Skip to main content

Machine Learning and Artificial Intelligence to Advance Earth System Science Opportunities and Challenges: Proceedings of a Workshop (2022) / Chapter Skim
Currently Skimming:

Emerging Approaches for Using, Interpreting, and Integrating ML/AI for Earth System Science
Pages 9-18

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 9...
... model state toward the observations when the latter are available, but this methodology is not sufficient for climate predictions and projections for which only historical observations are available, and the model is not completely accurate. Gentine suggested that the community work on developing new ML algorithms to assimilate data in order to correct structure, to better represent physical and biological processes, and to produce accurate projections (Figure 2)
Read the entire page →
From page 10...
... Beyond better understanding the Earth system, XAI can help identify problematic methods in ML algorithms, such as when neural networks trained to do image recognition get the right answer for the wrong reasons -- for example, learning to recognize a copyright symbol on a series of training images of horses instead of identifying the horse itself (Lapuschkin et al., 2019)
Read the entire page →
From page 11...
... If interpretable ML is built to be understood from start to finish, it has the potential to be easily understood by a wider audience. Building on the ideas Gentine raised about bringing physical constraints into ML algorithms, Barnes suggested that physical knowledge should be brought into the entire AI design framework.
Read the entire page →
From page 12...
... In order to best utilize all available Earth science information, Penny identified a number of needs including new global optimization methods; robust ML/AI methods that are less sensitive to noise and biases in training data; the ability to scale to high dimensions; and the identification of the best types of training data. Penny concluded with an example of the transformational potential of using data assimilation and ML/AI together.
Read the entire page →
From page 13...
... Barnes offered a vision in which all scientists across the career spectrum would have basic working knowledge of data science tools by offering and incentivizing training for all career levels. Dueben added that in order to train ML domain scientists, the Earth science community could train domain scientists to
Read the entire page →
From page 14...
... The other objective of this center is to increase the representation of underrepresented minorities in climate data science in order to have the broadest reach required to tackle climate change, which is affecting everyone. EMERGING OPPORTUNITIES FROM SOCIAL AND HUMAN ENGINEERED SYSTEMS Panelists in this session discussed opportunities for using ML/AI to understand social and human engineered systems, and the prospects for using ML/AI to integrate social and human engineered system science into Earth system science.
Read the entire page →
From page 15...
... about opportunities for AI in advancing climate science, mitigation, and adaptation, part of the greater Climate Change AI global initiative.9 AI is a powerful tool that can distill raw data into actionable information, improve predictions, optimize complex systems, and accelerate scientific modeling and discovery. Beyond applying existing AI tools to climate problems, climate-relevant problems can also be valuable in motivating fundamental innovations in AI, creating valuable use cases for hybrid physical models, transfer learning, interpretable and causal ML, and uncertainty quantification.
Read the entire page →
From page 16...
... In an effort to better understand the large uncertainties in human behavior, Chayes suggested using massive multiplayer games to see how individuals react and make decisions, including the unintended consequences of a policy intervention. Ganguly agreed that further opportunities exist to use participatory modeling and games in the climate action space to involve stakeholders and early adopters directly.
Read the entire page →
From page 17...
... Emerging Approaches for Using, Interpreting, and Integrating ML/AI for Earth System Science 17 forecasting and preventing locust outbreaks, which tend to affect the Global South. Snyder added that in order to address biases in the regions scientists choose to study, large ensembles of scenario modeling could be useful, in which perturbations across different variables in different sectors could generate a large ensemble of model outputs, upon which unsupervised ML methods can be deployed.
Read the entire page →

Key Terms

  • machine learning
  • domain scientists
  • artificial intelligence
  • data science
  • advance earth

  • human engineered
  • earth science
  • data sciences
  • science applications
  • barnes suggested

  • assimilation workflow
  • climate data
  • using data
  • training data
  • current data

  • climate platform
  • barnes noted
  • physical laws
  • physical constraints
  • integrate social

  • layerwise relevant
  • gentine raised
  • accurate projections
  • partial differential
  • emerging approaches

  • uncertainty quantification
  • relevant propagation
  • neural network
  • methods exist
  • differential equation


    • This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
    More information on Chapter Skim is available.