English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

By matching large-scale patterns in climate fields with patterns in observed station precipitation, this work explores seasonal predictability of precipitation in the contiguous United States for all seasons. Although it is shown that total seasonal precipitation and frequencies of less-than-extreme daily precipitation events can be predicted with much higher skill, the focus of this study is on frequencies of daily precipitation above the seasonal 90th percentile (P90), a variable whose skillful prediction is more challenging. Frequency of heavy daily precipitation is shown to respond to ENSO as well as to non-ENSO interannual and interdecadal variability in the North Pacific. Specification skill achieved by a statistical model based on contemporaneous SST forcing with and without an explicit dynamical atmosphere is compared and contrasted. Statistical models relating the SST forcing patterns directly to observed station precipitation are shown to perform consistently better in all seasons than hybrid (dynamical-statistical) models where the SST forcing is first translated to atmospheric circulation via three separate general circulation models and the dynamically computed circulation anomalies are statistically related to observed precipitation. Skill is summarized for all seasons, but in detail for January-February-March, when it is shown that predictable patterns are spatially robust regardless of the approach used. Predictably, much of the skill is due to ENSO. While the U.S. average skill is modest, regional skill levels can be quite high. It is also found that non-ENSO-related skill is significant, especially for the extreme Southwest and that this is due mostly to non-ENSO interannual and decadal variability in the North Pacific SST forcing. Although useful specification skill is achieved by both approaches, hybrid predictability is not pursued further in this effort. Rather, prognostic analysis is carried out with the purely statistical approach to analyze P90 predictability based on antecedent SST forcing. Skill at various lead times is investigated and it is shown that significant regional skill can be achieved at lead times of several months even in the absence of strong ENSO forcing.

Heavy Daily Precipitation Frequency over the Contiguous United States: Sources of Climatic Variability and Seasonal Predictability