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@ARTICLE{Chang:905407,
      author       = {Chang, Kai-Lan and Schultz, Martin and Lan, Xin and
                      McClure-Begley, Audra and Petropavlovskikh, Irina and Xu,
                      Xiaobin and Ziemke, Jerald R.},
      title        = {{T}rend detection of atmospheric time series},
      journal      = {Elementa},
      volume       = {9},
      number       = {1},
      issn         = {2325-1026},
      address      = {Washington, DC},
      publisher    = {BioOne},
      reportid     = {FZJ-2022-00659},
      pages        = {00035},
      year         = {2021},
      abstract     = {This paper is aimed at atmospheric scientists without
                      formal training in statistical theory. Its goal is to (1)
                      provide a critical review of the rationale for trend
                      analysis of the time series typically encountered in the
                      field of atmospheric chemistry, (2) describe a range of
                      trend-detection methods, and (3) demonstrate effective means
                      of conveying the results to a general audience. Trend
                      detections in atmospheric chemical composition data are
                      often challenged by a variety of sources of uncertainty,
                      which often behave differently to other environmental
                      phenomena such as temperature, precipitation rate, or stream
                      flow, and may require specific methods depending on the
                      science questions to be addressed. Some sources of
                      uncertainty can be explicitly included in the model
                      specification, such as autocorrelation and seasonality, but
                      some inherent uncertainties are difficult to quantify, such
                      as data heterogeneity and measurement uncertainty due to the
                      combined effect of short and long term natural variability,
                      instrumental stability, and aggregation of data from sparse
                      sampling frequency. Failure to account for these
                      uncertainties might result in an inappropriate inference of
                      the trends and their estimation errors. On the other hand,
                      the variation in extreme events might be interesting for
                      different scientific questions, for example, the frequency
                      of extremely high surface ozone events and their relevance
                      to human health. In this study we aim to (1) review trend
                      detection methods for addressing different levels of data
                      complexity in different chemical species, (2) demonstrate
                      that the incorporation of scientifically interpretable
                      covariates can outperform pure numerical curve fitting
                      techniques in terms of uncertainty reduction and improved
                      predictability, (3) illustrate the study of trends based on
                      extreme quantiles that can provide insight beyond standard
                      mean or median based trend estimates, and (4) present an
                      advanced method of quantifying regional trends based on the
                      inter-site correlations of multisite data. All
                      demonstrations are based on time series of observed trace
                      gases relevant to atmospheric chemistry, but the methods can
                      be applied to other environmental data sets.},
      cin          = {JSC},
      ddc          = {550},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / Earth System Data
                      Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000740665000001},
      doi          = {10.1525/elementa.2021.00035},
      url          = {https://juser.fz-juelich.de/record/905407},
}