🌍 CO2 just hit 420 ppm, a million-year high. Ice cores show levels never topped 300 ppm naturally. Decisions made now will last centuries. Sources: NOAA, NASA JPL #omgfacts# #ClimateChange# #Science#

Why Europe’s CO₂ Emissions Plunge Every Summer 🌍  This graphic by The European Correspondent is one of the many incredible data-driven charts and stories from creators featured on our website. ✅

Global greening due to CO2 has hit a new record high, according to the latest research published in Nature. But activists refuse to accept that the 'climate emergency' has failed to materialise, says Chris Morrison.

With global CO2 emissions rising even as UK industry is being cut to the bone, if climate scientists really believed their own tales of doom, why are they not urgently calling for Britain to adapt, asks Philip Leith.

Accurate modeling of CO₂ storage in the subsurface is a critical challenge for scaling Carbon Capture and Storage (CCS). 🌎 But here’s the catch: the inverse problem (recovering geological properties from sparse field observations) is severely ill-posed. 🤒 Traditional approaches either struggle with the scarcity of measured data or are too computationally expensive. 💸 In our new work, we introduce Fun-DDPS (Function-space Decoupled Diffusion Posterior Sampling), a generative framework that decouples the problem into two parts: a function-space diffusion model that learns a prior over geological parameters, and a differentiable Local Neural Operator surrogate for physics modeling and conditioning. ✨ Why does the decoupling matter? The diffusion prior handles the heavy lifting of recovering missing geological information, while the neural operator surrogate makes data assimilation fast and physically grounded — no expensive full-physics simulations in the loop. Key results on synthetic CCS datasets: ⏩ 11x improvement in forward modeling with only 25% observations ✅ Robust inverse modeling for data assimilation under sparse, noisy conditions 🔥 First rigorous validation of diffusion-based inverse solvers against asymptotically exact Rejection Sampling (RS) posteriors This points toward a scalable, practical path for uncertainty-aware subsurface characterization — something the CCS community needs as projects move from pilots to full-scale deployment. 📷 Huge kudos to my amazing collaborators — this work wouldn’t have been possible without them: @IsaacJu13 @AnimaAnandkumar Sally M Benson @ggg_www_ #CarbonCapture# #CO2Sequestration# #MachineLearning# #DiffusionModels# #NeuralOperators# #AI4Science# #CCS# #Sustainability#