The Influence of ENSO and macrocirculation patterns in climate change

Received: 04-Jan-2022, Manuscript No. M.No: puljeg-22-4407; Editor assigned: 09-Jan-2022, Pre QC No. M.No: puljeg-22-4407; Accepted Date: Jan 25, 2022; Reviewed: 20-Jan-2022 QC No. M.No: puljeg-22-4407; Revised: 24-Jan-2022, Manuscript No. M.No: puljeg-22-4407; Published: 31-Jan-2022, DOI: 10.37532/2591-7641.2022.6(1).3-4

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Abstract

Under climate change settings, the potential influence of the El NinoSouthern Oscillation (ENSO) and macro circulation patterns (CPs) on local precipitation is investigated and studied. Using two models, a Fuzzy Rule-Based Model (FRBM) and a Multivariate Linear Regression Model (MLRM), the link between the input and output variables is first established under current conditions, and then this historical relationship is extended under climate change conditions. These models use lagged ENSO data represented by the Southern Oscillation Index, SOI) and 500 hPa height data clustered into macro circulation patterns over the western United States as input variables, with the output being an estimate of monthly local precipitation at chosen Arizona sites. To make up for the loss of SOI data as a result of climate change, a number of scenarios are created by varying the parameters of the model. To compensate for the absence of SOI data due to climate change, multiple scenarios are created using a design of experiments framework that perturbs previous SOI data.

Keywords

Model with fuzzy rules; Linear regression with many variables; Design of an experiment; CPs and ENSO

Introduction

The increased danger of storms and floods, one of the likely repercussions of global warming, might be severe in some areas, resulting in unexpected storm impacts, changes in runoff, and record floods. On the other hand, global warming combined with the impact of an increasing human population on the planet might result in major water shortages in the future, particularly in arid and semi-arid regions. Predicting these occurrences is difficult since there are many unknowns when it comes to addressing concerns about water supplies under climate change, especially when elements such as the impacts of (ENSO) are factored into climate models. The smaller scale model’s output can then be used as input for climate change impact studies. The following are a few instances of downscaling.

The current work uses a fuzzy rule-based strategy to answer queries concerning local-scale precipitation under climate change conditions, which extends the downscaling approach.

The incorporation of ENSO information as inputs to the model is another expansion of the downscaling technique, as multiple studies have established a relationship between not just CPs and local precipitation, but also ENSO and local precipitation. As a result, combining data from the CPs and ENSO should offer a more accurate portrayal of the complicated climatic conditions in our research area, the Southwestern United States. Using GCM predictions for atmospheric CPs and analysing multiple distinct scenarios for anticipated changes in the ENSO phenomena under climate change is a reasonably straightforward way to minimise the uncertainty in precipitation projections under climate change [1].

The goal of this review is to provide an estimate of local precipitation under climate change, which may be used in future impact assessments. The following is how the paper is structured:

First, a mechanism for linking atmospheric CPs, ENSO, and local precipitation is devised. Then, based on this relationship, an estimate of how a change in climatic circumstances, such as CO2 doubling, would effect local hydrology is provided. In an experimental design scenario, the problem of inadequate ENSO-data under climate change is also addressed. The historical link between the model’s input variables and the output of daily precipitation at the specified stations in Arizona must be established in the initial step of the modelling process. The model is then expanded in the second phase to determine the connection as a result of climate change. To assess the models’ performance, a split-sampling technique is used: the data is split into a calibration and validation phase. The calibration set is used to establish the connection, whereas the validation set is used to evaluate the model by comparing the model’s conclusions to the original precipitation measurements to determine how well it represents these observed values. The Root Mean Squared Error (RMSE) and the correlation coefficient are used to calculate the difference between the predicted and observed output.

Causes

The El Nio–Southern Oscillation (ENSO) is a periodic irregular oscillation in winds and sea surface temperatures over the tropical eastern Pacific Ocean that affects most of the tropics and subtropics’ climate. El Nio is the warming phase of the sea temperature, whereas La Nia is the cooling phase. The Southern Oscillation is the atmospheric component that occurs in tandem with the shift in sea temperature: El Nio is associated by high air surface pressure in the tropical western Pacific, whereas La Nia is accompanied by low air surface pressure [2]. Each of the two phases lasts several months and occurs every few years, with various degrees of severity. The El Nio–Southern Oscillation is a single climate phenomenon that alternates between three phases on a periodic basis: neutral, La Nia, and El Nio. La Nia and El Nio are polar opposites that require specific modifications in the water and atmosphere.

