Santa Ana winds intensified the ongoing Franklin fire in Malibu, California, forcing mass evacuations and valiant firefighting efforts. These hot, dry, powerful winds desiccate vegetation, carry embers, and increase flame length, accelerating wildfire spread. From 1990 to 2009, 50% of burned area and 80% of wildfire economic loss in Southern California was linked to Santa Ana winds (Kolden et al., 2018). More recent examples include the 2017 Thomas and 2020 El Dorado fires, which together burned over 300,000 acres.
Climate change is increasing annual average air temperatures and wildfire burned area across the Western US (NOAA, NIFC), but its impact and Santa Ana winds is uncertain. These winds are generated by pressure gradients between the warm Great Plains and the cooler Pacific Ocean that typically form during the autumn months. Reanalysis of weather data from 1979-2020 reveals that strengthening temperature gradients between coastal California and the continental interior may be increasing the frequency of these events (Thompson et al., 2023). However, global climate model outputs suggest that the frequency of Santa Ana winds may decline over the 21st century as the annual window of favourable conditions narrows (Guzman-Morales and Gershunov, 2020).
Though the evolution of Santa Ana winds as the climate changes is unclear, their devastating effect on wildfire activity continues to increase. California’s rainy season arrives ~27 days later than it did in 1960 (Lukovic et al., 2021). A extended wait for wildfire-relieving rains prolongs the period where vegetation dried over the summer months is exposed to Santa Ana winds. As a result, we may see an increasing number of late-season Santa-Ana-fuelled fires like the Franklin fire in the future.
Solid and dashed blue lines show the mean monthly precipitation for California between 1943-1963 and 2010-2023, respectively (National Oceanographic and Atmospheric Association). Yellow bars show the monthly average number of Santa Ana events recorded between 1948 and 2012 (Guzman-Morales et al., 2016).
Earlier this year on September 27th Asheville, NC was the center of the devastation brought by hurricane Helene, with record-breaking rainfall which resulted in extreme flooding. The flooding inundated homes, businesses, and roads, displacing hundreds of residents and leading to billions of dollars in damages and hundreds of houses destroyed in Buncombe County. With 227 fatalities Helene became the deadliest hurricane to hit the US since Katrina in 2005.
With one of the longest daily rainfall records in the U.S., dating back to the 1870s, Asheville’s historical data highlights the peculiar nature of this event. The figure shows, for each year in the historical record, the maximum 24-hour (blue) and 72-hours (yellow) rainfall totals. While Helene’s 1-day rainfall (and peak rainfall rate) were significant but not unprecedented and were exceeded at least twice in the historical record, the 3-day total far exceeded anything in Asheville’s history. This record-breaking 3-day rainfall was fuelled by two days of heavy rain before Helene’s arrival, caused by a unique meteorological setup. The precursor rain saturated the ground and raised river levels, leaving the region vulnerable to catastrophic flooding once Helene made landfall.
This event highlights the growing challenge of preparing for extreme weather. The sequence and intensity of rainfall, rather than daily peaks alone, proved decisive in causing the devastation. At Inigo, we’re prioritizing the evaluation of flood risk models and maximizing our learning from these events to improve our preparedness and resilience in the face of increasingly severe weather patterns. Asheville’s experience underscores the urgency of better understanding flood risk and adapting to a changing climate to mitigate future risks.
AI weather forecasting is in the news again this week, with Google DeepMind’s release of GenCast. AI weather models have made rapid progress for forecasts up to 15 days in advance, but less progress has been made on the notoriously difficult problem of seasonal predictions. Accurate seasonal predictions would provide an opportunity for society to prepare well in advance of hazardous conditions. Here at Inigo we’ve spent the last few months refining our strategy for seasonal hurricane prediction. A major part of that work has been creating I-SPARK (Inigo Seasonal Prediction and Analysis of RisK), a new AI tool to help make accurate seasonal hurricane predictions at insurance relevant lead times of three to nine months.
I-SPARK uses freely available sea surface temperature forecasts to predict the level of risk for the upcoming hurricane season. The figure shows some of the sea surface temperature regions that the model will be using to make its December initiated forecast in a few weeks time. Interestingly, our model disregards most ENSO information until after spring due to low predictability. As the figure inset shows, the model skill rapidly increases by February, but then falls off in spring. This gives us confidence that useful hurricane risk information can be derived as early as February, but we need to do more work to understand why the model struggles in spring.
This work is being presented at the American Geophysical Union conference in early December, 2024. You can check out the full conference poster and additional details on GitHub. Please reach out if you want to learn more.
