Short-term Climate Characteristics at Ny-(A)lesund over the Arctic Tundra Area

Based on the Germany Koldwey Station's 1994-2003 conventional observation hourly data, this paper conducts a statistical analysis on the short-term climate characteristics for an arctic tundra region (Ny-(A)lesund island) where our first arctic expedition station (Huanghe Station) was located. Affected by the North Atlantic warming current, this area has a humid temperate climate, and the air temperature at Ny-(A)lesund rose above 0 ℃ even during deep winter season during our research period. The wind speed in this area was low and appeared most at southeast direction. We find that the temperature at Ny-(A)lesund rose in the faster rate (0.68 ℃/10 a) than those at the whole Arctic area. Compared with the floating ices where our expedition conducted in the Arctic, Ny-(A)lesund was warmer and more humid and had lower wind speed. Comparison of the near surface air temperature derived by NCEP/NCAR reanalysis to the conventional measurements conducted at the Koldwey site in Ny-(A)lesund area shows a good agreement for winter season and a significant difference for summer season.     

Filters for linear sea-wave prediction

Deterministic sea-wave prediction (DSWP) models are appearing in the literature designed for quiescent interval prediction in marine applications dominated by large swell seas. The approach has focused upon spectral methods which are straightforward and intuitively attractive. However, such methods have the disadvantage that while the sea is aperiodic in nature, the standard discrete spectral processing techniques force an absolutely periodic structure onto the resulting sea surface prediction models. As it is the shape of the sea surface that is important in such applications, particularly near the end of the domain which is important, the standard windowing techniques used in signal processing work to reduce leakage artifacts cannot be employed. This has necessitated the use of end matching methods that can be both inconvenient and may reduce the fraction of the time for which legitimate predictions are available. As a result, an investigation has been undertaken of the use of finite impulse response prediction filters to provide the necessary dispersive phase shifting required in DSWP systems. The present work examines the theoretical basis for such filters and explores their properties together with their application to both long and short crested swell seas. It is shown that wide band forms of such filters are only convergent in the sense of distributions having both infinite duration impulse responses and asymptotically divergent first derivatives. However, appropriate band limitation can produce useful finite impulse responses allowing implementation via standard discrete convolution methods. It is demonstrated that despite the prediction filters having a non-causal impulse response such filters can be used in practice due to a combination of the asymmetric nature of the impulse response and the fundamental nature of the prediction process. The findings are confirmed against actual sea-wave data.     

Prediction of UT1–UTC,LOD and AAM χ<Subscript>3</Subscript> by combination of least-squares and multivariate stochastic methods

This article presents the application of a multivariate prediction technique for predicting universal time (UT1–UTC), length of day (LOD) and the axial component of atmospheric angular momentum (AAM χ ₃). The multivariate predictions of LOD and UT1–UTC are generated by means of the combination of (1) least-squares (LS) extrapolation of models for annual, semiannual, 18.6-year, 9.3-year oscillations and for the linear trend, and (2) multivariate autoregressive (MAR) stochastic prediction of LS residuals (LS + MAR). The MAR technique enables the use of the AAM χ ₃ time-series as the explanatory variable for the computation of LOD or UT1–UTC predictions. In order to evaluate the performance of this approach, two other prediction schemes are also applied: (1) LS extrapolation, (2) combination of LS extrapolation and univariate autoregressive (AR) prediction of LS residuals (LS + AR). The multivariate predictions of AAM χ ₃ data, however, are computed as a combination of the extrapolation of the LS model for annual and semiannual oscillations and the LS + MAR. The AAM χ ₃ predictions are also compared with LS extrapolation and LS + AR prediction. It is shown that the predictions of LOD and UT1–UTC based on LS + MAR taking into account the axial component of AAM are more accurate than the predictions of LOD and UT1–UTC based on LS extrapolation or on LS + AR. In particular, the UT1–UTC predictions based on LS + MAR during El Niño/La Niña events exhibit considerably smaller prediction errors than those calculated by means of LS or LS + AR. The AAM χ ₃ time-series is predicted using LS + MAR with higher accuracy than applying LS extrapolation itself in the case of medium-term predictions (up to 100 days in the future). However, the predictions of AAM χ ₃ reveal the best accuracy for LS + AR.     

Testing earthquake prediction methods: «The West Pacific short-term forecast of earthquakes with magnitude MwHRV ≥ 5.8»

Analyzing the tables and probability maps posted by Yan Y. Kagan and David D. Jackson in April 2002–September 2004 at http://scec.ess.ucla.edu/~ykagan/predictions_index.html and the catalog of earthquakes for the same period, the conclusion is drawn that the underlying method could be used for prediction of aftershocks, while it does not outscore random guessing when main shocks are considered.     

