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Abstract The Miocene (23.03–5.33 Ma) is recognized as a period with close to modern‐day paleogeography, yet a much warmer climate. With large uncertainties in future hydroclimate projections, Miocene conditions illustrate a potential future analog for the Earth system. A recent opportunistic Miocene Model Intercomparison Project 1 (MioMIP1) focused on synthesizing published Miocene climate simulations and comparing them with available temperature reconstructions. Here, we build on this effort by analyzing the hydrological cycle response to Miocene forcings across early‐to‐middle (E2MMIO; 20.03–11.6 Ma) and middle‐to‐late Miocene (M2LMIO; 11.5–5.33 Ma) simulations with CO2concentrations ranging from 200 to 850 ppm and providing a model‐data comparison against available precipitation reconstructions. We find global precipitation increases by ∼2.1 and 2.3% per degree of warming for E2MMIO and M2LMIO simulations, respectively. Models generally agree on a wetter than modern‐day tropics; mid and high‐latitude, however, do not agree on the sign of subtropical precipitation changes with warming. Global monsoon analysis suggests most monsoon regions, except the North American Monsoon, experience higher precipitation rates under warmer conditions. Model‐data comparison shows that mean annual precipitation is underestimated by the models regardless of CO2concentration, particularly in the mid‐ to high‐latitudes. This suggests that the models may not be (a) resolving key processes driving the hydrological cycle response to Miocene boundary conditions and/or (b) other boundary conditions or processes not considered here are critical to reproducing Miocene hydroclimate. This study highlights the challenges in modeling and reconstructing the Miocene hydrological cycle and serves as a baseline for future coordinated MioMIP efforts.
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A dependent multimodel approach to climate prediction with Gaussian processes
Abstract Simulations of future climate contain variability arising from a number of sources, including internal stochasticity and external forcings. However, to the best of our abilities climate models and the true observed climate depend on the same underlying physical processes. In this paper, we simultaneously study the outputs of multiple climate simulation models and observed data, and we seek to leverage their mean structure as well as interdependencies that may reflect the climate’s response to shared forcings. Bayesian modeling provides a fruitful ground for the nuanced combination of multiple climate simulations. We introduce one such approach whereby a Gaussian process is used to represent a mean function common to all simulated and observed climates. Dependent random effects encode possible information contained within and between the plurality of climate model outputs and observed climate data. We propose an empirical Bayes approach to analyze such models in a computationally efficient way. This methodology is amenable to the CMIP6 model ensemble, and we demonstrate its efficacy at forecasting global average near-surface air temperature. Results suggest that this model and the extensions it engenders may provide value to climate prediction and uncertainty quantification.
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- PAR ID:
- 10414747
- Date Published:
- Journal Name:
- Environmental Data Science
- Volume:
- 1
- ISSN:
- 2634-4602
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1017/eds.2022.24
