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91 Refer to the description of the emissions scenarios in the Global
Climate Change section on pages 22-25. “Lower emissions scenario”
refers to IPCC SRES B1, “higher emissions scenario” refers
to A2 and “even higher emissions scenario” refers to A1FI.
92 IPCC Emissions Scenarios (Even Higher, Higher Emission
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Field, 2007: Global and regional drivers of accelerating CO2 emissions.
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of Energy, Office of Biological & Environmental Research,
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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>
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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.
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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,
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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.
94 Hare, B. and M. Meinshausen, 2006: How much warming are we
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96 Seidel, D.J., Q. Fu, W.J. Randel, and T.J. Reichler, 2008: Widening
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21-24.
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103 Rahmstorf, S., 2007: A semi-empirical approach to projecting
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105 Clark, P.U., A.J. Weaver, E. Brook, E.R. Cook, T.L. Delworth,
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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.
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States Historical Climatology Network Monthly Temperature Data
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108 Christensen, J.H., B. Hewitson, A. Busuioc, A. Chen, X. Gao, I.
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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.
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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, INMCM3,
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 welldocumented
“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].
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