Quantifying the Air Pollution Exposure Consequences Of" Distributed Electricity Generation

Total Page:16

File Type:pdf, Size:1020Kb

Quantifying the Air Pollution Exposure Consequences Of Energy Development and Technology 005R "Quantifying the Air Pollution Exposure Consequences of" Distributed Electricity Generation Garvin A. Heath, Patrick W. Granvold, Abigail S. Hoats and William W. Nazaroff May 2005 Revised Version: November 2005 This paper is part of the University of California Energy Institute's (UCEI) Energy Policy and Economics Working Paper Series. UCEI is a multi-campus research unit of the University of California located on the Berkeley campus. UC Energy Institute 2547 Channing Way Berkeley, California 94720-5180 www.ucei.org This report was issued in order to disseminate results of and information about energy research at the University of California campuses. Any conclusions or opinions expressed are those of the authors and not necessarily those of the Regents of the University of California, the University of California Energy Institute or the sponsors of the research. Readers with further interest in or questions about the subject matter of the report are encouraged to contact the authors directly. QUANTIFYING THE AIR POLLUTION EXPOSURE CONSEQUENCES OF DISTRIBUTED ELECTRICITY GENERATION Final Report Grant No. 07427 Prepared for the University of California Energy Institute Prepared by Garvin A. Heath, Patrick W. Granvold, Abigail S. Hoats and William W Nazaroff Department of Civil and Environmental Engineering University of California Berkeley, CA 94720-1710 Original version: May 2005 Revised version: November 2005 Note Concerning Revision Subsequent to the original version of this report being made publicly available, the authors discovered an error in the model inputs of population density. While none of the qualitative results and conclusions were affected by this error, the absolute value of intake fractions and intake-to-generation ratio were overestimated by a constant factor of 2.59 (representing the conversion from square kilometers to square miles). The error has been corrected in this revised version. Contents Note Concerning Revision................................................................................................... i Contents .............................................................................................................................iii Figures................................................................................................................................vi Tables................................................................................................................................. ix Abstract.............................................................................................................................. xi I. Introduction................................................................................................................. 1 II. Methods..................................................................................................................... 11 II.A Case Selection and Background Information ................................................... 12 II.A.1 Case Selection Criteria.............................................................................. 12 II.A.1.a Existing Units.................................................................................... 12 II.A.1.b Hypothetical DG Cases..................................................................... 16 II.A.2 Location and Background Information of Cases ...................................... 16 II.A.2.a Existing Units.................................................................................... 16 II.A.2.b Hypothetical DG (< 1 MW) Cases ................................................... 26 II.B Pollutant Selection ............................................................................................ 30 II.C Modeling Tools and Input Data ........................................................................ 31 II.C.1 Gaussian Plume Model ............................................................................. 31 II.C.1.a Gaussian Plume Model for Conserved Pollutants ............................ 31 II.C.1.b Gaussian Plume Model for Decaying Pollutants .............................. 35 II.C.2 Meteorological Parameters ....................................................................... 37 II.C.2.a Mixing Height................................................................................... 37 II.C.2.b Wind Speed and Direction................................................................ 40 II.C.2.c Atmospheric Stability Class.............................................................. 47 II.C.3 Population Parameters .............................................................................. 47 II.C.4 Modeling Designations and Data Inputs for Existing Units and Hypothetical DG Cases............................................................................. 48 II.C.4.a Modeling Designations ..................................................................... 48 II.C.4.b Data Inputs........................................................................................ 58 II.C.5 Pollutant Data............................................................................................ 71 II.D Modeled Parameters.......................................................................................... 73 II.D.1 Intake Fraction .......................................................................................... 73 II.D.1.a Intake Fraction for Conserved Pollutants ......................................... 