Climate Conversations, Episode 2: The Complexities of Climate Modeling

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Description

This week, the team explores the role of market forces in tackling climate change and are joined by special guest John Reilly, who discusses the complex nature of climate modeling, and the challenges faced by the climate science community in the current political climate.

Participants

Rajesh Kasturirangan

Curt Newton

Laura Howells

John Reilly

 

5 comments
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Topics

  • Climate Science
  • Politics

Comments

Diana Chapman Walsh's picture

Thanks for the good

Thanks for the good conversation, Rajesh, Curt and Laura. I'm grateful for your work. This second of your conversations provided fascinating food for thought about "modeling" of two kinds -- statistical and role (as in being a role model). John was a role model in this conversation with you --the scientist in society who hews to the facts and the findings while also struggling to communicate the urgency of this moment. He didn't mention, but hinted at, the NPR interview he did immediately after Trump appropriated and distorted his work. I agree that we need our scientists to stay with their science but when its integrity is being undermined, I think its valuable for the rest of us to feel them pushing back, in controlled anger and frustration. I wrote a blog post about that interview, and a more recent one that touched on the burden scientists carry and should not have to carry alone. Here are the links in case they're of interest.

https://medium.com/@dcdubs/we-are-still-in-3770aa83d57a
https://medium.com/@dcdubs/disaster-porn-and-composting-e0180a954921

Rick Shankman's picture

Rajesh, the déjà vu you are

Rajesh, the déjà vu you are feeling regarding renewables investment may be by design. Money is spent, a few get rich, but in the end with these recurring political cycles... a zero-sum game and maintenance of the status quo (a fossil fuel-based world economy).

Curt Newton's picture

I really appreciate the

I really appreciate the segment where John talks about emotion vs. pure scientific objectivity, and the need for scientists to have more follow-through conversations with communities as modeling data makes its way toward policy (starting around 22:10).