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Type: Text Ă— Subject: academic research Ă—

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    Earth system data cubes unravel global multivariate dynamics
    (Göttingen : Copernicus Publ., 2020) Mahecha, Miguel D.; Gans, Fabian; Brandt, Gunnar; Christiansen, Rune; Cornell, Sarah E.; Fomferra, Normann; Kraemer, Guido; Peters, Jonas; Bodesheim, Paul; Camps-Valls, Gustau; Donges, Jonathan F.; Dorigo, Wouter; Estupinan-Suarez, Lina M.; Gutierrez-Velez, Victor H.; Gutwin, Martin; Jung, Martin; Londoño, Maria C.; Miralles, Diego G.; Papastefanou, Phillip; Reichstein, Markus
    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. © 2020 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
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    Measuring Success: Improving Assessments of Aggregate Greenhouse Gas Emissions Reduction Goals
    (Chichester : John Wiley and Sons Inc, 2018) Jeffery, M.L.; GĂĽtschow, J.; Rocha, M.R.; Gieseke, R.
    Long-term success of the Paris Agreement will depend on the effectiveness of the instruments that it sets in place. Key among these are the nationally determined contributions (NDCs), which elaborate country-specific goals for mitigating and adapting to climate change. One role of the academic community and civil society in supporting the Paris Agreement is to assess the consistency between the near-term action under NDCs and the agreement's long-term goals, thereby providing insight into the chances of long-term success. Here we assess the strengths and weaknesses of current methods to estimate the effectiveness of the mitigation component of NDCs and identify the scientific and political advances that could be made to improve confidence in evaluating NDCs against the long-term goals. Specifically, we highlight (1) the influence of post-2030 assumptions on estimated 21st century warming, (2) uncertainties arising from the lack of published integrated assessment modeling scenarios with long-term, moderate effort reflecting a continuation of the current political situation, and (3) challenges in using a carbon budget approach. We further identify aspects that can be improved in the coming years: clearer communication regarding the meaning, likelihood, and timeframe of NDC consistent warming estimates; additional modeling of long-term, moderate action scenarios; and the identification of metrics for assessing progress that are not based solely on emissions, such as infrastructure investment, energy demand, or installed power capacity.