Cutting-edge technologies are reshaping how climate science operates, influencing everything from how we model the Earth's systems to how we interpret data. For example, machine learning is being used in various stages of climate science, from improving the quality of observations to refining models and processing data. A key challenge is: how do we harness the potential of these emerging technologies into tools that advance our understanding of the climate system?

Methods and Activities

Key activities will focus on ways to strengthen the sustainability of technical systems for working with observations and model outputs, and to channel emerging technologies for climate modelling and research, bringing best practice from research and operations closer together. Dedicated efforts will be made in the application of machine learning to develop parameterizations for sub grid-scale motions and post-processing of initialized climate prediction for services and societal applications; the integration of expertise from climate modellers, software and hardware engineers to progress mutual understanding of the climate community's needs and co-design new tools; the promotion of science applications through new sensors and citizen science and of interoperable forecast model databases; foster collaborations with technical panels and WCRP stakeholders and contributors.


Top level outcomes of the activities under this Objective are:

  1. a stronger engagement of the weather and climate modelling communities with emerging technologies;
  2. the promotion of innovation in observations such as robotic platforms and new sensors;
  3. advancing model-data fusion through interoperability frameworks and smart data bases; and
  4. a broadened application for sustainability of data and modelling infrastructure.