Inigo’s 2025 Seasonal Hurricane Outlook 

Key Points

• Seasonal hurricane forecasting is challenging, partly due to the limited predictive power of two key variables.
• Inigo has worked to improve our seasonal outlook methodology by incorporating additional regions of ocean temperatures into our forecasts and developing an in-house machine learning model called I-SPARK.
• Inigo’s outlook suggests a slightly above average 2025 hurricane season.

Seasonal Hurricane Forecasting Background

The Atlantic Ocean is starting to wake up and with it eyes are turning towards the upcoming hurricane season. In spring of each year various public and private entities forecast the level of expected hurricane activity during the upcoming Atlantic hurricane season that officially starts in June. 2024 brought forecasts warning of a potentially catastrophic hurricane season. Those forecasts largely verified with 5 US landfalls and NOAA declaring an extremely active season, but there were surprises with a lull during the typical peak of hurricane season in August and September. As we look ahead to 2025 it’s worth taking a moment to step back and think about how seasonal hurricane forecasts are made.

Two of the primary metrics used to predict seasonal hurricane activity are the sea surface temperatures (SST) in a region of the Atlantic Ocean called the Main Development Region (MDR) and a region of the Pacific Ocean called the El Nino Southern Oscillation (ENSO). There are a few ways to measure ENSO, but the most common is by using the SSTs in the Nino3.4 region (see map, Figure 2). When the MDR is warm and the Nino3.4 region is cool hurricane risk is elevated, while a cool MDR and warm Nino3.4 region generally reduces hurricane activity. Both of these regions have decades worth of scientific literature highlighting their importance and were the centrepieces of the elevated 2024 forecasts. Other variables such as vertical wind shear, Saharan Dust, the Madden-Julian Oscillation, and atmospheric humidity levels are important, but forecasting them months in advance is not currently possible. As a result we end up relying quite a bit on variables that are predictable, such as SST. While the MDR and the Nino3.4 SSTs are important for predicting seasonal hurricane activity, they are less accurate than desired. Even if we knew exactly what temperatures would be in these two regions during hurricane season, a simple leave one out multivariate regression model using data since 1980 can only explain 34% of the variability in Atlantic hurricane activity (Figure 1). More sophisticated modelling frameworks can improve this, but the problem still remains. Adding to the difficulty is that we don’t know what temperatures in those two regions will be during hurricane season as we rely on seasonal forecasts released by C3S to predict what the SSTs will be. This means that seasonal hurricane forecasts rely heavily on imperfect forecasts of two variables (MDR and Nino3.4) that can only explain part of the variability in hurricane activity. No wonder seasonal hurricane prediction is so difficult! As we get closer to hurricane season the C3S seasonal forecasts become more accurate and we can begin to use other variables in addition to SST. Both of these help create more accurate forecasts and are a big reason why the June forecasts from Colorado State University are much more accurate than their April forecasts (see page 45 of CSU Hurricane forecast verification).

Figure 1. While the MDR and the Nino3.4 region are helpful predictors of hurricane activity, they fail to capture the full range of observed variability.

Inigo’s Seasonal Hurricane Methodology

Given the historical difficulty of making seasonal hurricane forecasts in spring, what is Inigo doing to try and make our forecasts more accurate? Over the last few months we’ve been exploring what other regions of SST might be useful to include. We’ve done this by leveraging large sets of climate model simulations, using data driven ML approaches, and continuing our ongoing partnership with Reask. In addition, we’ve spent time thinking about what the ‘right’ variable to predict is. As an insurance company with significant US interests what we really want to know is the specific landfall information for every storm in the upcoming 2025 season. Unfortunately, this level of detail is impossible to know this far in advance so we have to settle for alternatives. The two complementary approaches that we highlight below predict different things, but help us form a more complete view of hurricane risk in 2025.

The first approach uses a machine learning model called I-SPARK (Inigo Seasonal Prediction and Analysis of RisK). This model uses current values and future predictions of dozens of regions of SST to forecast if the accumulated cyclone energy west of 60° longitude (ACE 60W) will be in the lower, middle, or upper tercile for the season (referred to as Low, Medium, or High risk). We have trained our I-SPARK model to predict ACE 60W as we have found that of all hurricane related variables, ACE 60W strikes the right balance between predictability and correlation with insured US hurricane losses. This approach is also qualitative, we are only predicting a risk category of ACE 60W rather than its exact value. While this has limitations, we’ve chosen this approach due to the historically low skill of hurricane predictions in the March and April timeframe. We’re currently working on a more quantitative prediction method to complement this categorical ML model.

