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Luke Gregor

Researcher at ETH Zurich

Publications -  32
Citations -  3119

Luke Gregor is an academic researcher from ETH Zurich. The author has contributed to research in topics: Environmental science & Geology. The author has an hindex of 10, co-authored 20 publications receiving 727 citations. Previous affiliations of Luke Gregor include University of Cape Town & Council of Scientific and Industrial Research.

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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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A multi-decade record of high-quality fCO2 data in version 3 of the Surface Ocean CO2 Atlas (SOCAT)

Dorothee C. E. Bakker, +103 more
TL;DR: This ESSD "living data" publication documents the methods and data sets used for the assembly of this new version of the SOCAT data collection and compares these with those used for earlier versions of the data collection.
Journal ArticleDOI

Global Carbon Budget 2021

Pierre Friedlingstein, +63 more
TL;DR: Friedlingstein et al. as mentioned in this paper presented and synthesized datasets and methodology to quantify the five major components of the global carbon budget and their uncertainties, including fossil CO2 emissions, land use and land-use change data and bookkeeping models.
Journal ArticleDOI

Global Carbon Budget 2022

Pierre Friedlingstein, +105 more
TL;DR: Friedlingstein et al. as mentioned in this paper presented and synthesized data sets and methodologies to quantify the five major components of the global carbon budget and their uncertainties, including fossil CO2 emissions, land use and land-use change data and bookkeeping models.
Journal ArticleDOI

A comparative assessment of the uncertainties of global surface ocean CO 2 estimates using a machine-learning ensemble (CSIR-ML6 version 2019a) – have we hit the wall?

TL;DR: In this paper, an ensemble average of six machine learning models (CSIR-ML6 version 2019a, Council for Scientific and Industrial Research -Machine Learning ensemble with Six members) is proposed to fill the gaps in sparse surface ocean CO2 measurements.