Data for Assessing Climate Risk

The State of the Global Climate 2025 report is out. Below are some key data points. ⬇️ • Greenhouse gases at record levels, highest in up to 800,000 years, continuing to increase the Earth’s energy imbalance • CO₂ concentrations reached ~423.9 ppm in 2024, with methane and nitrous oxide also at record highs • The last 3 years are the warmest in a 176 year record, with 2025 at ~1.43°C above pre industrial levels • The Earth’s climate system is more out of balance than at any point in observed history • Around 91% of excess energy is stored in the ocean, which reached record heat levels again in 2025 • Ocean warming is now more than twice as fast as in previous decades • Sea levels are ~11 cm higher than in 1993, with faster rates of increase in recent years • Arctic and Antarctic sea ice remain below average, with record low maximum extent in the Arctic • 8 of the 10 most severe glacier loss years since 1950 have occurred after 2016 • The ocean absorbs ~29% of CO₂ emissions, driving measurable acidification • Extreme events continue to intensify, with heatwaves, floods and droughts affecting multiple regions at the same time • Changes in rainfall patterns are increasing conditions suitable for dengue transmission The system is accumulating energy faster than it can release it. That accumulation is now translating into tangible constraints across business environments. Coastal assets face increasing exposure. Water availability is becoming less predictable. Food systems are more sensitive to climate variability. Heat is already affecting labor productivity and operational continuity in certain regions. This needs to be reflected in how decisions are made. Climate variables are now directly linked to financial performance, asset resilience and long term viability. Ignoring them creates blind spots in strategy, risk assessment and investment decisions. There is a need to integrate climate considerations into core business functions. This includes where operations are located, how infrastructure is designed, how supply chains are structured and how capital is allocated over time.

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘆𝗼𝘂 𝗰𝗼𝘂𝗹𝗱 𝗷𝘂𝘀𝘁 𝗰𝗵𝗮𝘁 𝘄𝗶𝘁𝗵 𝘀𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲 𝗱𝗮𝘁𝗮 𝘁𝗼 𝘂𝗻𝗰𝗼𝘃𝗲𝗿 𝗰𝗹𝗶𝗺𝗮𝘁𝗲 𝗿𝗶𝘀𝗸𝘀? 𝗡𝗼𝘄 𝘆𝗼𝘂 𝗰𝗮𝗻 𝘄𝗶𝘁𝗵 𝗙𝗦𝗤 𝗦𝗽𝗮𝘁𝗶𝗮𝗹 𝗔𝗴𝗲𝗻𝘁. The most critical climate datasets — heatwave projections, precipitation models, land surface temperature, drought indices — live as 𝗿𝗮𝘀𝘁𝗲𝗿 𝗱𝗮𝘁𝗮: dense grids of pixel values derived from satellite sensors and climate models. Turning them into actionable intelligence requires specialized GIS tooling, resampling pipelines, CRS transformations, and significant engineering overhead before joining them with contextual data like population density or land use. This friction is why climate risk analysis has historically been slow, expensive, and inaccessible outside specialist teams. 𝗙𝗦𝗤 𝗛3 𝗛𝘂𝗯 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝘁𝗵𝗮𝘁 𝗲𝗾𝘂𝗮𝘁𝗶𝗼𝗻 𝗲𝗻𝘁𝗶𝗿𝗲𝗹𝘆. 𝗙𝗦𝗤 𝗦𝗽𝗮𝘁𝗶𝗮𝗹 𝗔𝗴𝗲𝗻𝘁 𝗽𝘂𝘁𝘀 𝗶𝘁 𝘁𝗼 𝘄𝗼𝗿𝗸. FSQ H3 Hub's proprietary indexing pipeline converts raw raster datasets into 𝗛3 𝗵𝗲𝘅𝗮𝗴𝗼𝗻𝗮𝗹 𝗰𝗲𝗹𝗹𝘀 — making satellite-derived climate data available in clean, tabular form at a consistent spatial resolution. Every dataset shares the same H3 grid, so joining a Copernicus heatwave projection with a CHELSA precipitation model, a wildfire risk layer, and population density becomes a simple SQL join on a cell ID. No resampling. No CRS headaches. No bespoke ETL. 𝗙𝗦𝗤 𝗦𝗽𝗮𝘁𝗶𝗮𝗹 𝗔𝗴𝗲𝗻𝘁, built on this foundation, lets you converse with that unified data layer to surface climate insights at scale. 𝗦𝗼 𝘄𝗲 𝗽𝘂𝘁 𝗶𝘁 𝘁𝗼 𝘁𝗵𝗲 𝘁𝗲𝘀𝘁: "Do a temporal climate risk analysis for Europe — pick an area with the most interesting future climate impacts." The agent selected 𝗔𝗻𝗱𝗮𝗹𝘂𝘀𝗶𝗮, 𝗦𝗼𝘂𝘁𝗵𝗲𝗿𝗻 𝗦𝗽𝗮𝗶𝗻 — citing Mediterranean climate sensitivity, agricultural economy, water scarcity, and dense coastal populations. It tessellated the region into 113,437 𝗛3 𝗰𝗲𝗹𝗹𝘀 at resolution 8 (~460m), drawing on five datasets spanning climate projections (Copernicus RCP 8.5, CHELSA SSP370), environmental risk (Drivers of Forest Loss), population exposure (Global Population 2020), and land use (MODIS Land Cover). 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗿𝗲𝘃𝗲𝗮𝗹𝗲𝗱: By late century, Andalusia faces a compounding climate trajectory: +23.7 heatwave days/year in extreme risk zones (32% of the region); +2.13°C average warming by 2070–2100; 53% projected "Extremely Drier" with over 600mm precipitation loss; 5,519 high-risk cells with significant population exposure; and a wildfire-climate feedback loop accelerating vegetation loss and further warming. 𝗥𝗮𝘀𝘁𝗲𝗿 𝗱𝗮𝘁𝗮 𝗮𝘁 𝘁𝗵𝗲 𝘀𝗽𝗲𝗲𝗱 𝗼𝗳 𝗶𝗻𝘀𝗶𝗴𝗵𝘁. The world's most detailed climate record is largely trapped in formats accessible only to specialists. FSQ H3 Hub and FSQ Spatial Agent change that — delivering climate risk intelligence that scales as fast as the questions you can ask. Download Foursquare 𝗦𝗽𝗮𝘁𝗶𝗮𝗹 𝗗𝗲𝘀𝗸𝘁𝗼𝗽 to get started.

