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D. E. Harrison

Abstract

The monthly mean surface wind changes during recent ENSO events, as observed from 11 islands in the tropical Pacific, are described. Two different composite ENSO wind fields are evaluated and compared. The month-to-month wind changes during each event are also discussed.

The wind changes for each event between 1953 and 1980 except 1969 show several common features:

(i) Westerly anomalies appear first west of the date line and then at the date line sometime in summer (0) to fall (0), then intensify over the following several months. The anomalies are confined to within ±3° of the equator during this stage.

(ii) In either November (0), December (0), or January (+1) there is an abrupt southward shift of the narrow band of westerly anomalies, so that the maximum anomaly is then at ∼5°S latitude at the date line, and nearly normal conditions prevail north of the equator.

(iii) Westerly anomalies are gone or greatly reduced one to two months after the southward shift.

The event-to-event variations are considerable, particularly prior to July (0) and after February (+1), so that composites show much reduced anomaly amplitude and much smaller month-to-month anomaly changes than are typical of any given event. The large amplitude months of the composites show similarities with a composite by Rasmusson and Carpenter, but a number of significant differences are identified. These findings, and their relationship to existing simple ideas concerning tropical Pacific coupled ocean-atmosphere interactions, are discussed.

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David S. Gutzler
and
D. E. Harrison

Abstract

The longitude–height–time evolution of seasonally averaged wind anomalies over the near-equatorial eastern Indian and western Pacific Oceans is examined, using multiyear time series derived from a network of eight rawinsonde stations. Data at six pressure levels, between 850 and 150 mb, are considered. The first two modes of an empirical orthogonal function analysis of zonal wind fluctuations are cross correlated at lag, with spatial structures suggesting that the dominant pattern of variability on seasonal time scales is best described as a propagating oscillation. This space–time structure is confirmed using a complex empirical orthogonal function analysis, which indicates that over half of the interseasonal zonal wind variance at these stations is associated with an eastward-propagating mode (denoted E1). Wind anomalies described by E1 are negatively correlated in the upper and lower troposphere at each station, and are out of phase between the southern tip of India and the central Pacific, so that E1 can be interpreted as an eastward-propagating pattern of convergence/divergence along the equator. Variations in the phase of this mode are “phase-locked” to the annual cycle, and are highly correlated with a conventional Southern Oscillation Index. The wind anomaly field described by E1 evolves through a characteristic life cycle during El Niño events, which begins before the onset of ocean surface warming in the eastern Pacific; the anomaly pattern then propagates eastward during the course of the event.

These results are further confirmed by compositing wind anomalies with respect to the phones of the six most recent El Niño events. During the Northern Hemisphere autumn season prior to the onset of El Niño, anomalous low level convergence and upper level divergence are observed in the vicinity of Indonesia. This pattern subsequently propagates eastward, until the opposite pattern of anomalies is observed during the fully developed phase of El Niño, one year after the initial appearance of the atmospheric anomaly pattern. The eastward phase speed is much slower than an atmospheric Kelvin wave, suggesting that the wind anomalies are part of an air–sea interactive system.

The interevent variability for each phase of the six El Niño events in the data record is substantial; the significance of the composite anomaly pattern varies considerably from phase to phase. The composite is most robust for the Northern Hemisphere autumn season during the year in which ocean surface warming first occurs. It is particularly noteworthy that the evolution of wind anomalies over the far western Pacific prior to the 1982 event was not significantly different from previous events.

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D. S. Luther
and
D. E. Harrison

Abstract

The utility of studying low-frequency surface weather phenomena with long time series of meteorological data from tropical Pacific islands is demonstrated. The wind stress changes associated with El Niño events in the period 1950–78 are examined at seven locations. Zonal wind stress anomalies at the equator near the date line often exhibit strengthening and subsequent weakening of the trade winds prior to each El Niño, as originally suggested by Wyrtki. An exception is the weak 1963 El Niño, which is preceded by meridional wind stress anomalies at the equator. The strongest zonal and meridional wind stress anomalies, however, occur well after the first occurrence of anomalously warm water off the coast of Peru for each El Niño, in agreement with prior analyses of merchant marine data. Away from the equator, variability of the wind stress anomalies from one El Niño to the next is strong, leading to numerous discrepancies with published profiles of the “mean” El Niño wind changes.

Power spectra of wind stress from three island stations are compared with concurrent wind stress spectra computed from merchant marine data. Many disparities are found and can be attributed to (sometimes severe) aliasing in the ship data. Possible aliasing errors in the ship data time series are estimated by randomly subsampling the island data in order to mimic the ship data sampling. Sampling criteria, which depend upon the scientific application, are suggested in order to limit the alias noise in the ship data to acceptable amounts.

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D. E. Harrison
and
Paul S. Schopf

Abstract

The initial surface warming of the 1982 El Niño event was of quite different timing and pattern from that associated with most El Niño events; strong anomalous warming occurred first in July along the equator and subsequently along the South American coast. We show here that a simple advective model for tropical ocean surface warming can produce anomalous sea-surface temperature (SST) fields like those found in the first few months of the 1982 El Niño. The model physics assumes that the existing SST field is advected by anomalous currents to produce the anomalous warming, and that the anomalous currents are those induced subsequent to the passage of downwelling Kelvin wave front(s). With the initial SST field taken to be that of July 1982, the anomalous eastward currents of the model lead to a satisfactory prediction of the evolution of anomalous SST for several months. Numerical experiments with a fully nonlinear and thermally active ocean model support the physical relevance of the more idealized study.

