Keywords:Climate variability, Individual foraminifer analyses, Planktic foraminifera, Mg/Ca, d18O
Foraminifera are commonly used in paleoclimate reconstructions as they occur throughout the world’s oceans and are often abundantly preserved in the sediments. Traditionally, foraminifera-based proxies like d18O and Mg/Ca are analyzed on pooled specimens of a single species. Analysis of single specimens of foraminifera allows reconstructing climate variability on timescales related to El Niño-Southern Oscillation (ENSO) or seasonality, thus has the potential to shed more light on the hydroclimate changes in regions such as East Asia. However, quantitative calibrations between the statistics of individual foraminiferal analyses (IFA) and climate variability are still missing. We performed Mg/Ca and d18O measurements on single specimens from surface sediments (modern to 300 years old) from different settings to better understand the signal recorded by individual foraminifera. We used three species of planktic foraminifera (G. ruber (s.s.), T. sacculifer, and N. dutertrei) from the Indo-Pacific Warm Pool (IPWP) and one species (G. ruber (pink)) from the Gulf of Mexico (GoM). The Mg/Ca-d18O relationships for different species agree well with published calibrations. IFA statistics (both mean and standard deviation) of Mg/Ca and d18O between the different sites show a strong relationship indicating that both proxies are influenced by a common factor, most likely temperature variations during calcification. This strongly supports the use of IFA to reconstruct climate variability. However, our combined IFA data for the different species only show a weak relationship to seasonal and interannual temperature changes, especially when seasonal variability increases at a location. This suggests that the season and depth habitat of the foraminifera strongly affect IFA variability, such that ecology needs to be considered when reconstructing past climate variability. Further studies on surface sediments and depth-stratified plankton tows would allow better characterization of IFA signal, and ultimately improve the robustness of IFA-based climate variability reconstructions.