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Episode 7: Mark Cane – Part II

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Manage episode 313003663 series 3255360
Inhalt bereitgestellt von Deep Convection. Alle Podcast-Inhalte, einschließlich Episoden, Grafiken und Podcast-Beschreibungen, werden direkt von Deep Convection oder seinem Podcast-Plattformpartner hochgeladen und bereitgestellt. Wenn Sie glauben, dass jemand Ihr urheberrechtlich geschütztes Werk ohne Ihre Erlaubnis nutzt, können Sie dem hier beschriebenen Verfahren folgen https://de.player.fm/legal.

This second part of the interview with Mark Cane picks up where Part I left off – at MIT, in the middle of Mark’s PhD. A major focus of the interview is the discovery that made Mark’s career, when he and his student at the time, Steve Zebiak, developed the first dynamical model that could both simulate and predict El Niño events; and then how they ventured to
make an actual real-time prediction, of the 1985-86 event, and then publicized it. That was a bold and risky move, but it paid off.

“The fact that it worked as soon as we started doing realistic things actually added to the sense of confidence. […] We had sweated to make the model work but not to make the forecast work.
So I was fairly confident. And one thought, I thought, and others, that okay, this could do some good if we’re right and people paid attention.”

El Niño and its companion La Niña (collectively referred to as El Niño/Southern Oscillation, or ENSO) are drivers of the strongest year-to-year climate fluctuations on the planet. They alter patterns of weather variability in many places around the globe, including the frequency and severity of extreme events such as droughts, floods, heat waves, and tropical cyclones. Current forecast models can predict these events 6-12 months ahead, and their predictions help to reduce the impacts of ENSO on people and businesses.

The model developed by Mark Cane and Steve Zebiak laid the foundation for this huge success story — ironically, that breakthrough happened just after the biggest disappointment of Mark’s career: He had left MIT after being told that he wouldn’t get tenure. This was no doubt a difficult experience for Mark (and probably a decision MIT has come to regret), even though the many successes in his career by far outweigh that setback.

You can find more information about Mark and his work here.

The interview with Mark Cane was recorded in May 2019.

  continue reading

57 Episoden

Artwork
iconTeilen
 
Manage episode 313003663 series 3255360
Inhalt bereitgestellt von Deep Convection. Alle Podcast-Inhalte, einschließlich Episoden, Grafiken und Podcast-Beschreibungen, werden direkt von Deep Convection oder seinem Podcast-Plattformpartner hochgeladen und bereitgestellt. Wenn Sie glauben, dass jemand Ihr urheberrechtlich geschütztes Werk ohne Ihre Erlaubnis nutzt, können Sie dem hier beschriebenen Verfahren folgen https://de.player.fm/legal.

This second part of the interview with Mark Cane picks up where Part I left off – at MIT, in the middle of Mark’s PhD. A major focus of the interview is the discovery that made Mark’s career, when he and his student at the time, Steve Zebiak, developed the first dynamical model that could both simulate and predict El Niño events; and then how they ventured to
make an actual real-time prediction, of the 1985-86 event, and then publicized it. That was a bold and risky move, but it paid off.

“The fact that it worked as soon as we started doing realistic things actually added to the sense of confidence. […] We had sweated to make the model work but not to make the forecast work.
So I was fairly confident. And one thought, I thought, and others, that okay, this could do some good if we’re right and people paid attention.”

El Niño and its companion La Niña (collectively referred to as El Niño/Southern Oscillation, or ENSO) are drivers of the strongest year-to-year climate fluctuations on the planet. They alter patterns of weather variability in many places around the globe, including the frequency and severity of extreme events such as droughts, floods, heat waves, and tropical cyclones. Current forecast models can predict these events 6-12 months ahead, and their predictions help to reduce the impacts of ENSO on people and businesses.

The model developed by Mark Cane and Steve Zebiak laid the foundation for this huge success story — ironically, that breakthrough happened just after the biggest disappointment of Mark’s career: He had left MIT after being told that he wouldn’t get tenure. This was no doubt a difficult experience for Mark (and probably a decision MIT has come to regret), even though the many successes in his career by far outweigh that setback.

You can find more information about Mark and his work here.

The interview with Mark Cane was recorded in May 2019.

  continue reading

57 Episoden

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