Efficient MCMC for Climate Model Parameter Estimation: Parallel Adaptive Chains and Early Rejection

By Antti Solonen, P. Ollinaho, Marko Laine, Heikki Haario, Johanna Tamminen, H. Järvinen. 2012

95 citations recorded by the source corpus.

. The emergence of Markov chain Monte Carlo (MCMC) methods has opened a way for Bayesian analysis of complex models. Running MCMC samplers typically requires thousands of model evaluations, which can exceed available computer resources when this evaluation is computationally intensive. We will discuss two generally applicable techniques to improve the eﬃciency of MCMC. First, we consider a parallel version of the adaptive MCMC algorithm of Haario et al. (2001), implementing the idea of inter-chain adaptation introduced by Craiu et al. (2009). Second, we present an early rejection (ER) approach, where model simulation is stopped as soon as one can conclude that the proposed parameter value will be rejected by the MCMC algorithm. This work is motivated by practical needs in estimating parameters of climate and Earth system models. These computationally intensive models involve non-linear expressions of the geophysical and biogeochemical processes of the Earth system. Modeling of these processes, especially those operating in scales smaller than the model grid, involves a number of speciﬁed parameters, or ‘tunables’. MCMC methods are applicable for estimation of these parameters, but they are computationally very demanding. Eﬃcient MCMC variants are thus needed to obtain reliable results in reasonable time. Here we evaluate the computational gains attainable through parallel adaptive MCMC and Early Rejection using both simple examples and a realistic climate model.

Subfields: Global and Planetary Change, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b5ff14f9394b285f5ad63fbcb4d620806e92c173

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Citations in the corpus

PASTIS: Bayesian extrasolar planet validation. I. General framework, models, and performance (2014, background): “…a number 6 We note that this is a typical problem in MCMC algorithms which have not been solved yet and is the subject of current research (e.g. Solonen et al. 2012). of chains (usually more than 20, depending on the dimensionality of the problem) are started at different points in parameter…”
Bayesian computation: a summary of the current state, and samples backwards and forwards (2015, methodology): “While the approach has been successfully applied in some very challenging practical problems (Solonen et al. 2012; Richardson et al. 2010; Griffin et al. 2014), there are examples of seemingly reasonable adaptive algorithms that fail to converge to the intended target distribution (Bai et al.”
Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High‐Resolution Simulations (2017, methodology): “, 2013) or by minimizing deviations between simulated and observed longer-term aggregates of climate statistics, such as global-mean TOA radiative fluxes accumulated over seasons or years (e.g., Jackson et al., 2008; Jarvinen et al., 2010; Neelin et al., 2010; Solonen et al., 2012; Tett et al., 2013”