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  26. The spatial average of annual-average surface air temperatures around the globe is commonly referred to as the global average surface air temperature.
  27. Meier, M.F., M.B. Dyurgerov, U.K. Rick, S. O’Neel, W.T. Pfeffer, R.S. Anderson, S.P. Anderson, and A.F. Glazovsky, 2007:
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  32. Uncertainties in the data are an order of magnitude smaller than the trend according to Karl, T.R., J.R. Christy, R.A. Clarke, G.V. Gruza, J. Jouzel, M.E. Mann, J. Oerlemans, M.J. Salinger, and S.-W. Wang, 2001: Observed climate variability and change. In: Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change [Houghton, J.T., Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, K. Maskell, and C.A. Johnson (eds.)]. Cambridge University Press, Cambridge, UK, and New York, pp. 99-181.

    Temperature data:
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    Jones, P.D., M. New, D.E. Parker, S. Martin, and I.G. Rigor, 1999: Surface air temperature and its changes over the past 150 years. Reviews of Geophysics, 37(2), 173-199.

    Carbon dioxide data:
    Data from 1974 to present: Tans, P., 2008: Trends in Atmospheric Carbon Dioxide: Mauna Loa. NOAA Earth System Research Laboratory (ESRL). [Web site] <http://www.esrl.noaa.gov/gmd/ccgg/trends/> Data available at <ftp://ftp.cmdl.noaa.gov/ccg/co2/trends/co2_annmean_mlo.txt>

    1958-1974 data are from the Scripps Institution of Oceanography (Keeling) Mauna Loa Observatory record. <http://scrippsco2.ucsd.edu/>

    Pre-1958 values are annual points taken from a smooth fit to the Law Dome data: Etheridge, D.M., L.P. Steele, R.L.
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    The solar data are a composite of 3 different data sets:

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    Willson, R.C. and A.V. Mordvinov, 2003: Secular total solar irradiance trend during solar cycles 21023. Geophysical Research Letters, 30(5), 1199, doi:10.1029/2002GL016038.

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    Stabilization scenario (450 ppm): CCSP 2.1a Scenario Information 070707 data file. From: Clarke, L., J. Edmonds, H. Jacoby, H. Pitcher, J. Reilly, and R. Richels, 2007: Scenarios of Greenhouse Gas Emissions and Atmospheric Concentrations. Sub-report 2.1A of Synthesis and Assessment Product 2.1. U.S. Department of Energy, Office of Biological & Environmental Research, Washington, DC. The emissions and concentrations shown were from MINICAM 1 and 2. See CCSP 2.1A Executive summary for more information. Spread sheet available at <http://www.climate science.gov/Library/sap/sap2-1/finalreport/default.htm>

    Observations of CO2 emissions (Fossil Fuel CO2 Emissions graphic) are updates to: Marland, G., B. Andres, T. Boden, 2008: Global CO2 Emissions from Fossil-Fuel Burning, Cement Manufacture, and Gas Flaring: 1751-2005. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, Oak Ridge, TN. <http://cdiac.ornl.gov/ftp/ndp030/global.1751_2005.ems>

    Observations of CO2 concentrations (Atmospheric CO2 Concentrations graphic): Tans, P., 2008: Trends in Atmospheric Carbon Dioxide: Mauna Loa. NOAA Earth System Research Laboratory (ESRL). [Web site] <http://www.esrl.noaa.gov/gmd/ccgg/trends/> Data available at <ftp://ftp.cmdl.noaa.gov/ccg/co2/trends/co2_annmean_mlo.txt>
  93. CMIP3-A: This analysis uses 15 models simulations from the WCRP CMIP3 that were available at resolutions finer than 4 degrees (CCSM3.0, CSIRO, UKMO-HadCM3, IPSL, ECHAM5/MPI, CGCM3.1(T47), GFDL2.0, UKMO-HadGEM1, MIROC3.2(medres), MRI-CGCM2.3.2a, CNRM, GFDL2.1, INM-CM3, ECHO-G, PCM). See Wehner, M., 2005: Changes in daily precipitation and surface air temperature extremes in the IPCC AR4 models. US CLIVAR Variations, 3(3), 5-9.

    Hatching indicates at least two out of three models agree on the sign of the projected change in precipitation.

