Provincializing the Anthropocene

2018 ◽  
pp. 1-18 ◽  
Author(s):  
Kathleen D. Morrison
Keyword(s):  
Empirical Support ◽  
Earth System ◽  
Carbon Budgets ◽  
Earth System Science ◽  
System Science ◽  
The World ◽  
Agricultural Transitions ◽  
Geological Period

The proposal that we have entered a new geological period, the Anthropocene, has gained currency both inside and outside of scientific circles. It is, therefore, worth understanding where this idea comes from and how the science behind it has developed. This article discusses both the nature of empirical support for the Anthropocene proposal as well as the analytical apparatus supporting and surrounding it. To a surprising extent, the notion of an Anthropocene represents an effort to expand homogenized European historical experiences, frameworks and chronologies onto the rest of the world. This focus has serious consequences, for example in the carbon budgets calculated to accompany early agricultural transitions. Not only that deciding whether or not a new geological period is called for is, at present, unnecessary, but even more that there is something very troubling about earth system science built out so fundamentally from the history and ecology of one small part of the world.

scholarly journals Earth System Science Frontiers: An Early Career Perspective

2017 ◽  
Vol 98 (6) ◽  
pp. 1120-1127 ◽  
Author(s):  
Florian Rauser ◽  
Mohammad Alqadi ◽  
Steve Arowolo ◽  
Noël Baker ◽  
Joel Bedard ◽  
...  
Keyword(s):  
High Performance ◽  
Interdisciplinary Approach ◽  
Early Career ◽  
Earth System ◽  
Earth System Science ◽  
System Science ◽  
Human Footprint ◽  
The World ◽  
Optimal Information ◽  
Prediction Systems

Abstract The exigencies of the global community toward Earth system science will increase in the future as the human population, economies, and the human footprint on the planet continue to grow. This growth, combined with intensifying urbanization, will inevitably exert increasing pressure on all ecosystem services. A unified interdisciplinary approach to Earth system science is required that can address this challenge, integrate technical demands and long-term visions, and reconcile user demands with scientific feasibility. Together with the research arms of the World Meteorological Organization, the Young Earth System Scientists community has gathered early-career scientists from around the world to initiate a discussion about frontiers of Earth system science. To provide optimal information for society, Earth system science has to provide a comprehensive understanding of the physical processes that drive the Earth system and anthropogenic influences. This understanding will be reflected in seamless prediction systems for environmental processes that are robust and instructive to local users on all scales. Such prediction systems require improved physical process understanding, more high-resolution global observations, and advanced modeling capability, as well as high-performance computing on unprecedented scales. At the same time, the robustness and usability of such prediction systems also depend on deepening our understanding of the entire Earth system and improved communication between end users and researchers. Earth system science is the fundamental baseline for understanding the Earth’s capacity to accommodate humanity, and it provides a means to have a rational discussion about the consequences and limits of anthropogenic influence on Earth. Without its progress, truly sustainable development will be impossible.

scholarly journals Chlorophyll a fluorescence illuminates a path connecting plant molecular biology to Earth-system science

Nature Plants ◽  
2021 ◽  
Author(s):  
Albert Porcar-Castell ◽  
Zbyněk Malenovský ◽  
Troy Magney ◽  
Shari Van Wittenberghe ◽  
Beatriz Fernández-Marín ◽  
...  
Keyword(s):  
Molecular Biology ◽  
Chlorophyll A ◽  
Chlorophyll A Fluorescence ◽  
Plant Molecular Biology ◽  
Earth System ◽  
Earth System Science ◽  
System Science

Earth system science and remote sensing

1985 ◽  
Vol 73 (6) ◽  
pp. 1118-1127 ◽  
Author(s):  
F.P. Bretherton
Keyword(s):  
Remote Sensing ◽  
Earth System ◽  
Earth System Science ◽  
System Science

scholarly journals Multivariate anomaly detection for Earth observations: a comparison of algorithms and feature extraction techniques

2017 ◽  
Vol 8 (3) ◽  
pp. 677-696 ◽  
Author(s):  
Milan Flach ◽  
Fabian Gans ◽  
Alexander Brenning ◽  
Joachim Denzler ◽  
Markus Reichstein ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Anomaly Detection ◽  
Data Streams ◽  
Multivariate Data ◽  
Earth System ◽  
Earth System Science ◽  
Sample Mean ◽  
System Science ◽  
Detection Algorithms ◽  
Earth Observations

Abstract. Today, many processes at the Earth's surface are constantly monitored by multiple data streams. These observations have become central to advancing our understanding of vegetation dynamics in response to climate or land use change. Another set of important applications is monitoring effects of extreme climatic events, other disturbances such as fires, or abrupt land transitions. One important methodological question is how to reliably detect anomalies in an automated and generic way within multivariate data streams, which typically vary seasonally and are interconnected across variables. Although many algorithms have been proposed for detecting anomalies in multivariate data, only a few have been investigated in the context of Earth system science applications. In this study, we systematically combine and compare feature extraction and anomaly detection algorithms for detecting anomalous events. Our aim is to identify suitable workflows for automatically detecting anomalous patterns in multivariate Earth system data streams. We rely on artificial data that mimic typical properties and anomalies in multivariate spatiotemporal Earth observations like sudden changes in basic characteristics of time series such as the sample mean, the variance, changes in the cycle amplitude, and trends. This artificial experiment is needed as there is no gold standard for the identification of anomalies in real Earth observations. Our results show that a well-chosen feature extraction step (e.g., subtracting seasonal cycles, or dimensionality reduction) is more important than the choice of a particular anomaly detection algorithm. Nevertheless, we identify three detection algorithms (k-nearest neighbors mean distance, kernel density estimation, a recurrence approach) and their combinations (ensembles) that outperform other multivariate approaches as well as univariate extreme-event detection methods. Our results therefore provide an effective workflow to automatically detect anomalies in Earth system science data.

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