Low-frequency Trends Research Articles

Preliminary evaluation of the potential of tree-ring cellulose content as a novel supplementary proxy in dendroclimatology

Abstract. Cellulose content (CC (%)) in tree rings is usually utilised as a tool to control the quality of the α-cellulose extraction from tree rings in the preparation of stable-isotope analysis in wooden tissues. Reported amounts of CC (%) are often limited to mean values per tree. For the first time, CC (%) series from two high-Alpine species, Larix decidua Mill. (European Larch, LADE) and Pinus cembra L. (Swiss stone pine, PICE) are investigated in modern wood samples and Holocene wood remains from the Early and mid-Holocene. Modern CC (%) series reveal a species-specific low-frequency trend independent of their sampling site over the past 150 years. Climate–cellulose relationships illustrate the ability of CC (%) to record temperature in both species but for slightly different periods within the growing season. The Holocene CC (%) series illustrate diverging low-frequency trends in both species, independent of sampling site characteristics (latitude, longitude and elevation). Moreover, potential age trends are not apparent in the two coniferous species. The arithmetic mean of CC (%) series in the Early and mid-Holocene indicate low CC (%) succeeding cold events. In conclusion, CC (%) in tree rings show high potential to be established as novel supplementary proxy in dendroclimatology.

Monitoring long-term soundscape trends in U.S. Waters: The NOAA/NPS Ocean Noise Reference Station Network

The National Oceanic and Atmospheric Administration (NOAA)/National Park Service (NPS) Ocean Noise Reference Station (NRS) Network is an array of currently twelve calibrated autonomous passive acoustic recorders. The first NRS was deployed in June 2014, and eleven additional stations were added to the network during the following two years. The twelve stations record data that can be used to quantify baseline levels and multi-year trends in ocean ambient sound across the continental United States, Alaska, Hawaii, and island territories within and near to the United States Exclusive Economic Zone (U.S. EEZ). The network provides multi-year, continuous observations of low-frequency underwater sound between 10 Hz and 2000 Hz to capture anthropogenic, biological, and geophysical contributions to the marine soundscape at each location. Comparisons over time and among recording sites will provide information on the presence of calling animals and the prevalence of abiotic and anthropogenic activities that contribute to each soundscape. Implementation of the NRS Network advances broad-scale passive acoustic sensing capabilities within NOAA and the NPS and is an important tool for monitoring protected areas and marine species and assessing potential environmental impacts of anthropogenic noise sources. This analysis focuses on the first year of recordings and captures the wide variability of low-frequency sound levels among and within individual NRS sites over time. Continued data collection will provide information on long-term, low-frequency sound level trends within or near the U.S. EEZ and will be used to explore the value of using soundscape analysis to inform management and mitigation strategies.

Trends and low frequency variability of East Coast Lows in the twentieth century

East Coast Lows (ECLs) are important weather systems that affect the eastern seaboard of Australia. They have attracted research interest for both their destructive nature and water supplying capability. In this paper, three objective ECL tracking methods are applied to the twentieth century reanalysis ensemble (20CRV2C) for the period of 1851–2014 to identify historical trends and variability in ECLs. While the ensemble mean is unsuitable for tracking ECLs, when all methods are applied to the full 56-member ensemble there is large agreement between tracking methods as to the low-frequency variability and trends in ECLs. The uncertainty between 56 ensemble members has dramatically decreased in recent decades. For comparison, the three tracking methods are also applied to ERA-I reanalysis dataset for the overlapping time period (1980-2009). The inter-annual variability and monthly distribution of ECLs agrees well between different reanalysis for each of tracking methods. The most recent decade has had relatively low numbers of ECLs compared to the previous century.

Practice and Cognition of Midway VSP in High Temperature and High Pressure Field of South China Sea

Ying-Qiong Basin is a typical high-temperature and overpressure basin, which is the main battlefield of oil and gas exploration in South China Sea and has made great breakthroughs in recent years. During drilling process in high pressure, the relationship between the deep and the pressure is directly related to the drilling safety and costs. In order to improve prediction accuracy, the VSP operation is carried out through the midway, and three points have been obtained: 1) The VSP has a higher accuracy of the interface depth in certain depth range of the drill bit. 2) When the low-frequency trend prediction is accurate before the drill bit, interval velocity of the VSP inversion is consistent with the formation velocity. 3) The VSP pressure forecast is based on the inversion layer velocity and under-compaction pressure. If the velocity prediction is not accurate, the pressure forecast must be erroneous. If the pressure has other sources, the formation pressure is not accurate even if the inversion velocity is accurate. The application scope and exploration effect of midway VSP operation are summarized and applied to Ledong 10-1 block in Yinggehai basin, which realize the breakthrough in the field of high temperature overpressure and provide the basis for other similar exploration areas to do VSP operation.

