A comparison of a GCM response to historical anthropogenic land cover change and model sensitivity to uncertainty in present-day land cover representations

This study assesses the sensitivity of the fully coupled NCAR-DOE PCM to three different representations of present-day land cover, based on IPCC SRES land cover information. We conclude that there is significant model sensitivity to current land cover characterization, with an observed average global temperature range of 0.21 K between the simulations. Much larger contrasts (up to 5 K) are found on the regional scale; however, these changes are largely offsetting on the global scale. These results show that significant biases can be introduced when outside data sources are used to conduct anthropogenic land cover change experiments in GCMs that have been calibrated to their own representation of present-day land cover. We conclude that hybrid systems that combine the natural vegetation from the native GCM datasets combined with human land cover information from other sources are best for simulating such impacts. We also performed a prehuman simulation, which had a 0.39 K ~higher average global temperature and, perhaps of greater importance, temperature changes regionally of about 2 K. In this study, the larger regional changes coincide with large-scale agricultural areas. The initial cooling from energy balance changes appear to create feedbacks that intensify mid-latitude circulation features and weaken the summer monsoon circulation over Asia, leading to further cooling. From these results, we conclude that land cover change plays a significant role in anthropogenically forced climate change. Because these changes coincide with regions of the highest human population this climate impact could have a disproportionate impact on human systems. Therefore, it is important that land cover change be included in past and future climate change simulations.     

Integrity of an integrated GPS/INS system in the presence of slowly growing errors. Part II: analysis

This is the second of two consecutive papers (Part II) in this journal on the monitoring of the integrity of integrated GPS/INS systems. Part I established that the worst class of error for an integrated system in terms of failure detection performance is that of slowly growing errors (SGEs). It was also concluded that among the integration architectures, the tightly coupled architecture provides the best mechanism to tackle SGEs due to its simpler structure and accessibility to the relevant measurements. In a subsequent comparison of the existing integrity algorithms, the multiple solution separation (MSS) and autonomous integrity monitoring by extrapolation (AIME) methods were selected for further analysis because of their representative characteristics of the current integrity algorithms. This paper carries out a detailed investigation of the capability of the MSS and AIME algorithms to deal with the SGEs, using a realistic simulation platform and limited real data analysis. Results show that although it is possible for the existing algorithms to detect SGEs, the time it takes to detect them varies inversely to the rate of growth of the SGE. A new algorithm is developed to deal with this based on the concept of rate detection. Simulation results show that the new algorithm can detect SGEs earlier than the current algorithms. This is validated by preliminary tests with real data.     

Coherent Cherenkov radio pulses from hadronic showers up to EeV energies

The Cherenkov radio pulse emitted by hadronic showers of energies in the EeV range in ice is calculated for the first time using full three dimensional simulations of both shower development and the coherent radio pulse emitted as the excess charge develops in the shower. A Monte Carlo, ZHAireS, has been developed for this purpose combining the high energy hadronic interaction capabilities of AIRES, and the dense media propagation capabilities of TIERRAS, with the precise low energy tracking and specific algorithms developed to calculate the radio emission in ZHS. A thinning technique is implemented to allow the simulation of radio pulses induced by showers up to 10 EeV in ice. The code is validated comparing the results for electromagnetic and hadronic showers to those obtained with GEANT4 and ZHS codes. The contribution to the pulse of other shower particles in addition to electrons and positrons, mainly protons, pions and muons, is found to be below 3% for 10 PeV and above proton induced showers. The characteristics of hadronic showers and the corresponding Cherenkov frequency spectra are compared with those from purely electromagnetic showers. The dependence of the spectra on shower energy and high-energy hadronic model is addressed and parameterizations for the radio emission in hadronic showers in ice are given for practical applications.     

Australian baby boomers switched to more environment friendly modes of transport during the global financial crisis

The global financial crisis (GFC) in 2008 rocked economies around the world. Several intermediate outcomes of the GFC included loss of jobs and reduced income. Relatively little research has, however, examined the impacts of the GFC on individual level travel behaviour change. To address this shortcoming, HABITAT panel data were employed to estimate a multinomial logit model to examine mode switching behaviour between 2007 and 2009 of a baby boomers cohort in Brisbane, Australia—a city within a developed country that has been on many metrics the least affected by the GFC. In addition, a Poisson regression model was estimated to model the number of trips made by individuals in 2007, 2008, and 2009. The South East Queensland travel survey datasets were used to develop this model. Four linear regression models were estimated to assess the effects of the GFC on time allocated to travel during a day: one for each of the three travel modes including public transport, active transport, less environmentally friendly transport; and an overall travel time model irrespective of mode. The results reveal that the baby boomers switched to more environmentally friendly travel modes during the GFC.     

Extreme value theory-based integrity monitoring of global navigation satellite systems

Measurements consistency-based Receiver Autonomous Integrity Monitoring (RAIM) is the main technique for monitoring the integrity of global satellite navigation systems at the user-level. Existing RAIM algorithms utilize two tests, in the position domain a test for RAIM availability and in the measurement domain a test for failure detection. These tests involve the computation of three parameters: test statistic, decision threshold, and protection level. The test statistic is based on the actual measurements in the form of the sum of the squared errors (SSE). The decision threshold is chosen on the basis of the statistical characteristics of the SSE including the assumption that the errors are normally distributed. However, in practice residual, error distributions exhibit heavier tails than predicted by the Gaussian model. Therefore, this paper challenges the normality assumption of the residual navigation errors in three ways. First, real data are used to assess its impact on the traditional RAIM algorithm. Second, extreme value theory is applied to the tails, and the generalized extreme value (GEV) distribution is derived to capture residual navigation errors. Third, the performance of the traditional RAIM approach is compared with that employing the GEV distribution. The results demonstrate that the GEV model is a more accurate representation of the distribution of residual navigation errors than the conventional Gaussian model and should be used in the development of integrity monitoring algorithms.