DOCSLIB.ORG

Global and Regional Sea Level Rise Scenarios for the United States

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Global and Regional Sea Level Rise Scenarios for the United States NOAA Technical Report NOS CO-OPS 083 GLOBAL AND REGIONAL SEA LEVEL RISE SCENARIOS FOR THE UNITED STATES Photo: Ocean City, Maryland Silver Spring, Maryland January 2017 noaa National Oceanic and Atmospheric Administration U.S. DEPARTMENT OF COMMERCE National Ocean Service Center for Operational Oceanographic Products and Services Center for Operational Oceanographic Products and Services National Ocean Service National Oceanic and Atmospheric Administration U.S. Department of Commerce The National Ocean Service (NOS) Center for Operational Oceanographic Products and Services (CO- OPS) provides the National infrastructure, science, and technical expertise to collect and distribute observations and predictions of water levels and currents to ensure safe, efficient and environmentally sound maritime commerce. The Center provides the set of water level and tidal current products required to support NOS’ Strategic Plan mission requirements, and to assist in providing operational oceanographic data/products required by NOAA’s other Strategic Plan themes. For example, CO-OPS provides data and products required by the National Weather Service to meet its flood and tsunami warning responsibilities. The Center manages the National Water Level Observation Network (NWLON), a national network of Physical Oceanographic Real-Time Systems (PORTS®) in major U. S. harbors, and the National Current Observation Program consisting of current surveys in near shore and coastal areas utilizing bottom mounted platforms, subsurface buoys, horizontal sensors and quick response real time buoys. The Center: establishes standards for the collection and processing of water level and current data; collects and documents user requirements, which serve as the foundation for all resulting program activities; designs new and/or improved oceanographic observing systems; designs software to improve CO-OPS’ data processing capabilities; maintains and operates oceanographic observing systems; performs operational data analysis/quality control; and produces/disseminates oceanographic products. NOAA Technical Report NOS CO-OPS 083 Global and Regional Sea Level Rise Scenarios for the United States William V. Sweet National Oceanic and Atmospheric Administration, Center for Operational Oceanographic Products and Services, Silver Spring, MD, USA Robert E. Kopp Department of Earth & Planetary Sciences, Rutgers Energy Institute and Institute of Earth, Ocean & Atmospheric Sciences, Rutgers University–New Brunswick, New Brunswick, NJ, USA Christopher P. Weaver U.S. Environmental Protection Agency, Office of Research and Development, Research Triangle Park, NC, USA Jayantha Obeysekera South Florida Water Management District, West Palm Beach, FL Radley M. Horton Center for Climate Systems Research, Columbia University Earth Institute, New York, NY, USA E. Robert Thieler U.S. Geological Survey, Woods Hole, MA, USA Chris Zervas National Oceanic and Atmospheric Administration, Center for Operational Oceanographic Products and Services, Silver Spring, MD, USA January 2017 U.S. DEPARTMENT OF COMMERCE Penny Pritzker, Secretary National Oceanic and Atmospheric Administration Dr. Kathryn Sullivan, NOAA Administrator and Under Secretary of Commerce for Oceans and Atmosphere National Ocean Service Dr. Russell Callender, Assistant Administrator Center for Operational Oceanographic Products and Services Richard Edwing, Director NOTICE Mention of a commercial company or product does not constitute an endorsement by NOAA. Use of information from this publication for publicity or advertising purposes concerning proprietary products or the tests of such products is not authorized. TABLE OF CONTENTS LIST OF FIGURES ............................................................................................................................... IV LIST OF TABLES ................................................................................................................................. V EXECUTIVE SUMMARY ..................................................................................................................... VI 1.0 INTRODUCTION .....................................................................................................................1 2.0 SEA LEVEL RISE: HISTORIC INSIGHTS AND RECENT OBSERVATIONS ..........................................7 2.1 GLOBAL MEAN SEA LEVEL CHANGES ................................................................................................... 8 2.2 REGIONAL SEA LEVEL CHANGES .......................................................................................................... 9 2.3 RELATIVE SEA LEVELS ........................................................................................................................ 9 3.0 FUTURE SEA LEVELS: SCENARIOS AND PROBABILISTIC PROJECTIONS ..................................... 11 3.1 PROBABILISTIC GMSL RISE PROJECTIONS ........................................................................................... 12 3.2 UPPER AND LOWER GMSL RISE SCENARIO BOUNDS ............................................................................ 13 4.0 REGIONALIZATION OF THE GMSL RISE SCENARIOS ................................................................ 15 4.1 PROCESSES AFFECTING REGIONAL RSL CHANGE .................................................................................. 15 4.2 REGIONALIZATION METHOD ............................................................................................................ 