The mechanism and even the existence of the Atlantic Multidecadal Oscillation (AMO) have remained under debate among climate researchers, and the same applies to general temperature oscillations of a ...60–90‐year period. The objective of this study is to show that these temperature oscillations are real and not artifacts and that these oscillations have different external cosmic origins. The authors have studied how well the variations of astronomical harmonic resonances (AHR) could explain the 60‐year temperature variations, which are based on instrumental records and on the tree‐ring data of the supra‐long Scots pine tree‐ring record for northern Finnish Lapland (subsequently called the Finnish timberline pine chronology FTPC), stretching to the year 5634 BC. Powerful volcanic eruptions have significant temperature‐decreasing impacts, and they are the major disturbances to eliminate in analysis. The similarities between the temperatures of the tree‐ring trend and the AHR trend are easy to observe even by the naked eye. The statistical analysis shows that these two signals are statistically related. The analyses also show that the well‐known Gleissberg cycle of 88 years is the dominating cycle caused by the Suns' activity changes but the observed 60‐year cycle can be connected to the AHR cyclicity.
The analyses revealed the dominant periods of 57–60 years and 79–88 years. The shorter period could be linked to solar system barycenter movements caused by the harmonic resonances of Jupiter and Saturn. The solar activity changes could be related to a longer period, which is the multiple of an average solar cycle length of 11 years. The combination of these two cyclic periods produces a periodic cyclicity of AMO and PMO that matches the tree‐ring data variations from 1100 to 2020.
Monsoon and its teleconnection with earth system internal processes affect the spatiotemporal distribution of precipitation and water resources. In this paper, the wavelet coherence analysis has been ...utilized, a time and frequency domain methodology for comparing the spectral features of two independent time series superior to linear approaches. This technique is used to capture the significant modes of variabilities in the Indian Summer Monsoon Index (ISMI) and large‐scale climate indices (CIs) between ocean–atmosphere oscillations, like Indian Ocean Dipole (IOD), El Niño–Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Atlantic Multidecadal Oscillation (AMO), and Arctic Oscillation (AO) over Pakistan. Precipitation time series during 1960–2016 revealed significant interannual coherences with ISMI, whereas the remaining CIs (IOD, ENSO, PDO, SOI, NAO, AMO, and AO) revealed interannual, decadal and interdecadal coherences. However, AO revealed strongest coherences in R‐II, III, and VI at interdecadal scales among all CIs. Overall, the interannual cycles on ISMI are 2.8 years, IOD 1–5.3 years, PDO 0–5.3 years, SOI 1–5.3 years, NAO 0–5 years, AO 0–5 years, and AMO 0–8.3 years. Whereas, the remaining CIs shared interdecadal coherences over particular regions. The ISMI displayed coherences (except in the UIB) with the large‐scale CIs over various homogenous regions on an interannual scale. The dominant influence of ISMI is observed in R‐II and III; the significant coherences in R‐II ranged from ~8 to 32 months (~0.8–2.8 years). The IOD and NAO have major coherences than the remaining large‐scale CIs ranging from ~16 to 64 months (1.3–5.3 years). The AO has the most significant coherences observed in R‐II, III, and VI on the decadal/interdecadal scale from 128 months and above (almost 10–15 years). On a 1.0‐year timescale, all homogenous regions demonstrated strong intermittent coherence with ISMI, IOD, ENSO, PDO, SOI, NAO, AMO, and AO. These findings have substantial implications for decision‐makers and scientists in Pakistan looking to enhance water resource planning and operations in the face of future climate uncertainties.
During 1960–2016, the ISMI displayed the most coherences compared to the large‐scale CIs over various regions on an interannual scale. The dominant influence of ISMI is observed in R‐II and III. The significant coherences in R‐II ranged from ~8 to 32 months (~0.8–2.8 years) through the entire period, whereas the remaining CIs have an almost similar pattern of interannual coherences over various regions. However, IOD and NAO had major coherences compared to the remaining large‐scale CIs ranging from ~16 to 64 months (1.3–5.3 years). The AO had the most significant coherences observed in R‐II, III, and VI throughout the study on the decadal/interdecadal scale from 128 months and above (almost 10–15 years).
The occurrence of extreme precipitation events during Indian Summer Monsoon Rainfall (ISMR) has increased significantly in recent decades. Natural spatio-temporal variability of extreme precipitation ...events in India has been linked to various climatic variables like El Niño Southern Oscillation (ENSO), Equatorial Indian Ocean Oscillation (EQUINOO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO). In this study, extreme precipitation indices are used to characterize the ISMR extremes and possible individual and coupled association with climatic variables identified using wavelet analysis. Region-based analysis revealed that ENSO, EQUINOO, PDO, and AMO influence extreme precipitation events on spatio-temporal scales. Variability of the duration of extreme precipitation events strongly depends on the ENSO at interannual scale compared to the other climate variables whereas, total precipitation greater than 95th percentile and maximum consecutive 5-day precipitation values were significantly coherent on inter-decadal scale with ENSO, EQUINOO, and PDO. It is also found that the climate variables together cause variability in ISMR extremes, particularly AMO-ENSO-EQUINOO and AMO-ENSO-PDO combinations explain the variability better than any other combination. An increase in the number of climate variables did not improve the coherence, since these climatic variables are correlated with each other. Further, the decomposition of wavelets at different scales shows that more than half of the grid points considered were significant at interdecadal and multidecadal scales even though they are designated with different time scales. This indicates that the non-stationary behavior of the ISMR extremes is directly linked to the climatic variables at higher scales.
