Running Correlation Research Articles

Increase in the Intensity of Air–Sea Coupling in the Key ENSO Region during 1955–2020

The El Niño and Southern Oscillation (ENSO), a phenomenon of air–sea coupling in the tropical Pacific, has strong response to global climate change. In this study, the primary region where ENSO occurred during the period 1955–2020 was selected as the key ENSO region, and the changes in air–sea coupling in this region were explored. The New Southern Oscillation Index (NSOI), modified from the previous Southern Oscillation Index, represents atmospheric changes, and the Niño-3.4 index represents oceanic changes. The absolute value of the running correlation coefficient between the Niño-3.4 index and NSOI in the 121-month time window was defined as the Intensity of Air–Sea Coupling (IASC) in the key ENSO region. The results showed that the IASC has significantly increased, with a confidence level of 95%, during the period 1955–2020, and the range where the correlation coefficient between the Niño-3.4 index and the sea level pressure anomaly over the key ENSO region was greater than 0.6 has evidently expanded in the context of global warming, which corresponded to the increase in IASC. Moreover, the coupling positions of sea surface temperature and wind anomalies changed, tending to the east of the equatorial Pacific during 1977–1998, and to the west during 1999–2020.

Synthetic running correlation coefficients Understanding the physical world

Synthetic running correlation coefficients Understanding the physical world

Mathematical Proof of the Synthetic Running Correlation Coefficient and Its Ability to Reflect Temporal Variations in Correlation

Mathematical Proof of the Synthetic Running Correlation Coefficient and Its Ability to Reflect Temporal Variations in Correlation

Synchronous Characteristics of Precipitation Extremes in the Yangtze and Murray-Darling River Basins and the Role of ENSO

The floods caused by the extreme precipitation in the Yangtze River basin (YRB) and Murray–Darling River basin (MDRB), the largest basins in China and Australia, have significant impacts on the society and regional economies. Based on the spatial–temporal analysis of the daily precipitation extremes (DPEs) during 1982–2016, we found that for both basins, the whole-basin-type DPEs have the highest proportion and a synchronous DPE interannual variation characteristic exists in the two basins, with the 3-yr running correlation coefficient of the annual DPE days (DPEDs) reaching almost 0.7 (significant at the 0.01 level). The El Nino–Southern Oscillation (ENSO), which is one of the most significant climate disturbance factors in the world, plays an important role in modulating the variability of the DPEs in the two basins. Singular value decomposition (SVD) analysis revealed that both the YRB and the MDRB’s whole-basin-type DPEs are closely coupled with the procedure that the preceding winter eastern Pacific (EP)-type El Nino faded to a central Pacific (CP)-type La Nina. This means that the DPEs in the YRB and MDRB may synchronously occur more frequently when the above process occurs. Owing to the atmosphere–ocean interaction from the east–west dipole sea surface temperature (SST) anomaly pattern, the atmospheric circulation disturbance exhibits a pattern in which the equatorial eastern Pacific region is a mass source anomaly with a higher pressure, drier air, and weaker convection, while the equatorial western Pacific region is a mass sink anomaly with a lower pressure, wetter air, and stronger convection. Moreover, two wave trains that originated from the tropical western Pacific were found to extend to the YRB and MDRB. The interaction between the wave train’s interphase dynamics and water vapor transport disturbance results in the ascent conditions and enhanced water vapor transport, which leads to the synchronous occurrence of DPEs in the YRB and MDRB on an interannual scale.

