Current scientific knowledge on the future response of the climate system to human-induced perturbations is comprehensively captured by various model intercomparison efforts. In the preparation of the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC), intercomparisons were organized for atmosphere-ocean general circulation models (AOGCMs) and carbon cycle models, named "CMIP3" and "(CMIP)-M-4", respectively. Despite their tremendous value for the scientific community and policy makers alike, there are some difficulties in interpreting the results. For example, radiative forcings were not standardized across the various AOGCM integrations and carbon cycle runs, and, in some models, key forcings were omitted. Furthermore, the AOGCM analysis of plausible emissions pathways was restricted to only three SRES scenarios. This study attempts to address these issues. We present an updated version of MAGICC, the simple carbon cycle-climate model used in past IPCC Assessment Reports with enhanced representation of time-varying climate sensitivities, carbon cycle feedbacks, aerosol forcings and ocean heat uptake characteristics. This new version, MAGICC6, is successfully calibrated against the higher complexity AOGCMs and carbon cycle models. Parameterizations of MAGICC6 are provided. The mean of the emulations presented here using MAGICC6 deviates from the mean AOGCM responses by only 2.2% on average for the SRES scenarios. This enhanced emulation skill in comparison to previous calibrations is primarily due to: making a "like-with-like comparison" using AOGCM-specific subsets of forcings; employing a new calibration procedure; as well as the fact that the updated simple climate model can now successfully emulate some of the climate-state dependent effective climate sensitivities of AOGCMs. The diagnosed effective climate sensitivity at the time of CO2 doubling for the AOGCMs is on average 2.88 degrees C, about 0.33 degrees C cooler than the mean of the reported slab ocean climate sensitivities. In the companion paper (Part 2) of this study, we examine the combined climate system and carbon cycle emulations for the complete range of IPCC SRES emissions scenarios and the new RCP pathways.
The aim of this paper is to investigate the stable/unstable regimes of the non-static anisotropic filamentary stellar models in the framework of f (R, T, R mu nu T mu nu) gravity. We construct the field equations and conservation laws in the perspective of this model of gravity. The perturbation scheme is applied to the analysis of the behavior of a particular f (R, T, R mu nu T mu nu) cosmological model on the evolution of cylindrical system. The role of the adiabatic index is also checked in the formulations of the instability regions. We have explored the instability constraints in the Newtonian and post-Newtonian limits. Our results reinforce the significance of the adiabatic index and dark source terms in the stability analysis of celestial objects in modified gravity.
Atmospheric parameters strongly affect the performance of free-space optical communication (FSOC) systems when the optical wave is propagating through the inhomogeneous turbulence transmission medium. Developing a model to get an accurate prediction of the atmospheric turbulence strength (C-n(2)) according to meteorological parameters (weather data) becomes significant to understand the behavior of the FSOC channel during different seasons. The construction of a dedicated free-space optical link for the range of 0.5 km at an altitude of 15.25 m built at Thanjavur (Tamil Nadu) is described in this paper. The power level and beam centroid information of the received signal are measured continuously with weather data at the same time using an optoelectronic assembly and the developed weather station, respectively, and are recorded in a data-logging computer. Existing models that exhibit relatively fewer prediction errors are briefed and are selected for comparative analysis. Measured weather data (as input factors) and C-n(2) (as a response factor) of size [177, 147 x 4] are used for linear regression analysis and to design mathematical models more suitable in the test field. Along with the model formulation methodologies, we have presented the contributions of the input factors' individual and combined effects on the response surface and the coefficient of determination (R-2) estimated using analysis of variance tools. An R-2 value of 98.93% is obtained using the new model, model equation V, from a confirmatory test conducted with a testing data set of size [2000 x 4]. In addition, the prediction accuracies of the selected and the new models are investigated during different seasons in a one-year period using the statistics of day, week-averaged, month-averaged, and seasonal-averaged diurnal C-n(2) profiles, and are verified in terms of the sum of absolute error (SAE). A C-n(2) prediction maximum average SAE of 2.3 x 10(-13) m(-2/3) is achieved using the new model in a longer range of dynamic meteorological parameters during the different local seasons. (C) 2015 Optical Society of America
The last published version of The Society of Thoracic Surgeons (STS) Adult Cardiac Surgery Database (ACSD) risk models were developed in 2008 based on patient data from 2002 to 2006 and have been periodically recalibrated. In response to evolving changes in patient characteristics, risk profiles, surgical practice, and outcomes, the STS has now developed a set of entirely new risk models for adult cardiac surgery. New models were estimated for isolated coronary artery bypass grafting surgery (CABG [n = 439,092]), isolated aortic or mitral valve surgery (n = 150,150), and combined valve plus CABG procedures (n = 81,588). The development set was based on July 2011 to June 2014 STS ACSD data; validation was performed using July 2014 to December 2016 data. Separate models were developed for operative mortality, stroke, renal failure, prolonged ventilation, reoperation, composite major morbidity or mortality, and prolonged or short postoperative length of stay. Because of its low occurrence rate, a combined model incorporating all operative types was developed for deep sternal wound infection/mediastinitis. Calibration was excellent except for the deep sternal wound infection/mediastinitis model, which slightly underestimated risk because of higher rates of this endpoint in the more recent validation data; this will be recalibrated in each feedback report. Discrimination (c-index) of all models was superior to that of 2008 models except for the stroke model for valve patients. Completely new STS ACSD risk models have been developed based on contemporary patient data; their performance is superior to that of previous STS ACSD models.
We describe a new approach to speech recognition, in which all Hidden Markov Model (HMM) states share the same Gaussian Mixture Model (GMM) structure with the same number of Gaussians in each state. The model is defined by vectors associated with each state with a dimension of, say, 50, together with a global mapping from this vector space to the space of parameters of the GMM. This model appears to give better results than a conventional model, and the extra structure offers many new opportunities for modeling innovations while maintaining compatibility with most standard techniques.
Drawing on research undertaken in the history and philosophy of science, with particular reference to the extensive literature which discusses the use of models in biology and economics, we explore the question ‘Are Business Models useful?’ We point out that they act as various forms of model: to provide means to describe and classify businesses; to operate as sites for scientific investigation; and to act as recipes for creative managers. We argue that studying business models as models is rewarding in that it enables us to see how they embody multiple and mediating roles. We illustrate our ideas with reference to practices in the real world and to academic analyses, especially in this Special Issue on Business Models.