|Project name:||VERTICLIM – Atmospheric Vertical Structure and Trends in Climate Data|
Andrea K. Steiner
|Project team:||Projekt team: Florian Ladstädter (PostDoc Scientist) |
Hallgeir Wilhelmsen (PhD Student)
Barbara Angerer (Scientist)
Ulrich Foelsche (Senior Sci. Adviser)
Gottfried Kirchengast (Senior Sci. Adviser)
Barbara Scherllin-Pirscher (PostDoc Sci. Adviser)
|Partners:||Partners: Advisers: |
L. Haimberger, Dep. of Meteorology and Geophysics, Univ. of Vienna, Austria
G. C. Hegerl, School of GeoSciences, Univ. of Edinburgh, UK
Y.-H. Kuo, UCAR/NCAR, Boulder, CO, USA
K. B. Lauritsen, DMI, Copenhagen, Denmark
C. S. Long, CPC/NOAA College Park, USA
A. Mannucci, JPL/CalTech, Pasadena, CA, USA
C. Mears, RSS Santa Rosa, USA
G. Stiller, KIT Karlsruhe, GER
K. Taylor, PCMDI Livermore, USA
A. von Engeln, EUMETSAT, Darmstadt, GER
J. Wickert, German Research Centre for Geosciences (GFZ), Potsdam, GER
FWF – Austrian Science Fund
Observations of the Earth’s surface temperature provide undeniable evidence of a changing climate. While surface temperature trends are in accordance amongst different groups, there are still unresolved issues regarding upper-air climate trends. Though overall agreement on a global warming of the troposphere and a cooling of the stratosphere is given, the uncertainty in trend rates and their vertical structure is large and limits the ability to draw robust and consistent inferences about climate trends. This is stated as a key issue in the recent IPCC report implying the need for data with better accuracy.
Addressing this need is challenging since uncertainties exist in observations, reanalyses, and climate model output. Observations from weather satellites and balloons have several shortcomings since they were not intended to serve climate monitoring needs, which demand accurate and long-term stable measurements. In atmospheric reanalyses observations are integrated into models, containing effects of both observation and model errors. In current climate models discrepancies to observations are evident in vertical thermal structure and trends, especially in the upper troposphere and stratosphere.
Radio Occultation (RO) provides independent observations with beneficial characteristics in this context. The traceability to time measurements with precise atomic clocks assures a long‐term stable and consistent data record with global coverage. Accuracy, low structural uncertainty, and use for climate studies have been demonstrated. High quality and vertical resolution offer the distinct advantage for assessing the vertical thermodynamic structure.
The central aim of the project VERTICLIM is the exploration and evaluation of the vertical structure of atmospheric climate variability and climate trends, their regional imprints, and relevant processes from the surface to the stratopause. Focus periods are 2002–2015 with dense data set coverage for rigorous short-term study, and 1979–2015 with good coverage for complementary longer-term study. New insights will be gained on recent climatic changes in the troposphere and stratosphere by systematically exploiting upper-air records from observations and reanalyses, and climate models.
Evaluating the RO record as reference and the inter-consistency of other high-quality observations such as MIPAS, SABER, radiosondes, and of AMSU/SSU bulk temperatures, will provide essential new information on the climate quality of upper-air observations. Exploration of atmospheric key characteristics, including annual cycle, tropopause/stratopause dynamics, and climate variability modes will reveal key strengths, weaknesses, and improvement potential of reanalyses and models. Also new insights into the vertical structure of trends and their regional imprints in the atmosphere compared to the surface will be gained from RO and best-evaluated records.
Overall we aim with VERTICLIM to draw a picture of unprecedented quality and rigor of the vertical structure of atmospheric climate variability and trends and to reveal key skills of upper-air observations, reanalyses, and climate models with RO as reference climate data record.