Welcome to ACTM Gallery

ACTM Program Overview and Performers

Climate change, whether natural or human-driven, has huge potential impacts on geopolitical and economic stability, food and water security, and DoD missions and operations. Current climate models of highly complex, underlying physical processes are computationally intensive and provide limited actionable guidance to policy makers, especially on the risks and causes of sudden tipping-points, runaway feedback loops, and the strategic implications of potential adversarial activity. Third wave AI methods (e.g., neuro-symbolic hybrid AI models that can incorporate context and can extract causal factors and internal structure) have the potential to improve the accuracy of climate forecasts as well as improve the predictability of tipping points and to provide actionable guidance on new data when predictability remains poor. Faster learned models, particularly when used in conjunction with full-scale physics models for validation, will enable policy makers to better explore the climate impacts and risks of policy decisions. Quantifying climate risks is essential to prepare for a range of scenarios such as those related to DoD planning and decision support (e.g., arctic strategy/defense, regional destabilization, global power/economic realignment, base/force locations, and extreme weather threats) and to identify new potential high-value observations (e.g., stratospheric vs. ocean surface vs. deep ocean vs. arctic, etc.) to enhance confidence in forecasts.

The objective of ACTM program is to explore AI-assisted modeling of complex processes related to climate. The specific goals of this effort are to:

• Explore the use of third wave AI methods to enhance models of complex interconnected processes. In particular, to develop hybrid AI models of the climate and Earth system that capture missing physical, chemical, or biological processes with sufficient computational efficiency to explore decadal scale effects and characterize tipping points and bifurcations.

• Develop methods to assimilate diverse data into models and estimate the “value of new data” to enhance confidence in target-specific forecasts relative to state-of-the-art (SOTA) techniques.

Here is a list of projects in ACTM program and their official documentation links:

• AIBEDO: a hybrid AI framework to capture the effects of cloud properties on global circulation and regional climate patterns (PARC/University of Victoria/University of Washington)

• AI Methods for Solar Radiation Management Research (Colorado State University)

• Assessing Risks of High-Impact Climate Changes and Tipping Points with a Data-Informed Climate Model (Caltech)

• GAIA: Global AI Accelerator (STR, UNSW)

• Hybridizing Knowledge-Based and Machine Learning Models for Climate and Tipping-Point Prediction (UMD, TAMU)

Tutorials and Code Snippets

• Code Snippets

Julia (CliMA) Tutorials

• Caltech Hybrid Modeling Tools

Hybrid Models

• Gallery of Hybrid Models

Citation

The models in this website are funded under the DARPA AI-assisted Climate Tipping-point Modeling (ACTM) program.