Climate models and spatial scale limitations

Climate models have long struggled with coarse resolution, limiting precise climate risk insights. But AI-driven methods are now changing this, unlocking more detailed intelligence than traditional physics-based approaches. I recently spoke with a research scientist at Google Research who highlighted a promising new hybrid approach. This method combines physics-based General Circulation Models (GCMs) with AI refinement, significantly improving resolution. The process starts with Regional Climate Models (RCMs) anchoring physical consistency at ~45 km resolution. Then, it uses a diffusion model, R2-D2, to enhance output resolution to 9 km, making estimates more suitable for projecting extreme climate events. 🔥 About R2-D2 R2‑D2 (Regional Residual Diffusion-based Downscaling) is a diffusion model trained on residuals between RCM outputs and high-resolution targets. Conditioned on physical inputs like coarse climate fields and terrain, it rapidly generates high-res climate maps (~800 fields/hour on GPUs), complete with uncertainty estimates. ✅ Why this matters - Offers detailed projections of extreme climate events for precise risk quantification. - Delivers probabilistic forecasts, improving risk modeling and scenario planning. - Provides another high-resolution modeling approach, enriching ensemble strategies for climate risk projections. 👉 Read the full paper: https://lnkd.in/gU6qmZTR 👉 An excellent explainer blog: https://lnkd.in/gAEJFEV2 If your work involves climate risk assessment, adaptation planning, or quantitative modeling, how are you leveraging high-resolution risk projections?

The future of climate modelling? Global climate modelling - the type of modelling that the Coupled Model Intercomparison (CMIP) undertakes, and the type of modelling that underpins regional climate projections uses grid resolutions of roughly 100 x 100 km pixels. While you can downscale those in a variety of ways, there are always uncertainties, and these include the detail of how our large-scale climate responds to global warming. If we get that wrong it means the information fed into regional models is wrong and that is a problem to say the least. A summit held in Berlin recently explored ways forward, and there is a very nice report from that summit that proposes solution that is of order 3 billion euros ($5 billion Australian dollars) a year for each of 3-5 global modelling centres. This sounds a lot - but relative to the costs of climate change it is an investment with potentially large returns. The goal would be 3-5 modelling systems at kilometre resolution built with full-scale software engineering standards providing high quality projections for all countries This is not to replace the many existing modelling centres, rather it recognises that the requirements for kilometre resolution models are beyond the capability of most countries. There is a nice report on this at: https://lnkd.in/gmhjQZTb and the actual statement from the summit is available here: https://eve4climate.org/ As a footnote - if you are using kilometre resolution data sourced from climate models, ask why the community that builds our existing global models are arguing for large investment in creating tools to create kilometre resolution data. This rather opens up a question of how robust global modellers believe products purporting to provide kilometre resolution climate projections might be.

🌍 Climate scientists often face a trade-off: Global Climate Models (GCMs) are essential for long-term climate projections — but they operate at coarse spatial resolution, making them too crude for regional or local decision-making. To get fine-scale data, researchers use Regional Climate Models (RCMs). These add crucial spatial detail, but come at a very high computational cost, often requiring supercomputers to run for months. ➡️ A new paper introduces EnScale — a machine learning framework that offers an efficient and accurate alternative to running full RCM simulations. Instead of solving the complex physics from scratch, EnScale "learns" the relationship between GCMs and RCMs by training on existing paired datasets. It then generates high-resolution, realistic, and diverse regional climate fields directly from GCM inputs. What makes EnScale stand out? ✅ It uses a generative ML model trained with a statistically principled loss (energy score), enabling probabilistic outputs that reflect natural variability and uncertainty ✅ It is multivariate – it learns to generate temperature, precipitation, radiation, and wind jointly, preserving spatial and cross-variable coherence ✅ It is computationally lightweight – training and inference are up to 10–20× faster than state-of-the-art generative approaches ✅ It includes an extension (EnScale-t) for generating temporally consistent time series – a must for studying events like heatwaves or prolonged droughts This approach opens the door to faster, more flexible generation of regional climate scenarios, essential for risk assessment, infrastructure planning, and climate adaptation — especially where computational resources are limited. 📄 Read the full paper: EnScale: Temporally-consistent multivariate generative downscaling via proper scoring rules ---> https://lnkd.in/dQr5rmWU (code: https://lnkd.in/dQk_Jv8g) 👏 Congrats to the authors — a strong step forward for ML-based climate modeling! #climateAI #downscaling #generativeAI #machinelearning #climatescience #EnScale #RCM #GCM #ETHZurich #climatescenarios

Every year, natural disasters hit harder and closer to home. But when city leaders ask, "How will rising heat or wildfire smoke impact my home in 5 years?"—our answers are often vague. Traditional climate models give sweeping predictions, but they fall short at the local level. It's like trying to navigate rush hour using a globe instead of a street map. That’s where generative AI comes in. This year, our team at Google Research built a new genAI method to project climate impacts—taking predictions from the size of a small state to the size of a small city. Our approach provides: - Unprecedented detail – in regional environmental risk assessments at a small fraction of the cost of existing techniques - Higher accuracy – reduced fine-scale errors by over 40% for critical weather variables and reduces error in extreme heat and precipitation projections by over 20% and 10% respectively - Better estimates of complex risks – Demonstrates remarkable skill in capturing complex environmental risks due to regional phenomena, such as wildfire risk from Santa Ana winds, which statistical methods often miss Dynamical-generative downscaling process works in two steps: 1) Physics-based first pass: First, a regional climate model downscales global Earth system data to an intermediate resolution (e.g., 50 km) – much cheaper computationally than going straight to very high resolution. 2) AI adds the fine details: Our AI-based Regional Residual Diffusion-based Downscaling model (“R2D2”) adds realistic, fine-scale details to bring it up to the target high resolution (typically less than 10 km), based on its training on high-resolution weather data. Why does this matter? Governments and utilities need these hyperlocal forecasts to prepare emergency response, invest in infrastructure, and protect vulnerable neighborhoods. And this is just one way AI is turbocharging climate resilience. Our teams at Google are already using AI to forecast floods, detect wildfires in real time, and help the UN respond faster after disasters. The next chapter of climate action means giving every city the tools to see—and shape—their own future. Congratulations Ignacio Lopez Gomez, Tyler Russell MBA, PMP, and teams on this important work! Discover the full details of this breakthrough: https://lnkd.in/g5u_WctW  PNAS Paper: https://lnkd.in/gr7Acz25

The 5th United States National Climate Assessment is out, primarily based on high-resolution statistically downscaled CMIP6 Global Climate Models (GCMs) future projections. From being a poor man's methodology for getting around the scale mismatch between GCMs and policy-relevant decision-making merely a decade ago to taking center stage in a national climate assessment as the data for analyzing regional and local climate change and its impacts across the United States, statistical downscaling has had a stellar rise. But every fame comes with greater scrutiny, so there is a need to get under the hood and highlight some yeas and nays of statistical downscaling. In my latest blog post (https://lnkd.in/evA29i52), I explain why: 1) Process-based dynamical downscaling is still necessary despite the progress made in statistical downscaling techniques. 2) Dynamically downscaled data, when compared with statistically downscaled data, will likely have better-resolved scales at identical grid spacing. 3) If a coarse-resolution GCM produces precipitation without squall lines and mesoscale convective vortices, then a mathematically refined version of its precipitation through statistical downscaling would likely suffer from the same issues. 4) GCM's inability to heterogeneously simulate fine-scale responses to anthropogenic forcings, such as those related to pronounced warming over higher elevations due to changes in snow-albedo feedback, will remain uncorrected in high-resolution statistically downscaled data.