Challenges in climate pathway modelling

Time is rapidly running out to prevent exceeding 1.5°C of human-caused global warming since the preindustrial era—a critical threshold stated in the #parisagreement Most pathways that aim to keep warming below 1.5°C now involve a temporary "overshoot," where temperatures exceed this limit before being brought back down through #carbondioxide removal from the atmosphere (IPCC) However, a new study published in Nature reveals significant uncertainties and risks associated with this approach, notably: ▶️ Even pathways designed to limit warming to 1.5°C carry a notable risk of exceeding 2°C due to climate system uncertainties. ▶️ To reverse a temporary overshoot, we may need to remove hundreds of billions of tonnes of CO₂ by 2100—a scale that challenges our current technological and economic capacities. ▶️ Overshooting 1.5°C, even temporarily, can lead to irreversible consequences such as accelerated sea-level rise and loss of ecosystems, aligning with warnings from the World Meteorological Organization (WMO). The study suggests adopting "peak and decline" strategies that focus on rapid emissions reductions to minimize peak warming and developing sustainable CO₂ removal methods to reduce temperatures over time. What Can We Do? ✅ Immediate and substantial cuts in #greenhousegasemissions are essential. ✅ Scaling up CO₂ Removal Technologies can help hedge against higher-than-expected warming. ✅ Governments, businesses, and organizations must work together to implement sustainable solutions. This research underscores the urgency of proactive measures. Relying on future technological fixes for carbon removal is not enough; we must act now to reduce emissions and prevent irreversible damage to our planet. Read the article here 👇 https://lnkd.in/eXn-yC4j

“Fifty years into the project of modeling Earth’s future climate, we still don’t really know what’s coming. Some places are warming with more ferocity than expected. Extreme events are taking scientists by surprise. Right now, as the bald reality of climate change bears down on human life, scientists are seeing more clearly the limits of our ability to predict the exact future we face. The coming decades may be far worse, and far weirder, than the best models anticipated… This is a problem. The world has warmed enough that city planners, public-health officials, insurance companies, farmers, and everyone else in the global economy want to know what’s coming next for their patch of the planet… Today’s climate models very accurately describe the broad strokes of Earth’s future. But warming has also now progressed enough that scientists are noticing unsettling mismatches between some of their predictions and real outcomes… Across places where a third of humanity lives, actual daily temperature records are outpacing model predictions… And a global jump in temperature that lasted from mid-2023 to this past June remains largely unexplained… Trees and land are major sinks for carbon emissions, and that this fact might change is not accounted for in climate models. But it is changing: Trees and land absorbed much less carbon than normal in 2023, according to research published last October… The interactions of the ice sheets with the oceans are also largely missing from models, Schmidt told me, despite the fact that melting ice could change ocean temperatures, which could have significant knock-on effects… The models may be underestimating future climate risks across several regions because of a yet-unclear limitation. And, Rohde said, underestimating risk is far more dangerous than overestimating it.” #ClimateRisk #TransitionRisk https://lnkd.in/eiSRvUeF

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.

Climate models rely on weak data for durable #CarbonRemoval, yet these same models shape today’s climate policy Most climate policy experts tend to focus on the #NDCs as the fundamental tool for creating political buy-in to scale up durable removals. But what informs the NDCs? The #IPCC reports. What informs the IPCC reports? The Integrated Assessment Models (IAMs). The IPCC’s Sixth Assessment Report (AR6) illustrates the problem well. Of the 121 model runs in AR6 scenarios aligned with “well below 2°C” and “above 1.5°C” pathways: 120 deployed BECCS, 28 (!) deployed DACCS None (!) represented biochar or ERW. Carbon Direct has just published an in-depth analysis of the problem and potential solutions. The narrow scope of novel and durable carbon removals in IAMs also shapes many countries' NDCs and long-term strategies. I'd add that there is another important element - the IPCC guidelines for the national greenhouse gas inventories (the GHG accounting rules for the governments), which have also suffered from the same shortcomings. It's great to learn that Carbon Direct is collaborating with three leading research institutions with well-established IAMs: Pacific Northwest National Laboratory, Utrecht University, and the International Institute for Applied Systems Analysis, to close this gap and represent removals more accurately in climate modelling: updating the latest cost assumptions, learning curves, and growth constraints for existing carbon removal technologies, while adding new representations of DACCS, biochar, and ERW. Have a look at their short blog post laying out the key issues: https://lnkd.in/eEczTaW2 There's a link to a longer white paper at the end of the blog. It's well worth the read!

The biggest problem with today’s physical climate risk models isn’t the hazard data or climate variables, it’s how buildings are treated. In many models, building vulnerability is an afterthought, if it’s considered at all. Why? Because the current generation of climate risk tools relies on loss models originally developed for the insurance industry or publicly available resources like FEMA’s HAZUS. These models use top-down historical claims data, such as NFIP claims, and are built for portfolio-level or regional loss estimation, not for understanding specific losses or damage causes at individual properties. HAZUS itself acknowledges this limitation. When you look at the underlying claims data, the issue becomes clear: losses are highly scattered even for the same hazard intensity (e.g. flood depth vs loss data from NFIP claims collected since the 1970's). The best these models can do is group buildings into broad archetypes and fit loss curves through a wide cloud of outcomes to represent the average. But, there is no explanation of why one claim results in zero loss while another leads to a total loss for the same flood depth because that data isn't being collected. The insurance industry can live with this (for now) because the logic works good enough at scale. Across thousands of properties, severe over- and under-estimates tend to balance out. But at a single-asset level, that variance matters enormously. It can significantly alter perceptions of risk and more importantly, influence your decisions about individual buildings including retrofits, capital planning, acquisitions, or site selection. The disconnect between portfolio logic and asset-level decision-making is where many climate risk assessments fall short today. If we want climate risk analysis to inform real decisions, we need to model vulnerability very differently.

