Climate-scale features and weather prediction

🌍 The latest World Meteorological Organization El Niño/La Niña Update brings important news for climate-sensitive sectors: 🔹 Current cooler-than-average Pacific sea surface temperatures are expected to return to neutral conditions (neither El Niño nor La Niña) between March and May 2025 — with a 60% probability, rising to 70% by April-June 2025. 🔹 The risk of El Niño redeveloping in this period is negligible, but uncertainty remains high due to the spring predictability barrier, a well-known challenge for long-range climate forecasts. Why should businesses care? 👉 Seasonal climate forecasts like this are not just scientific updates — they are powerful risk management tools that translate into economic savings worth millions of dollars for industries like #agriculture, #energy, #transport, #supplychains, and #insurance. 👉 Understanding how ENSO patterns will evolve helps businesses: ✅ Plan for #climaterisks and supply chain disruptions ✅ Anticipate shifts in #wateravailability, #energydemand, and #cropyields ✅ Align operational strategies to climate-sensitive markets (#commodities, #foodsecurity, #renewableenergy production) ✅ Enhance disaster preparedness and reduce costly damage from climate extremes Even in a warming world, businesses can no longer rely solely on historical weather patterns — they must actively integrate climate intelligence into decision-making. 📊 The latest forecast also comes in the context of record-breaking heat: January 2025 was the hottest January ever recorded, despite the presence of weak La Niña conditions since December 2024. What’s next? WMO’s Global Seasonal Climate Updates (GSCU) provide even broader insights — covering key climate drivers like the North Atlantic Oscillation, Indian Ocean Dipole, and tropical Atlantic temperatures — all critical for regional weather and climate risks that matter to business continuity and resilience. ✅ Takeaway for leaders: Climate intelligence is a competitive advantage. Use it to future-proof your business strategy and build climate resilience into your operations. 💡 Want to stay ahead of #climaterisks and opportunities? Follow #WMO for ongoing updates and insights. https://lnkd.in/e-hXFXPC

Google DeepMind created a Gen AI model to predict extreme heat, and cyclones -- and it's faster and more accurate than traditional prediction models. It's going to be a huge deal as the climate crisis keeps getting worse. The model's called GenCast, and it uses a diffusion model, similar to those in image generation, adapted for Earth's spherical geometry. The model was trained on four decades of weather data from ECMWF's ERA5 archive. It generates 50+ possible weather scenarios, giving probabilistic ensemble forecasts. These forecasts predict daily weather and extreme events like cyclones with high accuracy. GenCast operates faster and more efficiently than traditional systems, needing just 8 minutes per forecast using TPUs. GenCast outperformed ECMWF’s ENS on 97.2% of forecasting targets, especially for extreme heat, wind, and cyclones. Its speed and precision help safeguard lives, improve renewable energy reliability, and support climate resilience. #GenAI #AI

