IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v569y2021ics0378437121000212.html
   My bibliography  Save this article

Maximal likely phase lines for a reduced ice growth model

Author

Listed:
  • Tsiairis, Athanasios
  • Wei, Pingyuan
  • Chao, Ying
  • Duan, Jinqiao

Abstract

We study the impact of white noise on transitions between metastable equilibrium states in a stochastic ice sheet model. Two methods to accomplish different objectives are employed. The maximal likely trajectory by maximizing the probability density function and numerically solving the Fokker–Planck equation shows how the system will evolve over time. We have especially studied the maximal likely trajectories starting near the ice-free metastable state, and examined whether they evolve to or near the ice-covered metastable state for certain parameters, in order to gain insights into how the ice sheet formed. Furthermore, for the transition from ice-covered metastable state to the ice-free metastable state, we study the most probable path for various noise parameters via the Onsager–Machlup least action principle. This enables us to predict and visualize the melting process of the ice sheet if such a rare event ever does take place.

Suggested Citation

  • Tsiairis, Athanasios & Wei, Pingyuan & Chao, Ying & Duan, Jinqiao, 2021. "Maximal likely phase lines for a reduced ice growth model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 569(C).
    • Handle: RePEc:eee:phsmap:v:569:y:2021:i:c:s0378437121000212
    DOI: 10.1016/j.physa.2021.125749
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437121000212
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2021.125749?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cheng, Xiujun & Wang, Hui & Wang, Xiao & Duan, Jinqiao & Li, Xiaofan, 2019. "Most probable transition pathways and maximal likely trajectories in a genetic regulatory system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 531(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hao, Mengli & Jia, Wantao & Wang, Liang & Li, Fuxiao, 2022. "Most probable trajectory of a tumor model with immune response subjected to asymmetric Lévy noise," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    2. Yang, Anji & Wang, Hao & Yuan, Sanling, 2023. "Tipping time in a stochastic Leslie predator–prey model," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    3. Han, Ping & Xu, Wei & Wang, Liang & Zhang, Hongxia & Ma, Shichao, 2020. "Most probable dynamics of the tumor growth model with immune surveillance under cross-correlated noises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:569:y:2021:i:c:s0378437121000212. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.