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Dudley B. Chelton

Abstract

The impact of SST specification on low-level winds in the operational ECMWF numerical weather prediction model is investigated in the eastern tropical Pacific from comparisons of ECMWF wind stress fields with QuikSCAT satellite scatterometer observations of wind stress during the August–December cold seasons of 2000 and 2001. These two time periods bracket the 9 May 2001 change from the Reynolds SST analyses to the Real-Time Global SST (RTG_SST) analyses as the ocean boundary condition in the ECMWF model. The ocean–atmosphere interaction in the eastern tropical Pacific that is clearly evident in QuikSCAT wind stress divergence and curl fields is also evident in the ECMWF winds, but is more than twice as strong in the 2001 cold season as in the 2000 cold season, due primarily to the improved spatial and temporal resolution of the RTG_SST analyses compared with the Reynolds SST analyses. While a significant improvement compared with 2000, the response of the 2001 ECMWF wind stress field to SST is only about half as strong as the coupling inferred from QuikSCAT data and satellite observations of SST from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). It is concluded that the underrepresentation of the ocean–atmosphere coupling is attributable partly to underrepresentation of SST gradients in the RTG_SST fields and partly to inadequacies of the ECWMF model. The latter may be due to errors in the parameterization of boundary layer processes or to insufficient horizontal or vertical resolution in the model.

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Dudley B. Chelton and Russ E. Davis

Abstract

Linear statistical estimators are used to examine 29 years of nonseasonal, monthly-mean, tide-gauge sea-level data along the west coast of North America. The objective is exploration of the structure, and causes of nearshore ocean variability over time scales of months to years at 20 stations from Alaska to Mexico. North of San Francisco, 50–60% of the sea-level variability reflects a simple inverse barometric response to local atmospheric pressure. These inverted barometer effects account for only 10–15% of the variance at stations to the south.

The dominant signal of inverse-barometer-corrected sea level represents a nearly uniform rise or fall of sea level everywhere along the eastern rim of the North Pacific. The interannual aspects of this large-scale sea-level variability are closely related to El Niño occurrences in the eastern tropical Pacific which appear to propagate poloward with phase speeds of ∼40 cm s−1. Higher frequency aspects of this large-scale sea-level variability appear to represent quasi-geostrophic currents driven by basin-wide scales of wind forcing over the North Pacific.

The nature of local (individual station) inverse-barometer-corrected sea-level variability is examined through a series of statistical models and the results are compared with existing dynamical models. The longshore component of wind stress generally forces a larger response than the onshore component (except in large semi-enclosed basins) but the important dynamical aspects of the wind field appear to be basin wide rather than local. The response is consistent with that expected from Ekman dynamics. An apparent non-barometric response to local atmospheric pressure is shown to partly represent an influence of sea-level anomalies farther south. Efforts to determine the nature of this indirect coupling between local pressure and sea level at stations to the south are somewhat limited by the ability of statistical estimators to accurately isolate the responses of sea level to a number of correlated inputs. However, evidence is presented indicating that part of the apparent non-barometric response is due to longshore wind-stress forcing at stations to the south. A response 30–50% greater than inverse barometric remains unexplained from Tofino to San Franciso.

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Dudley B. Chelton and Michael G. Schlax

Abstract

A formalism is presented for determining the wavenumber-frequency transfer function associated with an irregularly sampled multidimensional dataset. This transfer function reveals the filtering characteristics and aliasing patterns inherent in the sample design. In combination with information about the spectral characteristics of the signal, the transfer function can be used to quantify the spatial and temporal resolution capability of the dataset. Application of the method to idealized Geosat altimeter data (i.e., neglecting measurement errors and data dropouts) concludes that the Geosat orbit configuration is capable of resolving scales of about 3° in latitude and longitude by about 30 days.

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Dudley B. Chelton and Michael G. Schlax

Abstract

A technique previously developed for assessing the effects of sampling errors on sea surface height (SSH) fields constructed from satellite altimeter data is extended to include measurement errors, thus providing estimates of the total mean-squared error of the SSH fields. The measurement error contribution becomes an important consideration with the greater sampling density of a coordinated tandem satellite mission. Mean-squared errors are calculated for a variety of tandem altimeter sampling patterns. The resolution capability of each sampling pattern is assessed from a subjectively chosen but consistent set of criteria for the mean value and the spatial and temporal inhomogeneity of the root-mean-squared errors computed over a representative large collection of estimation times and locations.

For a mean mapping error threshold tolerance criterion of 25% of the signal standard deviation, the filter cutoff wavelength and period defining the resolution capability of SSH fields constructed from a tandem TOPEX/Poseidon (T/P) and Jason satellite sampling pattern with evenly spaced ground tracks are about 2.2° by 20 days. This can be compared with the resolution capability of about 6° by 20 days that can be obtained from a single altimeter in the T/P orbit. A tandem T/P–Jason mission with 0.75° spacing between simultaneously sampled parallel tracks that has been suggested for estimating geostrophic velocity yields an SSH mapping resolution capability of about 3.7° by 20 days. For the anticipated factor-of-2 larger orbit errors for ENVISAT compared with Jason, the resolution capability of a tandem JasonENVISAT scenario is about 3° by 20 days.

