Tropical Atmosphere Ocean Array Research Articles

Assessment of radiative heating errors in Tropical Atmosphere Ocean array marine air temperature measurements

We assess the radiative heating error affecting marine air temperature (MAT) measurements in the Tropical Atmosphere Ocean array. The error in historical observations is found to be ubiquitous across the array, spatially variable and approximately stationary in time. The error induces spurious warming during daytime hours, but does not affect night-time temperatures. The range encompassing the real, unknown daily- and monthly-mean values is determined using daytime and night-time mean temperatures as upper and lower limits. The uncertainty in MAT is less than or equal to 0.5 °C and 0.2 °C for 95% of daily and monthly estimates, respectively. Uncertainties impact surface turbulent heat flux estimates, with potentially significant influences on the quantification of coupled ocean-atmosphere processes.

A 31-year Global Diurnal Sea Surface Temperature Dataset Created by an Ocean Mixed-Layer Model

A dataset of hourly sea surface temperature (SST) from the period 1 January 1982 to 31 December 2012, and covering the global ocean at a resolution of 0.3° × 0.3°, was created using a validated ocean mixed-layer model (MLSST). The model inputs were heat flux and surface wind speed obtained from the Coupled Forecast System Reanalysis dataset. Comparisons with in-situ data from the Tropical Atmosphere Ocean array and the National Data Buoy Center showed that the MLSST fitted very well with observations, with a mean bias of 0.07°C, and a root-mean-square error (RMSE) and correlation coefficient of 0.37°C and 0.98, respectively. Also, the MLSST fields successfully reproduced the diurnal cycle of SST in the in-situ data, with a mean bias of −0.005°C and RMSE of 0.26°C. The 31-year climatology revealed that the diurnal range was small across most regions, with higher values in the eastern and western equatorial Pacific, northern Indian Ocean, western Central America, northwestern Australia, and several coastal regions. Significant seasonal variation of diurnal SST existed in all basins. In the Atlantic and Pacific basins, this seasonal pattern was oriented north–south, following the variation in solar insolation, whereas in the Indian basin it was dominated by monsoonal variability. At the interannual scale, the results highlighted the relationship between diurnal and interannual variations of SST, and revealed that the diurnal warming in the central equatorial Pacific could be a potential climatic indicator for ENSO prediction.

An ENSO prediction approach based on ocean conditions and ocean–atmosphere coupling

A simple statistical model for the El Nino–Southern Oscillation (ENSO) prediction is derived based on the evolution of the ocean heat condition and the oceanic Kelvin wave propagation associated with westerly wind events (WWEs) and easterly wind surges (EWSs) in the tropical Pacific. The multivariate linear regression model solely relies on the pentad thermocline depth anomaly evolution in 25 days along with the zonal surface wind modulation. It successfully hindcasts all ENSOs except for the 2000/01 La Nina, using the pentad (or monthly) mean tropical atmosphere ocean array data since 1994 with an averaged skill (measured by anomaly correlation) of 0.62 (or 0.67) with a 6-month lead. The exception is mainly due to the long-lasting cold sea surface temperature anomalies in the subtropics resulting from the strong 1998/99 La Nina, even though the tropical warm water volume (WWV) had rebounded and turned phases after 2000. We also note that the hindcast skill is comparable using pentad or monthly mean NCEP global ocean data assimilation system data for the same time period. The hindcast skill of the proposed statistical model is better than that based on the WWV index in terms of the monthly correlation, normalized RMSEs and ENSO occurrences, which suggest that including the evolution of the subsurface ocean temperature anomaly and the WWEs/EWSs in the central tropical Pacific can enhance the ability to predict ENSO. The hindcast skill is also comparable to the predictions using other dynamical and statistical models, indicating that these processes are the keys to ENSO development. The dynamics behind the statistical model are consistent with the physical processes of ENSO development as follows: the tropical WWV resulting from the interannually-varying meridional subtropical cell transport provides a sufficient heat source. When the seasonal phase lock of ocean–atmosphere coupling triggers the positive (negative) zonal wind anomaly in boreal summer and fall, an El Nino (a La Nina) will develop as evidenced by the Kelvin wave propagation. The triggering dynamic may be suppressed or enhanced by the influence of extratropical Pacific sea surface temperature.

