On downscaling probabilities for heavy 24-hour precipitation events at seasonal-to-decadal scales

Abstract

A B S T R A C T We examine a method for predicting variations in the probabilities and occurrence of intense local 24-hour precipitation events over seasonal, annual, and 5-yr intervals. The wet-day mean m can be used to describe the exponential distribution for the wet-day amounts, and provides a basis for forecasting the likelihood of seeing an event above a given threshold. A regression-based downscaling model is calibrated on annual m and wet-day frequency f w , respectively, and then tested on seasonal to multi-annual scales. The probabilities for heavy precipitation statistics are taken as the product between a fitted cumulative exponential distribution for the wet-day amounts m and f w . The annual number of heavy precipitation events is estimated from the annual probability, where a 90% confidence interval on the number is computed using the 5 and 95 percentiles of a binomial distribution for the predicted probability. The analysis identifies a strong link between large-scale predictors such as mean sea-level pressure or surface temperature and the wet-day frequency. There was a weaker dependency of the wet-day mean to large-scale predictors, which suggests that local-scale processes have a stronger influence on the 24-hour precipitation amounts. The results also suggest that similar physical processes influence the wet-day mean m on different timescales except for during winter, and that similar physical processes may explain the wet-day frequency on seasonal to annual time scales. The implication is that models calibrated on annual data samples in some cases can give skilful predictions on seasonal time scales.

On using principal components to represent stations in empirical–statistical downscaling

“…The set of station records for m was complete for the period 1907Á1997; however, since the NCEP/NCAR reanalysis only covered 1948Á2014, the downscaling was carried out for 1948Á1997 (50 yr). The saturation vapour pressure was used as predictor for m, and was estimated using the ClausiusÁClapeyron equation and the surface temperature taken from the reanalysis, as in Benestad and Mezghani (2015). The locations of all stations are shown in Fig.…”

Implications of a decrease in the precipitation area for the past and the future

“…This balance can also be expressed as − = 0 where is the mean evaporation over area with evaporation, A . An increase in the mean rate of evaporation is expected to be accompanied with an increase in amplified by A /A if all else is constant [9], and a reduced area of precipitation and constant or increased rate of evaporation are expected to be associated with increased intensity according to this simple expression. The area of evaporation may vary to some extent, although it is dominated by the oceans which take up approximately 70% of Earth's surface.…”

A simple equation to study changes in rainfall statistics

“…For instance, El Niño Southern Oscillation influences the number of wet days over Australia, and equation (1) can be used to estimate approximately how this teleconnection affects the likelihood of heavy precipitation and hence give an indicator for the risk of flooding [36]. Results from previous efforts suggest that f w and μ are influenced by the mean sea-level pressure (SLP) and surface air temperature (SAT) respectively [19], but only part of the variations in μ has been attributed to SAT. These results also suggest that biases in SLP and temperature in global climate models will lead to errors in simulated extreme precipitation.…”

“…Here we only looked at annually aggregated f w and μ and previous studies indicate that there usually is a mean seasonal cycle in both μ and f w [20,29]. Nevertheless, equation (1) appears to describe the statistics for the different seasons [19].…”

“…It tends to under-estimate the higher percentiles of daily precipitation [15], but this bias appears to be constant for a given percentile or returninterval and can be adjusted for [16,17]. Previous analysis of trends in the wet-day mean precipitation μ has been used to indicate trends in the upper percentiles [18] and this framework has provided a basis for downscaling the probability of heavy precipitation and the change in return-values [19,20]. There are also studies suggesting that extreme daily rainfall amounts increase with higher temperature as indicated by the Clausius-Clapeyron relation [21,22], however, the global mean precipitation amount is also constrained by the available energy [23].…”