Predictions at the seasonal to sub-seasonal scale are important for planning and decision-making in a variety of disciplines, and improving understanding and model skill at this timescale is a key research priority. An as yet underexplored approach to sub-seasonal prediction using data science and graph theory methods that are increasingly common to other fields outside of meteorology and climate science shows potential to improve predictions at this challenging timescale.
Process understanding of the Madden-Julian Oscillation (MJO) has increased dramatically over the past decade, but many observed features of the MJO are not well explained by physical mechanisms believed to underlie the phenomenon. New CVP-supported research published in the Journal of Climate examines Moist Static Energy (MSE) and moisture budgets to understand MJO moisture variations.
Research supported by CPO's Climate Variability and Predictability program (CVP) and published in the Journal of Climate tests the wintertime atmospheric response to North Atlantic Ocean circulation variability in CCSM4.
NOAA’s Climate Variability and Predictability (CVP) program competitively funded 2 new three-year projects totaling $2.4 million in grants and $598,000 in other awards to support 20 researchers, postdocs, and students at 10 institutions.
Americans’ health, security and economic wellbeing are tied to climate and weather. Every day, we see communities grappling with environmental challenges due to unusual or extreme events related to climate and weather.