Lag Correlation Research Articles

Forecasting COVID-19 Outbreak Through Fusion of Internet Search, Social Media, and Air Quality Data: A Retrospective Study in Indian Context

This article proposes a machine learning augmented technique to predict the coronavirus disease (COVID-19) outbreak in India by combining Internet search trends along with social media data retrieved from Twitter. A comprehensive list of suitable search words has been used to select a large collection of Tweets, and the Internet search trends of the same keywords have been fetched. First, a lag correlation analysis is conducted to find the number of days, ahead of the current time, required to make an accurate prediction of COVID-19 cases. Second, both shallow and deep learning methods are engaged to predict the number of COVID-19 cases in a specific geospatial location in India. Thereafter, statewise air pollution data collected from the Central Pollution Control Board, Government of India, are amalgamated to understand the effect of air pollution in spreading of COVID-19 disease. The air pollution monitoring parameters have been combined to understand their effects in the prediction of COVID-19 cases in the Indian context. Experimental results reveal that accurate predictions can be made 85 days ahead of the current time using the proposed method (r > 0.85), thereby establishing its ingenuity in the prediction of COVID-19 spread in advance.

Comprehensive meteorological factors analysis and lag correlation study for cyanobacterial blooms in shallow plateau lake

Cyanobacterial blooms (CBs) are a growing concern for shallow plateau lakes, and numerous studies have investigated the relationship between CBs and meteorological factors. However, these studies have typically lacked comprehensive analyses and neglected the impact of lag effects. This study employed Landsat satellite imagery to extract CBs information from Xingyun Lake from 1990 to 2019 and conducted a lag correlation analysis between meteorological factors ranging from 1 to 30 days with a 1-day step and CBs. The result show that the Sunshine Duration displayed a negative correlation with CBs at an 8-day lag, but when Sunshine Duration was <6 h, it exhibited a positive relationship with CBs at a 1-day lag. And daytime precipitation had a more substantial positive link with CBs than nighttime precipitation at a lag of 13 days. The aforementioned conclusions deepen our comprehension of the meteorological forces that drive cyanobacterial blooms in plateau lakes. Moreover, the maximum and minimum wind velocities were negatively and positively associated with CBs with lags of 20 and 29 days, respectively. In addition, relative humidity and atmospheric pressure were positively associated with cyanobacterial blooms 13–19 days and 3 days after their onset, respectively. The relationship between air temperature and cyanobacterial blooms in Xingyun Lake was weak. Our research emphasizes the significance of incorporating delayed effects and refined meteorological factors for accurate cyanobacterial bloom forecasting.

Comparing resting state and task-based EEG using machine learning to predict vulnerability to depression in a non-clinical population

Major Depressive Disorder (MDD) affects a large portion of the population and levies a huge societal burden. It has serious consequences like decreased productivity and reduced quality of life, hence there is considerable interest in understanding and predicting it. As it is a mental disorder, neural measures like EEG are used to study and understand its underlying mechanisms. However most of these studies have either explored resting state EEG (rs-EEG) data or task-based EEG data but not both, we seek to compare their respective efficacy. We work with data from non-clinically depressed individuals who score higher and lower on the depression scale and hence are more and less vulnerable to depression, respectively. Forty participants volunteered for the study. Questionnaires and EEG data were collected from participants. We found that people who are more vulnerable to depression had on average increased EEG amplitude in the left frontal channel, and decreased amplitude in the right frontal and occipital channels for raw data (rs-EEG). Task-based EEG data from a sustained attention to response task used to measure spontaneous thinking, an increased EEG amplitude in the central part of the brain for individuals with low vulnerability and an increased EEG amplitude in right temporal, occipital and parietal regions in individuals more vulnerable to depression were found. In an attempt to predict vulnerability (high/low) to depression, we found that a Long Short Term Memory model gave the maximum accuracy of 91.42% in delta wave for task-based data whereas 1D-Convolution neural network gave the maximum accuracy of 98.06% corresponding to raw rs-EEG data. Hence if one has to look at the primary question of which data will be good for predicting vulnerability to depression, rs-EEG seems to be better than task-based EEG data. However, if mechanisms driving depression like rumination or stickiness are to be understood, task-based data may be more effective. Furthermore, as there is no consensus as to which biomarker of rs-EEG is more effective in the detection of MDD, we also experimented with evolutionary algorithms to find the most informative subset of these biomarkers. Higuchi fractal dimension, phase lag index, correlation and coherence features were also found to be the most important features for predicting vulnerability to depression using rs-EEG. These findings bring up new possibilities for EEG-based machine/deep learning diagnostics in the future.

