PP43B-1469:
Multiproxy Reconstruction of Tropical Pacific Holocene Mean State Variability
Abstract:
The El Niño-Southern Oscillation (ENSO) is the most prominent mode of tropical Pacific climate variability, significantly impacting both regional and global climate. In the past, the mean state of the Pacific Ocean has differed from today as evidenced by variability in the zonal water column structure. Recent paleoproxy based studies of tropical Pacific hydrology and surface temperature variability have hypothesized that observed climatological changes over the Holocene are directly linked to ENSO and/or mean state variability, complementing studies that dynamically relate centennial scale ENSO variability to mean state changes. These studies have suggested that mid Holocene ENSO variability was low and the mean state was more “La Niña” like. In the late Holocene, data have been interpreted as indicating an increase in ENSO variability with a more moderate mean state. Here, we test the hypothesis that observed climatological changes in the eastern tropical Pacific are related to mean state or ENSO variability during the Holocene.We focus our study on three sediment core locations from the equatorial Pacific: the Indo-Pacific Warm Pool (BJ803-119 GGC, 117MC), the Line Islands in the central Pacific (ML1208-18GC) and the Galapagos Islands in the Eastern Cold Tongue (KNR195-5 43 GGC, 42MC). These sites lie in regions poised to provide evidence of basin-wide equatorial water column structure changes in response to mean state and/or long-term ENSO variability. We use a multiproxy approach with data from both organic (sterol abundances) and inorganic proxies (Mg/Ca and δ18O of 4 planktonic foraminiferal species, % G. bulloides) to reconstruct zonal tropical Pacific (sub)surface temperature and stratification gradients over the Holocene. This approach enables us to combine the strengths of each individual proxy to derive more robust records. To put our data in the context of the broader Pacific and Indo-Pacific regions, we compare our new data to published records.