National Oceanic and
Atmospheric Administration
United States Department of Commerce


 

FY 2013

TropFlux wind stresses over the tropical oceans: Evaluation and comparison with other products

Praveen Kumar, B., J. Vialard, M. Lengaigne, V.S.N. Murty, M.J. McPhaden, M.F. Cronin, F. Pinsard, and K. Gopala Reddy

Climate Dynam., 40(7–8), 2049–2071, doi: 10.1007/s00382-012-1455-4 (2013)


In this paper, we present TropFlux wind stresses and evaluate them against observations along with other widely used daily air-sea momentum flux products (NCEP, NCEP2, ERA-I and QuikSCAT). TropFlux wind stresses are computed from the COARE v3.0 algorithm, using bias and amplitude corrected ERA-I input data and an additional climatological gustiness correction. The wind stress products are evaluated against dependent data from the TAO/TRITON, PIRATA and RAMA arrays and independent data from the OceanSITES mooring networks. Wind stress products are more consistent amongst each other than surface heat fluxes, suggesting that 10 m-winds are better constrained than near-surface thermodynamical parameters (2 m-humidity and temperature) and surface downward radiative fluxes. QuikSCAT overestimates wind stresses away from the equator, while NCEP and NCEP2 underestimate wind stresses, especially in the equatorial Pacific. QuikSCAT wind stress quality is strongly affected by rain under the Inter Tropical Convergence Zones. ERA-I and TropFlux display the best agreement with in situ data, with correlations >0.93 and rms-differences <0.012 Nm−2. TropFlux wind stresses exhibit a small, but consistent improvement (at all timescales and most locations) over ERA-I, with an overall 17 % reduction in root mean square error. ERA-I and TropFlux agree best with long-term mean zonal wind stress observations at equatorial latitudes. All products tend to underestimate the zonal wind stress seasonal cycle by ~20 % in the western and central equatorial Pacific. TropFlux and ERA-I equatorial zonal wind stresses have clearly the best phase agreement with mooring data at intraseasonal and interannual timescales (correlation of ~0.9 versus ~0.8 at best for any other product), with TropFlux correcting the ~13 % underestimation of ERA-I variance at both timescales. For example, TropFlux was the best at reproducing westerly wind bursts that played a key role in the 1997–1998 El Niño onset. Hence, we recommend the use of TropFlux for studies of equatorial ocean dynamics.



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