Climate Change Risk Assessment Models

Climate models have long struggled with coarse resolution, limiting precise climate risk insights. But AI-driven methods are now changing this, unlocking more detailed intelligence than traditional physics-based approaches. I recently spoke with a research scientist at Google Research who highlighted a promising new hybrid approach. This method combines physics-based General Circulation Models (GCMs) with AI refinement, significantly improving resolution. The process starts with Regional Climate Models (RCMs) anchoring physical consistency at ~45 km resolution. Then, it uses a diffusion model, R2-D2, to enhance output resolution to 9 km, making estimates more suitable for projecting extreme climate events. 🔥 About R2-D2 R2‑D2 (Regional Residual Diffusion-based Downscaling) is a diffusion model trained on residuals between RCM outputs and high-resolution targets. Conditioned on physical inputs like coarse climate fields and terrain, it rapidly generates high-res climate maps (~800 fields/hour on GPUs), complete with uncertainty estimates. ✅ Why this matters - Offers detailed projections of extreme climate events for precise risk quantification. - Delivers probabilistic forecasts, improving risk modeling and scenario planning. - Provides another high-resolution modeling approach, enriching ensemble strategies for climate risk projections. 👉 Read the full paper: https://lnkd.in/gU6qmZTR 👉 An excellent explainer blog: https://lnkd.in/gAEJFEV2 If your work involves climate risk assessment, adaptation planning, or quantitative modeling, how are you leveraging high-resolution risk projections?

The Nature Communications article "How to stop being surprised by unprecedented weather" outlines a comprehensive framework to anticipate and manage the risks of extreme, previously unobserved weather events. The article’s central thesis is that surprise should not be the default response to such events—and that science, policy, and disaster planning can work in concert to build resilience. These methods help anticipate extreme weather events beyond what has occurred in the observational record: a. Conventional Statistical Methods - Use historical weather data and extreme value theory to estimate probabilities of rare events. Limitations: Short observational records, underestimation of extremes, and inability to simulate events beyond past climate conditions. b. Past Events and Proxy Data - Extend the view of climate risk through historical documentation, oral history, and paleoclimate proxies (tree rings, sediments, etc.). Benefits: Reveal long-term variability and past extremes that modern records miss. Limitations: Coarse resolution, dating uncertainty, and difficulty aligning with present-day conditions. c. Event-Based Storylines - Construct physically plausible scenarios of specific high-impact events using counterfactuals and modeling. Useful for local decision-making and public engagement. Limitations: Focused on specific events, often non-probabilistic, and dependent on expert input. d. Weather and Climate Model Data Exploration Mine large ensembles of model outputs (e.g., UNSEEN, SMILEs, CORDEX) for unobserved but plausible extremes. Enables exploration of events outside the observational record using physical consistency. Limitations: Computationally intensive, resolution trade-offs, and model biases.

Last week, CFA Institute published the report “Modeling Climate Transition Risk: A Network Approach,” authored by my colleague Raymond Pang and Gireesh Shrimali, Head of Transition Finance Research at Oxford Sustainable Finance Group and the Center for Greening Finance and Investment. The report uses a scenario-based approach to predict potential asset losses and systemic impacts to financial stability threatened by climate transition risks. Using a case study of developing countries in Asia, a region with prevalent climate transition risks, the authors model the cascade of losses between financial firms under different transition scenarios, exploring how these risks interact within an interconnected financial system. They also offer recommendations to firms and regulators on how best to mitigate these risks. In addition, accompanying the report is a Network Reevaluation Model, an interactive tool that demonstrates the network methodologies employed in the paper.  You can learn more and read the report here:

Our work "An Efficient SSP-based Methodology for Assessing Climate Risks of a Large Credit Portfolio" is out. This is joint work with Emmanuel Gobet and Ying Jiao. https://lnkd.in/eBnswcbx Comments welcome! Abstract: We examine climate-related exposure within a large credit portfolio, addressing transition and physical risks. We design a modeling methodology that begins with the Shared Socioeconomic Pathways (SSP) scenarios and ends with describing the losses of a portfolio of obligors. The SSP scenarios impact the physical risk of each obligor via a DICE-inspired damage function and their transition risk through production, requiring optimal adjustment. To achieve optimal production, the obligor optimizes various energy sources to align its greenhouse gas (GHG) emission trajectories with SSP objectives, while accounting for uncertainties in consumption trajectories. Ultimately, we obtain a Gaussian factor model whose dimension is of the order of the number of obligors. Two efficient dimension reduction methods (Polynomial Chaos Expansion and Principal Component Analysis) provide a fast and accurate method for analyzing credit portfolio losses.

