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Ming Ji and Ferdinand Baer

Abstract

A three-dimensional scale index based on spherical domain and quasigeostrophic scale analysis indicates a truncation limit of global atmospheric models that includes both horizontal and vertical dimensions. Applying such a scale index, a numerical experiment is designed using a simplified adiabatic version of the National Center for Atmospheric Research (NCAR) Community Climate Model (CCM0B) to examine, incorporating nonlinear dynamics alone, whether an optimal horizontal resolution for a nine-vertical-level (modes) global general circulation model can be achieved. In establishing appropriate vertical modes that can be uniquely scaled and are independent and physically relevant, an optimal distribution of levels, which has been developed, is utilized in the experiment.

The experimental results, which consist of a total of 110 individual integrations of the CCM0B with ten initial states for each of six horizontal truncations, appear to agree with the conclusions implied by the above referenced three-dimensional scale index; that is, a consistent horizontal resolution for a nine-vertical-level model should be in the range of triangular truncation T25 to T30 to yield optimal prediction results, considering, however, only the nonlinear dynamical aspect. It should be noted that due to the simplifications and idealizations made to carry out our experiment, additional studies under more realistic atmospheric conditions are necessary and are encouraged based on the results presented herein to further validate the existence of the consistency of three-dimensional model resolutions.

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David Halpern and Ming Ji

Abstract

The upper-ocean temperature distribution along the Pacific equator from 139° to 103°W was observed in January 1992 with temperature profiles recorded from a ship and inferred from an ocean general circulation model calculation involving data assimilation (i.e., hindcast). An El Niño episode was in progress. The 100-m-thick mixed layer depth, the mixed-layer temperature, and the depth-averaged temperature below the thermocline were similar in both data products. Considerable differences occurred in the representation of the 15°−25°C thermocline, such as the depth-averaged temperatures above and below the 20°C isotherm, the cast-west slope of the 20°C isotherm, and a 1000-km-wide depression. The longitudinal-averaged root-mean-square difference between the hindcast and observed depths of the center of the thermocline was 17 m. Most of the disparities could be attributed to a high wavenumber transient event that the model-based assimilation system was not intended to resolve.

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F. Baer and Ming Ji

Abstract

The vertical levels used in atmospheric models are selected for a variety of reasons, but the selection process has not been systematized. The study presented herein represents an attempt to do so. An atmospheric model is linearized about a state of rest and vertical modes are determined for the vertical structure equation-an ordinary differential equation-by solving a difference form of that equation. Since the solutions of the differential equation do not correspond to the solutions of the difference equation, the distribution of points used for the difference equation (the vertical levels) is adjusted until both sets of solutions coalesce. This distribution is considered the optimum set of model levels.

To test the impact of such a distribution on a numerical integration, the NCAR CCMOB is integrated using both its standard levels and the levels determined above. Comparisons of the integrations show that the solutions separate as time evolves, despite the fact that the initial conditions for the separate integrations are as similar as possible. The results suggest that care in selecting vertical levels is essential to successful integration and that perhaps a start on finding a systematic way of choosing levels has been made.

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Ming Ji and Ants Leetmaa

Abstract

In this study, the authors compare skills of forecasts of tropical Pacific sea surface temperatures from the National Centers for Environmental Prediction (NCEP) coupled general circulation model that were initiated using different sets of ocean initial conditions. These were produced with and without assimilation of observed subsurface upper-ocean temperature data from expendable bathythermographs (XBTs) and from the Tropical Ocean Global Atmosphere–Tropical Atmosphere Ocean (TOGA–TAO) buoys.

These experiments show that assimilation of observed subsurface temperature data in the determining of the initial conditions, especially for summer and fall starts, results in significantly improved forecasts for the NCEP coupled model. The assimilation compensates for errors in the forcing fields and inadequate physical parameterizations in the ocean model. Furthermore, additional skill improvements, over that provided by XBT assimilation, result from assimilation of subsurface temperature data collected by the TOGA–TAO buoys. This is a consequence of the current predominance of TAO data in the tropical Pacific in recent years.

Results suggest that in the presence of erroneous wind forcing and inadequate physical parameterizations in the ocean model ocean data assimilation can improve ocean initialization and thus can improve the skill of the forecasts. However, the need for assimilation can create imbalances between the mean states of the oceanic initial conditions and the coupled model. These imbalances and errors in the coupled model can be significant limiting factors to forecast skill, especially for forecasts initiated in the northern winter. These limiting factors cannot be avoided by using data assimilation and must be corrected by improving the models and the forcing fields.

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Ming Ji and Thomas M. Smith

Abstract

Two 11-yr Pacific Ocean simulations using an ocean general circulation model are compared with corresponding ocean analyses and with in situ observations from moorings and island tide gauges. The ocean simulations were forced by combining the climatological wind stress of Hellerman and Rosenstein with wind stress anomalies obtained from (a) The Florida State University surface wind analysis and (b) a two-member ensemble from an atmospheric model simulation. The ocean analyses were obtained by assimilating observed surface and subsurface temperatures into an ocean GCM, forced with the same wind stress anomaly fields used in the simulations.

The difference in thermocline depth between simulation and analysis using the same wind stress forcing is large in the off-equatorial regions near the North Equatorial Counter Current trough and in the South Pacific, suggesting that the mean climatological stress fields may be in error. The simulation results using the atmospheric GCM stress anomalies failed to show anomalous interannual sea level responses in the eastern equatorial Pacific, indicating that there are significant errors in the AGCM stress anomalies due to errors in the atmospheric model. The analyses show significant improvement over the comparable simulations when compared with the tide gauge data, indicating that assimilation of subsurface oceanic thermal data can compensate for stress-forcing errors and model errors on interannual timescales. However, the more accurate stress-forcing field leads to a better ocean analysis, indicating that the present density of temperature data is not sufficient to determine the ocean state.

