Climate Scenarios: What We Need to Know and How to Generate Them
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Climate Change and Human Health: Risks and Responses
Climate change and human health RISKS AND RESPONSES Editors A.J. McMichael The Australian National University, Canberra, Australia D.H. Campbell-Lendrum London School of Hygiene and Tropical Medicine, London, United Kingdom C.F. Corvalán World Health Organization, Geneva, Switzerland K.L. Ebi World Health Organization Regional Office for Europe, European Centre for Environment and Health, Rome, Italy A.K. Githeko Kenya Medical Research Institute, Kisumu, Kenya J.D. Scheraga US Environmental Protection Agency, Washington, DC, USA A. Woodward University of Otago, Wellington, New Zealand WORLD HEALTH ORGANIZATION GENEVA 2003 WHO Library Cataloguing-in-Publication Data Climate change and human health : risks and responses / editors : A. J. McMichael . [et al.] 1.Climate 2.Greenhouse effect 3.Natural disasters 4.Disease transmission 5.Ultraviolet rays—adverse effects 6.Risk assessment I.McMichael, Anthony J. ISBN 92 4 156248 X (NLM classification: WA 30) ©World Health Organization 2003 All rights reserved. Publications of the World Health Organization can be obtained from Marketing and Dis- semination, World Health Organization, 20 Avenue Appia, 1211 Geneva 27, Switzerland (tel: +41 22 791 2476; fax: +41 22 791 4857; email: [email protected]). Requests for permission to reproduce or translate WHO publications—whether for sale or for noncommercial distribution—should be addressed to Publications, at the above address (fax: +41 22 791 4806; email: [email protected]). The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of the World Health Organization concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. -
Climate Models and Their Evaluation
8 Climate Models and Their Evaluation Coordinating Lead Authors: David A. Randall (USA), Richard A. Wood (UK) Lead Authors: Sandrine Bony (France), Robert Colman (Australia), Thierry Fichefet (Belgium), John Fyfe (Canada), Vladimir Kattsov (Russian Federation), Andrew Pitman (Australia), Jagadish Shukla (USA), Jayaraman Srinivasan (India), Ronald J. Stouffer (USA), Akimasa Sumi (Japan), Karl E. Taylor (USA) Contributing Authors: K. AchutaRao (USA), R. Allan (UK), A. Berger (Belgium), H. Blatter (Switzerland), C. Bonfi ls (USA, France), A. Boone (France, USA), C. Bretherton (USA), A. Broccoli (USA), V. Brovkin (Germany, Russian Federation), W. Cai (Australia), M. Claussen (Germany), P. Dirmeyer (USA), C. Doutriaux (USA, France), H. Drange (Norway), J.-L. Dufresne (France), S. Emori (Japan), P. Forster (UK), A. Frei (USA), A. Ganopolski (Germany), P. Gent (USA), P. Gleckler (USA), H. Goosse (Belgium), R. Graham (UK), J.M. Gregory (UK), R. Gudgel (USA), A. Hall (USA), S. Hallegatte (USA, France), H. Hasumi (Japan), A. Henderson-Sellers (Switzerland), H. Hendon (Australia), K. Hodges (UK), M. Holland (USA), A.A.M. Holtslag (Netherlands), E. Hunke (USA), P. Huybrechts (Belgium), W. Ingram (UK), F. Joos (Switzerland), B. Kirtman (USA), S. Klein (USA), R. Koster (USA), P. Kushner (Canada), J. Lanzante (USA), M. Latif (Germany), N.-C. Lau (USA), M. Meinshausen (Germany), A. Monahan (Canada), J.M. Murphy (UK), T. Osborn (UK), T. Pavlova (Russian Federationi), V. Petoukhov (Germany), T. Phillips (USA), S. Power (Australia), S. Rahmstorf (Germany), S.C.B. Raper (UK), H. Renssen (Netherlands), D. Rind (USA), M. Roberts (UK), A. Rosati (USA), C. Schär (Switzerland), A. Schmittner (USA, Germany), J. Scinocca (Canada), D. Seidov (USA), A.G. -
