HR: 09:30h
AN: OS31A-07    [Abstracts]
TI: Small-Scale Variability in MODIS and Pathfinder Sea Surface Temperatures With Applications to Data Error Models for in Situ Observations
AU: * Kaplan, A
EM: alexeyk@ldeo.columbia.edu
AF: LDEO of Columbia University, P.O. Box 1000, Palisades, NY 10964, United States
AB: Sea surface temperature (SST) is arguably the most visible climate variable in the public forum of climate change debate. In climate change detection and attribution studies they are usually used in the form of gridded data sets which are analyzed statistically or serve as boundary conditions for atmospheric general circulation models. Therefore, it is of primary importance to ensure the optimality and reliability of gridded data sets, especially for the pre-satellite data period, including reliability of their error estimates. Most current methods of gridding blend together satellite and in situ data and involve a mixture of optimal interpolation (successive corrections), eigenvector reconstruction, bias correction techniques, and some forms of data assimilation. Analyses of the pre- satellite period depend on quite sparse in situ data as their inputs, but they usually try to make use of statistical information extracted from the satellite period. Therefore, the quality of these analyses and hence our ability to detect and properly attribute long-term climate change hinges on the quality of a priori statistical information obtained from the satellite data. Extensive satellite data was used in order to quantify and model in situ data errors, with the goal to improve pre-satellite era climate analyses. To this end intercomparisons of MODIS SST products, and their comparisons with Pathfinder V5 SST, and the in situ data collection ICOADS are presented. Small-scale and (within 1 degree bins) and short-term (within 1 month) variability of SST are estimated, using satellite data sets. These estimates are then successfully used to model the magnitude of the error in the binned in situ SST values from ICOADS.
DE: 4227 Diurnal, seasonal, and annual cycles (0438)
DE: 4260 Ocean data assimilation and reanalysis (3225)
DE: 4262 Ocean observing systems
SC: Ocean Sciences [OS]
MN: 2007 Joint Assembly