Event: S2S Extremes Workshop
This week, the International Research Institute for Climate and Society (IRI), the Extreme Weather & Climate Initiative (Extreme Weather), and the WWRP/WCRP Sub-Seasonal to Seasonal Prediction Project will hold a 2-day workshop at the Columbia University Lamont-Doherty Earth Observatory campus in Palisades, New York.
Adam Sobel, one of the co-organizers of the event, said that the workshop will bring many of the world’s leading scientists in the sub-seasonal to seasonal (S2S) field to the Lamont campus. During the event they will assess the state of the art in S2S prediction and define the directions in which new progress will be made.
Presenters come from academia, government and the private sector and represent a range of interests in understanding the latest science behind predictability of extreme weather and climate at the sub-seasonal to seasonal timescale, and in developing early warning products. Through a small set of invited talks, contributed posters and discussion sessions, the workshop will showcase the latest research on extremes using S2S models.
The S2S timescale, defined as the range of two weeks to several months, is a key time range, both from the perspective of the climatic drivers of extremes and for decision makers to have sufficient time to take preemptive actions.
“The science of predicting weather a few days ahead is about a century old, while seasonal climate prediction goes back a few decades,” said Sobel. “Both continue to evolve, but prediction on the subseasonal time scale, in between weather and climate, is altogether a new phenomenon, as our forecast models have only in the last decade or so begun to properly simulate the Madden-Julian oscillation, stratosphere-troposphere interactions and other phenomena which lead to predictability on this time scale.”
These recent developments, together with the establishment of the WWRP/WCRP S2S prediction project archive of operational model forecasts, provide a new opportunity to better understand the mechanisms behind extreme events with large societal impacts (e.g. floods, droughts, storms and heat and cold waves), as well as to improve their prediction and early warning.