An ocean data assimilation system in the Indian Ocean and west Pacific Ocean

The development and application of a regional ocean data assimilation system are among the aims of the Global Ocean Data Assimilation Experiment. The ocean data assimilation system in the regions including the Indian and West Pacific oceans is an endeavor motivated by this goal. In this study, we describe the system in detail. Moreover, the reanalysis in the joint area of Asia, the Indian Ocean, and the western Pacific Ocean (hereafter AIPOcean) constructed using multi-year model integration with data assimilation is used to test the performance of this system. The ocean model is an eddy-resolving, hybrid coordinate ocean model. Various types of observations including in-situ temperature and salinity profiles (mechanical bathythermograph, expendable bathythermograph, Array for Real-time Geostrophic Oceanography, Tropical Atmosphere Ocean Array, conductivity–temperature–depth, station data), remotely-sensed sea surface temperature, and altimetry sea level anomalies, are assimilated into the reanalysis via the ensemble optimal interpolation method. An ensemble of model states sampled from a long-term integration is allowed to change with season, rather than remaining stationary. The estimated background error covariance matrix may reasonably reflect the seasonality and anisotropy. We evaluate the performance of AIPOcean during the period 1993–2006 by comparisons with independent observations, and some reanalysis products. We show that AIPOcean reduces the errors of subsurface temperature and salinity, and reproduces mesoscale eddies. In contrast to ECCO and SODA products, AIPOcean captures the interannual variability and linear trend of sea level anomalies very well. AIPOcean also shows a good consistency with tide gauges.

Adaptive kernel smoothing regression for spatio-temporal environmental datasets

A method for performing kernel smoothing regression in an incremental, adaptive manner is described. A simple and fast combination of incremental vector quantization with kernel smoothing regression using adaptive bandwidth is shown to be effective for online modeling of environmental datasets. The approach proposed is to apply kernel smoothing regression in an incremental estimation of the (evolving) probability distribution of the incoming data stream rather than the whole sequence of observations. The method is illustrated on publicly available datasets corresponding to the Tropical Atmosphere Ocean array and the Helsinki Commission hydrographic database for the Baltic Sea.

Effects of Sea Level Data Assimilation by Ensemble Optimal Interpolation and 3D Variational Data Assimilation on the Simulation of Variability in a Tropical Pacific Model

Abstract Sea level anomalies (SLA) from the Ocean Topography Experiment (TOPEX)/Poseidon are assimilated with three-dimensional variational data assimilation (3DVAR) and ensemble optimal interpolation (EnOI) for the period of 1997–2001. When sea level data are assimilated, one major concern is how to project the surface information downward. In 3DVAR, downward projection is usually achieved by minimizing a cost function that computes the relations among temperature, salinity, and sea level. In EnOI, the surface information is propagated to other variables through a stationary ensemble. Their effects on the simulated variability are evaluated in a tropical Pacific Ocean model. When compared with different datasets, it is found that effects of 3DVAR and EnOI are different in several aspects. For sea level, the standard deviation is improved by both methods, but EnOI is more effective in the central/eastern Pacific. The SLA evolution is better reproduced with EnOI than with 3DVAR. For temperature, the model–reanalysis correlations are increased by 0.1–0.2 in the top 200 m with both methods, but EnOI is more effective, especially along the thermocline depth. When compared with the Tropical Atmosphere–Ocean array (TAO) profiles, evolution of the temperature reveals that 3DVAR tends to cause more errors during ENSO events. The correlations with TAO profile are increased by 0.1–0.3 with EnOI and are generally decreased by 0.1–0.3 with 3DVAR. For salinity, both methods have weak impact on the model–reanalysis correlations above the thermocline. Relative to 3DVAR, EnOI can increase the correlation by 0.2 below the thermocline. When compared with the TAO profiles, the differences are reduced to some extent with both methods, but 3DVAR is very negative on the simulated variability.