74 II.D.1.b Intake Fraction for a Decaying Pollutant.......................................... 75 II.D.1.c Intake Fraction Model....................................................................... 76 II.D.2 Intake-to-Generation Ratio ....................................................................... 76 III. Results and Discussion ......................................................................................... 77 III.A Intake Fraction Results ..................................................................................... 77 III.A.1 Conserved Pollutants Intake Fractions...................................................... 77 III.A.1.a Existing Units.................................................................................... 77 III.A.1.b Hypothetical DG Cases..................................................................... 83 III.A.1.c Comparisons Between Existing Units and Hypothetical DG Cases. 88 III.A.2 Results for Decaying Pollutant ................................................................. 93 iii III.A.3 Comparison to Previous Research ............................................................ 96 III.B Intake-to-Generation Ratio Results................................................................... 99 III.B.1 Existing Unit IGRs.................................................................................... 99 III.B.2 IGRs for Small-Scale DG Technologies (< 1 MW) and BACT-Controlled Existing Units Compared to the Existing Units...................................... 105 III.C Equalizing Exposure Burdens......................................................................... 108 III.D Summary of Results........................................................................................ 111 IV. Conclusions......................................................................................................... 119 V. Recommendations............................................................................................... 120 VI. Acknowledgements............................................................................................. 123 VII. References........................................................................................................... 124 VIII. Glossary of Terms, Abbreviations, Units of Measure and Symbols................... 132 Appendix A: Procedures for Developing Typical Mixing Height Years ....................... 140 A.1 Introduction......................................................................................................... 140 A.1.1 Input Data................................................................................................... 140 A.1.1.1 Revised Typical Meteorological Years (TMY2s)................................... 141 A.1.1.2 SAMSON Data ....................................................................................... 141 A.1.1.3 Radiosonde Data ..................................................................................... 141 A.1.2 Meteorological Preprocessors.................................................................... 142 A.1.2.1 Mixing Heights Program ........................................................................ 142 A.1.2.2 PCRAMMET .......................................................................................... 142 A.2 Methodology....................................................................................................... 142 A.2.1 Input Data................................................................................................... 143 A.2.1.1 Surface and Radiosonde Data Inclusion Criteria.................................... 143 A.2.1.2 Month/Year Pairings............................................................................... 144 A.2.1.3 Modifications Made to SAMSON Data.................................................. 144 A.2.1.4 Modifications Made to Radiosonde Data ............................................... 145 A.2.2 Candidate Months .....................................................................................
Recommended publications
  • Technical Implementation and Success Factors for a Global Solar and Wind Atlas Carsten Hoyer-Klick
    The Global Atlas for Solar and Wind Energy Carsten Hoyer-Klick Folie 1 CEM/IRENA Global Atlas for Solar and Wind Energy > Carsten Hoyer-Klick > IRENA End User Workshop Jan. 13th 2012, Abu Dhabi Getting Renewable Energy to Work Technology data and learning Available Resources Resource mapping Socio-economic Which and policy data Framework Economic + Political technologies Technical and are feasible? economical Potentials Setting the right the Setting How can RE Technology contribute to the deployment scenarios Best practices energy system? How to get them into the Strategies for market? Where to start? market development Legislation, incentives Political and financial Instruments RE-Markets Slide 2 CEM/IRENA Global Atlas for Solar and Wind Energy > Carsten Hoyer-Klick > IRENA End User Workshop Jan. 13th 2012, Abu Dhabi Project Development for Renewable Energy Systems Pre feasibility Resources Finding suitable (Atlas) sites with high The Atlas should Feasibility resolution maps few few data no no market Project developmentsupport the first steps and economic Engineering evaluations 1200 in potential assessment, ground 1000 satellite 800 Resources 600 W/m² Detailed 400 policy development200 0 13 14 15 16 17 18 day in march, 2001 (time series) and project pre feasibility, engineering with site Engineering specific data with high Construction temporal resolution as input to Commissioning simulation software data and servicesavailable Operation existing existing commerical market Slide 3 CEM/IRENA Global Atlas for Solar and Wind Energy > Carsten
