David W. Behringer

An Improved Coupled Model for ENSO Prediction and Implications for Ocean Initialization. Part I: The Ocean Data Assimilation System

Abstract An improved forecast system has been developed for El Nino–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.

An Improved Coupled Model for ENSO Prediction and Implications for Ocean Initialization. Part II: The Coupled Model

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 u...

Impacts of Argo salinity in NCEP Global Ocean Data Assimilation System: The tropical Indian Ocean

[1]xa0Salinity profiles collected by the International Argo Project (International Argo Project data are available at http://argo.jcommops.org) since 2000 provide us an unprecedented opportunity to study impacts of salinity data on the quality of ocean analysis, which has been hampered by a lack of salinity observations historically. The operational Global Ocean Data Assimilation System (GODAS) developed at the National Centers for Environmental Prediction (NCEP) assimilates temperature and synthetic salinity profiles that were constructed from temperature and a local T-S climatology. In this study, we assess impacts of replacing synthetic salinity by Argo salinity on the quality of the GODAS ocean analysis with a focus on the tropical Indian Ocean. The study was based on two global ocean analyses for 2001–2006 with (NCEP_Argo) and without (NCEP_Std) inclusion of Argo salinity. The quality of the ocean analyses was estimated by comparing them with various independent observations such as the surface current data from drifters, the salinity data from the Triangle Trans-Ocean Buoy Network moorings, and the sea surface height (SSH) data from satellite altimeters. We found that by assimilating Argo salinity, the biases in the salinity analysis were reduced by 0.6 practical salinity units (psu) in the eastern tropical Indian Ocean and by 1 psu in the Bay of Bengal. Associated with these salinity changes, the zonal current increased by 30–40 cm s−1 toward the east in the central equatorial Indian Ocean during the winter seasons. When verified against drifter currents, the biases of the annually averaged zonal current in the tropical Indian Ocean were reduced by 5–10 cm s−1, and the root-mean-square error of surface zonal current was reduced by 2–5 cm s−1. The SSH biases were reduced by 3 cm in the tropical Indian Ocean, the Bay of Bengal, and the Arabian Sea. These results suggest that the Argo salinity plays a critical role in improving salinity analysis, which in turn contributed to improved surface current and sea surface height analyses.

The beta spiral in the North Atlantic subtropical gyre

Abstract The results of the first hydrographic cruise specifically designed to test calculations of the β spiral are discussed. The method, first proposed by Stommel and Schott ( Deep-Sea Research , 24 , 325–329, 1977), permits the computation of absolute geostrophic velocities from observations of density alone. Several variations on the basic calculations were used and all gave consistent results. Sensitivity of the results to variations in the depth range of the data used in the calculations, a problem with earlier calculations based on historical data, was not a problem with these data. Comparisons of the new β spiral are made with β spirals computed from historical data and with the results of a separate calculation using the inverse theory discussed by Wunsch ( Reviews of Geophysics and Space Physics , 16 , 583–620, 1978).

Signatures of salinity variability in tropical Pacific Ocean dynamic height anomalies

[1]xa0The vertical variability of the salinity field, with emphasis on interannual timescales, is examined within the tropical Pacific Ocean (10°N–10°S) using a compilation of the conductivity-temperature-depth (CTD) casts for the period 1975–1998. Compared to the vertical dependence of temperature that exhibits a systematic maximum variability at the depth of the main thermocline, the salinity typically shows a maximum variability in the surface layers. One notable region where this rule is violated is the southwestern and central Pacific Ocean. Below the surface, salinity variability is correlated with the strong gradients in mean salinity above and below the subsurface salinity maximum. It is shown that using conventional mean T-S curves to estimate salinity profiles from temperature observations leads to strong biases because of the large scatter around the T-S relationships. From the surface to the bottom of the thermocline, the dispersion of the T-S diagrams is large regardless of whether the conditions are representative of “El Nino” or “La Nina” conditions. Error introduced in computing the dynamic height anomaly (DHA) is larger than 2 dyn. cm (dynamic centimeters) throughout the tropical Pacific if salinity variability is neglected. This error represents 30% of the total variability of the sea surface height in the western Pacific, and more than 50% in the south central Pacific. In order to estimate salinity variability when direct measurements are not available, an empirical orthogonal function analysis of existing temperature and salinity profiles was conducted. It is shown that a relatively low number of dominant modes, typically less than 6, are sufficient to explain 80% or more of the total variance. The separate contributions of the temperature and salinity fields to the DHA are then examined. The salinity contribution is generally smaller than the temperature contribution, though in some instances the two oppose one another, resulting in lowered dynamic height anomalies. These results confirm that salinity variability should not be neglected in ocean analyses that attempt to infer vertical changes in density from sea level fluctuations within the tropical Pacific Ocean.

Steric sea level variability (1993–2010) in an ensemble of ocean reanalyses and objective analyses

AbstractnQuantifying the effect of the seawater density changes on sea level variability is of crucial importance for climate change studies, as the sea level cumulative rise can be regarded as both an important climate change indicator and a possible danger for human activities in coastal areas. nIn this work, as part of the Ocean Reanalysis Intercomparison Project, the global and regional steric sea level changes are estimated and compared from an ensemble of 16 ocean reanalyses and 4 objective analyses. These estimates are initially compared with a satellite-derived (altimetry minus gravimetry) dataset for a short period (2003–2010). The ensemble mean exhibits a significant high correlation at both global and regional scale, and the ensemble of ocean reanalyses outperforms that of objective analyses, in particular in the Southern Ocean. The reanalysis ensemble mean thus represents a valuable tool for further analyses, although large uncertainties remain for the inter-annual trends. Within the extended intercomparison period that spans the altimetry era (1993–2010), we find that the ensemble of reanalyses and objective analyses are in good agreement, and both detect a trend of the global steric sea level of 1.0 and 1.1xa0±xa00.05 mm/year, respectively. However, the spread among the products of the halosteric component trend exceeds the mean trend itself, questioning the reliability of its estimate. This is related to the scarcity of salinity observations before the Argo era. Furthermore, the impact of deep ocean layers is non-negligible on the steric sea level variability (22 and 12xa0% for the layers below 700 and 1500xa0m of depth, respectively), although the small deep ocean trends are not significant with respect to the products spread.

Chapter 17 Analyzing the 1993-1998 interannual variability of NCEP model ocean simulations: The contribution of TOPEX/Poseidon observations

Abstract The National Centers for Environmental Prediction climate forecast system for El Nino Southern Oscillation (ENSO) includes a coupled ocean and atmosphere general circulation model. A critical element for a skillful forecast of ENSO is accurate initialization of the tropical Pacific Ocean component of the coupled system. For accurate ocean analyses, assimilation of both in-situ and satellite data is required to correct ocean model biases and to better capture sea surface temperature (SST) variability. Our results suggest that Topography Experiment (TOPEX)JPoseidon (TJP) sea level data have a strong potential for improving ocean analyses and coupled forecasts, in the same way that assimilation of T/P data improved model sea level. However, our present assimilation scheme corrects only temperature; TJP data can, therefore, only influence temperature. In the western tropical Pacific, salinity is an important contribution to sea level. Thus, TJP data need to be correctly partitioned between temperature and salinity for more accurate ocean analyses.

Hydrographic station data of five surveys of the beta-triangle in the eastern North Atlantic, 1978-1981

Funding was provided by the National Science Foundation under Grant nOCE B0-15789 and by the National Oceanic and Atmospheric Administration.