A new Middle Miocene Climatic Optimum (MMCO) global precipitation reconstruction using Bayesian and probability reconstruction techniques

GIBSON E. MARTHA; O'KEEFE JENNIFER M. K.; NUÑEZ OTAÑO N.B.; WARNY SOPHIE; POUND MATTHEW

Congreso; AGU Fall Meeting, New Orleans, LA & Online everywhere; 2021

American geophysical Union

The Middle Miocene Climatic Optimum (MMCO, 16.9?14.7 Ma) was the warmest interval of the Neogene and is a potential analogue for IPCC RCP 4.5?6.0 (intermediate scenarios). Global mean annual temperatures are estimated to have been 4?6°C warmer and pCO2 was slightly higher than present day (~500ppm), with an asymmetric latitudinal temperature gradient, tropical temperatures in the mid-latitudes and reduced polar ice sheets. However, our understanding of Middle Miocene terrestrial climate at broad spatial scales is still developing as there are difficulties reconciling proxy-based climate reconstructions with climate models. One of the current views of MMCO terrestrial climate is based on the Co-existence Approach which has given a broad view of global temperature and precipitation across the Neogene. However, the Co-existence Approach reconstructs an equal likelihood range for climate parameters that can be wide and therefore hampers our understanding the water cycle during the MMCO. These reconstructed ranges also hinder quantitative data-model comparisons (proxy-ranges vs. climate model uncertainty). Here we apply two climate reconstruction techniques to produce a new global precipitation reconstruction for the MMCO and compare them to the results of the Co-existence Approach. Quantitative estimates of mean annual precipitation and seasonality are derived from palaeobotanical records from over 150 globally distributed palaeobotanical sites. We use the probability-based models (CREST) Climate Reconstruction SofTware and (CRACLE) Climate Reconstruction Analysis using Coexistence Likelihood Estimation, that use Bayesian and likelihood estimation probability respectively to generate 2σ confidence intervals. This is the first application of these models at a global scale and their statistically generated terrestrial climate reconstruction for the MMCO will aid in the evaluation of climate reconstruction models in deep time, enabling an understanding of hydrology in the globally warmer conditions of the MMCO. This new reconstruction will also contextualise botanical and fungal biodiversity during the MMCO.