Statistical Downscaling And Bias Correction For Climate Research
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| Author | : Douglas Maraun |
| Publisher | : Cambridge University Press |
| Total Pages | : 365 |
| Release | : 2018-01-18 |
| Genre | : Mathematics |
| ISBN | : 1107066050 |
A comprehensive and practical guide, providing technical background and user context for researchers, graduate students, practitioners and decision makers. This book presents the main approaches and describes their underlying assumptions, skill and limitations. Guidelines for the application of downscaling and the use of downscaled information in practice complete the volume.
| Author | : Rasmus E. Benestad |
| Publisher | : World Scientific |
| Total Pages | : 228 |
| Release | : 2008 |
| Genre | : Science |
| ISBN | : 9812819126 |
Empirical-statistical downscaling (ESD) is a method for estimating how local climatic variables are affected by large-scale climatic conditions. ESD has been applied to local climate/weather studies for years, but there are few ? if any ? textbooks on the subject. It is also anticipated that ESD will become more important and commonplace in the future, as anthropogenic global warming proceeds. Thus, a textbook on ESD will be important for next-generation climate scientists.
| Author | : Rao Kotamarthi |
| Publisher | : Cambridge University Press |
| Total Pages | : 213 |
| Release | : 2021-02-11 |
| Genre | : Science |
| ISBN | : 1108587062 |
Downscaling is a widely used technique for translating information from large-scale climate models to the spatial and temporal scales needed to assess local and regional climate impacts, vulnerability, risk and resilience. This book is a comprehensive guide to the downscaling techniques used for climate data. A general introduction of the science of climate modeling is followed by a discussion of techniques, models and methodologies used for producing downscaled projections, and the advantages, disadvantages and uncertainties of each. The book provides detailed information on dynamic and statistical downscaling techniques in non-technical language, as well as recommendations for selecting suitable downscaled datasets for different applications. The use of downscaled climate data in national and international assessments is also discussed using global examples. This is a practical guide for graduate students and researchers working on climate impacts and adaptation, as well as for policy makers and practitioners interested in climate risk and resilience.
| Author | : Kazi F Ahmed |
| Publisher | : |
| Total Pages | : |
| Release | : 2011 |
| Genre | : Electronic dissertations |
| ISBN | : |
Global Climate Models (GCMs) are the typical sources of future climate data required for impact assessments of climate change. However, GCM outputs are related to model-related uncertainties and involve a great deal of biases. Bias correction of model outputs is, therefore, necessary before their use in impact studies. The coarse resolution of GCM simulations is another hindrance to their direct use in fine-scale impact analysis of climate change. Although downscaling of GCM outputs can be performed by dynamical downscaling using Regional Climate Models (RCMs), it requires large computational capacity. When daily climate data from multiple GCMs are required to be downscaled, dynamical downscaling may not be a feasible option. Statistical downscaling, in contrast, can be efficiently used to downscale a large number of GCM outputs at a fine temporal and spatial scale. This study performs the bias correction and statistical downscaling of daily maximum and minimum temperature and daily precipitation data from six GCM and four RCM simulations for the northeast United Stated (US). The spatial resolution of the data set is 1/8°x 1/8° and it spans from 2046 to 2065. This fine-scale daily climate data set, which has been created using Bias Correction and Spatial Downscaling (BCSD) approach, can be directly used in regional impact studies for the northeast US. Using both raw and bias corrected daily precipitation data from two GCMs and two RCMs, one extreme precipitation index has been analyzed for the observed climate. The comparison between the results demonstrates that bias correction is important not only for GCM outputs, but also for RCM outputs. When the same analysis has been performed for future climate, bias correction has led to a larger level of agreements among the models in predicting the magnitude and capturing the spatial trend for the extreme precipitation index. Moreover, five extreme climate indices have been analyzed at 1/8° spatial resolution for future climate using the bias corrected and statically downscaled data from multiple GCMs and RCMs. The incorporation of dynamical downscaling as an intermediate step has not led to any considerable changes from the results of statistical downscaling. Statistical downscaling with bias correction has been sufficient to create a fine-scale daily climate data set to be directly used in impact studies. The future means of five extreme climate indices, which have been calculated from GCM and RCM ensembles, have been compared to their observed means. The decrease in total number of frost days because of the future warming will be similar over the entire northeast region. The earlier arrival of spring will lead to an extended growing season and the magnitude of the changes will be larger in the coastal area. The comparison of precipitation extreme indices indicates an increase in the heavy precipitation events in future climate for most of the region.
| Author | : Douglas Maraun |
| Publisher | : Cambridge University Press |
| Total Pages | : 365 |
| Release | : 2018-01-18 |
| Genre | : Science |
| ISBN | : 110834030X |
Statistical downscaling and bias correction are becoming standard tools in climate impact studies. This book provides a comprehensive reference to widely-used approaches, and additionally covers the relevant user context and technical background, as well as a synthesis and guidelines for practitioners. It presents the main approaches including statistical downscaling, bias correction and weather generators, along with their underlying assumptions, skill and limitations. Relevant background information on user needs and observational and climate model uncertainties is complemented by concise introductions to the most important concepts in statistical and dynamical modelling. A substantial part is dedicated to the evaluation of regional climate projections and their value in different user contexts. Detailed guidelines for the application of downscaling and the use of downscaled information in practice complete the volume. Its modular approach makes the book accessible for developers and practitioners, graduate students and experienced researchers, as well as impact modellers and decision makers.
