The authors will present a broad variety of examples detailing practical and useful techniques for doing climate analysis with distributed data. Examples will showcase GrADS and the GrADS Data Server (GDS) as the client and server software, and will exploit a variety of data types, including in situ, satellite, and gridded model output. Techniques demonstrated will include basic math functions over spatial and temporal domains, grid and vector operations, intercomparison of data types, managing and comparing ensemble members, forecast evaluation on seasonal and shorter time scales, calculating climatologies and anomalies including those associated with phenomena such as ENSO, and strategies for model intercomparison projects.