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15 NOVEMBER 2006 A R O R A A N D B O E R 5875 The Temporal Variability of Soil Moisture and Surface Hydrological Quantities in a Climate Model VIVEK K. ARORA AND GEORGE J. BOER Canadian Centre for Climate Modelling and Analysis, Meteorological Service of Canada, University of Victoria, Victoria, British Columbia, Canada (Manuscript received 4 October 2005, in final form 8 February 2006) ABSTRACT The variance budget of land surface hydrological quantities is analyzed in the second Atmospheric Model Intercomparison Project (AMIP2) simulation made with the Canadian Centre for Climate Modelling and Analysis (CCCma) third-generation general circulation model (AGCM3). The land surface parameteriza- tion in this model is the comparatively sophisticated Canadian Land Surface Scheme (CLASS). Second- order statistics, namely variances and covariances, are evaluated, and simulated variances are compared with observationally based estimates. The soil moisture variance is related to second-order statistics of surface hydrological quantities. The persistence time scale of soil moisture anomalies is also evaluated. Model values of precipitation and evapotranspiration variability compare reasonably well with observa- tionally based and reanalysis estimates. Soil moisture variability is compared with that simulated by the Variable Infiltration Capacity-2 Layer (VIC-2L) hydrological model driven with observed meteorological data. An equation is developed linking the variances and covariances of precipitation, evapotranspiration, and runoff to soil moisture variance via a transfer function. The transfer function is connected to soil moisture persistence in terms of lagged autocorrelation. Soil moisture persistence time scales are shorter in the Tropics and longer at high latitudes as is consistent with the relationship between soil moisture persis- tence and the latitudinal structure of potential evaporation found in earlier studies. -
Developing and Applying Scenarios
3 Developing and Applying Scenarios TIMOTHY R. CARTER (FINLAND) AND EMILIO L. LA ROVERE (BRAZIL) Lead Authors: R.N. Jones (Australia), R. Leemans (The Netherlands), L.O. Mearns (USA), N. Nakicenovic (Austria), A.B. Pittock (Australia), S.M. Semenov (Russian Federation), J. Skea (UK) Contributing Authors: S. Gromov (Russian Federation), A.J. Jordan (UK), S.R. Khan (Pakistan), A. Koukhta (Russian Federation), I. Lorenzoni (UK), M. Posch (The Netherlands), A.V. Tsyban (Russian Federation), A. Velichko (Russian Federation), N. Zeng (USA) Review Editors: Shreekant Gupta (India) and M. Hulme (UK) CONTENTS Executive Summary 14 7 3. 6 . Sea-Level Rise Scenarios 17 0 3. 6 . 1 . Pu r p o s e 17 0 3. 1 . Definitions and Role of Scenarios 14 9 3. 6 . 2 . Baseline Conditions 17 0 3. 1 . 1 . In t r o d u c t i o n 14 9 3. 6 . 3 . Global Average Sea-Level Rise 17 0 3. 1 . 2 . Function of Scenarios in 3. 6 . 4 . Regional Sea-Level Rise 17 0 Impact and Adaptation As s e s s m e n t 14 9 3. 6 . 5 . Scenarios that Incorporate Var i a b i l i t y 17 1 3. 1 . 3 . Approaches to Scenario Development 3. 6 . 6 . Application of Scenarios 17 1 and Ap p l i c a t i o n 15 0 3. 1 . 4 . What Changes are being Considered? 15 0 3. 7 . Re p r esenting Interactions in Scenarios and Ensuring Consistency 17 1 3. 2 . Socioeconomic Scenarios 15 1 3. -
A Review of Climate Change Scenarios and Preliminary Rainfall Trend Analysis in the Oum Er Rbia Basin, Morocco
WORKING PAPER 110 A Review of Climate Drought Series: Paper 8 Change Scenarios and Preliminary Rainfall Trend Analysis in the Oum Er Rbia Basin, Morocco Anne Chaponniere and Vladimir Smakhtin Postal Address P O Box 2075 Colombo Sri Lanka Location 127, Sunil Mawatha Pelawatta Battaramulla Sri Lanka Telephone +94-11 2787404 Fax +94-11 2786854 E-mail [email protected] Website http://www.iwmi.org SM International International Water Management IWMI isaFuture Harvest Center Water Management Institute supportedby the CGIAR ISBN: 92-9090-635-9 Institute ISBN: 978-92-9090-635-9 Working Paper 110 Drought Series: Paper 8 A Review of Climate Change Scenarios and Preliminary Rainfall Trend Analysis in the Oum Er Rbia Basin, Morocco Anne Chaponniere and Vladimir Smakhtin International Water Management Institute IWMI receives its principal funding from 58 governments, private foundations and international and regional organizations known as the Consultative Group on International Agricultural Research (CGIAR). Support is also given by the Governments of Ghana, Pakistan, South Africa, Sri Lanka and Thailand. The authors: Anne Chaponniere is a Post Doctoral Fellow in Hydrology and Water Resources at IWMI Sub-regional office for West Africa (Accra, Ghana). Vladimir Smakhtin is a Principal Hydrologist at IWMI Headquarters in Colombo, Sri Lanka. Acknowledgments: The study was supported from IWMI core funds. The paper was reviewed by Dr Hugh Turral (IWMI, Colombo). Chaponniere, A.; Smakhtin, V. 2006. A review of climate change scenarios and preliminary rainfall trend analysis in the Oum er Rbia Basin, Morocco. Working Paper 110 (Drought Series: Paper 8) Colombo, Sri Lanka: International Water Management Institute (IWMI). -