A Climatology of Diurnal and Semidiurnal Surface Wind Variations over the Tropical Pacific Ocean Based on the Tropical Atmosphere Ocean Moored Buoy Array

Abstract Hourly measurements from 51 moored buoys in the Tropical Atmosphere Ocean array (9°N–8°S, 165°E–95°W) during 1993–2004 are used to document the climatological seasonal and annual mean patterns of diurnal and semidiurnal near-surface wind variability over the tropical Pacific Ocean. In all seasons, the amplitude of the semidiurnal harmonic is approximately twice as large as the diurnal harmonic for the zonal wind component, while the diurnal harmonic is at least 3 times as large as the semidiurnal harmonic for the meridional wind component, both averaged across the buoy array. Except for the eastern equatorial Pacific, the semidiurnal zonal wind harmonic exhibits uniform amplitude (∼0.14 m s−1) and phase [maximum westerly wind anomalies ∼0325/1525 local time (LT)] across the basin in all seasons. This pattern is well explained by atmospheric thermal tidal theory. The semidiurnal zonal wind signal is diminished over the cold surface waters of the eastern equatorial Pacific where it is associated with enhanced boundary layer stability. Diurnal meridional wind variations tend to be out of phase north and south of the equator (maximum southerly wind anomalies ∼0700 LT at 5°N and ∼1900 LT at 5°S), while a noon southerly wind anomaly maximum is observed on the equator in the eastern Pacific particularly during the cold season (June–November). The diurnal meridional wind variations result in enhanced divergence along the equator and convergence along the southern border of the intertropical convergence zone ∼0700 LT (opposite conditions ∼1900 LT); the amplitude of the divergence diurnal cycle is ∼5 × 10−7 s−1. The diurnal meridional wind variations are largely consistent with the diurnal pressure gradient force.

Comparison of Five Satellite-Derived Latent Heat Flux Products to Moored Buoy Data

Abstract Five satellite products of latent heat flux at the sea surface were compared to bulk fluxes calculated with data from 75 moored buoys, on almost 36 successive months from 1998 to 2000. The five products compared are the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Dataset (HOAPS-2), the Japanese Ocean Flux Datasets with Use of Remote Sensing Observations (J-OFURO), the Jones dataset, the Goddard Satellite-Based Surface Turbulent Fluxes, version 2 (GSSTF-2), and the Bourras–Eymard–Liu dataset (BEL). The comparisons were performed under tropical and midlatitude environmental conditions, with three datasets based on 66 Tropical Atmosphere–Ocean array (TAO) buoys in the tropical Pacific, nine National Data Buoy Center (NDBC) buoys off the U.S. coasts, and four Met Office/Météo-France (UK–MF) moorings west of the United Kingdom and France, respectively. The satellite products did not all compare well to surface data. However, for each in situ dataset (TAO, NDBC, or UK–MF) at least one satellite product was found that had a good fit to surface data, that is, an rms deviation of 15–30 W m−2. It was found that HOAPS-2, J-OFURO, GSSTF-2, and BEL satellite products had moderate systematic errors with respect to surface data, from −13 to 26 W m−2, and small biases at midlatitudes (6–8 W m−2). Most of the satellite products were able to render the seasonal cycle of the latent heat flux calculated with surface data. The estimation of near-surface specific humidity was found to be problematic in most products, but it was best estimated in the HOAPS-2 product. GSSTF-2 and J-OFURO strongly overestimated the surface flux variations in time and space compared to surface data and to a flux climatology. With respect to TAO data, Jones fluxes yielded good results in terms of rms deviation (27 W m−2) but also presented a large systematic deviation. Overall, for application of the satellite fluxes to the world oceans, it was found that HOAPS-2 was the most appropriate product, whereas for application to the Tropics, BEL fluxes had the best performance in rms with respect to TAO data (24 W m−2).