15 5.0 RESULTS .............................................................................................................................. 21 5.1 GLOBAL MEAN SEA LEVEL RISE SCENARIOS ........................................................................................ 21 5.2 GMSL RISE RATES THIS CENTURY AND RISE BEYOND 2100 .................................................................. 22 5.3 REGIONAL CLIMATE-RELATED RSL CHANGES....................................................................................... 24 5.4 NONCLIMATIC BACKGROUND RSL AND GPS VLM TRENDS ................................................................... 27 5.5 SCENARIO PROJECTIONS OF RELATIVE SEA LEVEL (RSL) ........................................................................ 29 6.0 USAGE OF SCENARIOS WITHIN A RISK-BASED CONTEXT ........................................................ 33 6.1 GENERAL GUIDELINES FOR SCENARIO SELECTION ................................................................................. 33 6.2 SCENARIO PROJECTIONS OF RSL AND TIDAL FLOOD FREQUENCIES: A NATIONAL PERSPECTIVE .................... 35 6.3 BUILDING FOR A MAJOR FLOOD EVENT: A CASE STUDY FOR SOUTH FLORIDA ........................................... 39 7.0 SUMMARY AND NEXT STEPS ................................................................................................ 43 ACKNOWLEDGEMENTS .................................................................................................................... 45 REFERENCES ....................................................................................................................................47 LIST OF APPENDICES ......................................................................................................................... 55 APPENDIX A. SEA LEVEL RISE AND COASTAL FLOOD HAZARD SCENARIOS AND TOOLS INTERAGENCY TASK FORCE ........................................................................................................... A-1 APPENDIX B. LOW AND HIGH CLIMATE-RELATED RSL CHANGE CORRESPONDING TO GMSL SCENARIOS ............................................................................................................ B-1 APPENDIX C. LOW AND HIGH TOTAL RSL CHANGE CORRSPONDING TO GMSL SCENARIOS ............ C-1 APPENDIX D. CMIP5 MODELS USED ............................................................................................. D-1 ACRONYMS ......................................................................................................................................... iii LIST OF FIGURES Figure 1. a) Multi-year empirical (smoothed) distributions for daily highest water levels in Norfolk, Va. (Sweet and Park 2014) for the 1960s and 2010s, showing extent that local RSL rise has increased the flood probability relative to impact thresholds defined locally by the National Weather Service (http://water.weather.gov/ahps) for minor (~0.5 m: nuisance level), moderate (~0.9 m) and major (~1.2 m: local level of Hurricane Sandy in 2012) impacts, relative to mean higher high water (MHHW) tidal datum of the National Tidal Datum Epoch (1983-2001). b) Due to RSL rise, annual flood frequencies (based upon 5-year averages) in Norfolk for recurrent nuisance tidal floods with minor impacts are accelerating, as shown by the quadratic trend fit (goodness of fit [R2]=0.84). Flood rates are rapidly increasing in similar fashions along dozens of coastal cities of the U.S. (e.g., Sweet et al., 2014; Sweet and Park, 2014; Sweet and Marra, 2016). .................................................................................................... 2 Figure 2. Schematic showing the intersection of scenario approaches with emission-dependent (conditional) probabilistic projections of sea level rise under the climate modeling community’s Representative Concentration Pathways (RCP) (van Vuuren et al., 2011). .......................................................................... 4 Figure 3. a) GMSL rise from -500 to 1900 CE from Kopp et al. (2016a)’s geological
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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
    [Show full text]
  • March 21–25, 2016
    FORTY-SEVENTH LUNAR AND PLANETARY SCIENCE CONFERENCE PROGRAM OF TECHNICAL SESSIONS MARCH 21–25, 2016 The Woodlands Waterway Marriott Hotel and Convention Center The Woodlands, Texas INSTITUTIONAL SUPPORT Universities Space Research Association Lunar and Planetary Institute National Aeronautics and Space Administration CONFERENCE CO-CHAIRS Stephen Mackwell, Lunar and Planetary Institute Eileen Stansbery, NASA Johnson Space Center PROGRAM COMMITTEE CHAIRS David Draper, NASA Johnson Space Center Walter Kiefer, Lunar and Planetary Institute PROGRAM COMMITTEE P. Doug Archer, NASA Johnson Space Center Nicolas LeCorvec, Lunar and Planetary Institute Katherine Bermingham, University of Maryland Yo Matsubara, Smithsonian Institute Janice Bishop, SETI and NASA Ames Research Center Francis McCubbin, NASA Johnson Space Center Jeremy Boyce, University of California, Los Angeles Andrew Needham, Carnegie Institution of Washington Lisa Danielson, NASA Johnson Space Center Lan-Anh Nguyen, NASA Johnson Space Center Deepak Dhingra, University of Idaho Paul Niles, NASA Johnson Space Center Stephen Elardo, Carnegie Institution of Washington Dorothy Oehler, NASA Johnson Space Center Marc Fries, NASA Johnson Space Center D. Alex Patthoff, Jet Propulsion Laboratory Cyrena Goodrich, Lunar and Planetary Institute Elizabeth Rampe, Aerodyne Industries, Jacobs JETS at John Gruener, NASA Johnson Space Center NASA Johnson Space Center Justin Hagerty, U.S. Geological Survey Carol Raymond, Jet Propulsion Laboratory Lindsay Hays, Jet Propulsion Laboratory Paul Schenk,
    [Show full text]
  • 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.
