Supplementary material to "How well do Earth System Models reproduce observed aerosol changes during the Spring 2020 COVID-19 lockdowns?"
Notice bibliographique
Résumé
S1 Observational Uncertainties and Sampling DifferencesThis work is based on comparisons between observed and simulated anomalies of AOD.In order to draw meaningful conclusions from these comparisons we need to assess how much of the difference between our datasets comes from differences in sampling, and how much can be attributed to uncertainties or biases in the products themselves.Here we assess the role of sampling differences in explaining the considerable spread between AOD estimates from different satellites, which can differ by 20-30% (Table S1).Our main analysis used monthly and regional mean data products.For the simulations, these means are spatiotemporally complete; the observed means use retrievals obtained at the satellite's particular overpass time, in clear-sky conditions (for the passive sensors), when the retrieval was successful and not prevented by a myriad of potential limitations such as sun glint or complex terrain.Here we conduct a systematic intercomparison between pairs of observational data products, with each pairing selected to isolate the effect of a particular sampling effect.Discrepancies that cannot be attributed to sampling differences are then used to estimate the degree of uncertainty in our observed AOD responses.Throughout, figures that do not require gridcellto-gridcell comparisons (spatial-mean timeseries, histograms) are generated at the products' native resolutions, and figures that do require gridcell-to-gridcell comparisons (scatter plots, calculation of correlation between datasets) are generated on fields that have been interpolated to a common 1 • ×1 • resolution. S1.1 Temporal SamplingWe first assess the effects of temporal sampling on our comparison between satellite observations, which are sampled only at a particular overpass time, and model simulations, which are integrated over the full diurnal cycle.Figure S1 compares threehourly, daily, and monthly AOD values for MAM 2004 from a sample CanAM5 simulation.Timeseries plot the evolution of AOD over the three-month period in each of our analysis regions, with blue, orange, and green lines corresponding to the different averaging timescales.A dotted black line indicates the diurnally-integrated, MAM-mean AOD.For all regions except the Northern Hemisphere, blue horizontal lines additionally show the mean of AOD for MAM 2004 sampled only at our 1 Table S1.Mean AOD over reference period (2015)(2016)(2017)(2018)(2019) from different satellites, and the spread in these estimates.The relative spread is calculated with respect to the mean of the highest and lowest AOD estimates in each region. RegionMODIS Aqua MISR CALIOP ASN ACROS-C absolute spread relative spread N. Hemis 0.231 0.190 0.178 0.181 0.053 26% E.China 0.483 0.331 0.445 0.451 0.152 30% India 0.402 0.365 0.459 0.477 0.112 27% Europe 0.157 0.125 0.127 0.124 0.033 23% Table S2.Mean AOD values obtained from 3-hourly CanAM output, when averaged over all of MAM 2004 ("full day") and when sampled only at satellite overpass times.Values in parentheses indicate the percent change in AOD when sampled at a particular overpass time as compared to the diurnally-integrated value.These relative differences are an order of magnitude smaller than the spread bewteen region-mean AOD from different satellites (Table S1).Region Full Day Aqua Day Overpass Aqua Night Overpass Terra Day Overpass N. Hemis 0.311 ---E.China 0.503 0.488 (-3.0%) 0.513 (+2.0%) 0.499 (-0.8%)India 0.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,002 | 0,011 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».