    We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset, <http://www-pcmdi.llnl.gov/projects/cmip/index.php>. Support of this dataset is provided by the Office of Science, U.S. Department of Energy. For an overview and documentation of the CMIP3 modelling activity, see Meehl, G.A., C. Covey, T. Delworth, M. Latif, B. McAvaney, J.F.B. Mitchell, R.J. Stouffer, and K.E. Taylor, 2007: The WCRP CMIP3 multi-model dataset: a new era in climate change research. Bulletin of the American Meteorological Society, 88(9), 1383-1394.
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  107. Temperatures for the contiguous U.S. are based on data from the U.S. Historical Climatology Network Version 2 (Menne et al. 2008). Temperatures for Alaska, Hawaii, and Puerto Rico are based on data from the Cooperative Observers Network adjusted to remove non-climatic influences such as changes in instruments and observer practices and changes in the station environment (Menne and Williams, 2008).

    U.S. time series on page 27 is calculated with data for the contiguous US, Alaska, and Hawaii. US map on page 28 lower left includes observed temperature change in Puerto Rico. Winter temperature trend map in the agriculture section, page 76, is for the contiguous US only.

    References for this endnote:
    Menne, M.J., C.N. Williams, and R.S. Vose, 2009: The United States Historical Climatology Network Monthly Temperature Data - Version 2. Bulletin of the American Meteorological Society, Early online release, 25 February 2009, doi:10.1175/2008BAMS2613.1

    Menne, M.J. and C.N. Williams Jr., 2008: Homogenization of temperature series via pairwise comparisons. Journal of Climate, 22(7), 1700-1717.
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  109. CMIP3-C: Analysis for the contiguous U.S. was based on methods described in: Hayhoe, K., D. Cayan, C.B. Field, P.C.
    Frumhoff, E.P. Maurer, N.L. Miller, S.C. Moser, S.H. Schneider, K.N. Cahill, E.E. Cleland, L. Dale, R. Drapek, R.M. Hanemann,
    L.S. Kalkstein, J. Lenihan, C.K. Lunch, R.P. Neilson, S.C. Sheridan, and J.H. Verville, 2004: Emission pathways, climate change, and impacts on California. Proceedings of the National Academy of Sciences, 101(34), 12422-12427; and Hayhoe, K., C. Wake, B. Anderson, X.-Z. Liang, E. Maurer, J. Zhu, J. Bradbury, A. DeGaetano, A.M. Stoner, and D. Wuebbles, 2008: Regional climate change projections for the Northeast USA. Mitigation and Adaptation Strategies for Global Change, 13(5-6), 425-436. This analysis uses 16 models simulations from the WCRP CMIP3. Where models had multiple runs, only the first run available from each model was used. See <http://gdo-dcp.ucllnl.org/downscaled_cmip3_projections/dcpInterface.html> for more information.

    The Alaskan projections are based on 14 models that best captured the present climate of Alaska; see Walsh, J.E., W.L. Chaman, V. Romanovsky, J.H. Christensen, and M. Stendel, 2008: Global climate model performance over Alaska and Greenland. Journal of Climate, 21(23), 6156-6174.

    Caribbean and Pacific islands analyses use 15 models simulations from the WCRP CMIP3 that were available at resolutions finer than 4 degrees (CCSM3.0, CSIRO, UKMO-HadCM3, IPSL, ECHAM5/MPI, CGCM3.1(T47), GFDL2.0, UKMO-HadGEM1, MIROC3.2(medres), MRI-CGCM2.3.2a, CNRM, GFDL2.1, INM-CM3, ECHO-G, PCM). See Wehner, M., 2005: Changes in daily precipitation and surface air temperature extremes in the IPCC AR4 models. US CLIVAR Variations, 3(3), 5-9.

    We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset, <http://www-pcmdi.llnl.gov/projects/cmip/index.php>. Support of this dataset is provided by the Office of Science, U.S. Department of Energy. For an overview and documentation of the CMIP3 modelling activity, see Meehl, G.A., C. Covey, T. Delworth, M. Latif, B. McAvaney, J.F.B. Mitchell, R.J. Stouffer, and K.E. Taylor, 2007: The WCRP CMIP3 multi-model dataset: a new era in climate change research. Bulletin of the American Meteorological Society, 88(9), 1383-1394.
  110. Detailed local-scale projections about temperature and precipitation changes displayed in this report were generated using well-documented “statistical downscaling” techniques [Wood et al., 2002] for the contiguous U.S. and Alaska. These techniques use statistical relationships between surface observations and climate simulations of the past to develop modifications for the global model results. These modifications are then applied to the climate projections for the future scenarios. The approach is also used to drive daily simulations by a well-established hydrological modeling framework for the contiguous U.S. [Liang et al., 1994]. This method, which modifies global climate model simulations to better account for landscape variations and other features affecting climate at the regional to local scale, has been previously applied to generate high-resolution regional climate projections for the Northeast, Midwest, Northwest, and Southwest [Wood et al., 2004; Hayhoe et al., 2004; Hayhoe et al., 2008; Cayan et al., 2008; Cherkauer et al., 2009]. Comparison of these methods with dynamically downscaled projections generated using regional climate model simulations provide strong justification for the use of such techniques [Wood et al., 2004; Hayhoe et al., 2008].