You might have seen news from our Google DeepMind colleagues lately on GenCast, which is changing the game of weather forecasting by building state-of-the-art weather models using AI. Some of our teams started to wonder – can we apply similar techniques to the notoriously compute-intensive challenge of climate modeling? General circulation models (GCMs) are a critical part of climate modeling, focused on the physical aspects of the climate system, such as temperature, pressure, wind, and ocean currents. Traditional GCMs, while powerful, can struggle with precipitation – and our teams wanted to see if AI could help. Our team released a paper and data on our AI-based GCM, building on our Nature paper from last year - specifically, now predicting precipitation with greater accuracy than prior state of the art. The new paper on NeuralGCM introduces 𝗺𝗼𝗱𝗲𝗹𝘀 𝘁𝗵𝗮𝘁 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝘀𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲 𝗱𝗮𝘁𝗮 𝘁𝗼 𝗽𝗿𝗼𝗱𝘂𝗰𝗲 𝗺𝗼𝗿𝗲 𝗿𝗲𝗮𝗹𝗶𝘀𝘁𝗶𝗰 𝗿𝗮𝗶𝗻 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻𝘀. Kudos to Janni Yuval, Ian Langmore, Dmitrii Kochkov, and Stephan Hoyer! Here's why this is a big deal: 𝗟𝗲𝘀𝘀 𝗕𝗶𝗮𝘀, 𝗠𝗼𝗿𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: These new models have less bias, meaning they align more closely with actual observations – and we see this both for forecasts up to 15 days, and also for 20-year projections (in which sea surface temperatures and sea ice were fixed at historical values, since we don’t yet have an ocean model). NeuralGCM forecasts are especially performant around extremes, which are especially important in understanding climate anomalies, and can predict rain patterns throughout the day with better precision. 𝗖𝗼𝗺𝗯𝗶𝗻𝗶𝗻𝗴 𝗔𝗜, 𝗦𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲 𝗜𝗺𝗮𝗴𝗲𝗿𝘆, 𝗮𝗻𝗱 𝗣𝗵𝘆𝘀𝗶𝗰𝘀: The model combines a learned physics model with a dynamic differentiable core to leverage both physics and AI methods, with the model trained directly on satellite-based precipitation observations. 𝗢𝗽𝗲𝗻 𝗔𝗰𝗰𝗲𝘀𝘀 𝗳𝗼𝗿 𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲! This is perhaps the most exciting news! The team has made their pre-trained NeuralGCM model checkpoints (including their awesome new precipitation models) available under a CC BY-SA 4.0 license. Anyone can use and build upon this cutting-edge technology! https://lnkd.in/gfmAx_Ju 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: Accurate predictions of precipitation are crucial for everything from water resource management and flood mitigation to understanding the impacts of climate change on agriculture and ecosystems. Check out the paper to learn more:  https://lnkd.in/geqaNTRP

New Research Shows Future Extreme Precipitation Will Intensify — Driven by Mesoscale Moisture Convergence A new study published in Nature Geoscience delivers one of the clearest pictures yet of how extreme precipitation will evolve in a warming world — and why high-resolution climate models (10–25 km) are essential for credible projections. What the researchers found 👉 Extreme precipitation will intensify sharply Using an ensemble of high-resolution simulations (CESM-HR), the study projects a ~41% increase in daily extreme precipitation over land by 2100 under high-emissions scenarios — far higher than estimates from standard low-resolution climate models. 👉 Dynamics — not just thermodynamics — drive future extremes While warming increases atmospheric moisture (~7% per °C), the study shows that mesoscale atmospheric dynamics—especially moisture convergence and strengthened updrafts—play an even larger role than previously understood. Low-resolution models (∼100 km) underestimate this dynamic contribution by a factor of three. 👉 High-resolution models better capture reality CESM-HR provides a much more faithful representation of observed extreme precipitation, outperforming coarse CMIP-class models in: ➡️ spatial distribution ➡️ event intensity ➡️ historical trends ➡️ mesoscale convective systems (MCSs) ➡️ multiscale interactions between storms, fronts, jets, and atmospheric rivers 👉 Mesoscale Convective Systems matter — a lot High-resolution simulations reveal that future increases in extreme precipitation are strongly linked to more frequent and more intense MCSs, especially in regions like: ➡️ Southeast US ➡️ South America’s La Plata Basin ➡️ Asian monsoon regions Coarse-resolution models largely miss MCSs entirely, leading to underestimates of future extreme rainfall. 👉 Stronger confidence in future projections The 10-member high-resolution ensemble shows robust, consistent increases across all members, providing higher signal-to-noise and more reliable insights for climate risk assessment. 👉 Why this matters Extreme rainfall is one of the most damaging climate risks — driving flash floods, landslides, infrastructure failures, and agricultural losses. This study highlights that standard global climate models may significantly underestimate future extremes because they cannot capture the mesoscale dynamics that amplify heavy rainfall. Scaling climate modeling to higher resolutions — and pairing it with emerging AI weather models — will be critical for governments, cities, and industries preparing for climate impacts. Link to article: Future extreme precipitation amplified by intensified mesoscale moisture convergence, Nature Geoscience (2025): https://lnkd.in/d9kgdbvh

