Large-scale Climate Oscillations Research Articles

Disentangling the spatially combined and temporally lagged influences of climate oscillations on seasonal droughts in the East Asian monsoon influenced Poyang Lake Basin

Large-scale climate oscillations are the main forcings affecting regional meteorological droughts and being relevant to sources of their predictability. However, the physical mechanism of atmospheric teleconnections with respect to regional droughts is still not fully understood. In this study, a univariate-to-multivariate analysis framework is proposed to disentangle the spatially combined and temporally lagged effects of multiple oceanic-atmospheric oscillations on meteorological droughts at regional scale. Our study focuses on the largest freshwater lake basin of China, the Poyang Lake basin (PLB). Pearson's correlation coefficient and cross-wavelet transform are used to analyze the pair-wise linear and non-linear correlations between droughts and each climate oscillation. Random forests model is used to reveal the combined influences of multiple climate oscillations. The associated atmospheric mechanism for the identified combination of climate indices with changing lags is explored by performing composite analysis. Regarding the spatially combined influences, the concurrence of El Niño-Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) are the most important drought precursors. Regarding the temporally lagged influences, ENSO with lag of 11 months and NAO with lag of 2–3 months trigger meteorological droughts. The combined effect of preceding winter El Niño and late-summer negative NAO is the primary cause for triggering autumn droughts. The positive Eurasian teleconnection pattern, triggered by ENSO and NAO and favorable for anomalous northerly currents, is the main drought-prone circulation pattern for the PLB. These findings contribute to improved understanding of joint effects of lagged teleconnections for meteorological droughts, which could eventually lead to more skillful seasonal drought forecasting.

Spatial diversity of atmospheric moisture transport and climate teleconnections over Indian subcontinent at different timescales

Regional weather and climate are generally impacted by global climatic phenomenon′s. Understanding the impact of global climate phenomenon′s on an atmospheric branch of the hydrological cycle is crucial to make advances in skillful precipitation forecast. The present study adopts a multiscale approach based on wavelets for unravelling the linkages between teleconnections and atmospheric moisture transport over homogeneous regions of Indian sub-continent. We investigated linkages between atmospheric moisture transport quantified as monthly integrated water vapor transport (IVT) during 1951–2022 over selected homogeneous regions and eight large scale climate oscillations using wavelet and global wavelet coherence. Our results indicate significant heterogeneity in linkages across different regions and across multiple timescales. In particular, the Indian Ocean Dipole (IOD) influence monthly IVT at intra-annual to inter-annual scale over all regions. The El Niño–Southern Oscillation (ENSO) have strong connection to monthly IVT at inter-annual scale whereas over west central region both IOD and ENSO strongly influence IVT at inter-decadal scale. While the Atlantic Multi-Decadal Oscillation and Pacific Decadal Oscillation have an impact on IVT in the north-east and southern regions, the Arctic Oscillation and North Atlantic oscillation have a strong inter-annual connection to IVT, majorly in the northwest and hilly regions. Overall, the methodology offers an effective approach for capturing the dynamics of atmospheric moisture transport in time–frequency space and provide a practical reference for prediction of atmospheric moisture transport linked precipitation over different regions of Indian subcontinent.

Univariate and bivariate spatiotemporal characteristics of heat waves and relative influence of large-scale climate oscillations over India