Chapter 3. Modelling the Climate System
Introduction to climate dynamics and climate modelling - http://www.climate.be/textbook Chapter 3. Modelling the climate system 3.1 Introduction 3.1.1 What is a climate model ? In general terms, a climate model could be defined as a mathematical representation of the climate system based on physical, biological and chemical principles (Fig. 3.1). The equations derived from these laws are so complex that they must be solved numerically. As a consequence, climate models provide a solution which is discrete in space and time, meaning that the results obtained represent averages over regions, whose size depends on model resolution, and for specific times. For instance, some models provide only globally or zonally averaged values while others have a numerical grid whose spatial resolution could be less than 100 km. The time step could be between minutes and several years, depending on the process studied. Even for models with the highest resolution, the numerical grid is still much too coarse to represent small scale processes such as turbulence in the atmospheric and oceanic boundary layers, the interactions of the circulation with small scale topography features, thunderstorms, cloud micro-physics processes, etc. Furthermore, many processes are still not sufficiently well-known to include their detailed behaviour in models. As a consequence, parameterisations have to be designed, based on empirical evidence and/or on theoretical arguments, to account for the large-scale influence of these processes not included explicitly. Because these parameterisations reproduce only the first order effects and are usually not valid for all possible conditions, they are often a large source of considerable uncertainty in models. -
Chapter 1 Ozone and Climate
1 Ozone and Climate: A Review of Interconnections Coordinating Lead Authors John Pyle (UK), Theodore Shepherd (Canada) Lead Authors Gregory Bodeker (New Zealand), Pablo Canziani (Argentina), Martin Dameris (Germany), Piers Forster (UK), Aleksandr Gruzdev (Russia), Rolf Müller (Germany), Nzioka John Muthama (Kenya), Giovanni Pitari (Italy), William Randel (USA) Contributing Authors Vitali Fioletov (Canada), Jens-Uwe Grooß (Germany), Stephen Montzka (USA), Paul Newman (USA), Larry Thomason (USA), Guus Velders (The Netherlands) Review Editors Mack McFarland (USA) IPCC Boek (dik).indb 83 15-08-2005 10:52:13 84 IPCC/TEAP Special Report: Safeguarding the Ozone Layer and the Global Climate System Contents EXECUTIVE SUMMARY 85 1.4 Past and future stratospheric ozone changes (attribution and prediction) 110 1.1 Introduction 87 1.4.1 Current understanding of past ozone 1.1.1 Purpose and scope of this chapter 87 changes 110 1.1.2 Ozone in the atmosphere and its role in 1.4.2 The Montreal Protocol, future ozone climate 87 changes and their links to climate 117 1.1.3 Chapter outline 93 1.5 Climate change from ODSs, their substitutes 1.2 Observed changes in the stratosphere 93 and ozone depletion 120 1.2.1 Observed changes in stratospheric ozone 93 1.5.1 Radiative forcing and climate sensitivity 120 1.2.2 Observed changes in ODSs 96 1.5.2 Direct radiative forcing of ODSs and their 1.2.3 Observed changes in stratospheric aerosols, substitutes 121 water vapour, methane and nitrous oxide 96 1.5.3 Indirect radiative forcing of ODSs 123 1.2.4 Observed temperature -
Improving Representations of Boundary Layer Processes