AVA Simultaneous Inversion of Prestack Seismic Data Using Particle Swarm Optimization

A new prestack AVA simultaneous inversion using particle swarm optimization algorithm is proposed, which can obtain the elastic parameters such as P-wave and S-wave impedance from P-wave reflection data simultaneously. Compared with the conventional AVA inversion based on generalized linear technique, this method does not depend on the initial model and can reach the global minimum. In order to increase the stability of the inversion, low-frequency trends of P-wave and S-wave impedances are built into the inversion. This method has been successfully applied to synthetic and field data. The estimated P-wave and S-wave impedances can be combined to derive other elastic parameters, which are sensitive for lithology identification and fluid prediction.

Industrial-age doubling of snow accumulation in the Alaska Range linked to tropical ocean warming

Future precipitation changes in a warming climate depend regionally upon the response of natural climate modes to anthropogenic forcing. North Pacific hydroclimate is dominated by the Aleutian Low, a semi-permanent wintertime feature characterized by frequent low-pressure conditions that is influenced by tropical Pacific Ocean temperatures through the Pacific-North American (PNA) teleconnection pattern. Instrumental records show a recent increase in coastal Alaskan precipitation and Aleutian Low intensification, but are of insufficient length to accurately assess low frequency trends and forcing mechanisms. Here we present a 1200-year seasonally- to annually-resolved ice core record of snow accumulation from Mt. Hunter in the Alaska Range developed using annual layer counting and four ice-flow thinning models. Under a wide range of glacier flow conditions and layer counting uncertainty, our record shows a doubling of precipitation since ~1840 CE, with recent values exceeding the variability observed over the past millennium. The precipitation increase is nearly synchronous with the warming of western tropical Pacific and Indian Ocean sea surface temperatures. While regional 20th Century warming may account for a portion of the observed precipitation increase on Mt. Hunter, the magnitude and seasonality of the precipitation change indicate a long-term strengthening of the Aleutian Low.

A hybrid model for dissolved oxygen prediction in aquaculture based on multi-scale features

To increase prediction accuracy of dissolved oxygen (DO) in aquaculture, a hybrid model based on multi-scale features using ensemble empirical mode decomposition (EEMD) is proposed. Firstly, original DO datasets are decomposed by EEMD and we get several components. Secondly, these components are used to reconstruct four terms including high frequency term, intermediate frequency term, low frequency term and trend term. Thirdly, according to the characteristics of high and intermediate frequency terms, which fluctuate violently, the least squares support vector machine (LSSVR) is used to predict the two terms. The fluctuation of low frequency term is gentle and periodic, so it can be modeled by BP neural network with an optimal mind evolutionary computation (MEC-BP). Then, the trend term is predicted using grey model (GM) because it is nearly linear. Finally, the prediction values of DO datasets are calculated by the sum of the forecasting values of all terms. The experimental results demonstrate that our hybrid model outperforms EEMD-ELM (extreme learning machine based on EEMD), EEMD-BP and MEC-BP models based on the mean absolute error (MAE), mean absolute percentage error (MAPE), mean square error (MSE) and root mean square error (RMSE). Our hybrid model is proven to be an effective approach to predict aquaculture DO.

Azimuthally anisotropic elastic impedance inversion for fluid indicator driven by rock physics

The normal-to-tangential fracture compliance ratio is usually used as a fracture fluid indicator (FFI) for fluid identification in fractured reservoirs. With a new parameterization for fracture weaknesses, we have defined a new FFI based on azimuthally anisotropic elastic impedance (EI) inversion and fractured anisotropic rock-physics models. First, we derived a new azimuthally anisotropic EI equation with a similar expression for the isotropic and anisotropic EI parts to remove the exponential correction of EI that is attributable to weak anisotropy. Then, we built a fractured anisotropic rock-physics model used for the estimation of well-log parameters for the normal and tangential fracture weaknesses, which built the initial background low-frequency trend of fracture weaknesses. Finally, based on the azimuthally anisotropic EI inversion method with the Cauchy-sparse and low-frequency information regularization, we estimated an FFI applied to fluid identification in fractured reservoirs. Tests on the synthetic and real data demonstrate that the anisotropic parameters related to fracture weaknesses can be estimated reasonably and stably and that our method appears to provide an alternative available for fluid identification in fractured reservoirs.

