Dudley B. Chelton

The hybrid precipitation index developed in Part I of this study is applied to investigate precipitation variability along the west coast of North America during the wet season November–March on monthly-to-interannual time scales. The variability in each of six regions considered in this study is negatively correlated with nearby 500-hPa geopotential height anomalies. Except in Southeast Alaska, these correlation patterns indicate that precipitation variability in each region is predominantly influenced by local atmospheric forcing analogous to the ridging of the westerly flow that has been studied extensively with regard to California drought variability. The first empirical orthogonal function (EOF) accounts for nearly all of the Southeast Alaska precipitation variability, which is controlled by the strength of the onshore flow rather than ridging. In association with this mode of variability, precipitation anomalies of opposite sign account for about 40% of the precipitation variance in Northern California and Oregon on all time scales. On short time scales, the second and third EOFs account primarily for precipitation variability in British Columbia/Washington and California, respectively. With increasing time scale, the third EOF diminishes in importance and the second EOF evolves into a pattern of synchronous precipitation anomalies of the same sign from British Columbia to Northern California. Precipitation variability in Southern California is only modestly related to precipitation elsewhere. With increasing time scale, Southern California precipitation variability becomes increasingly related to precipitation anomalies of opposite sign in Washington.

The filtering properties of the standardized precipitation index (SPI), the Palmer drought severity index (PDSI), and the model calibrated drought index (MCDI) are investigated to determine their relations to past, present, and future precipitation anomalies in regions with a wide diversity of precipitation characteristics. All three indices can be closely approximated by weighted averages of precipitation, but with different weighting. The SPI is well represented by one-sided, uniformly weighted averages; the MCDI is well represented by one-sided, exponentially weighted averages; and the PDSI is well represented by two-sided, exponentially weighted averages with much higher weighting of past and present precipitation than future precipitation. Detailed analyses identify interpretational complications and other undesirable features in the SPI and PDSI. In addition, the PDSI and MCDI are each restricted to single regionally specific “intrinsic” time scales that can significantly differ between the two indices. Inspired by the strengths of the SPI, PDSI, and MCDI, a hybrid index is developed that consists of exponentially weighted averages of past and present precipitation that are implicit in the PDSI and MCDI. The explicit specification of the exponential weighting allows users to control the time scale of the hybrid index to investigate precipitation variability on any time scale of interest. This advantage over the PDSI and MCDI is analogous to the controllability of the time scale of the SPI, but the exponentially fading memory is more physical than the uniform weighting of past and present precipitation in the SPI.

This study presents an assessment of the impact of a March 2006 change in the Met Office operational global numerical weather prediction model through the introduction of a nonlocal momentum mixing scheme. From comparisons with satellite observations of surface wind speed and sea surface temperature (SST), it is concluded that the new parameterization had a relatively minor impact on SST-induced changes in sea surface wind speed in the Met Office model in the September and October 2007 monthly averages over the Agulhas Return Current region considered here. The performance of the new parameterization of vertical mixing was evaluated near the surface layer and further through comparisons with results obtained using a wide range of sensitivity of mixing parameterization to stability in the Weather Research and Forecasting (WRF) Model, which is easily adapted to such sensitivity studies. While the new parameterization of vertical mixing improves the Met Office model response to SST in highly unstable (convective) conditions, it is concluded that significantly enhanced vertical mixing in the neutral to moderately unstable conditions (nondimensional stability [ ] between 0 and −2) typically found over the ocean is required in order for the model surface wind response to SST to match the satellite observations. Likewise, the reduced mixing in stable conditions in the new parameterization is also relatively small; for the range of the gradient Richardson number typically found over the ocean, the mixing was reduced by a maximum of only 10%, which is too small by more than an order of magnitude to be consistent with the satellite observations.

Large ocean eddies are the cause of some sea-surface height and chlorophyll anomalies previously ascribed to Rossby waves.

Ten years of sea‐surface height (SSH) fields constructed from the merged TOPEX/Poseidon (T/P) and ERS‐1/2 altimeter datasets are analyzed to investigate mesoscale variability in the global ocean. The higher resolution of the merged dataset reveals that more than 50% of the variability over much of the World Ocean is accounted for by eddies with amplitudes of 5–25 cm and diameters of 100–200 km. These eddies propagate nearly due west at approximately the phase speed of nondispersive baroclinic Rossby waves with preferences for slight poleward and equatorward deflection of cyclonic and anticyclonic eddies, respectively. The vast majority of the eddies are found to be nonlinear.

Satellite observations of wind stress and sea surface temperature (SST) are analyzed to investigate ocean–atmosphere interaction in the California Current System (CCS). As in regions of strong SST fronts elsewhere in the World Ocean, SST in the CCS region is positively correlated with surface wind stress when SST fronts are strong, which occurs during the summertime in the CCS region. This ocean influence on the atmosphere is apparently due to SST modification of stability and mixing in the atmospheric boundary layer and is most clearly manifest in the derivative wind stress fields: wind stress curl and divergence are linearly related to, respectively, the crosswind and downwind components of the local SST gradient. The dynamic range of the Ekman upwelling velocities associated with the summertime SST-induced perturbations of the wind stress curl is larger than that of the upwelling velocities associated with the mean summertime wind stress curl. This suggests significant feedback effects on the ocean, which likely modify the SST distribution that perturbed the wind stress curl field. The atmosphere and ocean off the west coast of North America must therefore be considered a fully coupled system. It is shown that the observed summertime ocean–atmosphere interaction is poorly represented in the NOAA North American Mesoscale Model (formerly called the Eta Model). This is due, at least in part, to the poor resolution and accuracy of the SST boundary condition used in the model. The sparse distribution of meteorological observations available over the CCS for data assimilation may also contribute to the poor model performance.

The value of Quick Scatterometer (QuikSCAT) measurements of 10-m ocean vector winds for marine weather prediction is investigated from two Northern Hemisphere case studies. The first of these focuses on an intense cyclone with hurricane-force winds that occurred over the extratropical western North Pacific on 10 January 2005. The second is a 17 February 2005 example that is typical of sea surface temperature influence on low-level winds in moderate wind conditions in the vicinity of the Gulf Stream in the western North Atlantic. In both cases, the analyses of 10-m winds from the NCEP and ECMWF global numerical weather prediction models considerably underestimated the spatial variability of the wind field on scales smaller than 1000 km compared with the structure determined from QuikSCAT observations. The NCEP and ECMWF models both assimilate QuikSCAT observations. While the accuracies of the 10-m wind analyses from these models measurably improved after implementation of the QuikSCAT data assimilation, the information content in the QuikSCAT data is underutilized by the numerical models. QuikSCAT data are available in near–real time in the NOAA/NCEP Advanced Weather Interactive Processing System (N-AWIPS) and are used extensively in manual analyses of surface winds. The high resolution of the QuikSCAT data is routinely utilized by forecasters at the NOAA/NCEP Ocean Prediction Center, Tropical Prediction Center, and other NOAA weather forecast offices to improve the accuracies of wind warnings in marine forecasts.

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.

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.