[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.

.[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.

Global Positioning System (GPS) radio occultation (RO) has provided continuous observations of the Earth's atmosphere since 2001 with global coverage, all-weather capability, and high accuracy and vertical resolution in the upper troposphere and lower stratosphere (UTLS). Precise time measurements enable long-term stability but careful processing is needed. Here we provide climate-oriented atmospheric scientists with multicenter-based results on the long-term stability of RO climatological fields for trend studies. We quantify the structural uncertainty of atmospheric trends estimated from the RO record, which arises from current processing schemes of six international RO processing centers, DMI Copenhagen, EUM Darmstadt, GFZ Potsdam, JPL Pasadena, UCAR Boulder, and WEGC Graz. Monthly-mean zonal-mean fields of bending angle, refractivity, dry pressure, dry geopotential height, and dry temperature from the CHAMP mission are compared for September 2001 to September 2008. We find that structural uncertainty is lowest in the tropics and mid-latitudes (50° S to 50° N) from 8 km to 25 km for all inspected RO variables. In this region, the structural uncertainty in trends over 7 yr is <0.03% for bending angle, refractivity, and pressure, <3 m for geopotential height of pressure levels, and <0.06 K for temperature; low enough for detecting a climate change signal within about a decade. Larger structural uncertainty above about 25 km and at high latitudes is attributable to differences in the processing schemes, which undergo continuous improvements. Though current use of RO for reliable climate trend assessment is bound to 50° S to 50° N, our results show that quality, consistency, and reproducibility are favorable in the UTLS for the establishment of a climate benchmark record

Abstract. Due to the measurement principle of the radio occultation (RO) technique, RO data are highly suitable for climate studies. RO profiles can be used to build climatological fields of different atmospheric parameters like bending angle, refractivity, density, pressure, geopotential height, and temperature. RO climatologies are affected by random (statistical) errors, sampling errors, and systematic errors, yielding a total climatological error. Based on empirical error estimates, we provide a simple analytical error model for these error components, which accounts for vertical, latitudinal, and seasonal variations. The vertical structure of each error component is modeled constant around the tropopause region. Above this region the error increases exponentially, below the increase follows an inverse height power-law. The statistical error strongly depends on the number of measurements. It is found to be the smallest error component for monthly mean 10 • zonal mean climatologies with more than 600 measurements per bin. Due to smallest atmospheric variability, the sampling error is found to be smallest at low latitudes equatorwards of 40 • . Beyond 40 • , this error increases roughly linearly, with a stronger increase in hemispheric winter than in hemispheric summer. The sampling error model accounts for this hemispheric asymmetry. However, we recommend to subtract the sampling error when using RO climatologies for climate research since the residual sampling error remaining after such subtraction is estimated to be only about 30 % of the original one or less. The systematic error accounts for potential residual biases in the measurements as well as in the retrieval process and generally dominates the total climatological error. Overall the total error in monthly means is estimated to be smaller than 0.07 % in refractivity Correspondence to: B. Scherllin-Pirscher (pirscher@ucar.edu) and 0.15 K in temperature at low to mid latitudes, increasing towards higher latitudes. This study focuses on dry atmospheric parameters as retrieved from RO measurements so for context we also quantitatively explain the difference between dry and physical atmospheric parameters, which can be significant at altitudes below about 6 km (high latitudes) to 10 km (low latitudes).

Abstract. The utilization of radio occultation (RO) data in atmospheric studies requires precise knowledge of error characteristics. We present results of an empirical error analysis of GPS RO bending angle, refractivity, dry pressure, dry geopotential height, and dry temperature. We find very good agreement between data characteristics of different missions (CHAMP, GRACE-A, and Formosat-3/COSMIC (F3C)). In the global mean, observational errors (standard deviation from "true" profiles at mean tangent point location) agree within 0.3 % in bending angle, 0.1 % in refractivity, and 0.2 K in dry temperature at all altitude levels between 4 km and 35 km. Above 35 km the increase of the CHAMP raw bending angle observational error is more pronounced than that of GRACE-A and F3C leading to a larger observational error of about 1 % at 42 km. Above ≈20 km, the observational errors show a strong seasonal dependence at high latitudes. Larger errors occur in hemispheric wintertime and are associated mainly with background data used in the retrieval process particularly under conditions when ionospheric residual is large. The comparison between UCAR and WEGC results (both data centers have independent inversion processing chains) reveals different magnitudes of observational errors in atmospheric parameters, which are attributable to different background fields used. Based on the empirical error estimates, we provide a simple analytical error model for GPS RO atmospheric parameters for the altitude range of 4 km to 35 km and up to 50 km for UCAR raw bending angle and refractivity. In the model, which accounts for vertical, latitudinal, and seasonal variations, a constant error is adopted around the tropopause region amounting to 0.8 % for bending angle, 0.35 % for refractivity, 0.15 % for dry pressure, 10 m for dry geopotential height, and 0.7 K for dry Correspondence to: B. Scherllin-Pirscher (pirscher@ucar.edu) temperature. Below this region the observational error increases following an inverse height power-law and above it increases exponentially. For bending angle and refractivity we also include formulations for error correlations in order to enable modeling of full error covariance matrices for these primary data assimilation variables. The observational error model is the same for UCAR and WEGC data but due to somewhat different error characteristics below about 10 km and above about 20 km some parameters have to be adjusted. Overall, the observational error model is easily applicable and adjustable to individual error characteristics.

