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David W. Behringer and Henry Stommel

Abstract

Charts are presented which show the seasonal and annual rates of heat gain of the tropical North Atlantic Ocean. These rates have been computed using subsurface oceanographic data and wind-stress data. In these computations the interseasonal rates of heat gain are determined primarily by the rate of local heating, and their magnitude, in general, is several times larger than the annual rate. The annual mean rate implies a net heat loss over much of the tropical ocean. The probable mechanism for this heat loss is an annual excess in cooling due to evaporation over heating due to the net incoming radiation. The present results show similarities to some results of previous authors who have based their calculations on bulk aerodynamical formulas and radiation estimates.

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Ming Ji, David W. Behringer, and Ants Leetmaa

Abstract

An improved forecast system has been developed and implemented for ENSO prediction at the National Centers for Environmental Prediction (NCEP). This system consists of a new ocean data assimilation system and an improved coupled ocean–atmosphere forecast model (CMP12) for ENSO prediction. The new ocean data assimilation system is described in Part I of this two-part paper.

The new coupled forecast model (CMP12) is a variation of the standard NCEP coupled model (CMP10). Major changes in the new coupled model are improved vertical mixing for the ocean model; relaxation of the model’s surface salinity to the climatological annual cycle; and incorporation of an anomalous freshwater flux forcing. Also, the domain in which the oceanic SST couples to the atmosphere is limited to the tropical Pacific.

Evaluation of ENSO prediction results show that the new coupled model, using the more accurate ocean initial conditions, achieves higher prediction skill. However, two sets of hindcasting experiments (one using the more accurate ocean initial conditions but the old coupled model, the other using the new coupled model but the less accurate ocean initial conditions), result in no improvement in prediction skill. These results indicate that future improvement in ENSO prediction skill requires systematically improving both the coupled model and the ocean analysis system. The authors’ results also suggest that for the purpose of initializing the coupled model for ENSO prediction, care should be taken to give sufficient weight to the model dynamics during the ocean data assimilation. This can reduce the danger of aliasing large-scale model biases into the low-frequency variability in the ocean initial conditions, and also reduce the introduction of small-scale noise into the initial conditions caused by overfitting the model to sparse observations.

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David W. Behringer, Ming Ji, and Ants Leetmaa

Abstract

An improved forecast system has been developed for El Niño–Southern Oscillation (ENSO) prediction at the National Centers for Environmental Prediction. Improvements have been made both to the ocean data assimilation system and to the coupled ocean–atmosphere forecast model. In Part I of a two-part paper the authors describe the new assimilation system. The important changes are 1) the incorporation of vertical variation in the first-guess error variance that concentrates temperature corrections in the thermocline and 2) the overall reduction in the magnitude of the estimated first-guess error. The new system was used to produce a set of retrospective ocean analyses for 1980–95. The new analyses are less noisy than their earlier counterparts and compare more favorably with independent measurements of temperature, currents, and sea surface height variability. Part II of this work presents the results of using these analyses to initialize the coupled forecast model for ENSO prediction.

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Femke C. Vossepoel and David W. Behringer

Abstract

In the primitive equation model for the tropical Pacific at the National Centers for Environmental Prediction, subsurface temperature observations are assimilated. The addition of TOPEX/Poseidon sea level observations to the NCEP assimilation scheme has resulted in large differences in sea level during 1996. These differences are suggested to be related to salinity variability.

A bivariate assimilation scheme is presented that corrects both temperature and salinity. The method is tested with synthetic data in an identical triplets experiment, in which a westerly wind burst is simulated. In this experiment, the correction of salinity improves the density simulation and attenuates errors in salinity. A four-year assimilation experiment with real data is performed to test the system’s performance for 1993–96. In this experiment, the assimilation of TOPEX/Poseidon observations improves dynamic height simulation without degrading the temperature field. This application of altimetry improves the mean salinity in the western equatorial Pacific and leads to a more pronounced salinity variability in the ocean model.

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Christophe Maes, David Behringer, Richard W. Reynolds, and Ming Ji

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 neglecting salinity variability on the dynamic height anomaly in the western tropical Pacific Ocean is addressed.

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Arun Kumar, Mingyue Chen, Yan Xue, and David Behringer

Abstract

Subsurface ocean observations in the equatorial tropical Pacific Ocean dramatically increased after the 1990s because of the completion of the TAO moored array and a steady increase in Argo floats. In this analysis the question explored is whether a steady increase in ocean observations can be discerned in improvements in skill of predicting sea surface temperature (SST) variability associated with El Niño–Southern Oscillation (ENSO)? The analysis is based on the time evolution of skill of sea surface temperatures in the equatorial tropical Pacific since 1982 based on a seasonal prediction system. It is found that for forecasts up to a 6-month lead time, a clear fingerprint of increases in subsurface ocean observations is not readily apparent in the time evolution of prediction skill that is dominated much more by the signal-to-noise consideration of SSTs to be predicted. Finding no clear relationship between an increase in ocean observations and prediction skill of SSTs, various possibilities for why it may be so are discussed. This discussion is to motivate further exploration on the question of the tropical Pacific observing system, its influence on the skill of ENSO prediction, and the capabilities of the current generation of coupled models and ocean data assimilation systems to take advantage of ocean observations.

