Mountain ranges favour vigorous Atlantic Meridional Overturning

We use a global Ocean-Atmosphere General Circulation Model (OAGCM) to show that the major mountain ranges of the world have a significant role in maintenance of the Atlantic Meridional Overturning Circulation (AMOC). A simulation with mountains has a maximum AMOC of 18 Sv (1 Sv=106 m3 s-1) compared with ~0 Sv for a simulation without mountains. Atlantic heat transport at 25N is 1.1 PW with mountains compared to 0.2 PW without. The difference in AMOC is due to major changes in surface heat and freshwater (FW) fluxes over the Atlantic. In the Pacific changed surface fluxes lead to a meridional overturning circulation of 10 Sv. Our results suggest that the effects of mountains on the large-scale atmospheric circulation is to force the ocean towards a state with a vigorous AMOC and with no overturning in the Pacific.

Seasonal temperature variability of the Neoglacial (3300–2500 BP) and Roman Warm Period (2500–1600 BP) reconstructed from oxygen isotope ratios of limpet shells (Patella vulgata), Northwest Scotland

Seasonal sea-surface temperaturevariability for the Neoglacial (3300–2500 BP) and Roman WarmPeriod (RWP; 2500–1600 BP), which correspond to the Bronze and Iron Ages, respectively, was estimated using oxygen isotope ratios obtained from high-resolution samples micromilled from radiocarbon-dated, archaeological limpet (Patella vulgata) shells. The coldest winter months recorded in Neoglacial shells averaged 6.6 ± 0.3 °C, and the warmest summer months averaged 14.7 ± 0.4 °C. One Neoglacial shell captured a year without a summer, which may have resulted from a dust veil from a volcanic eruption in the Katla volcanic system in Iceland. RWP shells record average winter and summer monthly temperatures of 6.3 ± 0.1 °C and 13.3 ± 0.3 °C, respectively. These results capture a cooling transition from the Neoglacial to RWP, which is further supported by earlier studies of pine history in Scotland, pollen type analyses in northeast Scotland, and European glacial events. The cooling transition observed at the boundary between the Neoglacial and RWP in our study also agrees with the abrupt climate deterioration at 2800–2700 BP (also referred to as the Subboreal/Subatlantic transition) and therefore may have been driven by decreased solar radiation and weakened North Atlantic Oscillation conditions.

Sampling series for determination of probability density of CDMA multiple access interference

Consideration is given to a standard CDMA system and determination of the density function of the interference with and without Gaussian noise using sampling theory concepts. The formula derived provides fast and accurate results and is a simple, useful alternative to other methods

Estimating reliability and resolution of probability forecasts through decomposition of the empirical score

References (20)Cited By (1)Export CitationAboutAbstract Proper scoring rules provide a useful means to evaluate probabilistic forecasts. Independent from scoring rules, it has been argued that reliability and resolution are desirable forecast attributes. The mathematical expectation value of the score allows for a decomposition into reliability and resolution related terms, demonstrating a relationship between scoring rules and reliability/resolution. A similar decomposition holds for the empirical (i.e. sample average) score over an archive of forecast–observation pairs. This empirical decomposition though provides a too optimistic estimate of the potential score (i.e. the optimum score which could be obtained through recalibration), showing that a forecast assessment based solely on the empirical resolution and reliability terms will be misleading. The differences between the theoretical and empirical decomposition are investigated, and specific recommendations are given how to obtain better estimators of reliability and resolution in the case of the Brier and Ignorance scoring rule.

Evaluating raw ensembles with the continuous ranked probability score

The continuous ranked probability score (CRPS) is a frequently used scoring rule. In contrast with many other scoring rules, the CRPS evaluates cumulative distribution functions. An ensemble of forecasts can easily be converted into a piecewise constant cumulative distribution function with steps at the ensemble members. This renders the CRPS a convenient scoring rule for the evaluation of ‘raw’ ensembles, obviating the need for sophisticated ensemble model output statistics or dressing methods prior to evaluation. In this article, a relation between the CRPS score and the quantile score is established. The evaluation of ‘raw’ ensembles using the CRPS is discussed in this light. It is shown that latent in this evaluation is an interpretation of the ensemble as quantiles but with non-uniform levels. This needs to be taken into account if the ensemble is evaluated further, for example with rank histograms.

‘NSC 68 and the Soviet Threat: A New Perspective on Western Threat Perception and Policy Making’

It has been argued that the US strategy paper NSC 68 of 1950 which ushered in the great Cold War rearmament process first in the US and then in NATO was a vast overreaction. This paper argues, by contrast, that given the intelligence about the Soviet Union's and its satellites' military buildup and the role of China in that period, this was a reasonable strategy to embrace.

