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Andreas Groth and Michael Ghil

region in a joint M-SSA analysis. The cleaner and sharper spectral results obtained herein support the previous authors’ findings of very similar interannual peaks in the Gulf Stream SST data and the North Atlantic Oscillation (NAO). Given that the proposed Monte Carlo test improves M-SSA’s discriminating power, our findings provide even stronger evidence for shared physical mechanisms between the Gulf Stream’s meandering and the atmospheric NAO. The remainder of the paper is organized as follows: In

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G. T. Aronica and B. Bonaccorso

November (more than 15%). 4. Hydrological analysis On the basis of the procedure outlined in Figure 1 , a hydrological analysis through the generation of synthetic rainfall, temperature, and discharge series has been carried out for assessing the current and the future hydropower potential consequent to the climate change scenarios described above. 4.1. Stochastic rainfall generator: Calibration and Monte Carlo generation The model was calibrated against 40-yr-long (1967–2006) daily rainfall series

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K. E. Kunkel and J. A. Weinman

1772 JOURNAL OF THE ATMOSPHERIC SCIENCES Vot. u~m33Monte Carlo Analysis of Multiply Scattered Lidar Returns K. E. Ktmx~O A~-O J. A. W~.nv~NDepartment of Meteorology, University of Wisamsin, Madison 53706(Manuscript received 6 March 1975, in revised form 25 March 1976)ABSTRACT Monte Carlo techniques are utilized to compute monostatic lidar returns from turbid atmospheres.Examples are evaluated for thick

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Lorenzo Tomassini, Peter Reichert, Reto Knutti, Thomas F. Stocker, and Mark E. Borsuk

second block contains climate sensitivity S , the indirect aerosol forcing scale, vertical ocean diffusivity K υ , and the transfer coefficient for sensible heat D . The two blocks are then sequentially updated in each iteration of the Markov chain Monte Carlo algorithm. d. Classes of priors The use of informative prior distributions in a Bayesian uncertainty analysis for climate sensitivity is controversial. One might argue that the knowledge used to construct such priors draws at least partly on

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Xin Zhao and Pao-Shin Chu

unknown observable ĥ . This formula is at the heart of Bayesian analysis. The MCMC approach is one of the efficient algorithms for Bayesian inference. The general Bayesian analysis method described above essentially involves calculating the posterior expectation where a ( θ ) can be any function conditional on model θ . This expectation, however, is very difficult to integrate in most practical models. Alternatively, a numerical way to calculate such an expectation is to use Monte Carlo integration

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Petra Friederichs and Andreas Hense

(1947) and Glynn and Muirhead (1978) . The most commonly applied method to test statistical significance in CCA analysis is a Monte Carlo test procedure. The idea is to generate a large number of independent realizations of the model under the assumption that a certain null hypothesis H 0 is true. In general, the sampling procedure generates a new realization by stochastically replacing the data fields in the time sequence. The distribution of one or more test variables, which represent a

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R. A. Raschke and S. K. Cox

intensitymeasurements is examined. Data were collected by a photodiode radiometer which measured incident radiationat angular fields of view of 2, 5, 10, 20 and 28-. Values of normalized annular radiance and transmittancewere calculated from the observations and compared to similar calculations from a Monte Carlo radiativetransfer model. The Monte Carlo results were for cloud optical depths of I through 6 over a spectral bandpassof 0.3 to 2.8 ~m. Eight case studies including high, middle and low clouds were

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Marie C. McGraw and Elizabeth A. Barnes

perform a Monte Carlo simulation in which we vary α , γ , and τ . First, we create a D time series with 550 steps following Eq. (1) . After discarding the first 50 values of D , we create R following Eq. (2) . We perform our regression analysis (discussed in the next section) and repeat this process 5000 times for each combination of α , γ , and τ . We test 20 values of α , ranging from 0 to 1; 20 values of γ , ranging from 0.005 to 15; and 15 values of τ , ranging from 1 to 15, to

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J. A. Weinman

forcloud, fog and haze models. The results are found to be in satisfactory agreement with results obtainedfrom the Monte Carlo analysis of Kunkel (1974) and the theory of light pulses doubly scattered by turbidatmospheres which was developed by Eloranta (1972).1. Introduction Pulses of light emitted by lasers which are reflectedfrom particles in the atmosphere provide a techniqueto probe the optical properties and the spatial distribution of the particles. A receiver consisting of a telescopewith a

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Pao-Shin Chu

spring of the year following an El Nifio. The reliability of the E1 Nifio composite hasbeen tested using a Monte Carlo simulation technique. Upper-air circulation patterns during the recent threeEl Nifio events are discussed in relation to drought winters in Hawaii. The more eastward elongated subtropicaljet stream in the North Pacific and the thermally induced local Hadley circulation in the central North Pacific,characteristics of El Nifio winters, are unfavorable for rainfall in Hawaii.1

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