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Are Olsen

Researcher at Geophysical Institute, University of Bergen

Publications -  167
Citations -  15623

Are Olsen is an academic researcher from Geophysical Institute, University of Bergen. The author has contributed to research in topics: Climate change & Sea surface temperature. The author has an hindex of 45, co-authored 149 publications receiving 10948 citations. Previous affiliations of Are Olsen include Bjerknes Centre for Climate Research & University of Gothenburg.

Papers
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Global Carbon Budget 2020

Pierre Friedlingstein, +95 more
TL;DR: In this paper, the authors describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties, including emissions from land use and land-use change data and bookkeeping models.
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Climatological mean and decadal change in surface ocean pCO2, and net sea–air CO2 flux over the global oceans

TL;DR: In this article, a global mean distribution for surface water pCO2 over the global oceans in non-El Nino conditions has been constructed with spatial resolution of 4° (latitude) × 5° (longitude) for a reference year 2000 based upon about 3 million measurements of surface water PCO2 obtained from 1970 to 2007.
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Global Carbon Budget 2018

Corinne Le Quéré, +84 more
TL;DR: In this article, the authors describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties, including emissions from land use and land-use change data and bookkeeping models.
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Global Carbon Budget 2016

Corinne Le Quéré, +71 more
TL;DR: In this article, the authors quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community.
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Global Carbon Budget 2015

C. Le Quéré, +76 more
TL;DR: In this article, the authors presented a methodology to quantify all major components of the global carbon budget, including their uncertainties, based on the combination of a range of data, algorithms, statistics, and model estimates and their interpretation by a broad scientific community.