Modelling extreme weather events

Dr Peter Sousounis, vice president and director of climate change research at Verisk, discusses how catastrophe models can help anticipate Black Swans.

Extreme weather events may be rare, but we do have the ability to model and forecast them. We know enough to be better prepared. Which begs the question, what type of weather or climate-related event should be considered a ‘black swan’, and can our predictive capabilities keep pace with a warming planet and the potential convergence of multiple low-probability catastrophes in a single year?

A good example of a true weather-related black swan is Hurricane Katrina in 2005. This hurricane made landfall as a Category 3 storm in Louisiana, with impacts being felt as far away as Mississippi. That area of the US had seen Category 3 hurricanes in the past, and from a hazard standpoint, the hurricane had an exceedance probability of 5%–In other words, there’s a 5% chance of a hurricane of this magnitude happening in any given year—not really an extremely rare event.

Losses of that magnitude from a storm have a 0.2% chance of happening in any given year. More significantly, it’s estimated that approximately 1,500 people lost their lives because of the storm, far in excess of the average loss of life from hurricanes in this region. The differential between storm intensity and storm impact demonstrates that the New Orleans area was not prepared for this type of storm.

Climate change

Climate change is making extreme events more intense and more frequent—in particular, heavy precipitation and more recently, wildfires. It is expected that events of unprecedented intensity will likely continue to occur (eg, Hurricane Harvey-like) leading to potentially high losses, so historical hindsight may not be sufficient.

Necessary foresight can come from climate models, and especially from those that simulate ultra-low probability ways in which the climate itself could change dramatically, such as from the Atlantic Meridional Overturning Circulation (a system of ocean currents) completely shutting down. While capabilities to provide that foresight may currently be hampered by model resolution, model biases, and model errors, these models, because of their physical underpinnings, can certainly give advanced notice to what might be in store as the climate continues to change—in terms of simulating cruder versions of events, which may be considered unprecedented when compared to model-generated versions of similar events from past climates.

For example, a model-simulated version of Hurricane Harvey for the current climate may only generate 50 inches of rainfall because of above-noted limitations, but if a future climate version were to yield 60 inches, that would be an indication that events causing even more rainfall are possible. Combined with scientific insight that can be enhanced through machine learning, physical downscaling the coarse model output to capture smaller-scale event features, and Monte Carlo simulations to generate probabilities, it may be possible to become aware of weather events currently deemed impossible—think of Hurricane Harvey-scale event impacting New York City, a European 2003 (or 1540) heat wave in contemporary China, or whether a tornado outbreak of the scale that hit the U.S. in 2011 could impact Europe in the future.

Moreover, because of their global perspective, such models could hint at what kinds of combinations of such extreme events would be possible in a region or around the world in the same year. The combined impact from hurricanes and wildfire in the US in both 2020 and 2017 was not necessarily by happenstance. Large-scale atmospheric circulations, the kind that general circulation models (GCMs) examine, can provide a conducive environment for such correlated extreme events to occur.

Wind shear

For both 2017 and 2020, for example, a moderate La Niña was in place; that weather phenomenon provided a large high-pressure ridge over the western US, which accelerates drying of vegetation and creating (wild)fire fuel load. It also enhances down-sloping winds across California, which can further promote drying and spread a fire once it has started. A La Niña also reduces wind shear over the Caribbean Main Development Region, which allows strong hurricanes to develop.

So, even though seasonal forecast models at the time did not tell us that there would be record-breaking wildfires and record-breaking hurricane activity in the U.S. in 2017 and 2020, perhaps we could have realized—based on past data—what the models were projecting for the summer and fall of 2017 and 2020, and we should have anticipated the elevated activity. Exacerbating this natural weather phenomenon, climate change has been increasing sea surface temperatures, which provide the primary fuel for hurricanes to develop, and it has been steadily increasing the dryness in the Western U.S.