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Large-scale atmospheric and oceanic circulation fluctuations have a strong impact on the global hydrological cycle and tropical cyclones (TC), which mainly generate intense precipitation and flooding. The primary objective of this research is to analyze the main climate indices (CI), which are techniques used to summarize complex climate information in simpler and more understandable forms. These indices are based on meteorological data, such as temperature, precipitation, humidity, and other atmospheric parameters to provide summary information about the climatic conditions in a particular region. Some common utilities and functions of climate indices are (i) climate monitoring; (ii) anomaly detection; (iii) agricultural planning; (iv) climate risk assessment; (v) scientific research; (vi) climate insurance; (vii) climate adaptation; and (viii) evaluation of water resources. CI play a crucial role in water management climate research and public policy planning, providing tools to understand and address challenges associated with climate conditions.
National University of San Agustin of Arequipa, Arequipa, Peru
Kenny Gonzales
Advance Engineering and Research, Urb. Colegio de Ingenieros E3, Cerro Colorado, Arequipa, Peru
*Address all correspondence to: hgonzalesz@unsa.edu.pe
1. Introduction
It is now well-recognized that large-scale atmospheric and oceanic circulation fluctuations have a strong impact on the global hydrological cycle. Such relationships are helpful in the global understanding of the non-stationary relationships that exist between ocean and atmosphere mean conditions and freshwater discharge integrated at a continental scale [1]. TC dominate natural hazard losses, these losses have increased substantially in recent decades, driven largely by increased exposure, and studies that isolate the hazard component by normalizing the losses for exposure find no linear long-term trend [2].
CI are tools used to summarize complex climate information in simpler and more understandable forms. CI play a crucial role in various fields, from agriculture and water management to climate research and public policy planning, providing tools to understand and address challenges associated with weather conditions.
These indices are based on meteorological data, such as temperature, precipitation, humidity, and others, to provide summarized information about weather conditions in a particular region. Here are some common utilities and functions of climate indices: (i) climate monitoring; (ii) anomaly detection; (iii) agricultural planning; (iv) climate risk assessment; (v) scientific research; (vi) climate insurance; (vii) climate adaptation; and (viii) water resources assessment.
There is a diversity of climatic or oceanic indices developed by different researchers worldwide, however, for this research, only the most well-known and used indices are described.
NAO, represents the dominant climate pattern, where a meridional shift of the atmospheric mass occurs over the North Atlantic area, Europe, and North America. The NAO measures the strength of westerly flow (positive with strong westerly winds and vice versa) and is an important source of seasonal to interdecadal variability, influenced by ocean and sea ice processes. The NAO and its recent counterpart, the Arctic Oscillation/Northern Hemisphere Annular Mode (AO/NAM), are the significant variability modes in the northern hemisphere winter climate [3].
NAO is based on the difference in the air pressure between the high area of the Azores (Azores Anticyclone) and the low area of Iceland (Iceland Anticyclone) and has two phases: positive and negative.
During positive phases of the NAO, weaker pressure occurs between Iceland and the Azores, generating above-average temperatures in the eastern US and throughout northern Europe and below-average temperatures in Greenland and throughout southern Europe, Europe, and the Middle East. This variability causes stronger wetter winds from the west toward Europe, causing milder, wetter winters in that region, as well as more frequent storms in the North Atlantic.
During the negative phase of the NAO, dominant processes occur for long periods, for example, in the mid-1950s until the winter of 1978/1979 (24 years), there were four periods of at least 3 years each in which the negative phase was dominant and the positive phase was absent.
Likewise, opposite patterns of temperature and precipitation anomalies occur (colder and drier winters in Europe). They are also associated with above-average rainfall in northern Europe and Scandinavia in winter, below-average rainfall in southern and central Europe, and a decrease in storms in the North Atlantic. Both phases of the NAO present prolonged periods of positive and negative phases generating changes in the location of the jet stream and the path of storms from the North Atlantic to Western and Central Europe and with large-scale modulations of the transport patterns of heat and humidity (Figure 1) [4].
Figure 1.
NAO, based on the atmospheric pressure, differences between Iceland and the Azores represent the dominant climate pattern in the North Atlantic Region, and its teleconnection is characterized by a meridional displacement of atmospheric mass.
NAO predicts winter weather patterns (temperature and precipitation) in Europe and North America, important for agriculture and water management.
The TNA index represents the damage potential of North Atlantic TCs due to the effect of winds and coastal surges using seasonal climate parameters of sea surface temperature and flow direction. These climatic parameters are indicators of the variables of intensity size and speed of advance of the TC. The potential damage from the cyclone is based on assessments of past periods expressed in actual damage and the characteristics of exposure and vulnerability [5].
