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Appendix C
Method for Developing Figure 3-1
T
o develop Figure 3-1 we applied the methods used in Easterling and
Wehner (2009) to examine the chances that any 10-year period in the
globally averaged annual temperature time series will have a negative
or positive trend. Here we extended this method two ways. First, in addi-
tion to the global analysis we did the same analysis for each region shown
in Figure 3-1. Second, we used projected surface temperatures from the
latest state-of-the-art climate models that are being used in the development
of the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment
Report (AR5) and available from the Coupled Model Intercomparison
Project Phase 5 (CMIP5) database (Taylor et al., 2012)
We used annual averaged surface temperature projections (e.g., one
temperature value per year) from simulations of the 21st century by six
different global climate modeling groups from the publicly available
CMIP5 database. The models include the Canadian Earth System Model
(CANESM2), the National Center for Atmospheric Research Community
Climate System Model (CCSM4), the French National Center for Meteo-
rological Research climate model (CNRM-CM5), the National Oceanic
and Atmospheric Administration Geophysical Fluid Dynamics Laboratory
(GFDL) climate model (GFDL-CM3), two GFDL earth system models
(GFDL-ESM2G and GFDL-ESM2M), and the Norwegian Earth System
Model. The models were run using the Representative Concentration Path-
way (RCP) 8.5 (Moss et al., 2010) greenhouse gas (GHG) forcing scenario
developed for the IPCC AR5 and available from the Earth System Grid
portal (http://www.earthsystemgrid.org [accessed November 15, 2012]).
Each modeling group provided a set or ensemble of two climate simula-
189
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190 CLIMATE AND SOCIAL STRESS
tions for the 21st century each forced with the RCP8.5 scenario but start-
ing with slightly different initial conditions to represent how the climate
might evolve for the 2000–2099 period. The RCP8.5 scenario is close to the
SRES A2 scenario used in previous IPCC reports and represents a business
as usual GHG increase of about 1 percent per year. Discussions of climate
models and their limitations are beyond the scope of this document, but
some can be found in Chapter 8 of the IPCC Fourth Assessment Report by
Working Group I (Randall et al., 2007).
An annually averaged surface temperature from each ensemble member
for the 2000–2050 period was used to calculate an annual temperature
time series for each region in Figure 3-1 and for the globe by averaging the
temperature across all model grid points in each region or for the globe,
which resulted in one time series of temperature for each region for each
model simulation (see Figure 2 from Easterling and Wehner, 2009, for an
example). This resulted in 14 time series for each region (seven models, two
simulations for each model). Ordinary least squares (OLS) trends were then
calculated for all running 10-year periods (e.g., 2000–2009, 2001–2010,
2002–2011, etc.) for each region’s time series for each model simulation.
Each region had 588 trends calculated because there are 42 overlapping
10-year periods for each time series and 14 total time series for each re-
gion. All trends for a given region were used to construct each probability
distribution function shown in Figure 3-1.
We restricted our analysis to the 2000–2050 model period because
the simulated change in global air temperature for this period generally is
linear, with acceleration typically starting after 2050. Additionally, the time
evolution of simulated global surface air temperature for the 2000–2050
period differs little between the different RCP forcing scenarios, with dif-
ferences becoming clear only after 2050. This allows us to generalize the
results shown in Figure 3-1 to any given 10-year period between 2000 and
2050 across a wide range of GHG emissions trajectories in the coming
decades.
REFERENCES
Easterling, D.R., and M.F. Wehner. 2009. Is the climate warming or cooling? Geophysical
Research Letters 36:8. doi:10.1029/2009GL037810.
Moss, R.H., J.A. Edmonds, K.A. Hibbard, M.R. Manning, S.K. Rose, D.P. van Vuuren, T.R.
Carter, S. Emori, M. Kainuma, T. Kram, G.A. Meehl, J.F.B. Mitchell, N. Nakicenovic,
K. Riahi, S.J. Smith, R.J. Stouffer, A.M. Thomson, J.P. Weyant, and T.J. Wilbanks. 2010.
The next generation of scenarios for climate change research and assessment. Nature
463:747–756.
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APPENDIX C 191
Randall, D.A., R.A. Wood, S. Bony, R. Colman, T. Fichefet, J. Fyfe, V. Kattsov, A. Pitman, J.
Shukla, J. Srinivasan, R.J. Stouffer, A. Sumi, and K.E. Taylor. 2007. Climate models and
their evaluation. Pp. 589–662 in Climate change 2007: The physical basis. Contribution
of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on
Climate Change, S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt,
M. Tignor, and H.L. Miller, Eds. New York: Cambridge University Press.
Taylor, K.E., R.J. Stouffer, and G.A. Meehl. 2012. An overview of CMIP5 and the experiment
design. Bulletin of the American Meteorological Society 93:485–498.
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