SPARC DATA INITIATIVE (Chemical Observations) Susann Tegtmeier 1 & Michaela I. Hegglin 2 1)IFM-GEOMAR, Kiel, Germany 2)University of Toronto, Toronto,

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Presentation transcript:

SPARC DATA INITIATIVE (Chemical Observations) Susann Tegtmeier 1 & Michaela I. Hegglin 2 1)IFM-GEOMAR, Kiel, Germany 2)University of Toronto, Toronto, Canada

Example from the UTLS chapter of the CCMVal report: MIPAS shows likely a too low amplitude (pers. communication G. Stiller IMK, Karlsruhe) ACE-FTS, despite potentially higher accuracy, shows very large std deviations, largely due to its restricted sampling coverage Comparison between models and the two data sets yield different outcome. Uncertainties in model assessment Seasonal cycle of H 2 O for 40°N-60°N MIPAS observations in black, ACE-FTS in grey MOTIVATION

SPARC CCMVal project (model-measurement intercomparisons): → variety of chemical observational data sets available → not necessarily known which data set is most reliable for a particular application → conflicting results when comparing models to different data sets → comparison less meaningful (analogous to SPARC Report No. 3 for the meteorological data sets) Need for an assessment of the available data sets for chemical trace gases

OBJECTIVES The proposed report will offer guidance for the use of chemical trace gas observations from space based instruments. Establishing a data portal for chemical observations in collaboration with the space agencies and assessing state of data availability. Compiling climatologies of chemical trace gases (e.g. zonal means, variability, seasonal evolution, annual means) in collaboration with the instrument PIs. Detailed inter-comparison of these climatologies (summarizing useful information and highlighting differences between the data sets) which will be published as a SPARC report.

SCOPE Vertically resolved measurements of chemical trace gas species from the upper troposphere to the middle mesosphere (approximately 5-65 km). The main focus is on satellite measurements but, where helpful, the evaluation may be enhanced through aircraft and balloon measurements. The main species the report is targeted at are CH 4, N 2 O, HNO 3, NO y, NO x, NO 2, HCl, Cl y, Br y, age of air (SF 6 and CO 2 ). Ozone and water vapor may also be treated briefly, heavily drawing from the SPARC water vapor and ozone initiatives.

DATA INITIATIVE The initiative is very timely given the fact that the “golden age” of stratospheric composition measurements of the past three decades is about to come to an end, it is highly likely that there will be much less measurements in the future, we need to capture existing knowledge on current and recent instruments before this knowledge is lost. In particular the report will also help to identify priorities for reprocessing data or enhanced validation efforts identify measurement gaps, which could motivate and provide support for future missions.

Satellite measurements: primary source of chemical data sets of the middle atmosphere during the last 30 years SATELLITE MEASUREMENTS

Variety of aircraft and balloon campaigns available (list not complete): SPARC Newsletter No. 26 (2006) AICRAFT AND BALLOON MEASUREMENTS provide high accuracy, high precision and high resolution data limited sampling in time and space

For some atmospheric missions climatologies have been developed, e.g. A stratospheric climatology for O 3, H 2 O, CH 4, NO x, HCl and HF derived from HALOE measurements (Grooß and Russell, ACP 2005). Odin stratospheric proxy NO y measurements and climatology (Brohede et al., ACP 2008). Nitric acid in the stratosphere based on Odin observations from 2001 to 2009 – Part 1: A global climatology (Urban et al., ACP, 2009). Towards a climatology of stratospheric bromine monoxide from SCIAMACHY limb observations (Sheode et al., ACPD 2006). Technical Note: A new global database of trace gases and aerosols from multiple sources of high vertical resolution measurements (Hassler et al., ACP, 2008). BrO climatology from OSIRIS (McLinden). A Stratospheric NO 2 Climatology from Odin/OSIRIS Limb-Scatter Measurements (Brohede at al., Can. J. Phys., 2007). DATA AVAILABILITY

EVALUATIONS Zonal mean climatologies Grooß and Russell, ACP 2005 HALOE NO x climatology January (sunset only) SCIAMACHY BrO climatology December-February Sheode et al., ACPD 2006

EVALUATIONS Seasonal evolution Brohede et al., ACP 2008 Odin NO y climatology seasonal cycle of monthly mean values Odin NO y climatology seasonal cycle of standard deviation

IDENTIFIED CONTACTS Author team (to be determined from instrument teams and analysts) Space Agencies: NASA: Ernest Hilsenrath CSA: Thomas Piekutowski ESA: Claus Zehner, Joerg Langen Experts / Advisory group (e.g. Bill Randel, Karen Rosenlof) SYNERGIES Atmospheric Composition Portal will be developed by ACC so far supported by NASA and DLR no homogenized data sets, but easy access to air quality data NASA MEaSUREs - GOZCARDS program lead: Lucien Froidevaux homogenized stratospheric data sets for American missions

Instrument teams MLS (AURA/UARS): Lucien Froidevaux, Michelle Santee, Nathaniel Livesey TES (AURA): Annemarie Eldering, John Worden, Jessica Neu ACE-FTS (SCISAT-1): Peter Bernath, Kaley Walker POAM II / III (SPOT-3,4): Karl Hoppel HALOE (UARS): James M. Russell III, John Anderson OSIRIS (ODIN): Doug Degenstein, Samuel Brohede SMR (ODIN): Donal Murtagh, Joachim Urban ILAS I / II (ADEOS-1,2): Tatsuya Yokota, Yasuhiro Sasano SAGE I / II / III: M. Patrick McCormick SME: Garry Rottman MIPAS (ENVISAT): Thomas von Clarmann, Bernd Funke SCIAMACHY (ENVISAT): John Burrows, Alexei Rozanov GOMOS: E. Kyröla, J. Tamminen SABER (Timed): James M. Russell III HIRDLS: John Gille AIM/SOFIE: James M. Russell III, Larry Godley

TIME LINE Report outline and author teams to be defined in spring 2010 First workshop in autumn 2010 Report to be completed by May 2012 DISCUSSION POINTS How much of aircraft, balloon, and ground-based measurements should be included, should such comparisons be done in separate papers? Should water vapour and ozone be included? What about CO, short-lived species, aerosols, PSCs? Technical issues like trends in data sets, sampling biases?