Please note: Significance Statements are currently only available for WAF and WCAS.
Public support of scientific research is of great potential benefit, both to the science and to society. To further the goal of expanding the impact of the science in our journals to society in general, AMS is encouraging the inclusion in submitted manuscripts of a plain language summary, or “significance statement,” aimed at the undergraduate-level scientist outside their field of specialty.
AMS journals is piloting this option with WAF and WCAS first, before expanding it to the full suite of journals. Those authors will have the option of including significance statements to set results in a broader context and in plain language suitable for a wider readership. Significance statements enhance accessibility and impact of your articles, extending their benefit to scientists in other fields and society as a whole.
A significance statement is not a summary or an abstract: instead, it provides additional context for why the work is relevant to science and society. Significance statements will be peer-reviewed and will appear after the abstract in the published paper. The statements must answer the following questions in 120 words or less, without jargon or technical wording:
Authors opting to provide a significance statement with their manuscript should include it immediately after the abstract in the manuscript file with a section title of Significance Statement. Two examples are given below.
Extratropical Transition of Hurricane Irene (2011) in a Changing Climate
According to a prior study, 46% of North Atlantic hurricanes transition into mid-latitude cyclones as they move poleward, potentially bringing hazardous weather to locations that rarely experience hurricane impacts. Previous studies also indicate that projected climate change will worsen the impacts of hurricanes and tropical storms, especially with increased rainfall. These factors motivated us to investigate how climate change could affect the characteristics of transitioning hurricanes. We utilized computer-based experiments to simulate hurricane Irene, which affected the eastern U.S. in 2011. In one simulation, we reproduced the observed storm. In an experimental simulation, we examined how the storm would change in a warmer climate. The future transitioning storm featured substantially heavier precipitation and stronger winds. In addition, the amount of time it took the storm to complete its transitioning was about 60% longer in the warmer simulations.
Using Deep Learning to Estimate Tropical Cyclone Intensity from Satellite Passive Microwave Imagery
This type of satellite imagery has been used routinely in forecasting for decades, and other techniques have been proposed that find empirical relationships between features in the imagery and tropical cyclone intensity. However, this study is unique because 1) it generates calibrated uncertainty values for every estimate; 2) it can work on corrupted images, partial scans and imprecisely centered features; and 3) it provides clear indicators of the biases in the training dataset, which points toward strategies for further improvement.