|Project name:||INDICATE - Indicators of Atmospheric Climate Change from Radio Occultation|
|Project leader:||Andrea K. Steiner|
|Project team:||Andrea K. Steiner|
Bettina C. Lackner
Senior support and advice:
|Partners:||G. Hegerl (School of Geosciences, Univ. of Edinburgh, Edinburgh, U.K.)|
J. Jungclaus, L. Kornblueh (MPI for Meteorology, Hamburg, D)
H. Doleisch, J. Kehrer, P. Muigg (Centre for Virtual Reality and Visualization - VRVis, Vienna, A)
FWF - Austrian Science Fund
|Duration:||Mar. 2006 - Jun. 2009|
Considerable efforts are undertaken by the international scientific community in global climate change research, but still large discrepancies and uncertainties exist regarding the detection, attribution and projections of climate trends. One main cause is the lack of suitably accurate and stable long-term climate observations, an urgent need which was addressed by the Intergovernmental Panel on Climate Change (IPCC) in its “high priority areas of actions” for future research in the IPCC Third Assessment Report 2001. Climate benchmark observations provided by the Radio Occultation (RO) technique using Global Navigation Satellite System (GNSS) signals are well suited to overcome this problem for atmospheric observation, due to their unique combination of properties of accuracy, long-term stability, global coverage, and all-weather capability. Highest accuracy of key climate variables (such as temperature and geopotential height of pressure levels) is obtained in the upper troposphere and lower stratosphere (UTLS), the changing thermal structure in this height domain being a sensitive indicator of climate change.
In this context the main aim of the proposed project was the exploration and provision of benchmark indicators of atmospheric climate change for the UTLS region by using available RO based climatologies and, for exploring the long-term value, “proxy” RO climatologies from re-analyses and climate model runs. Given the limited length of the available RO data (continuous since 2002 only), re-analyses were used to extend the observational datasets back to 1979. Furthermore, Global Climate Model (GCM) scenario simulations for the IPCC 4th Assessment Report (AR4) were used as multi-decadal “proxy” datasets out to year 2050. The datasets were systematically explored for finding the most robust and sensitive RO based change indicators both by testing pre-defined potentially useful indicators within a multi-model/multi-ensemble approach and by using a new visualization-driven 4D field exploration technique. Based on the identified most promising indicators, the trend detection capabilities of RO observations were investigated using methods of optimal trend detection (“fingerprinting”).
In summary INDICATE aimed at revealing optimal UTLS climate trend indicators available from RO combined with validating the skill of climate models with RO data, thereby making a substantial contribution to climate change monitoring and research.
Finalreport_p18733-n10_Indicate [pdf (0.4 MB)]