For mapping the SSH field, the tandem T/P–Jason sampling patterns with evenly spaced, interleaved ground tracks and either a 5-day or a 0-day offset is far better than the other tandem altimeter mission scenarios considered here. For the highest-resolution mapping, the 5-day offset is preferable to the 0-day offset. The scientific benefits of such a tandem mission are discussed in the context of two specific examples: Rossby wave dispersion and investigation of eddy–mean flow interaction.

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Eric D. Maloney and Dudley B. Chelton

Abstract

The ability of six climate models to capture the observed coupling between SST and surface wind stress in the vicinity of strong midlatitude SST fronts is analyzed. The analysis emphasizes air–sea interactions associated with ocean meanders in the eastward extensions of major western boundary current systems such as the Gulf Stream, Kuroshio, and Agulhas Current. Satellite observations of wind stress from the SeaWinds scatterometer on NASA’s Quick Scatterometer and SST from the Advanced Microwave Scanning Radiometer clearly indicate the influence of SST on surface wind stress on scales smaller than about 30° longitude × 10° latitude. Spatially high-pass-filtered SST and wind stress variations are linearly related, with higher SST associated with higher wind stress. The influence of SST on wind stress is also clearly identifiable in the ECMWF operational forecast model, having a grid resolution of 0.35° × 0.35° (T511). However, the coupling coefficient between wind stress and SST, as indicated by the slope of the linear least squares fit, is only half as strong as for satellite observations.

The ability to simulate realistic air–sea interactions is present to varying degrees in the coupled climate models examined. The Model for Interdisciplinary Research on Climate 3.2 (MIROC3.2) high-resolution version (HIRES) (1.1° × 1.1°, T106) and the NCAR Community Climate System Model 3.0 (1.4° × 1.4°, T85) are the highest-resolution models considered and produce the most realistic air–sea coupling associated with midlatitude current systems. Coupling coefficients between SST and wind stress in MIROC3.2_HIRES and the NCAR model are at least comparable to those in the ECMWF operational model. The spatial scales of midlatitude SST variations and SST-induced wind perturbations in MIROC3.2_HIRES are comparable to those of satellite observations. The spatial scales of SST variability in the NCAR model are larger than those in the ECMWF model and satellite observations, and hence the spatial scales of SST-induced perturbations in the wind fields are larger.

It is found that the ability of climate models to simulate air–sea interactions degrades with decreasing grid resolution. SST anomalies in the GFDL Climate Model 2.0 (CM2.0) (2.0° × 2.5°), Met Office Third Hadley Centre Coupled Ocean–Atmosphere General Circulation Model (HadCM3) (2.5° × 3.8°), and MIROC3.2 medium-resolution version (MEDRES) (2.8° × 2.8°, T42) have larger spatial scales and are more geographically confined than in the higher-resolution models. The GISS Model E20/Russell (4.0° × 5.0°) is unable to resolve the midlatitude ocean eddies that produce prominent air–sea interaction. Notably, MIROC3.2_MEDRES exhibits much weaker coupling between wind stress and SST than does the higher vertical and horizontal resolution version of the same model. GFDL CM2.0 and Met Office HadCM3 exhibit a linear relationship between SST and wind stress. However, coupling coefficients for the Met Office model are significantly weaker than in the GFDL and higher-resolution models. In addition to model grid resolution (both vertical and horizontal), deficiencies in the parameterization of boundary layer processes may be responsible for some of these differences in air–sea coupling between models and observations.

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Dudley B. Chelton and Michael H. Freilich

Abstract

Wind measurements by the National Aeronautics and Space Administration (NASA) scatterometer (NSCAT) and the SeaWinds scatterometer on the NASA QuikSCAT satellite are compared with buoy observations to establish that the accuracies of both scatterometers are essentially the same. The scatterometer measurement errors are best characterized in terms of random component errors, which are about 0.75 and 1.5 m s−1 for the along-wind and crosswind components, respectively.

The NSCAT and QuikSCAT datasets provide a consistent baseline from which recent changes in the accuracies of 10-m wind analyses from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the U.S. National Centers for Environmental Prediction (NCEP) operational numerical weather prediction (NWP) models are assessed from consideration of three time periods: September 1996–June 1997, August 1999–July 2000, and February 2002–January 2003. These correspond, respectively, to the 9.5-month duration of the NSCAT mission, the first 12 months of the QuikSCAT mission, and the first year after both ECMWF and NCEP began assimilating QuikSCAT observations. There were large improvements in the accuracies of both NWP models between the 1997 and 2000 time periods. Though modest in comparison, there were further improvements in 2002, at least partly attributable to the assimilation of QuikSCAT observations in both models.

There is no evidence of bias in the 10-m wind speeds in the NCEP model. The 10-m wind speeds in the ECMWF model, however, are shown to be biased low by about 0.4 m s−1. While it is difficult to eliminate systematic errors this small, a bias of 0.4 m s−1 corresponds to a typical wind stress bias of more than 10%. This wind stress bias increases to nearly 20% if atmospheric stability effects are not taken into account. Biases of these magnitudes will result in significant systematic errors in ocean general circulation models that are forced by ECMWF winds.

