Sensitivity of the tropical Pacific Ocean simulation to the temporal and spatial resolution of wind forcing

Abstract

Abstract. The effects of temporal and spatial smoothing of wind forcing were evaluated in a model simulation of the tropical Pacific Ocean variability during the onset phase of the 1997/1998 E1 Nifio. A total of 16 experiments were performed using the NASA scatterometer wind data smoothed at time intervals from 1 to 30 days and on spatial scales from 1 ø to 10 ø. A major effect of the temporal smoothing of winds is to warm sea surface temperature (SST) by reducing the energy input for vertical turbulent mixing. When the daily wind forcing was replaced by the monthly average, the mean SST increased by 0.5 ø to 1 ø over most of the tropical Pacific. The spatial smoothing of winds is not as effective as the temporal smoothing in causing SST warming, but it has a more severe influence on dynamical ocean response for smoothing scales above 5 ø . The onset of the 1997/1998 E1 Nifio can be successfully simulated using the wind forcing averaged to monthly intervals and 2 ø squares. For climate models the spatial smoothing of wind forcing on scales larger than the width of the equatorial waveguide is a more serious limitation than the temporal smoothing on scales up to 1 month. IntroductionThe upper ocean dynamical and thermal structures are largely determined by the wind forcing at the ocean surface. Thus the numerical ocean models that attempt to simulate these structures have to include a good representation of wind stress as a surface boundary condition. However, because of the general lack of wind observations over the ocean, modelers usually have no choice but to resort to the wind products with relatively coarse spatial and temporal resolution. For example, the commonly used Florida State University (FSU) wind analysis [GoMenberg and O'Brien, 1981] is monthly in time and virtually 2 ø latitude by 10 ø longitude in space, which is typical for wind analyses based mainly on shipboard observations. Although some high-resolution wind data from buoys have occasionally been used to drive small-scale ocean models for a limited period of time [e.g., Chen and Wang, 1990], large-scale climate models are invariably forced with low-resolution wind products such as the FSU analysis.The underlying assumption of our common practice with low-resolution wind data is that high-frequency, small-scale winds have insignificant influence on the low-frequency, largescale ocean variabilities. This might be true to some extent but is not likely to be generally applicable. For instance, using a quasi-geostrophic (OG) model of the North Pacific Ocean, Large et al. [1991] showed that high-frequency wind forcing accounts for a large portion of the barotropic ocean response A potentially important aspect of the high-frequency, smallscale wind forcing that has not been addressed previously is its effects on the upper ocean thermal structure, especially sea surface temperature (SST), a vital parameter in oceanatmosphere interaction. There are reasons to believe that these effects are far from negligible. First of all, winds can force turbulent m...

Statistical Characterization of Zonal and Meridional Ocean Wind Stress

“…Probability density functions and frequency spectra are used to describe the basic structure of wind variability. These are particularly relevant for ocean models that seek to understand the ocean response to stochastic wind forcing with known statistical characteristics (e.g., Sura and Penland 2002;Weijer 2005) or that evaluate the response of ocean circulation to highfrequency, high-wavenumber wind variability (e.g., Milliff et al 1996;Chen et al 1999;Milliff et al 1999).…”

Assessment of wind‐stress errors using bias corrected ocean data assimilation

“…A study by Chen et al (1999) looked at the temporal and spatial smoothing of National Aeronautics and Space Administration scatterometer wind data and its impact on simulations of the tropical Pacific, with a particular focus on impacts on sea surface temperature. Their conclusions were that spatial smoothing greater than the lengthscales associated with the equatorial wave guide negatively impacts on the model simulation and this provides a useful guide for seasonal forecasting studies.…”

AMSR and DFS synergy

“…Monitoring these terms is critical to the characterization of the hydrological cycle and climate changes [15], [16], [17]. The combined measurements of the vector wind, water vapor, SST, and rain will also help to monitor tropical climate including tropical convection systems and cloud clusters, the intertropical convergence zone (ITCZ) [18], [19], [20], and the interannual climate anomalies of El Nino/Southern Oscillation (ENSO) [21], [22], [23], which also influences the mid-and high-latitude climate variations. Scientific impacts of the combined mission will further extend to studies concerning various phenomena of the atmosphere and ocean, such as monsoon [24], tropical cyclones [25], [26], ocean-atmosphere coupling mechanism [27] - [36], ocean's response to wind and hydrologic forcing, and operational numerical weather prediction, and also to cryospheric studies and land applications.…”