.[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.

Existing upper air records of radiosonde and operational satellite data recently showed a reconciliation of temperature trends but structural uncertainties remain. GPS radio occultation (RO) provides a new high‐quality record, profiling the upper troposphere and lower stratosphere with stability and homogeneity. Here we show that climate trends are since recently detected by RO data, consistent with earliest detection times estimated by simulations. Based on a temperature change detection study using the RO record within 1995–2008 we found a significant cooling trend in the tropical lower stratosphere in February while in the upper troposphere an emerging warming trend is obscured by El Niño variability. The observed trends and warming/cooling contrast across the tropopause agree well with radiosonde data and basically with climate model simulations, the latter tentatively showing less contrast. The performance of the short RO record to date underpins its capability to become a climate benchmark record in the future.

[1] The sampling error of Global Positioning System (GPS) radio occultation (RO) derived temperature climatologies is computed over a representative time span of 2 years and compared for Sun-synchronous and non-Sun-synchronous Low Earth Orbit (LEO) satellites. The main focus lies on the sampling error's local time component, which is caused by incomplete sampling of the diurnal cycle and which depends on the geometry of the satellite orbits. The Sun-synchronous satellite MetOp (Meteorological Operational European weather satellite) and the non-Sun-synchronous satellite CHAMP (Challenging Minisatellite Payload), both carrying GPS RO instruments on board, serve as representative cases. For the Sun-synchronous satellite MetOp the local time component remains constant during the whole observation period such that the magnitude of the local time errors in monthly mean or longer-term mean RO climatologies is generally lower than ±0.1 K. Except for potential long-term effects of global warming on the diurnal cycle, which might require calibration, this small local time component is stable on decadal timescales and is mainly positive in the Northern Hemisphere and at low latitudes, whereas it is mainly negative in the Southern Hemisphere. These features are attributable to a slight orbit-determined asymmetry in local time sampling. The typical (temporally stable) local time error of an annual mean MetOp climatology resolved into 18 zonal bands amounts to $0.04 K. For the non-Sun-synchronous satellite CHAMP the local time error component in monthly mean RO climatologies is also small (up to about ±0.15 K) but more variable (about zero mean) at middle and high latitudes. At low latitudes it results in sinusoidally varying positive and negative deviations with a several-months period, resulting from the local time drift of the satellite. The magnitude of local time errors is slightly larger compared to MetOp since the monthly averaging period is too short for CHAMP to entirely sample a diurnal cycle; a longer averaging period further decreases CHAMP's local time component. An annual mean climatology resolved into 18 zonal bands shows for CHAMP a typical local time residual error component of $0.03 K. The overall evidence is that even with single RO satellites, monthly climatologies of high accuracy (sampling error <0.3 K) with the local time component being a minor part (<0.1 K to 0.15 K) can be obtained.Citation: Pirscher, B., U. Foelsche, B. C. Lackner, and G. Kirchengast (2007), Local time influence in single-satellite radio occultation climatologies from Sun-synchronous and non-Sun-synchronous satellites,

The detection of climate change signals in rather short satellite datasets is a challenging task in climate research and requires high-quality data with good error characterization. Global Navigation Satellite System (GNSS) radio occultation (RO) provides a novel record of high-quality measurements of atmospheric parameters of the upper-troposphere-lower-stratosphere (UTLS) region. Because of characteristics such as long-term stability, self calibration, and a very good height resolution, RO data are well suited to investigate atmospheric climate change. This study describes the signals of ENSO and the quasi-biennial oscillation (QBO) in the data and investigates whether the data already show evidence of a forced climate change signal, using an optimal-fingerprint technique. RO refractivity, geopotential height, and temperature within two trend periods (1995-2010 intermittently and 2001-10 continuously) are investigated. The data show that an emerging climate change signal consistent with the projections of three global climate models from the Coupled Model Intercomparison Project cycle 3 (CMIP3) archive is detected for geopotential height of pressure levels at a 90% confidence level both for the intermittent and continuous period, for the latter so far in a broad 508S-508N band only. Such UTLS geopotential height changes reflect an overall tropospheric warming. 90% confidence is not achieved for the temperature record when only large-scale aspects of the pattern are resolved. When resolving smaller-scale aspects, RO temperature trends appear stronger than GCM-projected trends, the difference stemming mainly from the tropical lower stratosphere, allowing for climate change detection at a 95% confidence level. Overall, an emerging trend signal is thus detected in the RO climate record, which is expected to increase further in significance as the record grows over the coming years. Small natural changes during the period suggest that the detected change is mainly caused by anthropogenic influence on climate.

In atmospheric and climate research, the increasing amount of data available from climate models and observations provides new challenges for data analysis. The authors present interactive visual exploration as an innovative approach to handle large datasets. Visual exploration does not require any previous knowledge about the data, as is usually the case with classical statistics. It facilitates iterative and interactive browsing of the parameter space to quickly understand the data characteristics, to identify deficiencies, to easily focus on interesting features, and to come up with new hypotheses about the data. These properties extend the common statistical treatment of data, and provide a fundamentally different approach. The authors demonstrate the potential of this technology by exploring atmospheric climate data from different sources including reanalysis datasets, climate models, and radio occultation satellite data. Results are compared to those from classical statistics, revealing the complementary advantages of visual exploration. Combining both the analytical precision of classical statistics and the holistic power of interactive visual exploration, the usual workflow of studying climate data can be enhanced.