1 2 3 4 5 6 Regional climate modeling over the Maritime Continent: Improving 7 representations of boundary layer processes 8 9 Rebecca L. Gianotti* and Elfatih A. B. Eltahir 10 11 Ralph M. Parsons Laboratory, Massachusetts Institute of Technology, 12 15 Vassar St, Cambridge MA 02139, USA 13 14 15 16 17 18 19 20 ELTAHIR Research Group Report #3, 21 March, 2014 1 Abstract 2 This paper describes work to improve the representation of boundary layer processes 3 within a regional climate model (Regional Climate Model Version 3 (RegCM3) coupled to 4 the Integrated Biosphere Simulator (IBIS)) applied over the Maritime Continent. In 5 particular, modifications were made to improve model representations of the mixed 6 boundary layer height and non-convective cloud cover within the mixed boundary layer. 7 Model output is compared to a variety of ground-based and satellite-derived observational 8 data, including a new dataset obtained from radiosonde measurements taken at Changi 9 airport, Singapore, four times per day. These data were commissioned specifically for this 10 project and were not part of the airport’s routine data collection. It is shown that the 11 modifications made to RegCM3-IBIS significantly improve representations of the mixed 12 boundary layer height and low-level cloud cover over the Maritime Continent region by 13 lowering the simulated nocturnal boundary layer height and removing erroneous cloud 14 within the mixed boundary layer over land. The results also show some improvement with 15 respect to simulated radiation and rainfall, compared to the default version of the model. -
Installing and Using the Hadley Centre Regional Climate Modelling System, PRECIS
Installing and using the Hadley Centre regional climate modelling system, PRECIS Version 1.9.4 www.metoffice.gov.uk/precis Simon Wilson, David Hassell, David Hein, Chloe Eagle, Simon Tucker, Richard Jones and Ruth Taylor April 12, 2012 Contents 1 Introduction 11 1.1 Background .............................. 11 1.2 Objectivesandstructureofthemanual . 12 2 Hardware, operating system and software environment 13 2.1 Recommended Hardware Configurations . 13 2.2 Multi-processorsystems . 14 2.3 InstallationofLinux ......................... 15 2.4 Compilers ............................... 16 2.5 System setup before installing PRECIS . 17 3 PRECIS software and installation 19 3.1 Introduction.............................. 19 3.2 Disklayout .............................. 20 3.3 Main steps in installation process . 21 3.4 Installation of PRECIS software and data . 21 3.5 InstallationofMetOfficedata . 25 3.5.1 Boundary data supplied on hard drive . 25 3.6 Installation verification . 27 3.7 InstallationofCDAT ......................... 28 4 Experimental design and setup 30 4.1 Experimentaldesign ......................... 30 4.1.1 Regional climate model . 30 2 4.1.2 Choice of driving model and forcing scenario . 30 4.1.3 Simulationlength. 36 4.1.4 Initial condition ensembles . 37 4.1.5 Choice of land surface scheme . 38 4.1.6 Outputdata.......................... 38 4.1.7 Spinup............................. 38 4.1.8 Choice of region . 39 4.1.9 Land-seamask ........................ 39 4.1.10 Altitude ............................ 40 4.1.11 Altitude of inland waters . 41 4.1.12 Soil and land cover . 41 4.1.13 RCM calendar and clock . 42 4.1.14 RCM Resolution . 42 4.1.15 Outputformat ........................ 43 4.1.16 Checklist............................ 43 5 Configuring an experiment with PRECIS 45 5.1 Introduction............................. -
Global Climate Models and Their Limitations Anthony Lupo (USA) William Kininmonth (Australia) Contributing: J
1 Global Climate Models and Their Limitations Anthony Lupo (USA) William Kininmonth (Australia) Contributing: J. Scott Armstrong (USA), Kesten Green (Australia) 1. Global Climate Models and Their Limitations Key Findings Introduction 1.1 Model Simulation and Forecasting 1.2 Modeling Techniques 1.3 Elements of Climate 1.4 Large Scale Phenomena and Teleconnections Key Findings Confidence in a model is further based on the The IPCC places great confidence in the ability of careful evaluation of its performance, in which model general circulation models (GCMs) to simulate future output is compared against actual observations. A climate and attribute observed climate change to large portion of this chapter, therefore, is devoted to anthropogenic emissions of greenhouse gases. They the evaluation of climate models against real-world claim the “development of climate models has climate and other biospheric data. That evaluation, resulted in more realism in the representation of many summarized in the