The Interconnection between the Periodicities of Solar Wind Parameters Based on the Interplanetary Magnetic Field Polarity (1967–2018): A Cross Wavelet Analysis

In this article, we have examined the possible interconnection and phase asynchrony between the periodicities of solar wind parameters (solar wind velocity $V$ , plasma density $N$ , and dynamic pressure $P$ ), and the interplanetary magnetic field (IMF) magnitude $B$ , taking into account the polarity state, toward ( $T$ ) and away ( $A$ ) from the Sun, of the IMF. For this purpose, the daily measurements of $V$ , $N$ , $P$ , and $B $ during the period 1967–2018, thus covering almost five solar cycles, were sorted into two groups according to the IMF sense. Days of mixed IMF polarity or having no data were eliminated from our analysis during the entire period. The monthly averages of $T$ and $A$ polarity groups have been calculated for each parameter. $B_{\mathrm{T}}$ ( $B_{\mathrm{A}}$ ), $V_{\mathrm{T}}$ ( $V_{\mathrm{A}}$ ), $N_{\mathrm{T}}$ ( $N_{\mathrm{A}}$ ), and $P_{\mathrm{T}}$ ( $P_{\mathrm{A}}$ ), refer to the monthly averages of $T$ ( $A$ ) polarity group for $B$ , $V$ , $N$ , and $P$ , respectively. In this study, three techniques were applied, including the running correlation analysis, cross-wavelet transform (XWT) and wavelet coherence (WTC) to the monthly means of each polarity group. Our results reveal that the running correlation coefficient for toward and away groups of each parameter reflect a positive correlation value during most of the entire period. The long-term periodicities within the 8–16 yr band are also detected for all toward and away components of the considered parameters with a significant common power during most of the entire period. Moreover, the WTC reflects an almost in-phase relationship behavior between the toward and away groups of each parameter during this range. On the other hand, within the 4–8 yr, 2–4 yr, and 1–2 yr bands, the toward and away groups of each solar wind parameter show mid-term periodicities at different periods, displaying sometimes significant powers.

How Robust is the Asian Precipitation–ENSO Relationship during the Industrial Warming Period (1901–2017)?

AbstractInstrumental observations (1901–2017) are used to uncover the seasonality, regionality, spatial–temporal coherency, and secular change of the relationship between El Niño–Southern Oscillation (ENSO) and Asian precipitation (AP). We find an abrupt seasonal reversal of the AP–ENSO relationship occurring from October to November in a large area of Asia north of 20°N due to a rapid northward shift of the ENSO-induced subsidence from Indonesia to the Philippines. We identified six subregions that have significant correlations with ENSO over the past 116 years with |r| > 0.5 (p < 0.001). Regardless of the prominent subregional differences, the total amount of AP during a monsoon year (from May to the next April) shows a robust response to ENSO with r = −0.86 (1901–2017), implying a 4.5% decrease in the total Asian precipitation for 1° of SST increase in the equatorial central Pacific. Rainfall in tropical Asia (Maritime Continent, Southeast Asia, and India) shows a stable relationship with ENSO with significant 31-yr running correlation coefficients (CCs). However, precipitation in North China, the East Asian winter monsoon front zone, and arid central Asia exhibit unstable relationships with ENSO. Since the 1950s, the AP–ENSO relationships have been enhanced in all subregions except over India. A major factor that determines the increasing trends of the AP–ENSO relationship is the increasing ENSO amplitude. Notably, the AP response is asymmetric with respect to El Niño and La Niña and markedly different between the major and minor ENSO events. The results provide guidance for seasonal prediction and a metric for assessment of climate models’ capability to reproduce the Asian hydroclimate response to ENSO and projected future change.

Transition Periods Between Sea Ice Concentration and Sea Surface Air Temperature in the Arctic Revealed by an Abnormal Running Correlation

This study used the synthetic running correlation coefficient calculation method to calculate the running correlation coefficients between the daily sea ice concentration (SIC) and sea surface air temperature (SSAT) in the Beaufort-Chukchi-East Siberian-Laptev Sea (BCEL Sea), Kara Sea and southern Chukchi Sea, with an aim to understand and measure the seasonally occurring changes in the Arctic climate system. The similarities and differences among these three regions were also discussed. There are periods in spring and autumn when the changes in SIC and SSAT are not synchronized, which is a result of the seasonally occurring variation in the climate system. These periods are referred to as transition periods. Spring transition periods can be found in all three regions, and the start and end dates of these periods have advancing trends. The multiyear average duration of the spring transition periods in the BCEL Sea, Kara Sea and southern Chukchi Sea is 74 days, 57 days and 34 days, respectively. In autumn, transition periods exist in only the southern Chukchi Sea, with a multiyear average duration of only 16 days. Moreover, in the Kara Sea, positive correlation events can be found in some years, which are caused by weather time scale processes.

