Modelling the surface mass balance of the Greenland ice sheet and neighbouring ice caps: A dynamical and statistical downscaling approach
Notice bibliographique
Résumé
The Greenland ice sheet (GrIS) is the world’s second largest ice mass, storing about one tenth of the Earth’s freshwater. If totally melted, global sea level would rise by 7.4 m, affecting low-lying regions worldwide. Since the mid-1990s, increased atmospheric and oceanic temperatures have accelerated GrIS mass loss through increased meltwater runoff and ice discharge from marine-terminating glaciers. To understand the causes of recent GrIS surface mass loss, we use the Regional Atmospheric Climate Model RACMO2. This meteorological model simulates the GrIS surface mass balance (SMB), i.e. the difference between snowfall accumulation and ablation from meltwater runoff. To cover a large domain at reasonable computational cost, RACMO2 is run at a relatively coarse horizontal resolution of 11 km (1958-2016). At this spatial resolution, the model does not well resolve small glaciated bodies, such as narrow glaciers and small peripheral ice caps (GICs), detached from the main ice sheet. Therefore, we developed a statistical downscaling algorithm that reprojects the RACMO2 output on a 1 km grid. This downscaled product allows to quantify mass changes of small ice masses in unprecedented detail. Using the downscaled data set, we identify 1997 as a tipping point for the mass balance of Greenland’s GICs. The GICs are located in relatively dry regions where summer melt nominally exceeds winter snowfall. To sustain these ice caps, the refreezing of meltwater in the snow is a key process. The snow acts as a ”sponge” that buffers a large fraction of meltwater, which subsequently refreezes in winter. The remaining meltwater runs off to the ocean and directly contributes to mass loss. Until 1997, the snow layer in the interior of these GICs could compensate for increased melt by refreezing more meltwater. Around 1997, following decades of increased melt, the snow became saturated with refrozen meltwater, so that any additional summer melt was forced to run off to the ocean, tripling the mass loss. We call this a tipping point, as it would take decades to regrow a new, healthy snow layer that could buffer enough summer meltwater. As a result, Greenland’s GICs are expected to undergo irreversible mass loss in the future. Similar mechanisms are at play in the Canadian Arctic Archipelago. While the northern ice caps, that are larger and more elevated, can still efficiently buffer meltwater in their extensive snow-covered accumulation zones, the southern smaller and lower-lying ice fields have already lost most of their meltwater retention capacity, causing uninterrupted mass loss during the last six decades. Consequently, these southern ice caps are expected to disappear within the next 400 years. For now, the main Greenland ice sheet is still safe, as porous snow in the extensive accumulation zone, covering about 90% of the GrIS, still buffers most of the summer melt. At the current rate of mass loss, it would still take 10,000 years to melt the GrIS completely. However, the tipping point reached for the peripheral GICs must be regarded as an alarm-signal for the GrIS in the near future, if temperatures continue to increase.
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 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 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 ».