The Earth’s climate system is a classical example of a multiscale, multiphysics dynamical system with an extremely large number of active degrees of freedom, exhibiting variability on scales ranging from micrometers and seconds to thousands of kilometers and centuries. Machine learning approaches present a timely opportunity to leverage the information content of large datasets generated by observational systems and models to improve scientific understanding and prediction capability of weather and climate dynamics. The workshop will bring together an interdisciplinary group of researchers in applied mathematics, climate science, and data science to discuss recent advances and future perspectives on machine learning for weather and climate applications, including feature extraction, subgrid-scale modeling, and statistical prediction. This workshop will include a poster session on Wednesday, November 2. In order to propose a poster, you must first register for the workshop, and then submit a poster proposal using the form that will become available on this page after you register. The registration form should not be used to propose a poster. The deadline for proposing a poster is October 25.