    References for this endnote:
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    Cherkauer, K. and T. Sinha, 2009: Hydrologic impacts of
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    Hayhoe, K., D. Cayan, C.B. Field, P.C. Frumhoff, E.P. Maurer, N.L. Miller, S.C. Moser, S.H. Schneider, K.N. Cahill, E.E.
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    Hayhoe, K., C. Wake, B. Anderson, X.-Z. Liang, E. Maurer, J. Zhu, J. Bradbury, A. DeGaetano, A.M. Stoner, and D. Wuebbles, 2008: Regional climate change projections for the Northeast USA. Mitigation and Adaptation Strategies for Global Change, 13(5-6), 425-436.

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    Maurer, E.P., A.W. Wood, J.C. Adam, D.P. Lettenmaier, and B. Nijssen, 2002: A long-term hydrologically-based data set of land surface fluxes and states for the conterminous United States. Journal of Climate, 15(22), 3237-3251.

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    Wood, A.W., E.P. Maurer, A. Kumar, and D.P. Lettenmaier, 2002: Long range experimental hydrologic forecasting for the eastern U.S. Journal of Geophysical Research, 107(D20), 4429, doi:10.1029/2001JD000659.
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    We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset, <http://www-pcmdi.llnl.gov/projects/cmip/index.php>. Support of this dataset is provided by the Office of Science, U.S. Department of Energy. For an overview and documentation of the CMIP3 modelling activity, see Meehl, G.A., C. Covey, T. Delworth, M. Latif, B. McAvaney, J.F.B. Mitchell, R.J. Stouffer, and K.E. Taylor, 2007: The WCRP CMIP3 multi-model dataset: a new era in climate change research. Bulletin of the American Meteorological Society, 88(9), 1383-1394.
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    â—¦ Operational implementation of this index is described at <http://www.ncdc.noaa.gov/oa/climate/research/stagnation/index.php>
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Photo And figure Credits

over: Blue marble, USA, NASA WorldWind

Inside Cover: USA map, NOAA/NCDC; Water glass, ©iStockphotos.com/luchschen; Tractor trailer ©iStockphotos.com/Clayton Hansen; Hospital scene, ©iStockphotos.com/Jeffrey Smith; San Francisco, Grant Goodge,STG Inc.

Table of Contents: Planet Earth, NASA; Water glass,©iStockphotos.com/luchschen; Tractor trailer©iStockphotos.com/Clayton Hansen; Hospital scene, ©iStockphotos.com/Jeffrey Smith; San Francisco, Grant Goodge,STG Inc.

Throughout the report, Adaptation title texture, ©iStockphotos.com/Bill Noll

Page 6, 13: Planet Earth, NASA

Page 38: Lake Effect snow, Dave Kelly, NOAA National Severe Storms Laboratory

Page 41: Water glass,©iStockphotos.com/luchschen; Skagit River, Philip Mote PhD, Climate Impacts Group, Univ. of Washington

Page 47: Turbidity in New York City’s Ashokan, Thurlough Smyth, Climate Change Planning,New York City Department of Environmental Protection, Bureau of Environmental Planning & Assessment

Page 48: Flood water road damage, Grant Goodge, STG Inc.

