Solar-Cycle Variation of Subsurface Meridional Flow Derived with Ring-Diagram Analysis     

R. Komm  I. González Hernández  R. Howe  F. Hill 《Solar physics》2015,290(11):3113-3136

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Low Speed Granular–Granular Impact Crater Opening Mechanism in 2D Experiments     

Roberto Bartali  Yuri Nahmad-Molinari  Gustavo M. Rodríguez-Liñán 《Earth, Moon, and Planets》2015,116(2):115-138

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Orbit propagation in Minkowskian geometry     

Javier Roa  Jesús Peláez 《Celestial Mechanics and Dynamical Astronomy》2015,123(1):13-43

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Frozen-anomaly transformation for the elliptic rendezvous problem     

Javier Roa  Jesús Peláez 《Celestial Mechanics and Dynamical Astronomy》2015,121(1):61-81

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Potential for small scale added value of RCM’s downscaled climate change signal   总被引：1，自引：1，他引：0  

Alejandro Di Luca  Ramón de Elía  René Laprise 《Climate Dynamics》2013,40(3-4):601-618

In recent decades, the need of future climate information at local scales have pushed the climate modelling community to perform increasingly higher resolution simulations and to develop alternative approaches to obtain fine-scale climatic information. In this article, various nested regional climate model (RCM) simulations have been used to try to identify regions across North America where high-resolution downscaling generates fine-scale details in the climate projection derived using the “delta method”. Two necessary conditions were identified for an RCM to produce added value (AV) over lower resolution atmosphere-ocean general circulation models in the fine-scale component of the climate change (CC) signal. First, the RCM-derived CC signal must contain some non-negligible fine-scale information—independently of the RCM ability to produce AV in the present climate. Second, the uncertainty related with the estimation of this fine-scale information should be relatively small compared with the information itself in order to suggest that RCMs are able to simulate robust fine-scale features in the CC signal. Clearly, considering necessary (but not sufficient) conditions means that we are studying the “potential” of RCMs to add value instead of the AV, which preempts and avoids any discussion of the actual skill and hence the need for hindcast comparisons. The analysis concentrates on the CC signal obtained from the seasonal-averaged temperature and precipitation fields and shows that the fine-scale variability of the CC signal is generally small compared to its large-scale component, suggesting that little AV can be expected for the time-averaged fields. For the temperature variable, the largest potential for fine-scale added value appears in coastal regions mainly related with differential warming in land and oceanic surfaces. Fine-scale features can account for nearly 60 % of the total CC signal in some coastal regions although for most regions the fine scale contributions to the total CC signal are of around ～5 %. For the precipitation variable, fine scales contribute to a change of generally less than 15 % of the seasonal-averaged precipitation in present climate with a continental North American average of ～5 % in both summer and winter seasons. In the case of precipitation, uncertainty due to sampling issues may further dilute the information present in the downscaled fine scales. These results suggest that users of RCM simulations for climate change studies in a delta method framework have little high-resolution information to gain from RCMs at least if they limit themselves to the study of first-order statistical moments. Other possible benefits arising from the use of RCMs—such as in the large scale of the downscaled fields– were not explored in this research.  相似文献   

Nonparametric analysis of high wind speed data     

Mario Francisco-Fernández  Alejandro Quintela-del-Río 《Climate Dynamics》2013,40(1-2):429-441

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   

Analysis of uncertainties in future climate projections for South America: comparison of WCRP-CMIP3 and WCRP-CMIP5 models     

Josefina Blázquez  Mario N. Nuñez 《Climate Dynamics》2013,41(3-4):1039-1056

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   

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   

Estimating Buoyancy Heat Flux Using the Surface Renewal Technique over Four Amazonian Forest Sites in Brazil     

Marcelo Zeri  Leonardo D. A. Sá  Carlos A. Nobre 《Boundary-Layer Meteorology》2013,149(2):179-196

The surface renewal (SR) method was applied for the first time to measurements of air temperature over four Amazonian forest sites and different seasons in order to obtain estimates of buoyancy heat flux. The required calibration of this method against eddy covariance resulted in a value for a specific parameter that is close to the range reported in other studies, contributing to the generalization of the SR method to different kinds of canopies. The comparison with fluxes obtained using the eddy covariance technique revealed a good match between the two methods for different sites, heights and seasons. Sites with high levels of non-stationarity in the signals of temperature and wind speed presented higher scatter in the regression with fluxes from eddy covariance. For a particular site with previously reported influence of low-frequency motions, the regression was only satisfactory, i.e., slope parameter close to unity and small offset, when oscillations with periods longer than $\approx $ ≈ 13 min were filtered out. The SR method has a great potential due to the simplicity of the instrumentation required. However, care should be taken when measuring under the influence of mesoscale motions, which can lead to high levels of non-stationarity, compromising the fundamental concepts of the SR theory.  相似文献   

The challenge to detect and attribute effects of climate change on human and natural systems     

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