The 2024 hurricane season began with the earliest Category 5 hurricane ever recorded. In an unprecedented twist there was an eerily long and unexpected lull during the peak season, before ending with a flurry of activity in the Gulf. Next month, Charles Powell, from the University of Cambridge, and I will be sharing a retrospective of 2024 as part of the work that Inigo is funding under its InSPIRe programme.
The chart compares the timing of the first and last major hurricanes for the climatological periods 1980-2020, 1980–2000 and 2000–2020 and compares these to 2024. The vertical lines denote the temperature range between the first and last major hurricane and the horizontal line the mean temperature. From the climatological data we can see that there is a fairly well-defined start to the first major hurricane in the season, but that the end of the season appears to be extending, with the last major hurricane occurring later in the 2000–2020 period compared with the earlier 1980–2000 period. The first major hurricane of 2024, Beryl, is a remarkable outlier becoming a major hurricane on the 30th of June. The last major hurricane of 2024, Rafael, reached major hurricane status on the 6th of November, which, while not unprecedented, is approximately two standard deviations from the long-term mean.
Our analysis shows that the average temperatures in the Atlantic main development region have increased between the early and later periods, 1980–2000 and 2000–2020, with 2024 being one of the hottest years on record. As global temperatures continue to rise due to climate change, warmer oceans are able to sustain major hurricanes earlier and later in the season potentially leading to extended hurricane seasons This could pose a challenge for the insurance sector, as amplified hurricane risk over a longer season increases the potential for catastrophic loss events.
Typhoon Shanshan made landfall in the Kagoshima Prefecture of southern Japan on August 29th bringing considerable flooding and rainfall with it. Some areas broke rainfall records with over 300mm of rain in 24 hours. Early forecasts showed the potential for even more catastrophic outcomes with the storm forecast to make landfall as a powerful typhoon between Osaka and Tokyo, a highly populated area. However, the storm ended up making a sharp turn westward before turning north and making landfall (yellow track on map). The map below shows the forecast tracks for the ECMWF traditional and AI models initialized on August 23rd with the forecast strength of the storm indicated by text in each track point.
Weather models generally did a poor job forecasting this storm. One of the first models to pick up on the westward turn was the AI model run by ECMWF (blue track), one of the world leaders in weather prediction. In many ways this was a win for the AI models, they accurately forecast the track of a dangerous storm hours and days before traditional weather models (the ECMWF traditional model is the orange track). However, this success came with a massive caveat. The ECMWF AI model forecast the westward turn, but also forecast the storm would never rise above tropical storm strength, a known and recurring issue for AI weather models. In reality, the storm briefly reached category 4 strength, with sustained winds of 215km/h, before weakening to a category 1 storm prior to landfall. All together, Typhoon Shanshan presented an interesting case study in the promise and shortcomings of AI weather models.
Inigo Welcomes Patrick Ball to the Climate Science Team.
“As a Risk Scientist within Inigo’s Catastrophe Research team, my role is to help our underwriters and exposure managers leverage the latest scientific advances and cutting-edge technology when assessing the likelihood and impact of natural disasters to our insurance portfolio. This work includes evaluating and optimising catastrophe models, collaborating with academic institutions and insurtech companies, and performing bespoke research projects. This scientific data-driven approach ensures Inigo stands out from the crowd! While our team covers all natural disasters, my specialty is earthquakes. I hold a masters and PhD in Geophysics from the Universities of Oxford and Cambridge, respectively, and prior to joining Inigo I worked as a researcher at several universities and a catastrophe model vendor”.
Idalia made landfall as a major hurricane this week in a season where we’ve been anticipating what might become of a Battle Royale between the anomalously warm seas in the Atlantic and an ongoing El Niño in the Pacific. A reminder: we’re not even climatologically half way through the season yet…
Taking a look at the sea surface temperatures in the Gulf of Mexico, it’s little surprise we saw a strong landfall: in the week leading up to Idalia, the sea in its path was the warmest it’s been in since 1982 (when this particular sea temperature dataset started). The chart here shows the average sea temperature for the period of Aug 23-29th for every year for the past 40 years in the red box shown that broadly straddles the region through which Idalia passed and developed. Naturally other factors will always influence how a storm develops, but you don’t typically get the heavy-hitters of hurricanes without the warmer seas.
And now for the rest of the season…
Lahaina, located on the northwest coast of the Hawaiian island of Maui, experienced a devasting wildfire in August 2023. Several factors likely contributed to the severity of this wildfire including enhancement of strong easterly winds by Hurricane Dora, Katabatic winds flowing from the West Maui Mountains, located to the east of Lahaina, and drought conditions.