Heat waves (HWs) have grown in frequency, duration, and intensity in last few decades globally and even the latest climate projections (CMIP6) reveal these to get worsen under enhanced global warming. HWs have adverse impacts over biophysical and human systems, therefore, to analyse the impacts of HWs accurately, it is required to understand the regional trend and variability associated with HWs. We carried out a bivariate copula-based joint exceedance probabilistic analysis of HW characteristics to better assess the vulnerability of Indian regions. The regions with comparatively higher HW frequency, higher intensity, longer duration, and lower joint return period of HW frequency and intensity were selected as vulnerable to HWs. Rajasthan, Madhya Pradesh, Uttar Pradesh, Punjab, Bihar, Chhattisgarh, Gujrat, Karnataka, Maharashtra, Telangana, Andhra Pradesh, and Odisha states of India have been found vulnerable and are projected to get even more vulnerable in the future as per the recent climate projections. To unveil the spatiotemporal variability in HW characteristics, we investigated the relative influence of crucial large-scale climate oscillations (LSCOs) i.e., ENSO, IOD, and AMO on HW by introducing these atmospheric variables as covariates in nonstationary extreme value theory (EVT)-based models. The statistical analysis from generalized extreme value distribution of temperature extreme with LSCOs reveals that ENSO and IOD simultaneously have been influencing the extreme in 21.2%, 25.9% and 23.1% of total region taken into consideration in April, May and June month respectively.

Exploring NAO influence on waterbirds abundance through hydrological changes in a Mediterranean coastal wetland

Predicting how waterbird populations may respond to climate change is a major challenge for conservation, which could be addressed by understanding the effects of large-scale climate oscillations, such as the North Atlantic Oscillation (NAO), on breeding population size. Here, we explore the relationship between the NAO position and the abundance of waterbird breeding pairs in a protected Mediterranean coastal wetland (Mouth of the Guadalhorce River, Málaga, southern Iberian Peninsula). We found a significant and negative relationship between the winter NAO index and the abundance of grebes (r=-0.72, N=15, p<0.01), rails (r=-0.74, N=15, p<0.01), diving ducks (r=-0.56, N=15, p<0.05) and dabbling ducks (r=-0.54, N=15, p<0.05). Our results suggest that this relation is mediated by the NAO indirect effects on wetland flooded surface via changes in winter precipitation and Mediterranean sea level. These results should be considered to design appropriate environmental management strategies devoted to preventing or mitigating potential deleterious effects of the NAO variability on Mediterranean wetlands ecosystems and preserving their valuable waterbird communities.

Rain triggers seasonal stratification in a temperate shelf sea

The North Atlantic Storm Track acts as a conveyor belt for extratropical cyclones that frequently deliver high winds and rainfall to northwest European shelf seas. Storms are primarily considered detrimental to shelf sea stratification due to wind-driven mixing countering thermal buoyancy, but their impact on shelf scale stratification cycles remains poorly understood. Here, we show that storms trigger stratification through enhanced surface buoyancy from rainfall. A multidecadal model confirms that rainfall contributed to triggering seasonal stratification 88% of the time from 1982 to 2015. Stratification could be further modulated by large-scale climate oscillations, such as the Atlantic Multidecadal Variability (AMV), with stratification onset dates being twice as variable during a positive AMV phase than a negative one. Further insights into how changing storm activity will impact shelf seas are discussed beyond the current view of increasing wind-driven mixing, with significant implications for marine productivity and ecosystem function.

Effects of Large-Scale Climatic Oscillations on the Variability of the Indian Summer Monsoon Rainfall

Effects of Large-Scale Climatic Oscillations on the Variability of the Indian Summer Monsoon Rainfall

Dynamic flood frequency analysis for west flowing rivers of Kerala, India

Conventional Flood Frequency Analysis (FFA) may underestimate flood quantiles and increase hydraulic infrastructure vulnerability in changing climates. This study uses annual maximum streamflow data from 17 hydrologic stations along west-flowing rivers in Kerala, India, for Non-Stationary (NS) FFA. The Generalized Extreme Value model with a linear temporal location parameter worked effectively for five stations. Kidangoor and Pattazhy stations must account for non-stationarity for longer return periods (RPs) (>50 years), whereas Neeleeswaram and Perumannu stations must for shorter RPs (<50 years). An extensive study was conducted for Neeleswaram station (Periyar basin) by simulating NS models incorporating four large-scale climate oscillations as covariates. The stationary assumption underestimated flood return levels of 2-year RP by about 61% which increases the flood risk leading to failure of hydraulic infrastructures. It was observed that the best fitted climate-based NS model achieves stationary return level of 150-years RP at 25-years RP itself. The study proved that the climate-based NS models captured the 2018 August Floods in Periyar basin better than stationary and time-based models. The regional variability in FF curve behaviour concludes that NSFFA for Kerala cannot be generalised and must be done at a local-scale.