Deciphering the unusual stellar progenitor of GRB 210704A

ABSTRACT GRB 210704A is a burst of intermediate duration (T90 ∼ 1–4 s) followed by a fading afterglow and an optical excess that peaked about 7 d after the explosion. Its properties, and in particular those of the excess, do not easily fit into the well-established classification scheme of gamma-ray bursts (GRBs) as being long or short, leaving the nature of its progenitor uncertain. We present multiwavelength observations of the GRB and its counterpart, observed up to 160 d after the burst. In order to decipher the nature of the progenitor system, we present a detailed analysis of the GRB high-energy properties (duration, spectral lag, and Amati correlation), its environment, and late-time optical excess. We discuss three possible scenarios: a neutron star merger, a collapsing massive star, and an atypical explosion possibly hosted in a cluster of galaxies. We find that traditional kilonova and supernova models do not match well the properties of the optical excess, leaving us with the intriguing suggestion that this event was an exotic high-energy merger.

Predicting Geostationary 40–150 keV Electron Flux Using ARMAX (an Autoregressive Moving Average Transfer Function), RNN (a Recurrent Neural Network), and Logistic Regression: A Comparison of Models

AbstractWe screen several algorithms for their ability to produce good predictive models of hourly 40–150 keV electron flux at geostationary orbit (data from GOES‐13) using solar wind, Interplanetary Magnetic Field, and geomagnetic index parameters that would be available for real time forecasting. Value‐predicting models developed using ARMAX (autoregressive moving average transfer function), RNN (recurrent neural network), or stepwise‐reduced regression produced roughly similar results. Including magnetic local time as a categorical variable to describe both the differing levels of flux and the differing influence of parameters improved the models (r as high as 0.814; Heidke Skill Score (HSS) as high as 0.663), however value‐predicting models did a poor job at predicting highs and lows. Diagnostic tests are introduced (cubic fit to observation‐prediction relationship and Lag1 correlation) that better assess predictions of extremes than single metrics such as root mean square error, mean absolute error, or median symmetric accuracy. Classifier models (RNN and logistic regression) were equally able to predict flux rise above the 75th percentile (HSS as high as 0.667). Logistic regression models were improved by the addition of multiplicative interaction and quadratic terms. Only predictors from 1 or 3 hr before were necessary and a detailed description of flux time series behavior was not needed. Stepwise selection of these variables trimmed non‐contributing parameters for a more parsimonious and portable logistic regression model that predicted as well as neural network‐derived models. We provide a logistic regression model (LL3: LogisticLag3) based on inputs measured 3 hr previous, along with optimal probability thresholds, for future predictions.

Impact of public health policy and mobility change on transmission potential of severe acute respiratory syndrome coronavirus 2 in Rhode Island, March 2020 – November 2021

ABSTRACT To study the SARS-CoV-2 transmission potential in Rhode Island (RI) and its association with policy changes and mobility changes, the time-varying reproduction number, Rt, was estimated. The daily incident case counts (16 March 2020, through 30 November 2021) were bootstrapped within a 15-day sliding window and multiplied by Poisson-distributed multipliers (λ = 4, sensitivity analysis: 11) to generate 1000 estimated infection counts, to which EpiEstim was applied to generate Rt time series. The median Rt percentage change when policies changed was estimated. The time lag correlations were assessed between the 7-day moving average of the relative changes in Google mobility data in the first 90 days, and Rt and estimated infection count, respectively. There were three major pandemic waves in RI in 2020–2021: spring 2020, winter 2020–2021 and fall-winter 2021. The median Rt fluctuated within the range of 0.5–2 from April 2020 to November 2021. Mask mandate (18 April 2020) was associated with a decrease in Rt (−25.99%, 95% CrI: −37.42%, −14.30%). Termination of mask mandates on 6 July 2021 was associated with an increase in Rt (36.74%, 95% CrI: 27.20%, 49.13%). Positive correlations were found between changes in grocery and pharmacy, Rt retail and recreation, transit, and workplace visits, for both Rt and estimated infection count, respectively. Negative correlations were found between changes in residential area visits for both Rt and estimated infection count, respectively. Public health policies enacted in RI were associated with changes in the pandemic trajectory. This ecological study provides further evidence of how non-pharmaceutical interventions and vaccination slowed COVID-19 transmission in RI.

