To 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 addition 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 Meteorological 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 Pathway (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-
tions for the 21st century each forced with the RCP8.5 scenario but starting 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 region. 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 differences 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.
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