Argentina
Tropical Cyclones, Historical
Cyclones are powerful, rotating storms that form over warm tropical and subtropical oceans and generally move from East to West before turning towards higher latitudes. These cyclones are known as Hurricanes in the Atlantic and Northeast Pacific basins, and as Typhoons in the Northwest Pacific basin. Cyclones pose a significant threat upon landfall, causing heavy rain, strong winds, flooding, and widespread damage, which can degrade water quality, spread disease, and destroy infrastructure. In addition to the immediate physical destruction, the aftermath often leads to long-term challenges, such as disruption of essential services, economic loss, and environmental degradation. To mitigate these impacts and support recovery, building broad resilience is essential.
This page presents observed Tropical Cyclones from the International Best Track Archive for Climate Stewardship (IBTrACS) but primarily relies on simulated Tropical Cyclones generated by the Columbia HAZard Model (CHAZ, Lee et al. 2018) for most of the analysis. In most tropical places the occurrence of Tropical Cyclones in any one place is still rare, therefore the historical record is generally too short to allow for a robust estimation of recurrence intervals of these storms. Such historical uncertainty can be reduced somewhat using models, where large ensembles of Tropical Cyclones can be generated. The CHAZ Model simulates tropical cyclones across the oceans and their impact on landfall by generating an extensive synthetic catalog of possible cyclone tracks, offering a more comprehensive view than what can be obtained from observational data alone. The results presented here are currently based solely on the CHAZ Model, albeit driven by a broad array of 12 different Global Circulation Models (from the CMIP6 ensemble). CCKP is offering these products as first examples that are designed to assist users in engaging with Tropical Cyclone data, forming a better understanding of their statistical properties today, and then working in a scenario context to consider possible changes in the future.
CCKP is grateful for the expertise and guidance of the Columbia HAZard Model (CHAZ) Team at Columbia University.
We classify Tropical Cyclones using the Saffir-Simpson Hurricane Scale, which uses maximum sustained wind speed.
- Tropical Storm: 34 to <64 knots (63 to <118.5 km/h)
- Category 1: 64 to <83 knots (118.5 to <154 km/h)
- Category 2: 83 to <96 knots (154 to <178 km/h)
- Category 3: 96 to <113 knots (178 to <209 km/h)
- Category 4: 113 to <137 knots (209 to <254 km/h)
- Category 5: >= 137 knots (>=254km/h)
Below, cyclone information is presented as storm tracks and probabilities (number of storms per year), along with aggregated data at the global average, per ocean basin, per Exclusive Economic Zone (EEZ), and by landfall for associated territories or countries.
Cyclones are fueled by the release of energy from condensation of moisture that has evaporated from the warm ocean surface. If the conduit of moisture can be sustained, which generally happens if the sea surface temperatures are above 26 or 27 degrees Celsius, the storm clusters grow, the central pressure drops, and the convection can become self-sustaining. The Coriolis effect tends to impose rotation on the evolving systems and eventually a Tropical Cyclone system can form that is increasingly taking on a concentric shape as it grows.
In the Northern Hemisphere, cyclones initially move westward and then curve northward where they get into contact with the prevailing westerly flow where they either dissipate due to windshear or where they turn into extratropical cyclones on the way eastward. Equally, in the Southern Hemisphere, they move westward before curving south and eventually southeastward. Cyclones cannot form around the equator (between approximately 4°S and 4°N) because the Coriolis effect is too weak there to maintain their rotation and thus the self-sustaining dynamic is not achieved. Cyclones are very rare in the South Atlantic and on the western coast of South America primarily because of the cold waters there.
01-Historical Tropical Cyclones Activity Maps and Statistics
The map below displays individual observed storm tracks on the left, with colors indicating their intensity. Given that reliable observational data is only available since 1980, we also provide simulated historical cyclone data on the right, categorized by storm type. See a more exhaustive description by clicking on 'Expand Details'.