🎉 We’re excited to publish Earthian 2025 Global Climate Risk Index. This release is the result of nearly two years of in-depth research into country-level climate risk. Measuring climate risk at a national scale is inherently complex—and becomes significantly more challenging when expanded across all countries globally. Earlier this year, during a discussion with Vlad Trusca, Manager at the United Nations Framework Convention on Climate Change (UNFCCC), we spoke about the scarcity—and importance—of organized, categorized climate risk data at a global scale. And today we are proud to share this list publicly and contribute a transparent, data-driven perspective to the climate risk conversation. Our assessment combines multiple dimensions of climate risk, including: Vulnerability Index: A country’s sensitivity to climate impacts and structural limitations in adapting Exposure Level: The degree to which a country is exposed to climate hazards Adaptation Capacity – The ability to respond to, absorb, and recover from climate impacts Primary Risks: The most significant climate threats facing each nation Together, these factors form a comprehensive view of relative climate risk across countries. 🔗 Explore the full ranking here: https://lnkd.in/eKiWjxyJ We welcome feedback, discussion, and collaboration as this work continues to evolve.

The Climate Risk Index (#CRI by Germanwatch e.V.) 2026 highlights a sobering reality: while extreme weather is becoming our "new normal," our global ability to track and prepare for it is dangerously lopsided. The World Meteorological Organization is at the forefront of identifying these gaps and driving the solutions we desperately need. According to the latest findings: 1️⃣ #WMO reporting confirmed that 2024 was shaped by the combined force of human-induced climate change and a strong El Niño, leading to record-breaking global temperatures. 2️⃣ Despite our technological advances, Least Developed Countries (LDCs) and Small Island Developing States (SIDS) are currently collecting and exchanging only 9% of the mandated data for the Global Basic Observing Network. 3️⃣ While the U.S. and E.U. benefit from 636 weather radar stations, the entire African continent—home to 1.2 billion people—has only 37. This data drought directly limits the accuracy of life-saving warnings. 4️⃣ Through the "Early Warnings for All" initiative #EW4ALL, the #WMO and its partners aim to ensure every person on Earth is protected by multi-hazard early warning systems by 2027. Currently, 113 countries have these systems in place, a vital step toward reducing disaster-related fatalities. Weather observations alone won't stop a storm, but without them, we cannot plan for a future that is already here. Closing the meteorological data gap is a matter of global justice. Imagine trying to navigate a ship through a treacherous, rocky strait at night. The Global North has high-powered floodlights and advanced sonar (636 radars), while the Global South is trying to navigate those same rocks with a single, flickering candle (37 radars). The WMO’s mission is to ensure that everyone, regardless of their coordinates, has the light they need to see the hazards ahead. https://lnkd.in/dR9AZ-Qe