    [Show full text]
  • The Nexus Solutions Tool (NEST): an Open Platform for Optimizing Multi-Scale Energy-Water-Land System Transformations
    https://doi.org/10.5194/gmd-2019-134 Preprint. Discussion started: 15 July 2019 c Author(s) 2019. CC BY 4.0 License. The Nexus Solutions Tool (NEST): An open platform for optimizing multi-scale energy-water-land system transformations Adriano Vinca1,2, Simon Parkinson1,2, Edward Byers1, Peter Burek1, Zarrar Khan3, Volker Krey1,4, Fabio A. Diuana5,1, Yaoping Wang1, Ansir Ilyas6, Alexandre C. Köberle7,5, Iain Staffell8, Stefan Pfenninger9, Abubakr Muhammad6, Andrew Rowe2, Roberto Schaeffer5, Narasimha D. Rao10,1, Yoshihide Wada1,11, Ned Djilali2, and Keywan Riahi1,12 1International Institute for Applied Systems Analysis, Austria 2Institute for Integrated Energy Systems, University of Victoria, Canada 3Joint Global Change Research Institute, Pacific Northwest National Laboratory, United States 4Dept. of Energy & Process Engineering, Norwegian University of Science and Technology, Norway 5Energy Planning Program, Federal University of Rio de Janeiro, Brazil 6Center for Water Informatics & Technology, Lahore University of Management Sciences, Pakistan 7Grantham Institute, Faculty of Natural Sciences, Imperial College London, United Kingdom 8Centre for Environmental Policy, Faculty of Natural Sciences, Imperial College London, United Kingdom 9 Dept. of Environmental Systems Science, ETH Zurich, Switzerland 10School of Forestry and Environmental Studies, Yale University, United States 11Department of Physical Geography, Faculty of Geosciences, Utrecht University, The Netherlands 12Institute for Thermal Engineering, TU Graz, Austria Correspondence: Adriano Vinca ([email protected]) Abstract. The energy-water-land nexus represents a critical leverage future policies must draw upon to reduce trade-offs be- tween sustainable development objectives. Yet, existing long-term planning tools do not provide the scope or level of integration across the nexus to unravel important development constraints.
    [Show full text]
  • The Water-Energy Nexus: Challenges and Opportunities Overview
    U.S. Department of Energy The Water-Energy Nexus: Challenges and Opportunities JUNE 2014 THIS PAGE INTENTIONALLY BLANK Table of Contents Foreword ................................................................................................................................................................... i Acknowledgements ............................................................................................................................................. iii Executive Summary.............................................................................................................................................. v Chapter 1. Introduction ...................................................................................................................................... 1 1.1 Background ................................................................................................................................................. 1 1.2 DOE’s Motivation and Role .................................................................................................................... 3 1.3 The DOE Approach ................................................................................................................................... 4 1.4 Opportunities ............................................................................................................................................. 4 References ..........................................................................................................................................................
    [Show full text]
  • The March 10, 2020 Council Packet May Be Viewed by Going to the Town of Frisco Website
    THE MARCH 10, 2020 COUNCIL PACKET MAY BE VIEWED BY GOING TO THE TOWN OF FRISCO WEBSITE. RECORD OF PROCEEDINGS WORK SESSION MEETING AGENDA OF THE TOWN COUNCIL OF THE TOWN OF FRISCO MARCH 10, 2020 5:00 PM Agenda Item #1: Excavation Code Amendment Discussion Agenda Item #2: Fuel Discussion Agenda Item #3: Remodel Design of Police Department Draft RECORD OF PROCEEDINGS REGULAR MEETING AGENDA OF THE TOWN COUNCIL OF THE TOWN OF FRISCO MARCH 10, 2020 7:00PM STARTING TIMES INDICATED FOR AGENDA ITEMS ARE ESTIMATES ONLY AND MAY CHANGE Call to Order: Gary Wilkinson, Mayor Roll Call: Gary Wilkinson, Jessica Burley, Daniel Fallon, Rick Ihnken, Hunter Mortensen, Deborah Shaner, and Melissa Sherburne Public Comments: Citizens making comments during Public Comments or Public Hearings should state their names and addresses for the record, be topic-specific, and limit comments to no longer than three minutes. NO COUNCIL ACTION IS TAKEN ON PUBLIC COMMENTS. COUNCIL WILL TAKE ALL COMMENTS UNDER ADVISEMENT AND IF A COUNCIL RESPONSE IS APPROPRIATE THE INDIVIDUAL MAKING THE COMMENT WILL RECEIVE A FORMAL RESPONSE FROM THE TOWN AT A LATER DATE. Mayor and Council Comments: Staff Updates: Presentation: Frisco’s Finest Award – Jeff Berino Consent Agenda: • Minutes February 25, 2020 Meeting • Warrant List • Purchasing Cards New Business: Agenda Item #1: First Reading Ordinance 20-03, an Ordinance Amending Chapters 65 and 180 of the Code of Ordinances of the Town of Frisco, Concerning Building Construction and Housing Standards, and the Unified Development Code, Respectively,
    [Show full text]
  • Opportunities for Energy Efficient Computing: a Study of Inexact General Purpose Processors for High-Performance and Big-Data Applications