What you can see in this figure
The map on the left displays observed tropical storms from the International Best Track Archive for Climate Stewardship (IBTrACS), covering the period from 1980 to 2023. It uses wind speed data provided by the USA agency (1-minute sustained wind speed). Each segment of the storm track (recorded every 3 hours) is color-coded based on the storm’s intensity. Note: 'USA wind' combines data from the National Hurricane Center (NHC), the Join Typhoon Warning Center (JTWC), the Central Pacific Hurricane Center (CPHC), and also includes data for the WMO Regional Specialised Meteorological Center at Miami and Honolulu (operated by NOAA).
Given that reliable observational data is only available since 1980, we also provide simulated historical cyclone data on the right to address these observational gaps. The simulated map shows the exceedance probability of a cyclone, calculated for the simulated historical period (1951–2014) using the Columbia HAZard Model (CHAZ). This probability is represented as the number of storms per year in a 0.5° grid of a given category or higher.
How to use?Users can adjust the left map visualization range from 0 to 2.4 (representing frequent events, occurring once every 5 months with a probability of 2.4), 0-0.1 (rare events, occurring once every 10 years with a probability of 0.1), and 0.01 (extremely rare events, occurring once every 100 years with a probability of 0.01).
When clicking on any Exclusive Economic Zone (EEZ), the EEZ and its corresponding territory or country are highlighted. The tooltip provides information on the EEZ's cyclone probability and return period, as well as information on landfall. Additionally, plots below are dynamically adjusted to reflect the selected EEZ, the corresponding ocean basin, and the associated landfall territory.
Understanding the Data: Implications and Utility
While differences in probability may be hard to discern on the left plot (which shows observed tracks), they are much more apparent in the probability plot. Notice how the North Atlantic, a region with high cyclone activity, is more dispersed, whereas the Western and Eastern North Pacific regions show much more concentrated cyclone activity in specific areas. You can also observe that higher categories are less probable, as expected.
What are some caveats and potential limitations to consider?
Individual storm tracks simulated by the statistical-dynamical CHAZ model broadly reflect samples of storm dynamics and evolution. However, the spatial detail of boundary conditions, such as influence from islands or dynamical ocean response are limited. Therefore, CHAZ results provides useful though not necessarily precise spatial detail in storm distributions. Individual grid-cells are to be interpreted with caution as it is neither the intent nor skill of the model to provide location specific accuracy. Basin and EEZ statistics are more appropriate for analysis. CHAZ has applied a bias correction to the simulated number of cyclones to match observed values. This correction varies between ocean and land areas, with a more aggressive adjustment applied for landfalls, resulting in a sharp drop in cyclone density over land. In the following, we display the number density using the ocean correction, even for land pixels, for illustrative purposes. However, when calculating spatially aggregated landfall statistics in the plots below, the correct correction factor for land areas is applied.
What you can see in this figure
Here, we compare the percentage contribution of each category level for all the cyclones (global) and for cyclones over the selected ocean basin, the selected EEZ, and the associated landfall territory (CHAZ modeled cyclone data).
Understanding the Data: Implications and Utility
Note how the percentage of low-intensity cyclones is usually much higher than high-intensity cyclones. Note how the proportion of low-category cyclones increases as the storm approaches land in most cases (from basin to EEZ to landfall).
What are the key caveats and limitations to consider? How was this calculated?
Each cyclone is counted once for the global average and for each ocean basin. Cyclones are categorized based on their maximum wind speed, with each cyclone assigned to the ocean basin where it achieved its peak wind speed. For each EEZ and country (or territory), cyclones are counted and categorized according to the maximum wind speed within that specific region (EEZ or land boundaries). Note that a cyclone may reach Category 5 within an EEZ but weaken to Category 3 upon making landfall. In this case, the storm is counted as a Category 5 cyclone for the EEZ, but as a Category 3 cyclone for the corresponding country or territory. Similarly, a cyclone might reach Category 5 in an EEZ and weaken to Category 3 in another EEZ. In that case, the same cyclone will be counted as Category 5 in the first EEZ, and as Category 3 in the second EEZ. Then, we calculate the percentage of each cyclone type.