[1] The vertical and spatial structure of the atmospheric El Niño-Southern Oscillation (ENSO) signal is investigated using radio occultation (RO) data from August 2006 to December 2010. Due to their high vertical resolution and global coverage, RO data are well suited to describe the full 3-dimensional ENSO structure in the troposphere and lower stratosphere. We find that interannual temperature anomalies in the equatorial region show a natural decomposition into zonal-mean and eddy (deviations from the zonal-mean) components that are both related to ENSO. Consistent with previous studies, we find that during the warm phase of ENSO, zonal-mean temperatures increase in the tropical troposphere and decrease in the tropical stratosphere. Maximum warming occurs above 8 km, and the transition between warming and cooling occurs near the tropopause. This zonal-mean response lags sea surface temperature anomalies in the eastern equatorial Pacific by 3 months. The atmospheric eddy component, in contrast, responds rapidly (within 1 month) to ENSO forcing. This signal features a low-latitude dipole between the Indian and Pacific Oceans, with off-equatorial maxima centered around 20 to 30 latitude in both hemispheres. The eddy response pattern attains maximum amplitude in the upper troposphere near 11 km and (with opposite polarity) in a shallow layer near the tropopause at approximately 17 km. The eddy ENSO signal tends to be out-of-phase between low and middle latitudes in both the troposphere and lower stratosphere. Citation: Scherllin-Pirscher, B., C. Deser, S.-P.Ho, C. Chou, W. Randel, and Y.-H. Kuo (2012), The vertical and spatial structure of ENSO in the upper troposphere and lower stratosphere from GPS radio occultation measurements, Geophys.

Abstract. Data consistency is an important prerequisite to build radio occultation (RO) climatologies based on a combined record of data from different satellites. The presence of multiple RO receiving satellites in orbit over the same time period allows for testing this consistency. We used RO data from CHAMP (CHAllenging Minisatellite Payload for geoscientific research), six FORMOSAT-3/COSMIC satellites (Formosa Satellite Mission 3/Constellation Observing System for Meteorology, Ionosphere and Climate, F3C), and GRACE-A (Gravity Recovery and Climate Experiment). We show latitude-altitude-resolved results for an example month (October 2007) and the temporal evolution of differences in a climate record of global and monthly means from January 2007 to December 2009. Latitude-and altitude-resolved refractivity and dry temperature climatologies clearly show the influence of different sampling characteristics; monthly mean deviations from the multi-satellite mean over the altitude domain 10 km to 30 km typically reach 0.1 % and 0.2 K, respectively. Nevertheless, the 3-yr average deviations (shorter for CHAMP) are less than 0.03 % and 0.05 K, respectively. We find no indications for instrument degradation, temporal inhomogeneities in the RO records, or temporal trends in sampling patterns. Based on analysis fields from ECMWF (European Centre for MediumRange Weather Forecasts), we can estimate -and subtract -the sampling error from each monthly climatology. After such subtraction, refractivity deviations are found reduced to <0.05 % in almost any month and dry temperature deviCorrespondence to: U. Foelsche (ulrich.foelsche@uni-graz.at) ations to <0.05 K (<0.02 % relative) for almost every satellite and month. 3-yr average deviations are even reduced to <0.01 % and <0.01 K (CHAMP: −0.05 K), respectively, establishing an amazing consistency of RO climatologies from different satellites. If applying the same processing scheme for all data, refractivity and dry temperature records from individual satellites with similar bending angle noise can be safely combined up to 30 km altitude (refractivity also up to 35 km) to a consistent single climate record of substantial value for climate monitoring in the upper troposphere and lower stratosphere.

Radio occultation (RO) sensing is used to probe the earth's atmosphere in order to obtain information about its physical properties. With a main interest in the parameters of the neutral atmosphere, there is the need to perform a correction of the ionospheric contribution to the bending angle. Since this correction is an approximation to first order, there exists an ionospheric residual, which can be expected to be larger when the ionization is high (day versus night, high versus low solar activity). The ionospheric residual systematically affects the accuracy of the atmospheric parameters at low altitudes, at high altitudes (above 25–30 km) it even is an important error source. In climate applications this could lead to a time dependent bias which induces wrong trends in atmospheric parameters at high altitudes. The first goal of our work was to study and characterize this systematic residual error. In a second step we developed a simple correction method, based purely on observational data, to reduce this residual for large ensembles of RO profiles. In order to tackle this problem, we analyzed the bending angle bias of CHAMP and COSMIC RO data from 2001–2011. We could observe that the nighttime bending angle bias stays constant over the whole period of 11 yr, while the daytime bias increases from low to high solar activity. As a result, the difference between nighttime and daytime bias increases from about −0.05 μrad to −0.4 μrad. This behavior paves the way to correct the solar cycle dependent bias of daytime RO profiles. In order to test the newly developed correction method we performed a simulation study, which allowed to separate the influence of the ionosphere and the neutral atmosphere. Also in the simulated data we observed a similar increase in the bias in times from low to high solar activity. In this simulation we performed the climatological ionospheric correction of the bending angle data, by using the bending angle bias characteristics of a solar cycle as a correction factor. After the climatological ionospheric correction the bias of the simulated data improved significantly, not only in the bending angle but also in the retrieved temperature profiles