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Yan Xue, Ants Leetmaa, and Ming Ji

Abstract

A series of seasonally varying linear Markov models are constructed in a reduced multivariate empirical orthogonal function (MEOF) space of observed sea surface temperature, surface wind stress, and sea level analysis. The Markov models are trained in the 1980–95 period and are verified in the 1964–79 period. It is found that the Markov models that include seasonality fit to the data better in the training period and have a substantially higher skill in the independent period than the models without seasonality. The authors conclude that seasonality is an important component of ENSO and should be included in Markov models. This conclusion is consistent with that of statistical models that take seasonality into account using different methods.

The impact of each variable on the prediction skill of Markov models is investigated by varying the weightings among the three variables in the MEOF space. For the training period the Markov models that include sea level information fit the data better than the models without sea level information. For the independent 1964–79 period, the Markov models that include sea level information have a much higher skill than the Markov models without sea level information. The authors conclude that sea level contains the most essential information for ENSO since it contains the filtered response of the ocean to noisy wind forcing.

The prediction skill of the Markov model with three MEOFs is competitive for both the training and independent periods. This Markov model successfully predicted the 1997/98 El Niño and the 1998/99 La Niña.

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Ming Ji, Ants Leetmaa, and Vernon E. Kousky

Abstract

In this paper, the authors discuss observed climatic variability from 1982 to early 1995 and emphasize the contrasts between the period of strong interannual variability during the 1980s and the period of more persistent features beginning in 1990. Three versions of the NCEP coupled forecast model, which were developed to predict interannual sea surface temperature variability in the equatorial Pacific, are described and their performance compared for those two periods.

Climatic variability during 1982–1992 in the tropical Pacific was dominated by strong low-frequency interannual variations characterized by three warm and two cold El Niño episodes. However, beginning in 1990, the climate state has been characterized by a pattern of persistent positive SST anomalies in the tropical Pacific, especially in the central Pacific near the date line, and weaker than normal trade winds. Superimposed on this were several occurrences of short-lived, generally small-amplitude warmings in the eastern equatorial Pacific. Some of the short-lived warmings amplified into mature warm episodes, such as in spring 1993 and in late 1994.

The NCEP coupled models showed useful skill in predicting low-frequency SST variability associated with warm episodes in the tropical Pacific during the 1982–1992 period. However, the short-lived warmings in spring 1993 and fall/winter 1994/95 were not well predicted by the NCEP coupled models. Neither were they predicted by most of the other dynamic or statistical forecast models. If these short-lived warmings truly represent a different behavior of the coupled ocean-atmosphere system on intraseasonal timescales, the skill levels that were developed for predicting the strong low-frequency SST variability of the 1980s are probably not relevant. The lead times for skillful forecasts of short-lived episodes such as those observed in recent years will no doubt be only a few months.

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Robert E. Livezey, Michiko Masutani, and Ming Ji

The feasibility of using a two-tier approach to provide guidance to operational long-lead seasonal prediction is explored. The approach includes first a forecast of global sea surface temperatures (SSTs) using a coupled general circulation model, followed by an atmospheric forecast using an atmospheric general circulation model (AGCM). For this exploration, ensembles of decade-long integrations of the AGCM driven by observed SSTs and ensembles of integrations of select cases driven by forecast SSTs have been conducted. The ability of the model in these sets of runs to reproduce observed atmospheric conditions has been evaluated with a multiparameter performance analysis.

Results have identified performance and skill levels in the specified SST runs, for winters and springs over the Pacific/North America region, that are sufficient to impact operational seasonal predictions in years with major El Niño–Southern Oscillation (ENSO) episodes. Further, these levels were substantially reproduced in the forecast SST runs for 1-month leads and in many instances for up to one-season leads. In fact, overall the 0- and 1-month-lead forecasts of seasonal temperature over the United States for three falls and winters with major ENSO episodes were substantially better than corresponding official forecasts. Thus, there is considerable reason to develop a dynamical component for the official seasonal forecast process.

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Ming Ji, Arun Kumar, and Ants Leetmaa

The Coupled Model Project was established at the National Meteorological Center (NMC) in January 1991 to develop a multiseason forecast system based on coupled ocean-atmosphere general circulation models. This provided a focus to combine expertise in near real-time ocean modeling and analyses situated in the Climate Analysis Center (CAC) with expertise in atmospheric modeling and data assimilation in the Development Division. Since the inception of the project, considerable progress has been made toward establishing a coupled forecast system. A T40 version of NMC's operational global medium-range forecast model (MRF) has been modified so as to have improved response to boundary forcing from the Tropics. In extended simulations, which are forced with observed historical global sea surface temperature (SST) fields, the model reproduces much of the observed tropical Pacific and North American rainfall and temperature variability. An ocean reanalysis has been performed for the Pacific basin starting from July 1982 to present and uses a dynamical model-based assimilation system. This also provides the ocean initial conditions for coupled forecast experiments. The current coupled forecast model consists of an active Pacific Ocean model coupled to the T40 version of the NMC's MRF. In the future, a global ocean model will be used to include climate information from the other ocean basins. The initial experiments focused on forecasting Northern Hemisphere winter SST anomalies in the tropical Pacific with a lead time of two seasons. The coupled model showed considerable skill during these experiments. Work is currently under way to quantify the skill in predicting climatic variability over North America.

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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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