💡 A Practical Guide to Climate Scenarios! Really pleased to have written the forward to this valuable report on the types and applications of climate scenarios by MSCI Inc. and my former United Nations Environment Programme Finance Initiative (UNEP FI) FI colleagues Looking for a handy summary of the types of scenarios from qualitative to quantitative? Here it is: 1. 𝗙𝘂𝗹𝗹𝘆 𝗡𝗮𝗿𝗿𝗮𝘁𝗶𝘃𝗲 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 These scenarios are qualitative descriptions of potential climate futures. ✅ Strengths: - Easily customizable - Useful for high-level strategic discussions - Can capture complex risks that are difficult to quantify ⚠️ Limitations: - Subjective and vulnerable to bias - Lack of numerical outputs makes them hard to integrate into risk models 2. 𝗤𝘂𝗮𝗻𝘁𝗶𝗳𝗶𝗲𝗱 𝗡𝗮𝗿𝗿𝗮𝘁𝗶𝘃𝗲 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 This type builds on fully narrative scenarios by adding expert-driven quantitative estimates (macroeconomic forecasts, asset class returns, regional physical risks). ✅ Strengths: - Balances qualitative storytelling with numerical data - Allows for scenario comparisons without requiring sophisticated models - Easier to communicate results with clear quantitative insights ⚠️ Limitations: - Can give a false sense of precision if assumptions are weak - Still dependent on subjective expert input, leading to potential biases 3. 𝗠𝗼𝗱𝗲𝗹-𝗗𝗿𝗶𝘃𝗲𝗻 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 These scenarios rely on integrated quantitative models to project how climate change and transition risks might evolve under different policy and economic conditions, using macroeconomic models, IAMs, and energy system models. ✅ Strengths: Highly structured and data-driven, reducing subjectivity. Can produce detailed, sector-specific outputs useful for investment decisions. Widely used by regulators and financial institutions for stress testing. ⚠️ Limitations: - Expensive and time-consuming to develop and maintain - “Black box” nature of complex models makes interpretation difficult - Results are only as good as underlying assumptions and data inputs 4. 𝗣𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘀𝘁𝗶𝗰 𝗦𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 Probabilistic models go beyond single-scenario forecasting by assigning probabilities, variance, and uncertainty estimates to different climate outcomes. ✅ Strengths: - Models uncertainty, improving risk management - Enables sophisticated stress testing for asset prices, portfolios, and corporate exposure - Valuable for insurance, catastrophe modeling, and financial risk assessments ⚠️ Limitations: - Highly complex and computationally demanding - Requires strong assumptions about uncertainty - Limited research on how climate change affects probability distributions #ClimateFinance #ClimateScenarios #SustainableInvesting #RiskManagement #ScenarioAnalysis #Risk #Finance

6-Step Methodology for Climate Risk Assessment 🌎 Addressing climate-related risks is increasingly essential as extreme weather events, resource scarcity, and ecosystem disruptions become more frequent and severe. Effective Climate Risk Management (CRM) equips governments, organizations, and communities with the tools to anticipate, prepare for, and mitigate these impacts. A structured approach to climate risk assessment not only identifies vulnerabilities but also informs proactive measures that protect lives, livelihoods, and essential infrastructure. The GP L&D’s 6-step methodology offers a practical, systematic framework for understanding and addressing climate risks, integrating these insights into public policies and investment decisions to build resilience and promote sustainable development. The first step in this methodology is to analyze the current status to determine information needs and set specific objectives. Establishing a clear baseline of vulnerabilities helps ensure that the entire process remains aligned with the climate resilience goals set out from the start. From here, a hotspot and capacity analysis is conducted, identifying regions and systems most exposed to climate risks—such as droughts or floods—and evaluating the local capacity to respond. This targeted analysis allows for efficient resource allocation by pinpointing areas of highest priority. The methodology then adapts to local contexts by developing a tailored approach that reflects unique socio-economic and environmental factors. This customization enhances the relevance and accuracy of the risk assessment, making it more actionable and specific to each setting. Following this, a comprehensive risk assessment is conducted, using both qualitative and quantitative measures to capture the full range of potential impacts. This dual assessment provides a complete understanding of direct impacts, such as infrastructure damage, and indirect consequences, like disruptions to livelihoods. An evaluation of risk tolerance follows, defining acceptable levels of risk and helping prioritize the most urgent interventions. This clarity on risk thresholds ensures that resources are directed to where they are most needed. Finally, the methodology identifies feasible, cost-effective measures to mitigate, adapt to, or prevent potential losses and damages. This step aligns recommended actions with budget and policy constraints, ensuring that interventions are practical and impactful. By adopting this structured approach, decision-makers can better manage climate risks, develop adaptive strategies, and enhance resilience tailored to local needs and resources. Source: Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) #sustainability #sustainable #business #esg #climatechange #climateaction