Relative influence of ENSO, IOD, and AMO over spatiotemporal variability of hydroclimatic extremes in Narmada basin, India

Abstract The Narmada basin is one of the major river basins of Central India. The basin frequently experiences droughts and floods due to its geography and uneven topography. Therefore, it is important to understand the spatiotemporal variability of hydroclimatic extremes over the basin. Large-scale climate oscillations (LSCOs) have been observed significantly affecting the patterns of hydroclimatic extremes at the basin and continental scale. In this study, we have analysed the relative influence of LSCOs (EL Nino-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Atlantic multidecadal oscillation (AMO)) over hydroclimatic extremes of the Narmada basin. Precipitation, temperature, and streamflow extremes were analysed in stationary and nonstationary frameworks of generalized extreme value distribution. The precipitation extremes, PRCPTOT and R95p were observed significantly influenced by ENSO, IOD, and AMO individually whereas extreme Rx5day was relatively more influenced by ENSO and AMO individually and collectively. Temperature extremes, TXx was significantly more influenced by ENSO alone (26.47% of the region), while TNx was observed to be substantially more influenced by ENSO and AMO. The upper Narmada basin was found vulnerable to flooding and whereas the basin was projected to experience more frequent and intense heatwave-associated disasters in long term.

Role of large-scale climate oscillations in precipitation extremes associated with atmospheric rivers: nonstationary framework

ABSTRACT Atmospheric rivers (ARs) are filamentary regions of high-water vapour flux in the lower troposphere that contribute significantly to poleward moisture movement in mid-latitude regions. Key characteristics (frequency, duration, and intensity) of ARs have been explored to recognize the regions vulnerable to AR-flood. To investigate the association of ARs with large-scale climate oscillations (LSCOs), precipitation extremes (PEs) maximum 1-day precipitation (Rx1day), maximum consecutive 5-day precipitation (Rx5day), precipitation amount from very wet days (R95pTOT) are explored in a non-stationary framework of generalized extreme value distribution, taking the Arctic Oscillation, North Atlantic Oscillation, El Niño Southern Oscillation, and Pacific Decadal Oscillation (PDO) as covariates. In almost 30% of regions around the globe, May-June-July-August-September (MJJAS) season PDO was found to be the relatively most influential covariate for capturing PEs. The west coast of North America and of Europe, southernmost South America, central East Asia, New Zealand, and Australia have been identified as the most critical regions associated with AR linked with PE-associated LSCOs.

Impacts of Large-Scale Climate Oscillations on Fluvial Sediments in Central Poland: Evidence from Geochronological Analysis

Abstract The article presents the results of a collective analysis of 181 radiocarbon and 38 luminescence dating results from the time range 50–11.7 cal kBP for samples originating in fluvial sedimentary environment in Łódź region (central Poland), south of the last glacial maximum (LGM) line. Four sites were selected for a general of fluvial deposits and the obtained dating results Struga Żłobnicka, Parchliny, Warenka, and Swędów regions. Based on the summed probability density function (PDF) curves, the study demonstrates the response of the fluvial sedimentary environment to events of overregional scale. The collective analysis of the radiocarbon age determinations from the Łódź region allows for observing the correlation between local environmental changes and large-scale changes recorded in the NGRIP core. The warming periods Greenland interstadials (GI)-3, GI-4, and GI-5.1, the cooling event GI-1b within the warming GI-1, as well as the transition between GI-1a and Greenland stadials (GS)-1, were most prominently recorded in the PDF curves. The collective analysis of the luminescence age determinations reflects a change in the river valleys, expressed by a strong aggradation during the cold maximum of the studied period.

