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Temperature-driven global sea-level variability in the Common Era

TLDR
This is the first, to the authors' knowledge, estimate of global sea-level (GSL) change over the last ∼3,000 years that is based upon statistical synthesis of a global database of regional sea- level reconstructions, and indicates that, without global warming, GSL in the 20th century very likely would have risen by between −3 cm and +7 cm, rather than the ∼14 cm observed.
Abstract
We assess the relationship between temperature and global sea-level (GSL) variability over the Common Era through a statistical metaanalysis of proxy relative sea-level reconstructions and tide-gauge data. GSL rose at 0.1 ± 0.1 mm/y (2σ) over 0-700 CE. A GSL fall of 0.2 ± 0.2 mm/y over 1000-1400 CE is associated with ∼ 0.2 °C global mean cooling. A significant GSL acceleration began in the 19th century and yielded a 20th century rise that is extremely likely (probability [Formula: see text]) faster than during any of the previous 27 centuries. A semiempirical model calibrated against the GSL reconstruction indicates that, in the absence of anthropogenic climate change, it is extremely likely ([Formula: see text]) that 20th century GSL would have risen by less than 51% of the observed [Formula: see text] cm. The new semiempirical model largely reconciles previous differences between semiempirical 21st century GSL projections and the process model-based projections summarized in the Intergovernmental Panel on Climate Change's Fifth Assessment Report.

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Impacts of 1.5°C Global Warming on Natural and Human Systems

Ove Hoegh-Guldberg, +86 more
TL;DR: In this article, the authors present a survey of women's sportswriters in South Africa and Ivory Coast, including: Marco Bindi (Italy), Sally Brown (UK), Ines Camilloni (Argentina), Arona Diedhiou (Ivory Coast/Senegal), Riyanti Djalante (Japan/Indonesia), Kristie L. Ebi (USA), Francois Engelbrecht (South Africa), Joel Guiot (France), Yasuaki Hijioka (Japan), Shagun Mehrotra (USA/India), Ant
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Assessing the impacts of 1.5 °C global warming - simulation protocol of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2b)

Katja Frieler, +60 more
TL;DR: In Paris, France, December 2015, the Conference of the Parties (COP) to the United Nations Framework Concerning on Climate Change (UNFCCC) invited the Inter- governmental Panel on Climate change (IPCC).

Global and Regional Sea Level Rise Scenarios for the United States

TL;DR: The Sea Level Rise and Coastal Flood Hazard Scenarios and Tools Interagency Task Force, jointly convened by the U.S. Global Change Research Program (USGCRP) and the National Ocean Council (NOC), began its work in August 2015.
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Reassessment of 20th century global mean sea level rise

TL;DR: A 20th-century GMSL reconstruction computed using an area-weighting technique for averaging tide gauge records that both incorporates up-to-date observations of vertical land motion (VLM) and corrections for local geoid changes resulting from ice melting and terrestrial freshwater storage and allows for the identification of possible differences compared with earlier attempts is presented.
References
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Climate change 2007: the physical science basis

TL;DR: The first volume of the IPCC's Fourth Assessment Report as mentioned in this paper was published in 2007 and covers several topics including the extensive range of observations now available for the atmosphere and surface, changes in sea level, assesses the paleoclimatic perspective, climate change causes both natural and anthropogenic, and climate models for projections of global climate.
Journal ArticleDOI

Monte Carlo Sampling Methods Using Markov Chains and Their Applications

TL;DR: A generalization of the sampling method introduced by Metropolis et al. as mentioned in this paper is presented along with an exposition of the relevant theory, techniques of application and methods and difficulties of assessing the error in Monte Carlo estimates.
Book

Gaussian Processes for Machine Learning

TL;DR: The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics, and deals with the supervised learning problem for both regression and classification.
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Climate change 2007: the physical science basis

TL;DR: In this article, Chen et al. present a survey of the state of the art in the field of computer vision and artificial intelligence, including a discussion of the role of the human brain in computer vision.
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