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Towards Quantifying the Uncertainty in Estimating Observed Scaling Rates.
Haider Ali1, Hayley J Fowler1, David Pritchard1
1School of Engineering Newcastle University Newcastle upon Tyne UK.
Geophysical Research Letters
|July 21, 2022
Summary
Data quality and analysis methods significantly impact estimates of precipitation extremes (PE) changes with warming. Using precise, quality-controlled data and robust methods reveals higher scaling rates, crucial for understanding future climate impacts.
Area of Science:
- Climate Science
- Atmospheric Science
- Hydrology
Background:
- Precipitation extremes (PE) are projected to intensify with global warming, following the Clausius-Clapeyron relationship.
- Previous research indicates variability in the precipitation-temperature relationship (scaling) due to thermodynamic and dynamic factors.
Purpose of the Study:
- To investigate how data quality and analytical methods influence the estimated scaling of short-duration precipitation extremes with temperature.
- To provide more accurate estimations of precipitation extremes under climate change.
Main Methods:
- Analysis of hourly precipitation (PPT) and daily dewpoint temperature (DPT) data from 2,905 US locations.
- Comparison of scaling rates using raw versus quality-controlled data.
- Evaluation of scaling based on precipitation measurement precision (0.25 mm vs. 2.54 mm).
- Assessment of methods with and without seasonality removal for PPT and DPT data.
Main Results:
- Quality-controlled data yielded higher median scaling (6.2%/K) compared to raw data (5.3%/K).
- Higher measurement precision (0.25 mm) showed greater scaling (median 7.8%/K) than lower precision (2.54 mm, median 6.6%/K).
- Removing seasonality from data resulted in higher median scaling (7.2%/K) than analyses including seasonality (6.2%/K).
Conclusions:
- The choice of data quality and analytical methods significantly affects the estimated rate of precipitation extreme intensification.
- Accurate assessment requires quality-controlled, high-precision observational data and robust statistical methods.
- Improved understanding of PE scaling is vital for climate change impact assessments and adaptation strategies.
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