Publisher: Copernicus Gesellschaft Mbh
Subjects: DOAJ:Earth and Environmental Sciences, SEA-SURFACE TEMPERATURE, [SDU.OCEAN] Sciences of the Universe [physics]/Ocean, Atmosphere, GC1-1581, [ SDU.OCEAN ] Sciences of the Universe [physics]/Ocean, Atmosphere, STERIC, G, Geography. Anthropology. Recreation, VARIABILITY, DOAJ:Oceanography, PACIFIC, ALTIMETRIC HEIGHT, ARGO, Oceanography, GE1-350, Environmental sciences, DATA ASSIMILATION, HEIGHT
ISI Document Delivery No.: 029CN Times Cited: 3 Cited Reference Count: 35 Cited References: AVISO, 2012, SSALTO DUACS US HDB BRETHERTON FP, 1976, DEEP-SEA RES, V23, P559, DOI 10.1016/0011-7471(76)90001-2 Buongiorno Nardelli B., 2012, OCEAN SCI DISCUSS, V9, P1045, DOI [10.5194/osd-9-1045-2012, DOI 10.5194/0SD-9-1045-2012] Cabanes C., 2011, CORIOLIS OCEAN DATAB Cabanes C., 2012, OCEAN SCI DISCUSS, V9, P1273, DOI DOI 10.5194/0SD-9-1273-2012 Dhomps AL, 2011, OCEAN SCI, V7, P175, DOI 10.5194/os-7-175-2011 Ferry N., 2010, MERCATOR OCEAN Q JAN Font J, 2010, P IEEE, V98, P5649 Fox DN, 2002, J ATMOS OCEAN TECH, V19, P240, DOI 10.1175/1520-0426(2002)019<0240:TMODAS>2.0.CO;2 Gaillard F., 2008, MERSEAWP05CNRSSTR001 Garric G., 2011, EGU GEN ASS 2011 VIE Gilson J, 1998, J GEOPHYS RES-OCEANS, V103, P27947, DOI 10.1029/98JC01680 Guinehut S, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2005GL025551 Guinehut S, 2004, J MARINE SYST, V46, P85, DOI 10.1016/j.jmarsys.2003.11.022 Haines K., 2012, OCEAN SCI, V8, P3330, DOI [10.5194/os-8-333-2012, DOI 10.5194/OS-8-333-2012] Ingleby B, 2007, J MARINE SYST, V65, P158, DOI 10.1016/j.jmarsys.2005.11.019 Larnicol G., 2006, ESA SP PUBL Le Traon PY, 1998, J ATMOS OCEAN TECH, V15, P522, DOI 10.1175/1520-0426(1998)015<0522:AIMMOM>2.0.CO;2 Lherminier P, 2007, J GEOPHYS RES-OCEANS, V112, DOI 10.1029/2006JC003716 Locarnini R., 2010, NOAA ATLAS NESDIS, V68 McCarthy MC, 2000, J GEOPHYS RES-OCEANS, V105, P19535, DOI 10.1029/2000JC900056 Meijers AJS, 2011, J ATMOS OCEAN TECH, V28, P548, DOI 10.1175/2010JTECHO790.1 Mulet S, 2012, DEEP-SEA RES PT II, V77-80, P70, DOI 10.1016/j.dsr2.2012.04.012 Oke P. 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EU ; CNES [82394/00] This work was carried out within the scope of the MyOcean project (Development and pre-operational validation of GMES Marine Core Services; 2009-2012) and was funded within the call for proposal EU FP7-SPACE-2007-1 (Grant Agreement nr. 218812) and with support from CNES under contract 82394/00. 3 COPERNICUS GESELLSCHAFT MBH GOTTINGEN OCEAN SCI; This paper describes an observation-based approach that efficiently combines the main components of the global ocean observing system using statistical methods. Accurate but sparse in situ temperature and salinity profiles (mainly from Argo for the last 10 yr) are merged with the lower accuracy but high-resolution synthetic data derived from satellite altimeter and sea surface temperature observations to provide global 3-D temperature and salinity fields at high temporal and spatial resolution. The first step of the method consists in deriving synthetic temperature fields from altimeter and sea surface temperature observations, and salinity fields from altimeter observations, through multiple/simple linear regression methods. The second step of the method consists in combining the synthetic fields with in situ temperature and salinity profiles using an optimal interpolation method. Results show the revolutionary nature of the Argo observing system. Argo observations now allow a global description of the statistical relationships that exist between surface and subsurface fields needed for step 1 of the method, and can constrain the large-scale temperature and mainly salinity fields during step 2 of the method. Compared to the use of climatological estimates, results indicate that up to 50% of the variance of the temperature fields can be reconstructed from altimeter and sea surface temperature observations and a statistical method. For salinity, only about 20 to 30% of the signal can be reconstructed from altimeter observations, making the in situ observing system essential for salinity estimates. The in situ observations (step 2 of the method) further reduce the differences between the gridded products and the observations by up to 20% for the temperature field in the mixed layer, and the main contribution is for salinity and the near surface layer with an improvement up to 30%. Compared to estimates derived using in situ observations only, the merged fields provide a better reconstruction of the high resolution temperature and salinity fields. This also holds for the large-scale and low-frequency fields thanks to a better reduction of the aliasing due to the mesoscale variability. Contribution of the merged fields is then illustrated to describe qualitatively the temperature variability patterns for the period from 1993 to 2009.
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