Human responses to early Holocene climate variability in eastern Fennoscandia

In eastern Fennoscandia numerous biological and physical proxy records provide ample evidence of Holocene climate-environment dynamics. The region therefore has great promise for studies concentrating on the impacts of past climate change on human populations in the early Holocene, that is, in the period that saw the beginning of postglacial human dispersal into the area.Here we provide a brief overview of the high and low frequency climate changes indicated by different proxy records in Finland and nearby areas in eastern Fennoscandia, and discuss the archaeological evidence for human responses to abrupt climate-related environmental change and low-frequency climate trends. The clearest archaeologically visible event-like responses seem to derive from ecotonal regions, i.e., the forest–tundra or coastal regions and suggest a correlation between ecological “hinge-regions” and the archaeologically clearest signs of hunter-gatherer responses to climate stress. However, the evidence of the abrupt climate events is often ambiguous and their influence on early Holocene human populations remains equivocal.

Interpreting the sea surface temperature warming trend in the Yellow Sea and East China Sea

Previous studies have demonstrated that the low-frequency sea surface temperature (SST) variability in the Yellow Sea and East China Sea (YECS) is linked to large-scale climate variability, but explanations on the mechanisms vary. This study examines the low-frequency variability and trends of some atmospheric and oceanic variables to discuss their different effects on the YECS warming. The increasing temperature trend is also observed at a hydrographic section transecting the Kuroshio. The increasing rate of ocean temperature decreases with depth, which might result in an increase in vertical stratification and a decrease in vertical mixing, and thus plays a positive role on the YECS warming. The surface net heat flux (downward positive) displays a decreasing trend, which is possibly a result of the YECS warming, and, in turn, inhibits it. Wind speeds show different trends in different datasets, such that its role in the YECS warming is uncertain. The trends in wind stress divergence and curl have large uncertainties, so their effects on SST warming are still unclear. The Kuroshio heat transport calculated in this study, displays no significantly increasing trend, so is an unlikely explanation for the SST warming. Limited by sparse ocean observations, sophisticated assimilative climate models are still needed to unravel the mechanisms behind the YECS warming.

Comparison of decision tree based classification strategies to detect external chemical stimuli from raw and filtered plant electrical response

Plants monitor their surrounding environment and control their physiological functions by producing an electrical response. We recorded electrical signals from different plants by exposing them to Sodium Chloride (NaCl), Ozone (O3) and Sulfuric Acid (H2SO4) under laboratory conditions. After applying pre-processing techniques such as filtering and drift removal, we extracted few statistical features from the acquired plant electrical signals. Using these features, combined with different classification algorithms, we used a decision tree based multi-class classification strategy to identify the three different external chemical stimuli. We here present our exploration to obtain the optimum set of ranked feature and classifier combination that can separate a particular chemical stimulus from the incoming stream of plant electrical signals. The paper also reports an exhaustive comparison of similar feature based classification using the filtered and the raw plant signals, containing the high frequency stochastic part and also the low frequency trends present in it, as two different cases for feature extraction. The work, presented in this paper opens up new possibilities for using plant electrical signals to monitor and detect other environmental stimuli apart from NaCl, O3 and H2SO4 in future.

Atmospheric Water Balance and Variability in the MERRA-2 Reanalysis

Abstract Closing and balancing Earth’s global water cycle remains a challenge for the climate community. Observations are limited in duration, global coverage, and frequency, and not all water cycle terms are adequately observed. Reanalyses aim to fill the gaps through the assimilation of as many atmospheric water vapor observations as possible. Former generations of reanalyses have demonstrated a number of systematic problems that have limited their use in climate studies, especially regarding low-frequency trends. This study characterizes the NASA Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) water cycle relative to contemporary reanalyses and observations. MERRA-2 includes measures intended to minimize the spurious global variations related to inhomogeneity in the observational record. The global balance and cycling of water from ocean to land is presented, with special attention given to the water vapor analysis increment and the effects of the changing observing system. While some systematic regional biases can be identified, MERRA-2 produces temporally consistent time series of total column water and transport of water from ocean to land. However, the interannual variability of ocean evaporation is affected by the changing surface-wind-observing system, and precipitation variability is closely related to the evaporation. The surface energy budget is also strongly influenced by the interannual variability of the ocean evaporation. Furthermore, evaluating the relationship of temperature and water vapor indicates that the variations of water vapor with temperature are weaker in satellite data reanalyses, not just MERRA-2, than determined by observations, atmospheric models, or reanalyses without water vapor assimilation.

