Study Highlights Growing Importance Of Multi-Day Storms In Future U.S. Flood Risk
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https://news.okstate.edu/articles/engineering-architecture-technology/2026/study-highlights-growing-importance-of-multi-day-storms-in-future-u.s.-flood-risk <-- shared technical article
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https://doi.org/10.1088/2752-5295/ae4f14 <-- shared paper
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“Extreme rainfall is projected to intensify as the climate warms, yet whether the greatest increases will occur in multi-day or single-day events remains uncertain. This knowledge gap is particularly pressing given recent catastrophic floods triggered by multi-day rainfall events, prompting the question of whether multi-day events could, in fact, intensify more than their daily counterparts, and by how much. This study addresses this question using an ensemble of 34 downscaled Earth System Models under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5), focusing on changes in extreme rainfall by the end of the century across ten regions of the contiguous United States. [Their] statistical framework evaluates model agreement, ensemble-mean changes, and the significance of these changes for both daily and multi-day rainfall extremes. Results show that extreme rainfall amounts are expected to increase for most regions and durations. The degree of intensification, however, depends strongly on event rarity and regional climate characteristics. Notably, in the U.S. western Gulf Coast region, very rare multi-day events (e.g., 500 year return period) are projected to intensify more than their daily counterparts, a phenomenon that could be explained by increased stalling of tropical cyclones, which can prolong heavy rainfall over multiple days. These results challenge the assumption that daily extremes dominate future risk and highlight the need to consider event duration when updating flood-hazard maps, design standards, and adaptation planning…”
#Flooding #FloodRisk #FloodInsurance #FloodAwareness #Explore #FloodPreparedness #FlashFlooding #ClimateResilience #climatechange #extremeweather #DisasterPreparedness #StormwaterManagement #FloodSafety #CommunityResilience #risk #hazard #model #modeling #floodrisk #multiday #rainfall #precipitation #storm #water #hydrology #hydrography #planning #policy #regulations #climatemodel #CONUS #USA #publicsafety #cost #economics #damage #loss #infrastructure #spatiotemporal #spatialanalysis #earthsystemmodels #forecasting #meteorology #designstandards #floodmapping #mitigation #flood

3 days, 9 models, 1 goal: benchmarking mortality in #EarthSystemModels 🌍

From data & model overviews to hands-on coding sessions, we worked toward a shared framework, common metrics, and first results for a joint paper.

Great collaboration across teams & time zones! 🚀

#AI4PEX #EarthSystemModels #ClimateScience

#AI4PEX postdoc position available at @unituebingen developing #MachineLearning approaches to improve #EarthSystemModels!

👉Join our team: https://uni-tuebingen.de/de/290466

#Postdoc #ClimateResearch #ResearchOpportunity

Postdoc position -- Machine Learning for Earth-Systems Modelling | Universität Tübingen

🌍 New series: Meet the #EarthSystemModels powering our project!

Over the next weeks we’ll share 5 unique #ESMs — what makes them special, the science they enable, & how we’ll advance them.

🚀 ICON-ESM, UKESM, CNRM-ESM, EC-EARTH, IPSL-CM.

#ClimateModeling #AI4Climate

đź’ˇ New paper!
Droughts are complex hydrometeorological phenomena driven by large-scale land–atmosphere feedbacks. Using a new parametric calibration algorithm improves Deep Learning framework reliability in drought detection as showcased on European drought events.

👉 Find out more: https://www.sciencedirect.com/science/article/pii/S1569843225002109

#EarthSystemModels #ClimateResearch

📢 News from #nextGEMS: Our latest #PolicyBrief is out—“Reaching the Last Mile with Km-Scale #EarthSystemModels.”

It highlights how km-scale models deliver sharper #climate insights, especially on extreme weather and regional projections. But better data ≠ better #policy—yet.

🔑 Key proposals:
Support transdisciplinary #collaboration
Remove barriers to data access
Recognise & retain early-career talent
Build public-private partnerships for future-ready infrastructure

We call on policy-makers and funders to help ensure this cutting-edge science reaches its societal potential.
📥 Read the brief via our Media Library

👀 A second brief is coming soon—stay tuned!

#H2020 #cinea

🚀 First General Assembly for #AI4PEX in Lund!
Last month partners from across Europe met to advance #EarthSystemModels with #AI, #ML & #EarthObservation. 4 days packed with:
✨ Shaping benchmarking tools
đź›  Reviewing progress & syncing milestones
🌍 Boosting outreach & community building
🤝 Hands-on collaboration

Big thanks to Lund University for hosting & to all driving this forward!
👉 www.ai4pex.org

#Climate #HorizonEU

đź’ˇ New Paper Out!
#MachineLearning (ML) improves #EarthSystemModels, but explaining its added value is tough. Inspired by climate model hierarchies, we propose using Pareto-optimal models to distill ML's added value in representing cloud cover, radiative transfer and tropical precipitation.
👉 Find out more: https://arxiv.org/abs/2408.02161#
#ClimateResearch
Distilling Machine Learning's Added Value: Pareto Fronts in Atmospheric Applications

The added value of machine learning for weather and climate applications is measurable through performance metrics, but explaining it remains challenging, particularly for large deep learning models. Inspired by climate model hierarchies, we propose that a full hierarchy of Pareto-optimal models, defined within an appropriately determined error-complexity plane, can guide model development and help understand the models' added value. We demonstrate the use of Pareto fronts in atmospheric physics through three sample applications, with hierarchies ranging from semi-empirical models with minimal parameters to deep learning algorithms. First, in cloud cover parameterization, we find that neural networks identify nonlinear relationships between cloud cover and its thermodynamic environment, and assimilate previously neglected features such as vertical gradients in relative humidity that improve the representation of low cloud cover. This added value is condensed into a ten-parameter equation that rivals deep learning models. Second, we establish a machine learning model hierarchy for emulating shortwave radiative transfer, distilling the importance of bidirectional vertical connectivity for accurately representing absorption and scattering, especially for multiple cloud layers. Third, we emphasize the importance of convective organization information when modeling the relationship between tropical precipitation and its surrounding environment. We discuss the added value of temporal memory when high-resolution spatial information is unavailable, with implications for precipitation parameterization. Therefore, by comparing data-driven models directly with existing schemes using Pareto optimality, we promote process understanding by hierarchically unveiling system complexity, with the hope of improving the trustworthiness of machine learning models in atmospheric applications.

arXiv.org

What are the climate models we are working on? Land model number 3:

ORCHIDEE (Organising Carbon and Hydrology In Dynamic Ecosystems), the land surface model of the IPSL #EarthSystemModels, where our focus is especially on the hydrological processes.
👉 Learn more: https://buff.ly/83X23ho

#ClimateResearch

ORCHIDEE – Website of ORCHIDEE, Vegetation Model