Reference : Deriving ocean climatologies with multivariate coupling
Scientific congresses and symposiums : Unpublished conference/Abstract
Physical, chemical, mathematical & earth Sciences : Earth sciences & physical geography
http://hdl.handle.net/2268/204925
Deriving ocean climatologies with multivariate coupling
English
Barth, Alexander mailto [Université de Liège > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER) >]
Alvera Azcarate, Aida mailto [Université de Liège > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER) >]
Beckers, Jean-Marie mailto [Université de Liège > Département d'astrophys., géophysique et océanographie (AGO) > GeoHydrodynamics and Environment Research (GHER) >]
2016
No
No
International
EGU
17–22 April 2016
[en] multivariate statistics ; ocean
[en] In situ measurements of ocean properties are generally sparsely distributed and thus undersample the ocean variability. Deriving ocean climatologies is a challenging task especially for biological and chemical parameters where the number of data is, by an order of magnitude, smaller than for physical parameters. However, physical and biogeochemical parameters are related through the ocean dynamics. In particular fronts visible in physical parameters are often related to gradients in biogeochemical parameters. Ocean climatologies are generally derived for different variables independently. For biogeochemical parameters, only the very large-scale variability can be derived for poorly sampled areas. Here we present a method to derive multivariate analysis taking the relationship between physical and biogeochemical variables into account. The benefit of this procedure is showed by using model data for salinity, nitrate and phosphate of the Mediterranean Sea. The model fields are sampled at the locations of true observations (extracted from the World Ocean Database 2013) and the analysed fields are compared to the original model fields. The multivariate analysis result in a reduction of the RMS error and to a better representation of the gradients
GHER/MARE/AGO
Union Européenne = European Union - UE = EU ; Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
Researchers ; Professionals
http://hdl.handle.net/2268/204925
http://meetingorganizer.copernicus.org/EGU2016/EGU2016-11879.pdf
FP7 ; 283607 - SEADATANET II - SeaDataNet II: Pan-European infrastructure for ocean and marine data management

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