Relative Contribution of External Forcing and Internal Climate Variability to Lagged Dry-Wet Swing Projections in North America
Bibliographic record
Abstract
Hydroclimatic whiplash, a lagged compound hazard combined with preceding drought (flood) and following flood (drought), may induce significant environmental, hydrological, and socio-economic impacts worldwide. North America is one of the hot spots becoming susceptible to the transitions or shifts between two extremes. These compound events are also expected to become more frequent and intense in the future under climate change. To better understand the climate influence, overall decadal changes in climate variables and related hydroclimatic swing events need to be analyzed considering two components: anthropogenic external forcing and natural internal variability. External forcing is induced by anthropogenic activities imposing greenhouse gas emissions on the climate system, resulting in the signal of global warming. Internal climate variability (ICV), also termed climate noise, is an irreducible uncertainty induced by the chaotic nature thus unpredictable evolution of the climate system. In this study, we use four single-model initial-condition large ensembles (SMILEs) under historical and future forcing scenarios (RCP8.5), CanLEAD-EWEMBI, CanLEAD-S14FD, CanRCM4-LE, and GFDL-SPEAR, to quantify the relative role of external forcing and ICV on variations in compound dry-wet swing events across North America. The SMILE enables the robust quantification of the externally forced response and internal variability via computation of ensemble statistics, provided the ensemble size is large enough. On the virtue of this advantage, the standardized precipitation evaporation index is estimated to identify dry and wet spells and their transitions based on ensemble pooling and threshold-based event extraction methods. Frequency, intensity, transition time, transition intensity, and relative role of preceding and following spells, etc. are quantified for each warming period (1.5°C-4 °C global warming levels) and compared with those of the baseline period to investigate their projected changes and trends. The relative contribution of ACC and ICV to compound dry-wet spells is quantified by the ratio of changes and trends in the ensemble mean and the spread (standard deviation) among the ensemble members of each SMILE, respectively. The results of this study suggest that hydroclimatic swing events across North America are expected to become more frequent and intensified in a warmer climate, which is induced by significant emergence of external forcing. In addition, the transition time and transition intensity are projected to be more dominated by anthropogenic forcing over ICV than other characteristics indicating that more abrupt and severe shifts can occur in the future. The findings of this study support the necessity of developing appropriate measures for mitigating the anthropogenic forcing impact because it increases the risk of lagged compound floods and droughts that can lead to severe disasters in North America.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 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".