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Climate change in infographics
\"Readers will learn about the causes and effects of climate change through colorful and clear graphics, such as maps, charts, and infographics. Book also includes a glossary, index, suggested books and websites, and a bibliography\"-- Provided by publisher.
Book
Small Islands in Peril?
This book explores the idea that small island communities could be regarded as canaries in the coal mine of sustainable development because of scientific and anecdotal evidence of a common link between rapid population growth, degradation of the local resource base, and intensification of disputes over the ownership and use of terrestrial and marine resources. The authors are all anthropologists with a specific interest in the question of whether the economic and social 'safety valves' that have previously served to break some of the feedback loops between these trends appear to be losing their efficacy. While much of the debate about economy–society–environment relationships on small islands has been overtaken by a narrow focus on the problem of climate change, the authors show that there are many other factors at work in the transformation of island lives and livelihoods.
eBook
Finding the Forest in the Trees
During the last few decades of the 20th century, the development of an array of technologies has made it possible to observe the Earth, collect large quantities of data related to components and processes of the Earth system, and store, analyze, and retrieve these data at will. Over the past ten years, in particular, the observational, computational, and communications technologies have enabled the scientific community to undertake a broad range of interdisciplinary environmental research and assessment programs. Sound practice in database management are required to deal with the problems of complexity in such programs and a great deal of attention and resources has been devoted to this area in recent years. However, little guidance has been provided on overcoming the barriers frequently encountered in the interfacing of disparate data sets. This book attempts to remedy that problem by providing analytical and functional guidelines to help researchers and technicians to better plan and implement their supporting data management activities.
eBook
Uncertainty Management in Remote Sensing of Climate Data
Great advances have been made in our understanding of the climate system over the past few decades, and remotely sensed data have played a key role in supporting many of these advances. Improvements in satellites and in computational and data-handling techniques have yielded high quality, readily accessible data. However, rapid increases in data volume have also led to large and complex datasets that pose significant challenges in data analysis. Uncertainty characterization is needed for every satellite mission and scientists continue to be challenged by the need to reduce the uncertainty in remotely sensed climate records and projections. The approaches currently used to quantify the uncertainty in remotely sensed data lack an overall mathematically based framework. An additional challenge is characterizing uncertainty in ways that are useful to a broad spectrum of end-users.
In December 2008, the National Academies held a workshop, summarized in this volume, to survey how statisticians, climate scientists, and remote sensing experts might address the challenges of uncertainty management in remote sensing of climate data. The workshop emphasized raising and discussing issues that could be studied more intently by individual researchers or teams of researchers, and setting the stage for possible future collaborative activities.
eBook
Finding the forest in the trees: the challenge of combining diverse environmental data : selected case studies
During the last few decades of the 20th century, the development of an array of technologies has made it possible to observe the Earth, collect large quantities of data related to components and processes of the Earth system, and store, analyze, and retrieve these data at will. Over the past ten years, in particular, the observational, computational, and communications technologies have enabled the scientific community to undertake a broad range of interdisciplinary environmental research and assessment programs. Sound practice in database management are required to deal with the problems of complexity in such programs and a great deal of attention and resources has been devoted to this area in recent years. However, little guidance has been provided on overcoming the barriers frequently encountered in the interfacing of disparate data sets. This book attempts to remedy that problem by providing analytical and functional guidelines to help researchers and technicians to better plan and implement their supporting data management activities.
eBook
Multivariate quantile mapping bias correction: an N-dimensional probability density function transform for climate model simulations of multiple variables
Most bias correction algorithms used in climatology, for example quantile mapping, are applied to univariate time series. They neglect the dependence between different variables. Those that are multivariate often correct only limited measures of joint dependence, such as Pearson or Spearman rank correlation. Here, an image processing technique designed to transfer colour information from one image to another—the N-dimensional probability density function transform—is adapted for use as a multivariate bias correction algorithm (MBCn) for climate model projections/predictions of multiple climate variables. MBCn is a multivariate generalization of quantile mapping that transfers all aspects of an observed continuous multivariate distribution to the corresponding multivariate distribution of variables from a climate model. When applied to climate model projections, changes in quantiles of each variable between the historical and projection period are also preserved. The MBCn algorithm is demonstrated on three case studies. First, the method is applied to an image processing example with characteristics that mimic a climate projection problem. Second, MBCn is used to correct a suite of 3-hourly surface meteorological variables from the Canadian Centre for Climate Modelling and Analysis Regional Climate Model (CanRCM4) across a North American domain. Components of the Canadian Forest Fire Weather Index (FWI) System, a complicated set of multivariate indices that characterizes the risk of wildfire, are then calculated and verified against observed values. Third, MBCn is used to correct biases in the spatial dependence structure of CanRCM4 precipitation fields. Results are compared against a univariate quantile mapping algorithm, which neglects the dependence between variables, and two multivariate bias correction algorithms, each of which corrects a different form of inter-variable correlation structure. MBCn outperforms these alternatives, often by a large margin, particularly for annual maxima of the FWI distribution and spatiotemporal autocorrelation of precipitation fields.