Page 51: Two Lake Powell photos, John C. Dohrenwend

Page 52: Water and energy corner schematic, Electric Power Research Institute, Palo Alto, CA and Tetra Tech Inc., Lafayette, CA

Page 59: St. Lucie power plant, CCSP SAP 4.5

Page 55: Map, Rocky Bilotta, Earth Resources Technology

Page 60: Hydro-electric dam, Philip Mote PhD, Climate Impacts Group, Univ. of Washington; Ship, NOAA

Page 61: Tractor trailer©iStockphotos.com/claylib; Houses against bridge, Associated Press/Jeff Roberson

Page 65: Kosrae Island photo, John E. Hay

Page 66: Flooded railway Plainfield, IA, Don Becker, USGS

Page 68: Broken bridge, Joe Furr

Page 75: CO2 plant photos, Lewis Ziska, USDA ARS

Page 80: Edith’s checkerspot butterfly, Camille Parmesan, University of Texas at Austin

Page 83: Rob Wu, CCSP SAP 4.3

Page 85: Polar bear, Mike Dunn, North Carolina Museum of Natural Sciences

Page 86: Pika, ©iStockphotos.com/Global Exposure; Climate change graphic depicting the changing forests, Boston Globe/Landov

Page 89: Hospital scene, ©iStockphotos.com/Jeffrey Smith

Page 91: Urban heat island, ©Crown Copyright and Licensing, Canada

Page 92: New York and Atlanta smog, Paul Grabhorn

Page 93: Los Angeles smog, ©iStockphotos.com/Stein Photo

Page 95: Mosquito, upper corner, ©iStockphotos.com/Julie Le de Leseleuc; Virus cycle illustration, Deborah Misch, STG Inc.

Page 97: Poison ivy plant and girl scratching, Sara Veasey, NOAA/NCDC

Page 98: EMS officer and woman, Sara Veasey, NOAA/NCDC

Page 99: San Franciso, Grant Goodge, STG Inc., Midwest flood, Associated Press/Jeff Roberson

Page 101: Person in floodwater, Associated Press/Dave Martin; Aerial flood, USGS

Page 109: New York City, Applied Science Associates, Inc.; Boston Deer Island sewage plant, Massachusetts Water Resources Authority

Page 113: Dry Lake Lanier, Kent Frantz, NOAA National Weather Service Southern Regional Headquarters

Page 114: Barrier islands , USGS

Page 115: Fish plant, Barataria Terrebonne; Flooded area, Dr. Kerry St. Pé of the Barataria-Terrebonne National Estuary program

Page 116: Flood levee, U.S. Army Corps of Engineers

Page 118: Chicago map, City of Chicago; Thermal image, Chicago Police Department and the Cook County Sheriff’s Department

Page 120: Flooded highway, Missouri Highway & Transportation Department/USGS; Flood of 1993, NOAA

Page 125: Dust bowl, NOAA

Page 126: Mallard duck, ©iStockphotos.com/Andy Gehrig

Page 127: Playa lakes, Katharine Hayhoe, Texas Tech University

Page 128: Seedlings, USDA/NRCS

Page 132: Map, Rocky Bilotta, Earth Resources Technology

Page 142: Alaska pipeline, SSgt. Joshua Strang, U.S. Air Force

Page 143: Leaning trees, NOAA

Page 147: El Niño billboard, NOAA National Weather Service Pacific Region Headquarters

Page 148: Micronesia, Eric L. Gilman

Page 150: Ghost swamp, USGS, National Wetlands Research Center

Page 152: Sea wall, Jack Pellette, National Weather Service

 

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Jack Kaye, Vice Chair,
National Aeronautics and Space Administration, Acting Director, U.S. Global Change Research Program

Allen Dearry
Department of Health and Human Services

Anna Palmisano
Department of Energy

Mary Glackin
National Oceanic and Atmospheric Administration

Charles Vincent
Department of Defense

William Hohenstein
Department of Agriculture

Linda Lawson
Department of Transportation

Thomas Armstrong
U.S. Geological Survey

Tim Killeen
National Science Foundation

Patrick Neale
Smithsonian Institution

William Breed
U.S. Agency for International Development

Joel Scheraga
Environmental Protection Agency

Jonathan Pershing
Department of State

 

Executive Office And Other Liaisons

Robert Marlay
Climate Change Technology Program

Katharine Gebbie
National Institute of Standards & Technology

Jason Bordoff
Council on Environmental Quality

Philip DeCola
Office of Science and Technology Policy

Stuart Levenbach
Office of Management and Budget

Margaret McCalla
Office of the Federal Coordinator for Meteorology

Howard Frumkin
Center for Disease Control

 

This book summarizes the science of climate change and the impacts of climate change on the United States, now and in the future. It is an authoritative scientific report written in plain language, with the goal of better informing public and private decision making at all levels.

 

 

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