Ocean Oscillation Research Articles

Climatic oscillation based 3-dimensional drought risk assessment over India

Drought risk assessment is essential for the efficient design of water resource infrastructures and agricultural planning. Conventional drought studies define droughts either in a spatial or temporal framework without considering the simultaneous temporal and spatial progression of droughts. Spatiotemporal drought (SPD), a dynamic and complex phenomenon, has been relatively understudied due to the challenges in developing algorithms for their identification. In this study, we use a 3-dimensional drought identification framework to identify the SPD events across India. Further, as large-scale ocean-atmospheric phenomena namely El Niño Southern Oscillation (ENSO) and Equatorial Indian Ocean Oscillation (EQUINOO) play an important role in modulating the occurrence of droughts in India, we investigated the influence of these climatic oscillations on SPD characteristics across different climatic regions of India. Further, ENSO-based drought risk assessment using drought characteristics and the synergy between univariate and multivariate drought risks are also evaluated. Results show that 1) there are significant seasonal and spatial variations in occurrences and magnitude of SPD characteristics for the different climate phases. SPDs during the ENSO positive (El Niño) and the EQUINOO negative phases have higher severity, especially over the monsoon core region. In northwest India, the median drought severity during El Niño events is over three times higher than La Niña events. Across most of India, a major portion of SPD event onset occurs during the monsoon seasons, however in the north-western and south-eastern regions an equal portion of drought onset occurs during the pre-monsoon and post-monsoon season, respectively. 2) Further, the ENSO-based drought risk assessment reveals that severe and longer-duration SPD events are associated with El Niño phases, making ENSO a crucial factor in drought frequency analysis. 3) The synergy between the univariate and multivariate drought risks conditioned on ENSO reveal that overall drought risks are linked to the magnitude of individual drought characteristics which have a strong association with ENSO phases. Thus, design criteria for drought resilient infrastructure should be based on the dominant drought characteristics influenced by ENSO phases for effective drought risk assessment. Overall, the study underscores the significant impact of ENSO on SPDs in India and the importance of incorporating ENSO information in drought frequency analysis for reliable risk assessments.

Tornado Occurrence in the United States as Modulated by Multidecadal Oceanic Oscillations Using Empirical Model Decomposition

Studies have analyzed U.S. tornado variability and correlated F1–F5 tornado occurrence with various natural climate oscillations and anthropogenic factors. Using a relatively new empirical mode decommission (EMD) method that extracts time-frequency modes adaptively without priori assumptions like traditional time-series analysis methods, this study decomposes U.S. tornado variability during 1954–2022 into intrinsic modes on specific temporal scales. Correlating the intrinsic mode functions (IMFs) of EMD with climate indices found that 1. the U.S. overall tornado count is negatively (positively) correlated with the Atlantic Multidecadal Oscillation (AMO) index (the Southern Oscillation Index (SOI)); 2. the negative (positive) correlation tends to be more prevalent in the western (eastern) U.S.; 3. the increase in weak (F1–F2) and decrease in strong (F3–F5) tornadoes after around 2000, when both the AMO and the Pacific Decadal Oscillation (PDO) shifted phases, are likely related to their secular trends and low-frequency IMFs; and 4. the emerging Dixie Tornado Alley coincides with an amplifying intrinsic mode of the SOI that correlates positively with the eastern U.S. and Dixie Alley tornadoes. The long-term persistence of these climate indices can offer potential guidance for future planning for tornado hazards.

Comparative analysis of SPI and SPEI drought indices for Montenegro and the impact of teleconnections

ABSTRACT After 2000, Montenegro has been hit by several severe droughts. The results presented in this study were based on the analysis of the standardized precipitation index (SPI) and the standardized precipitation evapotranspiration index (SPEI) for the period 1961–2020. They showed the prevalence of a negative trend in both indices and a significantly higher frequency of drought in the second half of the observed period (1991–2020). The SPEI index is a more representative indicator of drought, because in addition to precipitation, it also takes into account potential evapotranspiration. Using the rescaled adjusted partial sums (RAPS) method, it was determined that the tipping point when the SPEI suddenly fell was the mid-1980s. Out of the 15 analyzed teleconnection variation indicators (atmospheric and oceanic oscillations), 11 oscillations have a significant impact on one or more of the 24 analyzed SPEI time series. The consequences of the drought in Montenegro, as part of the Mediterranean, are most noticeable in the summer, primarily in the form of water shortages, dried vegetation, and frequent fires. Decision-makers in Montenegro should pay attention to this extreme, as drought could pose a serious problem under the projected warmer climate conditions.

