Where will we grow our food in 2050? 🌽 Watch how one of the spatial thinkers using GriidAI, Shreyas Pandharipande, is turning climate data into long-term agricultural resilience strategies below. 👇 youtu.be/l08ChdgS1LA?si=72vj… #PrecisionAgriculture #ClimateModeling #GeoAI

Very much looking forward to this week’s Modeling Climate-Economic Dynamics Workshop for Young Scholars organized by the @MFRProgram and hearing about some innovative research ideas from promising young scholars and our faculty speakers. Kicking off Day 1 with my colleague @DKeithClimate presenting his research on “Climate Engineering and Uncertainty.” #EconTwitter #ClimateEconomics #ClimateModeling #Uncertainty

How resilient is Earth’s climate? A new Smithsonian/UArizona study in Science reconstructs 485M years of global temperature. But that’s a backward-looking baseline with a 68% past probability band. What are the statistical odds for our actual future? 🧵👇 📈 The Long-Term Metric: Deep-time data reveals Earth System Sensitivity (ESS) is higher than we thought. Doubling atmospheric CO2 historically locked in a massive 8°C (14.4°F) long-term temperature rise once slow-moving feedbacks (like ice sheets) fully caught up. 🔮 The Future Odds (By 2100): Climate scientists use Shared Socioeconomic Pathways (SSPs) to calculate the statistical probabilities of where we go next: The Baseline Trend (SSP2-4.5): Current global policy tracks here with the highest probability. The statistical 90% confidence interval points to a 2.1°C to 3.5°C rise by 2100—an instantaneous vertical spike on a geological timescale. Aggressive Cuts (SSP1-2.6): Limits warming to under 2°C. Low probability—requires unprecedented geopolitical coordination. Worst-Case Burn (SSP5-8.5): Projects 4.4°C. Low probability—global market shifts toward renewables have mostly bent the curve away from this track. ⚠️ Tipping Point Probabilities: At our current trajectory ( 1.5°C to 2°C), statistical models show a ~70% chance of triggering a permanent Greenland Ice Sheet collapse, and a near 95% certainty of widespread Arctic permafrost thaw releasing unmodeled methane loops. 💥 The Catastrophic Black Swans: These variables throw all standard statistical probabilities out the window: #NuclearWinter / #NuclearAutumn: A full-scale exchange drops global temps by 5°C instantly via stratospheric soot. Even a limited 100-warhead regional conflict carries high odds of a global #NuclearFamine, cutting crop yields by 10-13%. Impact Events: A meteor or comet strike completely bypasses natural feedback loops, replacing gradual cycles with abrupt impact winters. The Relativistic Wildcard: An FTL or kinetic space alien impact introduces unmodeled physics entirely. 🔬 The Golden Rule: Always evaluate who funds the studies (NSF, Smithsonian, NASEM) and if the data is independently replicated. Models are only as solid as the real-world volcanic/wildfire analogs used to calibrate the code. #Paleoclimate #ClimateModeling #DataAssimilation #Astrophysics #NuclearWinter #ScientificSkepticism #EarthHistory

📢 𝗖𝗮𝗽𝗮𝗰𝗶𝘁𝘆 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗪𝗼𝗿𝗸𝘀𝗵𝗼𝗽 𝘂𝗻𝗱𝗲𝗿 𝗚𝗿𝗲𝗲𝗻 𝗦𝗸𝗶𝗹𝗹𝘀 𝗣𝗿𝗼𝗴𝗿𝗮𝗺 The Sustainable Development Policy Institute (SDPI), in collaboration with the Embassy of Denmark in Pakistan and International Islamic University, Islamabad is hosting a Capacity Building Workshop under its Green Skills Program themed “𝗘𝗺𝗽𝗼𝘄𝗲𝗿𝗶𝗻𝗴 𝗙𝘂𝘁𝘂𝗿𝗲 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 𝗳𝗼𝗿 𝗦𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗜𝗺𝗽𝗮𝗰𝘁”, titled: 𝗘𝗻𝗲𝗿𝗴𝘆 & 𝗖𝗹𝗶𝗺𝗮𝘁𝗲 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗟𝗼𝘄 𝗘𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 (𝗟𝗘𝗔𝗣) 🌟 Workshop Details 🕓 10:00 am – 04:30 pm 🗓️ April 16th, 2026 📍 Islamabad The workshop will equip students with essential knowledge and hands-on exposure to energy and climate modeling tools that are shaping Pakistan’s transition toward a low-emission future. 📌 Key Themes ✔ Introduction to energy and climate modeling using the #LEAP framework ✔ Scenario building for #SustainableEnergy pathways and #Low-Emission development ✔ Understanding policy linkages between #EnergyPlanning and #ClimateGoals ✔ Enhancing analytical and technical #GreenSkills for future climate leaders This workshop aims to empower youth with the technical expertise, analytical capacity, and confidence needed to contribute to evidence-based policymaking and sustainable, climate-resilient development in Pakistan. #GreenSkills #ClimateModeling #LEAP #EnergyTransition

