Search Results
You are looking at 1 - 2 of 2 items for :
- Author or Editor: Michael G. Schlax x
- Journal of Climate x
- Refine by Access: All Content x
Abstract
Satellite measurements of surface wind stress from the QuikSCAT scatterometer and sea surface temperature (SST) from the Tropical Rainfall Measuring Mission Microwave Imager are analyzed for the three-month period 21 July–20 October 1999 to investigate ocean–atmosphere coupling in the eastern tropical Pacific. Oceanic tropical instability waves (TIWs) with periods of 20–40 days and wavelengths of 1000–2000 km perturb the SST fronts that bracket both sides of the equatorial cold tongue, which is centered near 1°S to the east of 130°W. These perturbations are characterized by cusp-shaped features that propagate systematically westward on both sides of the equator. The space–time structures of these SST perturbations are reproduced with remarkable detail in the surface wind stress field. The wind stress divergence is shown to be linearly related to the downwind component of the SST gradient with a response on the south side of the cold tongue that is about twice that on the north side. The wind stress curl is linearly related to the crosswind component of the SST gradient with a response that is approximately half that of the wind stress divergence response to the downwind SST gradient. The perturbed SST and wind stress fields propagate synchronously westward with the TIWs. This close coupling between SST and wind stress supports the Wallace et al. hypothesis that surface winds vary in response to SST modification of atmospheric boundary layer stability.
Abstract
Satellite measurements of surface wind stress from the QuikSCAT scatterometer and sea surface temperature (SST) from the Tropical Rainfall Measuring Mission Microwave Imager are analyzed for the three-month period 21 July–20 October 1999 to investigate ocean–atmosphere coupling in the eastern tropical Pacific. Oceanic tropical instability waves (TIWs) with periods of 20–40 days and wavelengths of 1000–2000 km perturb the SST fronts that bracket both sides of the equatorial cold tongue, which is centered near 1°S to the east of 130°W. These perturbations are characterized by cusp-shaped features that propagate systematically westward on both sides of the equator. The space–time structures of these SST perturbations are reproduced with remarkable detail in the surface wind stress field. The wind stress divergence is shown to be linearly related to the downwind component of the SST gradient with a response on the south side of the cold tongue that is about twice that on the north side. The wind stress curl is linearly related to the crosswind component of the SST gradient with a response that is approximately half that of the wind stress divergence response to the downwind SST gradient. The perturbed SST and wind stress fields propagate synchronously westward with the TIWs. This close coupling between SST and wind stress supports the Wallace et al. hypothesis that surface winds vary in response to SST modification of atmospheric boundary layer stability.
Abstract
Two new high-resolution sea surface temperature (SST) analysis products have been developed using optimum interpolation (OI). The analyses have a spatial grid resolution of 0.25° and a temporal resolution of 1 day. One product uses the Advanced Very High Resolution Radiometer (AVHRR) infrared satellite SST data. The other uses AVHRR and Advanced Microwave Scanning Radiometer (AMSR) on the NASA Earth Observing System satellite SST data. Both products also use in situ data from ships and buoys and include a large-scale adjustment of satellite biases with respect to the in situ data. Because of AMSR’s near-all-weather coverage, there is an increase in OI signal variance when AMSR is added to AVHRR. Thus, two products are needed to avoid an analysis variance jump when AMSR became available in June 2002. For both products, the results show improved spatial and temporal resolution compared to previous weekly 1° OI analyses.
The AVHRR-only product uses Pathfinder AVHRR data (currently available from January 1985 to December 2005) and operational AVHRR data for 2006 onward. Pathfinder AVHRR was chosen over operational AVHRR, when available, because Pathfinder agrees better with the in situ data. The AMSR–AVHRR product begins with the start of AMSR data in June 2002. In this product, the primary AVHRR contribution is in regions near land where AMSR is not available. However, in cloud-free regions, use of both infrared and microwave instruments can reduce systematic biases because their error characteristics are independent.
Abstract
Two new high-resolution sea surface temperature (SST) analysis products have been developed using optimum interpolation (OI). The analyses have a spatial grid resolution of 0.25° and a temporal resolution of 1 day. One product uses the Advanced Very High Resolution Radiometer (AVHRR) infrared satellite SST data. The other uses AVHRR and Advanced Microwave Scanning Radiometer (AMSR) on the NASA Earth Observing System satellite SST data. Both products also use in situ data from ships and buoys and include a large-scale adjustment of satellite biases with respect to the in situ data. Because of AMSR’s near-all-weather coverage, there is an increase in OI signal variance when AMSR is added to AVHRR. Thus, two products are needed to avoid an analysis variance jump when AMSR became available in June 2002. For both products, the results show improved spatial and temporal resolution compared to previous weekly 1° OI analyses.
The AVHRR-only product uses Pathfinder AVHRR data (currently available from January 1985 to December 2005) and operational AVHRR data for 2006 onward. Pathfinder AVHRR was chosen over operational AVHRR, when available, because Pathfinder agrees better with the in situ data. The AMSR–AVHRR product begins with the start of AMSR data in June 2002. In this product, the primary AVHRR contribution is in regions near land where AMSR is not available. However, in cloud-free regions, use of both infrared and microwave instruments can reduce systematic biases because their error characteristics are independent.