What you can see in this figure
Here, we compare the annual total number of cyclones by category on a global scale, within the selected ocean basin, the EEZ, and those that make landfall.
Understanding the Data: Implications and Utility
These numbers will give you an idea of the absolute frequency of cyclones in a region, and how the number decays when landfall occurs.
What are some caveats and potential limitations to consider?
Take the local numbers with caution, as these models are designed to match ocean basin observations, not local specifics.
How was this calculated?
The calculation process is as follows: Each cyclone is counted once for the global average and for each ocean basin. Cyclones are categorized based on their maximum wind speed, with each cyclone assigned to the ocean basin where it achieved its peak wind speed. For each EEZ and country (or territory), cyclones are counted and categorized according to the maximum wind speed within that specific region (EEZ or land boundaries). Note that a cyclone may reach Category 5 within an EEZ but weaken to Category 3 upon making landfall. In this case, the storm is counted as a Category 5 cyclone for the EEZ, but as a Category 3 cyclone for the corresponding country or territory. Similarly, a cyclone might reach Category 5 in an EEZ and weaken to Category 3 in another EEZ. In that case, the same cyclone will be counted as Category 5 in the first EEZ, and as Category 3 in the second EEZ.
02-Basin Timeseries and Seasonal Cycle
Below is the simulated historical annual time series and average daily seasonality for three categories of tropical cyclones: tropical storms, minor cyclones (Category 1 and Category 2), and major cyclones (Category 3 and above) at the ocean basin level. You can select the desired ocean basin for analysis. Note that the Western Pacific basin excludes the South China and Eastern Archipelagic Seas, which are treated as a separate basin.
What you can see in this figure
The time series is designed to visualize annual variability, the total number of cyclones in the selected basin, and the percentage of cyclones by intensity. The plot shows the simulated number of storms per year over the chosen ocean basin, categorized into three groups: 1) tropical storms, 2) minor cyclones (Category 1 and Category 2), and 3) major cyclones (Category 3 and above).
Understanding the Data: Implications and Utility
The annual time series highlights interannual natural variability, a key factor in storm formation that leads to significant year-to-year fluctuations. For instance, El Niño events often increase tropical cyclone formation in the Eastern Pacific but suppress activity in the North Atlantic, Northwest Pacific, and Australian regions. However, it is important to note that interannual variability is less pronounced here than in reality due to the use of a multi-model ensemble.
What are the key caveats and limitations to consider?
It’s important to note that the interannual variability shown here is entirely independent of observations, as this model is based on free-running CMIP6 simulations. These simulations have their own inherent interannual variability and internal Niño cycles, meaning the events of a given year are not directly comparable to observational data. Additionally, interannual variability in the model tends to be lower due to the use of a multi-ensemble approach, rather than displaying individual ensembles separately. As a result, observations are expected to exhibit greater interannual variability.
What you can see in this figure
Here, you can see the daily averaged cyclone activity. The plot shows the averaged number of storms per day for the selected basin, normalized to represent events per 100 years (a standard approach for cyclones, as these are rare events).
Understanding the Data: Implications and Utility
The daily averaged annual climatology helps us identify the more active seasons, typically coinciding with warmer waters in late summer.Cyclone seasons typically peak in late summer (different in the northern and southern hemisphere) when ocean temperatures are at their warmest, providing the necessary heat and moisture to sustain cyclone development. Low wind shear—minimal variations in wind speed or direction with height—is also critical for cyclone formation and maintenance.
What are the key caveats and limitations to consider?
The monthly step-function in the seasonal cycle likely arises from the model's use of monthly data. Note that the total sum of daily values exceeds the annual average. This is because storms can remain active for several days. On average, tropical cyclones are active for 6 days, and each storm is counted daily until it becomes inactive or drops below cyclone-strength winds.