Heterogeneous spatiotemporal streamflow response to large-scale climate indexes in the Eastern Alps

Analyzing temporal and spatial variability of river discharge and the impacts of large-scale climate oscillations on hydrological systems are of particular interest in Alpine catchments, which have been proved to be especially sensitive to climatic drivers. The impact of climate oscillation indexes may show a delayed response and therefore the correlation between climatic drivers and streamflow is challenging to be properly identified. For this purpose, wavelet transform (WT) is recognized as a suitable tool able to determine the crucial scales of variability. In this work, first we explore the periodicities and the coherence among several climatic indexes: North Atlantic Oscillation Index (NAO), Mediterranean Oscillation Index (MO), Greenland Blocking Index (GB), and Artic Oscillation Index (AO). This analysis shows the complementary information that different oscillation indexes provide and the need to consider their impacts on streamflow simultaneously. Previous work revealed a heterogeneous and complex response of the Inn river basin at long temporal scales, which could not be linked to analyzed anthropogenic impacts such as dams’ construction and hydropower plants operation. Therefore, the trigger of changes in streamflow variability remained unclear. In this study, we elaborate a classification based on all considered indexes’ coherence with fifty gauging stations of the Inn river catchment. We quantify similarity among stations with a focus on yearly and longer temporal scales. The results highlight the heterogeneous response of the streamflow towards changes in climatic indexes and give an overview of the possible drivers of detected long-term alterations. NAO and GB extreme phases are connected with cold winters and hot summers. We observe that from the 1980s changes detected in the streamflow behavior at yearly and longer temporal scales are in line with emerging patterns of the climatic indexes, such as the shift from constant to intermittent periodicities of the MO index. AO coherence displays a higher complexity able to capture singular hydrologic behaviors (i.e., particular hydrological regime only detected for one gauging station, often connected to high altitude small basins). From the cluster analysis we can also derive how mean catchment elevation and geographical location can contribute to the explanation of the influence and teleconnection to the oscillation indexes, while glacierized area is not identified as a dominant characteristic. Thus, this research contributes to a better understanding of streamflow variability over the Eastern Alps, and the role of teleconnection patterns on this variability. These relationships can be also used to improve hydrological forecasting and water resources management in the Alpine region.

Evidence for increasing influence of atmospheric teleconnections on water levels in the Great Lakes

Understanding the variability and predominant drivers of water level fluctuations in large water bodies is critical for the development of proactive mitigation plans in the nearshore zone. Here, for the first time, we reconstructed the individual and collective influence of the Atlantic Multi-decadal Oscillation, Pacific Decadal Oscillation, region 3.4 of El Niño-Southern Oscillation, and North Atlantic Oscillation on Lake Huron-Michigan’s water budget components, including water levels, over-lake precipitation, evaporation, runoff, and river flow rates over a 115-year period. Conducting wavelet spectral analysis on the reconstructed impact of large-scale climate oscillations revealed the emergence of quasi-periodic and non-stationary fluctuations along with frequency shifts in hydrological variables, highlighting their increasingly stronger signature on water levels after 1980. After removing the atmospheric teleconnection effects, the residual hydrological time series provided evidence of gradual changes in runoff and river flow rates and their relationships with precipitation, reflecting the effects of intensifying anthropogenic activities on the regional water cycle over the past decades. Considering the uncertainty pertaining to the magnitude and frequency of atmospheric teleconnections in a changing climate, the findings of our study can offer a new perspective in our understanding of the drivers of water level variability along the shoreline and/or the navigability of shallow waters in the Great Lakes.