Recurrence duration statistics and time-dependent intrinsic correlation analysis of trading volumes: A study of Chinese stock indices

The trading volume in stock markets is known as an important variable which reflects the liquidity of the financial markets and therefore is regarded to be greatly important for the measurement of market liquidity risk. In this work, a new concept called recurrence duration is introduced for study of daily trading volumes, which is inspired by idea of the volatility duration that was proposed and studied in our previous work. The recurrence duration is thought as the shortest passing time that the following days’ trading volume takes to exceed or go below the current trading volume which is time-varying. Similar to the volatility duration distribution of the price returns, the power-law function could describe the empirical probability distribution of recurrence durations of trading volumes, and their tail distributions can be fitted by two stretched exponential functions. Further, the correlation relationships of trading volumes between Chinese stock indices as well as the correlations of recurrence durations are investigated. One approach employed is a recently proposed method, time-dependent intrinsic correlation (TDIC), which is based on the empirical mode decomposition (EMD) to decompose nonlinear and nonstationary signals into the intrinsic mode functions (IMFs), the instantaneous periods of which are used then in determination of the sizes of sliding windows to compute the running correlation coefficients for the multiscale signals. The empirical results reveal rich patterns of correlations for both trading volumes and recurrence durations at different scales for different modes. Another approach is the widely-used DCCA cross-correlation coefficient, by which the level of cross-correlation is measured for both original series and IMF modes of the stock indices.

Non-linear Dynamical Analysis of Intraspinal Pressure Signal Predicts Outcome After Spinal Cord Injury.

The injured spinal cord is a complex system influenced by many local and systemic factors that interact over many timescales. To help guide clinical management, we developed a technique that monitors intraspinal pressure from the injury site in patients with acute, severe traumatic spinal cord injuries. Here, we hypothesize that spinal cord injury alters the complex dynamics of the intraspinal pressure signal quantified by computing hourly the detrended fluctuation exponent alpha, multiscale entropy, and maximal Lyapunov exponent lambda. 49 patients with severe traumatic spinal cord injuries were monitored within 72 h of injury for 5 days on average to produce 5,941 h of intraspinal pressure data. We computed the spinal cord perfusion pressure as mean arterial pressure minus intraspinal pressure and the vascular pressure reactivity index as the running correlation coefficient between intraspinal pressure and arterial blood pressure. Mean patient follow-up was 17 months. We show that alpha values are greater than 0.5, which indicates that the intraspinal pressure signal is fractal. As alpha increases, intraspinal pressure decreases and spinal cord perfusion pressure increases with negative correlation between the vascular pressure reactivity index vs. alpha. Thus, secondary insults to the injured cord disrupt intraspinal pressure fractality. Our analysis shows that high intraspinal pressure, low spinal cord perfusion pressure, and impaired pressure reactivity strongly correlate with reduced multi-scale entropy, supporting the notion that secondary insults to the injured cord cause de-complexification of the intraspinal pressure signal, which may render the cord less adaptable to external changes. Healthy physiological systems are characterized by edge of chaos dynamics. We found negative correlations between the percentage of hours with edge of chaos dynamics (−0.01 ≤ lambda ≤ 0.01) vs. high intraspinal pressure and vs. low spinal cord perfusion pressure; these findings suggest that secondary insults render the intraspinal pressure more regular or chaotic. In a multivariate logistic regression model, better neurological status on admission, higher intraspinal pressure multi-scale entropy and more frequent edge of chaos intraspinal pressure dynamics predict long-term functional improvement. We conclude that spinal cord injury is associated with marked changes in non-linear intraspinal pressure metrics that carry prognostic information.

Wavelet pressure reactivity index: a validation study.