Physical climate risk data: the more we learn, the less we know? Khalid Azizuddin's recent piece in *Responsible Investor captures well what many practitioners are grappling with today: - asset-level data that remain incomplete or hard to interpret; - physical hazard exposure often disconnected from financial materiality; - little visibility on supply chains or customers; - adaptation and resilience efforts largely ignored; - and a risk of over-simplifying complex realities into a single “score.” Some three years ago, EDHEC Business School set out to address exactly these challenges, working to advance climate risk modelling and make decision-useful for investors, companies, and public authorities. In this work, we have developed: 🔹 a blueprint for a new generation of probabilistic climate scenarios; 🔹 high-resolution geospatial modeling capabilities to allow for geographic and sectoral downscaling, consistent with each scenario; 🔹 an open database of decarbonisation and resilience technologies through the #ClimaTech project, which officially launched this week. While the research is public, the new EDHEC Climate Institute has also been assisting a school-backed venture, Scientific Climate Ratings (SCR), which integrates this research to deliver forward-looking quantification of the #financialmateriality of climate risks for infrastructure companies and investors worldwide. While SCR provides a rating scale for comparability, it avoids the trap of over-simplification. Each rating is backed by probabilistic scenario modelling, analysis of physical and transition risk exposures, and explicit accounting for adaptation measures. The result is a synthesis that remains transparent, interpretable, and anchored in scientific rigour. Together, these initiatives aim to move the discussion from data abundance to decision relevance, equipping practitioners with tools that connect climate science, finance, and strategy.

I was wrong about climate adaptation planning. . . You do not have to start it from scratch. Here is a tool worth knowing. CRISP – Climate Risk Planning & Managing Tool for Development Programmes in Agri-food Systems. What makes CRISP powerful is its clear, structured categorisation of climate risk: - Hazards: rainfall, temperature, seasonality, wind, maritime-related - Impacts: biophysical and socioeconomic - Vulnerability: economic, human, institutional capacity, sensitivity to harm Exposure, Risk, and Adaptation options - Adaptation capitals: financial, human, natural, physical, political, and social Why is this useful? CRISP gives organisations a head start in climate risk assessment by breaking risk down into its core components - hazard, exposure, and vulnerability - and then linking these to adaptation options. This makes it especially valuable as a beginner or entry-level tool for teams starting work on climate adaptation in agri-food systems. The climate risk information in CRISP is presented using climate risk impact chains, helping users understand how hazards translate into real-world impacts. Importantly, adaptation options are embedded to highlight entry points for climate risk management aimed at reducing vulnerability. While the tool does require contextualisation to local conditions, it offers a strong foundation for informed decision-making and structured discussions on climate risk. If you’re working in climate action, agriculture, food systems, or development programmes, CRISP is definitely worth exploring. It is available for all agro-ecological contexts. Here is the link for the tool: https://crisp.eurac.edu/ Share widely with your fellow organisations working on climate adaptation in food systems. #climateadaptation #foodsystems #agriculture #farmsystems

How do we estimate climate change macroeconomic risks in The World Bank's Country Climate and Development Reports? We just published a methodological paper that present a methodology used in many of them, with our partners at Industrial Economics (IEc). The methodology captures a set of impact channels through which climate change affects the economy by (1) connecting a set of biophysical models to the macroeconomic model and (2) exploring a set of development and climate scenarios. The paper summarizes the results for five countries, highlighting the sources and magnitudes of their vulnerability - with estimated gross domestic product losses in 2050 exceeding 10 percent of gross domestic product in some countries and scenarios, although only a small set of impact channels is included. The paper also presents estimates of the macroeconomic gains from sector-level adaptation interventions, considering their upfront costs and avoided climate impacts and finding significant net gross domestic product gains from adaptation opportunities identified in the Country Climate and Development Reports. Finally, the paper discusses the limits of current modeling approaches, and their complementarity with empirical approaches based on historical data series. I think there are strong complementarity between empirical approaches (which measure historical aggregated impacts and are key for calibration and validation) and process-based modeling (which can consider possible thresholds in the future and run policy counterfactuals). The paper is here: https://lnkd.in/gpAURDV5. Comments welcome! Kodzovi ABALO, Ph.D, Brent Boehlert, Thanh Bui (Tania), Andrew Burns, Unnada Chewpreecha, Charl Jooste, Florent McIsaac, Kim Smet, Kenneth Strzepek, and Diego Castillo and Heather Ruberl.