The precipitation rate for all of Hawaii has been extracted from the NCEP Reanalysis data between 1980 and 2023 and is shown in the graph. The precipitation rate for February 2023 was the highest recorded since 1980 at nearly three times the average. Precipitation rates throughout the spring were above average but by June it is below average, with July being extremely dry in the bottom 5% of years.
The February and spring rainfall likely contributed to a growth of vegetation. During dry seasons an increase in fuel availability contributes to the wildfire risk. Precipitation rates over Hawaii have been well below normal for June and July leading to drought conditions across Hawaii. According to the US Drought Monitor the most severe droughts over Hawaii in South and West Maui (where Lahaina is located). It is likely that the drought conditions combined with an abundance of fuel from the wet February and spring were contributory factors in the severity of the Lahaina wildfire.
The Copernicus Climate Change Service produces monthly seasonal forecasts from many different weather forecasting centres to help us understand possible future conditions around the globe.
The chart below shows the expected rainfall through the months of September to November from five different seasonal forecast models. Greens show rainier conditions, brown shows drier conditions. The rainier conditions can be indicative of increased tropical cyclone activity. This year we have an El Nino – that usually weakens hurricane activity – but a warm Atlantic, that can increase activity. So, any indications of what is come in such a difficult year to forecast are always useful.
It’s interesting to note the green, wetter region across the Tropical Atlantic. However,this region exists more towards the central / eastern Atlantic, which may be indicative of a busier hurricane season here, but this anomalous wetness is reduced towards the eastern seaboard of the US – although rainfall is still expected to be slightly above average here. All to play for with the three key months ahead – but will we be spared hurricane landfalls with a busy tropical season staying over the sea?
by Charles Powell (University of Cambridge) and Ruth Petrie (Inigo)
Coming into the 2024 hurricane season, almost all forecasts pointed towards an above average season. The Barcelona Supercomputing Centre collates predictions from universities, government agencies and private companies. Across all entities there was an average of 11 hurricanes forecast for the 2024 season, well above the long-term average of 7 hurricanes per season. These forecasts for increased hurricane activity were first made in March-April and remained unchanged throughout the season, despite an unusual lull in activity during the early-to-middle part of the hurricane season that included the climatological peak month of activity in August. So, why did we expect such an active season?
Figure 1: daily average sea surface temperatures in the Atlantic main development region (inset). Record warm SSTs in 2023 and 2024 highlighted.
In March 2023, global average sea surface temperature (SST) reached record warm levels. In the subsequent 15 months, global average daily SSTs set records every day. How does this affect Atlantic hurricane activity? Hurricane activity is strongly correlated with SSTs in the Atlantic main development region (MDR) where most Atlantic hurricanes form (figure 1). This metric therefore forms a key component of seasonal forecasts of hurricane activity. Daily average SSTs in the MDR also reached record levels in June 2023 and have remained exceptionally above average ever since (figure 1). At Inigo we forecast that the August-September-October (ASO) MDR SSTs would be exceptionally warm once again, with a best estimate of 28.95°C (figure 2). With SSTs at such high levels, there is an enormous amount of extra energy available to fuel tropical cyclone intensification.
Figure 2: SST forecast for Atlantic MDR in 2024.
For tropical cyclones to form and intensify into hurricanes, a favourable atmospheric environment must exist for deep convection to initiate and organise into a tropical depression which, given the right circumstances, may then evolve into a hurricane.
The atmospheric conditions in the Atlantic can be heavily influenced by ‘teleconnections’ with atmospheric and oceanic drivers around the world. Of these teleconnections, the El-Niño Southern Oscillation (ENSO) exerts the largest influence on tropical cyclone activity in the Atlantic basin. The positive or ‘warm’ phase – El Niño – tends to inhibit activity by shifting major tropical convection into the Eastern Pacific. This enhances upper-level divergent outflow, resulting in stronger westerly zonal winds in the upper troposphere over the Caribbean and tropical Atlantic. The associated vertical wind shear interferes with the organisation of deep convection and prevents tropical cyclones from intensifying. The negative or ‘cold’ phase – La Niña – has the opposite effect, tending to enhance activity by reducing vertical wind shear in the Atlantic.
