CCSM coupled model is based on a framework which divides the complete climate system into component models connected by a coupler. This design requires four component models - atmosphere, sea-ice, land, and ocean - each connected to the Coupler, and each exchanging data with the Coupler only.
The Observational Data Atmosphere Model (latm) functions as the atmosphere component in a CCSM configuration. The latm atmosphere component interacts with the Coupler just like any atmosphere model would, but it is not an active model, rather, it takes atmosphere data from input data files and sends it to the Coupler, ignoring any forcing data received from the Coupler. Typically the input data files contain climatological or time averaged observational data, although some data fabrication is usually necessary as it's unlikely that real world observations exists for all the required fields. Such a "dummy" atmosphere model is useful for doing ocean + ice spinup runs.
This latm code is a variant of the datm code, the difference being that the datm cycles thru daily average data fields normally created by the CCSM active atmosphere component (CCM, now called CAM), while the latm cycles thru data from other sources (NCEP in particular). The latm cycles thru separate data file streams for atmospheric states, precipitation, and radiation. Further, these three data streams can have different sampling intervals, for example, radiation data can be daily average data while precipitation data is monthly average data. Note that this version of latm can only use NCEP data for all three data streams and the biases in these data produce solutions that are not of high scientific quality. The latm code is provided as an example of how one group of users altered the datm source code to cycle through different types of input data streams.
Important note: When assembling a CCSM configuration, the user must carefully consider the limitations and requirements of all components and make sure that the complete set of component models will interact in a meaningful way. In particular, the user must verify that the data provided by this model is adequate for their specific application.