Accumulated Cyclone Energy Research Articles

Projected increase in the frequency of extremely active Atlantic hurricane seasons.

Future changes to the year-to-year swings between active and inactive North Atlantic tropical cyclone (TC) seasons have received little attention, yet may have great societal implications in areas prone to hurricane landfalls. This work investigates past and future changes in North Atlantic TC activity, focusing on interannual variability and evaluating the contributions from anthropogenic forcing. We show that interannual variability of Atlantic TC activity has already increased, evidenced by an increase in the occurrence of both extremely active and inactive TC seasons. TC-resolving general circulation models project a 36% increase in the variance of North Atlantic TC activity, measured by accumulated cyclone energy, by the middle of the 21st century. These changes are the result of increased variability in vertical wind shear and atmospheric stability, in response to enhanced Pacific-to-Atlantic interbasin sea surface temperature variations. Robust anthropogenic-forced intensification in the variability of Atlantic TC activity will continue in the future, with important implications for emergency planning and societal preparedness.

Tropical cyclone activity over western North Pacific favors Arctic sea ice increase.

Teleconnections between the tropics and the Arctic have attracted a lot of scientific interest. However, the mechanisms by which tropical synoptic-scale systems influence the variability of Arctic sea ice remain unknown. In this study, we highlight the impacts of tropical cyclone (TC) activity over the western North Pacific (WNP) on Arctic Sea Ice Concentration (SIC) using observational evidence and climate model simulation experiments. Our findings demonstrate significant positive correlations between Accumulated Cyclone Energy (ACE) in the WNP and SIC in the Arctic-Pacific Sector (APS), particularly when considering a 30-day lag. The TC activity over the WNP induces Rossby wave train propagation towards the Arctic, leading to anomalous cyclonic circulation over the upper troposphere of the APS. The anomalous cyclone over the Arctic, on one hand, signifies the deepening of the Arctic polar vortex and diminishes adiabatic warming over the APS, subsequently inducing cooling and drying of the lower Arctic air. This process reduces downward longwave radiation, promoting an increase in September APS SIC. On the other hand, the anomalous cyclone over the Arctic hinders the export of sea ice and local melting processes throughout the Fram Strait. These findings contribute to a deeper comprehension of tropics-Arctic teleconnections.

Spatial Changes of Driving Parameters Affecting Cyclonic Activity over the North Indian Ocean from 1960 to 2020

This study undertakes a thorough analysis of the elements that influence the variability of tropical cyclones (TC) in the North Indian Ocean (NIO) from 1960 to 2020, with a specific focus on the periods before and after the monsoon season. The study utilizes historical satellite data to investigate the factors that impact the formation, strength, and trajectories of cyclones. The primary method for evaluating cyclone strength is by calculating the Accumulated Cyclone Energy (ACE). The study observes a decreasing trend in ACE levels during 1991–2005, which started increasing just after from 2006 to 2020. The Bay of Bengal (BoB) has a more uniform distribution of ACE in comparison to the Arabian Sea (AS), with higher average values and more variability over the Main Development Region (MDR), which is the area where cyclone development occurs most frequently. Cyclones of greater intensity generally occur following the monsoon season. Examination of storm paths reveals that cyclones with greater intensity frequently hit the northeastern and southeastern coastal regions of India. The study emphasizes notable discrepancies in parameters within the MDR, which impact cyclone strength and ACE values throughout various periods.

Assessing South Indian Ocean tropical cyclone characteristics in HighResMIP simulations