Non-stationary and non-Gaussian characteristics of wind speeds

Non-stationarity and non-Gaussian property are two of the most important characteristics of wind. These two features are studied in this study based on wind speed records measured at different heights from a 325 m high meteorological tower during the synoptic wind storms. By using the time-frequency analysis tools, it is found that after removing the low frequency trend of the longitudinal wind, the retained fluctuating wind speeds remain to be asymmetrically non-Gaussian distributed. Results show that such non-Gaussianity is due to the weak-stationarity of the detrended fluctuating wind speed. The low frequency components of the fluctuating wind speeds mainly contribute to the non-zero skewness, while distribution of the high frequency component is found to have high kurtosis values. By further studying the decomposed wind speed, the mechanisms of the non-Gaussian distribution are examined from the phase, turbulence energy point of view.

Investigating the multiscale variability and teleconnections of extreme temperature over Southern India using the Hilbert–Huang transform

This study investigates the variability of annual minimum and maximum surface temperature (T min and T max ) of three temperature homogeneous regions [East Coast (EC), Western Coast (WC) and Interior Peninsular (IP)] in southern India in multiple time scales and examines their teleconnections with different climatic indicators. First, the different temperature time series are decomposed into appropriate number of oscillatory modes using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm. Subsequently, the modes are subjected to spectral analysis employing the Normalized Hilbert Transform—Direct Quadrature (NHT–DQ) scheme to get instantaneous amplitudes and frequencies. Further, a detailed trend analysis of instantaneous amplitudes is performed, which showed that the recent changes in temperature since 1970s, over southern India are mainly attributed to the increase in amplitudes of the IMFs corresponding to inter-decadal periodicity in all the regions. Moreover, data of four climatic indicators such as Pacific Decadal Oscillation (PDO), Sunspot Number (SN), Total Solar Irradiance (TSI) and CO2 concentration are decomposed using CEEMDAN and compared with decompositions of T max and T min time series of all the three regions. From the cross correlation analysis of oscillatory modes, this study established the links of different climatic indicators with the extreme temperature of southern India, which are evident mainly at few low frequency modes and trend component. The close matching of periodicity of different lower modes of PDO series with that of maximum and minimum temperature of the different regions depicted the possible association of PDO with the temperature regime of southern India. This association is further investigated with a recently developed running correlation analysis method namely Time Dependent Intrinsic Correlation (TDIC), which deciphered a long range positive correlation between the PDO and minimum temperature series at inter annual mode of 7 years to inter decadal mode of 15 years at all the three regions of southern India. The association between PDO and maximum temperature at inter annual ranges is positive at EC and IP regions. The association between PDO and maximum temperature at inter decadal scale show regional differences, with no association at WC region, negative association at IP region and positive association at EC region.

McMC-based AVAZ direct inversion for fracture weaknesses

Considering that wide-azimuth seismic data contains abundant azimuthal amplitude information about the fractured reservoir with obvious characteristics of amplitude variation with incident angle and azimuth (AVAZ), azimuthal seismic data can be used for the inversion of anisotropic parameters in fractured reservoir. Fractured reservoir with a single set of vertically aligned fractures embedded in a purely isotropic background medium may be considered as a long-wavelength effective transversely isotropic medium with a horizontal symmetry axis (HTI). The normal and tangential fracture weaknesses are two key parameters to the evaluation of fracture properties in HTI media, thus the inversion of fracture weaknesses may be used for characterizing the anisotropy in fractured reservoir. The elastic properties of background isotropic media without fractures, however, do not cause azimuthal changes in AVAZ data compared to the fracture anisotropic properties, therefore simultaneous inversion for the background elastic parameters and fracture anisotropic parameters may be not stable. Thus we propose a method of azimuth-difference-based AVAZ direct inversion for fracture weaknesses. First, we extract the fracture symmetry axis azimuth based on the least square ellipse fitting (LSEF) method to obtain a linear AVAZ approximation. Then we build a fractured anisotropic rock-physics model for the estimation of anisotropic well-log information, building the initial background low-frequency trend of fracture weaknesses. Finally, an AVAZ direct inversion method of normal and tangential fracture weaknesses is proposed with the nonlinear Markov chain Monte Carlo (McMC) strategy. So we can eliminate the influence of isotropic background elastic properties on the fracture weakness properties and obtain the normal and tangential fracture weaknesses more stably. Tests on both 2D over-thrust model and real data demonstrate that the normal and tangential fracture weaknesses may be estimated more stably, and it turns out that fracture-developed zones can be identified using the estimated fracture weaknesses.

