Arrhenius Small Satellite
The objective of the Arrhenius program is two fold:
- To introduce a responsive small satellite structure into the national climate strategy that will provide observations that have tight tolerances for accuracy, sampling protocols, data availability, and cost, that meet the criteria for long-term stable climate records
- to investigate the systematic use of high accuracy satellite data to test the veracity of decadal climate predictions
For objective (1), the most important single requirement on climate data is that we leave for future investigators completely reliable and reproducible measurements of the climate. We also require measurements that are of very high accuracy if we are to detect the fingerprint of anthropogenic climate change before it becomes a major problem. Space measurements, in general, have not been designed to this level of accuracy, nor have they had the means for in-flight verification demonstrating that the required accuracy has been achieved. However, if accuracy is given the highest priority it is possible to realize key space benchmarks with existing technology. Two examples of realizable space benchmarks are radio refractivities from occultations of GPS satellite transmissions; and absolute, spectrally resolved thermal radiances. Resolved radiances provide a unique climate fingerprint. Not only do radiances measure directly the energy flow between Earth and space, the most important single factor determining climate change, but they capture a wide array of signatures involving every important climate variable. In addition, the use of interferometers for meteorological soundings and cloud and surface characterization have created a large base of experience with space systems. The appropriate use of resolved thermal radiances for climate research is probably the single most important and achievable development in climate observing.
For objective (2), the purpose of this research is to investigate the systematic use of climate quality satellite data to test the competence of decadal climate predictions. This task requires the existence of an operational climate model and satellite data optimized for the task. Both are essential for a strong U.S. climate initiative. The task of systematically applying observed data to climate model improvement is a field that demands innovation in both the mathematical structures selected to test the high-end forecasts as well as the observations central to the testing strategy. The subjects of 4-D assimilation and inverse modeling have now reached an advanced stage for weather models, but this has taken a lot of effort. Analogous climate activities will prove challenging, but fundamental advances will emerge from this union.
It is necessary that we push forward with both the experimental and modeling methodologies in parallel, both because they demand technical integration and because they possess similar time scales to converge.