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Global Climate Ecosystem

Global Climate Models

Global Climate Models to make Climate Predictions

Earth's Future Temperature

Earth Balance Models (EBMs)

Earth Models of Intermediate Complexity (EMICs)

General Climate Models (GCMs)

U.S. National AI Research Institutes

529 Collaborative Institutions

The U.S. National AI Research Institutes, led by the U.S. National Science Foundation (NSF), are strategic investments Behind AI Foundational Science and its use in Critical Sectors of Economy.

• They consist of 29 institutes that connect over 500 funded and collaborative institutions across the U.S. and around the world.

• Launched in 2020 and funded at about $20 million each over five years, these institutes represent one of the biggest public-private investments to date in AI research and development.

• Artificial Intelligence (AI) is built upon several foundational scientific principles, including logic, computation, and the study of the human mind. 

• Ancient philosophical inquiries into reasoning and knowledge laid the groundwork, while advancements in computer science, particularly in Machine Learning and Deep Learning, have driven its modern evolution.

• AI aims to create systems that can mimic human intelligence, enabling them to learn, reason, and solve problems.  

DARPA

The DARPA, U.S. Defense Advanced Research Projects Agency, AI-assisted Climate Tipping-point Modeling (ACTM) Program focuses on using AI and Machine Learning to improve our understanding and prediction of climate tipping points, which are critical thresholds that, when crossed, can lead to rapid and potentially irreversible changes in the Earth's climate system.

• The ACTM program aims to develop advanced AI Models that can better capture the complex interactions within the climate system and identify potential tipping points, along with their associated risks and potential cascading effects. 

Key Aspects of ACTM Program

AI-enhanced modeling:

• • The program seeks to integrate AI/ML Models with traditional physics-based climate models to create hybrid models that can better represent complex, interconnected processes and capture missing physical, chemical, or biological factors. 

Tipping Point Identification:

• A core goal is to identify tipping points, their thresholds, and the timeframes within which they might occur, with a focus on sudden and drastic changes. 

Causal Inference and Forecasting:

• The program aims to not only predict tipping points but also understand the causal factors driving them and improve the accuracy of forecasts. 

Data assimilation and high-value targets:

• ACTM seeks to identify high-value data collection targets that can help understand complex climate systems and track early warning signals of tipping points. 

Decision support:

• The program's findings are intended to inform decision-making by providing policymakers with a better understanding of the risks associated with climate tipping points and potential mitigation strategies. 

Addressing national security concerns:

• DARPA's involvement highlights the potential national security implications of climate change, particularly the risk of sudden and irreversible changes to key Earth systems. 

In essence, the ACTM program aims to leverage the power of AI to provide a more robust and timely understanding of climate tipping points, enabling better preparedness and potentially mitigating the most severe consequences of climate change, according to the program manager at DARPA. 

Climate Tipping-point Modeling

(ACTM)

The Defense Advanced Research Projects Agency (DARPA)'s AI-assisted Climate Tipping-point Modeling (ACTM) Program leverages Artificial Intelligence (AI) and Machine Learning (ML) to address a significant challenge in climate change: 

Understanding and Predicting Climate Tipping Points

ACTM Program

The ACTM program focuses on critical thresholds in the Earth's climate system where crossing a certain point can trigger abrupt and potentially irreversible, large-scale changes. 

• These "tipping points" can lead to new equilibrium states with significant impacts. Traditional climate models have limitations in capturing complex Earth system processes and are computationally intensive, making it difficult to gain actionable insights about sudden tipping points. 

• ACTM aims to overcome these limitations by developing hybrid AI models that combine AI/ML techniques with conventional physics-based climate models. 

• The program's goals include improving the representation of missing processes, enhancing computational efficiency for exploring decadal-scale effects, and developing methods for data assimilation. DARPA's involvement highlights the recognition of climate change and tipping points as potential threats to global stability and DoD operations. 

Examples of ACTM's Approach

ACTM Methodologies are being applied to improve the understanding and prediction of potential disruptions, such as the Atlantic Meridional Overturning Circulation (AMOC). 

• Researchers are also developing hybrid AI frameworks to capture the effects of cloud properties on climate and inform resilience planning. The program is also developing hybrid AI models to identify early warning signals for tipping points and using AI algorithms to analyze Solar Climate Intervention (SCI) scenarios, including risks and uncertainties. 

U.S. Government Source URLs

DARPA's ACTM Program

DARPA's AI Forward Initiative

NOAA's Center for Artificial Intelligence (NCAI)

NASA's Advanced Data Analytics Platform (ADAPT) & AI/ML

Department of Energy's AI Initiatives: 

Department of Energy's Policy AI & Clean Energy

Note: These resources provide valuable insights, but climate change research is ongoing, with government agencies continuously working to improve understanding and prediction capabilities, including exploring the broader implications of AI on climate.