Visual Analytics for Comparison of Ocean Model Output with Reference Data: Detecting and Analyzing Geophysical Processes Using Clustering Ensembles

Researchers assess the quality of an ocean model by comparing its output to that of a previous model version or to observations. One objective of the comparison is to detect and to analyze differences and similarities between both data sets regarding geophysical processes, such as particular ocean currents. This task involves the analysis of thousands or hundreds of thousands of geographically referenced temporal profiles in the data. To cope with the amount of data, modelers combine aggregation of temporal profiles to single statistical values with visual comparison. Although this strategy is based on experience and a well-grounded body of expert knowledge, our discussions with domain experts have shown that it has two limitations: (1) using a single statistical measure results in a rather limited scope of the comparison and in significant loss of information, and (2) the decisions modelers have to make in the process may lead to important aspects being overlooked.

1 Introduction

Geoscientific simulation models, in particular ocean and climate system models, consider complex interactions between processes in the Earth system [38]. For example, the salinity of the oceans affects the circulation within oceans, which in turn impacts the energy exchange between oceans and atmosphere. The latter influences air temperature, which may affect continental and sea ice. To come full circle, melting ice leads to freshwater influx into the oceans which influences their salinity.