Invasive-Weed-Optimization-Based Extreme Learning Machine for Prediction of Lake Water Level Using Major Atmospheric–Oceanic Climate Scenarios

Fresh water lakes are vulnerable assets that need to be protected against manmade/natural challenges like climate change and anthropogenesis activities. This study addresses the predictability of the lake water level changes based on the knowledge acquired directly from the climate data. Two fresh water lakes named Lake Iznik and Uluabat, located in Turkey, are addressed. Time series of the lake water levels during October 1990–September 2019 at a monthly scale, along with the corresponding anomalies of 24 Large-Scale Atmospheric–Oceanic Oscillations (LSAOOs) from around the globe, are used in the analysis. The relationship between variables and the structure of the models are initially acquired based on the significance of the dependence between climate indices and lake water levels with consideration of the significance of the Spearman rank-order coefficient. Then, the time series are divided into training (80%) and testing (20%) sets. The Extreme Learning Method (ELM), enhanced with the genetic algorithm (ELM-GA) and Invasive Weed Optimization (ELM-IWO), is then used in the predictive models. Based on the results, Lake Uluabat showed a stronger teleconnection with LSAOOs, while the ELM-GA for Lake Iznik and ELM-IWA for Lake Uluabat depicted the best performance in the prediction of lake water levels. Comparison of the enhanced ELM-IWO to the corresponding ELM-GA illustrates that the ELM-IWO reveals more acceptable results owing to its flexible nature.

Multiscale association between large‐scale climate variability modes and drought in India via wavelet analysis

AbstractExtreme events, such as droughts, are influenced by climate variability modes such as El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Atlantic Multi‐decadal oscillation (AMO) and Equatorial Indian Ocean Oscillation (EQUINOO). Due to the significant interdependency among the climatic indices at various frequencies, a time‐frequency framework is necessary to better understand the teleconnection. The present study evaluates the association between climate variability modes and drought index in India using different variants of wavelet analysis such as wavelet coherence analysis (WCA), partial wavelet coherence analysis (PWCA), multiple wavelet coherence analysis (MWCA) and wavelet reconstruction methods. In this study, four major climatic indices (ENSO, PDO, AMO, EQUINOO) and the drought index, standard precipitation index (SPI), at four different monthly time periods (1, 3, 6 and 12) are considered. The results from the WCA analysis highlight significant teleconnection spectra for ENSO, PDO and EQUINOO at 2–8, 8–16 and 16–32 year time‐frequency bands. Further, PWCA results reveal significant interdependency in the teleconnection of ENSO and PDO at all the scales, while ENSO and EQUINOO are found to be independent modes of interannual drought variability in India. Finally, MWCA results show that the combination of EQUINOO and ENSO better explains the interannual variability of droughts in Northwest and Central northeast India. The results of this study will help improve drought prediction and early warning systems in India, by selection of appropriate climatic oscillations for different regions in India.

La Niña's Teleconnection to the Indian Ocean Dipole Controlled by Its Longitudinal Position

AbstractWhile the prominent influence of El Niño‐Southern Oscillation (ENSO) on the Indian Ocean Oscillation (IOD) is widely recognized, intricate relationships between them are often invoked that introduce challenges into seasonal predictions. Previous studies have shown that different flavors of El Niño exhibit distinct associations with the IOD. In this study, we demonstrate that La Niña's teleconnection to the IOD is primarily controlled by its longitudinal position. Westward‐displaced La Niña events tend to produce stronger negative convection anomalies in the central Pacific and more pronounced Walk Circulation anomalies, thereby triggering strong negative IOD events. In contrast, eastward‐displaced La Niña events are usually accompanied by feeble convection response due to the excessively cold conditions in the cold tongue, yielding insignificant IOD response. The pivotal role of La Niña's longitudinal position on the IOD's response is realistically reproduced by targeted pacemaker experiments, providing new insights into inter‐basin climate connections.