Journal Article
Climate change in Nepal: a comprehensive analysis of instrumental data and people’s perceptions
Despite broad scientific consensus on climate change, public views may not always correspond with scientific findings. Understanding public perceptions of climate change is thus crucial to both identifying problems and delivering solutions. Investigations of climate change that integrate instrumental records and people’s perceptions in the Himalayas are scarce and fragmentary compared to other regions of the world. We analyzed nationally representative data (n = 5060) of local peoples’ perception of climate change in Nepal, and assessed annual and seasonal trends of temperature and precipitation, onsets of seasons, and trends of climate extremes, based on gridded climate datasets. We firstly used quantitative and spatial techniques to compare local perceptions and the instrumentally observed trends of climate variables. We then examined the possible association of demographic variables, place attachment, regional differences, and prior understanding of climate change with the accuracy of people’s perceptions. Instrumental evidence showed consistent warming, increasing hot days and nights, and increasing annual precipitation, wet spells, heavy precipitation and decreasing dry spells in Nepal. Our results indicate that locals accurately perceived the shifts in temperature but their perceptions of precipitation change did not converge with the instrumental records. We suggest that, in future as exposure to changes in weather, particularly extreme events, continues, people may become more likely to detect change which corresponds with observed trends. With some new methodological insights gained through integrating community perceptions with observed climate data, the results of this study provides valuable information to support policies to reduce climate-related risk and enhance climate change adaptation.
Journal Article
An Overview of the Global Historical Climatology Network-Daily Database
A database is described that has been designed to fulfill the need for daily climate data over global land areas. The dataset, known as Global Historical Climatology Network (GHCN)-Daily, was developed for a wide variety of potential applications, including climate analysis and monitoring studies that require data at a daily time resolution (e.g., assessments of the frequency of heavy rainfall, heat wave duration, etc.). The dataset contains records from over 80 000 stations in 180 countries and territories, and its processing system produces the official archive for U.S. daily data. Variables commonly include maximum and minimum temperature, total daily precipitation, snowfall, and snow depth; however, about two-thirds of the stations report precipitation only. Quality assurance checks are routinely applied to the full dataset, but the data are not homogenized to account for artifacts associated with the various eras in reporting practice at any particular station (i.e., for changes in systematic bias). Daily updates are provided for many of the station records in GHCN-Daily. The dataset is also regularly reconstructed, usually once per week, from its 20+ data source components, ensuring that the dataset is broadly synchronized with its growing list of constituent sources. The daily updates and weekly reprocessed versions of GHCN-Daily are assigned a unique version number, and the most recent dataset version is provided on the GHCN-Daily website for free public access. Each version of the dataset is also archived at the NOAA/National Climatic Data Center in perpetuity for future retrieval.
Journal Article
COSMOS-Europe: a European network of cosmic-ray neutron soil moisture sensors
Climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns, and reduced snow occurrence. While Europe has suffered from drought events in the last decade unlike ever seen since the beginning of weather recordings, harmonized long-term datasets across the continent are needed to monitor change and support predictions. Here we present soil moisture data from 66 cosmic-ray neutron sensors (CRNSs) in Europe (COSMOS-Europe for short) covering recent drought events. The CRNS sites are distributed across Europe and cover all major land use types and climate zones in Europe. The raw neutron count data from the CRNS stations were provided by 24 research institutions and processed using state-of-the-art methods. The harmonized processing included correction of the raw neutron counts and a harmonized methodology for the conversion into soil moisture based on available in situ information. In addition, the uncertainty estimate is provided with the dataset, information that is particularly useful for remote sensing and modeling applications. This paper presents the current spatiotemporal coverage of CRNS stations in Europe and describes the protocols for data processing from raw measurements to consistent soil moisture products. The data of the presented COSMOS-Europe network open up a manifold of potential applications for environmental research, such as remote sensing data validation, trend analysis, or model assimilation. The dataset could be of particular importance for the analysis of extreme climatic events at the continental scale. Due its timely relevance in the scope of climate change in the recent years, we demonstrate this potential application with a brief analysis on the spatiotemporal soil moisture variability. The dataset, entitled “Dataset of COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors”, is shared via Forschungszentrum Jülich: https://doi.org/10.34731/x9s3-kr48 (Bogena and Ney, 2021).
Journal Article