scholarly journals Earth system data cubes unravel global multivariate dynamics

2019 ◽  
Author(s):  
Miguel D. Mahecha ◽  
Fabian Gans ◽  
Gunnar Brandt ◽  
Rune Christiansen ◽  
Sarah E. Cornell ◽  
...  
Keyword(s):  
Data Streams ◽  
Multiple Time Scales ◽  
Earth System ◽  
Model Data ◽  
Data Cubes ◽  
Intensive Research ◽  
Data Intensive ◽  
The Earth ◽  
System Data ◽  
Multiple Variables

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.

scholarly journals Earth system data cubes unravel global multivariate dynamics

2020 ◽  
Vol 11 (1) ◽  
pp. 201-234 ◽  
Author(s):  
Miguel D. Mahecha ◽  
Fabian Gans ◽  
Gunnar Brandt ◽  
Rune Christiansen ◽  
Sarah E. Cornell ◽  
...  
Keyword(s):  
Data Integration ◽  
Data Streams ◽  
Scale Model ◽  
Earth System ◽  
Model Data ◽  
Data Cubes ◽  
Intensive Research ◽  
Data Intensive ◽  
The Earth ◽  
System Data

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.

Excitations of the Earth and Mars’ Variable Rotations by Surficial Fluids

2020 ◽  
Author(s):  
Yonghong Zhou ◽  
Xueqing Xu ◽  
Cancan Xu ◽  
Jianli Chen ◽  
David Salstein
Keyword(s):  
Angular Momentum ◽  
Multiple Time Scales ◽  
Global Dynamics ◽  
Multiple Time ◽  
Earth System ◽  
Polar Ice ◽  
Dynamic Interactions ◽  
Ice Caps ◽  
The Earth ◽  
Atmospheric Excitation

<p>The dynamic interactions that occur between the solid Earth and surficial fluids are related globally by conservation of angular momentum in the Earth system. Owing to this condition, the surficial fluids have shown to be main excitation sources of the Earth’s variable rotation on timescales between a few days and several years. Likewise, the Mars’ rotation changes due to variations of atmospheric circulation and surface pressure, and the variable Martian polar ice caps associated with the CO<sub>2</sub> sublimation/condensation effects. Investigations of the Earth and Mars’ rotations by surficial fluids may further our understandings of the Earth and planetary global dynamics. Here, we present our recent progresses on excitations of the Earth and Mars’ rotational variations on multiple time scales: (1) differences between the NCEP/NCAR and ECMWF atmospheric excitation functions of the Earth’s rotation, and (2) the Mars’ rotational variations and the dust cycles during the Mars Years 24-31.</p>

EASYDAB (Earth System Data Branding) for FAIR and Open Data

2021 ◽  
Author(s):  
Anette Ganske ◽  
Amandine Kaiser ◽  
Angelina Kraft ◽  
Daniel Heydebreck ◽  
Andrea Lammert ◽  
...  
Keyword(s):  
Open Data ◽  
Atmospheric Model ◽  
Research Data ◽  
Quality Data ◽  
Data Repository ◽  
Earth System ◽  
Model Data ◽  
Core Element ◽  
The Earth ◽  
High Maturity

<p>As in many scientific disciplines, there are a variety of activities in Earth system sciences that address the important aspects of good research data management. What has not been sufficiently investigated and dealt with so far is the easy discoverability and re-use of quality-checked data. This aspect is taken up by the EASYDAB label.</p><p>EASYDAB<sup>1</sup> is a currently developed branding for FAIR and open data from the Earth System Sciences. The branding can be adopted by institutions running a data repository which stores data from the Earth System Sciences. EASYDAB is always connected to a research data publication with DataCite DOIs. Data published under EASYDAB are characterized by a high maturity, extensive metadata information and compliance with a comprehensive discipline-specific standard. For these datasets, the EASYDAB logo is added to the landing page of the data repository. Thereby, repositories can indicate their efforts to publish data with high maturity.</p><p>The first standard made for EASYDAB is the ATMODAT standard<sup>2</sup>, which has been developed within the AtMoDat<sup>3</sup> project (Atmospheric Model Data). It incorporates concrete recommendations and requirements related to the maturity, publication and enhanced FAIRness of atmospheric model data. The requirements are for rich metadata with controlled vocabularies, structured landing pages, file formats (netCDF) and the structure within files. Human- and machine-readable landing pages are a core element of the ATMODAT standard and should hold and present discipline-specific metadata on simulation and variable level. </p><p>The ATMODAT standard includes checklists for the data producer and the data curator so that the compliance with the standard can easily be obtained by both sides. To facilitate automatic checking of the netCDF files headers, a checker program will also be provided and published with DOI. Moreover, a checker for the compliance with the requirements for the DOI Metadata will be developed and made openly available. </p><p>The integration of standards from other disciplines in the Earth System Sciences, such as oceanography, into EASYDAB is helpful and desirable to improve the re-use of reviewed, high-quality data. </p><p> <sup>1</sup>www.easydab.de</p><p><sup>2</sup>https://cera-www.dkrz.de/WDCC/ui/cerasearch/entry?acronym=atmodat_standard_en_v3_0</p><p><sup>3</sup>www.atmodat.de</p>

