Search

Tropical forests fix large quantities of carbon from the atmosphere every year; however, the fate of this carbon as it travels through ecosystem compartments is poorly understood. In particular, there is a large degree of uncertainty regarding the time carbon spends in an ecosystem before it is respired and returns to the atmosphere as CO2.We estimated the fate of carbon (trajectory of photosynthetically fixed carbon through a network of compartments) and its transit time (time it takes carbon to pass through the entire ecosystem, from fixation to respiration) for an old‐growth tropical forest located in the foothills of the Andes of Colombia.We show that on average, 50% of the carbon fixed at any given time is respired in <0.5 years, and 95% is respired in <69 years. The transit time distribution shows that carbon in ecosystems is respired on a range of time‐scales that span decades, but fast metabolic processes in vegetation dominate the return of carbon to the atmosphere.Synthesis. The transit time distribution integrates multiple ecosystem processes occurring at a wide range of time‐scales. It reconciles measurements of the age of respired CO2 with estimates of mean residence time in woody biomass, and provides a new approach to interpret other ecosystem level metrics such as the ratio of net primary production to gross primary production. [ABSTRACT FROM AUTHOR]

Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model–data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model–data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model–data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries. [ABSTRACT FROM AUTHOR]