A novel optimization method for star-sensor data processing

A novel optimization method for star sensor data processing has been proposed in this paper, by which the stability and accuracy of star sensor attitude outputs have been improved. Aiming at the problem that the star sensor has unacceptable single measurement error, which reduces the accuracy of navigation and dissatisfies the requirement of high accuracy in navigation system. Utilizing improved transformation, extraction, interpolation and filtering, then using wavelet analysis integrated with Allan variance, the star-sensor measurement data is processed to solve the problems mentioned above. By adopting this method, the star-sensor can achieve: (1) eliminate outliers accurately, to enhance the ability of error redundancy; (2) smooth and eliminate low frequency trends, to enhance the ability of interference resistance; (3) uniform data with equal interval and make it continuous, to enhance the stability; (4) restrain the internal noise and observation noise, to enhance the signal-noise-ratio; (5) de-noised with improved method, to promote star-sensor attitude output accuracy.

Improvement of isotope-based climate reconstructions in Patagonia through a better understanding of climate influences on isotopic fractionation in tree rings

Very few studies of stable isotopes exist across the Andes in South America. This study is the first presenting annually resolved chronologies of both δ18O and δ13C in Nothofagus pumilio and Fitzroya cupressoides trees from Northern Patagonia. Interannual variability in δ18O and δ13C was assessed over the period 1952–2011. Based on these chronologies, we determined the primary climatic controls on stable isotopes and tree physiological responses to changes in atmospheric CO2 concentrations (ca), temperature and humidity. Changes in specific intrinsic water use efficiency (iWUE) were inferred from variations in δ13C whereas the effects of CO2 increase on stomatal conductance were explored using δ18O. Over the 60-year period, iWUE increased significantly (by ca. 25%) in coincidence with the rise of ca. The two species appear to have different strategies of gas-exchange. Whereas iWUE variations were likely driven by both stomatal conductance and photosynthetic assimilation rates in F. cupressoides, they were largely related to stomatal conductance in N. pumilio. After removing the low-frequency trends related to increasing ca, significant relationships between δ13C and summer temperatures were recorded for both species. However, δ13C variations in F. cupressoides were more strongly influenced by summer temperatures than in N. pumilio. Our results advocate for an indirect effect of summer temperatures on stable isotope ratios, which is mostly influenced by sunlight radiation in F. cupressoides and relative humidity/soil moisture in N. pumilio. δ13C variations in F. cupressoides were spatially correlated to a large area south of 35°S in southern South America. These promising results encourage the use of δ13C variations in F. cupressoides for reconstructing past variations in temperature and large-scale circulation indexes such as the Southern Annular Mode (SAM) in the Southern Hemisphere.

Decreased Variability and Low Values of Perfusion Index on Day One Are Associated with Adverse Outcome in Extremely Preterm Infants

To develop new quantitative features for the Perfusion Index signal recorded continuously over the first 24 hours of life in a cohort of extremely low gestational age newborns and to assess the association of these features with normal and adverse short-term outcome. A cohort study of extremely low gestational age newborns. Adverse outcome was defined as early mortality before 72 hours of life, acquired severe periventricular-intraventricular hemorrhage, or severe cystic leukomalacia. Perfusion Index values were obtained from the plethysmographic signal of a pulse oximeter. Perfusion Index signals were separated into low-frequency (trend) and high-frequency (detrend) components. Three features were extracted during four 6-hour epochs: mean of the trend component (mean-trend), SD of the trend component (SD-trend), and SD of the detrend component (SD-detrend). The SD features represent long-term variability (SD-trend) and short-term variability (SD-detrend) of the Perfusion Index. A mixed-effects model was fitted to each feature. Ninety-nine infants were included in the analysis. Quadratic-time mixed-effects models provided the best fit for all 3 features. The mean-trend component was lower for the adverse outcome compared with the normal outcome group with a difference of 0.142 Perfusion Index (P = .001). SD-detrend component was also lower for the adverse compared with the normal outcome group, although this difference of 0.031 Perfusion Index/days2 was dependent on time (P < .001). Low values and reduced short-term variability of Perfusion Index on day 1 are associated with adverse outcome.