Reminder: Applications for the @MFRProgram 2026 Modeling Climate-Dynamics Workshop for Young Scholars are due February 1, 2026. The workshop will be held at @UChicago on May 27–29, 2026. Details: tinyurl.com/2ee2cymz #EconTwitter #climatemodeling #climatechange #macroeconomics @BeckerFriedman

Applications are now open for the @MFRProgram 2026 Modeling Climate-Dynamics Workshop for Young Scholars, held at the University of Chicago on May 27–29, 2026. Interested young scholars should submit applications by February 1, 2026. Learn more here: tinyurl.com/2ee2cymz   @UChicago #EconTwitter #climatemodeling #climatechange #macroeconomics @BeckerFriedman

THE FUTURE OF #DeSci IS NO LONGER “THE FUTURE” – IT STARTED TODAY! @DeepNodeAI just flipped the x1000 accelerator switch for decentralized science powered by real-world AI: Drug Discovery & Cancer Research no longer take 10 years → Now measured in months thanks to domain-specific, 100% verifiable compute environments Healthcare : High-performance, transparent compute accessible to doctors and labs worldwide Unbreakable Trust: Genomics and neuroscience data that can never be tampered with – fully on-chain, tamper-proof Collaboration 2.0: True DAO-style rewards: contribute models, datasets, or GPU power → earn $DN co-authorship equity in breakthroughs From curing cancer in biotech labs to climate modeling that saves the planet – Everything now runs on DeepNodeAI infrastructure, where science is no longer locked behind centralized walls. @MindoAI @TauntCoin #DeepNodeAI #DeSci #OpenScience #BiotechOnChain #Genomics #ClimateModeling #Web3ForGood

Vorticity Shows What HPC Can Unlock in Geoscience ow.ly/Bejw50Xui45 #Vorticity #HPC #Geoscience #DataVisualization #EarthScience #ComputationalScience #RemoteSensing #ClimateModeling #ScientificComputing #GeospatialAnalysis

🇪🇺 EC-EARTH: a European community climate model coupling ECMWF’s IFS atmosphere, NEMO ocean & LPJ-GUESS vegetation. We’re upgrading #LPJGUESS land processes and vegetation dynamics to better capture ecosystem –climate feedbacks. #ECEarth #ESM #ClimateModeling #ClimateScience

🌍🌊 CNRM-ESM: coupling climate physics & ocean biogeochemistry. Built by Météo-France, it couples atmospheric dynamics to NEMO–PISCESv2 ocean capturing nutrient-plankton-carbon cycles. Our updates: boost atm. physics, ocean dynamics & carbon–nutrient cycles. #ClimateModeling

🇬🇧🌊UKESM: The UK’s flagship #ESM linking atmosphere, ocean, land & ice with interactive carbon & chemistry cycles. What we do: upgrade ocean processes in HadGEM & NEMO, enhance JULES land surface, & improve climate–carbon feedbacks. #UKESM #ClimateModeling #ClimateScience

In the latest #ACMByteCast, @bruke_kifle hosts 2024 ACM Prize in Computing laureate @thoefler, Professor @ETH_en, Head @spcl_eth, Chief Architect AI/ML @cscsch. They talk application of #HPC in #ClimateModeling, #QuantumPhysics, #AI training & more. learning.acm.org/bytecast/ep…

Dr. Sameh Abdulah's latest presentation at #MUG25 dives into the future of scalable climate modeling, tightly coupled with HPC infrastructure🌍⚡️ Time to rethink MPI. #HPC #ClimateModeling #MUG2025 #ECRC #KAUST

Yuya Bana (APL) and Shingo Watanabe (RIGC) of JAMSTEC published a paper. #AtmosphericScience #ClimateModeling #QBO #AGCM Response of QBO Bias to Convection Scheme in an AGCM Journal of Geophysical Research: Atmospheres agupubs.onlinelibrary.wiley.…

