@article{HuangPengRudayaetal.2018,
  author    = {Huang, Xiaozhong and Peng, Wei and Rudaya, Natalia and Grimm, Eric C. and Chen, Xuemei and Cao, Xianyong and Zhang, Jun and Pan, Xiaoduo and Liu, Sisi and Chen, Chunzhu and Chen, Fahu},
  title     = {Holocene vegetation and climate dynamics in the Altai Mountains and Surrounding Areas},
  series = {Geophysical research letters},
  volume    = {45},
  journal   = {Geophysical research letters},
  number    = {13},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {0094-8276},
  doi       = {10.1029/2018GL078028},
  pages     = {6628 -- 6636},
  year      = {2018},
  abstract  = {A comprehensive understanding of the regional vegetation responses to long-term climate change will help to forecast Earth system dynamics. Based on a new well-dated pollen data set from Kanas Lake and a review on the published pollen records in and around the Altai Mountains, the regional vegetation dynamics and forcing mechanisms are discussed. In the Altai Mountains, the forest optimum occurred during 10-7ka for the upper forest zone and the tree line decline and/or ecological shifts were caused by climatic cooling from around 7ka. In the lower forest zone, the forest reached an optimum in the middle Holocene, and then increased openness of the forest, possibly caused by both climate cooling and human activities, took place in the late Holocene. In the lower basins or plains around the Altai Mountains, the development of protograssland or forest benefited from increasing humidity in the middle to late Holocene. Plain Language Summary In the Altai Mountains and surrounding area of central Asia, the previous studies of the Holocene paleovegetation and paleoclimate studies did not discuss the different ecological limiting factors for the vegetation in high mountains and low-elevation areas due to limited data. With accumulating fossil pollen data and surface pollen data, it is possible to understand better the geomorphological effect on the vegetation and discrepancies of vegetation/forest responses to large-scale climate forcing, and it is also possible to get reliable quantitative reconstructions of climate. Here our new pollen data and review on the published fossil pollen data will help us to look into the past climate change and vertical evolution of vegetation in this important area of the Northern Hemisphere. Based on our study, it can be concluded that the growth of taiga forest in the wetter areas may be promoted under a future warmer climate, while the forest in the relatively dry areas is liable to decline, and the different vegetation dynamics will contribute to future high-resolution coupled vegetation-climate model for Earth system modelling.},
  language  = {en}
}
@article{XuLiuLietal.2020,
  author    = {Xu, Yong and Liu, Xuemei and Li, Yongge and Metzler, Ralf},
  title     = {Heterogeneous diffusion processes and nonergodicity with Gaussian colored noise in layered diffusivity landscapes},
  series = {Physical review : E, Statistical, nonlinear and soft matter physics},
  volume    = {102},
  journal   = {Physical review : E, Statistical, nonlinear and soft matter physics},
  number    = {6},
  publisher = {American Physical Society},
  address   = {College Park},
  issn      = {2470-0045},
  doi       = {10.1103/PhysRevE.102.062106},
  pages     = {16},
  year      = {2020},
  abstract  = {Heterogeneous diffusion processes (HDPs) with space-dependent diffusion coefficients D(x) are found in a number of real-world systems, such as for diffusion of macromolecules or submicron tracers in biological cells. Here, we examine HDPs in quenched-disorder systems with Gaussian colored noise (GCN) characterized by a diffusion coefficient with a power-law dependence on the particle position and with a spatially random scaling exponent. Typically, D(x) is considered to be centerd at the origin and the entire x axis is characterized by a single scaling exponent a. In this work we consider a spatially random scenario: in periodic intervals ("layers") in space D(x) is centerd to the midpoint of each interval. In each interval the scaling exponent alpha is randomly chosen from a Gaussian distribution. The effects of the variation of the scaling exponents, the periodicity of the domains ("layer thickness") of the diffusion coefficient in this stratified system, and the correlation time of the GCN are analyzed numerically in detail. We discuss the regimes of superdiffusion, subdiffusion, and normal diffusion realisable in this system. We observe and quantify the domains where nonergodic and non-Gaussian behaviors emerge in this system. Our results provide new insights into the understanding of weak ergodicity breaking for HDPs driven by colored noise, with potential applications in quenched layered systems, typical model systems for diffusion in biological cells and tissues, as well as for diffusion in geophysical systems.},
  language  = {en}
}