Global compilation of surface mixed layer parameters (sedimentation rate, bioturbation depth, mixing intensity) from marine environments: The SMLBase v1.0

In the surface mixed layer (SML) of a sediment body, sedimentary particles such as skeletal remains are mixed and moved by a variety of processes. In its simplest form, their movement can be described by a burial component (advection, specified by the sedimentation rate), and a mixing component (diffusion, specified by mixing intensity and mixing depth; Guinasso and Schink 1975). The interaction of these two components plays an important role in different geoscientific disciplines, as it influences how information recorded by sedimentary particles enters the geohistorical record. After transiting the SML, younger particles can be buried below older particles (e.g., Dominguez et al., 2016). The resulting age inversions contradict the paradigm of age-depth modeling that the age of sedimentary particles strictly increases with their burial depth (Haslett and Parnell 2008). The stratigraphic offset of particles of identical age can be more than a meter (Tomašových et al., 2018), generating age inversions when individual particles are dated to construct age-depth models. Similarly, particles found at the same stratigraphic position can have ages that differ by millennia. This reduces the temporal resolution of stratigraphic time series and limits the fastest ecological change recognizable in the fossil record (Kidwell et al., 1991). Understanding how sedimentary particles are mixed and buried in the SML is thus crucial to the proper interpretation of geohistorical data. Multiple databases of surface sediment parameters that describe individual aspects of the surface mixed layer have been published in the past. This includes bioturbation depth and mixing coefficients in Teal et al. (2008), bioturbation intensity by Solan et al. (2019), and sedimentation rate and biodiffusion coefficient byMiddelburg et al. (1997). However, these databases record individual SML parameters from different sampling locations. As a result, they don’t allow simulating the SML. Here, I present a database of empirically informed models of the SML in marine environments that captures the dependencies between mixing and burial on a global scale. OPEN ACCESS