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Statistical Downscaling and Bias Correction for Climate Research

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TLDR
In this article, the main approaches including statistical downscaling, bias correction, and weather generators, along with their underlying assumptions, skill and limitations are presented, together with user context and technical background.
Abstract
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.

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The Self-Organizing Map

TL;DR: An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article, where the authors present an overview of their work.
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Mountain Weather and Climate

TL;DR: In this paper, the authors present a review of mountain bioclimatology and changes in mountain climates, and discuss the role of orography in the evolution of mountain climate.

ENSO representation in climate models: from CMIP3 to CMIP5

TL;DR: In this article, the ability of CMIP3 and CMIP5 coupled ocean-atmosphere general circulation models (CGCMs) to simulate the tropical Pacific mean state and El Nino-Southern Oscillation (ENSO) was analyzed.

The role of the stratosphere in the European climate response to El Niño

TL;DR: In this article, the authors use a general circulation model of the atmosphere, extended into the upper atmospheric layers, to provide end-to-end evidence for a global teleconnection pathway from the Pacific region to Europe via the stratosphere.
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Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change.

TL;DR: Analyzing co-occurring high sea level and heavy precipitation in Europe, it is shown that the Mediterranean coasts are experiencing the highest CF probability in the present, however, future climate projections show emerging high CF probability along parts of the northern European coast.
References
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Estimating the dimension of a model

TL;DR: In this paper, the problem of selecting one of a number of models of different dimensions is treated by finding its Bayes solution, and evaluating the leading terms of its asymptotic expansion.
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The NCEP/NCAR 40-Year Reanalysis Project

TL;DR: The NCEP/NCAR 40-yr reanalysis uses a frozen state-of-the-art global data assimilation system and a database as complete as possible, except that the horizontal resolution is T62 (about 210 km) as discussed by the authors.
Book

Generalized Linear Models

TL;DR: In this paper, a generalization of the analysis of variance is given for these models using log- likelihoods, illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables), and gamma (variance components).
Book

Principal Component Analysis

TL;DR: In this article, the authors present a graphical representation of data using Principal Component Analysis (PCA) for time series and other non-independent data, as well as a generalization and adaptation of principal component analysis.
Related Papers (5)