Kretschmer, Kerstin; Jonkers, Lukas; Kucera, Michal; Schulz, Michael (2018): Investigation of the seasonal and vertical habitats of planktonic foraminifera using an ecosystem modeling approach [dataset]. PANGAEA, https://doi.org/10.1594/PANGAEA.892469,

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Supplement to: Kretschmer, K et al. (2018): Modeling seasonal and vertical habitats of planktonic foraminifera on a global scale. Biogeosciences, 15, 4405-4429, https://doi.org/10.5194/bg-15-4405-2018

Species of planktonic foraminifera exhibit specific seasonal production patterns and different preferred vertical habitats. The seasonality and vertical habitats are not constant throughout the range of the species and changes therein must be considered when interpreting paleoceanographic reconstructions based on fossil foraminifera. Accounting for the effect of vertical and seasonal habitat tracking on foraminifera proxies at times of climate change is difficult because it requires independent fossil evidence. An alternative that could reduce the bias in paleoceanographic reconstructions is to predict species-specific habitat shifts under climate change using an ecosystem modeling approach. To this end, we present a new version of a planktonic foraminifera model, PLAFOM2.0, embedded into the ocean component of the Community Earth System Model, version 1.2.2. This model predicts monthly global concentrations of the planktonic foraminiferal species: Neogloboquadrina pachyderma, N. incompta, Globigerina bulloides, Globigerinoides ruber (white), and Trilobatus sacculifer throughout the world ocean, resolved in 24 vertical layers to 250m depth. The resolution along the vertical dimension has been implemented by applying the previously used spatial parameterization of biomass as a function of temperature, light, nutrition, and competition on depth-resolved parameter fields. This approach alone results in the emergence of species-specific vertical habitats, which are spatially and temporally variable. Although an explicit parameterization of the vertical dimension has not been carried out, the seasonal and vertical distribution patterns predicted by the model are in good agreement with sediment trap data and plankton tow observations. In the simulation, the colder-water species N. pachyderma, N. incompta, and G. bulloides show a pronounced seasonal cycle in their depth habitat in the polar and subpolar regions, which appears to be controlled by food availability. During the warm season, these species preferably occur in the subsurface, while towards the cold season they ascend through the water column and are found closer to the sea surface. The warm-water species G. ruber (white) and T. sacculifer exhibit a less variable shallow depth habitat with highest biomass concentrations within the top 40m of the water column. Nevertheless, even these species show vertical habitat variability and their seasonal occurrence outside the tropics is limited to the warm surface layer that develops at the end of the warm season. The emergence in PLAFOM2.0 of species-specific vertical habitats that are consistent with observations indicates that the population dynamics of planktonic foraminifera species may be driven by the same factors in time, space, and with depth, in which case the model can provide a reliable and robust tool to aid the interpretation of proxy records.

The model data was created using the planktonic foraminifera model PLAFOM2.0, which was embedded into the ocean component of the Community Earth System Model version 1.2.2 (for more details see Kretschmer et al., 2018).

The model results include monthly global concentrations (up to 250 m water depth) of the cold-water planktonic foraminiferal species Neogloboquadrina pachyderma, of the temperate-water species N. incompta and Globigerina bulloides, and of the warm-water species Globigerinoides ruber (white) and Trilobatus sacculifer.

The environmental data comprises of the modeled monthly global concentrations of small phytoplankton, diatoms, zooplankton, large detritus, and chlorophyll as well as of the modeled monthly global temperature distribution. Note the files including the environmental data were created using the split command. To merge these files into one file the command cat (cat split-environmentalData.tar.gz0* > environmentalData.tar.gz) should be used and afterwards the file(s) can be extracted from the created archive.