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Roland A. de Szoeke and Dudley B. Chelton

Abstract

A mechanism by which long planetary waves in the ocean may propagate significantly faster than the classical long baroclinic Rossby waves is investigated. The mechanism depends on the poleward thickening of intermediate density layers and the concomitant thinning of near-surface and deep layers. These features of the mass distribution are associated with the well-known homogenization of potential vorticity in intermediate density layers and with significantly elevated meridional potential vorticity gradients near the surface and somewhat at depth. The mechanism is explored in a simple three-layer model, in which the middle layer has zero potential vorticity gradient and is sandwiched between a surface layer with large potential vorticity gradient and a bottom layer with modest potential vorticity gradient. The effective phase speed of the planetary waves is merely the sum of the phase speeds of virtual baroclinic Rossby waves propagating on the individual layer interfaces as though the other interface were not there and as though there were no mean vertical shear. The mechanism is also examined for a continuous model with zero potential vorticity gradient throughout the interior and large virtual potential vorticity gradients near the surface and bottom. Planetary waves in these models can propagate westward up to twice as fast as baroclinic Rossby waves would through an ocean with the same vertical stratification, but no mean vertical shear. This explanation of the Rossby wave speedup complements a recent detailed theoretical calculation of planetary-wave phase speeds based on geostrophic velocity profiles from archived hydrographic data.

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Michael G. Schlax and Dudley B. Chelton

Abstract

Mean-squared errors of surface geostrophic velocity estimates from the crossover and parallel-track methods are calculated for altimeters in the Ocean Topography Experiment (TOPEX)/Poseidon and Jason orbits. As part of the crossover method analysis, the filtering properties and errors of cross-track speed estimates are examined. Velocity estimates from both the crossover and parallel-track methods have substantial mean-squared errors that exceed 20% of the signal standard deviation, differ systematically between the zonal and meridional components, and vary with latitude. The measurement errors on the zonal and meridional velocity component estimates from both methods increase at low latitudes owing to the inverse dependence of geostrophic velocity on the Coriolis parameter. Additional latitudinal variations result for the parallel-track method because of the poleward convergence of the satellite ground tracks and the presence of orbit error, and for the crossover method because of the changing angle between the ascending and descending ground tracks. At high latitudes, parallel-track estimates, have elevated measurement errors in both components, while only the zonal component is so affected for the crossover method. Along-track smoothing is efficient for mitigating measurement errors for crossover estimates, and the filtering properties of the smoothed estimates are simply related to the spectrum of cross-track speeds. Such smoothing is less effective for parallel-track estimates, and the filtering properties are more difficult to characterize because of the sampling geometry and the convergence of the parallel ground tracks at high latitudes.

If suitable along-track smoothing is applied in the crossover method, root-mean-squared errors (rmse's) of about 30% or less of the signal standard deviation can be obtained for each orthogonal velocity component over the latitude range 5°–60°. With 2-cm orbit errors, the parallel-track method yields estimates of the meridional velocity component with errors that exceed 40% at all latitudes. If orbit errors can be reduced to 1-cm standard deviation, the parallel-track method yields an rmse smaller than 30% in both orthogonal components for the latitude range 5°–55°.

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Michael G. Schlax and Dudley B. Chelton

Abstract

The frequency-domain characteristics of the successive corrections method in one dimension are investigated through the calculation of smoother weights and filter transfer functions. The successive corrections algorithm acts as a low-pass filter that behaves similarly to noniterative smoothers. The spectral content of fixed-span successive corrections estimates depends upon the number of iterations, the selected weighting function and the grid to which the dataset is interpolated. For a given weighting function and grid, increasing the number of iterations for the fixed-span case results in filter transfer functions with increased cutoff frequency and rolloff. Within data gaps, the use of more than one iteration leads to estimates that are more likely to be contaminated by high-frequency variability in the data. It is shown that variable-span successive corrections estimates are nearly independent of the choice of weights for the initial iterations and are almost equivalent to estimates obtained using a single iteration. The greater computational requirements of multiple-iteration successive corrections is a disadvantage for general applications.

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Lloyd J. Shapiro and Dudley B. Chelton

Abstract

In a recent paper, Lanzante reviewed methods for estimating the skill and significance of screening regression models through the use of Monte Carlo simulations. The strategies reviewed have several limitations that were not specified by the author. Due to the influence of true model skill, the Monte Carlo method provides an upper bound on the expected artificial skill, not the expected artificial skill itself as assumed. Lanzante emphasizes the advantages of the use of independent (uncorrelated) predictors. However, the disadvantages of their use and the advantages of dependent predictors in a screening regression were not considered.

The review of the effects of serial correlation on estimates of skill is misleading. The assertion that the formulations developed by Davis and Chelton are erroneous is incorrect. Moreover, contrary to the implication of the review, the use of effective sample size in tests of model significance has practical utility in applications including the Monte Carlo method.

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