findings of numerous peer- quantities and aspects of the climate system,” adding, reviewed scientific papers described in the different “it is extremely likely that human activities have subsections of this chapter, reveals the IPCC is caused more than half of the observed increase in overestimating the ability of current state-of-the-art global average surface temperature since the 1950s” GCMs to accurately simulate both past and future (p. 9 and 10 of the Summary for Policy Makers, climate. The IPCC’s stated confidence in the models, Second Order Draft of AR5, dated October 5, 2012). as presented at the beginning of this chapter, is likely This chapter begins with a brief review of the exaggerated. -
Tracking Climate Models
TRACKING CLIMATE MODELS CLAIRE MONTELEONI*, GAVIN SCHMIDT**, AND SHAILESH SAROHA*** Abstract. Climate models are complex mathematical models designed by meteorologists, geo- physicists, and climate scientists to simulate and predict climate. Given temperature predictions from the top 20 climate models worldwide, and over 100 years of historical temperature data, we track the changing sequence of which model currently predicts best. We use an algorithm due to Monteleoni and Jaakkola that models the sequence of observations using a hierarchical learner, based on a set of generalized Hidden Markov Models (HMM), where the identity of the current best climate model is the hidden variable. The transition probabilities between climate models are learned online, simultaneous to tracking the temperature predictions. On historical data, our online learning algorithm's average prediction loss nearly matches that of the best performing climate model in hindsight. Moreover its performance surpasses that of the average model predic- tion, which was the current state-of-the-art in climate science, the median prediction, and least squares linear regression. We also experimented on climate model predictions through the year 2098. Simulating labels with the predictions of any one climate model, we found significantly im- proved performance using our online learning algorithm with respect to the other climate models, and techniques. 1. Introduction The threat of climate change is one of the greatest challenges currently facing society. With the increased threats of global warming, and the increasing severity of storms and natural disasters, improving our understanding of the climate system has become an international priority. This system is characterized by complex and structured phenomena that are imperfectly observed and even more imperfectly simulated. -
Assimila Blank
NERC NERC Strategy for Earth System Modelling: Technical Support Audit Report Version 1.1 December 2009 Contact Details Dr Zofia Stott Assimila Ltd 1 Earley Gate The University of Reading Reading, RG6 6AT Tel: +44 (0)118 966 0554 Mobile: +44 (0)7932 565822 email: [email protected] NERC STRATEGY FOR ESM – AUDIT REPORT VERSION1.1, DECEMBER 2009 Contents 1. BACKGROUND ....................................................................................................................... 4 1.1 Introduction .............................................................................................................. 4 1.2 Context .................................................................................................................... 4 1.3 Scope of the ESM audit ............................................................................................ 4 1.4 Methodology ............................................................................................................ 5 2. Scene setting ........................................................................................................................... 7 2.1 NERC Strategy......................................................................................................... 7 2.2 Definition of Earth system modelling ........................................................................ 8 2.3 Broad categories of activities supported by NERC ................................................. 10 2.4 Structure of the report ...........................................................................................