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James A. Carton and Benjamin S. Giese

Abstract

This paper describes the Simple Ocean Data Assimilation (SODA) reanalysis of ocean climate variability. In the assimilation, a model forecast produced by an ocean general circulation model with an average resolution of 0.25° × 0.4° × 40 levels is continuously corrected by contemporaneous observations with corrections estimated every 10 days. The basic reanalysis, SODA 1.4.2, spans the 44-yr period from 1958 to 2001, which complements the span of the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA-40). The observation set for this experiment includes the historical archive of hydrographic profiles supplemented by ship intake measurements, moored hydrographic observations, and remotely sensed SST. A parallel run, SODA 1.4.0, is forced with identical surface boundary conditions, but without data assimilation. The new reanalysis represents a significant improvement over a previously published version of the SODA algorithm. In particular, eddy kinetic energy and sea level variability are much larger than in previous versions and are more similar to estimates from independent observations. One issue addressed in this paper is the relative importance of the model forecast versus the observations for the analysis. The results show that at near-annual frequencies the forecast model has a strong influence, whereas at decadal frequencies the observations become increasingly dominant in the analysis. As a consequence, interannual variability in SODA 1.4.2 closely resembles interannual variability in SODA 1.4.0. However, decadal anomalies of the 0–700-m heat content from SODA 1.4.2 more closely resemble heat content anomalies based on observations.

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Gennady A. Chepurin, James A. Carton, and Dick Dee

Abstract

Numerical models of ocean circulation are subject to systematic errors resulting from errors in model physics, numerics, inaccurately specified initial conditions, and errors in surface forcing. In addition to a time-mean component, the systematic errors include components that are time varying, which could result, for example, from inaccuracies in the time-varying forcing. Despite their importance, most assimilation algorithms incorrectly assume that the forecast model is unbiased. In this paper the authors characterize the bias for a current assimilation scheme in the tropical Pacific. The characterization is used to show how relatively simple empirical bias forecast models may be used in a two-stage bias correction procedure to improve the quality of the analysis.

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D. A. S. Patil, Brian R. Hunt, and James A. Carton

Abstract

Computational modeling is playing an increasingly vital role in the study of atmospheric–oceanic systems. Given the complexity of the models a fundamental question to ask is, How well does the output of one model agree with the evolution of another model or with the true system that is represented by observational data? Since observational data contain measurement noise, the question is placed in the framework of time series analysis from a dynamical systems perspective. That is, it is desired to know if the two, possibly noisy, time series were produced by similar physical processes.

In this paper simple graphical representations of the time series and the errors made by a simple predictive model of the time series (known as residual delay maps) are used to extract information about the nature of the time evolution of the system (in this paper referred to as the dynamics). Two different uses for these graphical representations are presented in this paper. First, a test for the comparison of two competing models or of a model and observational data is proposed. The utility of this test is that it is based on comparing the underlying dynamical processes rather than looking directly at differences between two datasets. An example of this test is provided by comparing station data and NCEP–NCAR reanalysis data on the Australian continent.

Second, the technique is applied to the global NCEP–NCAR reanalysis data. From this a composite image is created that effectively identifies regions of the atmosphere where the dynamics are strongly dependent on low-dimensional nonlinear processes. It is also shown how the transition between such regions can be depicted using residual delay maps. This allows for the investigation of the conjecture of Sugihara et al.: sites in the midlatitudes are significantly more nonlinear than sites in the Tropics.

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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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Edwin K. Schneider, Zhengxin Zhu, Benjamin S. Giese, Bohua Huang, Ben P. Kirtman, J. Shukla, and James A. Carton

Abstract

Results from multiyear integrations of a coupled ocean–atmosphere general circulation model are described. The atmospheric component is a rhomboidal 15, 18-level version of the Center for Ocean–Land–Atmosphere Studies atmospheric general circulation model. The oceanic component is the Geophysical Fluid Dynamics Laboratory ocean model with a horizontal domain extending from 70°S to 65°N. The ocean model uses 1.5° horizontal resolution, with meridional resolution increasing to 0.5° near the equator, and 20 vertical levels, most in the upper 300 m. No flux adjustments are employed.

An initial multiyear integration showed significant climate drift in the tropical Pacific sea surface temperatures. Several modifications were made in the coupled model to reduce these errors. Changes were made to the atmospheric model cloudiness parameterizations, increasing solar radiation at the surface in the western equatorial Pacific and decreasing it in the eastern Pacific, that improved the simulation of the time-mean sea surface temperature. Large errors in the wind direction near the western coast of South America resulted in large mean SST errors in that region. A procedure to reduce these errors by extrapolating wind stress values away from the coast to coastal points was devised and implemented.

Results from the last 17 years of a 62-yr simulation are described. The model produces a reasonably realistic annual cycle of equatorial Pacific sea surface temperature. However, the upper-ocean thermal structure has serious errors. Interannual variability for tropical Pacific sea surface temperatures, precipitation, and sea level pressure that resemble the observed El Niño–Southern Oscillation (ENSO) in structure and evolution is found. However, differences from observed behavior are also evident. The mechanism responsible for the interannual variability appears to be similar to the delayed oscillator mechanism that occurs in the real climate system.

The structure of precipitation, sea level pressure, and geopotential anomalies associated with the tropical Pacific sea surface temperature interannual variability are isolated and described. The coupled model is capable of producing structures that are similar to those observed.

It is concluded that atmosphere–ocean general circulation models are beginning to capture some of the observed characteristics of the climatology of the tropical Pacific and the interannual variability associated with the El Niño–Southern Oscillation. Remaining obstacles to realistic simulations appear to include ocean model errors in the eastern equatorial Pacific, errors associated with cloud–radiation interactions, and perhaps errors associated with inadequate meridional resolution in the atmospheric model equatorial Pacific.

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