Why Aren′t We Talking About Freddy?

Analysing Cyclone Freddy and the Urgent Need for Advanced Predictive Models

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Tropical Cyclone Freddy, which shattered records by lasting 36 days and covering over 12,785 km, starkly symbolises the changing global climate. Its exceptional characteristics underscore the urgent need to examine how climate change is reshaping weather patterns, particularly in terms of frequency, intensity, and longevity. Despite Freddy's implications, discussions within the (re)insurance industry are limited. This article explores why this phenomenon warrants more attention, the potential for similar occurrences in the Atlantic, the economic risks they pose, and why the (re)insurance industry must urgently update its predictive models and data infrastructure to stay relevant in an increasingly volatile climate landscape.

The Formation of Cyclone Freddy: A New Climate Reality

Global warming has significantly raised sea surface temperatures, especially in tropical regions where cyclones typically form. These warmer waters now act as a continuous energy source for storms, potentially allowing them to last longer and travel farther than before. Additionally, changing atmospheric patterns, including alterations in wind shear and the jet stream, are contributing to more erratic storm paths as evidenced by Cyclone Freddy which transitioned between cyclonic and extra-tropical states along its path in the Indian Ocean.

This new level of storm unpredictability poses significant challenges for forecasting, particularly in regions unaccustomed to such long-lasting and intense storms.

The Atlantic’s Vulnerability: A Prelude to Greater Storms?

The Atlantic basin, encompassing the Caribbean, Gulf of Mexico, and the eastern United States, has historically been vulnerable to powerful hurricanes. However, the dynamics of climate change suggest that this region could now face even more intense and prolonged storms, akin to Cyclone Freddy and financial implications for the (re)insurance industry could be catastrophic.

Critical risk factors in the region include 1) the ongoing warming of the Atlantic Ocean, which could create ideal conditions for forming long-lived storms; and 2) the potential weakening of the Atlantic Meridional Overturning Circulation (AMOC) due to global warming, which could lead to more stagnant and warmer waters in the North Atlantic, further increasing the likelihood of powerful storms.

Moreover, the jet stream’s increasing instability could result in more unpredictable storm tracks, posing new risks to areas traditionally considered safe from hurricanes. This shift could particularly impact the northeastern United States, a region that has historically seen fewer and less severe hurricanes compared to the south-east but could now face more frequent and intense storms.

Economic Implications: The Rising Cost of Climate Inaction

The economic implications of a Freddy-like storm hitting the Atlantic are profound. Coastal communities, already grappling with rising sea levels and increased flooding, would be particularly vulnerable to the sustained impact of such a storm. Prolonged high winds, heavy rainfall, and storm surges could cause widespread damage to infrastructure, homes, and businesses.

For the (re)insurance industry, the emergence of such storms presents a substantial financial risk. Current risk models, primarily based on historical data, may not accurately predict the frequency and severity of future storms, potentially leading to underpriced premiums and underfunded reserves. This misalignment between risk and pricing could result in significant financial losses for insurers, as claims outpace their projections, and lead to potential insolvencies.? For communities and businesses, there will also be the impact of increased premiums, restricted coverage, or even the withdraw of coverage altogether.

The Shortcomings of Current Climate Models

One of the most critical issues highlighted by Cyclone Freddy is the inadequacy of current climate models used by the (re)insurance industry. These models are typically static, relying on historical data to predict future risks. However, the rapidly changing climate means that historical data is no longer a reliable predictor of future events.

Traditional models also tend to focus on average conditions rather than extremes. This approach overlooks the increasing likelihood of extreme weather events, such as long-lived tropical cyclones, becoming more common due to climate change, leading to insufficient risk assessments and inadequate preparation.? Moreover, current models often fail to account for the complex interactions between different climate factors, such as the impact of global warming on sea surface temperatures, atmospheric conditions, and ocean currents. These interactions are critical to understanding the formation and behaviour of storms like Freddy, and their omission from traditional models leaves a significant gap in our ability to predict and prepare for future events.