Scenarios for Assessing Climate-Related Risks: New Short-Term Scenario Narratives The use of climate scenario analysis as a tool has become widespread, but a major gap exists in short-term scenarios that explore near-term risks, economic volatility, and potential systemic vulnerabilities. The need for short-term scenarios for climate scenario analysis has grown rapidly in recent years as financial institutions acknowledge the necessity of integrating climate commitments into their short-term planning strategies and addressing climate risks in the near term. However, the majority of currently available climate scenarios focus on long-term perspectives to explore climate risks, with only a limited number taking the short-term into account. This report, and the accompanying short-term climate scenarios tool, aim to bridge this gap in climate scenario analysis by identifying short-term scenario narratives for financial use. It serves as a guide to help financial institutions understand the implications and drivers of a range of short-term shocks. This report is accompanied by an Excel-based visualization tool with new scenarios that explore a set of macroeconomic, transition, and physical risk shocks, allowing users to explore combinations of these three types of shocks. Developed for asset managers, insurers, bankers, and investors. This report has been produced by United Nations Environment Programme Finance Initiative (UNEP FI) Risk Centre, a new virtual hub that is integrating resources to help UNEP FI’s members tackle sustainability risks, in partnership with the National Institute for Economic and Social Research. 🛠 Download the report and tool free here: https://lnkd.in/dC2aJij8 #scenarios #climate #climatescenarios #economics #climaterisk

📢 Research Alert: A Probabilistic Framework for Climate Scenario Analysis 🌍 "Median global warming expected at 2.7°C - well above the #ParisAgreement" As climate risks become central to #financial and #regulatory decision-making, one challenge remains critically unmet: most climate scenarios lack probabilistic grounding. To address this, the EDHEC Climate Institute with Lionel Melin, Riccardo Rebonato, FANGYUAN ZHANG has released a groundbreaking study: 📘 "How to Assign Probabilities to Climate Scenarios" This research proposes an innovative framework to quantify the likelihood of long-term temperature outcomes, enriching narrative-based scenarios with a probabilistic layer essential for asset pricing, risk management, and policy planning. ✅ Key contributions: • Based on 5,900+ Social Cost of Carbon estimates from 207 academic sources • Uses two rigorous methods: an elicitation-based approach and a maximum-entropy framework • Integrates real-world policy constraints and macroeconomic data 🔍 Findings: • 35–40% chance of >3°C warming by 2100 • The 1.5°C target is technologically feasible, but highly improbable • Median expected warming: 2.7°C - well above the Paris Agreement • Physical climate damages outweigh the cost of transition, emphasizing urgent financial realignment 🔗 The study also maps #probabilities onto Oxford Economics’ scenario framework, assigning over 90% likelihood to pathways involving limited or delayed emissions cuts: Climate Catastrophe, Climate Distress, and Baseline. 👉 A must-read for those in climate finance, regulatory strategy, and risk modeling. This research pushes the frontier in integrating uncertainty and feasibility into climate scenario analysis. #ClimateChange and #Mitigation remains both the greatest source of risk and of opportunity of our time. Let’s prepare! radicant bank #InvestInSolutionsNotProblems

