They warn that climate events exceed forecast models

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After the devastating impact of Hurricane Otis, we find ourselves at a pivotal moment in the history of climate prediction. This powerful hurricane slammed into the coast of Acapulco, Mexico, with 165 mph winds and torrential rains, claiming the lives of at least 48 people. However, what made this event exceptional was not just its strength, but the speed with which it escalated. In just 12 hours, it went from being a regular tropical storm to becoming a Category 5 hurricane, the most powerful and rarely seen category in the world. Proving that climate events exceed forecast models, according to The Conversation . Otis exposed the vulnerability of climate prediction This rare and alarming event, described by the US National Hurricane Center as a "nightmare scenario", broke records for the fastest rate of intensification in a 12-hour period in the eastern Pacific. Otis not only took residents and authorities by surprise, but also revealed that current weather events exceed forecast models.

Ravindra , a PhD specializing in the study of natural disasters with the aim of improving the ability to predict them and, ultimately, save lives, recognizes the fundamentals of addressing current challenges in climate prediction. At the same time, he recognizes the significant influence of rapid climate change on current forecasting capabilities. Predictive tools surpassed Belgium Phone Number List in the face of climate change At the core of weather prediction are computer programs, or “models,” that combine atmospheric variables such as temperature, humidity, wind and pressure with fundamental principles of physics. These models play an essential role in issuing early warnings and evacuation orders, but they have fundamental limitations and carry a significant degree of uncertainty, especially when dealing with rare or extreme weather events. This uncertainty arises from several factors, including the fundamentally chaotic nature of the climate system. Atmospheric processes are nonlinear, meaning that a small amount of uncertainty in initial conditions can lead to significant discrepancies in final forecasts. Therefore, the general practice today is to forecast a set of possible scenarios rather than predicting the single most likely scenario. One of the most notable challenges is the lack of complete historical data.



Events like Hurricane Otis may occur only once every several millennia, and, in many cases, we simply have not observed events of this magnitude before. For example, Ravindra Jayaratne points out, we don't know when the transformation of an eastern Pacific storm into a Category 5 hurricane last occurred overnight, if it ever happened. This lack of historical data makes it difficult for our models to address “one-in-a-thousand-year events.” Climate events exceed forecast models Added to this is that the complex physics that governs climate must be simplified in these predictive models. While this approach is effective for common situations, it falls short when it comes to the complexities of extreme events involving rare combinations of variables and factors. And then there are the “unknown factors,” those variables that current models cannot take into account because we are not aware of them or because they have not been integrated into predictive frameworks.