The IPCC provides temperature “projections” as part of their assessment reports. These projections are based on various “storyline” scenarios using various amounts of CO2 to drive the global circulation models.

Comparison of IPCC Third Assessment Report (TAR) 2001 and Assessment Report (AR4) 2007 The following figure shows Figure 9.14 from the TAR. It shows temperature projections to 2100: “results are relative to 1990 and shown for 1990 to 2100. future changes for the six illustrative SRES scenarios using a simple climate model tuned to seven AOGCMs. Also for comparison, following the same method, results are shown for IS92a. The dark blue shading represents the envelope of the full set of thirty-five SRES scenarios using the simple model ensemble mean results. The light blue envelope is based on the GFDL_R15_a and DOE PCM parameter settings. The bars show the range of simple model results in 2100 for the seven AOGCM model tunings.” The following figure shows Figure SPM-5 from the AR4. It shows temperature projections to 2100: “Solid lines are multi-model global averages of surface warming (relative to 1980-99) for the scenarios A2, A1B and B1, shown as continuations of the 20th century simulations. Shading denotes the plus/minus one standard deviation range of individual model annual averages. The orange line is for the experiment where concentrations were held constant at year 2000 values. The gray bars at right indicate the best estimate (solid line within each bar) and the likely range assessed for the six SRES marker scenarios. The assessment of the best estimate and likely ranges in the gray bars includes the AOGCMs in the left part of the figure, as well as results from a hierarchy of independent models and observational constraints.” In a revision of the AR4 Summary for Policymakers (2008) the following figure became the Figure SPM.5. The left-hand side of the figure shows the scenarios in terms of the GHG emissions. The warming and seal level rise estimates for the scenarios are summarized in the following table. The following figure compares the TAR (left) and AR4 (right) projections from the above figures. The main difference is that they don’t display the blue “envelope of the full set of thirty-five SRES scenarios” in the AR4 and the A1F1 scenario is no longer displayed as a plot on the graph. The following table compares the TAR with the AR4 in terms of temperature plots for various models for a couple of the scenarios. TAR AR4 Figure 9.6 from the TAR. “The time evolution of the globally averaged temperature change relative to the years (1961 to 1990) of the SRES simulations A2 (top) and B2 (bottom)” Figure 10.5 from the AR4. “Time series of globally averaged surface warming (surface air temperature change, °C) from the various global coupled models for the scenarios A2 (top), A1B (middle) and B1 (bottom). Numbers in parentheses following the scenario name represent the number of simulations shown. Values are annual means, relative to the 1980 to 1999 average from the corresponding 20th-century simulations, with any linear trends in the corresponding control run simulations removed. A three-point smoothing was applied. Multi-model (ensemble) mean series are marked with black dots” From the IPCC AR4 (Chapter 8 [http://ipcc-wg1.ucar.edu/wg1/Report/AR4WG1_Print_Ch08.pdf]): the spread of climate sensitivity estimates among current models arises primarily from inter-model differences in cloud feedbacks … Therefore, cloud feedbacks remain the largest source of uncertainty in climate sensitivity estimates. models exhibit a large range of global cloud feedbacks, with roughly half of the climate models predicting a more negative CRF [cloud radiative forcing] in response to global warming, and half predicting the opposite it is not yet possible to assess which of the model estimates of cloud feedback is the most reliable Despite considerable effort since the TAR, uncertainties remain in the representation of solar radiation in climate models … Difficulties in simulating absorbed solar and infrared radiation at the surface leads inevitably to uncertainty in the simulation of surface sensible and latent heat fluxes