Simulation of radon-222 with the GEOS-Chem global model: emissions, seasonality, and convective transport
Bibliographic record
Abstract
Abstract. Radon-222 (222Rn) is a short-lived radioactive gas naturally emitted from land surfaces and has long been used to assess convective transport in atmospheric models. In this study, we simulate 222Rn using the GEOS-Chem chemical transport model to improve our understanding of 222Rn emissions and surface concentration seasonality and characterize convective transport associated with two Goddard Earth Observing System (GEOS) meteorological products, the Modern-Era Retrospective analysis for Research and Applications (MERRA) and GEOS Forward Processing (GEOS-FP). We evaluate four global 222Rn emission scenarios by comparing model results with observations at 51 surface sites. The default emission scenario in GEOS-Chem yields a moderate agreement with surface observations globally (68.9 % of data within a factor of 2) and a large underestimate of winter surface 222Rn concentrations at Northern Hemisphere midlatitudes and high latitudes due to an oversimplified formulation of 222Rn emission fluxes (1 atom cm−2 s−1 over land with a reduction by a factor of 3 under freezing conditions). We compose a new global 222Rn emission scenario based on Zhang et al. (2011) and demonstrate its potential to improve simulated surface 222Rn concentrations and seasonality. The regional components of this scenario include spatially and temporally varying emission fluxes derived from previous measurements of soil radium content and soil exhalation models, which are key factors in determining 222Rn emission flux rates. However, large model underestimates of surface 222Rn concentrations still exist in Asia, suggesting unusually high regional 222Rn emissions. We therefore propose a conservative upscaling factor of 1.2 for 222Rn emission fluxes in China, which was also constrained by observed deposition fluxes of 210Pb (a progeny of 222Rn). With this modification, the model shows better agreement with observations in Europe and North America (> 80 % of data within a factor of 2) and reasonable agreement in Asia (close to 70 %). Further constraints on 222Rn emissions would require additional concentration and emission flux observations in the central United States, Canada, Africa, and Asia. We also compare and assess convective transport in model simulations driven by MERRA and GEOS-FP using observed 222Rn vertical profiles in northern midlatitude summer and from three short-term airborne campaigns. While simulations with both GEOS products are able to capture the observed vertical gradient of 222Rn concentrations in the lower troposphere (0–4 km), neither correctly represents the level of convective detrainment, resulting in biases in the middle and upper troposphere. Compared with GEOS-FP, MERRA leads to stronger convective transport of 222Rn, which is partially compensated for by its weaker large-scale vertical advection, resulting in similar global vertical distributions of 222Rn concentrations between the two simulations. This has important implications for using chemical transport models to interpret the transport of other trace species when these GEOS products are used as driving meteorology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".