Kelp dynamics and environmental drivers in the southern Salish Sea, British Columbia, Canada

The impacts of local-scale temperatures and winds on bull kelp (Nereocystis luetkeana) vary along a coastal gradient, while also being influenced by corresponding global-scale oceanic conditions. Around Vancouver Island and the Gulf Islands, BC, Canada, bull kelp floating canopies were mapped using high-resolution imagery from 2005 to 2022, whereas the largest kelp bed of the area was mapped with medium-resolution imagery spanning from 1972 to 2022. In order to understand spatial patterns of kelp resilience, the abiotic characteristics were used to organize the coastline into four clusters, ranging from the coldest and most exposed coast to a more sheltered and warmer location. Additionally, local-scale sea surface temperatures, winds, and marine heatwaves were categorized by global-scale temporal conditions defined by the positive/negative oceanic oscillations of the Oceanic Niño Index (ONI) and Pacific Decadal Oscillation (PDO). Comparing spatial and temporal categories, we observed that years with positive ONI and PDO, in particular the 2014–2019 period, concentrated most of the marine heatwaves and the spring temperature peaks. However, there are some indications of an underlying long-term trend. During the period 2020–2022, when ONI and PDO were negative, summer temperatures kept increasing and wind displayed a higher frequency of extreme events. Mapped kelp showed different trends to these stressors: the coldest and most exposed area showed a constant presence of kelp during the entire period, even dating back to 1972. Warmer and semi-sheltered coasts increased in kelp percentage cover after the positive ONI+PDO period of 2014–2019, and the coasts facing the Strait of Georgia displayed a lower kelp percentage cover than the other clusters. In summary, bull kelp was resilient in the study area, but for different reasons: colder and more exposed coasts had the most favorable conditions for kelp, but warmer and more sheltered coastal kelp beds may have benefited from wind-wave forcing.

How Have Hydrological Extremes Changed over the Past 20 Years?

Abstract Severe floods and droughts, including their back-to-back occurrences (weather whiplash), have been increasing in frequency and severity around the world. Improved understanding of systematic changes in hydrological extremes is essential for preparation and adaptation. In this study, we identified and quantified extreme wet and dry events globally by applying a clustering algorithm to terrestrial water storage (TWS) data from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (FO). The most intense events, ranked using an intensity metric, often reflect impacts of large-scale oceanic oscillations such as El Niño–Southern Oscillation and consequences of climate change. The severity of both wet and dry events, represented by standardized TWS anomalies, increased significantly in most cases, likely associated with intensification of wet and dry weather regimes in a warmer world, and consequently, exhibited strongest correlation with global temperature. In the Dry climate, the number of wet events decreased while the number of dry events increased significantly, suggesting a drying trend that may be attributed to climate variability and possible increases in irrigation and reliance on groundwater. In the Continental climate where temperature has risen faster than global average, dry events increased significantly. Characteristics of extreme events often showed strong correlations with global temperature, especially when averaged over all climates. These results suggest changes in hydrological extremes and underscore the importance of quantifying total water storage changes when studying hydrological extremes. Extending the GRACE/FO record, which spans 2002 to the present, is essential to continuously tracking changes in TWS and hydrological extremes.