Evolution of Solar Eruptive Events: Investigating the Relationships among Magnetic Reconnection, Flare Energy Release, and Coronal Mass Ejections

We study the evolution of solar eruptive events by investigating the temporal relationships among magnetic reconnection, flare energy release, and the acceleration of coronal mass ejections (CMEs). Leveraging the optimal viewing geometry of the Solar TErrestrial RElations Observatory (STEREO) relative to the Solar Dynamics Observatory (SDO) and the Reuven Ramaty High-Energy Solar Spectroscopic Imager (RHESSI) during 2010–2013, we identify 12 events with sufficient spatial and temporal coverage for a detailed examination. STEREO and SDO data are used to measure the CME kinematics and the reconnection rate, respectively, and hard X-ray (HXR) measurements from RHESSI provide a signature of the flare energy release. This analysis expands upon previous solar eruptive event timing studies by examining the fast-varying features, or “bursts,” in the HXR and reconnection rate profiles, which represent episodes of energy release. Through a time lag correlation analysis, we find that HXR bursts occur throughout the main CME acceleration phase for most events, with the HXR bursts lagging the acceleration by 2 ± 9 minutes for fast CMEs. Additionally, we identify a nearly one-to-one correspondence between bursts in the HXR and reconnection rate profiles, with HXRs lagging the reconnection rate by 1.4 ± 2.8 minutes. The studied events fall into two categories: events with a single dominant HXR burst and events with a train of multiple HXR bursts. Events with multiple HXR bursts, indicative of intermittent reconnection and/or particle acceleration, are found to correspond with faster CMEs.

Time Lag of Stem Water Deficit in Response to Increased Vapor Pressure Deficit

Abstract Vapor pressure deficit (VPD) shows a gradually increasing trend that forces trees to produce ecological memory with time-delay characteristics to drought year by year. The reversible stem diameter variation of trees can serve as an indicator for tree water status. In the context of global warming, these variations can dynamically monitor the response of stem water to the change of environmental water, especially to VPD. In this study, stem diameter variations of Larix gmelinii Rupr. and environmental factors were measured at high temporal resolution. We analyzed the tree water deficit–induced stem shrinkage (TWD) response to VPD variations. The results showed that variation of TWD lagged behind that of VPD to a certain extent, by about 3–5.5 h. Environmental water supply of trees under natural conditions can be divided into water deficit and adequate water supply. The division of the two water conditions was helpful to study the correlation between TWD and VPD. Observing the response of changes of stem diameter to the time lag of VPD is helpful to accurately represent the relationship of changes between moisture in the tree body and moisture in the air and to identify the stress effect of the lag in a short period of time. Study Implications: Stem diameter changes with change in moisture in the environment and usually reversibly shrinks under water deficits. Such reversible change in stem diameter is closely related to change in vapor pressure deficit, which is an environmental factor. Tree water deficit–induced trunk shrinkage, an index reflecting the change in stem diameter affected by moisture, had a time lag correlation with vapor pressure deficit in terms of variation characteristics. The time lag of the tree water deficit–induced trunk shrinkage response to vapor pressure deficit was 3–5.5 h. Just as the phenomenon of ecological memory cannot be ignored in an observational study with the year as the time scale, the phenomenon of time lag should not be ignored during an observational study of moisture stress at small time scales.

A combination-based machine learning algorithm estimating impacts of social, economic, and environmental on resident health—on China’s provincial panel data

The factors influencing residents health have become complex and intertwined with the development of economy and society. Traditional research with a single factor on health will not provide an accurate picture of the situation. This paper collects data on economic, environmental and social factors to estimate their impact on regional health. Considering the data is multi-source and complex, this paper proposes a combined feature importance algorithm, which weighted the feature importance of RF, XGB and SOIL. The algorithm does not depend on the data and adaptively approximates the true results. The results show that economic factors have a significant and direct impact on health, environmental factors have a lag correlation with health level, and social factors have a more complicated effect on health. Finally, we provide policy suggestions for health on economic, environmental, and social development.