AbstractSeveral damaging tropical cyclones (TCs) have occurred recently over the South Indian Ocean (SIO) region, causing enormous social and economic losses. Yet, while many studies have examined SIO TC characteristics using observations and reanalysis, only a few have assessed these characteristics specifically for this region in climate models, and fewer have investigated their projections under climate change. Here we do this for a historical (1980–2010) and future (2020–2050) period, using multimodel simulations from the High Resolution Model Intercomparison Project, as well as examine biases in the historical period relative to a reanalysis (ERA5). The models have horizontal resolutions of 25–50 km, which has enabled an improved ability to represent tropical cyclones globally in previous studies. TempestExtremes software is employed to detect tropical storm and cyclone tracks. In cases where TempestExtremes cannot be applied due to a lack of requisite variables in a dataset, we instead examine extreme wind speeds in that dataset. For the historical period, we find considerable variation in model biases compared to ERA5, which itself exhibits realistic spatial patterns of tracks and their monthly distribution. Models do at least agree on positive biases in track frequency east of Madagascar and somewhat in the Mozambique Channel. However, the models and ERA5 only produce Category 3 tropical cyclones at best. Wind speeds for 25 km resolution models have much larger positive biases than for 50 km ones, suggesting the former can simulate even higher‐category tropical cyclones. Considerable intermodel variation is also found in track changes between the future and historical periods. No systematic intercategory pattern of change exists, and low signal‐to‐noise may obscure any such patterns in the limited timespan of available data. Thus, no meaningful conclusions can be drawn regarding changes in track intensity. Nevertheless, track frequency broadly decreases across models for the region, as does accumulated cyclone energy. An east‐to‐west shift in track location from east of Madagascar toward the Mozambique Channel is also implied by track frequency and wind speed changes. Our findings provide information to potentially improve storm resiliency in this vulnerable region.

The 2023 Atlantic Hurricane Season: An Above-Normal Season despite Strong El Niño Conditions

Abstract The 2023 Atlantic hurricane season was above normal, producing 20 named storms, 7 hurricanes, 3 major hurricanes, and seasonal accumulated cyclone energy that exceeded the 1991–2020 average. Hurricane Idalia was the most damaging hurricane of the year, making landfall as a Category 3 hurricane in Florida, resulting in eight direct fatalities and 3.6 billion U.S. dollars in damage. The above-normal 2023 hurricane season occurred during a strong El Niño event. El Niño events tend to be associated with increased vertical wind shear across the Caribbean and tropical Atlantic, yet vertical wind shear during the peak hurricane season months of August–October was well below normal. The primary driver of the above-normal season was likely record warm tropical Atlantic sea surface temperatures (SSTs), which effectively counteracted some of the canonical impacts of El Niño. The extremely warm tropical Atlantic and Caribbean were associated with weaker-than-normal trade winds driven by an anomalously weak subtropical ridge, resulting in a positive wind–evaporation–SST feedback. We tested atmospheric circulation sensitivity to SSTs in both the tropical and subtropical Pacific and the Atlantic using the atmospheric component of the Community Earth System Model, version 2.3. We found that the extremely warm Atlantic was the primary driver of the reduced vertical wind shear relative to other moderate/strong El Niño events. The concentrated warmth in the eastern tropical Pacific in August–October may have contributed to increased levels of vertical wind shear than if the warming had been more evenly spread across the eastern and central tropical Pacific.

Remote effect of tropical south Atlantic sea surface temperature anomalies on April–June accumulated cyclone energy over the western North Pacific

Global processes and their teleconnections, such as the El Niño-Southern Oscillation (ENSO), have been shown to be a large driver of interannual changes in accumulated cyclone energy (ACE) of western North Pacific (WNP) tropical cyclones (TCs), with higher ACE during El Niño and lower ACE during La Niña. However, it remains uncertain whether interannual changes in WNP TC ACE are modulated by sea surface temperature anomalies (SSTAs) in other oceans. This study finds a significant negative correlation between WNP TC ACE during the early season (April–June) and simultaneous SSTAs over the tropical south Atlantic (TSA) in 1970–2021. On average, in warm TSA years, basinwide April–June ACE is significantly lower, with significant ACE decreases mainly occurring over the region spanning 5°–30°N, 115°–150°E. This is a result of reduced TC frequency, intensity and duration, due to a remote modulation of WNP environmental conditions by TSA SSTAs. In warm TSA years, there are significant decreases in 700–500-hPa relative humidity, 850-hPa relative vorticity and 200-hPa divergence and significant increases in 850–200-hPa vertical wind shear over the portion of the WNP with significant ACE reductions. These environmental changes can be linked to an anomalous Walker circulation induced by TSA SSTAs.