    Opportunities for Energy Efficient Computing: A Study of Inexact General Purpose Processors for High-Performance and Big-data Applications Peter Düben Jeremy Schlachter AOPP, University of Oxford EPFL Oxford, United Kingdom Lausanne, Switzerland [email protected] [email protected] Parishkrati, Sreelatha Yenugula, John Augustine Christian Enz Indian Institute of Technology Madras EPFL India Lausanne, Switzerland [email protected], [email protected] [email protected], [email protected] K. Palem T. N. Palmer Electrical Engineering and Computer Science AOPP, University of Oxford Rice University, Houston, USA Oxford, United Kingdom [email protected] [email protected] Abstract—In this paper, we demonstrate that disproportionate hardware artifacts such as adders, multiplier or FFT engines gains are possible through a simple devise for injecting inexactness [22], [23], [20], [21]. For additional references on inexact or approximation into the hardware architecture of a computing hardware, architectures and related topics, please also see [1], system with a general purpose template including a complete [32], [12], [39], [35], [2]. More recently, this work has also memory hierarchy. The focus of the study is on energy savings been extended to evaluations of large-scale applications—in possible through this approach in the context of large and challenging applications. We choose two such from different ends of the context of our own group, climate modeling and weather the computing spectrum—the IGCM model for weather and climate prediction served as the driving example in which the floating modeling which embodies significant features of a high-performance point operations were rendered inexact [9].
    [Show full text]
  • Building Performance Analysis Considering Climate Change
    Building performance analysis considering climate change Yiyu Chen1, Karen Kensek1, Joon-Ho Choi1, Marc Schiler1 1University of Southern California, Los Angeles, CA ABSTRACT: Climate change will significantly influence building performance in the future both through differences in energy consumption (perhaps drastically in certain climate zones) and by changing the thermal level of comfort that occupants experience. A building that meets the current energy consumption standard has the potential to become energy inefficient in the future, and well-meaning designers might be applying passive strategies for current conditions without understanding what impact their choices have under different climate change scenarios for their location. By analyzing a case study building’s thermal performance, specifically lifecycle energy use, for multiple climate change scenarios and in different climate zones, it is possible to inspect the resilience of passive design strategies over time. Varying the solar heat gain coefficient (SGHC) was the first strategy tested. To estimate the building’s future performance, three projected future climate conditions were created (for low, moderate, and severe climate change) in three climate zones for three different time periods (2020s, 2050s, and 2080s). The first step was to create new projected weather files. They were calculated from a world climate change weather file generation tool that was developed at the University of Southampton. Then the weather files were used in EnergyPlus to determine the energy consumption. Aggregated multiple runs provided lifecycle energy use for each of the three locations. Then the SHGC values were varied to determine which initial values provided the best long- term result. Although currently only SHGC was tested, this research provides a methodology for architects and engineers during the early stage of design that they can use to avoid the detrimental consequences that might be caused by climate change over the lifecycle of the building.
    [Show full text]
  • Energy Systems Modeling for Twenty-First Century Energy Challenges
    Energy systems modeling for twenty-first century energy challenges Stefan Pfenninger1,* Adam Hawkes2 James Keirstead1 8 January 2014 (author version of accepted paper) * Corresponding author: [email protected]. 1 Department of Civil and Environmental Engineering, Imperial College London 2 Centre for Environmental Policy, Imperial College London Published version: Renewable and Sustainable Energy Reviews, 33, pp. 74-86. DOI: 10.1016/j.rser.2014.02.003 Abstract Energy systems models are important methods used to generate a range of insight and analysis on the supply and demand of energy. Developed over the second half of the twentieth century, they are now seeing increased relevance in the face of stringent climate policy, energy security and economic development concerns, and increasing challenges due to the changing nature of the twenty-first century energy system. In this paper, we look particularly at models relevant to national and international energy policy, grouping them into four categories: energy systems optimization models, energy systems simulation models, power systems and electricity market models, and qualitative and mixed-methods scenarios. We examine four challenges they face and the efforts being taken to address them: (1) resolving time and space, (2) balancing uncertainty and transparency, (3) addressing the growing complexity of the energy system, and (4) integrating human behavior and social risks and opportunities. In discussing these challenges, we present possible avenues for future research and make recommendations to ensure the continued relevance for energy systems models as important sources of information for policy-making. Keywords: energy systems modeling; energy policy 1 Introduction Hamming (1962) argued that the purpose of computing is insight, not numbers.
