Effects of seafloor and laboratory dissolution on the Mg/Ca composition of Globigerinoides sacculifer and Orbulina universa tests - A laser ablation ICPMS microanalysis perspective

Partial or selective dissolution of planktonic foraminiferal tests on the seafloor has been shown to alter original test Mg/Ca compositions and thus may limit the accuracy of Mg/Ca-based thermometry for reconstructions of past sea surface temperatures. We have employed laser ablation ICPMS to determine the extent of dissolution-caused changes in Mg/Ca distribution across individual chamber walls of the planktonic foraminifera Globigerinoides sacculifer and Orbulina universa. G. sacculifer samples collected from a core-top depth transect in the NE Indian Ocean and laboratory dissolution experiments show little if any evidence of preferential removal of Mg-rich calcite layers by progressive dissolution of the tests. We attribute the absence of selective dissolution to the banded distribution of Mg across the chamber walls of these foraminiferal species and to the minimal presence of calcite crusts with relatively low-Mg composition on the outer surfaces of tests. Mg/Ca microanalyses of G. sacculifer from core-top samples further indicate that for samples collected above the calcite lysocline the effect of postdepositional dissolution on Mg/Ca sample mean values is minimal and within the uncertainty of Mg/Ca thermometry (i.e. ±0.4mmol/mol; ±0.8°C at ~28°C). Comparison with previously published results for G. sacculifer supports these observations. Simple modelling of G. sacculifer test dissolution indicates that selective removal of calcite with high-Mg/Ca values from within the final chamber of G. sacculifer test appears insufficient to cause the ~10% decrease in Mg/Ca values observed above calcite lysocline. These changes in test composition might be related to development/removal as a function of Δ[CO32-] of a thin diagenetic surface coating which has a relatively high-Mg/Ca composition (i.e. 20-25mmol/mol)

Mg/Ca variability in Globigerinoides ruber tests

Laser ablation inductively coupled plasma-mass spectrometry microanalysis of fossil and live Globigerinoides ruber from the eastern Indian Ocean reveals large variations of Mg/Ca composition both within and between individual tests from core top or plankton pump samples. Although the extent of intertest and intratest compositional variability exceeds that attributable to calcification temperature, the pooled mean Mg/Ca molar values obtained for core top samples between the equator and >30°S form a strong exponential correlation with mean annual sea surface temperature (Mg/Ca mmol/mol = 0.52 exp**0.076SST°C, r**2 = 0.99). The intertest Mg/Ca variability within these deep-sea core top samples is a source of significant uncertainty in Mg/Ca seawater temperature estimates and is notable for being site specific. Our results indicate that widely assumed uncertainties in Mg/Ca thermometry may be underestimated. We show that statistical power analysis can be used to evaluate the number of tests needed to achieve a target level of uncertainty on a sample by sample case. A varying bias also arises from the presence and varying mix of two morphotypes (G. ruber ruber and G. ruber pyramidalis), which have different mean Mg/Ca values. Estimated calcification temperature differences between these morphotypes range up to 5°C and are notable for correlating with the seasonal range in seawater temperature at different sites

LAtools: A data analysis package for the reproducible reduction of LA-ICPMS data

Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICPMS) is an increasingly popular analytical technique, that is able to provide spatially resolved, minimally destructive analyses of heterogeneous materials. The data produced by this technique are inherently complex, and require extensive processing and subjective expert interpretation to produce useful compositional data. At present, laboratories employ diverse protocols for data processing, and the reporting of these protocols is usually insufficient to allow data processing to be independently replicated, rendering the resulting data untraceable. Importantly, different expert users can obtain significantly different results from the same raw data using nominally identical processing workflows, depending on how ‘contaminants’ are identified and excluded, and which regions of signal are selected as representative of the composition of the sample. The irreproducibility of LA-ICPMS is a significant problem for the technique, but the complexity of the raw data has been a major hindrance to developing traceable data processing workflows. Here, we present LAtools – a free, open-source Python package for LA-ICPMS data processing designed with reproducibility at its core. The software performs basic data processing with similar efficacy to existing software, and brings a number of new data selection algorithms to facilitate reproducible reduction of LA-ICPMS data. We discuss the key advances of LAtools, and compare its output to trace metal analysis of marine CaCO3 (foraminifera) processed both manually and with Iolite, and to manually processed trace element data from zircon grains.This research was supported with funds from the UC Davis Department of Earth and Planetary Sciences, the Research School of Earth Sciences, ANU, as well as U.S. National Science Foundation awards to HJS (EAR-0946297 and OCE-1061676) and JF (OCE1261519), and Australian Research Council awards to SE (DP0990010 and DP110103158)

LAtools: A data analysis package for the reproducible reduction of LA-ICPMS data

Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICPMS) is an increasingly popular analytical technique, that is able to provide spatially resolved, minimally destructive analyses of heterogeneous materials. The data produced by this technique are inherently complex, and require extensive processing and subjective expert interpretation to produce useful compositional data. At present, laboratories employ diverse protocols for data processing, and the reporting of these protocols is usually insufficient to allow data processing to be independently replicated, rendering the resulting data untraceable. Importantly, different expert users can obtain significantly different results from the same raw data using nominally identical processing workflows, depending on how �contaminants� are identified and excluded, and which regions of signal are selected as representative of the composition of the sample. The irreproducibility of LA-ICPMS is a significant problem for the technique, but the complexity of the raw data has been a major hindrance to developing traceable data processing workflows. Here, we present LAtools � a free, open-source Python package for LA-ICPMS data processing designed with reproducibility at its core. The software performs basic data processing with similar efficacy to existing software, and brings a number of new data selection algorithms to facilitate reproducible reduction of LA-ICPMS data. We discuss the key advances of LAtools, and compare its output to trace metal analysis of marine CaCO3 (foraminifera) processed both manually and with Iolite, and to manually processed trace element data from zircon grains