Long term oscillations of Mediterranean sardine and anchovy explained by the combined effect of multiple regional and global climatic indices

It is widely known that the abundance and distribution dynamics of populations of small pelagic clupeid fish, such as sardines and anchovies, are affected by large-scale climate variability, which may lead to changeovers to new regimes of small pelagics. However, long-distance climatic oscillations, such as El Niño/La Niña and the Pacific Decadal Oscillation, have been little explored in the Western Mediterranean Sea. We investigated the possible effects of the South Oscillation Index (i.e. the atmospheric oscillation coupled with the El Niño/La Niña) and Pacific Decadal Oscillation on fluctuations in catches of European anchovy (Engraulis encrasicolus) and sardine (Sardina pilchardus) in the Western Mediterranean Sea, and their association with regional climate oscillations (i.e. the Atlantic Multidecadal Oscillation, the North Atlantic Oscillation, the Western Mediterranean Oscillation index, and the Arctic Oscillation). The study covered two periods: (a) landings between 1950 and 2016; and (b) abundance, biomass, and physical condition (i.e., relative condition index) between 2004 and 2016. The main large-scale climatic oscillations in the region were studied using General Additive Models to investigate the relationship between a time series of species measures of European sardine and anchovy from Geographical Subarea 06. Results show that the long-term Pacific Decadal Oscillation favours sardine landings, whereas the combined effect of the Western Mediterranean Oscillation Index and the Atlantic Multidecadal Oscillation favours anchovy. We discuss potential links between the present findings and changes in the plankton community caused by prevailing winds in the region driven by long-distance climate oscillations and their impact on the reduction in small pelagic fish populations in the study area.

Impact of individual and combined influence of large-scale climatic oscillations on Indian summer monsoon rainfall extremes

Impact of individual and combined influence of large-scale climatic oscillations on Indian summer monsoon rainfall extremes

Spatio-temporal rainfall variability and its linkage with large scale climate oscillations over the Republic of South Sudan

Climate change has distorted the rainfall spatio-temporal distribution. Therefore, understanding the spatio-temporal rainfall variation and its causes at meso and micro-level could provide insight for the water and agricultural development interventions. This article examines the spatio-temporal rainfall variability and driving factors in connection with large scale global oscillation. CHIRPS rainfall and NCEP/NCAR wind fields data from 1981 to 2019 were used. Rotaed Emperical Ortogonal Function (REOF) was employed to investigate spatio-temporal rainfall variability. Large scale moisture transport was studied using lower (1000–850 hPa), middle (600–500 hpa), and upper (300–100 hPa) level wind fields. The result reveals that in South Sudan rainfall dominant variance regions were not uniform across seasons. Northern, Southcentral, and Southeastern tip regions were the top three dominant variance regions which explain around 30 %, 25 % and 17 % of the total variation in REOF1, REOF2, and REOF3 during the spring season respectively. Likewise, during the summer season the dominant variance REOF1 (28 %), REOF2 (24 %), and REOF3 (9 %) was observed in South-east, West, and, North-west, whereas; the autumn dominant variance in North-west, North-east, and South-east explain about REOF1 (22.6 %), REOF2 (19.9 %), and REOF3 (16.7 %) of the total variation respectively. The alternative wet and dry years were causing drought and flooding in the study sites. Low-level southeasterly winds from India and over the equator from the Atlantic Ocean carry rain-bearing moisture to South Sudan. The winds converge and convection is triggered by African Easterly and Tropical Easterly jet favors convection during the summer and spring seasons. Sea Surface temperature shows a positive and negative correlation with rainfall variability. Rainfall spatio-temporal assessment is crucial for water resource management, agricultural productivity, and climate change mitigation.

Regional impact of large-scale climate oscillations on ice out variability in New Brunswick and Maine.

The available ice out (the date of disappearance of ice from a water body) records were analyzed from four relatively closely spaced lakes in southwestern New Brunswick (Harvey, Oromocto, Skiff) and eastern Maine (West Grand Lake), with the longest set of available observations being for Oromocto Lake starting in 1876. Results of a coherence analysis carried out on the ice out data from the four lakes indicates that there is regional coherence and correspondingly, that regional drivers influence ice out. These results also indicate that ice out dates for lakes from the region where records have not been kept can also be interpolated from these results. As the ice out record was coherent, further analysis was done for only Oromocto Lake on the basis of it having the longest ice out record. Cross-wavelet analysis was carried out between the ice out record and a variety of cyclic climate teleconnections and the sunspot record to identify which phenomena best explain the observed ice out trends. The most important observed contributors to ice out were the North Atlantic Oscillation (NAO) and the El Niño Southern Oscillation (ENSO), with observed periodicities at the interannual scale. At the decadal scale the Pacific Decadal Oscillation (PDO) and the 11-year solar cycle were the only patterns observed to significantly contribute to ice out.

