Abstract
AbstractWe assess the performance of different break detection methods on three sets of benchmark data sets, each consisting of 120 daily time series of integrated water vapor differences. These differences are generated from the Global Positioning System (GPS) measurements at 120 sites worldwide, and the numerical weather prediction reanalysis (ERA‐Interim) integrated water vapor output, which serves as the reference series here. The benchmark includes homogeneous and inhomogeneous sections with added nonclimatic shifts (breaks) in the latter. Three different variants of the benchmark time series are produced, with increasing complexity, by adding autoregressive noise of the first order to the white noise model and the periodic behavior and consecutively by adding gaps and allowing nonclimatic trends. The purpose of this “complex experiment” is to examine the performance of break detection methods in a more realistic case when the reference series are not homogeneous. We evaluate the performance of break detection methods with skill scores, centered root mean square errors (CRMSE), and trend differences relative to the trends of the homogeneous series. We found that most methods underestimate the number of breaks and have a significant number of false detections. Despite this, the degree of CRMSE reduction is significant (roughly between 40% and 80%) in the easy to moderate experiments, with the ratio of trend bias reduction is even exceeding the 90% of the raw data error. For the complex experiment, the improvement ranges between 15% and 35% with respect to the raw data, both in terms of RMSE and trend estimations.
Highlights
Water vapor is a key component for the Earth's climate as it is the most important natural greenhouse gas and responsible for the largest known feedback mechanism for amplifying climate change
These differences are generated from the Global Positioning System (GPS) measurements at 120 sites worldwide, and the numerical weather prediction reanalysis (ERA‐Interim) integrated water vapor output, which serves as the reference series here
As opposed to previous studies assessing the performance of break detection methods on benchmark time series (e.g., Venema et al, 2012), the analysis presented here is atypical in many aspects
Summary
Water vapor is a key component for the Earth's climate as it is the most important natural greenhouse gas and responsible for the largest known feedback mechanism for amplifying climate change (the water vapor feedback, see, for example, Soden & Held, 2006). Ning et al (2016) used a statistical test, the penalized maximal t test modified to account for first‐order autoregressive noise in time series (PMTred, see section 3.1.4), to detect inhomogeneities in the form of shifts in the mean (hereafter breaks or breakpoints) of the difference time series This approach allowed for identification of the breaks in the GPS IWV time series with the constraint that detected breaks could occur for inhomogeneities in the reference series (ERA‐Interim from the European Centre for Medium‐Range Weather Forecasts [ECMWF] reanalysis [Dee et al, 2011] in their case). We extend on this approach, but use both data sets to assess the performance of widely used break detection methods, as in, for example, Venema et al (2012) but for IWV data For this purpose, a synthetic benchmark data set was constructed by simulating the IWV differences between GPS and ERA‐Interim.
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