Seasonal forecasts for the 2024 hurricane season suggested the El Niño event seen throughout 2023 would quickly flip to La Niña, adding to concerns for a strong 2024 season. The probability and potential strength of the forecast La Niña from April 2024 is shown in figure 3. It was forecast by the Columbia Climate School International Research Institute for Climate and Society (IRI) that in the peak of hurricane season (ASO) there was an 80% chance of La Niña conditions. However, there were indications that it would likely not be a strong event with many forecasting centres predicting only a weak La Niña event. Nonetheless, with two of the biggest predictors of Atlantic hurricane activity suggesting enhanced hurricane activity in the Atlantic, expectations were set for a very active season.
Figure 3: The IRI ENSO predictions of a transition to La Niña for 2024 in April 2024.
The season started relatively slowly with the first named storm not forming until June 19th. This marked the latest start to a hurricane season since 2014. Activity soon picked up, with Hurricane Beryl becoming the earliest category 5 Atlantic hurricane on record at the end of June. From early July through to late September, despite a few named storms forming there was remarkably little activity. Whilst the season typically lasts from June to November, the climatological peak in activity occurs in August and September.
In 2024, after Beryl, there were no major hurricanes until the final week of September and only 4 named storms. A common measure of tropical cyclone activity is accumulated cyclone energy (ACE) which is calculated from maximum sustained winds; large ACE values indicate active periods. Figure 4 shows ACE in the North Atlantic in the 2024 season compared with climatology. After starting with a bang, ACE remained close to the climatological average through to August but then remained well below climatological levels during the peak season.
Figure 4: climatological and 2024 daily accumulated cyclone energy (ACE).
From late September, activity picked up dramatically. Four named storms formed in the space of a week, notably Hurricane Helene which formed in the Caribbean Sea and made landfall in the Big Bend region of Florida as a category 4 hurricane. Helene was an unusually fast-moving storm owing to interaction with a jet streak and a low pressure system centred over the central US. The divergence in the upper troposphere associated with the jet streak significantly enhanced moisture transport within Helene, resulting in catastrophic flooding in North Carolina.
In October, activity remained high with four named storms. All but one of these storms reached hurricane status. Notably, Hurricane Milton formed in the Gulf of Mexico and explosively intensified, undergoing the fastest 24-hour windspeed increase ever recorded in the Gulf of Mexico and becoming the most intense Atlantic hurricane (as measured by central pressure) since 2005.
Above average activity continued into the late season. In early November, Hurricane Rafael reached major hurricane status and tied the 1985 record for the strongest November hurricane in the Gulf of Mexico. In total, there were 18 named storms -less than the predicted 22. However, of these tropical storms, 11 became hurricanes and 5 went on to become major hurricanes, as was predicted. Although the 2024 season was inactive during the typical peak season, overall, 2024 has still been designated as hyperactive by NOAA, meaning total ACE exceeded 175% of the climatological median.
This year was really a tale of two seasons. From June to August, there were 5 named storms, of which one became a major hurricane. From September to November, there were 12 named storms, of which four became major hurricanes. So why was the early-to-mid season so quiet?
Answering this question is complex and multi-faceted. There is no single explanation for the relative inactivity, but we can point to a few factors that inhibited development of tropical cyclones.
Firstly, whilst SST forecasts were accurate – even slightly underestimating peak SSTs – the expected La Niña event developed more slowly than anticipated and therefore had little influence in the early and middle parts of the season.
Moreover, the regions where tropical cyclones form changes through the season – SSTs tend to lag atmospheric temperatures by a few months, such that peak SSTs occur around August and help to fuel the climatological peak in tropical cyclone activity. This allows tropical cyclones to form in the warm Caribbean Sea and Gulf of Mexico.
However, in the early-to-mid season when SSTs are (relatively) colder, most tropical cyclone formation arises from so-called ‘seed disturbances’, or more formally ‘tropical waves’, which are formed within the intertropical convergence zone (ITCZ) – a band of convection near the equator. These tropical cyclone seeds propagate westwards and enter the Atlantic off the coast of Africa. The disturbances promote organisation of deep convection which can then intensify into a tropical storm.
Figure 5: atmospheric setup which inhibited tropical cyclone development in the early-mid season.
The latitude of the band of convection that generates tropical cyclone ‘seeds’ varies annually. This year, the ITCZ shifted further northwards than usual. As a result, seed disturbances entered the Atlantic further north than a typical year in a region where SSTs were in fact slightly colder than average. Furthermore, this coincided with a positive North Atlantic Oscillation (NAO), where high atmospheric pressure over the Azores strengthens vertical wind shear off the west coast of Africa and drags dry air from the Sahara Desert out into the Atlantic.
