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In this paper, nonparametric curve estimation methods are applied to analyze time series of wind speeds, focusing on the extreme events exceeding a chosen threshold. Classical parametric statistical approaches in this context consist in fitting a generalized Pareto distribution (GPD) to the tail of the empirical cumulative distribution, using maximum likelihood or the method of the moments to estimate the parameters of this distribution. Additionally, confidence intervals are usually computed to assess the uncertainty of the estimates. Nonparametric methods to estimate directly some quantities of interest, such as the probability of exceedance, the quantiles or return levels, or the return periods, are proposed. Moreover, bootstrap techniques are used to develop pointwise and simultaneous confidence intervals for these functions. The proposed models are applied to wind speed data in the Gulf Coast of US, comparing the results with those using the GPD approach, by means of a split-sample test. Results show that nonparametric methods are competitive with respect to the standard GPD approximations. The study is completed generating synthetic data sets and comparing the behavior of the parametric and the nonparametric estimates in this framework. 相似文献
957.
This paper identifies two sources of uncertainties in model projections of temperature and precipitation: internal and inter-model variability. Eight models of WCRP-CMIP3 and WCRP-CMIP5 were compared to identify improvements in the reliability of projections from new generation models. While no significant differences are observed between both datasets, some improvements were found in the new generation models. For example, in summer CMIP5 inter-model variability of temperature was lower over northeastern Argentina, Paraguay and northern Brazil, in the last decades of the 21st century. Reliability of temperature projections from both sets of models is high, with signal to noise ratio greater than 1 over most of the study region. Although no major differences were observed in both precipitation datasets, CMIP5 inter-model variability was lower over northern and eastern Brazil in summer (especially at the end of the 21st century). Reliability of precipitation projections was low in both datasets. However, the signal to noise ratio in new generation models was close to 1, and even greater than 1 over eastern Argentina, Uruguay and southern Brazil in some seasons. 相似文献
958.
Winter-to-spring temperature dynamics in Turkey derived from tree rings since AD 1125 总被引:2,自引:1,他引:1
Ingo Heinrich Ramzi Touchan Isabel Dorado Liñán Heinz Vos Gerhard Helle 《Climate Dynamics》2013,41(7-8):1685-1701
In the eastern Mediterranean in general and in Turkey in particular, temperature reconstructions based on tree rings have not been achieved so far. Furthermore, centennial-long chronologies of stable isotopes are generally also missing. Recent studies have identified the tree species Juniperus excelsa as one of the most promising tree species in Turkey for developing long climate sensitive stable carbon isotope chronologies because this species is long-living and thus has the ability to capture low-frequency climate signals. We were able to develop a statistically robust, precisely dated and annually resolved chronology back to AD 1125. We proved that variability of δ13C in tree rings of J. excelsa is mainly dependent on winter-to-spring temperatures (January–May). Low-frequency trends, which were associated with the medieval warm period and the little ice age, were identified in the winter-to-spring temperature reconstruction, however, the twentieth century warming trend found elsewhere could not be identified in our proxy record, nor was it found in the corresponding meteorological data used for our study. Comparisons with other northern-hemispherical proxy data showed that similar low-frequency signals are present until the beginning of the twentieth century when the other proxies derived from further north indicate a significant warming while the winter-to-spring temperature proxy from SW-Turkey does not. Correlation analyses including our temperature reconstruction and seven well-known climate indices suggest that various atmospheric oscillation patterns are capable of influencing the temperature variations in SW-Turkey. 相似文献
959.
We focus on an island wake episode that occurred in the Madeira Archipelago region of the north-east Atlantic at $32.5^{\circ }\mathrm{N}, 17^{\circ }\mathrm{W}$ . The Weather Research and Forecasting numerical model was used in a (one-way) downscaling mode, considering initial and boundary conditions from the European Centre for Medium-range Weather Forecasts system. The current literature emphasizes adiabatic effects on the dynamical aspects of atmospheric wakes. Changes in mountain height and consequently its relation to the atmospheric inversion layer should explain the shift in wake regimes, from a ‘strong-wake’ to ‘weak-wake’ scenario. Nevertheless, changes in sea-surface temperature variability in the lee of an island can induce similar regime shifts because of exposure to stronger solar radiation. Increase in evaporation contributes to the enhancement of convection and thus to the uplift of the stratified atmospheric layer above the critical height, with subsequent internal gravity wave activity. 相似文献
960.
Dáithí Stone Maximilian Auffhammer Mark Carey Gerrit Hansen Christian Huggel Wolfgang Cramer David Lobell Ulf Molau Andrew Solow Lourdes Tibig Gary Yohe 《Climatic change》2013,118(2):381-395
Future scenarios of the energy system under greenhouse gas emission constraints depict dramatic growth in a range of energy technologies. Technological growth dynamics observed historically provide a useful comparator for these future trajectories. We find that historical time series data reveal a consistent relationship between how much a technology’s cumulative installed capacity grows, and how long this growth takes. This relationship between extent (how much) and duration (for how long) is consistent across both energy supply and end-use technologies, and both established and emerging technologies. We then develop and test an approach for using this historical relationship to assess technological trajectories in future scenarios. Our approach for “learning from the past” contributes to the assessment and verification of integrated assessment and energy-economic models used to generate quantitative scenarios. Using data on power generation technologies from two such models, we also find a consistent extent - duration relationship across both technologies and scenarios. This relationship describes future low carbon technological growth in the power sector which appears to be conservative relative to what has been evidenced historically. Specifically, future extents of capacity growth are comparatively low given the lengthy time duration of that growth. We treat this finding with caution due to the low number of data points. Yet it remains counter-intuitive given the extremely rapid growth rates of certain low carbon technologies under stringent emission constraints. We explore possible reasons for the apparent scenario conservatism, and find parametric or structural conservatism in the underlying models to be one possible explanation. 相似文献