The brain is vulnerable to damage from too little or too much blood flow. A physiological mechanism termed cerebral autoregulation (CA) exists to maintain stable blood flow even if cerebral perfusion pressure (CPP) is changing. A robust method for assessing CA is not yet available. There are still some problems with the traditional measure, the pressure reactivity index (PRx). We introduce a new method, the wavelet transform method (wPRx), to assess CA using data from two sets of controlled hypotension experiments in piglets: one set had artificially manipulated arterial blood pressure (ABP) oscillations; the other group were spontaneous ABP waves. A significant linear relationship was found between wPRx and PRx in both groups, with wPRx providing a more stable result for the spontaneous waves. Although both methods showed similar accuracy in distinguishing intact and impaired CA, it seems that wPRx tends to perform better than PRx, although not significantly so. We present a novel method to monitor cerebral autoregulation (CA) using the wavelet transform (WT). The new method is validated against the pressure reactivity index (PRx) in two piglet experiments with controlled hypotension. The first experiment (n=12) had controlled haemorrhage with artificial stationary arterial blood pressure (ABP) and intracranial pressure (ICP) oscillations induced by sinusoidal slow changes in positive end-expiratory pressure ('PEEP group'). The second experiment (n=17) had venous balloon inflation during spontaneous, non-stationary ABP and ICP oscillations ('non-PEEP group'). The wavelet transform phase shift (WTP) between ABP and ICP was calculated in the frequency range 0.0067-0.05Hz. Wavelet semblance, the cosine of WTP, was used to make the values comparable to PRx, and the new index was termed wavelet pressure reactivity index (wPRx). The traditional PRx, the running correlation coefficient between ABP and ICP, was calculated. The result showed a significant linear relationship between wPRx and PRx in the PEEP group (R=0.88) and non-PEEP group (R=0.56). In the non-PEEP group, wPRx showed better performance than PRx in distinguishing cerebral perfusion pressure (CPP) above and below the lower limit of autoregulation (LLA). When CPP was decreased below LLA, wPRx increased from 0.43±0.28 to 0.69±0.12 (P=0.003) while PRx increased from 0.07±0.21 to 0.27±0.37 (P=0.04). Moreover, wPRx provided a more stable result than PRx (SD of PRx was 0.40±0.07, and SD of wPRx was 0.28±0.11, P=0.001). Assessment of CA using wavelet-derived phase shift between ABP and ICP is feasible.

The Physical Significance of the Synthetic Running Correlation Coefficient and Its Applications in Oceanic and Atmospheric Studies

In order to study the temporal variations of correlations between two time series, a running correlation coefficient (RCC) could be used. An RCC is calculated for a given time window, and the window is then moved sequentially through time. The current calculation method for RCCs is based on the general definition of the Pearson product-moment correlation coefficient, calculated with the data within the time window, which we call the local running correlation coefficient (LRCC). The LRCC is calculated via the two anomalies corresponding to the two local means, meanwhile, the local means also vary. It is cleared up that the LRCC reflects only the correlation between the two anomalies within the time window but fails to exhibit the contributions of the two varying means. To address this problem, two unchanged means obtained from all available data are adopted to calculate an RCC, which is called the synthetic running correlation coefficient (SRCC). When the anomaly variations are dominant, the two RCCs are similar. However, when the variations of the means are dominant, the difference between the two RCCs becomes obvious. The SRCC reflects the correlations of both the anomaly variations and the variations of the means. Therefore, the SRCCs from different time points are intercomparable. A criterion for the superiority of the RCC algorithm is that the average value of the RCC should be close to the global correlation coefficient calculated using all data. The SRCC always meets this criterion, while the LRCC sometimes fails. Therefore, the SRCC is better than the LRCC for running correlations. We suggest using the SRCC to calculate the RCCs.

Compensatory-Reserve-Weighted Intracranial Pressure and Its Association with Outcome After Traumatic Brain Injury.

