Carbon cycling through Earth’s environment impacts the planet’s climate. An accurate understanding of the carbon cycle is critical to assessing potential ecosystemic impacts due to changes to its equilibrium. Recent advances in satellite observations have generated large datasets representing Earth’s ecosystems, affording Earth scientists the opportunity to understand these carbon cycles at unprecedented scales and resolutions. 

However, integrating satellite observations and terrestrial model data for interpretation is challenging due to complex interactions between variables, model uncertainties, and measurement errors. To address this gap, scientists at the NASA Jet Propulsion Laboratory developed the C Data-Model (CARDAMOM) framework which enhances scientific understanding of global carbon fluxes and their environmental implications. While this approach creates models that more accurately fit the data, its inherent complexity and high dimensionality makes it difficult to interpret, debug, and understand.

We partnered with CARDAMOM scientists to design CLOVE, a visual analytics approach to interpreting carbon cycle data. CLOVE allows scientists to meaningfully make sense of complex carbon cycle dynamics, facilitating exploration of key ecological relationships, patterns, and processes that would otherwise remain opaque or misunderstood. Combining a suite of representations within a single tool, CLOVE streamlines data interpretation through visual analysis, ultimately supporting new insights and improved predictions of environmental change.