Deploying climate models with real-world fidelity

Scientists Combine Climate Models For More Accurate Projections -- https://lnkd.in/ga-82Kaw <-- shared technical article -- https://lnkd.in/gHFTDAYj <-- shared paper -- Researchers... have created a new method for statistically analyzing climate models that projects future conditions with more fidelity. The method provides a way to adjust for models with high temperature sensitivities—a known problem in the community. By assigning different weights to models and combining them, the researchers estimate that the global temperature will increase between 2 and 5° Celsius by the end of the century. This projection, published in Nature Communications Earth & Environment [link above], aligns with previous projections, although this novel framework is more inclusive, avoiding the rejection of models that was common practice in previous methods... A key parameter for these models—known as equilibrium climate sensitivity or ECS—describes the relationship between change in carbon dioxide and corresponding warming. Although the Earth system has a true ECS, it is not a measurable quantity. Different lines of evidence can provide a plausible picture of the Earth's true ECS, which can alleviate the uncertainty of simulation models. However, many models assume a high ECS and predict higher temperatures in response to more atmospheric carbon dioxide than occurs in the real Earth system. Because these models provide estimates about future conditions to scientists and policymakers, it is important to ensure that they represent the conditions of the Earth as faithfully as possible. Previous methods mitigated this issue by eliminating models with a high ECS value. "That was a heavy-handed approach," said Massoud. "The models that were thrown out might have good information that we need, especially for understanding the extreme ends of things." "Instead, we adopted a tool called Bayesian Model Averaging, which is a way to combine models with varying influence when estimating their distribution," said Massoud. "We used this to constrain the ECS on these models, which enabled us to project future conditions without the 'hot model problem.'"... This new method provides a framework for how to best understand a collection of climate models. The model weights included in this research informed the Fifth National Climate Assessment, a report released on Nov. 14 that gauges the impacts of climate change in the United States. This project also supports the Earth System Grid Federation, an international collaboration led in the U.S. by DOE that manages and provides access to climate models and observed data…” #GIS #spatial #mapping #climatechange #spatialanalysis #spatiotemporal #model #modeling #numericmodeling #global #statistics #weighting #bayesian #modelaverging #climatesensivity #climatemodels #projection #ECS #earthsystem #ORNL

Google Research has released a new version of NeuralGCM, an AI powered climate model. The code, training data, and model checkpoints are now fully open source. This release focuses on improving the accuracy of rainfall simulations and adds new stochastic models. NeuralGCM is different from traditional climate models because it is trained using real-world rainfall measurements. The updated model addresses shortcomings of previous versions by improving the accuracy of rainfall simulations. It more realistically captures heavy rainfall events and daily rainfall patterns, outperforming even specialized, high-resolution models in test simulations.  Updated paper: https://lnkd.in/erV3BC_K  Original Paper: https://lnkd.in/esGzS_gN  Code: Apache V2 Model license: CC BY-SA 4.0 NeuralGCM Documentation: https://lnkd.in/eTuJgxSq NeuralGCM GitHub Repository: https://lnkd.in/eFu46tu5 Inference demo notebook: https://lnkd.in/eyusJpRC Checkpoints modification notebook: https://lnkd.in/e9kRvuYJ Credits for the update: Janni Yuval, Ian Langmore, Dmitrii Kochkov, Stephan Hoyer.

Nvidia just put the climate in a bottle. Their new generative AI model, Climate in a Bottle (cBottle), simulates Earth’s climate at an unprecedented 5km resolution. It’s the cornerstone of their Earth-2 platform, aimed at creating real-time digital twins of our planet. Why this matters: Insanely fast: Runs 1,000x faster than physics-based models using Nvidia GPUs Extreme event modeling: First AI model to simulate flash floods, cyclones, and localized weather accurately at kilometer scale 3000x data compression: 50 years of climate data, shrunk and supercharged for instant, low-energy predictions Downscaling unlocked: Localized risk insights, once a computational bottleneck, are now possible at scale Trained on both ERA5 (observational) and ICON (simulated) datasets, cBottle enables faster decisions on where to allocate resources, when to issue warnings, and how to plan infrastructure for a changing climate. It’s built on a cascaded diffusion architecture, trained on 64 H100 GPUs, and open-sourced by NVlabs. It’s a probabilistic model. But that’s exactly what scenario planning needs. High-res climate modeling is no longer a research luxury. #AI #ClimateTech #Nvidia #GenerativeAI #Earth2 #Climate