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US CLIVAR holds workshop on advancing scientific understanding of multi-year predictability

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Aiming to stimulate research into developing useful multi-year climate predictions, the US Climate Variability and Predictability (US CLIVAR) Program held its Societally-Relevant Multi-Year Climate Predictions Workshop during the last week of March. Similar to the 2022 US CLIVAR Summit, this workshop was in a hybrid format, with in-person activities held in Boulder, Colorado this time. The majority of the presentations focused on three main themes: multi-year processes in observations and models, current prediction efforts and modeling issues, and applications. In addition to the invited talks, participants were able to browse interactive poster sessions, in-depth plenary and breakout discussions, and a networking event.

CPO’s Climate Variability and Predictability (CVP)-supported PIs were among the invited speakers, sharing their research with participants. Martha Buckley (George Mason University) spoke on the multi-year predictability in the Atlantic Ocean, while Elizabeth Maroon (NCAR) presented a multi-model study of a subpolar North Atlantic cold extreme.

Research conducted by NOAA scientists was also highlighted in the meeting. As part of the multi-year processes in observations and models theme, GFDL’s Youngji Joh presented the latest findings on seasonal-to-decadal variability and predictability of the Kuroshio Extension. For the current efforts and modeling issues theme, a talk on the next generation seasonal to decadal prediction using the SPEAR model was given by Tom Delworth, also from GFDL. Finally, on the applications side, Stephanie Brodie (SWFSC) provided insight on the multi-year climate predictions to support fisheries management in a changing ocean, while Billy Sweet (NOS) spoke on NOAA’s sea level rise science and services.

US CLIVAR is funded by the CVP and MAPP Programs in CPO, and the GOMO Program Office. It also has interagency funding by NSF, NASA, DOE and NOAA.

For more information, contact Jose Algarin.

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