    [Show full text]
  • Renewable Energy and Efficiency Modeling Analysis Partnership REMI Regional Economic Models Inc
    Technical Report Renewable Energy and Efficiency NREL/TP-6A2-45656 Modeling Analysis Partnership (REMAP): September 2009 An Analysis of How Different Energy Models Addressed a Common High Renewable Energy Penetration Scenario in 2025 Nate Blair, Thomas Jenkin, James Milford, Walter Short, Patrick Sullivan National Renewable Energy Laboratory David Evans, Elliot Lieberman Environmental Protection Agency Gary Goldstein, Evelyn Wright International Resources Group Kamala R. Jayaraman, Boddu Venkatesh ICF International Gary Kleiman Northeast States for Coordinated Air Use Management Christopher Namovicz, Bob Smith Energy Information Administration Karen Palmer Resources for the Future Ryan Wiser Lawrence Berkeley National Laboratory Frances Wood OnLocation Technical Report Renewable Energy and Efficiency NREL/TP-6A2-45656 Modeling Analysis Partnership (REMAP): September 2009 An Analysis of How Different Energy Models Addressed a Common High Renewable Energy Penetration Scenario in 2025 Nate Blair, Thomas Jenkin, James Milford, Walter Short, Patrick Sullivan National Renewable Energy Laboratory David Evans, Elliot Lieberman Environmental Protection Agency Gary Goldstein, Evelyn Wright International Resources Group Kamala R. Jayaraman, Boddu Venkatesh ICF International Gary Kleiman Northeast States for Coordinated Air Use Management Christopher Namovicz, Bob Smith Energy Information Administration Karen Palmer Resources for the Future Ryan Wiser Lawrence Berkeley National Laboratory Frances Wood OnLocation Prepared under Task No. SAO1.1005 National Renewable Energy Laboratory 1617 Cole Boulevard, Golden, Colorado 80401-3393 303-275-3000 • www.nrel.gov NREL is a national laboratory of the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Operated by the Alliance for Sustainable Energy, LLC Contract No. DE-AC36-08-GO28308 NOTICE This report was prepared as an account of work sponsored by an agency of the United States government.
    [Show full text]
  • Innovations in Building Energy Modeling
    Innovations in Building Energy Modeling Research and Development Opportunities Report for Emerging Technologies November 2020 Innovations in Building Energy Modeling: Research and Development Opportunities Report Disclaimer This work was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, nor any of their contractors, subcontractors or their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or any third party’s use or the results of such use of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof or its contractors or subcontractors. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof, its contractors or subcontractors. ii Innovations in Building Energy Modeling: Research and Development Opportunities Report Acknowledgments Prepared by Amir Roth, Ph.D. Special thanks to the following individuals and groups. Robert Zogg and Emily Cross of Guidehouse (then Navigant Consulting) conducted the interviews, organized the stakeholder workshops, and wrote the first DRAFT BEM Roadmap. Janet Reyna of NREL (then an ORISE Fellow at BTO) provided extensive feedback on the initial DRAFT BEM Roadmap and helped create the organization for the subsequent DRAFT BEM Research and Development Opportunities (RDO) document.