Causes of the record-low Antarctic sea-ice in austral summer 2022

The Antarctic sea-ice area reached a record minimum during austral summer 2022 (December 2021 to February 2022), with particularly considerable loss in the West Antarctic. Based on lead–lag correlation analysis, the authors found that the record minimum was closely linked to the preceding near-strongest positive-phase Southern Annular Mode during August–October 2021 and stronger positive SST anomaly in the Maritime Continent during July–September 2021. The former remained at a historically near-strongest or strongest level because of the stratospheric cooling effect of the unprecedented stratospheric ozone depletion, which induced a deepened and southwestward-shifted Amundsen Sea Low (ASL), causing sea-ice retreat via horizontal wind anomalies. The latter persisted into summer and favored the development of La Niña, which generated deep convection over the southwestern subtropical Pacific and triggered a southeastward-propagated Rossby wave train, deepening the ASL remotely. The ozone reduction also induced surface warming in the West Antarctic by increasing the net downward shortwave radiation, thereby intensifying the sea-ice loss. Additionally, positive feedback between the SST, net shortwave radiation and cloudiness, along with the Ekman heat transport, amplified the surface warming.摘要2022年夏季 (2021年12月至2022年2月) 南极海冰面积达到历史新低, 西南极减少最显著. 2021年8∼10月南半球环状模接近历史最强和7∼9月海洋性大陆附近海温显著增暖是两个关键因素. 由于平流层臭氧破纪录减少使得前者维持历史最强或接近最强, 导致阿蒙森低压 (ASL) 加深并向西南移动, 有利于海冰减少. 后者持续到夏季, 有利于拉尼娜的发展, 通过激发罗斯贝波列加深ASL. 在热力上, 臭氧减少导致向下净短波辐射增加, 引起西南极增暖. 此外, 净短波辐射-海温-云形成正反馈, 与埃克曼输送一起, 放大表面增暖, 从而促进海冰融化.

Revisiting causality using stochastics on atmospheric temperature and CO 2 concentration

Revisiting causality using stochastics on atmospheric temperature and CO ₂ concentration

Generalized Multi-Lag Estimators (GMLE) for Polarimetric Weather Radar Observations

Observations of weather phenomenon by polarimetric pulsed-Doppler weather radars are employed worldwide to monitor impending severe storms, flash-floods, and other weather related public hazards. The basis for processing received meteorological signals from pulsed-radar waveforms relies on stochastic processes where the accurate estimation of radar variables from received signals in additive white noise is essential for meaningful interpretation of weather phenomena and algorithm-derived products. For polarimetric weather radars, these estimates are calculated from signal correlations in time and across the horizontal and vertical polarization channels. Conventional estimators only use 1 or 2 signal correlation time-lags and may not utilize all the available information intrinsic in the received signals. Weather-variable estimates could benefit from the use of all intrinsic characteristics in the received data; accordingly, more complex estimators use multiple lags to extract additional information. However, not all estimates are improved by the use of more lags; in fact, improvement in estimates depends on signal characteristics and requires that the additional correlation lags provide new information. In this article, we derive and examine general multi-lag estimators for reflectivity, differential reflectivity, polarimetric cross-correlation coefficient, and Doppler spectrum width. We compare the performance of these proposed estimators against conventional estimators using Monte-Carlo simulations on different meteorological signal characteristics to find estimators that can improve the quality of certain radar-variable estimates.

Dynamic Linkage(s) between Financial Innovation and Efficiency of Deposit Money Banks in Nigeria

The emergence of new technologies, changing customer expectations, and regulatory imperatives, among others, have driven the financial industry into an era where "digital innovations" thrive, culminating in the emergence and growth of innovative products such as agency banking, mobile/internet banking, mobile money, unstructured supplementary service data (USSD), just to name a few. There is increased customers' reliance on e-channels. This has further established the need for banks to engage in financial innovation for relevance, competitiveness, efficiency and growth. This study, therefore, examines the dynamic linkage(s) between financial innovation and the efficiency of deposit money banks (DMBs) in Nigeria. The population of the study comprises the 13 listed DMBs in Nigeria as of 31 December 2021, and these serve as the sample size. The period covered is 48 months -2016 and 2019. Data were collected from the bank's annual reports and Apex bank's statistical bulletin. Descriptive statistics, correlation and autoregressive distributed lag (ARDL) cointegration techniques were used for data analyses. The efficiency of DMBs was estimated using data envelopment analysis (DEA). Findings reveal that financial innovation has forward and backwards dynamic linkages with the efficiency of DMBs in Nigeria.

