Quantifying age and model uncertainties in palaeoclimate data and dynamical climate models with a joint inferential analysis
Carson, J.; Crucifix, M.; Preston, S.P.; Wilkinson, R.D.
The study of palaeoclimates relies on information sampled in natural archives such as deep sea cores. Scientific investigations often use such information in multi- stage analyses, typically with an age model being fitted to a core to convert depths into ages at stage one. These age estimates are then used as inputs to develop, calibrate, or select climate models in a second stage of analysis. Here we show that such multi-stage approaches can lead to misleading conclusions, and develop a joint inferential approach for climate reconstruction, model calibration, and age estimation. As an illustration, we investigate the glacial-interglacial cycle, fitting both an age model and dynamical climate model to two benthic sediment cores spanning the past 780 kyr. To show the danger of a multi-stage analysis we sample ages from the posterior distribution, then perform model selection conditional on the sampled age estimates, mimicking standard practice. Doing so repeatedly for different samples leads to model selection conclusions that are substantially different from each other, and from the joint inferential analysis. We conclude that multi- stage analyses are insufficient when dealing with uncertainty, and that to draw sound conclusions the full joint inferential analysis should be performed.
|Journal Article Type||Article|
|Publication Date||Apr 3, 2019|
|Journal||Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences|
|Publisher||Royal Society, The|
|Peer Reviewed||Peer Reviewed|
|APA6 Citation||Carson, J., Crucifix, M., Preston, S., & Wilkinson, R. (2019). Quantifying age and model uncertainties in palaeoclimate data and dynamical climate models with a joint inferential analysis. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 475(2224), doi:10.1098/rspa.2018.0854|
Quantifying Age and Model Uncertainties
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