Validation of Modelled Uplift Rates with Space Geodetic Data
Notice bibliographique
Résumé
Models of glacial isostatic adjustment (GIA) simulate the time-delayed viscoelastic response of the solid Earth to surface loading induced mainly by mass redistribution between ice and ocean during the last glacial cycle considering for rotational feedback, floating ice and moving coastlines. These models predict relative sea level change and surface deformation. The GIA component of present-day uplift is responsible for crustal uplift rates of more than 10 mm/year in areas such as Churchill (Canada) and Angermanland (Sweden). As GIA models have several uncertainties, the model output needs to be validated against observational data. Here, we validate displacements predicted by a GIA model code, VILMA-3D, by using space geodetically observed vertical land motion. We have created a GIA model ensemble using geodynamically constrained 3D Earth structures derived from seismic tomography to consider more realistic lateral variations in the GIA response. To validate the modelled uplift rates, we employ a multi-analysis-centre ensemble of GNSS station and geocentre motion coordinate solutions that have been assimilated into the latest international terrestrial reference frame (ITRF2020). Tectonic and weather signatures were reduced in estimating GNSS-derived velocities, and the trend signal is extracted from these GNSS time series with the STL method (seasonal-trend decomposition based on Loess).  Additionally, uplift rates observed within the ITRF2020 of VLBI, DORIS, and SLR are employed in this study. Because the geodetic stations are unevenly distributed, we employ a weighting scheme that involves the network density and the cross-correlation of the stations’ displacement time series. As measures of agreement for global and regional cases, we employ weighted root mean square error (RMSE) and weighted mean absolute error (MAE). With this validation, we determine the GIA model parameters that are most suitable for modelling present-day uplift rates and identify regions with the best and worst agreement.The results show an agreement between RMSE and MAE for the global case (all stations are considered) and the majority of regional cases, except for the farfield (away from formerly glaciated regions) and for North America. For the global case and for separate regions covered by the major ice sheets during glaciation (North America, Fennoscandia, Antarctica, Greenland), the best fit is performed by the GIA models with 3D Earth structures which show largest lateral variability in viscosity. For the GIA model with the best global fit, the MAE ranges between 0.03 and 0.98 for the respective regions British Isles, Antarctica, farfield, Fennoscandia and North America. In contrast, for the three regions with the lowest amount of observational data, Patagonia, Alaska and Greenland, the MAE is increased to values between 2.07 and 8.63. In general, the MAE ranges between 0.83 and 0.78 for the different GIA models when all stations are considered. Both the RMSE and the MAE show a larger spread between the regions than between the considered GIA models indicating the relevance of also evaluating regional differences in the model performance.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».