Accelerating Climate Action: A just transition in a post-Covid era. Book of abstracts, 9th SISC Annual Conference (online, 22-24 Set 2021)
Notice bibliographique
Résumé
Extended and intense biomass burning fires occurred in Northern Canada and on the Greenlandic West coast during summer 2017. The smoke plume emitted into the atmosphere was transported and spread in the Arctic, producing one of the most significant impacts ever observed in the region. Evidence of Canadian and Greenlandic wildfires was recorded at the Thule High Arctic Atmospheric Observatory (THAAO, 76.5°N, 68.8°W, www.thuleatmos-it.it) by a suite of instruments managed by ENEA, INGV, Univ. of Florence, and NCAR. Chemical tracers such as CO, HCN, H2CO, C2H6, and NH3 were measured in the atmospheric column above Thule from 19 August to 23 August by an FTIR. The aerosol optical depth measured by the AERONET sunphotometer was dominated by the fine fraction, reaching a peak value of about 0.86 on 21 August. An air sampler monitored several wildfire compounds at a 48-hour resolution. Groundbased radiometers allowed the quantification of the surface radiation budget at THAAO. Backward trajectories produced through HYSPLIT simulations (Stein et al., 2015) were also employed to understand the atmospheric dynamics indicating the origin of the transported smoke. MODTRAN6.0 radiative transfer model (Berk et al., 2014) was used to estimate the aerosol radiative effect (ARE) and the heating rate profiles at 78° SZA. Measured temperature profiles, integrated water vapour, surface albedo, spectral AOD and aerosol extinction profiles from CALIOP onboard CALIPSO satellite were used as model input. The shortwave ARE at the surface was -43.7 W/m2 at 78° solar zenith angle (SZA) for AOD=0.626. The peak aerosol heating rate (+0.5 K/day) was reached within the aerosol layer between 8 and 12 km, while the maximum radiative effect (-45.4 W/m2) was found at 3 km, below the most extensive aerosol layer. \nThe regional impact of the event observed between 15 and 25 August was investigated using MODTRAN to model the aerosol radiative effect efficiency (AREE) with measurements of AOD and surface albedo over land retrieved from MODIS. Instead, albedo data over the ocean were obtained from Jin et al. (2004). The radiative transfer model was fed with the atmospheric properties used in the ARE simulation at THAAO. The values of aerosol radiative effect efficiency (AREE) span from -3 W/m2 to -132 W/m2, depending on surface albedo and solar zenith angle. The fire plume covered a vast portion of the Arctic, with large values of the AOD reaching the eastern Greenlandic coast and with the shortwave ARE lasting for a few days. In particular, we calculated a cumulative ARE during the considered period and found a negative peak value of -120 TW on 21-22 August over the Arctic sector between 60°N - 80°N and 110°W - 0°E. Instead, the mean daily ARE shows values between -65 and -25 W/m2 between 15 and 25 August, being influenced by really large AOD values mainly during the first part of the period over northern Canada. This large amount of aerosol is also expected to influence cloud properties in the Arctic, producing significant indirect radiative effects. \n \nReferences \nBerk, A., P. Conforti, R. Kennett, T. Perkins, F. Hawes and J. van den Bosch (2014), "MODTRAN6: a major upgrade of the MODTRAN radiative transfer code", Proc. SPIE 9088, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XX, 90880H, 9088, 1-7. \nStein, A.F., R.R. Draxler, G.D. Rolph, B.J.B. Stunder, M.D. Cohen and F. Ngan (2015), “Noaa’s hysplit atmospheric transport and dispersion modeling system”, Bulletin of the American Meteorological Society, 96(12), 2059–2077. \nJin, Z., T.P. Charlock, W.L. Smith and K. Rutledge (2004), “A parameterization of ocean surface albedo”, Geophysical Research Letters, 31(22), 1–4.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,020 | 0,000 |
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; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».