    [Show full text]
  • Insights Into Paleoclimate Modeling
    Insights into paleoclimate modeling EMMA J. STONE1, P. BAKKER2, S. CHARBIT3, S.P. RITZ4 AND V. VARMA5 1School of Geographical Sciences, University of Bristol, UK; [email protected] 2Earth & Climate Cluster, Department of Earth Sciences, Vrije Universiteit Amsterdam, The Netherlands; 3Laboratoire des Sciences du Climat et de l'Environnement, CEA Saclay, Gif-sur-Yvette, France; 4Climate and Environmental Physics, Physics Institute and Oeschger Centre for Climate Change Research, University of Bern, Switzerland; 5Center for Marine Environmental Sciences and Faculty of Geosciences, University of Bremen, Germany We describe climate modeling in a paleoclimatic context by highlighting the types of models used, the logistics involved and the issues that inherently arise from simulating the climate system on long timescales. n contrast to "data paleoclimatologists" requires performing simulations on the al. 2011). Although computing advance- Past 4Future: Behind the Scenes 4Future: Past Iwho encounter experimental chal- order of thousands to tens of thousands ments have allowed transient climate lenges, and challenges linked to archive of model years and due to computa- experiments to be realized on long tim- sampling and working in remote and/or tional time this is not easily achievable escales, performing snapshot simulations difficult environments (e.g. Gersonde and with a GCM. Therefore, compromises are with EMICs or GCMs is still frequent and Seidenkrantz; Steffensen; Verheyden and required in terms of model resolution, useful (see Lunt et al. 2012). Genty, this issue) we give a perspective complexity, number of Earth system com- on the challenges encountered by the ponents and the timescale of the simula- Climate modeling by Past4Future "computer modeling paleoclimatologist".
    [Show full text]
  • Climate Modeling
    Climate Modeling Climate models are important tools for improving our understanding and predictability of climate behavior on seasonal, annual, decadal, and centennial time scales. Models investigate the degree to which observed climate changes may be due to natural variability, human activity, or a combination of both. Their results and projections provide essential information to better inform decisions of national, regional, and local importance, such as water resource management, agriculture, transportation, and urban planning. The Geophysical Fluid Dynamics Laboratory has been one of the world leaders in climate modeling and simulation for the past 50 years. Beginning in the 1960s, GFDL scientists developed the first coupled ocean-atmosphere general circulation climate model, and have continued to pioneer improvements and advances in a growing modeling community. State-of-the art climate modeling at GFDL requires vast computational resources, including supercomputers with thousands of processors and petabytes of data storage. What is a Global Climate Model? A global climate model (GCM) is a complex mathematical representation of the major climate system components (atmosphere, land surface, ocean, and sea ice), and their interactions. Earth's energy balance between the four components is the key to long- term climate prediction. The main climate system components treated in a climate model are: • The atmospheric component, which simulates clouds and aerosols, and plays a large role in transport of heat and water around the globe. • The land surface component, which simulates surface characteristics such as vegetation, snow cover, soil water, rivers, and carbon storing. • The ocean component, which simulates current movement and mixing, and biogeochemistry, since the ocean is the dominant reservoir of heat and carbon in the climate system.