It is the development of an index of cyclone damage potential formulated as [6]
CDP=4[(vm65)3+5(Rh50)]vtE1
where vm is the maximum surface wind speed (knots), Rh is the radius of hurricane-force winds (nautical miles), and vt is the forward translation speed (knots). The CDP applies to damage caused by wind, waves, and currents at sea as well as damage caused by intense winds on land and coastal flooding, excluding damage resulting from precipitation. The CDP coefficient scales from 0 to 10, and the damage is related to the duration of destructive winds (vt−1), not just their maximum value (vm).
TNA Positive: the SST in this region is above average, favoring the formation of hurricanes in the Atlantic.
TNA Negative: SSTs are below average; therefore, hurricane season is less active.
Applying the TNA index is important to evaluate winds, coastal waves, changes in SST, and forecast hurricane and TC activity in the tropical Atlantic region. Its use is essential to plan and reduce the adverse effects of climate (Figure 2).
Figure 2.
TNA, based on SST variations of the tropical Atlantic and regions of the Gulf of Mexico and the west coast of Africa, monitors hurricane and tropical cyclone activity and is developed using seasonal climate variables of relative SST and steering flow.
SAM describes the fluctuations in atmospheric pressure around the South Pole. It is the main mode of interannual atmospheric variability in the extratropics of the southern hemisphere (SH) (Atlantic Ocean, South America, Australia, New Zealand, southern Africa, and Antarctica), but is not consistent in the terrestrial areas of SH, indicating that the influence of the SAM index is modulated by regional effects. Teleconnections are stationary in regions with good proxy data coverage (South America, Tasmania, and New Zealand) [7].
Similarly, SAM is quantified by an empirical orthogonal function (EOF analysis) as EOF1 of the field or as the difference in mean sea level pressure (SLP) between mid and high latitudes [8]. Likewise, SAM also presents a barotropic vertical structure at the sea level. SAM and NAM (Northern Annular Mode) are used to determine the processes involving the troposphere and stratosphere in each hemisphere [9]. According to the EOF1 definition, SAM evolution would explain on average 30% of the climate variability of the hemisphere through the SLP or geopotential height [10].
Positive phase: it occurs during the summer, generating strengthening of the westerly winds associated with warming in the Peninsula. During the autumn, winter, and southern spring, warming occurs in the Antarctic Peninsula related to sea surface temperatures (SST) in the tropical Pacific and Atlantic.
Negative phase: it indicates a weakening of the processes generated in the positive phase.
The ENSO-Peninsula temperature relationship during autumn is weak on interannual time scales, and the tropical Pacific SST variability associated with the El Niño-Southern Oscillation (ENSO) is stronger mainly during winter and spring. These circulation changes dominate interannual temperature variability across the Antarctic Peninsula during summer and autumn.
The SAM index can help foresee anomalous climatic conditions, such as heat waves, drought, or intense rains, floods, as well as the impact on ecosystems and biodiversity. Likewise, knowledge of the SAM index can help plan water resources management and natural risk management (Figure 3) [11].
Figure 3.
SAM, based on fluctuations in atmospheric pressure over the South Atlantic; South America, Australia, New Zealand, Southern Africa, and Antarctica, presents positive and negative phases related to westerly winds associated with greater and lesser warming.
The AMO operates on a longer time scale with North Atlantic SST fluctuations that can last decades, with impacts on atmospheric circulation and surrounding regions, influencing the distribution of precipitation, temperature, and extreme events such as hurricanes and storms. Similarly, the AMO monitors climate variability during alternating warm and cold phases in large parts of the Northern Hemisphere and is related to rainfall in northeastern Brazil, African Sahel, Atlantic hurricanes, and summer weather in North America and Europe.
During the positive phase of the AMO, the SST is warmer than average, in most of the North Atlantic, except for the east coast of the United States. The atmospheric patterns that appear are anomalous low pressures over the Atlantic between 20°S and 50°N, cyclonic surface winds around the low, and reduced wind speeds over the tropical Atlantic. Warm SST anomalies in the tropical Atlantic (via atmospheric teleconnections) generate anomalous cooling in the equatorial eastern-central Pacific.
During the negative phase of AMO, more or less opposite conditions occur and the SST is cold in most of the North Atlantic, producing an increase in precipitation in the eastern tropical Atlantic.
Anticyclonic anomalies in the North and South Pacific generate equatorward winds, distributing along the coasts of North and South America and contributing to further cooling [12]. However, there are differences between SST and atmospheric anomalies during periods of the same phase, especially in the extratropics. The correlation between the AMO and air temperature anomalies is positive over much of the world between 40°S and 50°N [13].
