Rotation of the solar core from BiSON and LOWL frequency observations

Determination of the rotation of the solar core requires very accurate data on splittings for the low-degree modes which penetrate to the core, as well as for modes of higher degree to suppress the contributions from the rest of the Sun to the splittings of the low-degree modes. Here we combine low-degree data based on 32 months of observations with the BiSON network and data from the LOWL instrument. The data are analysed with a technique that specifically aims at obtaining an inference of rotation that is localized to the core. Our analysis provides what we believe is the most stringent constraint to date on the rotation of the deep solar interior.     

Abiotic Factors Influencing the Distribution of Vegetation in Coastal Estuary of the Liaohe Delta,Northeast China

There are divergent views about the dominant factors controlling vegetation distribution in coastal deltas. To determine the dominant factors controlling vegetation distribution, we explored the relationships between six abiotic factors (annual temperature, annual precipitation, soil organic matter, soil moisture, soil salinity, soil pH) and vegetation patterns along a belt transect throughout seven vegetation zones (bare beach, seepweed, common reed, meadow, rice, maize, woods) in the Liaohe Delta. The methods of data analysis included the Kriging interpolation method, grade-ranks method, correlation analysis, Euclidean distance analysis, and canonical correspondence analysis. Contrary to the view that climate controls vegetation pattern, our results suggested that climate had a limited influence on vegetation pattern. Edaphic factors were shown to exert the strongest influence on vegetation pattern, with soil salinity being identified as the dominant factor, followed by soil moisture, soil pH, and soil organic matter.     

The influence of interpolation and station network density on the distributions and trends of climate variables in gridded daily data

We study the influence of station network density on the distributions and trends in indices of area-average daily precipitation and temperature in the E-OBS high resolution gridded dataset of daily climate over Europe, which was produced with the primary purpose of Regional Climate Model evaluation. Area averages can only be determined with reasonable accuracy from a sufficiently large number of stations within a grid-box. However, the station network on which E-OBS is based comprises only 2,316 stations, spread unevenly across approximately 18,000 0.22° grid-boxes. Consequently, grid-box data in E-OBS are derived through interpolation of stations up to 500 km distant, with the distance of stations that contribute significantly to any grid-box value increasing in areas with lower station density. Since more dispersed stations have less shared variance, the resultant interpolated values are likely to be over-smoothed, and extreme daily values even more so. We perform an experiment over five E-OBS grid boxes for precipitation and temperature that have a sufficiently dense local station network to enable a reasonable estimate of the area-average. We then create a series of randomly selected station sub-networks ranging in size from four to all stations within the E-OBS interpolation search radii. For each sub-network realisation, we estimate the grid-box average applying the same interpolation methodology as used for E-OBS, and then evaluate the effect of network density on the distribution of daily values, as well as trends in extremes indices. The results show that when fewer stations have been used for the interpolation, both precipitation and temperature are over-smoothed, leading to a strong tendency for interpolated daily values to be reduced relative to the “true” area-average. The smoothing is greatest for higher percentiles, and therefore has a disproportionate effect on extremes and any derived extremes indices. For many regions of the E-OBS dataset, the station density is sufficiently low to expect this smoothing effect to be significant and this should be borne in mind by any users of the E-OBS dataset.     

海南抱板金矿田围岩蚀变带中绿泥石的特征及其意义

通过对海南抱板金矿田围岩蚀变带中绿泥石成分的分析，讨论了蚀变带中绿泥石的成分变化，并由此确定围岩蚀为形成于２９０～３００℃和氧逸度为１０＾－２６～１０＾－３４的条件下，结合含金石英脉的体包裹体研究，从蚀变到成矿物质沉淀，热液的温度和氧逸度是变化的。     

硼酸盐矿物的振动光谱研究：Ⅲ.理论分析

本文利用位置群分析和因子群分析得到硼酸盐矿物的振动模式，并利用正则坐标分析从实测振动频率计算了矿物的力常数，得出：（１）利用位置群分析与因子群分析，可以得到矿物振动光谱所应具有的谱带数目，但理论结果与实测谱图并不完全一致，一般情况下，位置群分析就能得到与实测谱图相近的谱带数目。（２）对于比较简单的配阴离子团，可以利用正则示分析由实测的振动光谱频率计算力常数，从而得到有关化学键的信息，或者由已知的力     

Anomalous variations in low-degree helioseismic mode frequencies

We compare changes in the frequencies of solar acoustic modes with degree between 0 and 2, as derived from Global Oscillation Network Group (GONG), Birmingham Solar Oscillations Network (BiSON) and Michelson Doppler Imager (MDI) spectra obtained between 1995 and 2003. We find that, after the solar-activity dependence has been removed from the frequencies, there remain variations that appear to be significant, and are often well correlated between the different data sets. We consider possible explanations for these fluctuations, and conclude that they are likely to be related to the stochastic excitation of the modes. The existence of such fluctuations has possible relevance to the analysis of other low-degree acoustic mode spectra such as those from solar-type stars.     

Extended search for correlation between solar mean magnetic field Bison data and coronal mass ejections

The Birmingham Solar-Oscillations Network (BiSON) has acquired high-precision solar mean magnetic field (SMMF) data on a 40-s cadence for a decade. We present attempts to compare such data from recent years with the occurrence of coronal mass ejections (CMEs) as recorded by LASCO, using correlation techniques applied to measurements from different BiSON instruments to maximise the sensitivity to CME-related SMMF responses. SMMF measurements were recorded at the time of occurrence of several hundred CMEs. No CME event shows a convincing response in our SMMF data at short periods setting a threshold amplitude of 12 mG. By averaging data sets we are able to set lower thresholds, which depend somewhat on the distribution of response strengths. A brief summary of the very first results of this study is also given in Chaplin et al.     

On the measurement precision of solar p-mode eigenfrequencies

We make use of 3456 d of observations of the low-ℓ p-mode oscillations of the Sun in order to study the evolution over time of the measurement precision of the radial eigenfrequencies. These data were collected by the ground-based Birmingham Solar-Oscillations Network (BiSON) between 1991 January and 2000 June. When the power spectrum of the complete time series is fitted, the analysis yields frequency uncertainties that are close to those expected from the returned coherence times of the modes. The slightly elevated levels compared with the prediction appear to be consistent with a degradation of the signal-to-noise ratio in the spectrum that is the result of the influence of the window function of the observations (duty cycle 71 per cent). The fractional frequency precision reaches levels of a several parts in 10⁶ for many of the modes. The corresponding errors reported from observations made by the GOLF instrument on board the ESA/NASA SOHO satellite, when extrapolated to the length of the BiSON data set, are shown to be (on average) about ∼25 per cent smaller than their BiSON counterparts owing to the uninterrupted nature of the data from which they were derived.
An analysis of the BiSON data in contiguous segments of different lengths, T , demonstrates that the frequency uncertainties scale as T ^−1/2. This is to be expected in the regime where the coherence (life) times of the modes, τ _{n ℓ}, are smaller than the observing time T (the 'oversampled' regime). We show that mode detections are only now beginning to encroach on the 'undersampled' regime (where   T < τ _{n ℓ})  .