We introduced 'compensatory-reserve-weighted intracranial pressure (ICP),' named 'weightedICP' for brevity, as a variable that may better describe changes leading to mortality after traumatic brain injury (TBI) overthe standard mean ICP. ICP was monitored prospectively in over 1023 sedated and ventilated patients. The RAP coefficient(R-correlation, A-amplitude, and P-pressure) was calculated as the running correlation coefficient between slow changes in thepulse amplitude of ICP and themean ICP. RAP has a value of 0 on the linear part of the pressure-volume curve anda value of +1 on the ascending exponential part. Then, RAP decreases towards zero or even becomes negative when ICP increases further-a phenomenon thought to be related tothe critical closing of cerebral vessels. In this study, we investigated a derived variable called weightedICP, calculated as ICP*(1-RAP). Mortality after TBI was associated with both elevated ICP and weightedICP. Analysis of variance showed higher values of test statistics for weightedICP (K=93) than for ICP (K=64) in outcome categorization. Additionally, receiver operator curve analysis indicated greater area under the curve for weightedICP (0.71) than for ICP (0.67) with respect to associated mortality; however, the difference was not statistically significant (p=0.12). The best threshold (maximizing sensitivity and specificity) was 19.5mmHg for mean ICP, and 8mmHg for weightedICP. Mortality rate expressed as a function of mean ICP and weightedICP showed an ascending profile in both cases. The proposed variable shows a significant association with mortality following head injury. It is sensitive to both the rising absolute ICP andto the critical deterioration of pressure-volume compensation.

Summer temperature in the eastern part of southern South America: its variability in the twentieth century and a teleconnection with Oceania

The 1907–2001 summer-to-summer surface air temperature variability in the eastern part of southern South America (SSA, partly including Patagonia) is analysed. Based on records from instruments located next to the Atlantic Ocean (36°S–55°S), we define indices for the interannual and interdecadal timescales. The main interdecadal mode reflects the late-1970s cold-to-warm climate shift in the region and a warm-to-cold transition during early 1930s. Although it has been in phase with the Pacific Decadal Oscillation (PDO) index since the 1960s, they diverged in the preceding decades. The main interannual variability index exhibits high spectral power at ~3.4 years and is representative of temperature variability in a broad area in the southern half of the continent. Eleven-years running correlation coefficients between this index and December-to-February (DJF) Nino3.4 show significant decadal fluctuations, out-of-phase with the running correlation with a DJF index of the Southern Annular Mode. The main interannual variability index is associated with a barotropic wavetrain-like pattern extending over the South Pacific from Oceania to SSA. During warm (cold) summers in SSA, significant anticyclonic (cyclonic) anomalies tend to predominate over eastern Australia, to the north of the Ross Sea, and to the east of SSA, whereas anomalous cyclonic (anticyclonic) circulation is observed over New Zealand and west of SSA. This teleconnection links warm (cold) SSA anomalies with dry (wet) summers in eastern Australia. The covariability seems to be influenced by the characteristics of tropical forcing; indeed, a disruption has been observed since late 1970s, presumably due to the PDO warm phase.

Exploring Cross-Cultural Differences In Social Knowledge Creation And Exchange: A Preliminary Examination

This paper chronicles an exploratory, in-progress research project that compares the findings of Hofstede’s cross-cultural research with those of Forrester’s Social Technographics research. The aim of the project is to determine if a relationship exists between cultural differences and social knowledge creation and exchange. Part one of the study mapped Davenport and Prusak’s information and knowledge creation theories to the six components of Forrester’s Social Technographics study (creators, critics, collectors, joiners, spectators, and inactives). Next, the Social Technographics results from 13 nations were compared with Hofstede’s four cultural dimensions (power distance, individualism, uncertainty avoidance, masculinity). The analysis included exploring the relationship visually using 24 scatter diagrams, running correlation coefficients (Peasson’s r) for each relationship, testing for significance of Pearson’s r, and finally conducting regression analyses on each relationship. Although the authors believe that culture influences behaviours, this study did not reveal any reasonable relationships between culture and placement along the Social Technographics. However, it is possible that there exists problems in the Hofstede scales. The Hofstede scales have been highly criticized in the literature. It may be that other cross-cultural models such as GLOBE, Schwartz, Triandis, or others may yield different results. In this regard, further research is necessary. The next phase of the project will compare Social Technographics with the GLOBE project findings.