Global-scale Multidecadal Variability (GMV) could be generated by the AMO through atmospheric teleconnections and atmosphere-ocean coupling mechanisms. Its horizontal pattern resembles that of the Interdecadal Pacific Oscillation (IPO) in the Pacific and the AMO in the Atlantic Ocean, whose processes could affect precipitation and global temperature around the world. Observations show that there is a strong negative correlation when AMO outperforms GMV throughout approximately 4–8 years [14].
The usefulness of this index allows us to make forecasts of climate patterns in the North Atlantic on a multidecadal time scale, as well as planning in the management of the agricultural sectors, water resource management, and understanding of climate variability in the Atlantic North and its impacts on the global climate (Figure 4).
Figure 4.
AMO operates on a longer time scale (decades) with fluctuations in the North Atlantic SST, influencing atmospheric circulation, precipitation, temperature, and extreme events such as hurricanes and storms. It is a near global scale mode of observed multidecadal climate variability with alternating warm and cool phases of the northern hemisphere.
The AO exerts a predominant influence on the variability of atmospheric circulation (atmospheric pressure) in the extratropical region of the northern hemisphere during the boreal winter, and the common feature is the oscillation of inverted sea level pressure (SLP) anomalies in high and mid-latitude regions [15].
Positive phase: there is lower atmospheric pressure in the Arctic and higher pressure in the mid-latitudes of the northern hemisphere, leading to a strengthening of the polar jet stream and the generation of strong winds.
Negative phase: during this phase, atmospheric pressure is highest in the Arctic and lowest in mid-latitudes, and this process weakens the polar jet stream and causes greater variability in the climate.
The AO is also known as the near-surface mean sea level pressure (MSLP) pattern related to the northern annular modes (NAM). It has its application in climate models to predict climate patterns or changes in high latitudes of the northern hemisphere, as well as understanding climate variability in the Arctic and its effects on the global climate. It is also useful in short- and long-term planning in sectors such as agriculture, energy, and transportation (Figure 5) [16].
Figure 5.
AO describes the variability of atmospheric pressure at high latitudes in the northern hemisphere (Arctic) and presents positive and negative phases related to atmospheric pressure.
ONI is used to determine the occurrence and duration of El Niño episodes, based on monitoring SST in the central Pacific Ocean. It is used to detect a level of SST above the average that is maintained for several months, covering both the beginning and the end of an El Niño episode.
When a seasonal anomaly of 0.5°C in temperature is recorded for the first time, it indicates a high probability that an El Niño event will develop; although to confirm stronger alerts, it is necessary to wait several months. For this reason, it was suggested that an ONI value of 0.7°C marks a critical point at which the El Niño phenomenon stabilizes, providing an additional period for social decision-makers to implement preventive actions. This indicates that the initial detection of a value of 0.7°C could reliably indicate the stabilization phase of the El Niño phenomenon, providing greater reliability to the current El Niño onset indicator, set at 0.5°C, thus allowing vulnerable communities to prepare for the foreseeable social and ecological impacts of the phenomenon [17].
ENSO indicators can be evidenced through the Oceanic Niño Index (ONI), as well as through variations in SST that influence the intensity of precipitation. Consequently, the presence of El Niño and La Niña causes a reduction or increase in precipitation in Indonesia [18, 19].
Positive phase: known as El Niño, SSTs in the central and eastern equatorial Pacific are warmer than normal (weakening trade winds and a reduction in the resurgence of cold waters). There is an increase in precipitation over the equatorial Pacific and drought in some regions such as Australia and western South America. In addition, it could lead to an increase in the frequency of tropical cyclones in the Pacific, as well as hotter and more arid climatic conditions in certain areas of Asia and Africa.
Negative phase: known as La Niña, SSTs in the central and eastern equatorial Pacific are colder than normal (strengthening of the trade winds and an increase in the resurgence of cold waters). There is increased rainfall over the western Pacific and drier conditions in the eastern equatorial Pacific. Additionally, it may result in increased hurricane intensity in the Atlantic and cooler, wetter weather conditions in some regions of Asia and Australia.
These ONI phases, El Niño and La Niña, have important implications on global climate patterns and can affect sectors such as agriculture, fisheries, food security, natural disasters, and other aspects of society and the economy (Figure 6).
Figure 6.
ONI describes the occurrence and duration of El Niño and La Niña episodes, based on SST monitoring in the central Pacific Ocean.
