DOE Call for White Papers Results in 155 Submissions

 
Published: 6 May 2021

White papers will inform the design of workshops for a new Artificial Intelligence for Earth System Predictability initiative

This schematic details the MODEX approach to scientific discovery (outer ring) and various DOE data, models, and analysis capabilities that should be linked as community resources based on open-science principles (inner sphere).
This schematic details the MODEX approach to scientific discovery (outer ring) and various DOE data, models, and analysis capabilities that should be linked as community resources based on open-science principles (inner sphere).

At the close of 2020, the U.S. Department of Energy’s (DOE) Earth and Environmental Systems Sciences Division (EESSD) issued a call for white papers for the Artificial Intelligence for Earth System Predictability (AI4ESP) initiative.

The papers would address the data-model integration grand challenge presented in the EESSD Strategic Plan. This plan describes five grand challenges that frame the division’s investments through 2023:

  • integrated water cycle
  • biogeochemistry
  • high-latitude science
  • drivers and responses
  • data-model integration.

Researchers were asked to emphasize quantifying and improving earth system predictability, particularly related to the integrative water cycle and associated water cycle extremes.

According to the DOE call, the white papers were intended to inform the design of three sequential workshops (conducted in 2021–2022) focused on answering the question of how DOE can directly leverage AI to engineer a substantial (paradigm-changing) improvement in earth system predictability.

The EESSD scientific communities responded with 155 white papers. Each may be reviewed on the AI4ESP website. The collection includes papers connected to data from the Atmospheric Radiation Measurement (ARM) user facility, as well as priority research areas for the Atmospheric System Research (ASR) program.

The AI4ESP initiative is a collaboration between DOE management and laboratories to understand the paradigm shift required to enable AI across the model-experiment (MODEX) enterprise, in part by determining the most impactful applications along the observation-modeling continuum. Learn more about this initiative.

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This work was supported by the U.S. Department of Energy’s Office of Science, through the Biological and Environmental Research program as part of the Atmospheric System Research program.