THE TIME-DEPENDENT INTRINSIC CORRELATION BASED ON THE EMPIRICAL MODE DECOMPOSITION

A Time-Dependent Intrinsic Correlation (TDIC) method is introduced. This new approach includes both auto- and cross-correlation analysis designed especially to analyze, capture and track the local correlations between nonlinear and nonstationary time series pairs. The approach is based on Empirical Mode Decomposition (EMD) to decompose the nonlinear and nonstationary data into their intrinsic mode functions (IMFs) and uses the instantaneous periods of the IMFs to determine a set of the sliding window sizes for the computation of the running correlation coefficients for multi-scale data. This new method treats the selection of the sliding window sizes as an adaptive process determined by the data itself, not a "tuning" process. Therefore, it gives an intrinsic correlation analysis of the data. Furthermore, the multi-window approach makes the new method applicable to complicated data from multi-scale phenomena. The synthetic and time series from real world are used to demonstrate conclusively that the new approach is far more superior over the traditional method in its ability to reveal detailed and subtle correlations unavailable through any other methods in existence. Thus, the TDIC represents a major advance in statistical analysis of data from nonlinear and nonstationary processes.

Spatial variation of the Arctic Oscillation and its long-term change

The Arctic Oscillation (AO) is spatially varied, but lack of study. The authors defined the spatial variation of AO as the temporal variation of the ensemble of grids with sea level pressure (SLP) varying consistent with the AO index in certain time span. The region of the ensemble is called as ‘AO-dominant region’, identified by the running correlation coefficient of gridded SLP with the AO index. The positive and negative AO-dominant regions show that the SLP oscillated sometimes between the polar and mid-latitude regions and sometimes between land and ocean. Along the Atlantic—Pacific section, the North Atlantic Oscillation exists as a stationary seesaw-like dipole, while on the Pacific side the oscillation is intermittent with lower intensity and swinging boundary. The long-term spatial variation of the AO with three stages is clearly identified by the relative area of the SLP anomaly regions. Positive SLP anomaly area dominated before 1970, showing the state before the global warming. Negative SLP anomaly area dominated during 1971–1995, indicating the effect of global warming before the Arctic warming being apparent. Since 1996 both positive and negative SLP anomaly areas are all small, being possibly caused by the sea-ice retreat during the Arctic warming.

BACCHUS temperature reconstruction for the period 16th to 18th centuries from Viennese and Klosterneuburg grape harvest dates

In the scientific project “Klosterneuburg Wine and Climate Change in Lower Austria” (BACCHUS), we focused on developing a grape harvest date (GHD) time series for the period 1523–2007 in the area of and around Vienna, one of the northeasternmost regions in Europe where vines are grown professionally. Since grape harvest dates are strongly influenced by spring to (early) summer temperatures, especially in a vine‐growing region at a climatic border, we found highly significant correlation coefficients between homogenized multiple monthly mean temperatures at Vienna, Hohe Warte, and GHD. For example, correlation values reach −0.76 (p = 0.01) between GHD and April to July mean temperature or −0.79 (p = 0.01) between GHD and May to July mean temperature. This made it possible to reconstruct May to July mean temperatures, starting in 1523. The years from 1775 to 1850 were used as calibration period for determining the temperature sensitivity of GHD, as the running correlation coefficients (10 year moving window) were most pronounced in this period, varying between almost −1 and −0.7 (p = 0.05). We found warm decades in the 16th century, at the beginning of our series, which were as warm as the 1990s. Afterwards the mean May to July temperatures started to drop; the coldest decade of the record was from 1771 to 1780. A constant temperature increase for more than 30 years, as from the 1970s to the present, seems to be unprecedented during the last 470 years.

Can the US stock market be Shanghaied?