Trend extraction of rail corrugation measured dynamically based on the relevant low-frequency principal components reconstruction

Rail corrugation dynamic measurement techniques are critical to guarantee transport security and guide rail maintenance. During the inspection process, low-frequency trends caused by rail fluctuation are usually superimposed on rail corrugation and seriously affect the assessment of rail maintenance quality. In order to extract and remove the nonlinear and non-stationary trends from original mixed signals, a hybrid model based ensemble empirical mode decomposition (EEMD) and modified principal component analysis (MPCA) is proposed in this paper. Compared with the existing de-trending methods based on EMD, this method first considers low-frequency intrinsic mode functions (IMFs) thought to be underlying trend components that maybe contain some unrelated components, such as white noise and low-frequency signal itself, and proposes to use PCA to accurately extract the pure trends from the IMFs containing multiple components. On the other hand, due to the energy contribution ratio between trends and mixed signals is prior unknown, and the principal components (PCs) decomposed by PCA are arranged in order of energy reduction without considering frequency distribution, the proposed method modifies traditional PCA and just selects relevant low-frequency PCs to reconstruct the trends based on the zero-crossing numbers (ZCN) of each PC. Extensive tests are presented to illustrate the effectiveness of the proposed method. The results show the proposed EEMD-PCA-ZCN is an effective tool for trend extraction of rail corrugation measured dynamically.

Circulation background of climate patterns in the past millennium: Uncertainty analysis and re-reconstruction of ENSO-like state

Abstract The question of whether or not global warming has paused since more than ten years ago, namely “warming hiatus”, has attracted the attention of climate science community including the IPCC. Some authors have attributed the “warming hiatus” to the internal changes in the climate system, i.e., the recombination of ocean-atmosphere circulations. Therefore, it is necessary to propose higher requirements on reconstructing circulation background of climate change for the past millennium. However, the analyses of changes in atmospheric circulation over the last millennium as well as the conclusions of related regional climate patterns are so widely different and contradictory, bringing uncertainties to our understanding of regional even global climate change to a great extent. On the other hand, in the last 10 years the high-precision U/Th-dated stalagmite oxygen isotope ratio ( δ 18O) sequences provided an accurate chronological frame for the paleoclimate study of the middle and late Pleistocene, in which all authors from China took the Chinese stalagmite δ 18O as the summer monsoon index without exception. However, this point of view misleads the climate scientists into thinking that the stalagmite δ 18O can be as the proxy of precipitation amount. Nevertheless, it is well known that all of these records have a lot in common in the low frequency trend. However, most sequences cannot be calibrated by instrumental precipitation records, and thus the uncertainty of the climate research framework of China and even of the world has increased. Therefore, it is imperative for climatology to clarify the origin of contradiction and to reduce the uncertainty as early as possible. On the basis of analyzing the significance of stalagmite δ 18O in the monsoon regions of China, the author tries to propose a new circulation proxy in this paper: integrating the Chinese stalagmite oxygen isotope sequence to reconstruct the tropical Pacific sea surface temperature gradient, i.e., the large-scale ENSO-like state over the past millennium. Furthermore, the author speculates that it was warm in the modern times and the Medieval Period, but the circulation recombination was different in both periods. And this inference could be supported by the longer record since Last Glacial Maximum. In other words, the attribution analysis of the identical low-frequency trends of Chinese stalagmite δ 18O on a largescale shows that the ENSO-like state controls the climate change in the monsoon regions of China at different time scales (from interannual to century or even longer time scales). Wherein the important connection of circulations is the western Pacific subtropical high (WPSH), that is to say, besides the interannual anddecadal time scales, the WPSH would possess the circulation mode on longer timescales. For example, we may discuss the change of the WPSH in the whole Holocene epoch, i.e., the half precession period. These discussions could make sense to the study of not only the paleoclimate but also the modern climate.