📍 Day 75 of #100DaysOfAI 🌍 𝐒𝐮𝐬𝐭𝐚𝐢𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐒𝐚𝐭𝐮𝐫𝐝𝐚𝐲 → 𝐀𝐈 + 𝐂𝐥𝐢𝐦𝐚𝐭𝐞 𝐑𝐢𝐬𝐤 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠 The climate crisis is no longer a future threat — it’s happening now, and it’s speeding up. Every year, we face more floods, wildfires, heatwaves, and rising sea levels. These are not isolated events — they affect lives, communities, economies, and ecosystems. That’s why climate risk modeling is so important. It helps us understand, prepare for, and reduce the impact of these events. And AI is making it better, faster, and more useful than ever. ---  𝐖𝐡𝐚𝐭 𝐢𝐬 𝐂𝐥𝐢𝐦𝐚𝐭𝐞 𝐑𝐢𝐬𝐤 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠? It’s a way to use data, tools, and models to predict how climate events may affect different areas — like cities, farms, homes, or businesses. Risks are usually grouped into: • 𝐏𝐡𝐲𝐬𝐢𝐜𝐚𝐥 𝐫𝐢𝐬𝐤𝐬 like storms and wildfires • 𝐓𝐫𝐚𝐧𝐬𝐢𝐭𝐢𝐨𝐧 𝐫𝐢𝐬𝐤𝐬 from changing policies and regulations • 𝐋𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐫𝐢𝐬𝐤𝐬 linked to lawsuits or insurance costs --- 𝐇𝐨𝐰 𝐀𝐈 𝐇𝐞𝐥𝐩𝐬: 1. Combines large amounts of climate, weather, and location data to find hidden patterns 2. Creates faster, more accurate forecasts at local and global levels 3. Maps high-risk zones to help with planning and infrastructure 4. Helps banks and insurance companies run stress tests to check future climate impact 5. Improves early warning systems so people can act before disaster strikes ---  𝐀 𝐅𝐞𝐰 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: • Some regions don’t have enough data • AI models can be complex and hard to explain • Training big AI systems uses a lot of energy — so we must align with Green AI principles --- We can’t solve the climate crisis with AI alone — but we can use it to be more prepared, more proactive, and more resilient. 📌 I’ve added relevant research papers and reports in the first comment if you want to go deeper. 📖 Read the full blog here: 🌍𝐀𝐈 + 𝐂𝐥𝐢𝐦𝐚𝐭𝐞 𝐑𝐢𝐬𝐤 𝐌𝐨𝐝𝐞𝐥𝐢𝐧𝐠 https://lnkd.in/edd2vCPq --- #AIWithAkanksha #SustainabilitySaturday #SwechAI #ClimateTech #ClimateRiskModeling #AIForGood #GreenAI #ResponsibleAI #AI4SDGs #FutureOfAI 🗓️ June 14, 2025

Scientific Climate Ratings (an EDHEC Venture) Scientific Climate Ratings (an EDHEC Venture) has been initiated by EDHEC Business School, building on two decades of excellence in financial research and innovation. Developed within a rich ecosystem which comprises notably the EDHEC Climate Institute (ECI), this venture represents the next generation of climate finance tools — combining academic rigor, quantitative depth, and practical relevance. THE MISSION OF SCIENTIFIC CLIMATE RATINGS (AN EDHEC VENTURE) SCR is an independent venture which provides forward-looking climate risk ratings that quantify the financial materiality of both transition and physical risks. Its objective is to support capital allocation decisions that reflect the true long-term exposure of assets to climate-related risks — starting with infrastructure and expanding to listed equities by 2026. Scientific Climate Ratings (an EDHEC Venture) introduces a groundbreaking two-tiered rating framework: Potential Climate Exposure Ratings (PCER): Measure current exposure to future risks based on prevailing policy and climate trajectories. Effective Climate Risk Ratings (ECRR): Quantify the financial impact of climate risks under probabilistic scenarios, integrating physical and transition risks directly into asset valuation models. It produces forward-looking assessments that measure the financial implications of climate risks for infrastructure companies and investors worldwide. Using detailed geospatial data, proprietary climate risk models, and an extensive financial dataset of infrastructure assets, SCR evaluates both risks related to the shift toward a low-carbon economy (transition risks) and risks resulting from climate-related events such as floods, storms, heatwaves, and wildfires (physical risks). https://lnkd.in/eZAiTjsz #climate #ratings #EDHEC