Assessment of present-day aerosol optical depth from modern aerosol-climate models, reanalyses, and satellite products
Bibliographic record
Abstract
Despite the implication of aerosols for radiative forcing, there are di erences in aerosol estimates from both, observations and models. This study quanti es di erences between current estimates of aerosol optical depth (AOD) to address two questions: (1) How well do we know the large-scale spatio-temporal pattern of present-day AOD across state-of-the-art data? (2) Has the representation of AOD improved across phases of aerosol-climate model intercomparison projects? To answer these questions, we analyze spatio-temporal patterns of the present-day monthly mean AOD from 94 global datasets. The data is taken from eight satellite retrievals, four aerosol-climate model intercomparison projects, two global reanalyses, one operational ensemble product, one climatology and one merged satellite product covering periods between 1998 and 2019. The evaluation includes new satellite data from SLSTR and aerosol-climate models of CMIP6 and AeroCom-III. The comprehensive data assessment allows us to evaluate the performance of individual products and models concerning di erent spatial and temporal aspects. Our assessment is based on metrics for a detailed investigation with respect to di erent spatio-temporal characteristics of AOD. Our results highlight spatio-temporal di erences in AOD across datasets, were the performance of individual data sets varies with respect to the di erent spatio-temporal metrics assessed. Global mean AOD of individual satellites ranges between -11% to +17% around a satellite mean of 0.14. The ensemble means from the aerosol-climate model intercomparison projects fall within the satellite range, but individual models can di er considerably. Reanalyses and climalologies are typically closer to the satellite mean than aerosol-climate models. No systematic improvement from earlier to later phases of CMIP and AeroCom is found, although some regional biases have been reduced. Compared to the satellite and reanalysis data, all aerosol-climate ensemble means tend to overestimate AOD along extra-tropical storm tracks and underestimate AOD in regions of high aerosol load in South America, South Africa, India, and Southeast Asia. The identi ed di erences may be used to guide further e orts to improve satellite retrievals and model simulations for aerosols. In addition, the uncertainty in observed AOD implies that a model evaluation based on a single satellite product might draw biased conclusions. This underlines the need for continued e orts to improve both model and satellite estimates of AOD to facilitate a better understanding of aerosol e ects in the Earth system. At the same time, our analysis suggests that an assimilation of multiple satellite products for AOD would be bene cial to account for observational uncertainty. Reference: Vogel, A., Alessa, G., Scheele, R., Weber, L., Dubovik, O., North, P., Fiedler, S. (2022). Uncertainty in aerosol optical depth from modern aerosol-climate models, reanalyses, and satellite products. Journal of Geophysical Research: Atmospheres, 127, e2021JD035483. https://doi.org/10.1029/2021JD035483
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".