ESP stands for Earth System Predictability and appears to be an initiative released during the remaining months of the Trump administration.

From predictions of individual thunderstorms to projections of long-term global change, knowing the degree to which Earth system phenomena across a range of spatial and temporal scales are practicably predictable is vitally important to society. Past research in Earth System Predictability (ESP) led to profound insights that have benefited society by facilitating improved predictions and projections. However, as there is an increasing effort to accelerate progress (e.g., to improve prediction skill over a wider range of temporal and spatial scales and for a broader set of phenomena), it is increasingly important to understand and characterize predictability opportunities and limits. Improved predictions better inform societal resilience to extreme events (e.g., droughts and floods, heat waves wildfires and coastal inundation) resulting in greater safety and socioeconomic benefits. Such prediction needs are currently only partially met and are likely to grow in the future. Yet, given the complexity of the Earth system, in some cases we still do not have a clear understanding of whether or under which conditions underpinning processes and phenomena are predictable and why. A better understanding of ESP opportunities and limits is important to identify what Federal investments can be made and what policies are most effective to harness inherent Earth system predictability for improved predictions.

They outline these primary goals:

  • Goal 1: Advance foundational understanding and theory for an improved knowledge of Earth system predictability of practical utility.
  • Goal 2: Reduce gaps in the observations-based characterization of conditions, processes, and phenomena crucial for understanding and using Earth system predictability.
  • Goal 3: Accelerate the exploration and effective use of inherent Earth system predictability through advanced modeling.
  • Cross-Cutting Goal 1: Leverage emerging new hardware and software technologies for Earth system predictability R&D.
  • Cross-Cutting Goal 2: Optimize coordination of resources and collaboration among agencies and departments to accelerate progress.
  • Cross-Cutting Goal 3: Expand partnerships across disciplines and with entities external to the Federal Government to accelerate progress.
  • Cross-Cutting Goal 4: Include, inspire, and train the next generation of interdisciplinary scientists who can advance knowledge and use of Earth system predictability.

Essentially the idea is to get it done with whatever means are available, including applying machine learning/artificial intelligence. The problem is that they wish to “train the next generation of interdisciplinary scientists who can advance knowledge and use of Earth system predictability”. Yet, interdisciplinary scientists are not normally employed in climate science and earth science research. How many of these scientists have done materials science, condensed-matter physics, electrical, optics, controlled laboratory experimentation, mechanical, fluid, software engineering, statistics, signal processing, virtual simulations, applied math, AI, quantum and statistical mechanics as prerequisites to beginning study? It can be argued that all the tricks of these trades are required to make headway and to produce the next breakthrough.

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