Indian summer monsoon variations could have affected the early Holocene woodland expansion in the Near East

Postglacial expansion of deciduous oak woodlands of the Zagros—Anti-Taurus Mountains, a major biome of the Near East, was delayed until the middle Holocene at ~6300 cal. yr BP. The current hypotheses explain this delay as a consequence of a regional aridity during the early Holocene, slow migration rates of forest trees, and/or a long history of land use and agro-pastoralism in this region. In the present paper, support is given to a hypothesis that suggests different precipitation seasonalities during the early Holocene compared with the late Holocene. The oak species of the Zagros—Anti-Taurus Mts, particularly Quercus brantii Lindl., are strongly dependent on spring precipitation for regeneration and are sensitive to a long dry season. Detailed analysis of modern atmospheric circulation patterns in SW Asia during the late spring suggests that the Indian Summer Monsoon (ISM) intensification can modify the amount of late spring and/or early summer rainfall in western/northwestern Iran and eastern Anatolia, which could in turn have controlled the development of the Zagros—Anti-Taurus deciduous oak woodlands. During the early Holocene, the northwestward shift of the Inter-Tropical Convergence Zone (ITCZ) could have displaced the subtropical anticyclonic belt or associated high pressure ridges to the northwest. The latter could, in turn, have prevented the southeastward penetration of low pressure systems originating from the North Atlantic and Black Sea regions. Such atmospheric configuration could have reduced or eliminated the spring precipitation creating a typical Mediterranean continental climate characterized by winter-dominated precipitation. This scenario highlights the complexity of biome response to climate system interactions in transitional climatic and biogeographical regions.

Probability of survival in a random exchange economy with dependent agents

In this paper I analyze the general equilibrium in a random Walrasian economy. Dependence among agents is introduced in the form of dependency neighborhoods. Under the uncertainty, an agent may fail to survive due to a meager endowment in a particular state (direct effect), as well as due to unfavorable equilibrium price system at which the value of the endowment falls short of the minimum needed for survival (indirect terms-of-trade effect). To illustrate the main result I compute the stochastic limit of equilibrium price and probability of survival of an agent in a large Cobb-Douglas economy.

An effective approach for land-cover classification from airborne lidar fused with co-registered data

Airborne lidar provides accurate height information of objects on the earth and has been recognized as a reliable and accurate surveying tool in many applications. In particular, lidar data offer vital and significant features for urban land-cover classification, which is an important task in urban land-use studies. In this article, we present an effective approach in which lidar data fused with its co-registered images (i.e. aerial colour images containing red, green and blue (RGB) bands and near-infrared (NIR) images) and other derived features are used effectively for accurate urban land-cover classification. The proposed approach begins with an initial classification performed by the Dempster–Shafer theory of evidence with a specifically designed basic probability assignment function. It outputs two results, i.e. the initial classification and pseudo-training samples, which are selected automatically according to the combined probability masses. Second, a support vector machine (SVM)-based probability estimator is adopted to compute the class conditional probability (CCP) for each pixel from the pseudo-training samples. Finally, a Markov random field (MRF) model is established to combine spatial contextual information into the classification. In this stage, the initial classification result and the CCP are exploited. An efficient belief propagation (EBP) algorithm is developed to search for the global minimum-energy solution for the maximum a posteriori (MAP)-MRF framework in which three techniques are developed to speed up the standard belief propagation (BP) algorithm. Lidar and its co-registered data acquired by Toposys Falcon II are used in performance tests. The experimental results prove that fusing the height data and optical images is particularly suited for urban land-cover classification. There is no training sample needed in the proposed approach, and the computational cost is relatively low. An average classification accuracy of 93.63% is achieved.

Sparse probability density function estimation using the minimum integrated square error

We develop a new sparse kernel density estimator using a forward constrained regression framework, within which the nonnegative and summing-to-unity constraints of the mixing weights can easily be satisfied. Our main contribution is to derive a recursive algorithm to select significant kernels one at time based on the minimum integrated square error (MISE) criterion for both the selection of kernels and the estimation of mixing weights. The proposed approach is simple to implement and the associated computational cost is very low. Specifically, the complexity of our algorithm is in the order of the number of training data N, which is much lower than the order of N2 offered by the best existing sparse kernel density estimators. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with comparable accuracy to those of the classical Parzen window estimate and other existing sparse kernel density estimators.

The probability of sale and price premiums in withdrawn auctioned properties

This paper examines the impact of the auction process of residential properties that whilst unsuccessful at auction sold subsequently. The empirical analysis considers both the probability of sale and the premium of the subsequent sale price over the guide price, reserve and opening bid. The findings highlight that the final achieved sale price is influenced by key price variables revealed both prior to and during the auction itself. Factors such as auction participation, the number of individual bidders and the number of bids are significant in a number of the alternative specifications.