Special Issue "Time Series Analysis of Global Climate Change"

A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Weather and Forecasting".

Deadline for manuscript submissions: 1 November 2021.

Special Issue Editor

Prof. Umberto Triacca
E-Mail
Guest Editor
University of L'Aquila/Department of Computer Engineering, Computer Science and Mathematics, via Vetoio 1, L'Aquila, 67100, Italy
Interests: climate change; cointegration; Granger causality; time series; vector autoregressive models

Special Issue Information

Dear Colleagues,

This Special Issue aims to promote the use of  time series  methods for the statistical analysis of climate data, with particular emphasis on the detection and attribution of climate change. Detection refers to the statistical assessment of the significance and relevance of the change occurring in the climate system, or in a natural or human system affected by climate. Attribution aims to quantify the links between observed climate variation and both human and natural drivers of change (anthropogenic forcing, solar variations, and volcanic eruptions).

We solicit the submission of papers that capture the essential features of climate series, such as possible non-stationarity, nonlinearity, seasonality, and cycles (for instance, related to transitory phenomena such as volcanic eruptions or the El Niño Southern Oscillation). We also welcome submissions in the field of  attribution of climate change highlighting interesting statistical challenges to which time series methods can contribute.  A further aim is to establish and to evaluate methods for predicting temperature trends and global sea level rise or other climate variables. Accurate and reliable decadal prediction is crucial for feeding impact models and correctly quantifying the real consequences on territories and ecosystems in the near future.

Prof. Umberto Triacca
Guest Editor

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. Forecasting is an international peer-reviewed open access quarterly 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 1000 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.

Keywords

  • climate change
  • forecasting
  • global warming
  • neural networks
  • time series methods

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
Visual Analytics for Climate Change Detection in Meteorological Time-Series
Forecasting 2021, 3(2), 276-289; https://0-doi-org.brum.beds.ac.uk/10.3390/forecast3020018 - 19 Apr 2021
Viewed by 767
Abstract
The importance of high-resolution meteorological time-series data for detection of transformative changes in the climate system is unparalleled. These data sequences allow for a comprehensive study of natural and forced evolution of warming and cooling tendencies, recognition of distinct structural changes, and periodic [...] Read more.
The importance of high-resolution meteorological time-series data for detection of transformative changes in the climate system is unparalleled. These data sequences allow for a comprehensive study of natural and forced evolution of warming and cooling tendencies, recognition of distinct structural changes, and periodic behaviors, among other things. Such inquiries call for applications of cutting-edge analytical tools with powerful computational capabilities. In this regard, we documented the application potential of visual analytics (VA) for climate change detection in meteorological time-series data. We focused our study on long- and short-term past-to-current meteorological data of three Central European cities (i.e., Vienna, Munich, and Zürich), delivered in different temporal intervals (i.e., monthly, hourly). Our aim was not only to identify the related transformative changes, but also to assert the degree of climate change signal that can be derived given the varying granularity of the underlying data. As such, coarse data granularity mostly offered insights on general trends and distributions, whereby a finer granularity provided insights on the frequency of occurrence, respective duration, and positioning of certain events in time. However, by harnessing the power of VA, one could easily overcome these limitations and go beyond the basic observations. Full article
(This article belongs to the Special Issue Time Series Analysis of Global Climate Change)
Show Figures

Figure 1

Back to TopTop