Effects of Solar Variability on Tropical Cyclone Activity

AbstractThe current study explores the relationship between solar variability and tropical cyclone (TC) activity using sunspot number (SSN) and TC best‐track data as respective proxies. We have considered six regions of the globe, for example, EP: Eastern Pacific, NA: North Atlantic, NI: North Indian, SI: South Indian, SP: South Pacific, and WP: Western Pacific. The results show strong anti‐correlation between yearly TC activity and yearly SSN while considering their 11‐year moving averages. This behavior is consistent for TC counts as well as accumulated cyclone energy. However, this is true only for the North Atlantic region. Overall, when we consider all regions together, more TCs (in terms of counts) are observed during lower solar activity periods (SSN < 50) as compared to higher solar activity conditions (SSN > 100). However, the yearly rates remain more or less similar. On the other hand, extreme TC events with a maximum wind speed of 137 knots and higher (category 5) are most likely to occur during the declining phase of a solar cycle and least likely to occur during the ascending phase or the maximum phase. Although solar activity levels are similar during the declining and ascending phases, the yearly occurrence rate is nearly double in the declining phase (1.123) as compared to that in the ascending phase (0.625).

Alternative point‐wise metrics for tropical cyclone risks

AbstractTo minimize the risks from tropical cyclones (TCs), the quantification and regular monitoring of TC activities are strongly needed. While the accumulated cyclone energy (ACE) (Bell et al., 2000, Climate assessment for 1999. Bulletin of the American Meteorological Society, 81, S1–S50) has been widely used for examining the level of basin‐wide TC activities, the conventional ACE does not discriminate the regional characteristics of TC risks. By introducing a point‐wise version of ACE, this paper proposes a geographical approach to the risk map for TCs. Here, the alternative metric is named the localized ACE (LACE), which interprets the TC risk directly felt by the local residents. Annual LACE at a geographical point measures TC activity by merging the quantities of frequency, intensity and duration factors, which contribute to the local TC activity in a year. In conjunction with LACE, a concept of LACE partial contribution (LACEP) is also proposed. The LACEP enables quantitative comparison of the contribution by each factor to the LACE, and thereby identifies the characteristics of the regional TC risks. To demonstrate the efficacy of the indices, that is, LACE and LACEP, this paper provides the response of the local TC activities to El Niño–Southern Oscillation in the western North Pacific and confirms the value of these indices.

Quantifying Downstream Climate Impacts of Sea Surface Temperature Patterns in the Eastern Tropical Pacific Using Clustering

El Niño–Southern Oscillation (ENSO) phases and flavors, as well as off-equatorial climate modes, strongly influence sea surface temperature (SST) patterns in the eastern tropical Pacific and downstream climate. Prior studies rely on EOFs (which characterize fractional SST variance) to diagnose climate-scale SST structures, limiting the ability to link individual ENSO flavors with downstream phenomena. Hierarchical and k-means clustering methods are used to construct Eastern Pacific patterns from the ERSST dataset spanning 1950 to 2021. Cluster analysis allows for the direct linkage of individual SST years/seasons to ENSO phase, providing insight into ENSO flavors and associated downstream impacts. In this study, four clusters are revealed, each depicting unique SST patterns influenced by ENSO and Pacific Meridional Mode (PMM) phases. A case study demonstrating the utility of the clusters was also carried out using accumulated cyclone energy (ACE) in the Atlantic and Eastern Pacific basins. Results showed that Eastern Pacific (EP) El Niño suppresses Atlantic tropical cyclone (TC) activity, while Central Pacific (CP) La Niña enhances it. Further, EP El Niño, coupled with positive PMM, amplifies ACE. Ultimately, the methods used herein offer a cleaner analysis tool for identifying dominant SSTA patterns and employing those patterns to diagnose downstream climatic effects.