A Standard for the FAIR publication of Atmospheric Model Data developed by the AtMoDat Project

2021 ◽  
Author(s):  
Andrea Lammert ◽  
Anette Ganske ◽  
Amandine Kaiser ◽  
Angelina Kraft
Keyword(s):  
Climate Model ◽  
Urban Climate ◽  
Open Data ◽  
Atmospheric Model ◽  
Scientific Data ◽  
Earth System ◽  
Model Data ◽  
Climate Research ◽  
Future Data ◽  
System Data

<p>Due to the increasing amount of data produced in science, concepts for data reusability are of immense importance. One aspect is the publication of data in a way that ensures that it is findable, reusable, traceable and comparable (FAIR<sup>1</sup> principles). However, putting these principles into practice often causes significant difficulties for researchers. Therefore some repositories accept datasets described only with the minimum metadata required for DOI allocation. Unfortunately, this contains not  enough information to conform to the FAIR principles - many research data cannot be reused despite having a DOI. In contrast, other repositories aid the researchers by providing advice and strictly controlling the data and their metadata. To simplify the process of defining the needed amount of metadata and of controlling the data and metadata, the AtMoDat<sup>2</sup> (Atmospheric Model Data) project developed a detailed standard for the FAIR publication of atmospheric model data.</p><p>For this purpose we have developed a concept for the “ideal” description of atmospheric model data. A prerequisite for this is the data publication with a DataCite DOI. The ATMODAT standard<sup>3</sup> was developed to implement this concept. The standard defines the data format as NetCDF, mandatory metadata (for DOI, landing page and data header), and naming conventions used in climate research - the Climate and Forecast conventions (CF-conventions<sup>4</sup>). However, many variable names used in urban climate research, for example, are not part of the CF-conventions. For this, standard names have to be defined together with the community and the inclusion in the list of CF-conventions has to be requested. Furthermore we developed and published Python routines which allow data producers as well as repositories to check model output data against the standard. </p><p>The ATMODAT standard will first be applied by the project partners of the two participating universities (University of Hamburg and Leipzig). Here, climate model data are processed with a post-processor in preparation for publication. Subsequently, the files including the specified metadata for the DataCite metadata schema will be published by the World Data Center for Climate<sup>5</sup> (WDCC). Data fulfilling the AtMoDat standard will be marked at the landing page by a special EASYDAB<sup>6</sup> (Earth System Data Branding) logo. EASYDAB is a currently developed branding for FAIR and open data from the Earth System Sciences. This indicates to future data users that the dataset is a verified dataset that can be easily reused. The standardization of the data and the further steps are easily transferable to data from other disciplines.</p><p>1 Wilkinson, M., Dumontier, M., Aalbersberg, I. et al.: The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18 </p><p>2 https://www.atmodat.de/</p><p>3 https://cera-www.dkrz.de/WDCC/ui/cerasearch/entry?acronym=atmodat_standard_en_v3_0</p><p>4 https://cfconventions.org/</p><p>5 https://cera-www.dkrz.de/WDCC/ui/cerasearch/</p><p>6 https://www.easydab.de/</p><p> </p>

AtMoDat: Improving the reusability of ATmospheric MOdel DATa with DataCite DOIs paving the path towards FAIR data

2020 ◽  
Author(s):  
Daniel Neumann ◽  
Anette Ganske ◽  
Vivien Voss ◽  
Angelina Kraft ◽  
Heinke Höck ◽  
...  
Keyword(s):  
Quality Indicator ◽  
System Modeling ◽  
Atmospheric Model ◽  
Atmospheric Modeling ◽  
Research Data ◽  
Earth System ◽  
Model Data ◽  
Earth System Modeling ◽  
The Earth ◽  
Generic Quality

