Two decades [1992–2012] of surface wind analyses based on satellite scatterometer observations

Abstract

Surface winds (equivalent neutral wind velocities at 10 m) from scatterometer missions since 1992 have been used to build up a 20-year climate series. Optimal interpolation and kriging methods have been applied to continuously provide surface wind speed and direction estimates over the global ocean on a regular grid in space and time. The use of other data sources such as radiometer data (SSM/I) and atmospheric wind reanalyses (ERA-Interim) has allowed building a blended product available at 1/4° spatial resolution and every 6 hours from 1992 to 2012. Sampling issues throughout the different missions (ERS-1, ERS-2, QuikSCAT, and ASCAT) and their possible impact on the homogeneity of the gridded product are discussed. In addition, we assess carefully the quality of the blended product in the absence of scatterometer data (1992 to 1999). Data selection experiments show that the description of the surface wind is significantly improved by including the scatterometer winds. The blended winds compare well with buoy winds (1992-2012) and they resolve finer spatial scales than atmospheric reanalyses, which make them suitable for studying air-sea interactions at mesoscale. The seasonal cycle and interannual variability of the product compare well with other long-term wind analyses. The product is used to calculate 20-year trends in wind speed, as well as in zonal and meridional wind components. These trends show an important asymmetry between the southern and northern hemispheres, which may be an important issue for climate studies. Highlights► 20-year blended of high-resolution (0.25deg, 6h) wind product from scaterrometry ► Blended product suitable for studying air-sea interactions at mesoscale. ► The product is used to calculate 20-year trends in wind speed, as well as in zonal and meridional wind components.A long record of ocean surface wind observations is essential for climate research and for addressing a variety of operational and scientific issues. Surface wind vectors are indeed the key drivers of oceanic and atmospheric processes that regulate the global and regional climate [e.g., Ricciardulli and Wentz, 2013]. Ocean winds are routinely used as the primary forcing function of numerical hydrodynamic models of the ocean circulation [e.g., Grima et al., 1999; Carton and Giese, 2009;Wunsch et al. 2009;Desbiolles et al., 2016] and of surface gravity waves [e.g., et al., 1988;Tolman 2002] at global and regional scales. Ocean winds are considered as the most important variable for investigating storm surges and wave forecasts at various space and time scales [Debernard et al., 2002]. They drive the variability of ocean processes such as coastal upwelling, primary productivity, cross-shelf transport, deep-water formation, ice transport, and they are of fundamental importance for the reliable estimation of air-sea momentum fluxes (wind stress vector), turbulent heat fluxes (latent and sensible), and gas exchanges (e.g. CO 2 and H 2 O). Longterm change in global winds is an important forcing and an indicator of cli...

ENSO impact on surface radiative fluxes as observed from space

“…For surface winds, we rely on ERA‐I and on the blended analysis of Desbiolles et al . [] that is available on a 1/4° grid, but only during 1992–2012.…”

Recipes for How to Force Oceanic Model Dynamics

“…Resolving mesoscale eddies in oceanic models is required to quantitatively reproduce key features of the ocean circulation such as the Western Boundary Currents (Chassignet & Marshall, 2013;McWilliams, 2008), the Southern Ocean overturning (e.g., Downes et al, 2018;Hallberg & Gnanadesikan, 2006), and the total heat transport and biogeochemical variability (Colas et al, 2012;Dong et al, 2014;Gruber et al, 2011;McGillicuddy et al, 2007;Renault, Deutsch, McWilliams, et al, 2016). Meanwhile, satellite sensors such as scatterometers (e.g., QuikSCAT) have been used to better understand mesoscale air-sea interactions and to demonstrate their global ubiquity and effects on 10-m wind and surface stress (Chelton et al, 2001(Chelton et al, , 2004(Chelton et al, , 2007Chelton & Xie, 2010;Cornillon & Park, 2001;Desbiolles et al, 2017;Gaube et al, 2015;Kelly et al, 2001;Renault, McWilliams, & Masson, 2017;O'Neill et al, 2010O'Neill et al, , 2012. They also motivated model developments and numerical studies that aim to understand the air-sea interaction effects in both the atmosphere and the ocean (Desbiolles et al, 2016;Hogg et al, 2009;Minobe et al, 2008;Oerder et al, 2016Oerder et al, , 2018Renault, Molemaker, Gula, et al, 2016;Seo et al, 2016Seo et al, , 2019Seo, 2017).…”

Coastal Upwelling Front Detection off Central Chile (36.5–37°S) and Spatio-Temporal Variability of Frontal Characteristics

“…However, when analyzing raw data, differences among the missions calibrations introduce bias in the wind stress tendency [38]. To solve this issue, wind data from sparse scatterometer fields (ERS, QuikSCAT, and ASCAT) have been processed by the IFREMER and compared to other data sources, such as radiometer data (SSM/I) and atmospheric wind reanalyses (ERA-Interim), to produce a new long time wind product described by Desbiolles et al [39]. This dataset provides 6 h wind stress maps at 1/4 • spatial resolution over the 2003-2016 period.…”

“…The imprint of CTWs is strong in the Northern HCS, but off Central Chile (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40) • S) their amplitude is largely attenuated so that the SST can be inferred from a one-dimensional model forced by the local wind stress and solar heating [24]. The region off Central-South Chile (35)(36)(37)(38)(39)(40) • S) is characterized by a strong seasonality in the coastal wind stress and the wind stress curl, with upwelling-favorable wind conditions from September to April (e.g., [13,31]). The upwelling-induced coastal cold tongue is present from November to May, suggesting a lag in the seasonal response of the SST to the wind conditions [13].…”