𝗕𝗥𝗘𝗔𝗞𝗜𝗡𝗚 𝗡𝗘𝗪𝗦- after 1,5 years, our 𝗖𝗹𝗶𝗺𝗮𝘁𝗲 𝗥𝗶𝘀𝗸 𝗦𝘁𝗿𝗲𝘀𝘀 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗠𝗲𝘁𝗵𝗼𝗱𝗼𝗹𝗼𝗴𝘆 benchmarking paper with UNEPFI is finally out! 💡What started as an idea almost 2 years ago is now finally coming to life. 🔭After numerous workshops and survey sessions with 20+ UNEPFI banks . 📝Long hours researching, discussing, summarizing, writing. Our report now synthesizes all this collected and public information in one place to help finance professionals 𝗻𝗮𝘃𝗶𝗴𝗮𝘁𝗲 𝗲𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝗰𝗹𝗶𝗺𝗮𝘁𝗲 𝗿𝗶𝘀𝗸 𝘀𝘁𝗿𝗲𝘀𝘀 𝘁𝗲𝘀𝘁𝗶𝗻𝗴 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 and strengthen their risk management capabilities. We have developed this report as collaboration between SAS and the United Nations Environment Programme Finance Initiative (UNEP FI) and their 20+ banks. Based on survey and workshops conducted with 20+ UNEPFI bank this report explores: 📌What are the common scenarios, approaches and assumptions applied by banks for assessing Transition and Physical risks 📌Which risk measures banks use to simulate the 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗶𝗺𝗽𝗮𝗰𝘁 𝗼𝗳 𝗰𝗹𝗶𝗺𝗮𝘁𝗲 𝗿𝗶𝘀𝗸𝘀 📌How do banks adress and manage 𝗠𝗼𝗱𝗲𝗹 𝗥𝗶𝘀𝗸 embedded in their climate risk models 📌To what extent is climate stress 𝗶𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗲𝗱 with bank's other forward looking processes📌How vital is the 𝘀𝘂𝗽𝗽𝗼𝗿𝘁𝗶𝗻𝗴 𝗿𝗼𝗹𝗲 𝗼𝗳 𝗮 𝗿𝗼𝗯𝘂𝘀𝘁 𝗜𝗧 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 for an efficient and integrated climate risk stress testing 📌𝗖𝗮𝘀𝗲 𝘀𝘁𝘂𝗱𝗶𝗲𝘀 & 𝗰𝗼𝗻𝗰𝗿𝗲𝘁𝗲 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 of how banks adress climate risk management 📌Expert perspectives on 𝘁𝗵𝗲 𝗿𝗼𝗮𝗱 𝗮𝗵𝗲𝗮𝗱 for the climate risk stress testing discipline Additionally, the report highlights areas for further enhancement in climate stress testing methodologies. By presenting 𝗴𝗼𝗼𝗱 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗳𝗼𝗿 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝗶𝗻𝗴 and identifying areas for prioritization I am confident this report will help institutions in 𝗺𝗼𝘃𝗶𝗻𝗴 𝘁𝗵𝗲𝗶𝗿 𝗖𝗹𝗶𝗺𝗮𝘁𝗲 𝗥𝗶𝘀𝗸 𝗦𝘁𝗿𝗲𝘀𝘀 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝘁𝗼 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗹𝗲𝘃𝗲𝗹. 🎢What a journey this was. I am so grateful for the opportunity to be part of this exercise and co-lead this great initiative. 👏My big thanks and congratulations go to the report team: Maheen Arshad, Melanie O'Toole, Arjun Mahalingam,David Trinh for this excellent work. The report download link can be found below. I am happy to provide more details and eager to hear your thoughts.

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

Zillow's New Move: Why Climate Scores Could Reshape Property Values 🏠 The world's largest real estate platform just added climate risk scores to every listing. Why? Because 80% of buyers now demand this data before purchasing. This isn't just another website update - it's a major market signal about the future of property values. Let's decode what this means: 1. The Market Reality • Insurance costs up 50% in high-risk areas since 2020 • Over half of listings face extreme heat exposure • 17% at major wildfire risk • 13% at major flood risk 2. The Buyer Shift • Climate data now essential to purchase decisions • First-time buyers prioritizing long-term climate safety • Insurance availability becoming deal-breaker • Risk scores affecting property negotiations 3. The Investment Impact 💡 • Banks updating lending criteria • Property values shifting based on risk exposure • New market for climate-resilient upgrades • Insurance companies restricting coverage in vulnerable areas Here's why this matters: When climate risk becomes visible on every property listing, it forces the market to properly price these risks. This could trigger the largest repricing of real estate assets in modern history. Question for real estate professionals: How are you preparing clients for this new reality where climate risk directly impacts property values? #RealEstate #ClimateRisk #PropertyValues #MarketSignals