scholarly journals Review of ”Earth system data cubes unravel global multivariate dynamics” by Mahecha et al. (esd-2019-62)

2019 ◽  
Author(s):  
Anonymous
Keyword(s):  
2019 ◽  
Author(s):  
Miguel D. Mahecha ◽  
Fabian Gans ◽  
Gunnar Brandt ◽  
Rune Christiansen ◽  
Sarah E. Cornell ◽  
...  

Abstract. Understanding Earth system dynamics in the light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing inter-disciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model-data. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple time-scales; and (3) data-model integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. Latest developments in machine learning, causal inference, and model data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries.


2020 ◽  
Vol 11 (1) ◽  
pp. 201-234 ◽  
Author(s):  
Miguel D. Mahecha ◽  
Fabian Gans ◽  
Gunnar Brandt ◽  
Rune Christiansen ◽  
Sarah E. Cornell ◽  
...  

Abstract. Understanding Earth system dynamics in light of ongoing human intervention and dependency remains a major scientific challenge. The unprecedented availability of data streams describing different facets of the Earth now offers fundamentally new avenues to address this quest. However, several practical hurdles, especially the lack of data interoperability, limit the joint potential of these data streams. Today, many initiatives within and beyond the Earth system sciences are exploring new approaches to overcome these hurdles and meet the growing interdisciplinary need for data-intensive research; using data cubes is one promising avenue. Here, we introduce the concept of Earth system data cubes and how to operate on them in a formal way. The idea is that treating multiple data dimensions, such as spatial, temporal, variable, frequency, and other grids alike, allows effective application of user-defined functions to co-interpret Earth observations and/or model–data integration. An implementation of this concept combines analysis-ready data cubes with a suitable analytic interface. In three case studies, we demonstrate how the concept and its implementation facilitate the execution of complex workflows for research across multiple variables, and spatial and temporal scales: (1) summary statistics for ecosystem and climate dynamics; (2) intrinsic dimensionality analysis on multiple timescales; and (3) model–data integration. We discuss the emerging perspectives for investigating global interacting and coupled phenomena in observed or simulated data. In particular, we see many emerging perspectives of this approach for interpreting large-scale model ensembles. The latest developments in machine learning, causal inference, and model–data integration can be seamlessly implemented in the proposed framework, supporting rapid progress in data-intensive research across disciplinary boundaries.


2008 ◽  
Vol 1 (1) ◽  
pp. 17-51 ◽  
Author(s):  
G. Shaffer ◽  
S. Malskær Olsen ◽  
J. O. Pepke Pedersen

Abstract. A new, low-order Earth System Model is described, calibrated and tested against Earth system data. The model features modules for the atmosphere, ocean, ocean sediment, land biosphere and lithosphere and has been designed to simulate global change on time scales of years to millions of years. The atmosphere module considers radiation balance, meridional transport of heat and water vapor between low-mid latitude and high latitude zones, heat and gas exchange with the ocean and sea ice and snow cover. Gases considered are carbon dioxide and methane for all three carbon isotopes, nitrous oxide and oxygen. The ocean module has 100 m vertical resolution, carbonate chemistry and prescribed circulation and mixing. Ocean biogeochemical tracers are phosphate, dissolved oxygen, dissolved inorganic carbon for all three carbon isotopes and alkalinity. Biogenic production of particulate organic matter in the ocean surface layer depends on phosphate availability but with lower efficiency in the high latitude zone, as determined by model fit to ocean data. The calcite to organic carbon rain ratio depends on surface layer temperature. The semi-analytical, ocean sediment module considers calcium carbonate dissolution and oxic and anoxic organic matter remineralisation. The sediment is composed of calcite, non-calcite mineral and reactive organic matter. Sediment porosity profiles are related to sediment composition and a bioturbated layer of 0.1 m thickness is assumed. A sediment segment is ascribed to each ocean layer and segment area stems from observed ocean depth distributions. Sediment burial is calculated from sedimentation velocities at the base of the bioturbated layer. Bioturbation rates and oxic and anoxic remineralisation rates depend on organic carbon rain rates and dissolved oxygen concentrations. The land biosphere module considers leaves, wood, litter and soil. Net primary production depends on atmospheric carbon dioxide concentration and remineralization rates in the litter and soil are related to mean atmospheric temperatures. Methane production is a small fraction of the soil remineralization. The lithosphere module considers outgassing, weathering of carbonate and silicate rocks and weathering of rocks containing old organic carbon and phosphorus. Weathering rates are related to mean atmospheric temperatures. A pre-industrial, steady state calibration to Earth system data is carried out. Ocean observations of temperature, carbon 14, phosphate, dissolved oxygen, dissolved inorganic carbon and alkalinity constrain air-sea exchange and ocean circulation, mixing and biogeochemical parameters. Observed calcite and organic carbon distributions and inventories in the ocean sediment help constrain sediment module parameters. Carbon isotopic data and carbonate vs. silicate weathering fractions are used to estimate initial lithosphere outgassing and rock weathering rates. Model performance is tested by simulating atmospheric greenhouse gas increases, global warming and model tracer evolution for the period 1765 to 2000, as forced by prescribed anthropogenic greenhouse gas inputs and other anthropogenic and natural forcing. Long term, transient model behavior is studied with a set of 100 000 year simulations, forced by a slow, 5000 Gt C input of CO2 to the atmosphere, and with a 1.5 million year simulation, forced by a doubling of lithosphere CO2 outgassing.


