[1] To examine the suitability of GPS radio occultation (RO) observations as a climate benchmark data set, this study aims at quantifying the structural uncertainty in GPS RO-derived vertical profiles of refractivity and measured refractivity trends obtained from atmospheric excess phase processing and inversion procedures. Five years (2002)(2003)(2004)(2005)(2006) of monthly mean climatologies (MMC) of retrieved refractivity from the experiment aboard the German satellite CHAMP generated by four RO operational centers were compared. Results show that the absolute values of fractional refractivity anomalies among the centers are, in general, 0.2% from 8 to 25 km altitude. The median absolute deviations among the centers are less than 0.2% globally. Because the differences in fractional refractivity produced by the four centers are, in general, unchanging with time, the uncertainty of the trend for fractional refractivity anomalies among centers is ±0.04% per 5 years globally. The primary cause of the trend uncertainty is due to different quality control methods used by the four centers, which yield different sampling errors for different centers. We used the National Centers for Environmental Prediction reanalysis in the same period to estimate sampling errors. After removing the sampling errors, the uncertainty of the trend for fractional refractivity anomalies among centers is between À0.03 and 0.01% per 5 years. Thus 0.03% per 5 years can be considered an upper bound in the processing scheme-induced uncertainty for global refractivity trend monitoring. Systematic errors common to all centers are not discussed in this article but are generally believed to be small.

[1] To examine the claim that Global Positioning System (GPS) radio occultation (RO) data are useful as a benchmark data set for climate monitoring, the structural uncertainties of retrieved profiles that result from different processing methods are quantified. Profile-to-profile comparisons of CHAMP (CHAllenging Minisatellite Payload) data from January 2002 to August 2008 retrieved by six RO processing centers are presented. Differences and standard deviations of the individual centers relative to the inter-center mean are used to quantify the structural uncertainty. Uncertainties accumulate in derived variables due to propagation through the RO retrieval chain. This is reflected in the inter-center differences, which are small for bending angle and refractivity increasing to dry temperature, dry pressure, and dry geopotential height. The mean differences of the time series in the 8 km to 30 km layer range from À0.08% to 0.12% for bending angle, À0.03% to 0.02% for refractivity, À0.27 K to 0.15 K for dry temperature, À0.04% to 0.04% for dry pressure, and À7.6 m to 6.8 m for dry geopotential height. The corresponding standard deviations are within 0.02%, 0.01%, 0.06 K, 0.02%, and 2.0 m, respectively. The mean trend differences from 8 km to 30 km for bending angle, refractivity, dry temperature, dry pressure, and dry geopotential height are within AE0.02%/5 yrs, AE0.02%/5 yrs, AE0.06 K/5 yrs, AE0.02%/5 yrs, and AE2.3 m/5 yrs, respectively. Although the RO-derived variables are not readily traceable to the international system of units, the high precision nature of the raw RO observables is preserved in the inversion chain.

High quality observations of the atmosphere are particularly required for monitoring global climate change. Radio occultation (RO) data, using Global Navigation Satellite System (GNSS) signals, are well suited for this challenge. The special climate utility of RO data arises from their long-term stability due to their self-calibrated nature. The German research satellite CHAllenging Minisatellite Payload for geoscientific research (CHAMP) continuously records RO profiles since August 2001 providing the first opportunity to create RO based climatologies for a multi-year period of more than 5 years. A period of missing CHAMP data from July 3, 2006 to August 8, 2006 can be bridged with RO data from the GRACE satellite (Gravity Recovery and Climate Experiment). We have built seasonal and zonal mean climatologies of atmospheric (dry) temperature, microwave refractivity, geopotential height and pressure with 10°lat-itudinal resolution. We show representative results with focus on dry temperatures and compare them with analysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Although we have available only about 150 CHAMP profiles per day (compared to millions of data entering the ECMWF analyses) the overall agreement between 8 and 30 km altitude is in general very good with systematic differences \0.5 K in most parts of the domain. Pronounced systematic differences (exceeding 2 K) in the tropical tropopause region and above Antarctica in southern winter can almost entirely be attributed to errors in the ECMWF analyses. Errors resulting from uneven sampling in space and time are a potential error source for single-satellite climatologies. The average CHAMP sampling error for seasonal zonal means is \0.2 K, higher values occur in restricted regions and time intervals which can be clearly identified by the sampling error estimation approach we introduced (which is based on ECMWF analysis fields). The total error of this new type of temperature climatologies is estimated to be \0.5 K below 30 km. The recently launched Taiwan/U.S. FORMOSAT-3/COSMIC constellation of 6 RO satellites started to provide thousands of RO profiles per day, but already now the single-satellite CHAMP RO climatologies improve upon modern operational climatologies in the upper troposphere-lower stratosphere and can act as absolute reference climatologies for validation of more bias-sensitive climate datasets and models.

.[1] Observation of the atmospheric climate and detection of changes require high quality data. Radio Occultation (RO) using Global Positioning System (GPS) signals is based on time measurements with precise atomic clocks. It provides a long-term stable and consistent data record with global coverage and favorable error characteristics. Highest quality and vertical resolution is given in the upper troposphere and lower stratosphere (UTLS). RO data exist from the GPS/Met mission within 1995-1997, and continuous observations are available since 2001. We give a review on studies using RO data for climate monitoring and change detection in the UTLS and discuss RO characteristics and error estimates, climate change indicators, trend detection, and comparison to conventional upper-air data. These studies showed that RO parameters cover the whole UTLS with useful indicators of climate change, being most robust in the tropics. Refractivity is most sensitive in the lower stratosphere (LS) and tropopause region, pressure/geopotential height and temperature over the UTLS region. An emerging climate change signal in the RO record can be detected for geopotential height of pressure levels and for temperature, reflecting warming of the troposphere and cooling of the LS. The results are in agreement with trends in radiosonde and ERA-Interim records. Climate model trends basically agree as well but they show less warming/cooling contrast across the tropical tropopause. (Advanced) Microwave Sounding Unit LS bulk temperature anomalies show significant differences to RO. Overall, the quality of RO climate records is suitable to fulfill the requirements of a global climate change monitoring system.