Four complementary approaches could collectively predict the "unprecedented" in weather, informing disaster preparation. Climate change is increasing the frequency and intensity of record-breaking weather events worldwide, from heat domes to unseasonal floods. These events often catch communities unprepared because they exist beyond our lived experience and historical records. A new perspective provides an overview of scientific approaches to identify unprecedented weather before it occurs, informing emergency management. The research team identified four complementary lines of evidence that together provide a robust framework: conventional statistical methods using observations, analysis of past events from historical records and proxies, event-based storylines, and weather/climate model explorations. When applied together — as demonstrated in their case study of extreme heat in the Netherlands — these approaches revealed that temperatures of up to 48°C are physically possible in regions previously thought to have maximums below 40°C. This work has significant implications for building climate resilience, which the authors conceptualize as a pyramid with transformative adaptation as the foundation, supported by incremental infrastructure improvements and reactive early warning systems. By Timo Kelder, Dorothy Heinrich, Lisette Klok, Vikki Thompson, Henrique Goulart, Ed Hawkins, Louise Slater and al.

🌍 Global Wind Circulation: The Engine of Our Planet's Climate 🌬️ Global wind circulation is a fascinating phenomenon that often goes unnoticed, yet it plays a crucial role in our planet's climate. This process not only impacts global weather patterns but also influences our daily lives, agriculture, commerce, and much more. How Does It Work? Unequal Solar Heating: The Earth doesn’t receive the same amount of solar energy everywhere. Regions near the equator are much warmer than the poles. This unequal heating creates pressure differences that cause air to move. Air Movement: Warm air rises, creating areas of low pressure, while cold air sinks, forming high-pressure areas. This movement is what we know as wind. Coriolis Effect: As the Earth rotates, winds are deflected. In the Northern Hemisphere, they turn to the right; in the Southern Hemisphere, they turn to the left. This deflection shapes the wind patterns we experience! Global Wind Belts: There are three main wind belts in each hemisphere: Trade Winds: Consistent winds blowing from east to west, vital for tropical weather. Westerlies: Winds blowing from west to east, influencing temperate climates. Polar Easterlies: Winds contributing to the cold conditions at the poles. Why Is It Important? Climate and Agriculture: Wind circulation affects ocean currents and, consequently, the climate. This directly impacts agriculture and food production. Weather Prediction: The interaction between different wind systems helps us predict the weather and understand climatic phenomena like storms and hurricanes. Climate Change: Wind circulation is changing due to global warming, meaning we need to pay attention to these patterns to prepare for and adapt to an uncertain future. Final Thoughts Next time you feel a breeze, remember it’s part of a complex global system that influences our daily lives. Understanding these processes is essential for tackling climate challenges and developing sustainable solutions. What are your thoughts on the relationship between wind circulation and climate change? I’d love to hear from you! #GlobalWindCirculation #ClimateChange #GlobalClimate #ClimateScience #Sustainability #EnvironmentalEducation

