Advances in Subseasonal to Seasonal Climate Prediction in South America
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Climatology".
Deadline for manuscript submissions: 30 April 2026 | Viewed by 85
Special Issue Editors
Interests: climate variability; precipitation; climate modeling
Interests: seasonal; climate prediction
Interests: climate; climate modeling; synoptic meteorology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Subseasonal to seasonal (S2S) climate predictions are essential for supporting decision-making across agriculture, water management, energy, disaster risk reduction, and public health in South America. The region is highly exposed to hydroclimatic extremes such as droughts, floods, and heatwaves, making reliable forecasts crucial for resilience, adaptation, and sustainable development.
Recent advances in dynamic, statistical, hybrid, and AI-based models, along with tools, platforms, and methodologies for prediction development and delivery offer new opportunities to enhance forecast skill and usability. Progress in data assimilation, ensemble forecasting, and machine learning further improves predictability. Challenges remain, however, in addressing model biases, downscaling, regionalization, and integrating forecasts into decision-making.
This Special Issue seeks contributions that cover the full spectrum of S2S climate prediction research and practice—from model development and methodological innovations to applications in climate-sensitive sectors and societal- and policy-relevant dimensions of forecast use. Both methodological advances and case studies demonstrating practical value are encouraged.
Potential topics include, but are not limited to, the following:
- Development and evaluation of S2S prediction systems in South America;
- Forecast downscaling, bias correction, and regional applications;
- Predictability of extreme events and compound hazards;
- Advances in dynamic, statistical, hybrid, and AI-based approaches for S2S prediction;
- Role of climate variability in shaping predictability over South America;
- Tools, platforms, and methodologies to support climate prediction development and delivery;
- User engagement, communication, and integration of forecasts into decision-making and policy.
By combining diverse perspectives, this volume aims to advance both the science and practice of climate prediction in South America.
Dr. Renata Gonçalves Tedeschi
Dr. Anita Drumond
Prof. Dr. Michelle Simões Reboita
Guest Editors
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 submissions that pass pre-check are 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 250 words) can be sent to the Editorial Office for assessment.
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 2400 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
- S2S prediction
- climatology
- AI-models
- dynamic models
- atmospheric variables
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