PurposeThe purpose of this paper is to examine the relationship between the US and China stock markets between 2000 and 2007. This study attempts to categorize the event on February 27, 2007, i.e. 9 per cent plunge in Shanghai stock market followed by the $1.5 trillion global market shake out, as irrational, i.e. herd mentality.Design/methodology/approachTo test for this relationship, the Morgan Stanley Capital International daily price index data was collected from April 15, 2002 to April 12, 2007. Daily Dow Jones Industrial Average (DJIA), Nikkei 225 (Nikkei), Hang Seng Index, and the Shanghai Stock Exchange Composite Index (SSECI) were collected from finance.yahoo.com from January 1, 2000 until April 3, 2007. The running beta and correlation coefficients, defined as the cumulative coefficients, are used to determine the co‐movement of the SSECI and DJIA.FindingsThe strength of the relationship between the US and China stock markets has significantly increased since 2005, maybe attributed to China's policy change in 2005 to move toward a more free market economy. Because of the unique characteristics of China's stock market, it is hard to conclude that the $1.5 trillion global market shake out was ignited by the 9 per cent plunge in the Shanghai stock market on February 27, 2007.Research limitations/implicationsChina's economic reform is unique since the country followed no blue print for the economic institutions to model after and policies were adopted through experimentation. Fueled by its fast growing economy (10.4 per cent in 2005 and 10.7 per cent in 2006), using past patterns or trends to predict the future of China's financial market requires further research as its stock market emerges. Research in this area requires more observations as China's stock market grows and becomes more transparent.Practical implicationsResults here suggest that the strength of the relationship between the US and China stock markets has significantly increased since 2005 and that China's 2005 policy moves toward a more free market economy are most likely responsible.Originality/valueA better understanding of the influence of China's emerging stock market on the global stock market offers significant value to portfolio managers worldwide.

A Running Average Method for Predicting the Size and Length of a Solar Cycle

The running correlation coefficient between the solar cycle amplitudes and the max-max cycle lengths at a given cycle lag is found to vary roughly in a cyclical wave with the cycle number, based on the smoothed monthly mean Group sunspot numbers available since 1610. A running average method is proposed to predict the size and length of a solar cycle by the use of the varying trend of the coefficients. It is found that, when a condition (that the correlation becomes stronger) is satisfied, the mean prediction error (16.1) is much smaller than when the condition is not satisfied (38.7). This result can be explained by the fact that the prediction must fall on the regression line and increase the strength of the correlation. The method itself can also indicate whether the prediction is reasonable or not. To obtain a reasonable prediction, it is more important to search for a running correlation coefficient whose varying trend satisfies the proposed condition, and the result does not depend so much on the size of the correlation coefficient. As an application, the peak sunspot number of cycle 24 is estimated as 140.4±15.7, and the peak as May 2012±11 months.

Core region of Arctic Oscillation and the main atmospheric events impact on the Arctic

Arctic Oscillation (AO) is a seesaw pattern in which sea level pressure (SLP) at the polar and middle latitudes in North Hemisphere fluctuates between positive and negative phases. It has been used as a representative atmospheric circulation index to express climate change. The purpose of this paper is to reveal the regional difference of SLP variation. By calculating the correlation coefficients of AO Index with all the gridded SLPs, we have uncovered a special region named as Arctic Oscillation Core Region (AOCR), where the running correlation coefficients (RCC) between gridded SLP and AO index are all negative. The averaged SLP of this region correlates significantly with the AO index. The correlations between local SLPs and AO index outside of AOCR are weaker than those inside. RCC analysis reveals several strong discrepant events different from AO. These events occurred in the years of 1954, 1955, 1962, 1971, 1982/83, 1995, 1996/97, 1998, and 1999. A comparison of these events and the ENSO or PDO indices suggests that the events in 1982/83 and 1998 are probably associated with the ENSO processes. Events centered in other years are likely connected with PDO, which reached their minima in the years of 1950, 1955, 1962 and 1971. The result in this study provides an alternative insight to look at the mechanism of the variation of Arctic Oscillation.