Spatial correlation bias in late-Cenozoic erosion histories derived from thermochronology

The potential link between erosion rates at the Earth’s surface and changes in global climate has intrigued geoscientists for decades1,2 because such a coupling has implications for the influence of silicate weathering3,4 and organic-carbon burial5 on climate and for the role of Quaternary glaciations in landscape evolution1,6. A global increase in late-Cenozoic erosion rates in response to a cooling, more variable climate has been proposed on the basis of worldwide sedimentation rates7. Other studies have indicated, however, that global erosion rates may have remained steady, suggesting that the reported increases in sediment-accumulation rates are due to preservation biases, depositional hiatuses and varying measurement intervals8–10. More recently, a global compilation of thermochronology data has been used to infer a nearly twofold increase in the erosion rate in mountainous landscapes over late-Cenozoic times6. It has been contended that this result is free of the biases that affect sedimentary records11, although others have argued that it contains biases related to how thermochronological data are averaged12 and to erosion hiatuses in glaciated landscapes13. Here we investigate the 30 locations with reported accelerated erosion during the late Cenozoic6. Our analysis shows that in 23 of these locations, the reported increases are a result of a spatial correlation bias—that is, combining data with disparate exhumation histories, thereby converting spatial erosion-rate variations into temporal increases. In four locations, the increases can be explained by changes in tectonic boundary conditions. In three cases, climatically induced accelerations are recorded, driven by localized glacial valley incision. Our findings suggest that thermochronology data currently have insufficient resolution to assess whether late-Cenozoic climate change affected erosion rates on a global scale. We suggest that a synthesis of local findings that include location-specific information may help to further investigate drivers of global erosion rates.