Variation of Total Electron Content in the Quiet and Disturbed Period and Their Correlation with Solar Wind Parameters

Total electron content and electron density are the fundamental parameters that determine the main properties of the ionosphere. We have observed variation of Global Positioning System (GPS) derived Vertical Total Electron Content (VTEC) on three different geomagnetic events. The observed VTEC data is recorded from four GPS stations at different locations (87.26°E, 26.48°N); (85.79°E, 27.87°N); (84.57°E, 28.17°N), and (86.70°E, 27.81°N). To determine the severity of storms, we analysed the north-south component of interplanetary magnetic field (IMF-Bz), solar wind parameters- solar wind speed (Vsw) and solar wind dynamic pressure (Psw) ,and geomagnetic indices- Dst index, Kp index and Auroral Electrojet (AE). For all the studied event days, we observed intensified VTEC on geomagnetically disturbed days over quiet days. The VTEC enhancement was significantly high on the severely disturbed day, followed by the moderate storm and the minor storm.The study made to observe association of VTEC with different interplanetary and geomagnetic indices shows that during all studied event days, VTEC enhancement is positively correlated with Vsw, Psw, AE and Kp with cross-correlation coefficient above 0.8 at zero time lag and strong negative correlation with Dst index. In contrast, the correlation of IMF-Bz vary with the intensity of storm. Our finding show a significant variation in VTEC during the geomagnetic disturbances, supporting previous studies on ionospheric responses to geomagnetic storms as well as theoretical assumptions.

Spring Land Temperature in Tibetan Plateau and Global-Scale Summer Precipitation: Initialization and Improved Prediction

Abstract Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface ­temperatures show a lag correlation with summer precipitation in several remote regions, but current global land–atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the “Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction” (LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global “hotspot” regions identified here; the ensemble means in some “hotspots” produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.

Impact of ENSO on the Entrance of the Indonesian Throughflow: The Oceanic Wave Propagation

AbstractA regional ocean model simulation for the period from 2000 through 2016 is used to study oceanic wave propagation in the Indonesian seas. The model simulation compares favorably with the interannual variability of the satellite derived sea level anomaly and the observed currents in the entrance passages of ITF. Lag correlation analysis suggests that the interannual sea level anomaly and opposite responses of the velocity above and below the thermocline in the western (the Sulawesi Sea and the Makassar Strait) and eastern (the Halmahera Sea) pathways of ITF are associated with the ENSO induced Rossby waves from the equatorial Pacific rather than the local wind forcing. In particular, during La Niña years ENSO induced anomalies traveling through the western pathway are comparable to those through the traditional eastern waveguide, suggesting the importance of both pathways in the propagation of ENSO signals in the Indonesian seas. Different roles of the western and eastern pathways in transmitting the ENSO signals into the Indonesian seas are associated with the nonlinear processes in the Sulawesi Sea, which appears to be linked with different states of the western boundary currents during the warm and cold ENSO phases.

Decadal variability of the interannual climate predictability associated with the Indo-Pacific oceanic channel dynamics in CCSM4

The lag correlations between the observed sea surface temperature anomalies (SSTA) in the southeastern tropical Indian Ocean in fall and those in the Pacific cold tongue at the one-year time lag are calculated in running windows of 7–11 years and are shown to have decadal variability. Similar decadal variability has also been identified in the historical simulations of the Community Climate System Model version 4 (CCSM4) that participates in the Coupled Model Intercomparison Project phase-5 (CMIP5). During the positive phases of the decadal variability, the significant lag correlations are diagnosed to be dominated by the oceanic channel dynamics, i.e., upwelling Kelvin waves associated with the Indian Ocean Dipole (IOD) force enhanced Indonesian Throughflow transport anomalies and western Pacific thermocline anomalies to propagate eastward, resulting in SSTA in the central-eastern equatorial Pacific Ocean. During the negative phases of the decadal variability, the subsurface lag correlations suggest that the western Pacific Ocean are still dominated by the oceanic channel dynamics, but do not correlate well with the SSTA in the cold tongue due to a deeper thermocline in the eastern equatorial Pacific. The IOD-ENSO teleconnection is not affected significantly by the anthropogenic forcing during either the positive or the negative decadal phases. The CCSM4 model is found to underestimate Indonesian Throughflow transport variability but overestimate the westerly wind anomalies in the western-central equatorial Pacific, which forces unrealistic anomalies in the equatorial Pacific Ocean associated with the IOD.

