Theoretical arguments and climate projections suggest that extreme precipitations are expected to increase in a warmer climate. To validate this scenario, statistical testing procedures are often employed to analyse observed and simulated precipitation records. Recent work warned about possible misinterpretation of trend tests if the presence of serial correlation and field significance are not considered when applied to hydrological data. In this study, these two aspects have been extensively investigated using time series of frequencies (or counts) of rainfall extremes derived from (i) long-term (100 years) daily precipitation records retrieved by 1087 rain gauges of the Global Historical Climate Network database and (ii) several reanalysis products. Multiple Monte Carlo experiments have been conducted involving random synthetic count time series generated with the Poisson first-order Integer-valued AutoRegressive model (Poisson-INAR(1)) and characterized by different sample size, level of autocorrelation, and trend magnitude. The main results are as follows. (1) Empirical autocorrelations are highly consistent with those exhibited by uncorrelated and non-stationary count time series, while empirical trends cannot be explained as the exclusive effect of autocorrelation; furthermore, taking into account the impact of serial correlation has a limited impact on tests’ performance. (2) Accounting for field significance improves interpretation of test results by limiting type-I errors but may decreases the test power; results of local tests could complement field significance outcomes and help identify regions with potentially significant changes where several trends with coherent sign are detected. (3) In the analysis of count time series, statistical trend tests based on linear and Poisson regression are more powerful than nonparametric tests, such as Mann-Kendall. These findings are used to properly investigate the existence of significant changes in the reference frequency time series. Several evident spatial patterns of statistically significant increasing (decreasing) trends emerge mainly in northern and eastern North America, northwestern South America, south Africa, some central regions of China and India and, more uncertainly, northern parts of Australia (southwestern North America, northeastern south America, central parts of Eurasia and southwestern and southeastern coastal regions of Australia).

A global scale statistical trend assessment on extreme precipitation frequencies

FARRIS, STEFANO
2022-04-27T00:00:00+02:00

Abstract

Theoretical arguments and climate projections suggest that extreme precipitations are expected to increase in a warmer climate. To validate this scenario, statistical testing procedures are often employed to analyse observed and simulated precipitation records. Recent work warned about possible misinterpretation of trend tests if the presence of serial correlation and field significance are not considered when applied to hydrological data. In this study, these two aspects have been extensively investigated using time series of frequencies (or counts) of rainfall extremes derived from (i) long-term (100 years) daily precipitation records retrieved by 1087 rain gauges of the Global Historical Climate Network database and (ii) several reanalysis products. Multiple Monte Carlo experiments have been conducted involving random synthetic count time series generated with the Poisson first-order Integer-valued AutoRegressive model (Poisson-INAR(1)) and characterized by different sample size, level of autocorrelation, and trend magnitude. The main results are as follows. (1) Empirical autocorrelations are highly consistent with those exhibited by uncorrelated and non-stationary count time series, while empirical trends cannot be explained as the exclusive effect of autocorrelation; furthermore, taking into account the impact of serial correlation has a limited impact on tests’ performance. (2) Accounting for field significance improves interpretation of test results by limiting type-I errors but may decreases the test power; results of local tests could complement field significance outcomes and help identify regions with potentially significant changes where several trends with coherent sign are detected. (3) In the analysis of count time series, statistical trend tests based on linear and Poisson regression are more powerful than nonparametric tests, such as Mann-Kendall. These findings are used to properly investigate the existence of significant changes in the reference frequency time series. Several evident spatial patterns of statistically significant increasing (decreasing) trends emerge mainly in northern and eastern North America, northwestern South America, south Africa, some central regions of China and India and, more uncertainly, northern parts of Australia (southwestern North America, northeastern south America, central parts of Eurasia and southwestern and southeastern coastal regions of Australia).
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11584/333634
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