Abstract
Droughts and associated near-surface temperature anomalies can be attributed to amplified vertical subsidence and anomalous anticyclonic circulations from dynamic perspectives. However, two open and interesting issues remain unknown: 1) whether hydrometeorological situations under droughts can be reproduced directly utilizing variability of atmospheric dynamics and 2) what specific roles atmospheric dynamics play in drought reconstruction. To explore these questions, this study employs three kinds of dynamic features (i.e., vertical velocity, relative vorticity, and horizontal divergence) for hydrometeorological reconstruction (e.g., precipitation and near-surface air temperature) under drought situations through a so-called XGBoost (extreme gradient boosting) ensemble learning method. The study adopts two different reconstruction schemes (i.e., statistically preexisting dynamic–hydrometeorological relationships and interannual variability) and finds dynamically based reconstruction feasible. The three main achievements are as follows. 1) Regarding different hydrometeorological situations reconstructed with preexisting dynamic–hydrometeorological relationships, good reconstruction performance can be captured with the same or different lead times, depending on whether the evolution of dynamic anomalies (e.g., vertical motion and relative vorticity) is temporally asynchronous. 2) Reconstruction on the interannual scale performs relatively well, seemingly regardless of seasonality and drought-inducing mechanisms. 3) More importantly, from interpretable perspectives, global-scale analysis of dynamic contributions helps discover unexpected dynamic drought-inducing roles and associated latitudinal modulation. That is, low-level cyclonic/anticyclonic anomalies contribute to drought development in the northern middle and high latitudes, while upper-level vertical subsidence contributes significantly to tropical near-surface temperature anomalies concurrent with droughts. These achievements could provide guidance for dynamically based drought monitoring and prediction in different geographic regions.
| Original language | English |
|---|---|
| Pages (from-to) | 1507-1524 |
| Journal | Journal of Hydrometeorology |
| Volume | 23 |
| Issue number | 9 |
| Online published | 23 Sept 2022 |
| DOIs | |
| Publication status | Published - Sept 2022 |
Research Keywords
- Atmospheric circulation
- Drought
- Hydrologic cycle
- Machine learning
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