SOI, one of the world’s most important climate indices, is a measure of the difference in air pressure in the Pacific Ocean, from Tahiti in the southeast to Darwin in the west. SOI is used to monitor and anticipate variations in both ENSO and the Walker Circulation (WC). During El Niño episodes, for example, the WC weakens, and the SOI tends to show negative values. Climate fluctuations associated with alterations in the WC exert a significant influence on the climate, ecosystems, agriculture, and communities in various regions of the planet.
Related research has shown that (i) WC and SOI have weakened in recent decades, (ii) WC tends to decrease in climate models in response to higher concentrations of greenhouse gases in the atmosphere, as well as it has also been determined that the atmospheric pressure at the mean sea level (MSLP) between the eastern and western equatorial Pacific tends to increase during the twenty-first century, and not decrease.
Under global warming, the MSLP tends to increase in both the Darwin region and Tahiti which is located in a large region where the MSLP tends to increase in response to global warming.
The influence of global warming is already having a significant impact on the long-term variability of SOI, accounting for 45% of the standard deviation of the 30-year moving averages of the SOI. This proportion is estimated to increase to approximately 340% by the end of the twenty-first century. The implications of these findings for understanding recent climate change and seasonal prediction are discussed [20, 21].
Positive phase: during this phase, atmospheric pressure is higher than normal in the central and eastern Pacific region and lower than normal in the western Pacific region. This may indicate La Niña conditions and is associated with the cooling of sea surface waters in the central and eastern Pacific.
Negative phase: in this phase, atmospheric pressure is lower than normal in the central and eastern Pacific region and higher than normal in the western Pacific region. This would indicate El Niño conditions and is associated with a warming of sea surface waters in the central and eastern Pacific.
These phases of the SOI are fundamental to understanding the El Niño-Southern Oscillation (ENSO) phenomenon and its effects on global climate patterns. However, the SOI is not a good indicator of long-term equatorial changes related to global warming.
The SOI is used to monitor the evolution of ENSO and provide information on the phases of El Niño and La Niña, as well as used in climate models to predict the occurrence and intensity of El Niño and La Niña events. These predictions are important to assist in resource planning and management in sectors such as agriculture, water, and energy management, as well as preparing for potential climate impacts, such as drought and floods (Figure 7).
Figure 7.
SOI, monitors and anticipates variations in both ENSO and the Walker Circulation (WC) and presents positive and negative phases associated with cooling and warming of SST.
MEI, uses several ocean-atmosphere variables (sea level atmospheric pressure (SLP), SST, zonal and meridional components of the surface wind, and outgoing longwave radiation (OLR)) in the tropical Pacific basin, to monitor and evaluate the ENSO phenomenon more completely and flexibly. The MEI stands out as the only ENSO index that considers spatial variations of its main components concerning the seasonal cycle. Furthermore, it represents a weighted average of all these characteristics [22].
The spectral analysis carried out using appropriate data reduction techniques on the monthly values of the MEI (1950–2008) has revealed the presence of a significant 60-month cycle, with a statistical reliability greater than 99%. The highest MEI values (typical of El Niño events, (Figure 8a)) and the lowest MEI values (typical of La Niña events, (Figure 8b)) coincide with the highest and lowest points of the 60-month cycle [23].
Figure 8.
MEI uses several ocean-atmosphere variables (SST, wind at different levels of the atmosphere, and cloudiness) in the tropical Pacific basin, to monitor and evaluate the ENSO phenomenon more completely and flexibly. (a) The highest values of MEI (typical of El Niño events). (b) The lowest values of MEI (typical of La Niña events). Source: Adapted https://psl.noaa.gov/enso/mei/. 2024.
The extreme points of the MEI show a correlation with the extremes in the fluctuations of the atmospheric CO2 concentration and are inversely related to the extremes of the variation in the length of day (LOD) rate. This indicates a strong connection between both local and global mechanisms in the Earth-atmosphere system, which are remarkably coupled and synchronized at various scales [24].
One of the applications of the MEI index is the prediction of the occurrence and intensity of El Niño and La Niña, which allows planning the management of risks associated with extreme climate phenomena, such as droughts, floods, and storms for the benefit of agriculture, as well as to implement water and soil management practices (Figure 8).
RII has been designed to be applied in the Atlantic and Northeast Pacific basins, using large-scale predictor from the Statistical Hurricane Intensity Prediction Scheme (SHIPS), that is, to calculate the probability of rapid intensification (RI) occurring in the next 24 hours, using a linear discriminant analysis. The individual predictive factors used by RII show variations between the two basins. In the Atlantic basin, the most important factors include the change in intensity in the last 12 hours, the divergence at higher levels, and the vertical shear. In the case of the eastern basin of the North Pacific, the factors with the greatest weight are the change in intensity in the last 12 hours, the symmetry of the internal convection areas, the difference between the current intensity, and the maximum potential of the system.
