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 […]
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.
New research finds that internal atmospheric variability plays a role alongside anthropogenic greenhouse gases in the warming of the upper Arctic Ocean over the last forty years.
The Climate Model Development Task Force’s has been working for over three years to advance NOAA’s climate models in support of improved sub-seasonal to seasonal predictions.
A new NOAA report describes high-priority recommendations to improve the way NOAA develops and operates models.
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.
Reanalysis and reforecast data from NOAA’s premiere forecast system are now fully available for the public’s use.
In a new study published in the Journal of the Meteorological Society of Japan, Xie et al. address the continued role of the Zebiak-Cane coupled model in ENSO forecasting.
CORE-II is the Coordinated Ocean-ice Reference Experiments are a CLIVAR model intercomparison effort that examines a group of global ocean-sea ice models under a common atmospheric state to facilitate improved understanding and modeling of the ocean.
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.