Towards the construction of regional marine radiocarbon calibration curves: an unsupervised machine learning approach

Radiocarbon may serve as a powerful dating tool in palaeoceanography, but its accuracy is severely limited by the need to calibrate radiocarbon dates to calendar ages. A key problem is that marine radiocarbon dates must be corrected for past offsets from either the contemporary atmosphere (i.e. ‘reservoir age’ offsets) or a modelled estimate of the global average surface ocean (i.e. delta-R offsets). This presents a challenge because the spatial distribution of reservoir ages and delta-R offsets can vary significantly, particularly over periods of major marine hydrographic and/or carbon cycle change such as the last deglaciation. Modern reservoir age/delta-R estimates therefore have limited applicability.  The construction of regional marine calibration curves could provide a solution to this challenge, if coherent regions could be defined. Here, we use unsupervised machine learning techniques to define distinct regions of the surface ocean that exhibit coherent behaviour in terms of their radiocarbon age offsets from the contemporary atmosphere (R-ages). We investigate the performance of different clustering algorithms applied to outputs from different numerical models. Comparisons between the cluster assignments across model runs confirm some robust regional patterns that likely arise from constraints imposed by large-scale ocean and atmospheric physics. At the coarsest scale, regions of coherent R-age variability are associated with the major ocean basins. By further dividing basin-scale shape-based clusters into amplitude-based subclusters, we recover regional associations that cohere with known modern oceanographic processes, such as increased high latitude R-ages, or the propagation of R-age anomalies from the Southern Ocean to the Eastern Equatorial Pacific. We show that the medoids for these regional sub-clusters provide significantly better approximations of simulated local R-age variability than constant offsets from the global surface average. The proposed clusters are also found to be broadly consistent with existing reservoir age reconstructions that span the last ~30 ka. We therefore propose that machine learning provides a promising approach to the problem of defining regions for which marine radiocarbon calibration curves may eventually be generated.