The second approach focuses on seven key regions of SST (Figure 2) and leverages Reask data to create quantitative estimates of what the upcoming season may bring given the forecast SST conditions in each of those seven regions. Reask provides us with hundreds of thousands of stochastic hurricane tracks tied to environmental conditions. By selecting only the Reask data that is connected to the environmental conditions we expect in 2025, we can create a 2025-specific stochastic hurricane set. This builds upon our approach from last year, and we’ve found the inclusion of seven SST regions instead of the two used last year (MDR and Nino3.4) yields more accurate forecasts over the last 20 years. Seasonal forecasting is always a humbling exercise, and we will no doubt learn something in 2025 that we can apply to 2026, but we feel confident that our methodology is moving in the right direction.

Figure 2. In 2024 the Inigo team focused on SSTs in two regions, Nino3.4 and the MDR, but have expanded this year to using the seven highlighted in this map.

As evidence of the improvement from increasing the number of SST regions used, if we repeat the example from Figure 1, where we used MDR and Nino3.4 values during hurricane season to predict the number of named storms that season, but now include our seven regions of interest, we see a noticeable increase in the variability explained from 34% to 45%  (Figure 3). Not all historical years see improvement with the inclusion of seven variables instead of two, but most do. While not shown here, we see similar improvements using seven regions instead of two when using the Reask data.

Figure 3. Comparison of using two versus seven regions of SST to predict the number of named storms in the Atlantic Ocean. Overall, using seven regions yields noticeable improvement. Similar improvement is seen forecasting other variables such as ACE 60W.

2025 Hurricane Outlook

Getting to the primary question of interest, what does Inigo think the 2025 hurricane season has in store for us? Our I-SPARK model predicts that ACE 60W will fall in the upper tercile, which would be indicative of increased hurricane risk, particularly near the US coastline (Figure 4). However, the likelihood of an upper tercile risk year has been decreasing in recent months. Using Shapley values  we can discern that the model has backed off its earlier confidence for an active year primarily due to cooling in the Atlantic Ocean from January to early March, which have brought temperatures from near-record warmth to above average. While a below average year is still unlikely, the odds of an average year have been increasing.

Figure 4. Our I-SPARK ML model predicts the likelihood of a risk category for the upcoming season. While the odds of a ‘High Risk’ season are diminishing, the model predicts this is still the most likely outcome.

Our second approach uses the predicted SSTs in seven different regions and Reask data to create a stochastic hurricane set for the 2025 season. This method produces a similar result to the ML model, above average risk, but not nearly as dire as the 2024 seasonal forecasts were. This above average risk is primarily driven by warmer than average SSTs that are persisting in the Atlantic Ocean, but is also influenced by a number of other SST regions. To better understand how each of the seven SST regions are influencing risk we can visualize what the forecasts are for hurricane season compared to the observations since 1990 (Figure 5). The forecasts are from the C3S seasonal forecasts issued by a number of leading national modelling centres and we exclude modelling centres with exceptionally poor historical performance. A few items that stand out for the SST forecasts: the MDR is forecast to be below the record-breaking 2023 and 2024 values, but still above the long-term average and with a tail risk for very warm temperatures in 2025. This tail risk is partially supported by ocean heat content values (a measure of the amount of heat in the upper layer of the ocean) that are still quite high, particularly in the western MDR. The Iberian Coast SSTs might surpass 2024 for the warmest year, potentially raising risk via connections with the North Atlantic Oscillation (Elsner et al., 1999). The forecasts for the Nino1.2 and Nino3.4 regions quite uncertain and we are just emerging from the spring predictability barrier that makes ENSO forecasting difficult in winter and early spring. There is some indication that the Nino1.2 region could tilt towards the El Nino (warmer, and lower hurricane activity) phase and it has rapidly warmed since January, but whether that persists through hurricane season is an open question. Given these uncertain forecasts for the Nino3.4 and Nino1.2 regions we are not heavily relying on the either region in making our seasonal forecast.

Figure 5. Historical SSTs and the forecast distributions for this upcoming hurricane season for the 7 different regions we are primarily focusing on in 2025.

So, where does this leave us overall? Unlike 2024 the signals are not all pointing towards the same outcome. While some regions, the MDR and the Iberian Coast, point towards elevated risk, others, Nino1.2 and the Atlantic Nino, point towards average or diminished risk. Overall our I-SPARK model and our Reask-supported methodology both point towards slightly elevated risk. While our current best estimate for the 2025 season is slightly elevated risk, we also have reasons for cautious optimism thanks to recent cooling trends. Here’s to hoping for a quiet 2025 hurricane season!