Summary and Conclusions

The potential of RO accessible trend indicators was demonstrated by means of climate simulations of three representative IPCC AR4 models (ECHAM5, CCSM3, HadCM3). The model simulations, investigated for the years 1961-2060 show a strong climate change signal in height regions depending on the respective physical processes governing the individual parameters.

Based on suitable criteria—including direction of trends, trend significances, and goodness-of-fit—the RO accessible parameters allow the identification of geographical regions and height domains qualified for best trend indicators. Spring and autumn show similar trend patterns, but clearest results in GCM simulations are found in the summer season.

• Refractivity is a suitable trend indicator between 300 hPa and 100 hPa as well as between 30 hPa and 10 hPa.

• Geopotential height, together with refractivity the most pristine RO parameter, is a suitable trend indicator in a wide height domain (400-20 hPa), which is partly not covered by the other parameters.

• Temperature trends are best represented in the UT (400-100 hPa) and LS (30-10 hPa), except over continental regions of winter hemispheric mid and high latitudes.

• Specific humidity shows best results for zonal means and the Arctic region for all seasons in the lower troposphere where RO data are well sensitive to humidity.

For the UTLS—our RO focus region—refractivity and geopotential height alone are adequate trend indicators. Temperature can be used as additional indicator especially in the upper troposphere. Together, the RO accessible parameters can provide a dataset for the UTLS region fulfilling the needs of climate monitoring and diagnosis for long-term stable, self-calibrated, and well height-resolved global data.

Acknowledgements We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP's Working Group on Coupled Modelling (WGCM) for their roles in making available the WCRP CMIP3 multi-model dataset. Support of this dataset is provided by the Office of Science, U.S. Department of Energy. This work was funded by the Austrian Science Fund (FWF) Project INDICATE P18733-N10.

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