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Rodert L. Molinari, John F. Festa, and David W. Behringer

Abstract

Monthly mean dynamic height topographies for the upper 500 m of the Gulf of Mexico, seasonal mean topographies for the upper 1000 m and annual topographies for the deep flow are presented. The dynamic height values on a 1° × 1° grid were determined from observed temperature values and salinities derived from mean T-S relations. The seasonal intrusion of the Loop Current is observed and found to vary directly with the geostrophic transport through the Yucatan Straits. At the Straits, the transport in the upper 500 m is a maximum in June. The transports in the upper 500 m of an anticyclone in the western Gulf are a maximum in winter and summer, and a minimum in spring and fall. There is a permanent westerly flow on the Texas Shelf. After turning cyclonically, this flow joins the eastward transport of the northern limb of the anticyclone in the western Gulf of Mexico. Most of this eastward flow recirculates in the anticyclone; however, a portion flows cast across the central Gulf to become entrained in the Loop Current. The deep circulation between 1500 and 3000 m is dominated by an anticyclonic gyre which fills the entire deep basin.

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Ming Ji, Richard W. Reynolds, and David W. Behringer

Abstract

In this study, two sets of Pacific Ocean analyses for 1993–96 were analyzed. Both analyses were produced with the assimilation of subsurface temperature data from expendable bathythermographs and tropical atmosphere–ocean moorings. In addition one analysis also assimilated sea level data from TOPEX/Poseidon. Sea level variability in the two analyses agreed well with each other, and both agree with tide gauge and altimetry data for 1993–95. However, beginning in late 1995 through 1996, large sea level differences of 5–8 cm were found in the tropical western Pacific between the two analyses. Furthermore, large sea level discrepancies were also found between dynamic height estimated from TAO temperatures and tide gauge–altimetry observations in the same region during 1996. These discrepancies are consistent with the sea level differences between the two model based analyses.

Historical conductivity–temperature–depth data along 165°E near the equator were also analyzed and it was found that salinity variability on interannual timescale can result in a sea level variability of at least −5 dyn cm to +6 dyn cm. These results suggest that the sea level discrepancy in 1996 is likely due to inadequate salinity information both in estimating dynamic height from TAO temperature and in the data assimilation system used here, which corrects only temperature field.

The sea level error that resulted from inadequate salinity variability has a significant projection onto the second sea level anomaly EOF, which is linked to the onset phase of ENSO. This suggests that the error in the ocean initial conditions due to underestimate of interannual salinity variations in 1996 could impact the accuracy of ENSO prediction. Results from a twin experiment that uses two summer 1996 ocean initial conditions to hindcast for winter 1996/97 equatorial Pacific SST anomalies appear to support this hypothesis.

The results also pointed to a weakness of the present univariate assimilation system, which corrects only temperature. The improved sea level variability comes at the expense of reduced accuracy in temperature. A better solution would be a bivariate data assimilation system, which corrects both salinity and temperature, producing more accurate and consistent ocean initial conditions for ENSO prediction.

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John G. W. Kelley, David W. Behringer, H. Jean Thiebaux, and Bhavani Balasubramaniyan

Abstract

The real-time, three-dimensional, limited-area Coastal Ocean Forecast System (COFS) has been developed for the northwestern Atlantic Ocean and implemented at the National Centers for Environmental Prediction. COFS generates a daily nowcast and 1-day forecast of water level, temperature, salinity, and currents. Surface forcing is provided by 3-h forecasts from the National Weather Service's Eta Model, a mesoscale atmospheric prediction model. Lateral oceanic boundary conditions are based on climatic data. COFS assimilates in situ sea surface temperature (SST) observations and multichannel satellite SST retrievals for the past 48 h. SST predictions from the assimilating and nonassimilating versions of COFS were compared with independent observations and a 14-km-resolution multichannel SST analysis. The assimilation of SST data reduced the magnitude and the geographic extent of COFS's characteristic positive temperature bias north of the Gulf Stream. The root-mean-square SST differences between the COFS predictions and in situ observations were reduced by up to 47%–50%. Qualitative comparisons were also made between predictions from the assimilating and nonassimilating versions and thermal profiles measured by expendable bathythermographs. These comparisons indicated that the assimilation scheme had positive impact in reducing temperature differences in the top 300 m at most locations. However, the subsurface comparisons also show that, in dynamically complex regions such as the Gulf Stream, the continental slope, or the Gulf of Maine, the data assimilation system has difficulty reproducing the observed ocean thermal structure and would likely benefit from the direct assimilation of observed profiles.

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Stephen G. Penny, David W. Behringer, James A. Carton, and Eugenia Kalnay

Abstract

Seasonal forecasting with a coupled model requires accurate initial conditions for the ocean. A hybrid data assimilation has been implemented within the National Centers for Environmental Prediction (NCEP) Global Ocean Data Assimilation System (GODAS) as a future replacement of the operational three-dimensional variational data assimilation (3DVar) method. This Hybrid-GODAS provides improved representation of model uncertainties by using a combination of dynamic and static background error covariances, and by using an ensemble forced by different realizations of atmospheric surface conditions. An observing system simulation experiment (OSSE) is presented spanning January 1991 to January 1999, with a bias imposed on the surface forcing conditions to emulate an imperfect model. The OSSE compares the 3DVar used by the NCEP Climate Forecast System (CFSv2) with the new hybrid, using simulated in situ ocean observations corresponding to those used for the NCEP Climate Forecast System Reanalysis (CFSR).

The Hybrid-GODAS reduces errors for all prognostic model variables over the majority of the experiment duration, both globally and regionally. Compared to an ensemble Kalman filter (EnKF) used alone, the hybrid further reduces errors in the tropical Pacific. The hybrid eliminates growth in biases of temperature and salinity present in the EnKF and 3DVar, respectively. A preliminary reanalysis using real data shows that reductions in errors and biases are qualitatively similar to the results from the OSSE. The Hybrid-GODAS is currently being implemented as the ocean component in a prototype next-generation CFSv3, and will be used in studies by the Climate Prediction Center to evaluate impacts on ENSO prediction.

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