Sources used:
http://www.mpimet.mpg.de/en/wissenschaft/modelle/echam.html
http://www.mpimet.mpg.de/en/wissenschaft/modelle/mpiom/mpiom-description.html
http://www.ipcc-data.org/ar4/model-MPIM-ECHAM5-change.html
http://cera-www.dkrz.de/
http://www.ccsm.ucar.edu/models/ccsm3.0/
http://www.cgd.ucar.edu/ccr/strandwg/CCSM3_AR4_Experiments.html
http://www.ccsm.ucar.edu/experiments/ccsm3.0/
http://www.ipcc-data.org/ar4/model-NCAR-CCSM3-change.html
https://www.earthsystemgrid.org/
http://ipcc-wg1.ucar.edu/wg1/Report/AR4WG1_Print_Ch11.pdf

ECHAM5_MPI-OM

ECHAM5_MPI-OM is coupled climate model consisting of atmospheric general circulation model (ECHAM5) and MPI-OM ocean-sea ice component developed at the Max Planck Institute for Meteorology (MPIM).

ECHAM5 is the 5th generation of the ECHAM general circulation model developed at the Max Planck Institute for Meteorology in Hamburg evolving originally from the spectral weather prediction model of the European Centre for Medium Range Weather Forecasts (ECMWF; Simmons et al. (1989)), which can be configured to resolve the atmosphere up to 10 hPa for tropospheric studies, or up to 0.01 hPa for middle atmosphere studies.
Model homepage: http://www.mpimet.mpg.de/en/wissenschaft/modelle/echam.html

The Max-Planck- Institute ocean model (MPIOM) is the ocean- sea ice component of the Max-Planck- Institute climate model (Roeckner et al., 2006; Jungclaus et al., 2006). MPIOM is a primitive equation model (C-Grid, z- coordinates, free surface) with the hydrostatic and Boussinesq assumptions. It includes an embedded dynamic/ thermodynamic sea ice model with a viscous- plastic rheology following Hibler (1979) and a bottom boundary layer scheme for the flow across steep topography. A model description can be found at Marsland et al. (2003).
Model Homepage: http://www.mpimet.mpg.de/en/wissenschaft/modelle/mpiom/mpiom-description.html

IPCC AR4 SRES scenarios data overview and results:
http://www.ipcc-data.org/ar4/model-MPIM-ECHAM5-change.html

Data output is available at:   http://cera-www.dkrz.de/

NCAR-CCSM3

The Community Climate System Model (CCSM) is a coupled climate model for simulating the earth's climate system, composed of four separate models simultaneously simulating the earth's atmosphere, ocean, land surface and sea-ice, and one central coupler component.
Model Homepage: http://www.ccsm.ucar.edu/models/ccsm3.0/

Overview of experiments can be found at:

• http://www.cgd.ucar.edu/ccr/strandwg/CCSM3_AR4_Experiments.html
• http://www.ccsm.ucar.edu/experiments/ccsm3.0/
IPCC AR4 SRES scenarios data overview and results:   http://www.ipcc-data.org/ar4/model-NCAR-CCSM3-change.html

CCSM3 output data is disseminated via the Earth System Grid (ESG): https://www.earthsystemgrid.org/

PRUDENCE project for European Climate Change

From IPCC WG1 AR4 Report: "Regional Climate Projections":   http://ipcc-wg1.ucar.edu/wg1/Report/AR4WG1_Print_Ch11.pdf

The Prediction of Regional scenarios and Uncertainties for Defining European Climate change risks and Effects (PRUDENCE) project involved more than 20 European research groups. The main objectives of the project were to provide dynamically downscaled high-resolution climate change scenarios for Europe at the end of the 21st century, and to explore the uncertainty in these projections. Four sources of uncertainty were studied:

1. Sampling uncertainty due to the fact that model climate is estimated as an average over a finite number (30) of years,
2. Regional model uncertainty due to the fact that RCMs use diff erent techniques to discretize the equations and to represent sub-grid eff ects,
3. Emission uncertainty due to choice of IPCC SRES emission scenario, and
4. Boundary uncertainty due to the diff erent boundary conditions obtained from different global climate models.
Each PRUDENCE experiment consisted of a control simulation representing the period 1961 to 1990 and a future scenario simulation representing 2071 to 2100. A large fraction of the simulations used the same boundary data (from the Hadley Centre Atmospheric Model (HadAM3H) for the A2 scenario) to provide a detailed understanding of the regional model uncertainty. Some simulations were also made for the B2 scenario, and by using driving data from two other GCMs and from diff erent ensemble members from the same GCM. More details are provided in, for example, Christensen et al. (2007), Deque et al. (2005) and http://prudence.dmi.dk.