Translating Climate Models into Actionable Insights

NASA just trained a 3 billion parameter model on 100 million MODIS satellite images. Google released foundation models that reason across geospatial datasets. Yet most institutional investors still use Excel to assess physical climate risk. I met with a CRO of a $200B AUM fund last week. They were proud of their "advanced" climate risk system. It was a spreadsheet with color-coded cells. This gap between new technology and status quo is where revenue opportunity lives. Today's geospatial foundation models don't just find patterns. They understand causality across space and time. SatVision-TOA can predict the shape of objects in cloud-obscured images with 93% accuracy while spotting features for deeper analysis. Let's explore what this means for institutional investors: 1. Risk assessment is becoming multi-dimensional - models understand how risks compound across variables - demographic shifts, infrastructure resilience, economic activity, and climate patterns. 2. The speed of insight has accelerated exponentially - What used to take months of analysis can now be generated in minutes. 3. Power is now the only constraint, and space infra investment is now viable - Space solar power, orbital data centers, in-orbit manufacturing: geospatial AI can model the terrestrial economic impacts of these technologies years before deployment. (I've watched portfolio managers' eyes widen when we discussed how we can project the value of space-based solar transmission to specific grid-constrained regions) At Sust Global , we're embedding these foundation models into our geospatial AI platform. Not just layering data, but enabling true cross-domain reasoning. Last quarter, a client used our platform to identify real estate assets with both high climate resilience and proximity to emerging demographic booms. They executed a $300M allocation based on insights that didn't exist in any conventional dataset. That's the real breakthrough: finding opportunities others can't see by connecting domains others don't combine. Climate risk data can't exist in isolation. Neither can space technology. The future belongs to those who can reason across all these domains simultaneously. Curious how geospatial foundation models can unlock insights for your portfolio? Let's connect.

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

I recently had the opportunity to discuss with Marcia Toledo Sotillo, director of #adaptation and #resilience at the UN High-Level Climate Champions, about our experience in democratizing climate risk assessment. The Race to Resilience campaign, led by the High-Level Climate Champions, has set an ambitious goal: enhance the resilience of 4 billion people by 2030 by mobilizing action from the so-called "non-state actors" - local communities, NGOs, companies... Assessing and understanding climate risks is the first step of this transformation. Thanks to five decades of intensive research in climate science and earth system modeling, information about future climate and its impacts is available at unprecedented precision and depth. However, accessing and interpreting this data can be almost impossible for non-state actors, as it requires very specific technical and scientific expertise. Since 2019, Callendar has been bridging this gap by developing tools that transform scientific data into ready-to-use, localized information. Our solutions cater to a wide range of stakeholders, from billion-dollar industrial projects to individuals. In 2024, we delivered climate risk assessments - such as submersion, heatwave or wildfire - to over 230,000 people in France. While a far cry of the 4 billion target, it represents a scalable model that can be replicated globally. I strongly believe that delivering high-quality, actionable climate impact assessments to half of humanity within 5 years is technically feasible. However, there is no one-size-fits-all solution for adaptation. Climate impacts vary greatly from one place to another and solutions must be tailored to local contexts. To be truly effective, our approach requires both global endorsement and local collaboration, ensuring that communities have access to tools and support tailored to their specific needs.

🌍 White paper alert: Check out my white paper written for the Anwar Gargash Diplomatic Academy, "How Artificial Intelligence Can Accelerate Global Climate Action." https://lnkd.in/eXXucw_X Nearly a year after COP28 in Dubai marked the conclusion of the Paris Agreement's First Global Stocktake, a key challenge emerged: managing the vast and varied data sources that required consolidation and analysis. I was asked to assess the potential of AI in tackling this complexity—specifically in integrating diverse types of climate data and information, spanning from earth observations and physical climate metrics to policy documents, sociodemographic insights, and individual-level data. Through three case applications (although there are many many more, check out climatechange.ai for a great wiki cataloguing AI-climate applications.) Some key findings: 🌍 AI has the power to fill in crucial data gaps that slow down climate action, especially for non-state and subnational actors. These groups play key roles but often go underreported. With AI-driven tools for tracking, analysis, and policy evaluation, we can better integrate their contributions and push forward the goals of the Paris Agreement. 📊 Enhancing Emissions Tracking: Machine learning (ML) is a game-changer for emissions tracking, particularly in challenging areas like land use and urban emissions. Advanced data integration can bring greater accuracy to GHG measurements, and predictive models can even forecast future emissions to support international transparency standards. 🔍 🌧️ AI for Risk Assessment & Adaptation: From flood risks to urban resilience, AI is proving invaluable in risk analysis. Tools like computer vision and NLP track and evaluate adaptation efforts, helping us anticipate and manage climate risks with greater precision. ⚠️ Challenges Remain: Despite AI's immense potential, we face hurdles like transparency, bias, and the high energy use of AI models. I stress the need for human-centered design, diverse data sources, and clear protocols to ensure AI is used fairly, ethically, and sustainably. Looking forward to hearing your thoughts! #climateaction #cop29 #AI #NLP #machinelearning #earthobservation #globalstocktake

Climate scenario analysis 101 🌍 A great resource from MSCI outlines the fundamentals of climate scenario analysis and how it supports decision making in finance and business. Scenario analysis provides a structured way to evaluate how climate risk and transition pathways may influence markets, portfolios, and corporate strategies. For companies, this is increasingly relevant. Climate change is driving shifts in policy, technology, and consumer demand, and businesses need tools that test strategies across multiple possible outcomes. MSCI describes four types of scenarios. Fully narrative scenarios are qualitative frameworks that help map potential risk pathways and identify emerging issues in the early stages of analysis. Quantified narrative scenarios combine narratives with numerical estimates. They allow organizations to assign data to possible futures, creating an entry point to quantify risks before moving to more complex models. Model driven scenarios are developed with integrated assessment models that merge economic, energy, land use, and climate systems. These scenarios are widely applied by regulators and investors for stress testing and forecasting. Probabilistic scenarios introduce probability distributions to reflect uncertainty across multiple futures. This approach is useful for assessing financial risk exposure and for stress testing under varying climate conditions. Each scenario type has clear strengths and limitations. Narrative approaches are flexible and cost effective, while model based and probabilistic approaches provide more detail and credibility but require technical expertise and resources. MSCI proposes a progressive method that combines different types of scenarios. Organizations can begin with narratives, advance through quantification, refine insights with models, and ultimately integrate scenario analysis into strategy and governance. For business leaders, the implications are significant. Scenario analysis helps evaluate exposure to transition and physical risks, assess regulatory impacts, and identify opportunities emerging in a low carbon economy. It also strengthens strategic foresight. By translating complex climate science into structured outputs, it enables boards and executives to take informed decisions on risk and resilience. As expectations on sustainability rise, climate scenario analysis is becoming an essential capability for companies seeking to manage uncertainty and position themselves for long term competitiveness. Source: MSCI #sustainability #business #sustainable #esg