Why is the atmosphere over land becoming drier? Exploring the roles of atmospheric and land-surface processes on relative humidity
Notice bibliographique
Résumé
Relative humidity (RH) over land has declined steeply since 2000. This drying is broadly consistent from the edge of the deep tropics to the mid-latitudes of both hemispheres, whereas regions equatorward and poleward show increasing RH trends. The drying trend observed in the gridded global humidity dataset, HadISDH, is not captured by the CMIP5 climate models. This can be mostly explained through thermodynamic drivers, i.e. faster land-than-ocean warming under global warming. Insufficient water vapour is thus evaporated and transported from the oceans to keep RH over land constant. However, there are notable regional and seasonal differences in the trend. This thesis explores how dynamical and terrestrial drivers, which are less well represented in the models, can explain changes in RH. RH was analysed regionally. Strong drying trends were found over eastern Brazil, Tibet, the Caspian Sea, California, Mongolia, southern Africa, southwestern Greenland, eastern USA and the Red Sea. Strong wetting trends were found over Scandinavia, northwestern India and eastern Canada. The relationship between these regional trends and a range of dynamical drivers (precipitation, sea surface temperatures [SST], wind direction and speed, as well as pressure systems and the most common modes of climate variability) were explored. The influence of terrestrial drivers was also examined through evaporation and soil moisture, terrestrial water storage, the vegetation structure, and the modelled carbon cycle response to increased CO2 through CMIP5 experiments. Key findings are as follows. The thermodynamic driver can be detected on small scales (e.g. the Caspian Sea). Of the dynamical drivers, a latitudinal shift of the Intertropical Convergence Zone due to tropical Atlantic SST changes reduced precipitation and thus water availability for RH over eastern Brazil. A wind direction change on different spatial scales leads to changes in RH in many regions (e.g. Greenland, southern Africa, eastern Canada). This work found a complex interplay of modes of variability behind the dynamical drivers, often influencing the RH trend through extreme years. In terms of terrestrial drivers, anthropogenic water management and land cover/land-use change affected surface and underground water availability over northern India, Mongolia and Tibet, and a modelled response of plants to increased CO2 was found to decrease specific humidity and RH by a small amount. Despite widespread drying trends, no evidence of large-scale effects from non-thermo-dynamical drivers could be found. Instead, dynamical and terrestrial drivers were found to influence RH on regional to sub-regional and seasonal scales, and complex interactions between the drivers and RH were found. Drivers such as the El Niño Southern Oscillation (ENSO) were found to influence strong peaks/troughs in RH in a number of regions which influenced trends over short timescales. This small-scale variability in drivers may indicate why climate models do not closely replicate the RH decline.
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) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».