Impact of MJO Propagation Speed on Active Atlantic Tropical Cyclone Activity Periods

AbstractThe Madden‐Julian Oscillation (MJO) is often used for subseasonal forecasting of tropical cyclone (TC) activity. However, TC activity still has considerable variability even given the state of the MJO. This study evaluates the connection between MJO propagation speed with Atlantic TC activity and possible physical mechanisms guiding this relation. We find the Atlantic sees the highest accumulated cyclone energy (ACE) during MJO phase 2. However, the odds of above average ACE in the Atlantic is greatest during slow MJO propagation. We find that slow propagation of the MJO results in lower vertical wind shear anomalies over the Caribbean and main development region compared with typical MJO propagation. Typical MJO propagation produces an amplified height pattern and lower height anomalies along the region of the tropical upper tropospheric trough which is known to impede Atlantic TC activity. Slow MJO propagation sees weaker height anomalies over the Atlantic.

Decadal Shift in the Relationship between Accumulated Cyclone Energy of Tropical Cyclones in the Northwest Pacific and ENSO

Decadal Shift in the Relationship between Accumulated Cyclone Energy of Tropical Cyclones in the Northwest Pacific and ENSO

An Extratropical Pathway for the Madden–Julian Oscillation’s Influence on North Atlantic Tropical Cyclones

Abstract This study investigates the combined impacts of the Madden–Julian oscillation (MJO) and extratropical anticyclonic Rossby wave breaking (AWB) on subseasonal Atlantic tropical cyclone (TC) activity and their physical connections. Our results show that during MJO phases 2–3 (enhanced Indian Ocean convection) and 6–7 (enhanced tropical Pacific convection), there are significant changes in basinwide TC activity. The MJO and AWB collaborate to suppress basinwide TC activity during phases 6–7 but not during phases 2–3. During phases 6–7, when AWB occurs, various TC metrics including hurricanes, accumulated cyclone energy, and rapid intensification probability decrease by ∼50%–80%. Simultaneously, large-scale environmental variables, like vertical wind shear, precipitable water, and sea surface temperatures become extremely unfavorable for TC formation and intensification, compared to periods characterized by suppressed AWB activity during the same MJO phases. Further investigation reveals that AWB events during phases 6–7 occur in concert with the development of a stronger anticyclone in the lower troposphere, which transports more dry, stable extratropical air equatorward, and drives enhanced tropical SST cooling. As a result, individual AWB events in phases 6–7 can disturb the development of surrounding TCs to a greater extent than their phases 2–3 counterparts. The influence of the MJO on AWB over the western subtropical Atlantic can be attributed to the modulation of the convectively forced Rossby wave source over the tropical eastern Pacific. A significant number of Rossby waves initiating from this region during phases 5–6 propagate into the subtropical North Atlantic, preceding the occurrence of AWB events in phases 6–7.

Exploring the Complex Dynamics of Tropical Cyclone Activity in the Southern Indian Ocean: A Multidecade Analysis

This study analyzes tropical cyclones (TCs) in the Southern Indian Ocean from 1970 to 2022, utilizing data from the International Best Track Archive for Climate Stewardship (IBTrACS) and Sea Surface Temperature (SST) data from ERA-5. Trends are assessed with Sen's slope method. Significant TC shifts are evident. Categories 1-2 TCs have substantially reduced in number, duration, and speed, indicating an overall decline in activity. Conversely, more powerful TCs (categories 3-5) have intensified, driven by rising SST. This intensification is associated with alarming increases in the Power Dissipation Index (PDI) and Accumulated Cyclone Energy (ACE), reflecting heightened destructive potential. TCs predominantly occur during DJFM, influenced by elevated SST compared to MJJASO. Climate drivers, including El Niño-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Madden-Julian Oscillation (MJO), significantly affect TC characteristics. Elevated TC activity coincides with La Niña, neutral ENSO phases, and positive IOD events. At the same time, MJO influences are nuanced, leading to a slight TC activity decline, potentially due to data limitations in specific MJO phases. This study offers vital climatological insights into evolving TC risks in the Southern Indian Ocean, aiding agencies and decision-makers in understanding TC-related risks in the region.

