In a rapidly changing world grappling with more frequent and extreme weather events, our understanding of climate change from past patterns is obsolete and could be dangerously misleading. Climate change, as concluded by the IPCC’s sixth assessment report in 2021, is reshaping our reality. What was once considered “rare” is becoming commonplace, and our tools for prediction need a serious overhaul.
It’s more than hotter summers and heatwaves. Our entire weather system is shifting, changing the way rain, plants, and other elements interact. This shift has been described as “global weirding,” and it’s leading to a world where weather gets more unpredictable and intense, and the complex systems that make up our planetary environment are changing.
How is climate change linked to extreme weather?
In an era of global weirding, nothing is as it seems, or intuitively should be. For example, historically the types of vegetation in an area are closely shaped by prevailing climate conditions, with dense vegetation in wet rainforests and sparse plant life and arid conditions in deserts. But with “global weirding,” these norms are disrupted: deserts may experience more rainfall, while rainforests might dry out. This creates environments with dense vegetation but insufficient rain, meaning a much higher risk of severe wildfires in areas where they were previously rare.
This global weirding and uncertainty is also apparent when looking at new patterns of tropical cyclones, or hurricanes and typhoons as they’re called in different parts of the world. The warming air and seas, shifting wind patterns, and changing ocean currents might all play a role in their increasing intensity. But even the experts at the IPCC tread lightly when talking about this link, and many models point to a relative continuation in frequency, but an extreme increase in severity when compared to previous patterns.
And climate change doesn’t just bring isolated events. It’s like a domino effect. Think about Hurricane Ian’s destruction made worse by constantly rising sea levels and intense rainfalls. Or the strong winds that led to devastation across Mau. Or the massive drought in California, fueled by extreme heat that dries out plants, setting the stage for devastating wildfires.
Just to further confuse matters, even when we move to mitigate our impact on the climate, it can have weird consequences. For example, laws banning sulphur-based fuels used by tankers in key shipping lanes were aimed at reducing emissions. In the long-term this will have benefits, but in the short term it has led to an increase in temperatures across many of these areas, caused by the removal of the sulphur-based compounds which were shielding areas of the planet from more extreme weather conditions.
How do we adapt?
In all of this chaos, predicting future climate risk is becoming a tougher game. In the past, teams would build regression-based models which rely on historical data for future events. But that approach is becoming less reliable as climate-induced disasters change in frequency and intensity. Places that once saw occasional grass fire now experience intense blazes. There are tropical cyclones that venture to unusual northern territories like Canada and Alaska, and floods deemed “one in a 1000-year” events happen with startling regularity.
Even more eye-opening, recent wild weather shifts – such as ferocious wildfires and temperature surges – are outpacing even the top predictions, including those from the IPCC. The newly introduced CMIP6 models, even when evaluated against past events, have consistently underestimated the severity of these weather extremes.
So, where does this leave us? I see three key points that can help us understand our planet better in an era of global weirding.
- We need to acknowledge that using solely historic data for future predictions no longer works. Our climate’s fundamentals are shifting, and our models need to reflect that.
- We need to harness new data sources. The richness of data now available to us – from real-time satellite imagery to advanced marine heat wave metrics – provides a promising foundation.
- We need to harness AI-based technologies. Companies using AI to identify and analyse climate risks are getting better at integrating an array of new data streams into modelling outcomes through using deep learning and AI techniques. AI can help us to integrate more recent variation in temperatures, and help us to understand changes in the next 5 years, rather than predicting what the world could look like in 2100.
This is not just about adapting our models but also our mindset. Recognising that both adaptation and mitigation are no longer choices but necessities. It’s high time for policymakers, businesses, and individuals to embed climate considerations into long-term strategies, ensuring we’re investing in the risk models that are equipped to face the multifaceted challenges of our changing climate.
Josh Gilbert is co-founder and CEO of Sust Global, a geospatial AI company fusing satellite data, climate models and deep learning techniques to help investors and businesses to integrate climate-aware risk management strategies.