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312 results in Statistics for environmental sciences

Page 9 of 16

  • Authors’ preface

  • Kym Anderson, University of Adelaide, Signe Nelgen, University of Adelaide, Vicente Pinilla, University of Zaragoza
  • Book:
    Global Wine Markets, 1860 to 2016
    Published by:
    The University of Adelaide Press
    Published online:
    27 July 2018
    Print publication:
    31 January 2018, pp xxxi-xxxii

  • Summary

    This latest edition of our Statistical Compendium is the tenth version to be prepared since 1998. It has been compiled by the Wine Economics Research Centre of the University of Adelaide. It updates data to 2016 but also revises past data, and it expands on earlier editions in a number of ways. For example, we include many more tables to show wine's consumption relative to that of other beverages, from A.J. Holmes and K. Anderson, Annual Database of National Beverage Consumption Volumes and Expenditures, 1950 to 2015, at www.adelaide.edu.au/wine-econ/databases/alcohol-consumption, July 2017. As well, an extra century of historical data are included as ten-year averages, drawing on a new annual database that is freely available as K. Anderson and V. Pinilla (with the assistance of A.J. Holmes), Annual Database of Global Wine Markets, 1835 to 2016, Wine Economics Research Centre, at www.adelaide.edu.au/wine-econ/databases/global-wine-history, November 2017. This edition is thus a major improvement over previous editions, the pre- 2009 ones of which were compiled by the Centre for International Economic Studies (CIES) at the University of Adelaide, and by the Centre of Policy Studies (CoPS) at Monash University, as follows:

  • • Anderson, K. and S. Nelgen (2011), Global Wine Markets, 1961 to 2009: A Statistical Compendium, Adelaide: University of Adelaide Press.

  • • Rothfield, J. and G. Wittwer (2007), The Global Wine Statistical Compendium, 1961 to 2006, Adelaide: Australian Wine and Brandy Corporation for CIES and CoPS.

  • • Wittwer, G. and J. Rothfield (2006), The Global Wine Statistical Compendium, 1961 to 2005, Adelaide: Australian Wine and Brandy Corporation for CIES and CoPS.

  • • Wittwer, G. and J. Rothfield (2005), The Global Wine Statistical Compendium, 1961 to 2004, Adelaide: Australian Wine and Brandy Corporation for CIES and CoPS.

  • • Wittwer, G. and Anderson, K. (2004), The Global Wine Statistical Compendium, 1961 to 2003, Adelaide: Australian Wine and Brandy Corporation for CIES and CoPS. Re-published in 2009 by the University of Adelaide Press.

  • • Anderson, K. and Norman, D. (2003), Global Wine Production, Consumption and Trade, 1961 to 2001: A Statistical Compendium, Adelaide: Centre for International Economic Studies.

  • 6 - Mathematical and Statistical Methods

  • from Part I - Background and Fundamentals
  • Douglas Maraun, Karl-Franzens-Universität Graz, Austria, Martin Widmann, University of Birmingham
  • Book:
    Statistical Downscaling and Bias Correction for Climate Research
    Published online:
    27 December 2017
    Print publication:
    18 January 2018, pp 41-86

  • Summary

    Statistical downscaling relies on a wide range of statistical concepts. This chapter presents a concise overview of these concepts. We attempt to explain the different issues as simply as possible but with the necessary detail and background to successfully implement a broad range of statistical downscaling methods. Section 6.1 gives an overview of random variables and probability, including the most relevant probability distributions, and some very basic introduction to the modelling of extreme values. Parameter estimation, that is, basis for calibrating statistical models, is introduced in Section 6.2. Here we focus on the widely used concept of maximum likelihood but give a brief overview of Bayesian estimators as well. Regression models, the backbone of many statistical downscaling models, are presented in Section 6.3, including a discussion of statistical model selection and evaluation. Weather generators are specific stochastic processes – these are laid out briefly in Section 6.4. Finally, predictors are often post-processed based on principal component analysis, and some statistical downscaling methods employ canonical correlation analysis. These pattern methods are introduced in Section 6.5. In the following, we will write scalars in normal font, while vectors and matrices will be written in bold font. We assume that readers have basic knowledge in significance testing – it will not be introduced in this chapter but only occurs in the context of model selection. In fact, significance testing is very useful in many contexts, but it is not essential to implement a statistical downscaling method. Also, even though relevant for some predictor transformations, we do not discuss clustering algorithms.

    Random Variables and Probability Distributions

    Events and Random Variables

    In synoptic meteorology, the atmospheric circulation is often characterised by discrete weather types. At each day, the actual circulation might randomly assume one of these types. In statistics, the space of all possible weather types is called sample space or event space S. For the considered weather types, the sample space could be, for example, S = (W,NW,N,NE, E, SE, S,SW). Subsets of the event space, for example, SE, or (NW, N, NE) (i.e. all Northerly weather types) are called events.

  • 11 - Perfect Prognosis

  • from Part II - Statistical Downscaling Concepts and Methods
  • Douglas Maraun, Karl-Franzens-Universität Graz, Austria, Martin Widmann, University of Birmingham
  • Book:
    Statistical Downscaling and Bias Correction for Climate Research
    Published online:
    27 December 2017
    Print publication:
    18 January 2018, pp 141-169

  • Summary

    State-of-the-art GCMs do not simulate regional-scale climate processes and also have a limited ability to represent resolved meso-scale processes. But GCMs often do have reasonable skill simulating synoptic-scale variability, such as the passage of cyclones and anticyclones. Perfect prognosis (PP) statistical downscaling exploits this skill to simulate regional climate. The approach builds upon synoptic meteorology and climatology, which analyses the relationship between synoptic scale and regional weather (Hewitson and Crane 1996).