Impacts of stronger winds and less sea ice on Canadian Beaufort Sea shelf ecosystems since the late 1990s

Continuous and multi-decadal records of faunal abundance and diversity helping to identify the impacts of ongoing global warming on aquatic ecosystems are rare in the coastal Arctic. Here, we used a 50-year-long microfaunal record from a sediment core collected in the Herschel Basin (YC18-HB-GC01; 18 m water depth) to document some aspects of the environmental responses of the southern Canadian coastal Beaufort Sea to climate change. The microfaunal indicators include benthic foraminiferal assemblages, ostracods and tintinnids. The carbonate shells of two foraminiferal species were also analyzed for their stable isotope signatures (δ13C and δ18O). We compiled environmental parameters from 1970 to 2019 for the coastal region, including sea ice data (break-up date, freeze-up date, open season length and mean summer concentration), the wind regime (mean speed, direction of strong winds and the number of storms), hydrological data (freshet date, freshet discharge and mean summer discharge of the Firth and the Mackenzie rivers), and air temperature. Large-scale atmospheric patterns were also taken into consideration. Time-constrained hierarchal clustering analysis of foraminiferal assemblages and environmental parameters revealed a near-synchronous shift around the late 1990s. The microfaunal shift corresponds to an increased abundance of taxa tolerant to variable salinity, turbulent bottom water conditions, and turbid waters towards the present. The same time interval is marked by stronger easterly winds, more frequent storms, reduced sea-ice cover, and a pervasive anticyclonic circulation in the Arctic Ocean (positive Arctic Ocean Oscillation; AOO+). Deeper vertical mixing in the water column in response to intensified winds was fostered by increased open surface waters in summer leading to turbulence, increased particle loading and less saline bottom waters at the study site. Stronger easterly winds probably also resulted in enhanced resuspension events and coastal erosion in addition to a westward spreading of the Mackenzie River plume, altogether contributing to high particulate-matter transport. Increase food availability since ∼2000 was probably linked to enhanced degradation of terrestrial organic carbon, which also implies higher oxygen consumption. The sensitivity of microfaunal communities to environmental variations allowed capturing consequences of climate change on a marine Arctic shelf ecosystem over the last 50 years.

A teleconnection study between oceanic oscillations and trends in precipitation extremes in the Paraíba do Sul River Basin

A teleconnection study between oceanic oscillations and trends in precipitation extremes in the Paraíba do Sul River Basin

Механизм компенсации Бьеркнеса как возможный триггер низкочастотной изменчивости Арктического усиления

The causes of Arctic amplification are widely debated, and a cohesive picture has not been obtained yet. This study has investigated the role of the Atlantic meridional oceanic and atmospheric heat transport into the Arctic in the emergence of Arctic amplification. The integral advective fluxes in the layer of Atlantic waters and in the lower troposphere were considered. The results show a strong coupling between the meridional heat fluxes and regional Arctic amplification in the Eurasian Arctic on the decadal time scales (10–15 years). We argue that the low-frequency variability of Arctic amplification is regulated via the chain of oceanic heat transport — atmospheric heat transport — Arctic amplification. The atmospheric response to the ocean influence occurs with a delay of three years and is attributed to the Bjerknes compensation mechanism. In turn, the atmospheric heat and moisture transport directly affects the magnitude of Arctic amplification, with the latter lagging by one year. Thus, the variability of oceanic heat transport at the southern boundary of the Nordic Seas might be a predictor of the Arctic amplification magnitude over the Eurasian Basin of the Arctic Ocean with a lead time of four years. The results are consistent with the concept of the decadal Arctic climate variability expressed via the Arctic Ocean Oscillation index.

Comparisons of climate change characteristics in typical arid regions of the Northern Hemisphere

In recent years, with the frequent occurrence of severe drought events, climate change in arid regions has become one of the research hotspots. However, previous studies mainly focused on a specific arid region, and the correlations and differences of drought among various arid regions have not been clearly understood. In this study, based on the latest monthly gridded dataset of the CRU, we compare the characteristics of climate change and its relationship with large-scale oceanic oscillation indexes in the three typical arid regions of Pan-Central-Asia (PCA), North America (NAm) and North Africa (NAf) in multiple perspectives. The results show that the precipitation in the PCA and NAm has increased obviously over the past 80 years, while the NAf precipitation has decreased. After the 1980s, the climate in the PCA and NAm show warm-wet types. This type of the former continues to the present, but the latter’s has changed to a warm-dry type since the 21st century. The NAf climate remains the warm-dry type since the 1990s. Nonetheless, the arid and semi-arid climate patterns in the three typical arid regions remain unchanged. The NAm precipitation has an anti-phase variability pattern compared with the NAf precipitation on both interdecadal and multi-decadal time scales. The Pacific Decadal Oscillation (PDO) has a great influence on the precipitation of the PCA and NAm. The temperature of three arid regions is significantly related to the variations in the Arctic oscillation (AO). In the inland arid region, the contribution of strong warming effect during cold season to the whole year is much greater than that during warm season, while the contribution of the coastal arid regions in warm season is greater. The precipitation in the mid-latitude arid regions is dominated by cold-season precipitation regardless of whether these regions are near the sea or not. The precipitation in the low-latitude arid regions has little difference between cold and warm seasons.