Multivariate Error Covariance Estimates by Monte Carlo Simulation for Assimilation Studies in the Pacific Ocean

Abstract One of the most difficult aspects of ocean-state estimation is the prescription of the model forecast error covariances. The paucity of ocean observations limits our ability to estimate the covariance structures from model–observation differences. In most practical applications, simple covariances are usually prescribed. Rarely are cross covariances between different model variables used. Here a comparison is made between a univariate optimal interpolation (UOI) scheme and a multivariate OI algorithm (MvOI) in the assimilation of ocean temperature profiles. In the UOI case only temperature is updated using a Gaussian covariance function. In the MvOI, salinity, zonal, and meridional velocities as well as temperature are updated using an empirically estimated multivariate covariance matrix. Earlier studies have shown that a univariate OI has a detrimental effect on the salinity and velocity fields of the model. Apparently, in a sequential framework it is important to analyze temperature and salinity together. For the MvOI an estimate of the forecast error statistics is made by Monte Carlo techniques from an ensemble of model forecasts. An important advantage of using an ensemble of ocean states is that it provides a natural way to estimate cross covariances between the fields of different physical variables constituting the model-state vector, at the same time incorporating the model’s dynamical and thermodynamical constraints as well as the effects of physical boundaries. Only temperature observations from the Tropical Atmosphere–Ocean array have been assimilated in this study. To investigate the efficacy of the multivariate scheme, two data assimilation experiments are validated with a large independent set of recently published subsurface observations of salinity, zonal velocity, and temperature. For reference, a control run with no data assimilation is used to check how the data assimilation affects systematic model errors. While the performance of the UOI and MvOI is similar with respect to the temperature field, the salinity and velocity fields are greatly improved when the multivariate correction is used, as is evident from the analyses of the rms differences between these fields and independent observations. The MvOI assimilation is found to improve upon the control run in generating water masses with properties close to the observed, while the UOI fails to maintain the temperature and salinity structure.

Designing Efficient Observing Networks for ENSO Prediction

The Tropical Atmosphere Ocean (TAO) array of moored buoys in the tropical Pacific Ocean is a major source of data for understanding and predicting El Nino-Southern Oscillation (ENSO). Despite the importance of the TAO array, limited work has been performed where observations are most important for predicting ENSO effectively. To address this issue, this study performs a series of observing system simulation experiments (OSSEs) with a linearized intermediate coupled ENSO model, stochastically forced. ENSO forecasts are simulated for a variety of observing network configurations, and forecast skill averaged over many simulated ENSO events is compared. The first part of this study examined the relative importance of sea surface temperature (SST) and subsurface ocean observations, requirements for spacing and meridional extent of observations, and important regions for observations in this system. Using these results as a starting point, this paper develops efficient observing networks for forecasting ENSO in this system, where efficient is defined as providing reasonably skillful forecasts for relatively few observations. First, efficient networks that provide SST and thermocline depth data at the same locations are developed and discussed. Second, efficient networks of only thermocline depth observations are addressed, assuming that many SST observations are available from another source (e.g., satellites). The dependence of the OSSE results on the duration of the simulated data record is also explored. The results suggest that several decades of data may be sufficient for evaluating the effects of observing networks on ENSO forecast skill, despite being insufficient for evaluating the long-term potential predictability of ENSO.

Adjustment of Near-Equatorial Wind Stress with Four-Dimensional Variational Data Assimilation in a Model of the Pacific Ocean

Abstract A four-dimensional variational scheme is described in which ocean observations are assimilated into an ocean general circulation model using wind stress forcing fields as control variables. Idealized (“twin”) experiments are performed to evaluate the possibility of reconstructing wind stress variability and its oceanic response from synthetic observations of the ocean state. Two types of wind stress errors are considered: time-varying errors associated with a wind burst and constant errors associated with a wind stress bias. Both sets of experiments demonstrate that the spatial structure of the wind stress variations is well reconstructed, while the estimation of their amplitude and time evolution is less accurate. Sparser equatorial sampling, similar to that of the Tropical Atmosphere–Ocean array, only slightly degrades the analysis. Omitting velocity and salinity observations leads to a less accurate amplitude and time evolution of the wind stress increment. Still, general features are captured...