    DEFINITION 11.1 In PP statistical downscaling, a statistical model that links largescale predictors to local-scale predictands is calibrated to observed data. The statistical model is then applied to predictors simulated by climate models.

    Comprehensive reviews about PP statistical downscaling methods for climate change studies have been published by Hewitson and Crane (1996), Wilby and Wigley (1997), Zorita and von Storch (1997), Fowler et al. (2007), Maraun et al. (2010a) and Schoof (2013); region-specific reviews are, for example, Hanssen-Bauer et al. (2005) for Scandinavia and Jacobeit et al. (2014) for the Mediterranean.

    In the following, we will first introduce the assumptions underlying the PP approach (Section 11.1). The most widely used PP methods will be presented in Section 11.2. The skill to apply a PP method in a specific context is very much determined by the structure of the method. These issues will be discussed in depth in Section 11.3. Issues related to the use of different predictand variables, in particular impact-relevant predictands, will be summarised in Section 11.4. Crucial for any PP method to work sensibly under climate is a careful predictor choice. Relevant assumptions and potential predictors for temperature and precipitation will be discussed in detail in Section 11.5. Section 11.6 lays out the statistical construction of a PP method, with a focus on climate change applications. A cookbook is finally given in Section 11.7. Several advanced PP methods condition weather generators on large-scale atmospheric predictors. Given the peculiarities of these method types, we will discuss them individually in Chapter 13. The following discussions on assumptions and predictor requirements, however, apply equally to these methods.

  • 2 - Regional Climate

  • from Part I - Background and Fundamentals
  • Douglas Maraun, Karl-Franzens-Universität Graz, Austria, Martin Widmann, University of Birmingham
  • Book:
    Statistical Downscaling and Bias Correction for Climate Research
    Published online:
    27 December 2017
    Print publication:
    18 January 2018, pp 9-15

  • Summary

    Before discussing regional climate modelling in more detail, it is sensible to briefly sketch regional climate itself, and the factors controlling regional climate and climate change.We use the term rather loosely, spanning a range of scales below the continental and synoptic scale. Some may prefer to distinguish between regional and local climate – we decided to use only one term – which scales we refer to should become clear from the context. In fact, we will discuss that it is essential to define the relevant scales for any user case individually. In some situations, a region might be a whole country, in other situations a district or even just a specific valley.

    Regional climate is determined by a vast number of climatic processes spanning global to local scales. To successfully model regional climate change, it is essential to successfully model the processes relevant for the specific application. We will therefore sketch these processes in the following, considering the Alps as a showcase.

    Large- and Planetary-Scale Processes

    The global temperature field is to a first order determined by the influence of latitude and land–sea distribution on the radiative balance, and the different thermal capacity of land and ocean. The Alps are located in the mid-latitudes, in a temperate climate (Figure 2.1, left). The mid-latitudes are a region with a strong meridional temperature gradient and high baroclinic instability, which controls the position of the jet stream and fuels the North Atlantic storm track (Hoskins and Valdes 1990, Lynch and Cassado 2006). Jet streaks, regions of acceleration and divergence in the jet stream, control the genesis of cyclones. The upper-level winds also steer the path of cyclones. Conversely, the passage of cyclones along the storm track drives the jet stream, intricately linking the two phenomena (Woollings 2010). The Alps are located just south of the climatological mean position of the polar front and polar jet in both summer and winter. Cyclones and anticyclones advect different air masses to central Europe. For instance, arctic continental air brings cold and dry air, tropical maritime air is warm and moist, tropical continental air is hot and dry. The jet stream itself follows planetary-scale Rossby waves meandering around the globe.

  • 17 - A Regional Modelling Debate

  • from Part III - Downscaling in Practice and Outlook
  • Douglas Maraun, Karl-Franzens-Universität Graz, Austria, Martin Widmann, University of Birmingham
  • Book:
    Statistical Downscaling and Bias Correction for Climate Research
    Published online:
    27 December 2017
    Print publication:
    18 January 2018, pp 263-268

  • Summary

    Along with the increasing amount of research in climate downscaling, a critical debate about the limitations and suitability of downscaling has arisen. The debate revolves around essentially two questions: first, are climate models skillful to provide userrelevant information about climate change? Some researchers argue that GCMs cannot provide skillful input to downscaling, and downscaling itself cannot add value to GCM simulations. Second, is downscaling necessary, or could it be avoided by bottomup approaches to decision making? We will review these two discussions and critically comment on the issues raised.

    Are Climate Models Fit for Purpose?

    Kundzewicz and Stakhiv (2010) discuss whether climate models are “ready for prime time” in climate impact research. They argue that GCMs have originally been developed to advise mitigation policies but are more and more applied also to inform adaptation decisions. Whereas for the former purpose, a broad representation of global climate change is sufficient, the latter requires accurate projections of regional changes, in particular of highly uncertain processes such as the hydrological cycle. The authors argue that climate models are not (yet) skillful for direct application in adaptation planning. This view has been shared by other authors (e.g. Pielke and Wilby 2012); it is based on a series of claims (Kundzewicz and Stakhiv 2010, Pielke and Wilby 2012): first, GCMs do not skillfully include all first order forcings and feedbacks; second, GCMs do not skillfully simulate relevant regional processes such as El Niño; third, GCMs do not reproduce observed trends; and fourth, downscaling cannot improve GCM simulations. The first three claims are related to GCM skill, the fourth to downscaling skill and added value. In the following we will discuss these issues.

    Skill of GCMs

    Pielke et al. (2009) argue that current GCM projections do not consider all first-order forcings that determine future climate, such as the effect of aerosols or changes in land use and land cover. Over recent years, these aspects have more and more been in the focus of climate research and featured prominently in the most recent IPCC assessment report (Boucher et al. 2013, Myhre et al. 2013).

Page 9 of 16