Changes in Extreme Precipitation on the Tibetan Plateau and Its Surroundings: Trends, Patterns, and Relationship with Ocean Oscillation Factors

The Tibetan Plateau is among the region’s most sensitive areas to global climate change. The observation data from 113 meteorological stations on the Tibetan Plateau and surrounding regions in China for 1971–2017 were used to analyze the periodic oscillations and trends in precipitation and extreme precipitation on multiple time scales to ensemble empirical mode decomposition. The relationship between extreme precipitation and sea-surface temperature (SST) anomalies was also explored. The results were as follows. (1) The timing of extreme-precipitation events in the highlands is consistent, with increased total precipitation and increased frequency, intensity, and extreme values of extreme precipitation. (2) Changes in temperature and precipitation are not completely synchronized. The total extreme precipitation, number of extreme-precipitation days, maximum single-day precipitation, and extreme single-day precipitation intensity all showed increases with fluctuations; the quasi-3-year oscillation contributes the most to the extreme precipitation. PRCPTOT is most strongly correlated with R10 and R95p. (3) The spatiotemporal patterns of the first and second empirical orthogonal function modes of the indices differed significantly and were not spatiotemporally uniform, but exhibited local clustering. (4) The Indian Ocean Warm Pool Strength and Western Pacific Warm Pool Strength indices were most highly correlated with each extreme-precipitation index, and the timings of the extreme-precipitation events lagged behind those of the SST anomalies. This study improves our understanding of extreme precipitation events in the context of climate warming and provides a basic analysis for the further assessment and prediction of extreme precipitation on the Tibetan Plateau and the surrounding ecologically fragile areas.

Indian Monsoon Teleconnections and the Impact of Correcting Tropical Diabatic Heating

Abstract The Indian summer monsoon is partly modulated by persistent remote forcing from the tropical Indo-Pacific, evident in the dominant observed teleconnection patterns, namely, El Niño–Southern Oscillation (ENSO) and the equatorial Indian Ocean Oscillation (EQUINOO). In the atmosphere, these teleconnections are presumably driven by diabatic heating, primarily associated with the release of latent heat in condensation with rainfall. However, in coupled atmosphere–ocean models, biases result in large systematic errors in tropical heating. This study seeks to understand the extent that teleconnections are forced by tropical heating and whether or not correcting tropical heating biases improves monsoon prediction skill. We examine a series of reforecasts made with the NCEP Climate Forecast System version 2 in which the “added heating” technique is applied to largely remove tropical heating biases. We isolate the ENSO and EQUINOO signals and examine the ability to reproduce and predict these teleconnections in the model run with and without tropical heating correction. Improving ENSO and EQUINOO-related heating does result in increased prediction skill in monsoon circulation teleconnection patterns. Prediction of other relevant tropical and subtropical circulation indices is improved; however, the impact on the Indian monsoon as a whole is limited. EQUINOO exhibits large internal variability in the model, and despite imposing realistic EQUINOO heating, the monsoon circulation is relatively insensitive in the model. This suggests that either the EQUINOO teleconnection in nature does not emerge as a forced response to tropical heating, and/or the model is unable to reproduce the relationship due to separate deficiencies. Significance Statement India receives over 80% of its annual rainfall during the summer in association with the monsoon. A strong socioeconomic dependence on agriculture makes India sensitive to year-to-year variations in monsoon rainfall, thus predicting and understanding such variations is of great value. Coincident changes in tropical atmospheric heating (and cooling) may be more predictable and presumably impact the monsoon; however, causality has yet to be demonstrated and quantified, particularly for the tropical Indian Ocean. This motivates our modeling study to diagnose the role of tropical heating for the Indian monsoon and whether or not correcting heating errors improves monsoon prediction.

