07-30, 19:50–20:00 (UTC), Blue
Here we provides a uniform interface to climate models of varying complexity and completeness. Models that range from low dimensional to whole Earth System models are
ran and analyzed via this simple interface. Three examples illustrate this framework as applied to:
- a stochastic path (zero-dimensional, Julia function)
- a shallow water model (two-dimensional, Julia package)
- a general circulation model (high-dim., feature-rich, fortran, MPI)
Key objectives of this project include:
- make it as easy to run complex models as it is to run simple ones and, hopefully, so easy that that they can all be used interactively in classrooms
- enable the Julia community to access widely-used, full-featured models right now and comfortably using notebooks, IDEs, terminal, and batch (1).
- enable the climate science community to leverage the booming Julia ecosystem for analyzing model output and experimenting with models (2).
- provide basic pipelining (e.g. Channel), book-keeping (e.g. Git), and documenting features (e.g. Pkg) to make complex workflows easier to reproduce, modify, and share with others.
(1) The MITgcm, used as example, has configurations for Ocean, Atmosphere, Cryosphere, Biosphere in forward as well as an adjoint mode (via AD).
(2) Both on-premise or via cloud based environments.
I work as a reseach scientist at the Massachusetts Institute of Technology (MIT) where I investigate oceanography and climate. As part of the Department of Earth, Atmospheric and Planetary Sciences, my work focuses on ocean modeling and the analysis of global ocean data sets such as Argo profile collections, satellite records of sea level, or ocean color retrievals. I co-develop computer programs in various languages and carry out ocean state estimation using the MIT general circulation model in order to interpolate and interpret ocean observations. My scientific interests include: ocean circulation and climate variability; tracer transport and turbulent transformation processes; interaction of ecological, geochemical, and physical processes; global cycles of heat, water, and carbon; observational statistics; forward and inverse modeling.