RO measurements have been continuously available since 2001 only, which can be considered as too short for climatological trend analysis. Thus, the potential of climate monitoring by RO accessible parameters is explored by using simulations of three representative global circulation models of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4). The Atmosphere-Ocean GCMs participating in the IPCC model comparison represent the most advanced and comprehensive set of climate simulations so far produced (Cordero and de Forster 2006). We concentrate on the 1961-2060 period employing a concatenation of 20th century and Special Report on Emission Scenarios (SRES) A2 as well as B1 runs of the three GCMs:

• CCSM3 from the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) (Collins et al. 2006), 5 runs A2, 7 runs B1;

• ECHAM5 from the Max-Planck-Institute for Meteorology in Hamburg (MPI) (Roeckner et al. 2003), 3 runs A2, 3 runs B1;

• HadCM3 from the Hadley Centre for Climate Prediction and Research of the UK-MetOffice (Gordon et al. 2000; Pope et al. 2000), 1 run A2, 1 run B1.

The data are available for download from the WCRP CMIP31 multi-model database.2 The models' climate projections, based upon forcings of varying strengths, form the basis of our knowledge about the anticipated range of possible future climate change. Model results differ depending on the kind of natural and anthropogenic forcings used within the simulations. Besides greenhouse gas forcings, which are prescribed by the A2 and B1 scenarios, the use of ozone forcing plays an essential role for simulating stratospheric temperatures and thus models including ozone forcings should be given preference in stratospheric studies. All selected models use ozone depletion and recovery forcings in their simulations (Roeckner et al. 2005, Jerry Meehl, NCAR/USA, Tim Johns, MetOffice/U.K., Jonathan Gregory, MetOffice/U.K., personal communication, 12/2007), and thus are qualified for our study. Furthermore, the three selected models show different characteristics of internal variation and intensity of circulation (ECHAM5 stronger than CCSM3, HadCM3 in between, cf. Leroy et al. 2006; Meehl et al. 2007). They cover a good range of variability and thus comprise a representative set of GCMs with respect to the whole set of IPCC AR4 models (Reichler and Kim 2008).

The original data, featuring different resolutions (of at least about 2.5° x 2.5°), were regridded to a common 2.5° x 2.5° grid in latitude and longitude. For every seasonal and annual mean we analyzed refractivity (N), geopotential height (Z), temperature (T), and specific humidity (q)—parameters which are provided by RO measurements—on 18 pressure levels ranging from 1000-10 hPa. While Z, T, and q fields are available for download from the WCRP CMIP3 multi-model database, N was derived using the classical Smith-Weintraub formula (Smith and Weintraub 1953).

Even though global-scale climate statistics, such as global mean climatologies of atmospheric parameters, provide in general good indicators for global climate change, regional statistics allow more sophisticated and detailed interpretations.

Thus, a combination of 6 large-scale zonal bands and 30 regions was used (Fig. 1). The zonal bands (marked at the left and right border) range from global mean climatologies over hemispheric means to zonal bands of 30° in latitude. The 30 regions follow the definition in Chapter 11 (Christensen et al. 2007) of the IPCC AR4. They include 22 land regions (solid lines in Fig. 1), 6 regions covering oceanic areas (dotted lines in Fig. 1) and 2 covering the polar caps (ARC, ANT, marked at the right side of Fig. 1), respectively. These IPCC region definitions are similar to those of Giorgi and Francisco (2000). The combination of the 30 IPCC regions and the 6 zonal bands is hereinafter addressed as IPCC+ regions. The regional parameter values were derived for each pressure level from the basic 2.5° x 2.5° grid using area-weighted averaging.

1World Climate Research Programme's (WCRP's) Working Group on Coupled Modelling (WGCM), 11/2007

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