Effects of Oceanic–Atmospheric Oscillations on Rivers

In this Special Issue, we invited scientists devoted to research on the impacts of the ocean and atmosphere oscillations on the climate and weather patterns, resulting in disturbances in the hydrological phenomena. In our view, the main goal has been successfully reached. This Special Issue received investigations based on measurements, modelling and experiments, related to a wide array of changes in river and lake hydrology on different scales, from local and regional to global approaches. We strongly believe that the readers of journal Water can benefit from these new findings and learn more about effects of the ocean and atmosphere on hydrology using the published papers and share the presented results with the scientific community, policymakers and stakeholders [...]

Variability and Changes in Rainfall Observed Across Jharkhand Region (India)

Abstract: Rainfall variability has a substantial impact on water supplies, agricultural output, and, as a result, the economy. It examines the historical spatiotemporal variability and trend of rainfall on Jharkhand's annual and seasonal time series state over a 60-year period (1954–2013). The goal of this study was to find trends in long and short-term changes in rainfall amounts in the Jharkhand region at various spatial scales. With the help of the wavelet technique, we were able to determine the periodicity of rainfall over time and identify active and break days in the monsoon season. When the OLR positive anomaly increases, rainfall decreases (Break days), and when the OLR negative anomaly increases, rainfall increases (Active days). The Indian summer monsoon extreme is also strongly linked to the Equatorial Indian Ocean Oscillation (EQUINOO), which is based on surface zonal wind across the central equatorial Indian Ocean. Because the Bay of Bengal is next to Jharkhand, local disturbances or cyclonic events are also discovered and their impact on rainfall is investigated. Keywords: Rainfall, ENSO, Wavelet Transform, Active and Break days, Cyclone, Climate Change.

Tradeoffs of managing cod as a sustainable resource in fluctuating environments.

Sustainable human exploitation of living marine resources stems from a delicate balance between yield stability and population persistence to achieve socioeconomic and conservation goals. But our imperfect knowledge of how oceanic oscillations regulate temporal variation in an exploited species can obscure the risk of missing management targets. We illustrate how applying a management policy to suppress fluctuations in fishery yield in variable environments (prey density and regional climate) can present unintended outcomes in harvested predators and the sustainability of harvesting. Using Atlantic cod (Gadus morhua, an apex predatory fish) in the Barents Sea as a case study we simulate age-structured population and harvest dynamics through time-varying, density-dependent and density-independent processes with a stochastic, process-based model informed by 27-year monitoring data. In this model, capelin (Mallotus villosus, a pelagic forage fish), a primary prey of cod, fluctuations modulate the strength of density-dependent regulation primarily through cannibalistic pressure on juvenile cod survival; sea temperature fluctuations modulate thermal regulation of cod feeding, growth, maturation, and reproduction. We first explore how capelin and temperature fluctuations filtered through cod intrinsic dynamics modify catch stability and then evaluate how management to suppress short-term variability in catch targets alters overharvest risk. Analyses revealed that suppressing year-to-year catch variability impedes management responses to adjust fishing pressure, which becomes progressively out of sync with variations in cod abundance. This asynchrony becomes amplified in fluctuating environments, magnifying the amplitudes of both fishing pressure and cod abundance and then intensifying the density-dependent regulation of juvenile survival through cannibalism. Although these transient dynamics theoretically give higher average catches, emergent, quasicyclic behaviors of the population would increase long-term yield variability and elevate overharvest risk. Management strategies that overlook the interplay of extrinsic (fishing and environment) and intrinsic (life history and demography) fluctuations thus can inadvertently destabilize fish stocks, thereby jeopardizing the sustainability of harvesting. These policy implications underscore the value of ecosystem approaches to designing management measures to sustainably harvest ecologically connected resources while achieving socioeconomic security.

