A comparison of atmospheric CO <sub>2</sub> flux signals obtained from GEOS-Chem flux inversions constrained by in situ or GOSAT observations
Bibliographic record
Abstract
Abstract. The CO2 flux signal is defined as the difference of the four-dimensional CO2 field obtained by integrating an atmospheric transport model with posterior fluxes and that obtained with prior fluxes. It is a function of both the model and the prior fluxes and it can provide insight into how posterior fluxes inform CO2 distributions. Here, we use the GEOS-Chem transport model constrained by either GOSAT or in situ observations to obtain two sets of posterior flux estimates in order to compare the flux signals obtained from the two different observing systems. Flux signals are also computed using two different models. The global flux signal in the troposphere primarily reflects the northern extratropics whereas the global flux signal in the stratosphere mainly reflects tropical contributions. While both observing systems constrain the global budget for 2010 equally well, stronger seasonal variations of the flux signal are obtained with GOSAT. Posterior CO2 distributions obtained with in situ observations better agree with TCCON measurements over an 18-month time period, but GOSAT-informed posterior fluxes better constrain the seasonal cycle at northern extratropical sites. Zonal standard deviations of the flux signal exceed the minimal value (defined by uncertainty in meteorological analyses) through most of the year when GOSAT observations are used, but when in situ observations are used, the minimum value is exceeded only in boreal summer. This indicates a potential for flux estimates constrained by GOSAT data to retrieve spatial structures within a zonal band throughout the year in the tropics and through most of the year in the northern extratropics. Verification of such spatial structures will require a dense network of independent observations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".