    [Show full text]
  • The Jormungand Climate Model
    The Jormungand Climate Model By Christopher V. Rackauckas Abstract The geological and paleomagnetic record indicate that around 750 million and 580 millions years ago glaciers grew near the equator, though as of yet we do not fully understand the nature of these glacia- tions. The well-known Snowball Earth Hypothesis states that the Earth was covered entirely by glaciers. However, it is hard for this hypothesis to account for certain aspects of the biological evidence such as the survival of photosynthetic eukaryotes. Thus the Jormungand Hypothesis was developed as an alternative to the Snowball Earth Hypothesis. In this paper we investigate previous models of the Jormungand state and look at the dynamics of the Hadley cells to develop a new model to represent the Jormungand Hypothesis. We end by solving for an analytical approximation to the model using a nite Legendre expansion and geometric singular perturbation theory. The resultant model gives a stable equilibrium point near the equator with strong hysteresis that satises the Jormungand Hypothesis. 1 Introduction Geological and paleomagnetic evidence indicates that glaciers grew near the equator during at least two time periods between 750 million and 580 million years ago in what is known as the Neoproterozoic era [3, 4]. To explain these ndings, the Snowball Earth Hypothesis was proposed. The Snowball Earth hypothesis states the the Earth's surface was covered entirely by glaciers. However, biological evidence such as the survival of photosynthetic eukaryotes has lead some researchers to support alternative hypotheses [1]. Many of these alternatives are unable to satisfy the strong hysteresis of CO2 seen in the data (which is the existence of two stable states over a large range of CO2 values).
    [Show full text]
  • Climate Investigations Using Ice Sheet and Mass Balance Models with Emphasis on North American Glaciation Sean David Birkel
    The University of Maine DigitalCommons@UMaine Electronic Theses and Dissertations Fogler Library 2010 Climate Investigations Using Ice Sheet and Mass Balance Models with Emphasis on North American Glaciation Sean David Birkel Follow this and additional works at: http://digitalcommons.library.umaine.edu/etd Part of the Glaciology Commons Recommended Citation Birkel, Sean David, "Climate Investigations Using Ice Sheet and Mass Balance Models with Emphasis on North American Glaciation" (2010). Electronic Theses and Dissertations. 97. http://digitalcommons.library.umaine.edu/etd/97 This Open-Access Dissertation is brought to you for free and open access by DigitalCommons@UMaine. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of DigitalCommons@UMaine. CLIMATE INVESTIGATIONS USING ICE SHEET AND MASS BALANCE MODELS WITH EMPHASIS ON NORTH AMERICAN GLACIATION By Sean David Birkel B.S. University of Maine, 2002 M.S. University of Maine, 2004 A THESIS Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy (in Earth Sciences) The Graduate School The University of Maine December, 2010 Advisory Committee: Peter O. Koons, Professor of Earth Sciences, Advisor George H. Denton, Libra Professor of Earth Sciences, Co-advisor Fei Chai, Professor of Oceanography James L. Fastook, Professor of Computer Science Brenda L. Hall, Professor of Earth Science Terrence J. Hughes, Professor Emeritus of Earth Science ©2010 Sean David Birkel All Rights Reserved iii CLIMATE INVESTIGATIONS USING ICE SHEET AND MASS BALANCE MODELS WITH EMPHASIS ON NORTH AMERICAN GLACIATION By Sean D. Birkel Thesis Advisor: Dr. Peter O. Koons An Abstract of the Dissertation Presented in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy (in Earth Sciences) December, 2010 This dissertation describes the application of the University of Maine Ice Sheet Model (UM-ISM) and Environmental Change Model (UM-ECM) to understanding mechanisms of ice-sheet/climate integration during ice ages.
