Use of climate model large ensembles to study the impact of \nclimate change on future extreme droughts
Bibliographic record
Abstract
The evolution of droughts in a changing climate has received increasing attention from the scientific community and the public. Recent studies looking at the evolution of future droughts have found that droughts are expected to become more severe. The ocean is the main driver of internal climate variability, and regional hydroclimatic variability can be related to largescale climate oscillations. This study explored the evolution of the frequency of short- and long-term extreme droughts at the global and continental scales, and studied the relationship between future climate variability and large-scale oscillations. A better understanding of the evolution of future extreme droughts and their relationship with climate variability is the key to better adapt to the changing climate. \n \nTwo climate model large ensembles, the 50-member Canadian Earth System Model (CanESM2) and the 40-member Community Earth System Model (CESM1), both under the Representative Concentration Pathway 8.5 were used in this work. Monthly precipitation outputs were used to calculate the Standard Precipitation Index (SPI) to quantify meteorological droughts at the global and North American scales for the near- (2036-2065) and far-future (2070-2099) periods. In a second step, the evolution of hydrological droughts over 4521 North American catchments was assessed using the Streamflow Drought Index (SDI). In the last step, the contribution to internal variability of three large-scale climatic indices was studied. The impact of the El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO) on climate anomalies at the catchment scale was studied. The constructive and destructive interactions between those three indices were also studied over the 1961-2010 historical period. \n \nCompared against observations, both ensembles do a reasonable job at replicating patterns of mean annual precipitation and interannual variability over the 1981 2010 reference period. The projected changes in precipitation for both models are consistent with the predicted IPCC trends. Both climate models project increases in extreme meteorological drought frequency over many of the world’s regions. The spatial patterns of regions with worsening droughts match those of projected change in mean annual precipitation, although the former is more extensive, indicating that changes in internal variability will increase drought frequency even in some regions projected to see increased mean annual precipitation. The projected increase in meteorological drought frequency is more significant for short-term June-July-August (JJA) doughts and for the larger return periods. Large increases in frequency are observed in many regions, all the way up to 20 times for the 100-year JJA drought indicating a return period shift from 100 to 5 years. \n \nResults show widely different patterns for future changes in extreme hydrological droughts compared to meteorological ones. Hydrological droughts, which combine the effect of preci-pitation and temperature changes, show a mostly uniform pattern of large to very large increases in drought frequency. This shows that the projected increase in temperature is a main driver of future extreme hydrological droughts, sufficient to overcome the projected increase in mean summer precipitation projected for many North American catchments. Predicted changes for both meteorological and hydrological droughts get consistently worse for the longer considered return periods. In other words, frequency changes for the 100-year droughts are more important than those expected for the 2- and 20-year droughts. \n \nAs to the control of large-scale oscillations on climatic anomalies at the catchment scale, it was found that ENSO dominates annual precipitation variability over North America whereas mean annual temperature is mostly influenced by AMO over most of North America. The impact of PDO is comparatively weaker. The dominant roles of ENSO on precipitation and AMO on temperature are preserved but reinforced or diminished by the strong interactions between oscillations. A negative ENSO (La Niña) coupled with a positive AMO brings climate conditions favorable to droughts. \n \nThis Thesis illustrates the impact of anthropogenic forcing and internal variability on future drought frequency under changing climate. The results provide much-needed knowledge necessary to better adapt to a changing climate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".