An analysis of forecast accuracy was performed by comparing climatological data for the 2006 and 2007 hurricane seasons, the results of which demonstrated that RII probabilistic forecasts are consistently accurate in both basins. Furthermore, using deterministic forecasts, RII was monitored to outperform all other available operational tools in terms of probability of detection (POD) and false alarm rate (FAR) [25]. Likewise, through the use of satellite images around the center of a TC, a characteristic pattern of precipitation rings has been found, which is associated with RI. By coupling the ring criterion with the RII index of the SHIPS, the probability of RI increased almost twofold. This suggests that both the ring and the SHIPS RII index contain independent information for predicting RI.
RII allows determining the probability that a tropical cycle will experience a rapid increase in its intensity. This information is valuable for forecasting agencies as well as for decision-making in terms of evacuations and emergency preparations. Additionally, RII can be combined with other criteria such as satellite-detected precipitation ring patterns to further improve the accuracy of predicting the rapid intensification of tropical cyclones (Figure 9) [26].
Figure 9.
RII, based on SST, atmospheric humidity, wind speed, and direction in different layers of the atmosphere, using mathematical models is designed for the prediction of hurricane intensity, that is, the probability of rapid intensification (RI) occurring in the next 24 hours (Atlantic and Northeast Pacific Basins).
11. Genesis Potential Index (GPI)
GPI is an indicator to evaluate the probability of development of TC, its argumentation includes the processes that drive climate variability and the future change of the genesis of TC (TCG). The GPI of a TC estimates oceanic and atmospheric parameters, based on the understanding of oceanic impacts, where many factors are evaluated and discriminated, resulting in a GPI index. However, the precise determination of the contribution of thermodynamic environmental factors compared to dynamic factors in the formation of convective storms remains a challenge, especially in the context of global warming [27].
Through stepwise logarithmic regression analysis and considering 11 dynamic and thermodynamic factors, four main dynamic factors have been identified; the absolute vorticity at 850 hPa, the vertical velocity at 500 hPa, the vertical variation of the wind in the troposphere, and the vorticity of the zonal wind shear at 500 hPa. These four main dynamic factors have a significant influence on both the current simulation and the future projection of global warming. The dynamical GPI, which is composed of these four dynamical parameters, provides a valuable diagnostic tool for understanding future changes in tropical storm formation. At the same time, it improves the ability to represent interannual variations in the frequency of tropical storm formation in the oceans of the western Pacific and southern hemisphere [28].
Its application of the GPI helps to forecast and monitor the potential for the development of tropical cyclones and the intensity they could reach in a given region. It is important for preparation, mitigation, and response to possible extreme weather events (Figure 10).
Figure 10.
GPI, based on atmospheric parameters such as SST, low-level cyclonic vorticity, atmospheric instability, vertical wind shear, and atmospheric humidity, evaluates the probability of TC development, based on the understanding of oceanic impacts, where several factors are evaluated and discriminated.
12. The Pacific Decadal Oscillation Index (PDO)
The PDO is a crucial indicator of decadal climate variability in the Pacific, defined as the main empirical orthogonal function of North Pacific Sea Surface Temperature anomalies, which some experts have compared to a long-lasting pattern in the Pacific climate, similar to El Niño. Others have described it as a combination of two, sometimes independent, modes of climate variability in the Pacific, showing differences in terms of the location and temporal characteristics of variations in SST in the North Pacific. A growing body of evidence highlights the notable influence of the PDO in the southern hemisphere, with significant effects on climatic conditions in the mid-latitudes of the South Pacific Ocean, Australia, and South America.
PDO presents positive and negative phases. During positive phases of the PDO, SSTs in the North Pacific tend to be warmer than normal, while during negative phases, they tend to be colder than normal. These phases can persist for decades before changing, and their effects can be felt in different parts of the world, influencing climate patterns and weather phenomena. During the periods between 1890 and 1924, as well as between 1947 and 1976, a prevalence of “cold periods” of the PDO was observed. On the other hand, between 1925 and 1946, as well as from 1977 to the mid-1990s, “warm periods” of the PDO predominated.
Based on climate reconstructions from tree ring and coral records in the Pacific, it is suggested that variations in the PDO (along different time scales) can date back at least to the year 1600, although various indirect reconstructions show significant discrepancies, just as PDO fluctuations during the twentieth century showed two general patterns of periodicity (one from 15 to 25 years and another from 50 to 70 years), the mechanisms underlying this variability are still not fully understood [29].