2017 ◽  
Vol 9 (2) ◽  
pp. 765-777 ◽  
Author(s):  
George A. Riggs ◽  
Dorothy K. Hall ◽  
Miguel O. Román

Abstract. Knowledge of the distribution, extent, duration and timing of snowmelt is critical for characterizing the Earth's climate system and its changes. As a result, snow cover is one of the Global Climate Observing System (GCOS) essential climate variables (ECVs). Consistent, long-term datasets of snow cover are needed to study interannual variability and snow climatology. The NASA snow-cover datasets generated from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra and Aqua spacecraft and the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) are NASA Earth System Data Records (ESDR). The objective of the snow-cover detection algorithms is to optimize the accuracy of mapping snow-cover extent (SCE) and to minimize snow-cover detection errors of omission and commission using automated, globally applied algorithms to produce SCE data products. Advancements in snow-cover mapping have been made with each of the four major reprocessings of the MODIS data record, which extends from 2000 to the present. MODIS Collection 6 (C6; https://nsidc.org/data/modis/data_summaries) and VIIRS Collection 1 (C1; https://doi.org/10.5067/VIIRS/VNP10.001) represent the state-of-the-art global snow-cover mapping algorithms and products for NASA Earth science. There were many revisions made in the C6 algorithms which improved snow-cover detection accuracy and information content of the data products. These improvements have also been incorporated into the NASA VIIRS snow-cover algorithms for C1. Both information content and usability were improved by including the Normalized Snow Difference Index (NDSI) and a quality assurance (QA) data array of algorithm processing flags in the data product, along with the SCE map. The increased data content allows flexibility in using the datasets for specific regions and end-user applications. Though there are important differences between the MODIS and VIIRS instruments (e.g., the VIIRS 375 m native resolution compared to MODIS 500 m), the snow detection algorithms and data products are designed to be as similar as possible so that the 16+ year MODIS ESDR of global SCE can be extended into the future with the S-NPP VIIRS snow products and with products from future Joint Polar Satellite System (JPSS) platforms. These NASA datasets are archived and accessible through the NASA Distributed Active Archive Center at the National Snow and Ice Data Center in Boulder, Colorado.


2021 ◽  
Author(s):  
Scarlet Stadtler ◽  
Julia Kowalski ◽  
Markus Abel ◽  
Ribana Roscher ◽  
Susanne Crewell ◽  
...  

<p>Artificial intelligence (AI) methods currently experience rapid development and are also used more and more frequently in environmental and Earth system sciences. To date however, this is often done in the context of isolated rather than systematic solutions. In particular, for researchers there is often a discrepancy between the requirements of a solid and technically sound environmental data analysis and the availability of modern AI methods such as deep learning. Their systematic use is not yet established in environmental and Earth system sciences.</p><p>The recently started KI:STE project bridges this gap with a dedicated strategy that combines both, the development of AI applications and a strong training and network concept, thereby covering  different relevant aspects of environmental and Earth system research. It creates the technical prerequisites to make high-performance AI applications on environmental data portable for future users and to establish environmental AI as a key technology. </p><p>Specifically, within KI:STE an AI-platform is envisioned which unifies machine learning (ML) workflows designed to study five core Earth system topics: cloud variability, hydrology, earth surface processes, vegetation health and air quality. All of them are strongly coupled and will profit from ML, e.g. to extend locally available information into global maps, or the track the interplay of spatio-temporal variability on different scales along process cascades. Besides being already connected across disciplines in the classical sense, KI:STE aims to furthermore bridge between these different topics by jointly addressing cutting edge ML research questions beyond pure algorithmic approaches. In particular, we will put emphasize on an explainable AI approach, which itself is a yet to be explored highly relevant topic within the Earth system sciences. It has the potential to connect the interdisciplinary work on yet another level.</p><p>KI:STE will also launch an e-learning platform in order to support the usage of the AI-platform as well as to communicate the knowledge to adequately use ML techniques within the different Earth system science domains.</p>


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