Special Issue "Paleoclimate and Its Connection with Future Climate Change"
Deadline for manuscript submissions: 31 October 2020.
Interests: Climate variability; paleoclimate dynamics; paleoclimate reconstruction; climate change; regional downscaling; emerging infectious diseases
Interests: forest resilience; tipping point; extreme climates; nonlinear system theory; dendroclimatology; carbon cycle; atmosphere–biosphere interactions; abrupt climate transition
Special Issues and Collections in MDPI journals
Special Issue in Remote Sensing: Forest Resilience to Extreme Events
Interests: Climate variability; impacts of climate change; historical climate; climate teleconnections
Interests: paleoclimate; paleooceanography; paleoenvironment using geological archives (especially coral geochemical proxy Sr/Ca and d18O)
As confidence is growing in our physical understanding of cause–effect relations within the Earth system, there remains a pressing need to expand our view of past climatic changes in order to increase confidence in the projected climate change scenarios. The rate at which our current climate system is moving further out of the observed range of variability is without precedence in the instrumental period. Facing uncharted future territory, scientists have long explored information from past climates to study a wider dynamic range of climate variability. In this Special Issue, we invite the scientific community to highlight their progress and advances made in connecting paleoclimate research with the fundamental scientific questions concerning future climate change, such as: How can paleoclimate help to reduce uncertainty in climate sensitivity estimates? What scaling laws are govern natural climate variability on interannual to millennial timescales? What can we learn from past climate reconstructions about changes in the natural modes of variability in response to external forcing? Can we predict future abrupt climate change based on paleoclimatic event histories? We call for contributions to this Special Issue that highlight advances in proxy system modeling, reconstructions of past climates, data assimilation of proxy records, or machine-learning-based paleoclimate analysis methods, as well as detection and attribution methods or dynamical system analyses in the context of paleoclimate applications.
Dr. Oliver Elison Timm
Prof. Chuixiang Yi
Prof. Daoyi Gong
Dr. Sri Yudawati Cahyarini
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- climate sensitivity;
- natural variability;
- climate models;
- nonlinear dynamics;
- feedback analysis;
- glacial–interglacial cycles;
- proxy archives;
- proxy system modeling;
- external forcing;
- detection and attribution;
- machine learning;
- data assimilation.