It has been shown that the PDO can be reconstructed from a representation of sea surface temperature anomalies in the North Pacific, using a first-order autoregressive model and influenced by low-temperature variability in the Aleutians, the El Niño-Southern Oscillation (ENSO) phenomenon, and the zonal advection of oceanic anomalies in the Kuroshio-Oyashio extension. The latter originates from oceanic Rossby waves, driven by Ekman pumping in the North Pacific. The response patterns of sea surface temperature to these processes are not perpendicular and determine the spatial characteristics of the PDO. It is concluded that the PDO is not an independent dynamical mode but rather arises from the superposition of fluctuations in sea surface temperature with different dynamical origins [30].
PDO is used for the prediction of climate patterns such as droughts and floods, and the frequency and severity of events such as El Niño and La Niña, providing information on long-term SST trends in the North Pacific, is also an important topic of scientific research to better understand long-term climate variations and their impact on marine and terrestrial ecosystems. Likewise, understanding the positive and negative phases of PDO can help in the sustainable management of natural resources (Figure 11).
Figure 11.
PDO is an indicator of climate variability over decades with a long-lasting pattern in the Pacific climate similar to El Niño. PDO presents positive and negative phases. Positive phase: North Pacific SSTs are warmer than normal. Negative phase: the opposite of the positive phase. These phases can last decades before change, and their effects can be felt in different parts of the world.
13. The Madden-Julian Oscillation Index (MJO)
The MJO index is a measure that describes the variability of rain and wind patterns in the tropical region of the Indian Ocean and the Pacific Ocean, allowing us to characterize the phase and intensity of the Madden-Julian Oscillation (natural cycle of changes in convection and winds in the equatorial region, characterized by two areas, one of suppressed rainfall and one of enhanced rainfall, each cycle of the oscillation lasts 30–60 days and is split into eight phases of equal length).
The MJO, like weather, has pulses of variability with a finite predictability time scale (predictability of at least 2–3 weeks), that is, future states of the MJO can be predicted up to a certain time in advance based on the knowledge of current and past states. Teleconnection links linked to MJO have a significant effect on circulations outside tropical regions, influencing meteorological and climate events such as monsoons in Southeast Asia, tropical storm formation in the Indian Ocean and the Pacific, the North Atlantic Oscillation, and the Pacific-North America pattern, and can also affect precipitation patterns in Australia and India. However, the accuracy of the MJO has been a considerable challenge in many general circulation models (GCMs), as these often introduce significant biases in the fundamental atmospheric fluxes. This makes it difficult to adequately reproduce the teleconnection patterns associated with the MJO and the resulting impacts in the extratropics. Previous studies have revealed that the spatial arrangement of MJO-associated warming in the Indo-Pacific (MJO models) has a limited impact on the ability to predict teleconnection patterns [31].
These studies suggest that the sensitivity to the location of tropical heating near the subtropical jet is minimal. However, it has been observed that warming of the MJO east of the dateline could modify teleconnection trajectories in the North American region [32].
The MJO index is useful for short- and medium-term climate prediction in various regions of the world, improving predictions of extreme weather events, such as droughts, floods, and tropical cyclones, as well as to better understand climate variability on a global scale (Figure 12).
Figure 12.
MJO describes the variability of rainfall and wind patterns in the tropical Indian Ocean and Pacific Ocean, as well as to better understand climate variability on a global scale.
14. Indian Ocean Dipole Index (IOD)
IOD measures climate variability in the Indian Ocean region and its effects on regional and global climate. Determining the fluctuation of SST differences between two opposite regions in the western Indian Ocean and the eastern Indian Ocean plays an important role in predicting the El Niño Southern Oscillation in the tropical Pacific. IOD manifests itself as abnormal changes in SST, and atmospheric pressure in different parts of the Indian Ocean, and this process also generates positive and negative phases.
During the positive phase, the waters in the western Indian Ocean tend to be warmer than normal, while in the eastern Indian Ocean, they are colder than normal [33].
The Niño 3.4 index and IOD can be used to indicate drought phenomena, especially the 2-month main anomaly SST parameter in MJJ and NDJ [34].
The decade-long signal of the IOD index is closely related to the depth anomaly at which the 20°C isotherm is found in the ocean, suggesting that oceanic processes play an important role in the long-term variation of the IOD. Furthermore, this signal is also linked to anomalies in zonal winds, and in tropical regions, it is interpreted to have a decadal modulation of events occurring over single and multi-year periods [35].
The application of IOD allows the prediction of extreme weather events, droughts, floods, and storms, which can assist in disaster planning and mitigation, as well as natural resource management and agriculture in the Indian Ocean region (Figure 13).
Figure 13.
