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modeling

NOAA Sea Ice Modeling Collaboration Workshop Report Defines Priorities and Key Collaborations 

A report has just been published detailing the accomplishments of the NOAA Sea Ice Modeling Collaboration Workshop, held at the University of Colorado, Boulder in April 2023. The Sea Ice Modeling Collaboration Team organized this workshop, a group dedicated to advancing cross-OAR and NOAA-wide sea ice modeling activities that is sponsored by CPO’s Climate Observations and Monitoring (COM) and Climate […]

NOAA Sea Ice Modeling Collaboration Workshop Report Defines Priorities and Key Collaborations  Read More »

Investigating the role of Sea-Surface Salinity (SSS) in simulating historical AMOC decadal variation

This new study aims to advance decadal prediction models by exploring regionally-dependent coupled sea-surface dynamics at work in the Atlantic and Pacific in initialized prediction ensembles.

Investigating the role of Sea-Surface Salinity (SSS) in simulating historical AMOC decadal variation Read More »

A new OAR technical report informs the development of next-generation NOAA climate reanalysis

A new OAR/Climate Program Office (CPO) Report, summarizing key outcomes of the May 2015 NOAA Climate Reanalysis Task Force Technical Workshop held at the NOAA Center for Weather and Climate Prediction in College Park, Maryland, has just been published.

A new OAR technical report informs the development of next-generation NOAA climate reanalysis Read More »

Novel data science approaches could drive advances in seasonal to sub-seasonal predictions of precipitation

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.

Novel data science approaches could drive advances in seasonal to sub-seasonal predictions of precipitation Read More »

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