Why is the atmosphere over land becoming drier? Exploring the roles of atmospheric and land-surface processes on relative humidity
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".