Is It Time to Mandate Advanced Predictive Models in (Re)Insurance?

Given the limitations of existing models, there is an urgent need for the (re)insurance industry to adopt more fluid and dynamic predictive tools that can better reflect the new climate realities. Mandating the use of advanced climate models in risk assessments for insurance purposes would be a significant step forward in improving the protection of communities and businesses from the devastating impacts of climate change.

However, mandating the use of advanced models is not merely a policy change; it requires a substantial investment in technology, infrastructure, and skilled personnel. The current lack of data insights and predictive environments is already costing the P&C and L&H (re)insurance industry dearly, particularly as we collectively face climate change as the world’s number one risk. Minimal investments focused on mitigating climate change are no longer viable.

The shift towards more advanced predictive models necessitates significant investments in technology, infrastructure, and human resources. (Re)insurers must invest in the development of sophisticated data collection and analysis systems that can provide real-time insights into emerging climate patterns. This includes integrating satellite data, advanced weather forecasting models, and AI-driven analytics that can identify and respond to new climate trends as they develop. To offset the carbon footprint of increased AI use in climate modelling, the (re)insurance industry will have to prioritise the use of renewable energy to power data centres, optimise AI algorithms for energy efficiency, and invest in carbon offset programs. Additionally, adopting sustainable AI practices, enhancing transparency in reporting energy consumption, and leveraging AI to optimise energy use can further reduce emissions.

In addition to technological investments, the industry must also focus on building the necessary infrastructure to support these advanced models. This includes the development of high-performance computing systems capable of processing vast amounts of climate data and generating accurate, real-time predictions. Furthermore, there is a need for robust data storage and management systems that can securely handle the influx of new information and ensure its accessibility for decision-making processes.

Equally important is the investment in skilled personnel. The successful implementation of advanced climate models requires a workforce with expertise in climate science, data analytics, and AI. (Re)insurers must prioritise the recruitment and training of individuals with these skills, ensuring they have the knowledge and capabilities to interpret and apply the insights generated by these new tools effectively.

CEOs and boards of brokers and (re)insurers must prioritise investments in technology and data infrastructure to stay competitive in an increasingly volatile climate landscape.

Harnessing AI for Better Climate Forecasting

The latest advancements in predictive climate models are heavily driven by the integration of AI, which are making these models more accurate, efficient, and capable of handling complex climate scenarios. One of the most significant breakthroughs is the development of AI-enhanced General Circulation Models (GCMs), such as the NeuralGCM, which combines machine learning with traditional physics-based models. This hybrid approach has shown superior performance in predicting extreme weather events like cyclones, offering a more robust tool for climate forecasting.

Additionally, a new algorithm developed by researchers at the University of Oxford has drastically reduced the time required for Earth System Models to reach equilibrium—a process known as "spin-up." This advancement allows for quicker and more accurate simulations of climate change scenarios, which is crucial for informing policy decisions and improving the reliability of climate projections.

Moreover, AI-powered models are beginning to rival traditional methods in both accuracy and efficiency, potentially transforming the future of weather forecasting and climate modelling. However, these models still face challenges, particularly in generalising predictions for unprecedented weather conditions, which underscores the need for continued research and development in this area.

These advancements are vital for the (re)insurance industry as they provide more precise tools to assess risks associated with increasingly severe and unpredictable weather patterns driven by climate change.

Embracing a New Era of Climate Adaptation

Cyclone Freddy serves as a stark reminder of the evolving nature of climate change and the urgent need for new approaches to risk assessment and disaster preparedness. The emergence of such extreme weather events underscores the limitations of current climate models and highlights the need for more fluid and dynamic predictive tools.

As we move forward, it is essential that we embrace a new era of climate adaptation, one that is informed by the latest science and driven by critical technologies. By doing so, we can better prepare for the challenges ahead and ensure a more resilient future for all.

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