Analysing frozen Foraminifera by Cryostage LA-ICPMS: Neogene CO2, patterns, cycles, and climate sensitivity.

Introduction

Knowledge of past concentrations of atmospheric carbon dioxide (CO2) has transformed our understanding of earth’s climate: past, present, and future. Ice cores have yielded 800,000 years of high resolution and high precision data, revolutionising Earth system science.

Marine Sediment Records

Marine sediment records provide the opportunity for highly resolved records of ocean and past ocean pH (and therefore ocean and atmosphere CO2 content) over millions of years by means of the δ11B-pH proxy. However, present progress is currently hampered by:

• Large sample size requirements
• Intensive laboratory techniques limiting throughput and applicability

Innovation is therefore required to answer a number of key questions, including:

1. How do spatial variations in ocean carbon flux cause natural climate variability?
2. Does climate sensitivity vary with background state?

ForCry Approach

By analysing small samples frozen into a blank-free ice puck, ForCry exploits the increased sensitivity of laser ablation δ11B analysis to enable the next generation of past climate data to be recovered.

Advantages of the ForCry Method

This novel approach allows analyses to be conducted on ≤20 individual plankton, representing a ~10-fold reduction in sample size while maintaining precision. Additionally, this approach:

• Removes costly chemical purification steps
• Leads to higher throughput, making high-resolution CO2 reconstructions using the δ11B-pH proxy a viable option to augment and extend the ice core CO2 records.

Objectives of ForCry

ForCry has 4 objectives:

1. To develop this novel technique and optimise the hardware for efficient data collection from carbonates and other archives.
2. To calibrate through culture and field sample collection the δ11B-pH proxy in multiple species of foraminifera.
3. To map ocean pH change in four dimensions using a data inversion model.
4. To reconstruct CO2 change at high resolution over key past intervals (e.g., Mid-Miocene) that are highly relevant to a warm future, thus probing the state dependency of climate sensitivity.