Sea Level Rise, after Church and White (2006)

Modeling done with a Bayesian Rauch-Tung-Striebel algorithm, estimating priors of variance for observations and state by using a stationary bootstrap for the series using Politis and Romano algorithm.

Updated, 30th September 2021

Zhu, Yingli, Gary T. Mitchum, Kara S. Doran, Don P. Chambers, and Xinfeng Liang. “Distinguishing between Regression Model Fits to Global Mean Sea Level Reconstructions.” Journal of Geophysical Research: Oceans: e2021JC017347.

The paper is behind a paywall. The link is to the corresponding preprint.

About ecoquant

See https://667-per-cm.net/about. Retired data scientist and statistician. Now working projects in quantitative ecology and, specifically, phenology of Bryophyta and technical methods for their study.
This entry was posted in Bayesian, carbon dioxide, civilization, climate, climate education, conservation, consumption, ecology, economics, education, efficiency, energy, energy reduction, engineering, environment, forecasting, geoengineering, geophysics, humanism, MCMC, meteorology, oceanography, optimization, physics, politics, rationality, reasonableness, science, statistics. Bookmark the permalink.

3 Responses to Sea Level Rise, after Church and White (2006)

  1. Pingback: HadCRUT4 and GISTEMP series filtered and estimated with simple RTS model | Hypergeometric

  2. Pingback: Warming is proportional to CUMULATIVE CARBON EMISSIONS, not emission intensity | Hypergeometric

  3. Pingback: illustrating particle filters and Bayesian fusion using successive location estimates on the unit circle | Hypergeometric

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