Dynamics and mixing in the Upper Ocean Layer

S'estudia la resposta de la capa de barreja oceànica al forçament atmosfèric considerant dades obtingudes durant 12 dies d'abril del 2001 a 42 estacions a través de l'Atlàntic nord seguint aproximadament la latitud de 53ºN. Aquestes dades inclouen, a més de variables atmosfèriques, mesures de CTD, velocitats amb ADCP i dades de microestructura obtingudes amb un perfilador de caiguda lliure. En aquest últim cas, s'han desenvolupat tècniques de processament de les dades que també es presenten aquí. El transsecte estudiat segueix la posició climatològica del rotacional mitjà anual del vent igual a zero i travessa el corrent del Labrador i algunes branques i meandres del Corrent Atlàntic Nord. El forçament atmosfèric es va caracteritzar per vents intensos i fluxos superficials de calor negatius, tot i que, tal com es dedueix de la comparació del gruix de la capa de barreja amb la longitud de Monin-Obukov, la barreja induïda pel vent va dominar sobre la convectiva durant tot el transsecte.

A compositional approach to stable isotope data analysis

Isotopic data are currently becoming an important source of information regarding sources, evolution and mixing processes of water in hydrogeologic systems. However, it is not clear how to treat with statistics the geochemical data and the isotopic data together. We propose to introduce the isotopic information as new parts, and apply compositional data analysis with the resulting increased composition. Results are equivalent to downscale the classical isotopic delta variables, because they are already relative (as needed in the compositional framework) and isotopic variations are almost always very small. This methodology is illustrated and tested with the study of the Llobregat River Basin (Barcelona, NE Spain), where it is shown that, though very small, isotopic variations comp lement geochemical principal components, and help in the better identification of pollution sources

Mixing compositions and other scales

Theory of compositional data analysis is often focused on the composition only. However in practical applications we often treat a composition together with covariables with some other scale. This contribution systematically gathers and develop statistical tools for this situation. For instance, for the graphical display of the dependence of a composition with a categorical variable, a colored set of ternary diagrams might be a good idea for a first look at the data, but it will fast hide important aspects if the composition has many parts, or it takes extreme values. On the other hand colored scatterplots of ilr components could not be very instructive for the analyst, if the conventional, black-box ilr is used. Thinking on terms of the Euclidean structure of the simplex, we suggest to set up appropriate projections, which on one side show the compositional geometry and on the other side are still comprehensible by a non-expert analyst, readable for all locations and scales of the data. This is e.g. done by defining special balance displays with carefully- selected axes. Following this idea, we need to systematically ask how to display, explore, describe, and test the relation to complementary or explanatory data of categorical, real, ratio or again compositional scales. This contribution shows that it is sufficient to use some basic concepts and very few advanced tools from multivariate statistics (principal covariances, multivariate linear models, trellis or parallel plots, etc.) to build appropriate procedures for all these combinations of scales. This has some fundamental implications in their software implementation, and how might they be taught to analysts not already experts in multivariate analysis