Construction of Temperature Climate Data Records in the Upper Troposphere and Lower Stratosphere Using Multiple RO Missions from April 2006 to July 2023 at NESDIS/STAR

An accurate UTLS temperature trend estimate is vital to understanding climate variation and its causes. In the past 40 years, spaceborne microwave (MW) radiometers and radiosonde observations (RAOBs) have been primary data sources for upper-air temperature trend detection. However, evaluating long-term temperature changes from these platforms is challenging owing to the trend uncertainties caused by the instrument changes over time, changes in the observational network, and inconsistent calibration and correction approaches of the inter-satellite offsets among missions. Complemented with MW sounders and RAOBs, Global Navigation Satellite System (GNSS) Radio Occultation (RO) data are useful for weather forecasting, atmospheric studies, and climate monitoring.

In this study, we developed a new monthly zonal mean climatology (MMC) in the upper troposphere and lower stratosphere (UTLS) from April 2006 to July 2023 using the dry temperature profiles from multiple GNSS RO missions processed by the GNSS RO Science and Data Center (SDC) at the NOAA Center for Satellite Applications and Research (STAR). The multiple RO missions include Formosa Satellite Mission 3/Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC-1), Formosa Satellite Mission 7/ COSMIC-2, SPIRE, and Meteorological Operational satellite (MetOp)-A, -B, -C.

To ensure the consistency of the data used in STAR-MMC construction, we compared the collocated dry temperature profiles from multiple STAR-processed RO missions. We found that above 20 km, COSMIC-2 has a positive bias against Metop-B/C that could increase the temperature trend by 0.05K/Dec, exceeding the measurement stability required by the Global Climate Observing System (GCOS). Therefore, COSMIC-2 data above 20 km are excluded from generating the multi-mission MMC. We further investigated the possible causes of COSMIC-2 positive bias against other missions.

We used a reanalysis model to estimate and remove the sampling error in STAR-MMC. The robustness of this sampling error correction method is validated through three different reanalysis models, i.e., fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA-5), Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and Japanese 55-year Reanalysis (JRA-55). The result shows the uncertainty caused by using various models in the correction method is negligible.

This STAR-MMC is then compared with the ROM SAF MMC and the MMC derived from ERA-5, MERRA-2, and JRA-55 reanalyses, exhibiting good agreement. Various climate signals, such as Quasi-Biennial Oscillation (QBO) and El Niño–Southern Oscillation (ENSO), can be identified from STAR MMC. The global temperature trends present a transition from a prominent warming of 0.309 ± 0.085 K/Decade in the upper troposphere to a robust cooling of -0.281 ± 0.044 K/Decade in the mid-stratosphere, consistent with the well-known response of the UTLS region to long-term global warming. These results demonstrate that STAR MMC can capture climate signals and monitor long-term climate change.

Disclaimer: The scientific results and conclusions, as well as any views or opinions expressed herein, are those of the author(s) and do not necessarily reflect those of NOAA or the Department of Commerce.