Spatiotemporal Heterogeneity in Precipitation over China and Its Connections with Large-Scale Climate Oscillations—A Moisture Budget Perspective

Abstract Climate change can lead to variations in the probability distribution of precipitation. In this study, quantile regression (QR) is undertaken to identify the quantile trends in precipitation over China and to examine the quantile effects of various climate oscillations on precipitation. The results show that the quantile trends show apparent seasonal variations, with a greater number of stations showing trends in winter (especially at quantile levels ≥ 0.5), and larger average magnitudes of trends at nearly all quantile levels in summer. The effects of El Niño–Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and Pacific decadal oscillation (PDO) exhibit evident variations with respect to the quantile level. Spatial clusters are subsequently identified based on the quantile trends, and the individual and combined effects from the teleconnection patterns are further investigated from the perspective of moisture budget. Seven spatial clusters with distinct seasonal quantile trends can be identified; three of them are located in southeastern China and are characterized by increasing trends in summer and winter precipitation. Summer precipitation over this region is positively influenced by ENSO and negatively influenced by NAO, with the former affecting both the dynamic and thermodynamic components of vertically integrated moisture divergence and the latter affecting only the dynamic component. The interaction effect of ENSO and NAO on summer precipitation anomalies in months that are extremely wetter than normal is statistically significant. In comparison, winter precipitation in this region is under the positive influence of ENSO and NAO and the negative influence of PDO; the effect of ENSO on moisture convergence can be mainly attributed to its dynamic component.

Change patterns of precipitation anomalies and possible teleconnections with large-scale climate oscillations over the Yangtze River Delta, China

Abstract In view of the key factor in regional hydrological processes and water resource management, the temporal patterns of precipitation anomalies and oscillations were detected by the Quantile Perturbation Method (QPM) and the Singular Spectrum Analysis (SSA) Method, and the spatial patterns were identified using the Principal Component Analysis (PCA) Method. In addition, the teleconnections and lagged influence with large-scale climate oscillations in the Yangtze River Delta (YRD) of China from 1957 to 2016 were also analyzed. Results showed that, temporally, the main oscillations of precipitation were all found to be 2, 7–11 and 3–4 years in the annual and seasonal scales. Precipitation quantiles are subject to strong temporal oscillations at (multi-)decadal time scales, with high and low anomalies at specific periods. Spatially, the whole region could be divided into two main sub-regions in annual and seasonal scales, respectively. Among the selected large-scale climate oscillations in this study, the Pacific Decadal Oscillation (PDO) has a stronger influence on precipitation in March, July and September, but significant correlations were detected in more than 18% of the total stations. These stations were mainly in the southeast regions. The North Pacific index (NP) controlled the precipitation in February (13.95% of the total stations) and October (37.21% of the total stations) in the north region. Generally, the indicators of the Southern Oscillation Index (SOI) and Oceanic Niño 4 SST Index (ONI) had the strongest influence in regional precipitation variations, but significant correlations were detected in more than 20% of the total stations in March, September, October and November. Also, large-scale climate oscillations have a delayed way on precipitation. Among the oscillations, the Arctic Oscillation (AO) and North Atlantic Oscillation (NAO) showed that significant cross-correlations on precipitation were 0 and 3–5 months, respectively. NP showed significant cross-correlations with precipitation in many stations when the lag time was 0–3 months. Generally, the PDO, SOI and ONI have a greater influence in the south region, mainly with the lag time of 0–3, 2–3 and 1–5 months, respectively. The results will provide a basis for taking relevant measures to deal with problems of meteorological disaster and water supplement under climate change.