<p>The generation of high quality research data is expensive. The FAIR principles were established to foster the reuse of such data for the benefit of the scientific community and beyond. Publishing research data with metadata and DataCite DOIs in public repositories makes them findable and accessible (FA of FAIR). However, DOIs and basic metadata do not guarantee the data are actually reusable without discipline-specific knowledge: if data are saved in proprietary or undocumented file formats, if detailed discipline-specific metadata are missing and if quality information on the data and metadata are not provided. In this contribution, we present ongoing work in the AtMoDat project, -a consortium of atmospheric scientists and infrastructure providers, which aims on improving the reusability of atmospheric model data.<br>  <br>Consistent standards are necessary to simplify the reuse of research data. Although standardization of file structure and metadata is well established for some subdomains of the earth system modeling community – e.g. CMIP –, several other subdomains are lacking such standardization. Hence, scientists from the Universities of Hamburg and Leipzig and infrastructure operators cooperate in the AtMoDat project in order to advance standardization for model output files in specific subdomains of the atmospheric modeling community. Starting from the demanding CMIP6 standard, the aim is to establish an easy-to-use standard that is at least compliant with the Climate and Forecast (CF) conventions. In parallel, an existing netCDF file convention checker is extended to check for the new standards. This enhanced checker is designed to support the creation of compliant files and thus lower the hurdle for data producers to comply with the new standard. The transfer of this approach to further sub-disciplines of the earth system modeling community will be supported by a best-practice guide and other documentation. A showcase of a standard for the urban atmospheric modeling community will be presented in this session. The standard is based on CF Conventions and adapts several global attributes and controlled vocabularies from the well-established CMIP6 standard.<br>  <br>Additionally, the AtMoDat project aims on introducing a generic quality indicator into the DataCite metadata schema to foster further reuse of data. This quality indicator should require a discipline-specific implementation of a quality standard linked to the indicator. We will present the concept of the generic quality indicator in general and in the context of urban atmospheric modeling data. </p>

scholarly journals A Regional Earth System Data Lab for Understanding Ecosystem Dynamics: An Example from Tropical South America

2021 ◽  
Vol 9 ◽  
Author(s):  
Lina M. Estupinan-Suarez ◽  
Fabian Gans ◽  
Alexander Brenning ◽  
Victor H. Gutierrez-Velez ◽  
Maria C. Londono ◽  
...  
Keyword(s):  
South America ◽  
Primary Production ◽  
Data Streams ◽  
Ecosystem Functioning ◽  
Earth System ◽  
Tropical Ecosystems ◽  
Study Region ◽  
Wide Range ◽  
System Data ◽  
Multiple Data Sets

Tropical ecosystems experience particularly fast transformations largely as a consequence of land use and climate change. Consequences for ecosystem functioning and services are hard to predict and require analyzing multiple data sets simultaneously. Today, we are equipped with a wide range of spatio-temporal observation-based data streams that monitor the rapid transformations of tropical ecosystems in terms of state variables (e.g., biomass, leaf area, soil moisture) but also in terms of ecosystem processes (e.g., gross primary production, evapotranspiration, runoff). However, the underexplored joint potential of such data streams, combined with deficient access to data and processing, constrain our understanding of ecosystem functioning, despite the importance of tropical ecosystems in the regional-to-global carbon and water cycling. Our objectives are: 1. To facilitate access to regional “Analysis Ready Data Cubes” and enable efficient processing 2. To contribute to the understanding of ecosystem functioning and atmosphere-biosphere interactions. 3. To get a dynamic perspective of environmental conditions for biodiversity. To achieve our objectives, we developed a regional variant of an “Earth System Data Lab” (RegESDL) tailored to address the challenges of northern South America. The study region extensively covers natural ecosystems such as rainforest and savannas, and includes strong topographic gradients (0–6,500 masl). Currently, environmental threats such as deforestation and ecosystem degradation continue to increase. In this contribution, we show the value of the approach for characterizing ecosystem functioning through the efficient implementation of time series and dimensionality reduction analysis at pixel level. Specifically, we present an analysis of seasonality as it is manifested in multiple indicators of ecosystem primary production. We demonstrate that the RegESDL has the ability to underscore contrasting patterns of ecosystem seasonality and therefore has the potential to contribute to the characterization of ecosystem function. These results illustrate the potential of the RegESDL to explore complex land-surface processes and the need for further exploration. The paper concludes with some suggestions for developing future big-data infrastructures and its applications in the tropics.

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