Using Large Skeletal Architecture Fossil Coral Rubble to Reconstruct the Central Tropical Pacific Climate

By Ainsley Lord

Faculty Mentor: Dr. Pamela Grothe

Abstract

The tropical Pacific is a major driver of global climate variability
through the El Niño Southern Oscillation (ENSO). However, the impact
of warming sea surface temperature (SST) and/or freshening sea
surface salinity (SSS) on ENSO from anthropogenic climate change is
poorly understood due to limited continuous instrumental and
paleoclimate records before 1950 CE. Kiritimati Island (1.8° N, 157.4° W)
lacks continuous high-resolution records from the reliable coral
climate proxy Porites, shifting the focus to shorter Porites coral rubble
sequences. However, this coral rubble approach requires dozens of
overlapping samples and would benefit from the addition of other
coral genera. Preliminary work with modern large skeletal
architectural corals from Kiritimati Island, Favia and Hydnophora
species, as climate recorders is promising. Additionally, these corals
are abundant in the rubble fields and their slightly slower growth
rates provide longer climate sequences. Here, we test young fossil
corals from the Hydnophora species found on the rubble fields of
Kiritimati Island dated from the 1900’s that overlap existing
paleoclimate records from this region. First, we quantify the level of
diagenesis prevalent in each coral using both scanning electron
microscopy imaging and X-ray diffraction. Then, we analyze each
coral’s geochemistry, drilling every 1 mm along the densest skeletal
feature, for oxygen isotopes (δ O), a combined SST and SSS proxy, and
Sr/Ca, which tracks SST. Preliminary coral δ O time series are
correlated with both Porites and instrumental SST records where they
overlap. This research helps us expand the coral paleoclimate archive
on Kiritimati Island with the goal of creating a nearly continuous high resolution climate record back to 1800 C.E. This record is critical to
quantify the impact of anthropogenic climate change on this region
and aiding climate models for future climate predictions under
greenhouse gas forcing.


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