Quantum Simulation: Advancing Science & Climate Understanding Short key points: Precision Climate Models: Simulates complex, chaotic systems like weather and climate with higher accuracy. Atomic-Level Research: Explores molecular behavior to advance material science and chemistry. Solving Complex Equations: Tackles hard physical problems that classical systems can't handle efficiently. Cross-Field Collaboration: Merges physics, climate science, and chemistry for breakthrough research. #QuantumSimulation #ClimateModeling #PhysicsInnovation #QuantumComputing #EnvironmentalTech #ScientificDiscovery

34 supercomputers with 35 Petaflops capacity deployed across India: IT Ministry yespunjab.com/?p=118123 #NationalSupercomputingMission #SupercomputingIndia #NSM #HighPerformanceComputing #ResearchAndDevelopment #IndianRD #AItraining #PARAMRudra #ClimateModeling #Supercomputing

🌐 Contextual Evolution in Decentralized AI: Exploring #AlloraNetwork Dynamic Intelligence System 🟪🔹 #gAllora how #DynamicIntelligenceSystem redefines inference frameworks through contextual adaptability. Let’s dive into its core mechanisms and implications. 🔹 Challenges of Static Inference Models Static models falter in nonstationary environments due to their inability to handle distributional shifts. In domains like #HighFrequencyTrading or #ClimateModeling, this leads to increased prediction variance and suboptimal outcomes. #AlloraNetwork counters this with a system designed for real-time contextual recalibration. 🔹 Core Mechanism: ➡️ Inference Weighting: #AlloraNetwork uses #ContextAwareInferenceSynthesis to dynamically weight worker inferences based on loss forecasts tailored to current conditions. ➡️ Real-Time Adaptation: For instance, in #CryptoPricePrediction, models are reweighted to account for market-specific volatility, ensuring precision during rapid shifts. 🔹 Evolutionary Feedback The #ForecastImpliedInference mechanism drives system evolution by integrating worker-driven loss forecasts into a continuous feedback loop. This allows #AlloraNetwork to refine model selection iteratively, optimizing for contexts where static models fail, such as sudden #MarketSentiment changes. 🔹 Active Set Management with #MeritBasedSortition ➡️ Performance-Driven Selection: #MeritBasedSortition ensures the active set comprises models with the highest contextual accuracy, using a performance-weighted approach. ➡️ Continuous Improvement: Underperforming models rotate out but can re-enter by improving, fostering a self-evolving inference pool. 🔹 Implications for Decentralized Systems #DynamicIntelligenceSystem enhances robustness in decentralized AI by reducing reliance on static datasets. Its adaptability minimizes error rates in dynamic scenarios—early data suggests a 15% improvement in prediction accuracy over static baselines in volatile conditions. This makes #AlloraNetwork a strong candidate for applications like #SupplyChainOptimization. 🔹 Scalability and Resilience Built on #Ethereum layer 2, #AlloraNetwork ensures scalability for high-frequency tasks. The #ReputerSystem, backed by #ALLO token staking, maintains inference quality, enhancing resilience against failures—a key advantage over centralized frameworks. #AlloraNetwork sets a new standard for AI adaptability, with potential to redefine decentralized intelligence in #DeFi and beyond. Its focus on contextual evolution positions it as a leader in next-gen AI systems. #DecentralizedAI #ContextualAI 🟪 #AlloraNetwork

🔹 #CollaborativeIntelligence with #AISwarms #HyperbolicLabs enables #AISwarms for distributed problem-solving. Coordination: Agents collaborate via #HyperdOS, using #PoSP for #TrustlessInteractions. Applications: #ClimateModeling and #SupplyChainOptimization benefit from swarm-driven #ComputeDistribution, enhancing #TaskEfficiency. 🧵🔽🐋#HyperBolic #HyperBolicLabs #gHyperbolic

📢 Discover the EarthNet2021 Dataset for Surface Forecasting! 🛰️ Multispectral & depth map dataset (600GB) 🌎 Includes Sentinel-2 imagery, topography, and meteorological variables 📡 32,000 minicubes with 5-daily time series for deep learning applications 📊 Ideal for geo-/bio-physical parameter estimation & time-series analysis 👩‍🔬 Supports research in climate modeling, land-use change, and remote sensing 📄 Learn more at bit.ly/EarthNet #RemoteSensing #EarthNet2021 #Sentinel2 #DeepLearning #TimeSeries #ClimateModeling #GIS #EarthObservation #Multispectral #BigData

📄 New Blog Post! For a summary of our latest paper on the integration of plant acclimation into the NOAH-MP land surface model, check out our blog: 👉research.reading.ac.uk/lemon… 🔬 Better models = Better climate predictions. 🌎 #LSMs #ClimateModeling #CarbonCycle #LEMONTREE