IOD measures climate variability in the Indian Ocean region and its effects on regional and global climate (western Indian Ocean and eastern Indian Ocean) and plays an important role in predicting the El Niño Southern Oscillation in the tropical Pacific. IOD manifests itself as abnormal changes in SST and atmospheric pressure (it presents positive and negative phases), in different parts of the Indian Ocean.
15. Pacific Regional Equatorial Index (PREI)
This PREI Index was developed to determine the occurrence of a unique ENSO that occurred in 2017 specifically in the northern area of Peru, whose occurrence is not common. For example, a previous similar event occurred in early 1925. This type of ENSO was called Coastal ENSO by the Multisectoral Commission in Charge of the National Study of the El Niño Phenomenon (ENFEN).
The Coastal ENSO event that occurred in 2017 was one of the first recorded with Meteorological satellites, whose occurrence is due to a succession process of Modoki ENSO and Modoki La Niña events [36]. Likewise, it is also mentioned that the mechanism for the Coastal El Niño is the combined effect of local winds and equatorial Kelvin waves that caused the extreme coastal El Niño phenomenon of 2017, amplified by the positive Bjerknes feedback. Furthermore, pre-existing SST warming along the western coast of subtropical South America also favored anomalous northerly winds [37].
Its usefulness lies in differentiating the occurrence of a traditional ENSO and a Coastal ENSO, which have different formation processes, and allowing the prediction of extreme climate events such as precipitation and flooding in the northern area of Peru.
Table 1 presents a summary of the description and applications of the different climate indices developed in the present study.
Climate Index
Description
Applications
North Atlantic Oscillation (NAO)
Based on atmospheric pressure differences between Iceland and the Azores NAO positive: strong, moist winds, and more frequent storms in the North Atlantic. High rainfall in Scotland and southwestern Norway. Warm and wet winters in Western Europe. NAO negative: colder, drier winters in Europe and fewer storms in the North Atlantic
Prediction of weather patterns; temperature and precipitation, important for the management of agriculture and water resources
Tropical North Atlantic Index (TNA)
Based on SST variations of the tropical Atlantic, regions of the Gulf of Mexico and the west coast of Africa. Monitors hurricane and tropical cyclone activity. TNA positive: the SST is above average, favoring the formation of hurricanes in the Atlantic. TNA negative: the SST is below average. Therefore, the hurricane season is less active.
Predicts the intensity, size and activity of hurricanes and TCs in the tropical Atlantic region. Important for planning and mitigating the impacts of these climate events.
South Atlantic Oscillation Index (SAM)
Based on fluctuations in the atmospheric pressure over the South Atlantic, South America, Australia, New Zealand, Southern Africa, and Antarctica. Positive phase: it occurs during the summer, with west winds associated with warming in the Peninsula. Negative phase: indicates a weakening of the processes generated in the positive phase.
Prediction of heat waves, drought, heavy rain (floods), and storm. Important for the management of water resources and natural risks, as well as the mitigation of the impact on ecosystems and biodiversity.
Atlantic Multidecadal Oscillation Index (AMO)
It operates on a longer time scale (decades) with fluctuations in the North Atlantic SST, influencing atmospheric circulation, precipitation, temperature, and extreme events such as hurricanes and storms. Positive phase: SSTs in the North Atlantic are higher than normal. Negative phase: SSTs in the North Atlantic are lower than average.
Forecasting climate patterns in the North Atlantic on a multi-decadal time scale. It allows planning in the management of water resources, energy, and agriculture.
Arctic Oscillation Index (AO)
Describes the variability of atmospheric pressure at high latitudes in the northern hemisphere (Arctic) Positive phase: atmospheric pressure; lowest in the Arctic and highest in mid-latitudes of the northern hemisphere, strengthening of the polar jet stream and strong winds. Negative phase: atmospheric pressure; highest in the Arctic and lowest in mid-latitudes, weakening the polar jet stream and greater variability in climate.
Application in climate models, to predict climate patterns in high latitudes of the northern hemisphere (Arctic) and their effects on global climate. Short- and long-term planning in agriculture, energy, and transportation
Oceanic Niño Index (ONI)
Describes the occurrence and duration of El Niño and La Niña episodes, based on SST monitoring in the central Pacific Ocean. Positive phase: known as El Niño, SSTs in the central and eastern equatorial Pacific are warmer than normal. Negative phase: known as La Niña, SSTs in the central and eastern equatorial Pacific are colder than normal.
Monitoring and forecasting the occurrence of El Niño and La Niña events, which can generate increased rainfall in the equatorial Pacific and drought in regions such as Australia and western South America and vice versa. Helps plan and mitigate its possible impacts.