Tropical Cyclonic Energy Variability in North Indian Ocean: Insights from ENSO

Tropical cyclones (TC) are one of the deadliest natural meteorological hazards with destructive winds and heavy rains, resulting losses often reach billions of dollars, imposing a substantial and long-lasting burden on both local and national economies. The El-Niño Southern Oscillation (ENSO), a tropical ocean–atmosphere interaction, is known to significantly impact cyclonic systems over global ocean basins. This study investigates the variability of TC activity in the presence of ENSO over the North Indian Ocean (NIO), comprising the Arabian Sea (ARB) and the Bay of Bengal (BOB) basins during the pre- and post-monsoon season, using accumulated cyclone energy (ACE) over the last 29 years. Our analysis reveals a significant rise in tropical cyclone energy intensity over the past two decades, with eight of the ten most active years occurring since the 2000s. Total ACE over the NIO is found to be higher in La-Niña. Higher ACE observed over ARB is strongly associated with a combination of elevated sea surface height (SSH) anomaly and low vertical wind shear during the El-Niño episodes, with higher sea surface temperatures (SST) during the post-monsoon season. Whereas in the BOB, El Niño not only reduces ACE, but also decreases basin-wide variability, and more pronounced effects during the post-monsoon season, coinciding with warmer SST and higher SSH along the coast during La-Niña.

Evaluation of Multiweek Tropical Cyclone Forecasts in the Philippines

Abstract In the pursuit of providing tropical cyclone (TC) forecasts beyond the conventional time scales covered by weather forecasting in the Philippines, this study has examined the multiweek (i.e., from week 1 to week 4) TC forecast skill in the country. TC forecasts derived from three ensemble models, namely, the NCEP Climate Forecast System version 2 (CFSv2), the European Centre for Medium-Range Weather Forecasts Ensemble Prediction System (ECMWF), and the NCEP Global Ensemble Forecast System version 12 (GEFSv12) from 6 October 2020 to 31 October 2021 were verified. Results revealed that the ECMWF model is consistently the most skillful in multiweek TC prediction over the domain bounded by 110°–155°E and 0°–27°N in the western North Pacific. The ECMWF obtained hit rates ranging from 0.25 to 0.31, low false alarm rates of 0–0.33, and the highest equitable threat scores among the models. In contrast to this, the GEFSv12 and CFSv2 models had varying skills, with the former performing better in the first two weeks and the latter in longer lead times. It is further revealed that the three models generally underestimate the observed number of storms, storm days, and accumulated cyclone energy. Moreover, the study shows that the forecast TC tracks have a significant (p < 0.05) positional bias toward the right of observed tracks beyond week 1, and that they tend to propagate slower than observations especially in week 1 and week 2. These findings contribute to better understanding the strengths and limitations of these ensemble models useful for eventual provision of multiweek TC forecasts in the Philippines.

Use of threshold parameter variation for tropical cyclone tracking

Abstract. Assessing the capacity of numerical models to produce viable tropical cyclones, as well as assessing the climatological behavior of simulated tropical cyclones, requires an objective tracking method. These make use of parameter thresholds to determine whether a detected feature, such as a vorticity maximum or a warm core, is strong enough to indicate a tropical cyclone. The choice of parameter thresholds is generally subjective. This study proposes and assesses the parallel use of many threshold parameter combinations, combining a number of weaker and stronger values. The tracking algorithm succeeds in tracking tropical cyclones within the model data, beginning at their aggregation stage or shortly thereafter and ending when they interact strongly with extratropical flow and transition into extratropical cyclones or when their warm core decays. The sensitivity of accumulated cyclone energy to tracking errors is assessed. Tracking errors include the faulty initial detection and termination of valid tropical cyclones and systems falsely identified as tropical cyclones. They are found to not significantly impact the accumulated cyclone energy. Thus, the tracking algorithm produces an adequate estimate of the accumulated cyclone energy within the underlying data.

