Climate Computing

Coding at the Cutting Edge of Climate Science and Computing

PI: Tapio Schneider and Andrew Stuart
SASE: Julia Sloan, Scholar, and Nat Efrat-Henrici, Scholar

Atmospheric CO2 concentrations now exceed 400 parts per million, a level last seen over 3 million years ago, when Earth was 2–3°C warmer and the sea level was 10–20 meters higher than today. Even with reduced CO2 emissions, Earth will continue to warm and the sea level will continue to rise, but it is unclear by how much. The uncertainties in global climate change projections have remained mostly unchanged since the 1960s, despite more than a 108 -fold increase in computer performance. These uncertainties impede society’s ability to assess environmental risks and take effective adaptation measures.

The Climate Modeling Alliance (CliMA) is building the first Earth system model (ESM) that automatically learns from diverse data sources to produce accurate climate predictions with quantified uncertainties. By fusing models and measurements through data assimilation and machine learning techniques, CliMA seeks to radically improve upon existing ESMs. In addition, CliMA is utilizing recent advances in computing technology to make models faster and their spatial grids finer, making it possible to better resolve important small-scale processes.

The Schmidt Academy Scholars are working with CliMA to implement, test, calibrate, and optimize components of the ESM. They are developing software at the cutting edge of science and high-performance computing, from low-level compute kernels that are optimized to run on CPUs and GPUs, to general models of cloud turbulence and microphysics.

climate computing

From Schneider, Tapio & Lan, Shiwei & Stuart, Andrew & Teixeira, João. (2017). Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations. Geophysical Research Letters. 10.1002/2017GL076101.