Good morning LinkedIn Community, Meteorologists, and Atmospheric Scientists everywhere, Today I had a request to run a WRF model over Algeria by Issam LAGHA. However, given the size of #Algeria, it is impractical to run the whole country at a 4km resolution on my desktop computer, as mentioned in my previous post on the Great Horn of Africa. So, working with ISSAM, we decided to focus on the northern half of Algeria where the capital city of #Algiers is located. Algeria is the largest country in Africa and has a varied climate, ranging from Mediterranean in the north to arid and semi-arid conditions in the Sahara desert, which covers more than 80% of the country. For this model run, I chose the CONUS physics package for the namelist options for both domains. Domain Details: Domain 1: 20km grid 100x100 domain size Timestep: 120s Domain 2: 4km grid 251x251 domain size Timestep: 24s For this particular case, I started with NOAA: National Oceanic & Atmospheric Administration'sCenter for Environmental Protection's (#NCEP) Global Forecast System (#GFS) 00Z data on July 17th, 2024, and used it as boundary conditions for WRF for 48 hours into the future. This particular model run over Northern Algeria took ~2 hours to complete the 48-hour forecast. Overall, I am pleased with the results from this test. The model can adequately resolve the low-level surface features of the land breeze from the Sahara and the sea breeze from the Mediterranean Sea. You can see that the Tell Atlas Mountains affect the climate of Algeria by blocking the moisture from the Mediterranean Sea and causing a rain shadow downwind. Looking at the synoptic scale, the area of high pressure in the upper atmosphere is easily resolved, including the short waves that propagate across the country. Short waves in the upper atmosphere help promote lift, and if you look closely, you can see where the short waves are present, the convection occurred. Overall, the WRF is a powerful tool that should be utilized, in my opinion, by more forecasters and scientists around the world. The ability to take coarse resolution global model data and downscale it to the meso-scale level can add a lot to research and forecasting. It gives scientists and forecasters more tools for their weather toolbox when determining what will happen, which in turn can help save lives and property. This aligns well with the World Meteorological Organization's #EarlyWarningsForAll initiative. The purpose of Early Warnings for All is to help save lives from severe weather when it occurs. Utilizing regional weather models like WRF can provide more lead time ahead of these events, complementing the #WMO's initiative to protect life and property. If you are interested in what the WRF can do and would like to see your country or area featured in one of my posts, please add your information and country below in the comments. #WRFModel #Meteorology #WeatherForecasting #AlgeriaWeather #GlobalForecasting #WeatherModeling

Helped by anticipated El Nino, marine heatwave aerial coverage increases from 23% now to 37% by late 2026 according to NOAA. Combining NOAA and Climate Impact Company (CIC) identification of current global sea surface temperature anomalies (SSTA) and the International Multi-Model Ensemble (IMME) SSTA forecast for APR/MAY/JUN 2026 renders expansion of marine heatwave (MHW) risk for 2026. According to NOAA, aerial coverage of global MHW’s is 23% in FEB-26 and forecast to increase to 30% by mid-year and 37% by end of 2026. El Nino development will, in part add to the global oceanic warming in 2026. Despite the warming, two notable cooler trends into mid-year are forecast including the North Atlantic warm hole (NAWH) south of Greenland and an expansive Amundsen Sea WH westward to the Ross Sea. The catalyst to warm holes is freshwater runoff from rapid ice-melt from polar land masses (Greenland and Antarctica). Large MHW’s and WH’s are significant contributors to regional climate and must be considered, along with ENSO, to generate climate forecasts (or explain previous climate observations).   CIC identifies MHW’s outside of the tropics (NOAA includes the tropics). Likely the largest influence on climate in the northern hemisphere as mid-2026 approaches is the semi-permanent (since 2018) expansion of the Kuroshio MHW east of Asia running northeastward past the Dateline. Strong MHW’s during the warm season are often well-correlated to stronger than normal high pressure ridging in the middle troposphere which increases risk of anomalous heat and drought affecting nearby land masses (implicating Japan and possibly China). Similarly, MHW’s off Baja California and strengthening in the Gulf of Mexico and eastward could elevate subtropical ridge intensity affecting Mexico and possibly the Southern U.S. during warm season.   A plethora of MHW’s located in the southern hemisphere will have tendency to weaken or become less organized as the winter climate arrives. Exceptions are MHW’s southeast of Australia and in the Southern Indian Ocean (according to the IMME forecast). In the mid-troposphere, large low-pressure troughs have tendency to form across or downwind WH’s. Significant upper troughing is possible given the size of the Amundsen/Ross WH which increases risk of chilly air masses emitted into South America during the winter season. 

