Accurate dating of stalagmites from low seasonal contrast tropical Pacific climate using Sr 2D maps, fabrics and annual hydrological cycles
Abstract Tropical Pacific stalagmites are commonly affected by dating uncertainties because of their low U concentration and/or elevated initial 230Th content. This poses problems in establishing reliable trends and periodicities for droughts and pluvial episodes in a region vulnerable to climate ch...
Guardado en:
Autores principales: | , , , , , , , |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/aef2194afd9541e38a13ad5ffcd1a266 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Sumario: | Abstract Tropical Pacific stalagmites are commonly affected by dating uncertainties because of their low U concentration and/or elevated initial 230Th content. This poses problems in establishing reliable trends and periodicities for droughts and pluvial episodes in a region vulnerable to climate change. Here we constrain the chronology of a Cook Islands stalagmite using synchrotron µXRF two-dimensional mapping of Sr concentrations coupled with growth laminae optical imaging constrained by in situ monitoring. Unidimensional LA-ICP-MS-generated Mg, Sr, Ba and Na variability series were anchored to the 2D Sr and optical maps. The annual hydrological significance of Mg, Sr, Ba and Na was tested by principal component analysis, which revealed that Mg and Na are related to dry-season, wind-transported marine aerosols, similar to the host-rock derived Sr and Ba signatures. Trace element annual banding was then used to generate a calendar-year master chronology with a dating uncertainty maximum of ± 15 years over 336 years. Our approach demonstrates that accurate chronologies and coupled hydroclimate proxies can be obtained from speleothems formed in tropical settings where low seasonality and problematic U–Th dating would discourage the use of high-resolution climate proxies datasets. |
---|