Introduction

Introduction

A Decadal Climate Service for Insurance: Skillful Multiyear Predictions of North Atlantic Hurricane Activity and U.S. Hurricane Damage

Abstract North Atlantic Ocean hurricane activity exhibits significant variation on multiannual time scales. Advance knowledge of periods of high activity would be beneficial to the insurance industry as well as society in general. Previous studies have shown that climate models initialized with current oceanic and atmospheric conditions, known as decadal prediction systems, are skillful at predicting North Atlantic hurricane activity averaged over periods of 2–10 years. We show that this skill also translates into skillful predictions of real-world U.S. hurricane damage. Using such systems, we have developed a prototype climate service for the insurance industry giving probabilistic forecasts of 5-yr-mean North Atlantic hurricane activity, measured by the total accumulated cyclone energy (ACE index), and 5-yr-total U.S. hurricane damage (given in U.S. dollars). Rather than tracking hurricanes in the decadal systems directly, the forecasts use a relative temperature index known to be strongly linked to hurricane activity. Statistical relationships based on past forecasts of the index and observed hurricane activity and U.S. damage are then used to produce probabilistic forecasts. The predictions of hurricane activity and U.S. damage for the period 2020–24 are high, with ∼95% probabilities of being above average. We note that skill in predicting the temperature index on which the forecasts are based has declined in recent years. More research is therefore needed to understand under which conditions the forecasts are most skillful. Significance Statement The purpose of this article is to explain the science and methods behind a recently developed prototype climate service that uses initialized climate models to give probabilistic forecasts of 5-yr-mean North Atlantic Ocean hurricane activity, as well as 5-yr-total associated U.S. hurricane damage. Although skill in predicting North Atlantic hurricane activity on this time scale has been known for some time, a key result in this article is showing that this also leads to predictability in real-world damage. These forecasts could be of benefit to the insurance industry and to society in general.

Analysis of shift in Indian monsoon and prediction of accumulated cyclone energy in Indian subcontinent using deep learning

Every year India faces many cyclones and erratic monsoon seasons are common in recent times. Cyclones destroy the infrastructure and lead to loss of life and damage property in coastal areas. The agriculture sector is also affected by random and unexpected rainfall. In recent years, India gets rainfall during the harvest season which leads to financial loss. Also, the number of drought events is on the rise in the Indian subcontinent as the rainwater is not managed properly. Farmers need to know whether the monsoon rainfall pattern has been shifted or not and need to shift their agricultural activity accordingly to handle the impacts of climate change. From the rainfall and accumulated cyclone energy (ACE) data analysis, it is found that monsoon seasons in India are not shifted, but, rainfall is intense during the initial months of each monsoon season. ACE values are predicted using techniques such as ARIMA, LSTM, Prophet, and stacked ensemble with multi-layer perceptron. Based on the experimental results, the proposed stacked ensemble model achieves 91% accuracy.

Accumulated Cyclone Energy-Based Tropical Cyclone Return Periods in Florida

This article introduces an accumulated cyclone energy (ACE) approach for estimating the return period of tropical cyclone (TC) wind risk in Florida. As opposed to calculating return periods directly from maximum sustained wind speed, the ACE-based approach also describes the duration of the strong winds, giving an additional dimension to the assessment of TC wind risks. Because Florida is a peninsula, TCs can move across the state within six hours of landfall, causing an underestimation of the inland wind footprint if only the six-hour reanalysis track points are employed as an input data source. This study uses four different scenarios and an inland exponential decay function to interpolate the wind speed between the six-hour reanalysis track points to feed the ACE-based return period calculation based on a 121-year record from 1900 to 2020. South Florida has the shortest return period (five to ten years) of TCs with an ACE equivalent to one hour of hurricane intensity (≥ 64 kt; 1 kt ∼ 0.51 m s−1) caused by intense historical hurricane strikes, and Polk County in inland central Florida has an equal return period due to frequent and long-duration TC occurrences, acting as an intersection for landfalling TCs in the Florida peninsula.