Self-lofting of wildfire smoke in the troposphere and stratosphere caused by radiative heating: simulations vs space lidar observations
Bibliographic record
Abstract
Abstract. Wildfire smoke is known as a highly absorptive aerosol type in the shortwave wavelength range. The absorption of Sun light by optically thick smoke layers results in heating of the ambient air. This heating is translated into self-lofting of the smoke up to more than 1 km in altitude per day. This study aims for a detailed analysis of tropospheric and stratospheric smoke lofting rate simulations as well as comparisons between modeled and observed smoke lofting rates. One of the main goals is to demonstrate that self-lofting processes can explain observed smoke lofting in the free middle and upper troposphere up to the tropopause and into the lower stratosphere without the need for pyrocumulonimbus convection. Simulations are conducted by using the ECRAD (European Centre for Medium-RangeWeather Forecasts Radiation) scheme. As input parameters thermodynamic profiles from CAMS (Copernicus Atmosphere Monitoring Service) reanalysis data, aerosol profiles from ground-based lidar observations, radiosonde potential temperature profiles, CALIOP (Cloud Aerosol Lidar with Orthogonal Polarization) aerosol measurements, and MODIS (Moderate Resolution Imaging pectroradiometer) aerosol optical depth retrievals were used. The uncertainty analysis revealed that the lofting rate sensitively depends on the aerosol optical thickness (AOT), layer thickness, layer height, and the black carbon to organic carbon fraction. We also looked at the influence of different meteorological parameters such as cloudiness, relative humidity, and potential temperature gradient. Largest sensitivities between 30 % and 50 % were found for variation of AOT, black carbon fraction, and cloudiness. Uncertainty in the self-lofting estimations grows with longevity of the smoke layers. In recent years, several major wildfire events occurred and injected smoke into the upper troposphere and lower stratosphere. Self-lofting processes led to the ascend of these smoke plumes. CALIPSO measurements show that in 2017, Canadian wildfire smoke plumes ascended by about 10 km in one month. In 2020, Australian wildfire smoke layers were lofted by around 20 km in two months. In 2019 and 2021, significant self-lofting of tropospheric smoke was observed in Siberia. Smoke was injected to around 4 km height and reached the tropopause within less than a week. These four examples, observed with CALIOP, are presented in this study. The observed CALIOP ascent rates are compared to the calculated ascent rates using the ECRAD model heating rate simulations.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".