We have developed an algorithm testbed designed for end-to-end simulation of atmospheric lidar measurements. The testbed is designed for maximum flexibility to allow for the rapid prototype and development of trace gas remote sensing systems. It not only provides a means of simulating sensor performance during the sensor design phase, but is flexible and modular to allow the same set of algorithm tools and techniques to incorporate measured data from existing sensors. Further, the output retrieval performance parameters can be fed directly into data assimilation schemes in order to fully test the utility of the data for end-user applications. This paper describes the application of this testbed to a LIDAR system designed to measure atmospheric carbon dioxide (CO2). We provide results of sensor simulation studies and show how assimilation of data from a satellite-based LIDAR system would enhance the modeling and simulation of sources and sinks of atmospheric CO2.