How well do the reanalysis datasets capture hot and cold extremes and their trends in India?
Bibliographic record
Abstract
Much of the Earth's surface lacks long-term in-situ measurement of essential meteorological variables. Climate reanalysis datasets provide an alternative in data-sparse regions, sometimes replacing gauge-based observations for climatological studies, however, they have inherent biases. Reanalysis is now available at finer spatial and temporal resolutions, that can be considered for hydrological and climatological studies. Although the assessment of reanalysis datasets is common at a daily, monthly, or seasonal scale, how the recent generation reanalysis captures the spatial pattern of extreme temperature events, and their trends remains an open question. In this study, two regional (IMDAA and EARS) and five global (ERA5-Land, ERA5, MERRA2, CFSR, and JRA3Q) reanalysis datasets are evaluated with a gauge-based gridded temperature dataset from the India Meteorological Department (IMD) to assess their suitability for studying extreme temperature events and their trends over India. Fifteen hot and cold extremes indices are identified to characterize extremes covering frequency, intensity, and duration of extreme temperature events. The study finds that no single reanalysis outperforms others for all the extreme indices when compared to the IMD gridded data, however, a select few (e.g., ERA5, ERA5L, MERRA2, and JRA3Q) better perform in reproducing the observed spatial pattern of extreme events and their changes across different regions of India. It is also noted that in response to global warming, the frequency, duration, and magnitude of extreme hot events are rising, and cold events are decreasing in India which is also captured by most of these reanalyses. Overall, the increase in hot extremes is more prominent in the south of the tropics and the decline in cold extremes is more evident in the north. However, the trend areas and magnitudes of the reanalysis datasets were not similar in comparison to trends from a regional station-based gridded dataset. Thus, care should be taken when selecting datasets for such applications and interpreting their trends. • The frequency, duration, and magnitude of extreme cold events are decreasing over India, while the hot extremes are rising. • Reanalysis has good potential for identifying frequency and duration-based extreme indices. • The reanalysis datasets capture the general warming trend over India, with some regional over and underestimation. • Compared to other reanalysis datasets, the estimated trends from ERA5 better matched with the IMD datasets.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".