Link between Arctic ozone and the stratospheric polar vortex

The stratospheric ozone layer protects life on earth by preventing solar ultraviolet radiation from reaching the surface. Owing to the large population in the Northern Hemisphere and extreme ozone loss in the Arctic, changes in Arctic stratospheric ozone (ASO) and their causes have attracted broad attention recently. Using monthly mean data during the period 1980–2020 from MERRA-2, the relationship between the stratospheric polar vortex (SPV) and ASO, along with the relative contributions of chemical and dynamic processes associated with the SPV to changes in ASO, were examined in this study. Results showed that the ASO in March has a strong out-of-phase link with the strength of the SPV in March, with no obvious lead–lag correlations, i.e., an increase (decrease) in ASO corresponds to a weakened (strengthened) SPV. Further analysis suggested that the strong out-of-phase link between the SPV and ASO is related to changes in Brewer–Dobson circulation (BDC). Strong SPV events, accompanied by a low temperature condition and weakened upward propagation of planetary waves over the Arctic in the stratosphere, result in weakened BDC. The weakened downwelling at high latitudes tends to transport less ozone-rich air in the upper stratosphere at lower latitudes into the lower stratosphere at high latitudes, facilitating a decrease in ASO. The BDC's vertical velocity plays the dominant role in modulating ASO.摘要利用1980–2020年MERRA-2资料, 分析了平流层极涡 (Stratospheric polar vortex, SPV) 和北极臭氧 (Arctic stratospheric ozone, ASO) 的关系, 评估了与SPV相关的化学, 动力过程在其中的相对作用. 结果表明, 3月份ASO与同期SPV强度反相关最大. SPV-ASO二者反相关与平流层剩余环流 (Brewer-Dobson circulation, BDC) 变化密切相关. 强SPV伴随的北极平流层低温条件和行星波向上传播减弱, 导致BDC减弱, 减弱的BDC下沉支将低纬度平流层上层臭氧含量较低的空气输送到北极平流层低层, 从而导致ASO减少. BDC垂直速度在其中起主导作用.

Assessment of the oceanic channel dynamics responsible for the IOD-ENSO precursory teleconnection in CMIP5 climate models

Existed studies have suggested a precursory relation between Indian Ocean Dipole (IOD) and El Niño and the Southern Oscillations (ENSO) with 1-year time lag. The underlying mechanisms were attributed to atmospheric bridge and/or oceanic channel processes. In this study, the oceanic channel dynamics in 23 climate models of the Coupled Model Intercomparison Project phase 5 (CMIP5) are assessed by correlation analyses in comparison with observations. The results show that the lag correlations between the IOD and ENSO anomalies associated with oceanic channel are significant, suggesting important role of oceanic channel dynamics in the cross-basin teleconnection in the analyzed CMIP5 models, consistent with observational analyses. In comparison, the correlations associated with atmospheric bridge are highly dispersive among the models and generally inconsistent with the observational analyses, suggesting model deficiencies. In a single climate model, the lag correlations associated with oceanic channel dynamics are consistent among different ensemble experiments, whereas those associated with atmospheric bridge processes are dispersive.

Objective determination of the winter monsoon onset dates and its interannual variability in Malaysia

AbstractThe northeast/winter monsoon over Malaysia often leads to extreme rainfall events and floods over the windward side of terrain due to the strong northeasterly winds. Comparatively, much less rain falls over the other are sheltered by the mountain ranges. Based on this consideration, the onset of monsoon in the region is best determined operationally using 925 hPa winds. Therefore, we propose the first, simple, single‐variable‐based method to determine the onset dates of winter monsoon is yet reliable and can be closely monitored for operational purposes. The onset date is defined in the third pentad when the average of three pentads of 925 hPa northerly wind speed is greater than 1 m·s−1, and at least one of three pentads must be greater than 2.5 m·s−1. The study also investigates the relationship between the interannual variability of onset date and El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). Late‐onset can be expected when La Niña develops in the boreal fall season. However, a similar association between monsoon onset and El Niño condition was not observed. The lead–lag correlation between the IOD and the onset dates shows that the onset is significantly negatively correlated with the IOD. During negative (positive) IOD events, a delay (early) onset is expected. Results also show that the relationship between the onset and IOD is more robust and linear than the ENSO.