Intensifying hydroclimatic swings under a warming climate: Disentangling anthropogenic climate change and internal variability in North America
Bibliographic record
Abstract
Hydroclimatic Swing Events, characterized by alternating droughts and floods, are expected to increase in both frequency and intensity across several regions in North America. These compound extremes pose significant threats to the environment, agriculture, and water resource management compared to isolated events, necessitating a deeper understanding of their behavior. This study investigates the relative contributions of anthropogenic forcing (ACC) and internal climate variability (ICV) to the characteristics of hydroclimatic swing events, utilizing four single-model initial-condition large-ensembles, including CanRCM4-LE, CanLEAD-EWEMBI, CanLEAD-S14FD, and GFDL-SPEAR, with a total number of 180 runs to offer a comprehensive view of shifting risks. Long-term projections reveal robust trends in most characteristics emerging from ICV-induced fluctuations during periods of +4.0 °C warming, which become discernible within +2.0 or + 3.0 °C warming periods. Based on the ratio-based analysis, more abrupt and intense events are likely to be influenced by the strengthened ACC rather than ICV regardless of events, transition types, and models. Spatial analysis reveals differences between the northern and southern regions of North America, pinpointing ACC hotspots in the Rocky Mountains spanning from Canada to California. Overall, our results indicate that transitions between extreme phases of hydroclimatic swings can become more abrupt and intense, largely affected by ACC. This underscores the heightened risk of exacerbated environmental, hydrological, and socio-economic conditions in the future due to the emergence of lagged compound drought and flood events driven by anthropogenic activity. • Proposes novel metrics (TAI, CPF) to characterize hydroclimatic swing events. • Assesses the relative roles of anthropogenic forcing and internal variability using four large ensemble climate models. • Finds more frequent, intense, and abrupt swing events under higher warming, with ACC dominance emerging by 2 to 3 °C. • Identifies regional hotspots of ACC influence across mid-latitude North America and the Rockies. • Highlights the potential of TAI and CPF as early indicators for climate risk and adaptation planning
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".