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Global Climate Change01:50

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Throughout its ~4.5 billion year history, the Earth has experienced periods of warming and cooling. However, the current drastic increase in global temperatures is well outside of the Earth’s cyclic norms, and evidence for human-caused global climate change is compelling. Paleoclimatology, the study of ancient climate conditions, provides ample evidence for human-caused global climate change by comparing recent conditions with those in the past.
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The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
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Using Generative Art to Convey Past and Future Climate Transitions
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Big Data Challenges in Climate Science.

John L Schnase1, Tsengdar J Lee2, Chris A Mattmann3

  • 1NASA Goddard Space Flight Center, Greenbelt, MD 20771 USA.

IEEE Geoscience and Remote Sensing Magazine
|November 12, 2019
PubMed
Summary

Climate science research relies on high-quality data, facing big data challenges due to its global scale. This paper explores solutions for data publication, analytics, and interoperability within the Earth System Grid Federation (ESGF).

Keywords:
Ana4MIPsCAaaSCMIPCREATE-IPESGFESGF-CWTIPCCOCWObs4MIPsWPS

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Area of Science:

  • Climate Science
  • Data Science
  • Computational Science

Background:

  • Climate research generates massive, globally distributed datasets.
  • Activities like CMIP, Obs4MIPs, Ana4MIPs, CREATE-IP, and IPCC highlight the need for robust data infrastructure.
  • The scale and complexity of climate data necessitate advanced cyberinfrastructure.

Purpose of the Study:

  • To provide an overview of big data challenges in climate science.
  • To present technical solutions for advancing climate data management and analysis.
  • To discuss improvements in data publication, climate analytics as a service, and interoperability.

Main Methods:

  • Overview of current climate science data practices.
  • Identification of big data problems in climate research.
  • Discussion of technical solutions and cyberinfrastructure development.

Main Results:

  • Climate science faces significant big data challenges.
  • The Earth System Grid Federation (ESGF) is a key cyberinfrastructure.
  • Advancements are being made in data publication, climate analytics, and interoperability.

Conclusions:

  • Improved cyberinfrastructure is crucial for climate science.
  • Technical solutions are being developed to manage and analyze large climate datasets.
  • Enhanced data publication, analytics, and interoperability are essential for future research.