@article{4775a645633b4ed98c82f71fb9a0bccc,
title = "The effects of time-varying observation errors on semi-empirical sea-level projections",
abstract = "Sea-level rise is a key driver of projected flooding risks. The design of strategies to manage these risks often hinges on projections that inform decision-makers about the surrounding uncertainties. Producing semi-empirical sea-level projections is difficult, for example, due to the complexity of the error structure of the observations, such as time-varying (heteroskedastic) observation errors and autocorrelation of the data-model residuals. This raises the question of how neglecting the error structure impacts hindcasts and projections. Here, we quantify this effect on sea-level projections and parameter distributions by using a simple semi-empirical sea-level model. Specifically, we compare three model-fitting methods: a frequentist bootstrap as well as a Bayesian inversion with and without considering heteroskedastic residuals. All methods produce comparable hindcasts, but the parametric distributions and projections differ considerably based on methodological choices. Our results show that the differences based on the methodological choices are enhanced in the upper tail projections. For example, the Bayesian inversion accounting for heteroskedasticity increases the sea-level anomaly with a 1% probability of being equaled or exceeded in the year 2050 by about 34% and about 40% in the year 2100 compared to a frequentist bootstrap. These results indicate that neglecting known properties of the observation errors and the data-model residuals can lead to low-biased sea-level projections.",
author = "Ruckert, {Kelsey L.} and Yawen Guan and Bakker, {Alexander M.R.} and Forest, {Chris E.} and Klaus Keller",
note = "Funding Information: We thank Stefan Rahmstorf for providing his global sea-level model (http://www.sciencemag.org/content/317/5846/1866.4/suppl/DC1 ). We gratefully acknowledge the comments from P. Applegate, the editor, and the reviewers on draft versions of this paper. This work was partially supported by the US Department of Energy, Office of Science, Biological and Environmental Research Program, Integrated Assessment Program, Grant No. DE-SC0005171 with additional support from the National Science Foundation (NSF) through the Network for Sustainable Climate Risk Management (SCRiM) under NSF cooperative agreement GEO-1240507, and the Penn State Center for Climate Risk Management. We also acknowledge the World Climate Research Programme's Working Group on Coupled Modelling and thank the climate modeling groups that participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5; http://cmip-pcmdi.llnl.gov/cmip5/ ), which supplied the climate model output used in this paper (listed in SI Table 3 ). The US Department of Energy's Program for Climate Model Diagnosis and Intercomparison, in partnership with the Global Organization for Earth System Science Portals, provides coordinating support for CMIP5. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s). Data and codes are available through the corresponding author and will be available at https://github.com/scrim-network/Ruckertetal_SLR2016 upon publication. Publisher Copyright: {\textcopyright} 2016, The Author(s).",
year = "2017",
month = feb,
day = "1",
doi = "10.1007/s10584-016-1858-z",
language = "English (US)",
volume = "140",
pages = "349--360",
journal = "Climatic Change",
issn = "0165-0009",
publisher = "Springer Netherlands",
number = "3-4",
}