-Sea Res. I 56, Wijffels, S., Firing, E., Toole, J., The mean structure and variability of the Mindanao Current at 8 N. J. Geophys. Res. 100, Wyrtki, K., Indonesian through flow and the associated pressure gradient. J. Geophys. Res.-Oceans 92 (C12),