Simulation of Indian summer monsoon rainfall, interannual variability and teleconnections: evaluation of CMIP6 models

We analyse the performance of global climate models of 6 $$\mathrm{th}$$ generation of Coupled Model Intercomparison Project (CMIP6) in simulating climatological summer monsoon rainfall over India, interannual variability (IAV) of all-India summer monsoon rainfall (ISMR) and its teleconnections with rainfall variability over equatorial Pacific and Indian Oceans. The multimodel ensemble mean (MME) of 61 CMIP6 models shows the best skill in simulating mean monsoon rainfall over India compared to the MMEs of 6 $$\mathrm{th}$$ generation atmosphere-only models (AMIP6) and the previous generations of Atmospheric and Coupled Model Intercomparison Projects (AMIPs and CMIPs). Systematic improvement and reduction in bias are evident from lower to higher AMIPs/CMIPs. Still, there exists dry bias over a narrow region of the monsoon zone of central India besides wet and cold bias over the surrounding oceans. The persistence of errors in atmosphere-only models hints that the source of errors could be with atmosphere models. Fifteen CMIP6 models selected through objective criteria, perform the best in simulating mean monsoon, IAV of ISMR, the strong inverse relationship between ISMR and Boreal summer El Niño-Southern Oscillation (ENSO), and the inverse relationship between all-India rainfall and north–west tropical Pacific rainfall in June. Several models reproduce the dipole structure of Equatorial Indian Ocean Oscillation (EQUINOO) with the centres over western and eastern equatorial Indian Ocean. But, ISMR-EQUINOO relationship in many of them is opposite to the observed. Our analysis implies the need for capturing ISMR-EQUINOO link to improve the simulation of IAV of ISMR which is crucial for reliable monsoon prediction and projection.

A novel deep neural network model approach to predict Indian Ocean dipole and Equatorial Indian Ocean oscillation indices

Indian Ocean Dipole (IOD) and Equatorial Indian Ocean oscillation (EQUINOO) are important climatic system oscillation events in the Indian Ocean region that affects the Indian summer monsoon rainfall (ISMR). The prime focus of this study is to deliberate the influence of these events on ISMR and an attempt has been made to predict these events for future time scales using a Long short term memory (LSTM) deep learning model. LSTM is a special kind of recurrent neural network (RNN) which specializes in learning long-term dependencies and extracting important features. The features learnt by the model is then ranked using correlational analysis (linear and nonlinear). This approach helps in selecting decisive and imperative set of relevant predictors, which can be employed to predict IOD and EQUINOO. Nonlinear correlational identified predictors are found to forecast with greater precision as to their linear counterparts. The model-calibrated correlation coefficient for IOD and for EQUNIOO was 0.90 and 0.88 respectively at a lead of 5 months. Our proposed model was observed to work at par with the other existing models in terms of various statistical evaluation measures.

Forecasting river basin yield using information of large-scale coupled atmospheric–oceanic circulation and local outgoing longwave radiation

ABSTRACT Global and local climate parameters influence the distribution of precipitation over continents. The spatio-temporal distribution of rainfall and the depth of rainfall over a river basin influence the river basin yield. This study deals with the prediction of the river basin scale yield of the ‘Upper Bhima River basin’ from the Maharashtra State of India. The information on large-scale circulation patterns El Nino-Southern Oscillation (ENSO), Equatorial Indian Ocean Oscillation (EQUINOO) index, Multivariate ENSO Index (MEI), and local meteorological input viz. Outgoing longwave radiation (OLR) has been used to predict the river basin scale yield. The Artificial Intelligence (AI) tool – Genetic Programming (GP) – is used for developing prediction models. Ten different combinations of input variables are attempted for the development of monthly river basin yield models to arrive at the best input variable combination for the best predictions with varying lead times. Also, three combinations of input variables were tested for the prediction of ‘Seasonal Yield’. The findings of this research work indicate that GP-derived monthly River Basin Scale Yield forecasting models are successful in the prediction of yield with a correlation coefficient of 0.83. The seasonal yields could be predicted with a correlation coefficient of 0.75.