A regional high-resolution carbon flux inversion of North America for 2004
Bibliographic record
Abstract
Abstract. Resolving the discrepancies between NEE estimates based upon (1) ground studies and (2) atmospheric inversion results, demands increasingly sophisticated techniques. In this paper we present a high-resolution inversion based upon a regional meteorology model (RAMS) and an underlying biosphere (SiB3) model, both running on an identical 40 km grid over most of North America. Previous papers have utilized inversion regions formed by collapsing biome-similar grid cells into large aggregated regions. The effect of this is that the NEE correction imposed on forested regions on the east coast of the United States might be the same as that imposed on forests on the west coast of the United States while, in reality, there likely exist subtle differences in the two areas, both natural and anthropogenic. Our current inversion framework utilizes a combination of previously employed inversion techniques while allowing carbon flux corrections to be biome independent. Temporally and spatially high-resolution results utilizing biome-independent corrections provide insight into carbon dynamics in North America. In particular, we analyze hourly CO2 mixing ratio data from a sparse network of eight towers in North America for 2004. A prior estimate of carbon fluxes due to gross primary productivity (GPP) and ecosystem respiration (ER) is constructed from the SiB3 biosphere model on a 40 km grid. A combination of transport from the RAMS and the parameterized chemical transport model (PCTM) models is used to forge a connection between upwind biosphere fluxes and downwind observed CO2 mixing ratio data. A Kalman filter procedure is used to estimate weekly corrections to biosphere fluxes based upon observed CO2. RMSE-weighted annual NEE estimates, over an ensemble of potential inversion parameter sets, show a mean estimate 0.57 Pg/yr sink in North America. We perform the inversion with two independently derived boundary inflow conditions and calculate jackknife-based statistics to test the robustness of the model results. We then compare final results to estimates obtained from the CarbonTracker inversion system and the Ameriflux network. Results are promising, showing the ability to correct carbon fluxes from the biosphere models over annual and seasonal time scales, as well as over the different GPP and ER components, and also providing interesting hypotheses for future work.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".