Southern Oscillation Index (SOI)
Monitors and anticipates variations in both ENSO and the Walker Circulation (WC). Positive phase: indicates La Niña conditions, associated with cooling of sea surface waters in the central and eastern Pacific. Negative phase: indicates El Niño conditions, associated with a warming of sea surface waters in the central and eastern Pacific.
Monitors and predicts the evolution and occurrence of ENSO; El Niño y La Niña Resource planning and management; agriculture, water, and energy, as well as preparing for possible climate impacts, such as drought and floods.
Multivariate ENSO Index (MEI)
MEI uses several ocean-atmosphere variables (SST, wind at different levels of the atmosphere, and cloudiness) in the tropical Pacific basin, to monitor and evaluate the ENSO phenomenon more completely and flexibly.
Prediction of the occurrence and intensity of El Niño and La Niña, allowing planning for risk management associated with extreme climate events, such as droughts, floods, and storms.
Rapid Intensity Index (RII)
Based on SST, atmospheric humidity, wind speed, and direction in different layers of the atmosphere, using mathematical models. Designed for the prediction of hurricane intensity, that is, the probability of rapid intensification (RI) occurring in the next 24 hours (Atlantic and Northeast Pacific Basins)
Prediction that a tropical cycle experiences a rapid increase in intensity. This information is valuable for decision-making in terms of evacuations and emergency preparations.
Genesis Potential Index (GPI)
Based on atmospheric parameters, SST, low-level cyclonic vorticity, atmospheric instability, vertical wind shear, and atmospheric humidity. It evaluates the probability of TC development, based on the understanding of oceanic impacts, where several factors are evaluated and discriminated.
Forecast and monitor tropical cyclone development potential and intensity in a given region. Important for preparation, mitigation, and response to possible extreme weather events.
Pacific Decadal Oscillation Index (PDO)
PDO is an indicator of climate variability over decades with a long-lasting pattern in the Pacific climate similar to El Niño. PDO presents positive and negative phases. Positive phase: North Pacific SSTs are warmer than normal. Negative phase: the opposite of the positive phase. These phases can last decades before changing, and their effects can be felt in different parts of the world.
Predicting weather patterns such as droughts, floods, and the severity of events such as El Niño and La Niña. Understanding the positive and negative phases of PDO can help in sustainable natural resource management.
Madden-Julian Oscillation Index (MJO)
The MJO index describes the variability of rainfall and wind patterns in the tropical Indian Ocean and Pacific Ocean, as well as to better understand climate variability on a global scale.
It allows short- and medium-term climate prediction in various regions of the world of extreme weather events, such as droughts, floods, and tropical cyclones.
Indian Ocean Dipole Index (IOD)
It measures climate variability in the Indian Ocean region and its effects on regional and global climate (western Indian Ocean and eastern Indian Ocean) and plays an important role in predicting the El Niño Southern Oscillation in the tropical Pacific. It presents positive and negative phases.
It allows the prediction of extreme weather events, droughts, floods, and storms in the Indian Ocean region.
Pacific Regional Equatorial Index (PREI)
PREI was developed to determine the occurrence of an ENSO, which occurred in 2017 specifically in the northern area of Peru (called coastal ENSO) whose occurrence is not common.
PREI allows the difference between the occurrence of a traditional ENSO and a Coastal ENSO, allowing the prediction of extreme precipitation and flooding in the northern area of Peru.
Table 1.
Summary of the description and applications of the different Climate Indices developed in the present study.
16. Conclusions
16.1 Tropical cyclones
The TNA is an indicator of the key parameters of TC intensity, size, and forward speed that constitute an index of existing cyclonic damage potential.
16.2 Southern hemisphere
SAM and SOI represent the atmospheric variability that involves the troposphere and stratosphere in the southern hemisphere and in the extratropical southern hemisphere.
16.3 North hemisphere
NAO, AMO, and RII represent dominant climate patterns in the North Atlantic and eastern North Pacific regions.
16.4 Extratropical
AO, MEI, GPI, and PDO. They generally represent the variability of atmospheric circulation and oceanic parameters in the extratropical region of the northern hemisphere.
16.5 Tropical
ONI and IOD represent climate variability in the central Pacific Ocean.
16.6 Tropical atmosphere
MJO is an index that represents the dominant mode of intraseasonal variability in the tropical atmosphere.
Conflict of interest
The authors declare no conflict of interest.
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Written By
Edgard Gonzales and Kenny Gonzales
Submitted: 15 February 2024Reviewed: 16 February 2024Published: 17 July 2024
Open access peer-reviewed chapter - ONLINE FIRST
