Operational climate prediction frameworks

𝗧𝗵𝗲 𝗡𝗚𝗙𝗦 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝗯𝗶𝗴— for the first time, we now have 𝘴𝘩𝘰𝘳𝘵-𝘵𝘦𝘳𝘮 𝘤𝘭𝘪𝘮𝘢𝘵𝘦 𝘴𝘤𝘦𝘯𝘢𝘳𝘪𝘰𝘴 tailored for 𝘀𝘁𝗿𝗲𝘀𝘀 𝘁𝗲𝘀𝘁𝗶𝗻𝗴, 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝘀𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗻𝗲𝗮𝗿-𝘁𝗲𝗿𝗺 𝗺𝗮𝗰𝗿𝗼 𝗿𝗶𝘀𝗸. 🔸 This isn't about 2050. It's the next five years, i.e. 𝟮𝟬𝟮𝟱–𝟮𝟬𝟯𝟬. 🔸 This isn't abstract. It's 𝗚𝗗𝗣 𝘀𝗵𝗼𝗰𝗸𝘀, 𝗰𝗿𝗲𝗱𝗶𝘁 𝗿𝗶𝘀𝗸, 𝗶𝗻𝗳𝗹𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝘂𝗻𝗲𝗺𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁. 𝗧𝗵𝗲𝘀𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝘀𝗵𝗼𝗿𝘁-𝘁𝗲𝗿𝗺 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀: 1.  A smooth transition ("Highway to Paris") 2.  A delayed, abrupt policy shift ("Sudden Wake-Up Call") 3.  Physical risk disasters without transition ("Disasters & Policy Stagnation") 4.  A fragmented world with climate chaos and policy misalignment ("Diverging Realities") These scenarios are a wake-up call for taking short-term climate risks seriously. ➤ Delaying climate action could increase global 𝗚𝗗𝗣 𝗹𝗼𝘀𝘀𝗲𝘀 𝗯𝘆 𝗼𝘃𝗲𝗿 𝟯𝘅, and unemployment spikes by 1.3 percentage points (Sudden Wake-Up Call vs Highway to Paris). ➤ Climate disasters aren’t just regional anymore. Floods, fires and droughts in Asia or Africa can cut European 𝗚𝗗𝗣 𝗯𝘆 𝟭.𝟳%, driven by supply chain exposure. ➤ Credit risk spreads explode in carbon-intensive sectors. In some cases, default probabilities jump by 20–30 percentage points, stressing banks and insurers alike. ➤ Green sectors could lose out if the transition is abrupt, fragmented, or disrupted by physical shocks. 𝗛𝗲𝗿𝗲 𝗶𝘀 𝘄𝗵𝘆 𝘁𝗵𝗲𝘀𝗲 𝘀𝗰𝗲𝗻𝗮𝗿𝗶𝗼𝘀 𝗮𝗿𝗲 𝗮 𝗴𝗮𝗺𝗲-𝗰𝗵𝗮𝗻𝗴𝗲𝗿 ➤ For the first time, compound hazards—droughts, floods, wildfires—are modelled together, showing how climate risk can become systemic through trade, finance, and supply chains. ➤ Monetary policy is now integrated, so climate shocks affect interest rate paths, inflation dynamics, and macroeconomic volatility. ➤ Financial contagion is now factored in. Using advanced modelling, the framework maps how climate-related losses feed into default risk, cost of capital, and sectoral investment flows. ➤ Sector-by-sector and region-by-region outcomes now include asset-level exposure, probability of default, and sovereign bond repricing, offering tools fit for risk management. 𝗠𝘆 𝘁𝗮𝗸𝗲 This release is a step-change in how we understand and model climate risk. These scenarios are critical because they model economic and financial impacts on business over the next five years. A timeline relevant for senior management, boards and shareholders. Because these scenarios capture dynamic feedback loops, sector-specific capital costs, and second-round effects that ripple through the financial system, the risk science is taken to a whole new level. These real-world complexities have been missing from science to date, which is why these scenarios are so critical. #NGFS #NetZero #ClimateRisk _____________ For updates, follow me on LinkedIn: Scott Kelly

Four complementary approaches could collectively predict the "unprecedented" in weather, informing disaster preparation. Climate change is increasing the frequency and intensity of record-breaking weather events worldwide, from heat domes to unseasonal floods. These events often catch communities unprepared because they exist beyond our lived experience and historical records. A new perspective provides an overview of scientific approaches to identify unprecedented weather before it occurs, informing emergency management. The research team identified four complementary lines of evidence that together provide a robust framework: conventional statistical methods using observations, analysis of past events from historical records and proxies, event-based storylines, and weather/climate model explorations. When applied together — as demonstrated in their case study of extreme heat in the Netherlands — these approaches revealed that temperatures of up to 48°C are physically possible in regions previously thought to have maximums below 40°C. This work has significant implications for building climate resilience, which the authors conceptualize as a pyramid with transformative adaptation as the foundation, supported by incremental infrastructure improvements and reactive early warning systems. By Timo Kelder, Dorothy Heinrich, Lisette Klok, Vikki Thompson, Henrique Goulart, Ed Hawkins, Louise Slater and al.

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.