@article{HerzschuhCaoLaeppleetal.2019,
  author    = {Herzschuh, Ulrike and Cao, Xianyong and Laepple, Thomas and Dallmeyer, Anne and Telford, Richard J. and Ni, Jian and Chen, Fahu and Kong, Zhaochen and Liu, Guangxiu and Liu, Kam-Biu and Liu, Xingqi and Stebich, Martina and Tang, Lingyu and Tian, Fang and Wang, Yongbo and Wischnewski, Juliane and Xu, Qinghai and Yan, Shun and Yang, Zhenjing and Yu, Ge and Zhang, Yun and Zhao, Yan and Zheng, Zhuo},
  title     = {Position and orientation of the westerly jet determined Holocene rainfall patterns in China},
  series = {Nature Communications},
  volume    = {10},
  journal   = {Nature Communications},
  publisher = {Nature Publ. Group},
  address   = {London},
  issn      = {2041-1723},
  doi       = {10.1038/s41467-019-09866-8},
  pages     = {8},
  year      = {2019},
  abstract  = {Proxy-based reconstructions and modeling of Holocene spatiotemporal precipitation patterns for China and Mongolia have hitherto yielded contradictory results indicating that the basic mechanisms behind the East Asian Summer Monsoon and its interaction with the westerly jet stream remain poorly understood. We present quantitative reconstructions of Holocene precipitation derived from 101 fossil pollen records and analyse them with the help of a minimal empirical model. We show that the westerly jet-stream axis shifted gradually southward and became less tilted since the middle Holocene. This was tracked by the summer monsoon rain band resulting in an early-Holocene precipitation maximum over most of western China, a mid-Holocene maximum in north-central and northeastern China, and a late-Holocene maximum in southeastern China. Our results suggest that a correct simulation of the orientation and position of the westerly jet stream is crucial to the reliable prediction of precipitation patterns in China and Mongolia.},
  language  = {en}
}
@article{ReschkeKunzLaepple2018,
  author    = {Reschke, Maria and Kunz, Torben and Laepple, Thomas},
  title     = {Comparing methods for analysing time scale dependent correlations in irregularly sampled time series data},
  series = {Computers \& geosciences : an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets ; an official journal of the International Association for Mathematical Geology},
  volume    = {123},
  journal   = {Computers \& geosciences : an international journal devoted to the publication of papers on all aspects of geocomputation and to the distribution of computer programs and test data sets ; an official journal of the International Association for Mathematical Geology},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0098-3004},
  doi       = {10.1016/j.cageo.2018.11.009},
  pages     = {65 -- 72},
  year      = {2018},
  abstract  = {Time series derived from paleoclimate archives are often irregularly sampled in time and thus not analysable using standard statistical methods such as correlation analyses. Although measures for the similarity between time series have been proposed for irregular time series, they do not account for the time scale dependency of the relationship. Stochastically distributed temporal sampling irregularities act qualitatively as a low-pass filter reducing the influence of fast variations from frequencies higher than about 0.5 (Delta t(max))(-1) , where Delta t(max), is the maximum time interval between observations. This may lead to overestimated correlations if the true correlation increases with time scale. Typically, correlations are underestimated due to a non-simultaneous sampling of time series. Here, we investigated different techniques to estimate time scale dependent correlations of weakly irregularly sampled time series, with a particular focus on different resampling methods and filters of varying complexity. The methods were tested on ensembles of synthetic time series that mimic the characteristics of Holocene marine sediment temperature proxy records. We found that a linear interpolation of the irregular time series onto a regular grid, followed by a simple Gaussian filter was the best approach to deal with the irregularity and account for the time scale dependence. This approach had both, minimal filter artefacts, particularly on short time scales, and a minimal loss of information due to filter length.},
  language  = {en}
}