Can climate models reproduce observed trends? The answer can be challenging. Our new review paper in Science Advances led by Isla Simpson and Tiffany Shaw discusses challenges and ways forward in confronting climate models and observations. It's tricky. Climate models and observations may disagree (1) by chance, due to unforced internal variability, (2) due to error in the model response, (3) due to inaccurate prescribed external forcings, (4) due to incomplete or uncertain observations or (5) due to inappropriate comparison methods. The paper discusses ways forward in disentangling the reasons for potential mismatches between observed and simulated trends. It provides a long catalogue of examples of success, discrepancies and unclear situations that require further attention. https://lnkd.in/dHrEJfDh Let by Isla Simpson and Tiffany Shaw with Paulo Ceppi, Amy Clement, Erich Fischer, Kevin Grise, Angeline Pendergrass, James Screen, Robert Jinglin Wills, Tim Woollings, Russell Blackport, Joonsuk Kang, and Stephen Po-Chedley supported by US CLIVAR

Happy to share a recent working paper, "Impact of Climate Scenario Choices on Climate Financial Risk Assessment," with colleagues at the Oxford Sustainable Finance Group, UK Centre for Greening Finance and Investment (CGFI), and Theia Finance Labs. Key takeaways: 1.    Widespread heterogeneity in climate scenario providers and trajectories indicate large uncertainty for financial institutions in assessing corporate climate transition scenario pathways. 2.    This has significant implications for climate financial stress testing that are premised on climate scenario pathways to meet certain temperature targets and policy ambitions. 3.    A consistent, bottom–up, climate financial stress test is applied to 3,419 power companies using different scenario trajectories and provides two main impacts: net present value (NPV) and probability of default (PD). 4.    Five scenarios are compared under a goal of reaching a global average surface temperature increase of below 2°C, and four scenarios are compared under a goal of reaching global Net Zero by 2050. 5.    Distribution of NPV changes under the stress test show that there are significant differences based on the climate scenario. This can impact the assessment of market and credit risk for companies. 6.    Analysis of individual power technologies indicate that the heterogeneity in company performance is technology specific and likely driven by assumptions in Integrated Assessment Models. 7.    Renewable power companies show improvement in NPV under any stress scenario, but there is some disagreement on the extent to which coal, gas, and oil companies show reduction in NPV. 8.    Hydro and nuclear technology power companies show the greatest uncertainty in financial performance (i.e., NPV) depending on the climate scenario being used. 9.    Results of probability of default (PD) change show similarly conflicting results with high variation in a company’s PD, however we observe higher levels of agreement between scenarios compared to NPV change. 10. Further research is needed to address both the uncertainty and assumptions in climate scenario trajectories as they are applied to financial climate risk analysis. #climaterisk #transitionrisk #stresstests #scenarioanalysis #integratedassessmentmodel #powergeneration #netpresentvalue #probabilityofdefault https://lnkd.in/gV2swsNr

Infrastructure in #northern regions is increasingly threatened by climate change, mainly due to #permafrost thaw. This has direct implications for community #safety, #economic development, and long-term #resilience of built assets across Arctic and sub-Arctic regions. A key challenge in integrating climate change into geotechnical and #infrastructure #design in northern regions is the representation of the surface energy balance (SEB). In our newly published paper in Cold Regions Science and Technology, titled “Ground surface boundary condition methods for analysis of climate-driven permafrost thaw: A comparative study and long-term projections for Nunavik, Canada,” led by my former PhD student Dr. Ali Gheysari, we present a data-driven, #machinelearning-based approach to represent ground surface thermal forcing in permafrost simulations using #ERA5-Land climate reanalysis data through 2100. We compare this ML-based method with SEB heat-flux approaches and traditional n-factors to evaluate how different ground surface boundary condition methods influence projections of climate-driven permafrost thaw. Using #Nunavik as a case study, we provide a comparative assessment of commonly used modeling approaches and show that surface forcing choices can significantly alter long-term #thaw predictions. These differences directly affect #risk assessments and #engineering design decisions for infrastructure systems in cold regions. Our objective was to identify the most effective approach for predicting ground surface temperatures to support climate-resilient design of northern infrastructure. Results indicate that the ML-based method outperforms both SEB heat-flux and n-factor approaches, with substantially lower prediction errors. The feasibility of long-term thermal analysis using ML-predicted ground surface temperatures is demonstrated through a permafrost case study in #Salluit, where active layer thickness and talik development are projected under moderate and extreme climate scenarios by the end of the 21st century. We also discuss the applicability and limitations of surface boundary condition methodologies, including the limited suitability of n-factors for long-term analysis and the sensitivity of SEB heat-flux methods to input data and thermal imbalance. The findings highlight the importance of selecting appropriate boundary condition methodologies to improve the reliability of geotechnical analyses in cold regions. Link to the paper: https://lnkd.in/gAHZ4VkG

📢 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