Towards Quantifying the Uncertainty in Estimating Observed Scaling Rates.

ABSTRACT

Short-duration precipitation extremes (PE) increase at a rate of around 7%/K explained by the Clausius-Clapeyron relationship. Previous studies show uncertainty in the extreme precipitation-temperature relationship (scaling) due to various thermodynamic/dynamic factors. Here, we show that uncertainty may arise from the choice of data and methods. Using hourly precipitation (PPT) and daily dewpoint temperature (DPT) across 2,905 locations over the United States, we found higher scaling for quality-controlled data, all locations showing positive (median 6.2%/K) scaling, as compared to raw data showing positive (median 5.3%/K) scaling over 97.5% of locations. We found higher scaling for higher measurement precision of PPT (0.25 mm median 7.8%/K; 2.54 mm median 6.6%/K). The method that removes seasonality in PPT and DPT gives higher (with seasonality median 6.2%/K; without seasonality median 7.2%/K) scaling. Our results demonstrate the importance of quality-controlled, high-precision observations and robust methods in estimating accurate scaling for a better understanding of PE change with warming.