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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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Craig M. Risien and Dudley B. Chelton

Abstract

Global seasonal cycles of the wind and wind stress fields estimated from the 8-yr record (September 1999–August 2007) of wind measurements by the NASA Quick Scatterometer (QuikSCAT) are presented. While this atlas, referred to here as the Scatterometer Climatology of Ocean Winds (SCOW), consists of 12 variables, the focus here is on the wind stress and wind stress derivative (curl and divergence) fields. SCOW seasonal cycles are compared with seasonal cycles estimated from NCEP–NCAR reanalysis wind fields. These comparisons show that the SCOW atlas is able to capture small-scale features that are dynamically important to both the ocean and the atmosphere but are not resolved in other observationally based wind atlases or in NCEP–NCAR reanalysis fields. This is particularly true of the wind stress derivative fields in which topographic, SST gradient, and ocean current influences on surface winds are plainly visible. Discussions of five example regions are presented to highlight these seasonally recurring small-scale features. It is expected that the SCOW atlas will prove valuable to researchers conducting hydrographic and modeling studies.

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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 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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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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Richard W. Reynolds and Dudley B. Chelton

Abstract

Six different SST analyses are compared with each other and with buoy data for the period 2007–08. All analyses used different combinations of satellite data [for example, infrared Advanced Very High Resolution Radiometer (AVHRR) and microwave Advanced Microwave Scanning Radiometer (AMSR) instruments] with different algorithms, spatial resolution, etc. The analyses considered are the National Climatic Data Center (NCDC) AVHRR-only and AMSR+AVHRR, the Navy Coupled Ocean Data Assimilation (NCODA), the Remote Sensing Systems (RSS), the Real-Time Global High-Resolution (RTG-HR), and the Operational SST and Sea Ice Analysis (OSTIA); the spatial grid sizes were , respectively. In addition, all analyses except RSS used in situ data. Most analysis procedures and weighting functions differed. Thus, differences among analyses could be large in high-gradient and data-sparse regions. An example off the coast of South Carolina showed winter SST differences that exceeded 5°C.

To help quantify SST analysis differences, wavenumber spectra were computed at several locations. These results suggested that the RSS is much noisier and that the RTG-HR analysis is much smoother than the other analyses. Further comparisons made using collocated buoys showed that RSS was especially noisy in the tropics and that RTG-HR had winter biases near the Aleutians region during January and February 2007. The correlation results show that NCODA and, to a somewhat lesser extent, OSTIA are strongly tuned locally to buoy data. The results also show that grid spacing does not always correlate with analysis resolution.

The AVHRR-only analysis is useful for climate studies because it is the only daily SST analysis that extends back to September 1981. Furthermore, comparisons of the AVHRR-only analysis and the AMSR+AVHRR analysis show that AMSR data can degrade the combined AMSR and AVHRR resolution in cloud-free regions while AMSR otherwise improves the resolution. These results indicate that changes in satellite instruments over time can impact SST analysis resolution.

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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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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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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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Dudley B. Chelton and Frank J. Wentz

Obtaining global sea surface temperature (SST) fields for the ocean boundary condition in numerical weather prediction (NWP) models and for climate research has long been problematic. Historically, such fields have been constructed by a blending of in situ observations from ships and buoys and satellite infrared observations from the Advanced Very High Resolution Radiometer (AVHRR) that has been operational on NOAA satellites since November 1981. The resolution of these global SST fields is limited by the sparse coverage of in situ observations in many areas of the World Ocean and cloud contamination of AVHRR observations, which can exceed 75% over the subpolar oceans. As clouds and aerosols are essentially transparent to microwave radiation, satellite microwave observations can greatly improve the sampling and resolution of global SST fields. The Advanced Microwave Scanning Radiometer on the NASA Earth Observing System (EOS) Aqua satellite (AMSR-E) is providing the first highly accurate and global satellite microwave observations of SST. The potential for AMSR-E observations to improve the sampling, resolution, and accuracy of SST fields for NWP and climate research is demonstrated from example SST fields and from an investigation of the sensitivity of NWP models to specification of the SST boundary condition.

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