    [Show full text]
  • UNEP Guide for Energy Efficiency and Renewable Energy Laws
    UNEP Guide for Energy Efficiency and Renewable Energy Laws United Nations Environment Programme, Pace University Law School Energy and Climate Center UNEP United Nations Environment Programme i Published by the United Nations Environment Programme (UN Environment) September 2016 UNEP Guide for Energy Efficiency and Renewable Energy Laws – English ISBN No: 978-92-807-3609-0 Job No: DEL/2045/NA Reproduction This publication may be reproduced in whole or in part and in any form for educational and non profit pur- poses without special permission from the copyright holder, provided that acknowledgement of the source is made. UN Environment Programme will appreciate receiving a copy of any publication that uses this material as a source. No use of this publication can be made for the resale or for any other commercial purpose whatsoever without the prior permission in writing of UN Environment Programme. Application for such permission with a statement of purpose of the reproduction should be addressed to the Communications Division, of the UN Environment Programme, P.O BOX 30552, Nairobi 00100 Kenya. The use of information from this document for publicity of advertising is not permitted. Disclaimer The contents and views expressed in this publication do not necessarily reflect the views or policies of the UN Environment Programme or its member states. The designations employed and the presentation of materials in this publication do not imply the expression of any opinion whatsoever on the part of UN Environ- ment concerning the legal status of any country, territory or its authorities, or concerning the delimitation of its frontiers and boundaries.
    [Show full text]
  • Cooling Energy Implications of Occupant Factor in Buildings Under Climate Change
    sustainability Article Cooling Energy Implications of Occupant Factor in Buildings under Climate Change Ji Hyun Lim and Geun Young Yun * ID Department of Architectural Engineering, Kyung Hee University, Gyeonggi-do 17104, Korea; [email protected] * Correspondence: [email protected]; Tel.: +82-31-201-3859 Received: 24 October 2017; Accepted: 6 November 2017; Published: 7 November 2017 Abstract: Occupant factor is a critical element in the overall energy consumption of buildings. This study aims to reveal how occupant factors influence the energy consumption of example buildings in Seoul, Tokyo, and Hong Kong under climate change projections, and to prioritize factors with energy saving potential for buildings in consideration of future climate change. The study finds that the cooling degree-hours base of 23.3 ◦C in the three cities sharply increases with future climate change by analyzing future hourly weather data produced herein. Simulations are made with EnergyPlus Runtime Language (Erl) for modeling occupant behavior. The simulation results reveal that a dynamic thermostat control based on an adaptive comfort model is an effective method to reduce cooling energy consumption under future climate change, reducing cooling energy consumption by up to 18% in some instances. In particular, we reveal that a combined application of the adaptive comfort control, nighttime ventilation, and the use of occupancy-based lighting and equipment result in reducing cooling energy consumption by 28%. The outcomes of this study are potentially useful in providing cost-effective solutions to adapt buildings for future climate change with simple modifications to occupant behavior. Also, the roles of renewable energy are briefly discussed.
    [Show full text]
  • Extreme Weather in a Changing Climate
    Extreme weather in a changing climate ENERGY MODELING FORUM Workshop on Climate Change Impacts and Integrated Assessment (CCI/IA) Snowmass, Colorado July 23, 2014 Michael F. Wehner Lawrence Berkeley National Laboratory / UC Berkeley [email protected] Extreme Weather in a changing climate What does the IPCC have to say about temperature extremes? • AR5 ES: – It is virtually certain that, in most places, there will be more hot and fewer cold temperature extremes as global mean temperatures increase – Under RCP8.5 it is likely that, in most land regions, a current 20-year high temperature event will occur more frequently by the end of the 21st century (at least doubling its frequency, but in many regions becoming an annual or two-year event) and a current 20- year low temperature event will become exceedingly rare. IPCC AR5 AR5 Figure 12.13 • Expert Team on Climate Change Detection and Indices (ETCCDI) • Designed for “detection and attribution”. • Not necessarily relevant to impacts. • Not very “extreme”. • Multi-model averages. • Annualized. • All results presented here are for the end of this century relative to present. • http://www.cccma.ec.gc.ca/data/climdex/ Sillmann, J., V. V. Kharin, F. W. Zwiers, X. Zhang, and D. Bronaugh, 2013a: Climate extremes indices in the CMIP5 multi- model ensemble. Part 1: Model evaluation in the present climate. J. Geophys. Res., doi:10.1002/jgrd.50203. Sillmann, J., V. V. Kharin, F. W. Zwiers, X. Zhang, and D. Bronaugh, 2013b: Climate extremes indices in the CMIP5 multi- model ensemble. Part 2: Future projections. J. Geophys. Res., doi:10.1002/jgrd.50188.
    [Show full text]