Indicators of Climate Change in the Northeast 2005

Climate changes. It always has and always will. What is unique in modern times is that human activities are now a significant factor causing climate to change. This is evident in the recent rise in key greenhouse gases, such as carbon dioxide (CO2), in the atmosphere, and in the recent increase in global temperatures in the lower atmosphere and in the surface ocean. The evidence presented in this report clearly illustrates that climate in New England is also changing. Over the past 100 years, and especially the last 30 years, all of the climate change indicators for the region reveal a warming trend. While at this point we cannot prove conclusively that this regional warming is due to human actions, the warming is fully consistent with what we would expect from global warming caused by increasing greenhouse gas concentrations. There is no question that human induced climate change is a phenomenon that humans will have to deal with in the coming decades. The good news is that, because we are the primary source of pollution that is likely causing our atmosphere and oceans to warm, we can also do something about it by changing specific policies and behaviors. It is our hope that by presenting this information in a succinct format, more people will understand the nature and scope of the problem and, therefore, be willing to make the changes necessary to address the significant societal challenge posed by climate change

Assessing the climate vulnerability of the world’s natural and cultural heritage

Climate change is the fastest-growing global threat to the world’s natural and cultural heritage. No systematic approach to assess climate vulnerability of protected areas and their associated communities has existed—until now. The Climate Vulnerability Index (CVI) is scientifically robust, transparent, and repeatable, and has now been applied to various World Heritage properties. The CVI builds upon an established Intergovernmental Panel on Climate Change (IPCC) framework to systematically assess vulnerability through a risk assessment approach that considers the key values of the World Heritage property in question and identifies key climate stressors. The CVI process is then used to assess the climate-related vulnerability of the community (including local residents, domestic visitors, and international tourists) associated with the World Heritage property considering economic, social, and cultural connections. Climate impacts are increasingly adding to a wide range of compounding pressures (e.g., increasing tourism, infrastructure development, changing land use practices) that are affecting places, people, customs, and values. Applications of the CVI to date have led to commitments to integrate outcomes into relevant management plans, and to periodically repeat the process, enabling responsive management to changing future circumstances. The CVI has also demonstrated its potential applicability for protected areas beyond World Heritage properties. The CVI process engages local community members in determining impacts, provides opportunities for identifying adaptation and impact mitigation within the community, and aids broader communication about key climate issues

Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus

In systems of multiple agents, identifying the cause of observed agent dynamics is challenging. Often, these agents operate in diverse, non-stationary environments, where models rely on hand-crafted environment-specific features to infer influential regions in the system's surroundings. To overcome the limitations of these inflexible models, we present GP-LAPLACE, a technique for locating sources and sinks from trajectories in time-varying fields. Using Gaussian processes, we jointly infer a spatio-temporal vector field, as well as canonical vector calculus operations on that field. Notably, we do this from only agent trajectories without requiring knowledge of the environment, and also obtain a metric for denoting the significance of inferred causal features in the environment by exploiting our probabilistic method. To evaluate our approach, we apply it to both synthetic and real-world GPS data, demonstrating the applicability of our technique in the presence of multiple agents, as well as its superiority over existing methods.Comment: KDD '18 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Pages 1254-1262, 9 pages, 5 figures, conference submission, University of Oxford. arXiv admin note: text overlap with arXiv:1709.0235

GlobalWarming and Terrestrial Biodiversity Decline

This study demonstrates that rapid rates of global warming are likely to increase rates of habitat loss and species extinction, most markedly in the higher latitudes of the Northern Hemisphere. Extensive areas of habitat may be lost to global warming and many species may be unable to shift their ranges fast enough to keep up with global warming. Rare and isolated populations of species in fragmented habitats or those bounded by large water bodies, human habitation and agriculture are particularly at risk, as are montane and arctic species

Generating and using truly random quantum states in Mathematica

The problem of generating random quantum states is of a great interest from the quantum information theory point of view. In this paper we present a package for Mathematica computing system harnessing a specific piece of hardware, namely Quantis quantum random number generator (QRNG), for investigating statistical properties of quantum states. The described package implements a number of functions for generating random states, which use Quantis QRNG as a source of randomness. It also provides procedures which can be used in simulations not related directly to quantum information processing.Comment: 12 pages, 3 figures, see http://www.iitis.pl/~miszczak/trqs.html for related softwar

Observations and Recommendations for the Calibration of Landsat 8 OLI and Sentinel 2 MSI for Improved Data Interoperability

Combining data from multiple sensors into a single seamless time series, also known as data interoperability, has the potential for unlocking new understanding of how the Earth functions as a system. However, our ability to produce these advanced data sets is hampered by the differences in design and function of the various optical remote-sensing satellite systems. A key factor is the impact that calibration of these instruments has on data interoperability. To address this issue, a workshop with a panel of experts was convened in conjunction with the Pecora 20 conference to focus on data interoperability between Landsat and the Sentinel 2 sensors. Four major areas of recommendation were the outcome of the workshop. The first was to improve communications between satellite agencies and the remote-sensing community. The second was to adopt a collections-based approach to processing the data. As expected, a third recommendation was to improve calibration methodologies in several specific areas. Lastly, and the most ambitious of the four, was to develop a comprehensive process for validating surface reflectance products produced from the data sets. Collectively, these recommendations have significant potential for improving satellite sensor calibration in a focused manner that can directly catalyze efforts to develop data that are closer to being seamlessly interoperable