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Full-Text Articles in Physical Sciences and Mathematics
Linear Trend Detection In Serially Dependent Hydrometeorological Data Based On A Variance Correction Spearman Rho Method, Wenpeng Weng, Yuanfang Chen, Stefan Becker, Bo Liu
Linear Trend Detection In Serially Dependent Hydrometeorological Data Based On A Variance Correction Spearman Rho Method, Wenpeng Weng, Yuanfang Chen, Stefan Becker, Bo Liu
Publications and Research
Hydrometeorological data are commonly serially dependent and thereby deviate from the assumption of independence that underlies the Spearman rho trend test. The presence of autocorrelation will influence the significance of observed trends. Specifically, the positive autocorrelation inflates Type І errors, while it deflates the power of trend detection in some cases. To address this issue, we derive a theoretical formula and recommend an appropriate empirical formula to calculate the rho variance of dependent series. The proposed procedure of the variance correction for the Spearman rho method is capable of mitigating the effect of autocorrelation on both, Type І error and …
Global Trends In Extreme Precipitation: Climate Models Versus Observations, Behzad Asadieh, Nir Y. Krakauer
Global Trends In Extreme Precipitation: Climate Models Versus Observations, Behzad Asadieh, Nir Y. Krakauer
Publications and Research
Precipitation events are expected to become substantially more intense under global warming, but few global comparisons of observations and climate model simulations are available to constrain predictions of future changes in precipitation extremes. We present a systematic global-scale comparison of changes in historical (1901–2010) annual-maximum daily precipitation between station observations (compiled in HadEX2) and the suite of global climate models contributing to the fifth phase of the Coupled Model Intercomparison Project (CMIP5). We use both parametric and non-parametric methods to quantify the strength of trends in extreme precipitation in observations and models, taking care to sample them spatially and temporally …