Evaluating Observing Requirements for ENSO Prediction: Experiments with an Intermediate Coupled Model

The Tropical Atmosphere Ocean (TAO) array of moored buoys in the tropical Pacific Ocean is a major source of data for understanding and predicting the El Niño–Southern Oscillation (ENSO). Despite the importance of the TAO array, limited work has been performed to date on the number and locations of observations required to predict ENSO effectively. To address this issue, this study performs a series of observing system simulation experiments (OSSEs) with a linearized intermediate coupled ENSO model, stochastically forced. ENSO forecasts are simulated for a number of observing network configurations, and forecast skill averaged over 1000 years of simulated ENSO events is compared. The experiments demonstrate that an OSSE framework can be used with a linear, stochastically forced ENSO model to provide useful information about requirements for ENSO prediction. To the extent that the simplified model dynamics represent ENSO dynamics accurately, the experiments also suggest which types of observations in which regions are most important for ENSO prediction. The results indicate that, using this model and experimental setup, subsurface ocean observations are relatively unimportant for ENSO prediction when good information about sea surface temperature (SST) is available; adding subsurface observations primarily improves forecasts initialized in late summer. For short lead-time (1–2 month) forecasts, observations within approximately 3° of the equator are most important for skillful forecasts, while for longer lead-time forecasts, forecast skill is increased by including information at higher latitudes. For forecasts longer than a few months, the most important region for observations is the eastern equatorial Pacific, south of the equator; a secondary region of importance is the western equatorial Pacific. These regions correspond to those where the leading singular vector for the ENSO model has a large amplitude. In a continuation of this study, these results will be used to develop efficient observing networks for forecasting ENSO in this system.

Estimating Sea Level Slope on the Equator

Abstract A new method for diagnosing the zonal gradient of sea level on the equator from in situ data is proposed and validated using satellite altimetry. A no-turbulent-stress-level boundary condition in the Pacific Equatorial Undercurrent core constrains the average flux of zonal momentum above, uniquely determining the zonal sea level gradient. The method is applied to simultaneous data from Tropical Atmosphere–Ocean Array moorings spanning 154°–165°E and ADCP moorings in the vicinity of 157.5°E from 6 October 1992 to 21 December 1993. An independent estimate of sea level slope is obtained from contemporaneous daily equatorial crossings of the first European Remote Sensing Satellite (ERS-1) and Ocean Topography Experiment (TOPEX)/Poseidon altimeters. The slope is also estimated by assuming no pressure gradient at 500-m depth. When accelerations and the pressure gradient can be estimated at the same location, 5-day averages of the no-stress-level estimate explain 59% of the variance in the altimetry dat...

Second and first baroclinic Kelvin modes in the equatorial Pacific at intraseasonal timescales

TOPEX/Poseidon sea level and time series from the TAO (Tropical Atmosphere Ocean) array of moorings over 1992–1999 are used to investigate the spatiotemporal characteristics of equatorial Kelvin waves in the Pacific at period shorter than 180 days. Spectral analyses show high oceanic energy in two separate bands of period, around 70 days and 120 days. Signals are coherent in both bands all along the equator. Ocean General Circulation Model (OGCM) simulations forced by different wind stress fields are also analyzed and exhibit the same features. Observed and modeled sea level, dynamic height and 20°C isotherm depth, band‐pass filtered around 70 days, show an eastward propagation at phase speed of 2.4–2.9 m s−1, typical of the first baroclinic Kelvin mode. The meridional and vertical structures of the equatorial waves at the onset of the 1997 El Niño are also those expected for a first Kelvin mode. At 120‐day period, phase speeds, meridional and vertical structures are closer to those expected for a second baroclinic mode. Both modes are nevertheless present in the two frequency bands. A simple Kelvin wave model shows that the wind at 120‐day period seems to force the Kelvin waves at the same period. However, the predominance of second baroclinic mode at 120‐day period cannot be explained solely by the characteristics of the wind. OGCM simulations rather suggest the importance of internal oceanic processes such as vertical redistribution of energy from first to second baroclinic mode as the waves propagate eastward. Model outputs over 1984–1999 indicate that these equatorial Kelvin waves at 70‐ and 120‐day period may have an effect on El Niño since they seem to be stronger at its onset.

