Data and modelling requirements for CO2 inversions using high-frequency data

We explore the future possibilities for CO2 Source estimation from atmospheric concentration data by performing synthetic data experiments. Synthetic data are used to test seasonal CO2 inversions using high-frequency data. Monthly CO2 sources over the Australian region are calculated for inversions with data at 4-hourly frequency and averaged over 1 d, 2.5 d, 5 d, 12.17 d and 1 month. The inversion quality, as determined by bias and uncertainty, is degraded when averaging over longer periods. This shows the value of the strong but relatively short-lived signals present in high-frequency records that are removed in averaged and particularly filtered records. Sensitivity tests are performed in which the synthetic data are 'corrupted' to simulate systematic measurement errors such as intercalibration differences or to simulate transport modelling errors. The inversion is also used to estimate the effect of calibration offsets between sites. We find that at short data-averaging periods the inversion is reasonably robust to measurement-type errors. For transport-type errors, the best results are achieved for synoptic (2-5 d) timescales. Overall the tests indicate that improved source estimates should be possible by incorporating continuous measurements into CO2 inversions.