Asymmetric vegetation responses to mid-Holocene aridity at the prairie-forest ecotone in south-central Minnesota

The mid-Holocene (ca. 8000-4000 cal yr BP) was a time of marked aridity throughout much of Minnesota, and the changes due to mid-Holocene aridity are seen as an analog for future responses to global warming. In this study, we compare the transition into (ca. 9000-7000 yr ago) and out of (ca. 5000-2500 yr ago) the mid-Holocene (MH) period at Kimble Pond and Sharkey Lake, located along the prairie forest ecotone in south-central Minnesota, using high resolution (∼ 5-36 yr) sampling of pollen, charcoal, sediment magnetic and loss-on-ignition properties. Changes in vegetation were asymmetrical with increasing aridity being marked by a pronounced shift from woodland/forest-dominated landscape to a more open mix of grassland and woodland/savanna. In contrast, at the end of the MH, grassland remained an important component of the landscape despite increasing effective moisture, and high charcoal influxes (median 2.7-4.0 vs. 0.6-1.7 mm² cm^− 2 yr^− 1 at start of MH) suggest the role of fire in limiting woodland expansion. Asymmetric vegetation responses, variation among and within proxies, and the near-absence of fire today suggest caution in using changes associated with mid-Holocene aridity at the prairie forest boundary as an analog for future responses to global warming.     

Predicting average annual groundwater levels from climatic variables: an empirical model

On the basis of one-dimensional theoretical water flow model, we demonstrate that the groundwater level variation follows a pattern similar to recharge fluctuation, with a time delay that depends on the characteristics of aquifer, recharge pattern as well as the distance between the recharge and observation locations. On the basis of a water budget model and the groundwater flow model, we propose an empirical model that links climatic variables to groundwater level. The empirical model is tested using a partial data set from historical records of water levels from more than 80 wells in a monitoring network for the carbonate rock aquifer, southern Manitoba, Canada. The testing results show that the predicted groundwater levels are very close to the observed ones in most cases. The overall average correlation coefficient between the predicted and observed water levels is 0.92. This proposed empirical statistical model could be used to predict variations in groundwater level in response to different climate scenarios in a climate change impact assessment.     

The climatic impacts of land surface change and carbon management, and the implications for climate-change mitigation policy

Strategies to mitigate anthropogenic climate change recognize that carbon sequestration in the terrestrial biosphere can reduce the build-up of carbon dioxide in the Earth’s atmosphere. However, climate mitigation policies do not generally incorporate the effects of these changes in the land surface on the surface albedo, the fluxes of sensible and latent heat to the atmosphere, and the distribution of energy within the climate system. Changes in these components of the surface energy budget can affect the local, regional, and global climate. Given the goal of mitigating climate change, it is important to consider all of the effects of changes in terrestrial vegetation and to work toward a better understanding of the full climate system. Acknowledging the importance of land surface change as a component of climate change makes it more challenging to create a system of credits and debits wherein emission or sequestration of carbon in the biosphere is equated with emission of carbon from fossil fuels. Recognition of the complexity of human-caused changes in climate does not, however, weaken the importance of actions that would seek to minimize our disturbance of the Earth’s environmental system and that would reduce societal and ecological vulnerability to environmental change and variability.     

水库地震主震发生时间的预测

水利工程对水库地震的抗震设防要求事先对水为地震进行预测。预测内容包括蓄水后诱发地震的危险性（可能性、）最大震级、最大震的地占紧及不库地震对场址地震动参数的影响。除主震时间的预测外，本文第一作者都曾提出过初步的预测方法。本文认为，水库地震前震波速比异常、非线性灰色模型以及前震系列回归等方法，是预测主震时间的有前途的方法。     

雅江地块的震兆分析与强震危险性探讨

分析了雅江地块的构造环境、地震活动和震兆异常，探讨2001年雅江6．0级地震、2002年新龙5．3级地震发生后雅江地块的强震趋势。重点对理塘-德巫断裂带，新龙、白玉、德格及贡觉一带地区的异常地震活动图像进行分析，认为该区存在强震填空的潜在危险性。     

The possible connection between ionization in the atmosphere by cosmic rays and low level clouds

Recent analysis of monthly mean cloud data from the International Satellite Cloud Climatology Project uncovered a strong correlation between low cloud and the cosmic ray flux for extensive regions of the Earth. Additional data have been recently released covering the period up to September 2001 with which we have made a new study of the geographical variation of the correlation between low cloud and predicted ionization level from cosmic rays at an altitude of 2 km. When analysed globally, we find that the correlations do not correspond to the latitude variation of cosmic ray flux and they are not field significant. Nonetheless they appear to be marginally field significant over broad latitude and longitude bands with a peak positive correlation at 50 degrees North and South and a tendency to negative correlation at lower latitudes. The correlation is strongest over the North and South Atlantic. Several of these features are consistent with the predictions of the electroscavenging process.We use a simple model to calculate the climatic impact should the correlation be confirmed. We show that, under the most favorable conditions, a reduction in low cloud cover since the late 19th century, combined with the direct forcing by solar irradiance can explain a significant part of the global warming over the past century, but not all. However, this computation assumes that there is no feedback or changes in cloud at other levels.