| Author | : Maike Holthuijzen |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2022 |
| Genre | : Climatic changes |
| ISBN | : |
High-resolution, bias-corrected climate data is necessary for climate impact studies and modeling efforts at local scales. General circulation models (GCMs) provide important information about historical and future larger-scale climate trends, but their spatial resolution is too coarse to investigate localized effects of climate processes. Additionally, raw GCM output is characterized by some degree of bias. Two post-processing procedures known as downscaling and bias-correction are typically applied to raw climate model output prior to its use in further modeling applications. Downscaling is the process in which data at a coarse spatial scale is transformed to a fine spatial scale. Bias-correction refers to a collection of methods in which climate model output is adjusted such that its statistical properties (e.g. mean, variance, and potentially higher moments) resemble those of observations in a common climatological period. Bias-correction is a challenge, due to relatively short calibration and long future time periods and potential spatial misalignment issues between griddedclimate model output and observed data. Issues that warrant further research are 1) spatially-coherent bias-correction, 2), processing of extremes, 3) temporally coherent bias-correction, and 4) balancing the bias-correction of future model output with the preservation of the climate change signal. Performing spatially-coherent bias-correction is particularly difficult, as model and observed data must be present in the same location where bias-correction is applied. Depending on the type of observed data used, this may not be the case. Extremes are challenging to represent accurately during bias-correction, because extreme values in both observed and model data are highly variable, limited, and there is greater uncertainty regarding their correction. Finally, very few bias-correction methods explicitly correct temporal dependence structures of model output. However, it is important that the temporal dependence of model data resembles that of observed data, as climate variability is closely linked to temporal dependence. In this body of work, I developed methodological workflows to generate high-resolution climate data products in which 1) bias-correction is carried out in a spatially-coherent manner, and 2) precipitation extremes are accurately represented. I also created a new, two-step bias-correction approach in which the temporal dependence and distributional properties of model output are corrected. This method allows for sensible bias-correction in both historical and future time periods and minimizes distortion to the future climate change signal.
| Author | : Hans von Storch |
| Publisher | : Cambridge University Press |
| Total Pages | : 979 |
| Release | : 2002-02-21 |
| Genre | : Science |
| ISBN | : 1139425099 |
Climatology is, to a large degree, the study of the statistics of our climate. The powerful tools of mathematical statistics therefore find wide application in climatological research. The purpose of this book is to help the climatologist understand the basic precepts of the statistician's art and to provide some of the background needed to apply statistical methodology correctly and usefully. The book is self contained: introductory material, standard advanced techniques, and the specialised techniques used specifically by climatologists are all contained within this one source. There are a wealth of real-world examples drawn from the climate literature to demonstrate the need, power and pitfalls of statistical analysis in climate research. Suitable for graduate courses on statistics for climatic, atmospheric and oceanic science, this book will also be valuable as a reference source for researchers in climatology, meteorology, atmospheric science, and oceanography.
| Author | : Nguyen Trung Viet |
| Publisher | : Springer Nature |
| Total Pages | : 1483 |
| Release | : 2019-09-25 |
| Genre | : Science |
| ISBN | : 9811502919 |
This book presents selected articles from the International Conference on Asian and Pacific Coasts (APAC 2019), an event intended to promote academic and technical exchange on coastal related studies, including coastal engineering and coastal environmental problems, among Asian and Pacific countries/regions. APAC is jointly supported by the Chinese Ocean Engineering Society (COES), the Coastal Engineering Committee of the Japan Society of Civil Engineers (JSCE), and the Korean Society of Coastal and Ocean Engineers (KSCOE). APAC is jointly supported by the Chinese Ocean Engineering Society (COES), the Coastal Engineering Committee of the Japan Society of Civil Engineers (JSCE), and the Korean Society of Coastal and Ocean Engineers (KSCOE).
| Author | : The BACC II Author Team |
| Publisher | : Springer |
| Total Pages | : 515 |
| Release | : 2015-04-03 |
| Genre | : Science |
| ISBN | : 3319160060 |
This book is an update of the first BACC assessment, published in 2008. It offers new and updated scientific findings in regional climate research for the Baltic Sea basin. These include climate changes since the last glaciation (approx. 12,000 years ago), changes in the recent past (the last 200 years), climate projections up until 2100 using state-of-the-art regional climate models and an assessment of climate-change impacts on terrestrial, freshwater and marine ecosystems. There are dedicated new chapters on sea-level rise, coastal erosion and impacts on urban areas. A new set of chapters deals with possible causes of regional climate change along with the global effects of increased greenhouse gas concentrations, namely atmospheric aerosols and land-cover change. The evidence collected and presented in this book shows that the regional climate has already started to change and this is expected to continue. Projections of potential future climates show that the region will probably become considerably warmer and wetter in some parts, but dryer in others. Terrestrial and aquatic ecosystems have already shown adjustments to increased temperatures and are expected to undergo further changes in the near future. The BACC II Author Team consists of 141 scientists from 12 countries, covering various disciplines related to climate research and related impacts. BACC II is a project of the Baltic Earth research network and contributes to the World Climate Research Programme.
| Author | : Daniel S. Wilks |
| Publisher | : Academic Press |
| Total Pages | : 698 |
| Release | : 2011-05-20 |
| Genre | : Mathematics |
| ISBN | : 0123850223 |
This revised and expanded text explains the latest statistical methods that are being used to describe, analyze, test, and forecast atmospheric data. It features numerous worked examples, illustrations, equations, and exercises with separate solutions. The book will help advanced students and professionals understand and communicate what their data sets have to say, and make sense of the scientific literature in meteorology, climatology, and related disciplines.
