IGAC/SPARC Chemistry-Climate Model Initiative

At the IGAC/SPARC Chemistry-Climate Model Workshop held in Davos in May 2012 four expert teams have been formed to foster collaborations between modelers and observationalists for an improved model evaluation. Several activities are currently underway that aim at a better access and comparability to observations. This website provides links to some of the ongoing activities.

For further information, please also see the CCMI simulation document available here.

1. Stratospheric composition satellites (Expert group leaders: Susann Tegtmeier and Michaela Hegglin, co-chairs of the SPARC Data Initiative)

This expert group provides the link between instrument teams (primarily via the SPARC Data Initiative) and CCMI with the goal to improve the comparability between models and limb-viewing satellite observations from the upper troposphere to the lower mesosphere. Key issues of model-measurement comparisons in this region are 1) knowledge of the quality of the available data sets for particular evaluations and 2) the impact of different sampling patterns on the representativeness of the data sets, which is especially important for trace gas species with strong diurnal cycles. The expert group will provide guidance on these issues based on the results of the SPARC Data Initiative. This international team effort is currently carrying out a comprehensive inter-comparison of vertically-resolved monthly zonal mean trace gas climatologies derived from most available stratospheric limb satellite measurements with the goal to gain better knowledge of the uncertainty and limitations of the different data sets. A list of zonal monthly mean climatologies (which will be directly comparable to model T2M fields and will be made publicly available by autumn 2013) can be found in Table S3 of the CCMI document. Along with the climatologies, the sampling patterns of the different satellite instruments will be provided. The latter can be used to sub-sample model fields in order to account for diurnal sampling issues affecting the zonal mean climatologies of shorter-lived species. The team will help to extend currently available satellite databases by additional parameters, classes of data sets (e.g., single profile information) and satellite instruments, and will provide updated stratospheric tracer diagnostics for implementation into the NCAR-Diagnostic Tool or via the obs4MIP data base (TBD). An extension of the SPARC Data Initiative into the upper troposphere and lower mesosphere covering aspects of 3D and shorter-term availability is in its planning phase.(More details on the Initiative can be found at the SPARC Data Initiative website.

2. Tropospheric composition satellites (Expert group leader: Bryan Duncan)

There is now a wealth of satellite data with which to evaluate processes and trace gas distributions within models. Each of these datasets has its own strengths/limitations and often provides complementary information to other datasets. A proper comparison between satellite observations and models requires sampling the model output at the times and locations of the measurements and interpolating the model data to the observed vertical levels. Comparisons to satellite data should in addition consider a priori profiles and averaging kernels from the retrievals when sampling model output to, for example, calculate tropospheric columns for trace gas species. During the last few years, several satellite simulators have been developed which either involve on-line calculations or post-processing to provide model output more directly comparable to remote sensing observations from satellites. Several models now have the capability to sample model output along sun-synchronous satellite orbits (see for example the SORBIT routine in Jöckel et al. (2010)). To facilitate and encourage a proper comparison to satellite data, we therefore provide local times and measured species for some remote sensing products that could potentially be used for evaluating trace gases, see Tables S1  in the CCMI simulation document which is available here.

3. Ground‐based measurements (Expert group leader: Johannes Stähelin)

A document describing the availability of ground based measurements and suggestions for comparisons to ground-based datais available here. These comparisons are possible with the standard monthly output generated using CMOR tables.

4. Insitu aircraft measurements (Expert group leader: Tom Ryerson)

The website of the CCMI insitu aircraft measurement expert group can be found here. This website includes a collection of campaigns, flightpath data, and observational data that can be used for the evaluation of CCMs.

Comparisons to more local measurements made for example during in-situ aircraft campaigns exhibit the problem of a mismatch of spatial and temporal scales between observations and models. CCMs and ESMs usually run at horizontal resolutions of a few hundred kilometers, whereas field experiments sample local air masses. Similar to sampling model output along sun-synchronous satellite orbits, several models now have the capability to interpolate the model data to the flight path during the model simulation (see for example the S4D routine in Jöckel et al. (2010)). This comparison is very useful in particular for the REF-C1SD simulation, which has specified dynamics matching the meteorological situation of particular years and thus allows a more direct comparison. To facilitate this comparison, we provide the flight paths of several aircraft campaigns at the CCMI website in NASA AMES format. Here we will provide links to flight path and obeservationl data that can be used for model evaluation. Updates will be made available on this website.

The charge of the expert team on insitu aircraft measurements will be to identify methodology to meaningfully evaluate CCM simulations against in-situ observations via analyses that bridge the disparate temporal and spatial scales.  Following the successful CCMVal exercise, carry out observation/model comparisons by improving access to vetted in-situ data sets to facilitate validation of model input inventories, to assess simulations of atmospheric processes, and to evaluate simulations of longer-term trends.