Statistical downscaling and simulation of daily temperature extremes
Bibliographic record
Abstract
There is now a broad scientific consensus that the global climate is changing in ways that could have a profound impact on human society and the natural environment over the coming decades. In particular, changes in the frequency and magnitude of extreme temperatures are likely to have more substantial impacts on the environment and human activities than changes in the mean temperature. The present study is therefore addressing three main objectives: (a) to propose a systematic data analysis method for characterizing the variability of daily extreme temperatures at different sites; (b) to develop new statistical downscaling models that could accurately describe the linkage between large-scale climate variables and the characteristics of temperature extremes at a local site; and (c) to develop a stochastic method for simulating accurately the extreme temperature processes. Firstly, a systematic data analysis procedure was proposed for analyzing the variability of daily maximum (Tmax) and minimum (Tmin) temperature characteristics. The suggested procedure consists of performing a detailed statistical analysis of twelve relevant temperature indices that are important for various practical application purposes: mean of diurnal temperature range, frost season length, growing season length, freeze and thaw cycle, 90th percentile of Tmax, 10th percentile of Tmin, means and standard deviations of Tmax, Tmin, and the daily mean temperature. The suggested method was applied to the analysis of daily Tmax and Tmin data for 20 stations in Quebec. The available records used are different from station to station, varying from 44 years to 107 years. In general, it was found that, depending on the temperature index considered as well as on the particular season of the year, there are some significant increasing or decreasing trends at some locations in Quebec. Results of this analysis would provide valuable information on the temporal and spatial variations of daily extreme temperature processes in the region. Furthermore, it can be observed that no systematic spatial variability of the increasing or decreasing trends of any of the twelve temperature indices considered could be identified for a given area in Quebec. Secondly, two new statistical downscaling models were proposed using the stepwise and robust regression methods in order to describe the linkage between largescale climate variables and the characteristics of Tmax and Tmin at a local site. The performance of these two models was tested using daily extreme temperature data available at Dorval Airport station in Quebec and the NCEP data for 25 different climate variables for the 1961-1990 period. It was found that the proposed stepwise and robust regression downscaling models can provide accurate estimates of fundamental statistical and physical properties of Tmax and Tmin. In addition, it has been observed that three climate variables, the mean sea level pressure, the 850hPa-geopotential height, and the near surface specific humidity, had the most significant effect on Tmax and Tmin at Dorval Airport. Furthermore, as compared with the popular SDSM model, the stepwise and robust regression models can provide more accurate estimates of the local Tmax and Tmin characteristics. In particular, the robust regression model was found to be the most accurate. Finally, a new stochastic simulation procedure was developed in this study for simulating the Tmax and Tmin temperature time series at a local site using the combination of the first-order autoregressive AR(1) model and the SVD technique. Results of the evaluation of the proposed AR(1)-SVD simulation method using daily extreme temperature data at Dorval Airport for the 1961-1990 period have indicated the feasibility of this method in describing accurately the observed basic statistical properties (mean, standard deviation, and first order autocorrelation) of the daily Tmax and Tmin time series at a local site.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".