A multivariate EMD-LSTM model aided with Time Dependent Intrinsic Cross-Correlation for monthly rainfall prediction

Accurate prediction of rainfall is a complex problem because of the large number of controlling factors, complex interrelationships between them and the multiscaling behaviour of the process. Because of the multiscaling behaviour of the problem, the use of hybrid modelling involving decomposition techniques is preferable over strenuous physical models and standalone driven techniques for accurate rainfall predictions. This study proposes a novel hybrid modelling framework integrating Long Short Term Memory (LSTM) and Multivariate Empirical Mode Decomposition (MEMD) aided with Time Dependent Intrinsic Cross-Correlation (TDICC) analysis algorithm for monthly rainfall predictions. The application of the proposed model is demonstrated for monthly rainfall prediction of 2005–2015 period at All India spatial domain considering El-Niño Southern Oscillation(ENSO), Indian Ocean Dipole(IOD) and five antecedent values of rainfall are the input variables. The proposed framework first uses MEMD to obtain a set of orthogonal components namely Intrinsic Mode Functions (IMFs) identifying and aligning the common scales embedded in the multiple input variables considered. Subsequently, scale specific rainfall information are predicted by incorporating them on relevant IMFs and their significant lags identified through TDIC and TDICC analyses. Final aggregation of predicted rainfall components from different scales gives the monthly rainfall of a generic time step ahead. The efficacy of the proposed MEMD-TDICC-LSTM framework is compared with five other hybrid models such as MEMD-TDIC-ANN, MEMD-TDICC-ANN, MEMD-ACO-ANN, MEMD-ACO-LSTM and MEMD-TDIC-LSTM, which used Time Dependent Intrinsic Correlation (TDIC) and Ant Colony Optimization (ACO) for predictor selection and Artificial Neural Network (ANN) as modelling tool. The study has used different graphical representations and ten different statistical performance evaluation measures for the prediction of validation period, it is observed that the proposed model could achieve a predictive skill of 0.98, Nash-SutcliffeEfficiency (NSE) of 0.95 and Index of Agreement (IA) of 0.91 which is better than all the five remaining models. The capability of MEMD-TDICC-LSTM model to predict the extremes is also confirmed by using the bar graph for the drought year rainfall of 2009 and is observed that the model is successful in capturing the extremes with an annual rainfall 975.16 mm closer to observed value 927.3 mm. MEMD algorithm facilitates the inclusion of multiple large scale climatic oscillations as inputs and their multi time scale decomposition, TDICC helps to fix the relevant inputs at different time scales and the LSTM functions as the robust modelling tool. Further, the framework using this specific combination resulted in substantial reduction in modelling complexity and faster execution, as the approach considers only the most relevant and significant inputs in the process.

Regionalization of climate teleconnections across Central Asian mountains improves the predictability of seasonal precipitation

Mountains play a critical role in water cycles in semiarid regions by providing for the majority of the total runoff. However, hydroclimatic conditions in mountainous regions vary considerably in space and time, with high interannual fluctuations driven by large-scale climate oscillations. Here, we investigated teleconnections between global climate oscillations and the peak precipitation season from February to June in the Tian-Shan and Pamir Mountains of Central Asia. Using hierarchical climate regionalization, we identified seven subregions with distinct precipitation patterns, and assessed correlations with selected climate oscillations at different time lags. We then simulated the seasonal precipitation in each subregion from 1979 to 2020 using the most prevalent teleconnections as predictors with support vector regression (SVR). Our findings indicate that the El Niño–Southern Oscillation, the Pacific Decadal Oscillation, and the Eastern Atlantic/West Russia pattern are among the major determinants of the seasonal precipitation. The dominant lead-lag times of these oscillations make them reliable predictors ahead of the season. We detected notable teleconnections with the North Atlantic Oscillation and Scandinavian Pattern, with their strongest associations emerging after onset of the season. While the SVR-based models exhibit robust prediction skills, they tend to underestimate precipitation in extremely wet seasons. Overall, our study highlights the value of appropriate spatial and temporal aggregations for exploring the impacts of climate teleconnections on precipitation in complex terrains.