scispace - formally typeset
R

Raphael Neukom

Researcher at Oeschger Centre for Climate Change Research

Publications -  53
Citations -  3935

Raphael Neukom is an academic researcher from Oeschger Centre for Climate Change Research. The author has contributed to research in topics: Climate change & Climate model. The author has an hindex of 25, co-authored 48 publications receiving 3109 citations. Previous affiliations of Raphael Neukom include University of Bern & University of Zurich.

Papers
More filters
Journal ArticleDOI

Continental-scale temperature variability during the past two millennia

Moinuddin Ahmed, +86 more
- 21 Apr 2013 - 
TL;DR: The authors reconstructed past temperatures for seven continental-scale regions during the past one to two millennia and found that the most coherent feature in nearly all of the regional temperature reconstructions is a long-term cooling trend, which ended late in the nineteenth century.
Journal ArticleDOI

A global multiproxy database for temperature reconstructions of the Common Era

Julien Emile-Geay, +108 more
- 11 Jul 2017 - 
TL;DR: A community-sourced database of temperature-sensitive proxy records from the PAGES2k initiative, suited to investigations of global and regional temperature variability over the Common Era, and is shared in the Linked Paleo Data (LiPD) format, including serializations in Matlab, R and Python.
Journal ArticleDOI

No evidence for globally coherent warm and cold periods over the preindustrial Common Era

TL;DR: No evidence for preindustrial globally coherent cold and warm epochs is found, indicating that preindustrial forcing was not sufficient to produce globally synchronous extreme temperatures at multidecadal and centennial timescales, and provides strong evidence that anthropogenic global warming is not only unparalleled in terms of absolute temperatures, but also unprecedented in spatial consistency within the context of the past 2,000 years.
Journal ArticleDOI

The influence of sampling design on tree-ring-based quantification of forest growth

TL;DR: It is found that commonly applied sampling designs can impart systematic biases of varying magnitude to any type of tree-ring-based investigations, independent of the total number of samples considered.