Benefits of integrating climatology in AI models

Further progress in AI+climate modeling "Applying the ACE2 Emulator to SST Green's Functions for the E3SMv3 Global Atmosphere Model". Building on ACE2 model which uses our spherical Fourier neural operator (SFNO) architecture, this work shows that ACE2 can replicate climate model responses to sea surface temperature perturbations with high fidelity at a fraction of the cost. This accelerates climate sensitivity research and helps us better understand radiative feedbacks in the Earth system. Background: The SFNO architecture was first used in training FourCastNet weather model, whose latest version (v3) has state-of-art probabilistic calibration. AI+Science is not just about blindly applying the standard transformer/CNN "hammer". It is about carefully designing neural architectures that incorporate domain constraints like geometry and multiple scales, while being expressive and easy to train. SFNO accomplishes both: it incorporates multiple scales, and it respects the spherical geometry and this is critical for success in climate modeling. Unlike short-term weather, which requires only a few autoregressive steps for rollout, climate modeling requires long rollouts with thousands or even greater number of time steps. All other AI-based models fail for long-term climate modeling including Pangu and GraphCast which ignore the spherical geometry. Distortions start building up at the poles since the models assume domain is a rectangle, and they lead to catastrophic failures. Structure matters in AI+Science!

We know the Earth is getting warmer, but not what it means specifically for different regions. To figure this out, scientists do climate modelling. 🔎 🌍 , Google Research has published groundbreaking advancements in climate prediction using the power of #AI! Typically, researchers use "climate modelling" to understand the regional impacts of climate change, but current approaches have large uncertainty. Introducing NeuralGCM: a new atmospheric model that outperforms existing models by combining AI with physics-based modelling for improved accuracy and efficiency. Here’s why it stands out: ✅ More Accurate Simulations When predicting global temperatures and humidity for 2020, NeuralGCM had 15-50% less error than the state-of-the-art model "X-SHiELD". ✅ Faster Results NeuralGCM is 3,500 times quicker than X-SHiELD. If researchers simulated a year of the Earth's atmosphere with X-SHiELD, it would take 20 days to complete —  whereas NeuralGCM achieves this in just 8 minutes. ✅ Greater Accessibility Google Research has made NeuralGCM openly available on GitHub for non-commercial use, allowing researchers to explore, test ideas, and improve the model’s functionality. The research showcases AI’s ability to help deliver more accurate, efficient, and accessible climate predictions, which is critical to navigating a changing global climate. Read more about the team’s groundbreaking research in   Nature Portfolio’s  latest article! → https://lnkd.in/e-Etb_x4 #AIforClimateAction #Sustainability #AI

🌍 White paper alert: Check out my white paper written for the Anwar Gargash Diplomatic Academy, "How Artificial Intelligence Can Accelerate Global Climate Action." https://lnkd.in/eXXucw_X Nearly a year after COP28 in Dubai marked the conclusion of the Paris Agreement's First Global Stocktake, a key challenge emerged: managing the vast and varied data sources that required consolidation and analysis. I was asked to assess the potential of AI in tackling this complexity—specifically in integrating diverse types of climate data and information, spanning from earth observations and physical climate metrics to policy documents, sociodemographic insights, and individual-level data. Through three case applications (although there are many many more, check out climatechange.ai for a great wiki cataloguing AI-climate applications.) Some key findings: 🌍 AI has the power to fill in crucial data gaps that slow down climate action, especially for non-state and subnational actors. These groups play key roles but often go underreported. With AI-driven tools for tracking, analysis, and policy evaluation, we can better integrate their contributions and push forward the goals of the Paris Agreement. 📊 Enhancing Emissions Tracking: Machine learning (ML) is a game-changer for emissions tracking, particularly in challenging areas like land use and urban emissions. Advanced data integration can bring greater accuracy to GHG measurements, and predictive models can even forecast future emissions to support international transparency standards. 🔍 🌧️ AI for Risk Assessment & Adaptation: From flood risks to urban resilience, AI is proving invaluable in risk analysis. Tools like computer vision and NLP track and evaluate adaptation efforts, helping us anticipate and manage climate risks with greater precision. ⚠️ Challenges Remain: Despite AI's immense potential, we face hurdles like transparency, bias, and the high energy use of AI models. I stress the need for human-centered design, diverse data sources, and clear protocols to ensure AI is used fairly, ethically, and sustainably. Looking forward to hearing your thoughts! #climateaction #cop29 #AI #NLP #machinelearning #earthobservation #globalstocktake