Studies related to the combined effect of different atmospheric oscillations on the ocean surface waves are limited. The present study focuses on the change in wave climatology due to the combined ...effect of Boreal Summer Intra-Seasonal Oscillation (BSISO) and El Niño Southern Oscillation (ENSO) using 40 years of reanalysis data on the Asian Summer Monsoon Region (ASMR). Composite analysis of surface wind, significant wave height, wind-sea, swell and mean wave period are analysed corresponding to different ENSO–BSISO phase combinations. The study showed noteworthy differences in wave parameters when ENSO–BSISO phases are analysed together. In El Niño–BSISO combined phase, enhancement of north-easterly wind causes the advancement in the reversal of wind direction (north-easterly to monsoon enhanced south-westerly) and disrupted propagation of positive wind and wave anomalies. Conversely, during La Niña–BSISO co-occurrence, south-westerlies are enhanced and as a result, the negative north-easterly anomalies are disrupted. In all the ENSO–BSISO combined phases, maximum wave height variability occurs at the South China Sea owing to the combined action of the north Indian Ocean (NIO) and Western North Pacific (WNP) surface wind forcing. High fluctuations in T
m
over the NIO and WNP are observed during different ENSO–BSISO phase combinations. The phase relationship of H
s
with T
m
and the significant height of wind-sea (H
sw
) and swell (H
ss
) are assessed to understand the propagation of swells. Due to the effect of multiple atmospheric perturbations, significant changes in H
s
occur over the coastal regions of the NIO.
The advent of space-based missions like Kepler has revolutionized the study of solar-type stars, particularly through the measurement and modeling of their resonant modes of oscillation. Here we ...analyze a sample of 66 Kepler main-sequence stars showing solar-like oscillations as part of the Kepler seismic LEGACY project. We use Kepler short-cadence data, of which each star has at least 12 months, to create frequency-power spectra optimized for asteroseismology. For each star, we identify its modes of oscillation and extract parameters such as frequency, amplitude, and line width using a Bayesian Markov chain Monte Carlo "peak-bagging" approach. We report the extracted mode parameters for all 66 stars, as well as derived quantities such as frequency difference ratios, the large and small separations and the behavior of line widths with frequency and line widths at with , for which we derive parametrizations; and behavior of mode visibilities. These average properties can be applied in future peak-bagging exercises to better constrain the parameters of the stellar oscillation spectra. The frequencies and frequency ratios can tightly constrain the fundamental parameters of these solar-type stars, and mode line widths and amplitudes can test models of mode damping and excitation.
Based on multiple data sets and methods, this study investigates the impacts of intra‐seasonal oscillations (ISOs) on the South China Sea summer monsoon (SCSSM) withdrawal. A daily SCSSM withdrawal ...date is established, which can capture reasonably the consistent transition of low‐level zonal wind from westerly to easterly over the South China Sea (SCS). The bandpass‐filtered outgoing longwave radiation and low‐level winds are then composited with respect to the monsoon withdrawal date. It is found that a 30–60‐day oscillation originating from the equatorial Indian Ocean and a quasi‐biweekly oscillation (QBWO) propagating from the equatorial western Pacific collectively contribute to the SCSSM withdrawal. Under the background of slow annual cycle (weak zonal wind during late September), the local convectively inactive phases of these ISOs induce anomalous easterly winds, which could trigger monsoon withdrawal over the SCS. The boreal summer ISO indices also confirm that both the 30–60‐day oscillation and the QBWO could modulate SCSSM withdrawal, which is more likely to occur when the suppressed convection caused by these ISOs is either encroaching on or occupying the SCS.
We use a neural network‐based estimate of the sea surface partial pressure of CO2 (pCO2) derived from measurements assembled within the Surface Ocean CO2 Atlas to investigate the dominant modes of ...pCO2 variability from 1982 through 2015. Our analysis shows that detrended and deseasonalized sea surface pCO2 varies substantially by region and the respective frequencies match those from the major modes of climate variability (Atlantic Multidecadal Oscillation, Pacific Decadal Oscillation, multivariate ENSO index, Southern Annular Mode), suggesting a climate modulated air‐sea exchange of CO2. We find that most of the regional pCO2 variability is driven by changes in the ocean circulation and/or changes in biology, whereas the North Atlantic variability is tightly linked to temperature variations in the surface ocean and the resulting changes in solubility. Despite the 34‐year time series, our analysis reveals that we can currently only detect one to two periods of slow frequency oscillations, challenging our ability to robustly link pCO2 variations to climate variability.
Plain Language Summary
In our study we show that there is a link between the amount of carbon in the surface ocean and natural climate variability. We find that this variability is very different between different oceanic regions, but most of the observed variability is on decadal timescales and longer. Current data products therefore do not extend long enough in time to robustly detect long‐term oscillations of the surface ocean carbon content.
Key Points
Frequency of observation‐based pCO2 variability corresponds to frequencies in AMO, PDO, MEI, and SAM index
The majority of the ocean variability is driven by circulation/biology, whereas the North Atlantic signal is temperature driven
Decadal pCO2 signals emerge in all basins; their detection is limited by the short observational record
Extreme 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.
Teleconnection between large‐scale climatic oscillations and droughts across India has been studied in a multiscale framework, wavelet and its variants. The methodology is shown in the figure below.
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