The anomalous horizontal advection model can also account for the initial SST evolution during the more common type of El Niño event. The reason that a similar anomalous current field can produce two such different warming patterns is that the gradients of SST along the equator have strong seasonal variation. If anomalous eastward currents are generated along the equator between February and April, when the climatological zonal SST gradient is small, little equatorial warming will occur and so coastal warming is observed first; this is the case in most El Niño events. But if the same anomalous currents occur later in the year, when there is typically a strong zonal temperature gradient, strong equatorial surface warming will occur prior to coastal warming, as happened in 1982. The pattern of SST changes resulting from remote westerly wind changes in the tropical Pacific thus is very strongly linked to the annual cycle of SST.

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D. E. Harrison
and
D. S. Gutzler

Abstract

We examine the variability of monthly mean winds at 850 mb and the surface from five island stations in the tropical western Pacific Ocean. Climatological winds and (850 mb-surface) wind shear are evaluated and used to construct time series of monthly mean wind and shear anomalies. Wind variance at 850 mb tends to be substantially greater than at the surface, and large temporal variations in shear are found. Prominent anomalies are associated with El Niño–Southern Oscillation periods. Composite El Niño event anomalies are examined; it is found that the westerly wind anomalies associated with warm central Pacific sea surface temperatures are stronger at 850 mb than at the surface, and that the anomalous (850 mb-surface) shears are as large as the surface wind anomalies themselves.

Several simple techniques are described to investigate the feasibility of estimating surface wind anomalies from 850 mb wind anomalies. Because strong correlations exist between the zonal winds at these levels, zonal estimate errors can be reduced to ≈0.5 m s−1 if known shear statistics are included in the estimate algorithm. Estimates which extrapolate cloud level wind anomalies to the surface using only climatological shear are shown to produce much greater surface wind errors. If these results are representative and if accurate monthly mean winds at 850 mb can be obtained from cloud motion vectors, then very useful low-frequency surface wind fields can be derived from cloud motion data.

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RUSSELL L. ELSBERRY
and
E. J. HARRISON JR.

Abstract

A 10-level primitive-equation model designed for prediction of circulations within a limited region of the Tropics is described. The model is initialized from wind and temperature information. During the course of the integration two types of oscillations arose from the imposed horizontal boundary conditions—a short-period height oscillation and a longer period kinetic energy oscillation. The character of the two oscillations and the boundary conditions needed to remove the oscillations are described.

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M. J. Harrison
,
A. Rosati
,
B. J. Soden
,
E. Galanti
, and
E. Tziperman

Abstract

This paper presents a quantitative methodology for evaluating air–sea fluxes related to ENSO from different atmospheric products. A statistical model of the fluxes from each atmospheric product is coupled to an ocean general circulation model (GCM). Four different products are evaluated: reanalyses from the National Centers for Environmental Prediction (NCEP) and the European Centre for Medium-Range Weather Forecasts (ECMWF), satellite-derived data from the Special Sensor Microwave/Imaging (SSM/I) platform and the International Satellite Cloud Climatology Project (ISCCP), and an atmospheric GCM developed at the Geophysical Fluid Dynamics Laboratory (GFDL) as part of the Atmospheric Model Intercomparison Project (AMIP) II. For this study, comparisons between the datasets are restricted to the dominant air–sea mode.

The stability of a coupled model using only the dominant mode and the associated predictive skill of the model are strongly dependent on which atmospheric product is used. The model is unstable and oscillatory for the ECMWF product, damped and oscillatory for the NCEP and GFDL products, and unstable (nonoscillatory) for the satellite product. The ocean model is coupled with patterns of wind stress as well as heat fluxes. This distinguishes the present approach from the existing paradigm for ENSO models where surface heat fluxes are parameterized as a local damping term in the sea surface temperature (SST) equation.

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R. E. Evans
,
M. S. J. Harrison
,
R. J. Graham
, and
K. R. Mylne

Abstract

One possible method of incorporating model sensitivities into ensemble forecasting systems is to combine ensembles run from two or more models. Furthermore, the use of more than one analysis, to which perturbations are added, may provide further unstable directions for error growth not present with a single analysis.

Results are presented from recent investigations into the potential benefit of combining ensembles from the systems of the European Centre for Medium-Range Weather Forecasts and The Met. Office of the United Kingdom. The multimodel and multianalysis ensemble significantly outperforms either individual system in many performance aspects, including deterministic and probabilistic forecast skill, spread–skill correlations, and breadth of synoptic information. It is demonstrated that these improvements are achieved through the combination of independent, useful information contained in the individual systems, and not through simple cancellation of biases that could occur when ensembles from two different forecast systems are combined. In addition, results indicate that model dependencies are at least comparable with analysis dependencies on medium-range timescales, and so in general both models and both analyses are required in the joint ensemble for the largest benefits.

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