Mixed layer depth variability over the global ocean

The spatial and monthly variability of the climatological mixed layer depth (MLD) for the global ocean is examined using the recently developed Naval Research Laboratory (NRL) Ocean Mixed Layer Depth (NMLD) climatologies. The MLD fields are constructed using the subsurface temperature and salinity data from the World Ocean Atlas 1994 [Levitus et al., 1994; Levitus and Boyer, 1994]. To minimize the limitations of these global data in the MLD determination, a simple mixing scheme is introduced to form a stable water column. Using these new data sets, global MLD characteristics are produced on the basis of an optimal definition that employs a density‐based criterion having a fixed temperature difference of ΔT = 0.8°C and variable salinity. Strong seasonality of MLD is found in the subtropical Pacific Ocean and at high latitudes, as well as a very deep mixed layer in the North Atlantic Ocean in winter and a very shallow mixed layer in the Antarctic in all months. Using the climatological monthly MLD and isothermal layer depth (ILD) fields from the NMLD climatologies, an annual mean ΔT field is presented, providing criteria for determining an ILD that is approximately equivalent to the optimal MLD. This enables MLD to be determined in cases where salinity data are not available. The validity of the correspondence between ILD and MLD is demonstrated using daily averaged subsurface temperature and salinity from two moorings: a Tropical Atmosphere Ocean array mooring in the western equatorial Pacific warm pool, where salinity stratification is important, and a Woods Hole Oceanographic Institute (WHOI) mooring in the Arabian Sea, where strongly reversing seasonal monsoon winds prevail. In the western equatorial Pacific warm pool the use of ILD criterion with an annual mean ΔT value of 0.3°C yields comparable results with the optimal MLD, while large ΔT values yield an overestimated MLD. An analysis of ILD and MLD in the WHOI mooring show that use of an incorrect ΔT criterion for the ILD may underestimate or overestimate the optimal MLD. Finally, use of the spatial annual mean ΔT values constructed from the NMLD climatologies can be used to estimate the optimal MLD from only subsurface temperature data via an equivalent ILD for any location over the global ocean.

Physical control of biological processes in the central equatorial Pacific Ocean

A five-component (phytoplankton, zooplankton, ammonium, nitrate, detritus) physical–biological model was developed to investigate the effects of physical processes on daily to interannual time scales, on the lower trophic levels of the central equatorial Pacific. Many of the biological processes included in the ecosystem model respond to environmental fluctuations with time scales between 1 and 10 d, which are not typically resolved by basin- to global-scale circulation models. Therefore, the equatorial Pacific ecosystem model is forced using daily information (solar radiation, velocity, temperature) from the Tropical Atmosphere Ocean (TAO) mooring array. The ecosystem model also requires vertical velocity information which necessitated the development of a method for computing daily vertical velocities from the TAO array. Much of the variability in primary production, plankton and nutrient concentrations observed in 1992 during the US Joint Global Ocean Flux Study Equatorial Pacific Process Study time-series cruises (TS1 and TS2), is well reproduced in the model simulations. Simulations demonstrate that lower primary productivities during TS1 as compared to TS2 result from the deeper thermocline that persisted during TS1 as a result of El Niño conditions; however, because of the simultaneous reduction in grazing pressure, simulated chlorophyll levels are similar for these two time periods. Simulations of this single-species ecosystem model successfully reproduce data collected both during and after the El Niño, suggesting that species composition changes are not of first-order importance when examining the effects of the 1991–92 El Niño on the equatorial Pacific ecosystem. A 60–70% increase in chlorophyll concentration and a 400% increase in the chlorophyll contribution by diatoms was associated with the passage of a tropical instability wave (20-d period) across the study site during TS2. This period of high chlorophyll concentration and diatom abundance coincided temporally with strong northward velocities and strong downwelling velocities in the upper euphotic zone. Observations and simulations suggest that this increase in chlorophyll concentration and change in species composition not only results from in situ diatom growth stimulated by increased iron concentrations, but also results from the advection of diatoms toward the convergent front located along the leading (western) edge of the instability wave. Equatorially trapped internal gravity waves can also stimulate in situ phytoplankton growth as high-frequency vertical motions introduce limiting micronutrients, such as iron, into the euphotic zone. Because iron can be taken up by the picoplankton on time scales much shorter than the wave period (6–8 days), these waves may provide a mechanism for effecting a large flux of iron into the euphotic zone. Exclusion of these high-frequency motions results in an iron flux to the euphotic zone that may be underestimated by more than 30%.

