The Resolvable Scales of Regional‐Scale CO<sub>2</sub> Transport in the Context of Imperfect Meteorology: The Predictability of CO<sub>2</sub> in a Limited‐Area Model
Bibliographic record
Abstract
Abstract Transport model error is an important source of uncertainty when estimating surface CO2 fluxes via an atmospheric model inversion. In this study, the transport error due to uncertainty of meteorological fields is investigated with a high resolution, limited‐area model. We characterize the extent to which errors in meteorological initial conditions (ICs) and lateral boundary conditions (LBCs) impact the quality of atmospheric CO2 transport across spatial scales. A series of experiments is conducted using different meteorological ICs and LBCs that possess varying levels of accuracy. We find that the transport error of CO2 is more sensitive to errors in meteorology at smaller scales O(10 km) than at larger scales O(1,000 km), and that surface CO2 fluxes can explain the predictability of CO2 at the largest scales. We also determine the spatial scales resolvable in the context of uncertain meteorology. These findings have implications for the development of regional‐scale inverse modeling systems. When assimilating CO2 observations near the surface, using accurate meteorological ICs is important for resolving fine‐scale spatial variability of CO2 because CO2 transport at lower levels is more sensitive to meteorological ICs and surface CO2 fluxes than to meteorological LBCs. However, when assimilating aircraft CO2 measurements or XCO2 satellite retrievals which contain information at higher altitudes, using accurate meteorological LBCs is also important. Improvement in meteorological inputs through a data assimilation system could be helpful in further resolving finer spatial scales of CO2 at regional scales.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".