Normally, the Humboldt Current, which flows northwards along South America’s west coast, transports relatively cold water from the Southern Ocean to the tropics, where it is increased by upwelling off the coast of Peru [3]. Trade winds around the equator induce ocean currents in the eastern Pacific to pull water from deeper layers of the ocean to the top, cooling the ocean surface. This frigid water moves westward around the equator under the influence of the equatorial trade winds, where it is gently warmed by the sun [4]. As a result, sea surface temperatures in the western Pacific are normally warmer than those in the eastern Pacific, by around 8-10°C (14- 18°F). This warmer part of the ocean is a source of convection, which is linked to cloudiness and rain. A pressure gradient force resulting from a high pressure system over the eastern Pacific Ocean and a low pressure system over Indonesia causes the Walker circulation. The tropical Indian, Pacific, and Atlantic basins all have Walker circulations, which produce westerly surface winds in northern summer in the first basin and easterly winds in the second and third basins. As a result, there are significant asymmetries in the temperature structure of the three seas. In the east, the equatorial Pacific and Atlantic both have cool surface temperatures in the northern summer, but only the western Indian Ocean has lower surface temperatures [5]. Variations in surface temperature correspond to changes in thermocline depth.

The Walker circulation evolves over time in response to variations in surface temperature. Some of these shifts are compelled by external forces, such as the sun’s yearly movement into the Northern Hemisphere in the summer. Other changes appear to be the consequence of coupled ocean-atmosphere feedback, in which easterly winds, for example, cause the sea surface temperature to drop in the east, increasing the zonal heat contrast and therefore amplifying easterly winds across the basin. These unusual easterlies cause greater equatorial upwelling and increase the thermocline in the east, increasing the southerlies’ initial cooling.

Bjerknes was the first to suggest this combined ocean-atmosphere feedback. The equatorial cold tongue is created by easterly winds from an oceanographic standpoint [6]. Cross-equatorial wind would cease if the Earth’s temperature were symmetric around the equator, and the cold tongue would be considerably weaker and have a significantly different zonal structure than it does now.

Oscillations in sea surface temperature

The Walker circulation is visible at the surface during non-El Nio circumstances as easterly trade winds that carry water and air warmed by the sun toward the west. This also causes ocean upwelling off Peru’s and Ecuador’s coasts, bringing nutrient-rich cold water to the surface and enhancing fishing supplies. Sea surface temperatures in the Nio 3.4 zone, which runs from the 120th to 170th meridians west longitude astride the equator and five degrees of latitude on either side, are monitored by the National Oceanic and Atmospheric Administration in the United States. This area is located around 3,000 kilometers (1,900 miles) southeast of Hawaii. An El Nio (or La Nia) is deemed in progress if the region’s most recent three-month average is higher than (0.9°F) above (or below) normal for that time.

ENSO conditions are classified as neutral if the temperature deviation from climatology is less than (0.9°F). The transition between ENSO’s warm and cold phases is known as neutral circumstances. During this period, ocean temperatures, tropical precipitation, and wind patterns are all around average. Nearly half of all years are in a state of neutrality.

El Nio occurs when the Walker circulation weakens or reverses while the Hadley circulation increases, causing the ocean surface to be warmer than usual due to little or no upwelling of cold water offshore northern South America. Spanish pronunciation is a term used to describe a band of warmer-than-average ocean water temperatures that forms off the coast of South America on a regular basis. Because periodic warmth in the Pacific near South America is frequently seen around Christmas, the capitalised word El Nio relates to the Christ child, Jesus.

It is a phase of the ‘El Nio–Southern Oscillation’ (ENSO), which refers to changes in the temperature of the tropical eastern Pacific Ocean’s surface and in the tropical western Pacific’s air surface pressure.

SOUTHERN OSCILLATION

El Nio’s atmospheric component is the Southern Oscillation. An oscillation in surface air pressure between the tropical eastern and western Pacific Ocean waters makes up this component. The Southern Oscillation Index measures the strength of the Southern Oscillation (SOI) [7]. The SOI is calculated using variations in the differential in surface air pressure between Tahiti (in the Pacific) and Darwin, Australia (on the Indian Ocean). El Nio episodes feature a negative SOI, which means the pressure over Tahiti is lower and the pressure over Darwin is greater. Positive SOI means greater pressure in Tahiti and lower pressure in Darwin during La Nia events. Because of deep convection over warm water, low atmospheric pressure occurs over warm water and high pressure occurs over cold water. El Nio events are characterised as a period of prolonged warmth in the central and eastern tropical Pacific Ocean, which results in a weakening of the Pacific trade winds and a decrease in rainfall over eastern and northern Australia. When compared to El Nio, La Nia occurrences are described as prolonged cooling of the central and eastern tropical Pacific Ocean, leading to a strengthening of the Pacific trade winds and the opposite consequences in Australia.

Conclusion

The CP ENSO has a distinct set of impacts than the typical EP ENSO. The El Nio Modoki causes more storms to make landfall in the Atlantic Ocean. In contrast to a traditional La Nia, La Nia Modoki causes an increase in rainfall throughout northwestern Australia and the northern Murray–Darling basin. La Nia Modoki also enhances the frequency of cyclonic storms over the Bay of Bengal while decreasing the frequency of severe storms in the Indian Ocean. Because of the asymmetric nature of ENSO’s warm and cold phases, some studies have been unable to make such distinctions for La Nia, both in observations and in climate models. However, some sources indicate that there is a variation on La Nia, with cooler waters in the central Pacific and average or warmer water temperatures in both the eastern and western Pacific, as well as eastern Pacific Ocean currents flowing in the opposite direction than in traditional cyclones.

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