Big news from MetraWeather - we now support Climate Outlook data from #CAMS. Our system now supports ingest of Copernicus climate data, specifically NetCDF4 files - which means we can use the incredible data from #Copernicus alongside our wider near-term forecast data. It is still early days, but moving forward we are working with existing and new customers to make sure this data gets used responsibly and distributed an a considered, responsible and unbiased way. This specific data is the CMIP6 outlook for the number of days where maximum temperature will exceed 40C. - The data is structured according to Climate scenarios, intended to provide an idea of what life will look like once we reach +2C and +4C (above 1900 levels). You will also notice the pressure fields - these are the Mean Sea Level Pressure anomaly forecast - essentially the change in pressure. I feel this provides a brilliant insight into the 'Why' - which helps you to build a better understanding of how the world is likely to change. A few things that jumped out at me: 1) Higher pressure over Central America - Increasing severe heat days across eastern Mexico and central-southern US states. 2) Signal for higher pressure west of Europe - not included in the animation but the CMIP6 shows a clear N-S line for rainfall impacts, with S Europe much drier and N Europe much wetter than 1900 levels. 3) Lower pressure across the Arabian Peninsula, resulting in a much higher percentage of days over 40C, stretching all the way around the Arabian Sea into Pakistan and NW India. 4) Stronger trough over Western Australia - potentially resulting in more severe heat days across central Australia. It is worth pointing out that these areas are generally hot historically, so it is natural that in a warming climate these areas would increase above this arbitrary threshold more often - but these datasets are intended to provide an easy to understand summary of an extraordinarily complex dataset. The other point that needs to be mentioned is that the climate Atlas includes valuable information on a confidence interval - which I have not been able to incorporate YET, but hope to in the future. There is uncertainty in any forecast, and one spanning over 50 years especially so - but these are the best estimates from some of the best models in the world from some of the smartest people in the world. #Weatherscape - Tell Better Weather Stories #COP29 MetraWeather / MetService

The North Atlantic Oscillation (NAO) could reach unprecedented magnitudes by the end of the century, leading to severe impacts such as increased flooding and storm damage in northern Europe. The NAO is a large-scale atmospheric pressure see-saw in the North Atlantic and is a key driver of winter weather patterns in the U.K., western Europe and eastern U.S. (pic 4,5,6,7) It is measured by the gradient between High pressure over the Azores and Low pressure over Iceland and controls the strength of the prevailing winds. A new study, led by a team of climate scientists, identifies climatological water vapor as a significant factor governing differences in long-term fluctuations in the NAO across climate model simulations. The research shows that errors in current climate models relating to water vapor lead to uncertainty in predictions of the NAO's future behavior. Taking account of these errors reveals a substantial response of the NAO to volcanic eruptions and greenhouse gases. These new findings have major implications for understanding and preparing for extreme weather events. New study suggests that taking model projections at face value could leave society unprepared for impending extremes. Mitigation efforts are crucial to prevent the severe impacts associated with an unprecedented increase in the NAO. Under a scenario with very high concentrations of greenhouse gases by the end of the century, the NAO will increase to levels never before seen, posing severe risks of impacts from extreme weather such as flooding and storm damage. However, these impacts could be mitigated through efforts to reduce greenhouse gas emissions. This study shows that better understanding the response of atmospheric circulation to greenhouse gases is crucial for anticipating what climate change has in store for the U.K. Key findings from the study include: Some of the model differences in NAO projections are due to climatological water vapor errors in the models. The research reveals the NAO's significant response to external forcings such as volcanic eruptions and greenhouse gases. The study also takes into account the "Signal to Noise Paradox," which suggests that climate models may underestimate the magnitude of the real-world NAO changes. The research results underscore the importance of mitigation efforts to avoid severe impacts from an unprecedented increase in the NAO. The study highlights the need for improved climate models to better predict future changes in the regional climate.