#Panoply nasa archive

For the pre-clustering analysis the user may choose a basin (North Atlantic or Southern Ocean), a data type (observations or model), and which function to run, in order to produce any of the corresponding paper figures.The procedure is split in three steps: pre-clustering, clustering and post-clustering. All functions are located in the OCS_toolbox together with the GUI script to allow for customization.В The scripts have been tested in MATLAB R2015a and R2016a. The Ocean Carbon States toolbox is a collection of MATLAB scripts that allows users to prepare the datasets and implement the cluster analysis, as described in detail in Latto and Romanou (2018). The objective method is meant to complement and not replace visual inspection of the datasets. To ensure that the number of clusters that was predetermined is representative of the system, we developed an objective method where k is determined based on a sensitivity test that compares the average distance of all monthly 2D histograms to the centroid of their assigned cluster versus that of other clusters, using an assigned score which quantifies that distance. This algorithm iteratively searches for a predefined number of clusters (k), converging when the squared error between the mean of each cluster and the 2D histograms assigned per cluster is minimized. The statistical method used here to determine the pCO 2-SST regimes in the North Atlantic is the k-means clustering method, which partitions and allocates the spatially and temporally defined 2D histograms of pCO 2-SST into groups, called clusters or regimes.

#Panoply nasa archive

Download the database and scripts (1.5 MB ZIP).ДЄn archive of this version is also available at doi:10.5281/zenodo.996892.In future work, both the toolbox and the database will be extended to include more functionality and datasets. The toolbox presented here comprises the methodology and the scripts used to extract the Ocean Carbon States and the database includes results from applying the method in the simple case of a climatological dataset, in order to test its applicability. In future work, other key variables of the ocean carbon cycle will be investigated. The variables, pCO 2 and SST, are chosen such that they represent the main pathways that the ocean carbon concentration changes namely, changes in air-sea flux and ocean biogeochemistry, and in ocean circulation. In this study, the self-organized patterns of pCO 2 and SST are calculated.

The 'Ocean Carbon States' Database: A proof-of-concept application of cluster analysis in the ocean carbon cycle, As we identify and compare the spatial and temporal patterns extracted from observational, but also model datasets, we gain insight into the physical and biogeochemical processes controlling the ocean carbon cycle in nature as well as the skill with which these processes are simulated by the models.ДЄ detailed description of the technique and its application to two ocean basins in observations and models of the ocean carbon cycle is provided in: These regimes are obtained using an advanced data-mining technique, cluster analysis, which organizes the multivariate data in groups of similar behavior. The вЂњOcean Carbon StatesвЂќ represent the regimes of variability of the ocean carbon cycle, as expressed by the partial pressure of CO 2 (pCO 2) in the ocean and sea surface temperature (SST). Ocean Carbon States - Database and Toolbox