Retrospective Analysis of the Salinity Variability in the Western Tropical Pacific Ocean Using an Indirect Minimization Approach

Abstract Empirical orthogonal functions of the combined variability of temperature and salinity have been used as basis functions for the indirect reconstruction of salinity from observations of temperature alone. The method employs a weighted least squares procedure that minimizes the misfit between the reconstructed temperature and the observed temperature, but also constrains the variability of the reconstructed salinity to remain within specified bounds. The method has been tested by fitting to temperature profiles from the Tropical Atmosphere Ocean array along 165°E in the western equatorial Pacific Ocean (8°N–8°S) for the 1986–97 period. Comparisons of the reconstructed salinity field with sea surface salinity and conductivity–temperature–depth data and of the reconstructed dynamic height with TOPEX/Poseidon observations of sea level demonstrate the reliability of the method. The reconstructed data successfully capture the upper-ocean variability at annual to ENSO timescales. The impact of neglectin...

Adventures in Data Analysis: The TAO Array and the 1997–1998 El Niño

Traditional undergraduate education in earth sciences does not emphasize data acquisition, analysis, or assessment. However, arrival of the information age dictates that earth sciences graduates be imbued with fundamental skills to organize, evaluate and process large data sets. Fortunately, the proliferation of remotely sensed data and its availability via the Internet provides many opportunities for earth science educators to meet these needs. Exercises to introduce students to data analysis have been designed utilizing data from the Tropical Atmosphere–Ocean (TAO) Array and the 1997–1998 El Nino episode in the tropical Pacific Ocean. The TAO Array is a grid of 69 buoys moored across the equatorial Pacific Ocean (8°N to 8°S and 95°W to 143°E) recording environmental data relevant to El Nino—Southern Oscillation (ENSO) processes. Data from the TAO Array is available in near-real-time (http://www.pmel.noaa.gov/toga-tao/realtime.html) or as archived ASCII files (http://www.pmel.noaa.gov/toga-tao/data-delivery.html) providing daily (sometimes hourly) records of environmental parameters for each buoy in the grid. Student exercises in data analysis begin with downloading data from buoy locations, parsing the data into spreadsheets, and organizing data by environmental parameter into yearly and monthly data sets. Analyses of reconstructed data include calculations of long-term averages of environmental parameters, seasonal climatologies, monthly climatologies and calculation of long-term, seasonal, and monthly anomalies. Finally, monthly anomaly maps produced by students are loaded sequentially into GIF-animation software to create time-series images illustrating the progress and development of the 1997–1998 El Nino event.

Dynamical Analysis of Seasonal and Interannual Variability in the Equatorial Pacific*

Abstract Time series measurements from the Tropical Atmosphere Ocean array during the 1980s and 1990s are used to analyze the dynamics of the long-term mean, the seasonal cycle, and interannual variability in the upper 250 m of the equatorial Pacific. The analysis is based on a diagnosis of the zonal momentum equation using surface winds, ocean temperatures, and currents between 165°E and 110°W along the equator. In the mean, the balance is mainly between zonal wind stress and depth-integrated zonal pressure gradient, in good agreement with linear Sverdrup theory. For the seasonal cycle, the balance is mainly between wind stress, pressure gradient, and local acceleration, with differences between the stress and pressure gradient leading to accelerations and decelerations of the South Equatorial Current and Equatorial Undercurrent. On interannual timescales, local acceleration is much smaller than either zonal wind stress or zonal pressure gradient, indicating that to zeroth order, variations in the equato...

Seasonal Variability in the Equatorial Pacific

Abstract Five to nine years of observations from the Tropical Atmosphere Ocean array in the tropical Pacific are used to document the seasonal cycle of surface winds, zonal currents, SST, thermocline depth, and dynamic height. Along the equator, the normally westward surface zonal current reverses direction in late boreal spring and early summer. This seasonal variation in the zonal surface current propagates westward, as do seasonal variations in the Equatorial Undercurrent and zonal surface winds at the equator. At 5°N and 5°S, the seasonal variations in the 20°C isotherm and dynamic height also propagate westward. Conversely at the equator in the eastern and central Pacific, the variations in 20°C isotherm depth and dynamic height propagate eastward. These seasonal variations are interpreted by means of a simple dynamical model based on linear equatorial wave theory. Model results indicate that seasonal variability between 5°N and 5°S is dominated by wind-forced equatorial Kelvin waves and first meridi...