    [Show full text]
  • 2010-2011 Science Planning Summaries
    Find information about current Link to project web sites and USAP projects using the find information about the principal investigator, event research and people involved. number station, and other indexes. Science Program Indexes: 2010-2011 Find information about current USAP projects using the Project Web Sites principal investigator, event number station, and other Principal Investigator Index indexes. USAP Program Indexes Aeronomy and Astrophysics Dr. Vladimir Papitashvili, program manager Organisms and Ecosystems Find more information about USAP projects by viewing Dr. Roberta Marinelli, program manager individual project web sites. Earth Sciences Dr. Alexandra Isern, program manager Glaciology 2010-2011 Field Season Dr. Julie Palais, program manager Other Information: Ocean and Atmospheric Sciences Dr. Peter Milne, program manager Home Page Artists and Writers Peter West, program manager Station Schedules International Polar Year (IPY) Education and Outreach Air Operations Renee D. Crain, program manager Valentine Kass, program manager Staffed Field Camps Sandra Welch, program manager Event Numbering System Integrated System Science Dr. Lisa Clough, program manager Institution Index USAP Station and Ship Indexes Amundsen-Scott South Pole Station McMurdo Station Palmer Station RVIB Nathaniel B. Palmer ARSV Laurence M. Gould Special Projects ODEN Icebreaker Event Number Index Technical Event Index Deploying Team Members Index Project Web Sites: 2010-2011 Find information about current USAP projects using the Principal Investigator Event No. Project Title principal investigator, event number station, and other indexes. Ainley, David B-031-M Adelie Penguin response to climate change at the individual, colony and metapopulation levels Amsler, Charles B-022-P Collaborative Research: The Find more information about chemical ecology of shallow- USAP projects by viewing individual project web sites.
    [Show full text]
  • The Risk of Sea Level Rise
    THE RISK OF SEA LEVEL RISE:∗ A Delphic Monte Carlo Analysis in which Twenty Researchers Specify Subjective Probability Distributions for Model Coefficients within their Respective Areas of Expertise James G. Titus∗∗ U.S. Environmental Protection Agency Vijay Narayanan Technical Resources International Abstract. The United Nations Framework Convention on Climate Change requires nations to implement measures for adapting to rising sea level and other effects of changing climate. To decide upon an appropriate response, coastal planners and engineers must weigh the cost of these measures against the likely cost of failing to prepare, which depends on the probability of the sea rising a particular amount. This study estimates such a probability distribution, using models employed by previous assessments, as well as the subjective assessments of twenty climate and glaciology reviewers about the values of particular model coefficients. The reviewer assumptions imply a 50 percent chance that the average global temperature will rise 2°C degrees, as well as a 5 percent chance that temperatures will rise 4.7°C by 2100. The resulting impact of climate change on sea level has a 50 percent chance of exceeding 34 cm and a 1% chance of exceeding one meter by the year 2100, as well as a 3 percent chance of a 2 meter rise and a 1 percent chance of a 4 meter rise by the year 2200. The models and assumptions employed by this study suggest that greenhouse gases have contributed 0.5 mm/yr to sea level over the last century. Tidal gauges suggest that sea level is rising about 1.8 mm/yr worldwide, and 2.5-3.0 mm/yr along most of the U.S.
    [Show full text]
  • Ozone Depletion, Ultraviolet Radiation, Climate Change and Prospects for a Sustainable Future Paul W
    University of Wollongong Research Online Faculty of Science, Medicine and Health - Papers: Faculty of Science, Medicine and Health Part B 2019 Ozone depletion, ultraviolet radiation, climate change and prospects for a sustainable future Paul W. Barnes Loyola University New Orleans, [email protected] Craig E. Williamson United Nations Environment Programme, Environmental Effects Assessment Panel, Miami University, [email protected] Robyn M. Lucas Australian National University (ANU), United Nations Environment Programme, Environmental Effects Assessment Panel, University of Western Australia, [email protected] Sharon A. Robinson University of Wollongong, [email protected] Sasha Madronich United Nations Environment Programme, Environmental Effects Assessment Panel, National Center For Atmospheric Research, Boulder, United States, [email protected] See next page for additional authors Publication Details Barnes, P. W., Williamson, C. E., Lucas, R. M., Robinson, S. A., Madronich, S., Paul, N. D., Bornman, J. F., Bais, A. F., Sulzberger, B., Wilson, S. R., Andrady, A. L., McKenzie, R. L., Neale, P. J., Austin, A. T., Bernhard, G. H., Solomon, K. R., Neale, R. E., Young, P. J., Norval, M., Rhodes, L. E., Hylander, S., Rose, K. C., Longstreth, J., Aucamp, P. J., Ballare, C. L., Cory, R. M., Flint, S. D., de Gruijl, F. R., Hader, D. -P., Heikkila, A. M., Jansen, M. A.K., Pandey, K. K., Robson, T. Matthew., Sinclair, C. A., Wangberg, S., Worrest, R. C., Yazar, S., Young, A. R. & Zepp, R. G. (2019). Ozone depletion, ultraviolet radiation, climate change and prospects for a sustainable future. Nature Sustainability, Online First 1-11. Research Online is the open access institutional repository for the University of Wollongong.
    [Show full text]