Together these factors resulted in a poor environment for tropical cyclone development (figure 5). Note that it is difficult to disentangle these two atmospheric events; a positive NAO favours a northward shifted subtropical high whilst a northward shifted ITCZ can also reinforce the atmospheric circulation associated with a positive NAO.
In addition to interannual and seasonal variability in the latitude of seed disturbances and the NAO phase, the tropical troposphere is gradually becoming more stable as the troposphere warms in response to climate change. In particular, the upper troposphere warms faster than the lower troposphere, reducing the atmospheric temperature gradient and therefore inhibiting tropical convection, which limits tropical storm development. Figure 6 shows the potential temperature difference between the lower and upper troposphere during August & September, the peak months of activity.
The temperature difference is decreasing over time and has been particularly low this year, which may have contributed to the lull in activity. Note that the figure shows potential temperature as it is a better indicator of atmospheric stability than temperature alone as it accounts for pressure changes as well as temperature changes.
Figure 6: potential temperature difference between 200 hPa (~12km) and 850 hPa (~1.5km) in the MDR weakening over time, increasing atmospheric stability.
In the late season, as is typical, the jet weakened, and tropical cyclogenesis moved further west as SSTs reached their peak in the Caribbean Sea and Gulf of Mexico. As in the MDR, SSTs in the Gulf of Mexico were well above average (figure 7). On sub-seasonal timescales, the , exerts significant influence on tropical cyclone (TC) activity.
As with ENSO, the MJO can strengthen or weaken vertical wind shear depending on its phase, as well as promoting uplift of air which supports tropical cyclone development. In the early season, the MJO was weak and played little role. However, from August onwards there was a higher than usual amount of MJO activity which may have contributed to the sudden uptick in activity. In summary, the favourable environment and westward shift in tropical cyclogenesis into the anomalously warm Gulf of Mexico allowed for increased TC activity later in the season.
Figure 7: daily SSTs in the Gulf of Mexico.
Perhaps the most notable aspect of the 2024 season is the proportion of named storms that developed into hurricanes. Out of a total 18 named storms, 11 developed into hurricanes and 5 of these were major hurricanes. On average, there are 14 named storms each season, 7 become hurricanes and 3 go on to become major hurricanes. The 2024 season therefore saw both a higher proportion of named storms developing into hurricanes and a higher proportion of these becoming major hurricanes – should we expect this to continue in the future?
Despite modern advances in computing power, global climate models cannot explicitly resolve tropical cyclones. Nonetheless, by considering a range of models, we can develop confidence in predictions of tropical cyclone behaviour in the future climate. Assessments of a range of climate models suggest that global average TC intensity will increase, as well as the proportion of TCs that reach category 4 and above.
Owing to differences in model output, there are less confident projections that the frequency of TCs will decrease whilst the frequency of major TCs increases. In broad terms, these changes can be explained by the increase in stability in the tropical troposphere due to different warming rates with height, and an increase in energy available to fuel TCs owing to higher SSTs. In the context of these projections, the 2024 season may indicate what we see in a future climate: a greater proportion of storms reaching hurricane and even major hurricane status, amplifying the risk of large losses. The 2024 season also saw an unusually active late season.
A recent study suggests that the Atlantic hurricane season may increase by up to a month with warm northern tropical Atlantic SSTs under La Niña conditions. This can easily be understood in the context of increasingly warm SSTs: in figure 1 and 7, we see that SSTs are above the 27C temperature threshold needed for tropical cyclone development for a much longer period compared to climatology.
In the future, we may look back on the 2024 hurricane season as a near-miss; whilst hyperactive overall, the peak season was unusually quiet. The atmosphere and ocean conditions were primed for extreme activity throughout the season, with record warm SSTs across the Atlantic – which are likely to get warmer. However, La Niña developed later than anticipated and variability in smaller-scale processes in the atmosphere stepped in to dampen activity in the Atlantic during peak season.
The last few months of the season were perhaps a view into what hurricane seasons may look like in the future, especially when driven by La Niña conditions and with favourable conditions for TC development. The difficulties in predicting tropical cyclone activity – let alone the complexity of forecasting the potential for individual systems to develop into major hurricanes and make landfall – were laid bare in the 2024 season.
Climate science tells us that we should expect SSTs to continue to warm whilst the tropical atmosphere becomes more stable. However, the response of the ENSO and MJO teleconnections to a warming climate are not yet fully understood and therefore difficult to predict. As shown in 2024, these drivers can play a significant role in modulating TC activity on shorter timescales, adding another layer of complexity to seasonal hurricane forecasting.
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