Applying ML to local climate patterns

🌍 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

🚨 𝐍𝐞𝐰 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐀𝐥𝐞𝐫𝐭: 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐏𝐫𝐞𝐜𝐢𝐩𝐢𝐭𝐚𝐭𝐢𝐨𝐧 𝐢𝐧 𝐖𝐞𝐬𝐭 𝐀𝐟𝐫𝐢𝐜𝐚🌍🌧️ Thrilled to share a new peer-reviewed article I co-authored: “𝑭𝒖𝒕𝒖𝒓𝒆 𝑷𝒓𝒆𝒄𝒊𝒑𝒊𝒕𝒂𝒕𝒊𝒐𝒏 𝑪𝒉𝒂𝒏𝒈𝒆 𝒊𝒏 𝑾𝒆𝒔𝒕 𝑨𝒇𝒓𝒊𝒄𝒂 𝑼𝒔𝒊𝒏𝒈 𝑵𝑬𝑿-𝑮𝑫𝑫𝑷-𝑪𝑴𝑰𝑷6 𝑴𝒐𝒅𝒆𝒍𝒔 𝑩𝒂𝒔𝒆𝒅 𝒐𝒏 𝑴𝒖𝒍𝒕𝒊𝒑𝒍𝒆 𝑴𝒂𝒄𝒉𝒊𝒏𝒆 𝑳𝒆𝒂𝒓𝒏𝒊𝒏𝒈 𝑨𝒍𝒈𝒐𝒓𝒊𝒕𝒉𝒎𝒔” — now published! 🔍 Using five cutting-edge machine learning algorithms trained on NASA's NEX-GDDP-CMIP6 datasets, we examined how climate change could reshape rainfall patterns in West Africa under two key Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5). 💡 𝐾𝑒𝑦 𝑇𝑎𝑘𝑒𝑎𝑤𝑎𝑦𝑠: • Significant increases in precipitation are projected for both mid (2040–2070) and long-term (2070–2100) periods, especially under high-emission scenarios. • Gradient Boosting Regressor emerged as the most effective ML tool for simulating future rainfall. • Our findings carry critical implications for water resource planning, agriculture, and climate resilience strategies in West Africa. 📍This work is vital for #researchers, #policymakers, and #climatepractitioners working in the region, helping drive data-informed decisions in the face of an evolving climate. Emmanuel Dioha Eun Sung Chung Hassen Babaousmail (李想), Ph.D. #ClimateScience #MachineLearning #WestAfrica #ClimateChange #NEXGDDP #PrecipitationTrends #CMIP6 #Sustainability #PolicyImpact

𝐍𝐞𝐰 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐂𝐨𝐦𝐩𝐚𝐫𝐢𝐧𝐠 𝐃𝐞𝐞𝐩 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡𝐞𝐬 𝐟𝐨𝐫 𝐅𝐥𝐨𝐨𝐝 𝐅𝐨𝐫𝐞𝐜𝐚𝐬𝐭𝐢𝐧𝐠 Flood forecasting presents unique ML challenges: multi-modal data fusion (meteorological, geographical, and soil variables), high-resolution spatial modeling, and capturing complex temporal dynamics. While foundation models promise transfer learning benefits, they can struggle with domain adaptation from global patterns to local contexts. Eric Wanjau and Samuel Maina explored data-driven approaches to flood extent forecasting in Rwanda, a region particularly vulnerable to flooding due to its mountainous terrain and increasingly frequent heavy rainfall. They compared three approaches: - A standard U-Net architecture - A ClimaX variant trained from scratch - Fine-tuned ClimaX model ClimaX is a transformer-based weather and climate foundation model. They found that a ClimaX variant trained from scratch with a linear projection decoder outperformed both the U-Net baseline and the fine-tuned ClimaX models. Perhaps most interesting was that pre-training on coarse global climate data didn't transfer effectively to the high-resolution local forecasting task in Rwanda. This suggests that foundation models might need region-specific pre-training at appropriate resolutions to bridge the gap between global patterns and local flood dynamics. https://lnkd.in/evmPuRje #ComputerVision #MachineLearning #FloodForecasting #FoundationModels