Introducing the Reuters Climate Monitor

By • • Responsible Business USA 2026 in Boston

Slides

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Slide 1

Introducing the
Reuters Climate Monitor
Tracking climate change at the speed of news

Slide 2

Table of contents
❖   Who I am

❖   The problem with climate data

❖   How we think we can solve it

❖   What we’ve made

❖   We hear from you

Slide 3

Who I am
I’m Ben Welsh. I’m from Iowa.

I joined Reuters three years
ago.

I lead a specialized team that
automates data to cover
breaking news.

Slide 4

What does that look like?
❖   Our goal is to learn from the speed, scale
    and extreme success of live election
    results, coronavirus trackers and similar
    products

❖   Our automated systems collect raw data,
    refine them into journalism and then
    deliver charts, dashboards, databases
    and other outputs to our customers.

❖   With continual updates, interactive inputs
    and limitless scale, they serve clients and
    readers in ways a traditional story cannot.

Slide 5

For example…
We’ve developed a pipeline
that automatically plots data
using our newsroom’s
chart-marking tool.

It produces hundreds of
ready-to-run charts each
week, covering markets,
economics and public
opinion.

Slide 6

Aiming high
Because we focus on
coverage of the top
storylines, our automated
charts appear on Reuters
homepage more than five
times per week.

Slide 7

Our new front
Now we’re expanding our
focus to extreme weather,
another routine source of
breaking news.

Slide 8

The problem with climate data
❖   The challenge: Can our newsroom use
    data to better cover the day-to-day
    impact of climate change?

❖   The issue: Most climate data is global in
    scope, slow to update and centered on
    the upward creep of arcane statistics.

❖   The result: It has little to add to coverage
    of fast-moving catastrophes, such as last
    summer’s European heat wave. A key
    piece of context is missing from our
    reporting.

Slide 9

How we think we can solve it
❖   We sought out more timely climate
    coverage demands data that’s localized,
    easy to grasp and quick to update.

❖   We surveyed our competitors, interviewed
    more than a dozen climate experts and
    reviewed the open-source science.

❖   We found an emerging set of data sources
    yet to be fully utilized by the press that fit
    the bill.

Slide 10

The big idea
❖   Create a data dashboard that, by automatically
    comparing today’s weather against the historic
    record, identifies temperature extremes as they
    occur

❖   Deliver a comprehensive, real-time view of how
    climate change is unleashing dangerous new
    weather patterns, with the ability to focus in on
    any location on Earth

❖   Pass muster with the world’s leading climate
    scientists

Slide 11

What we’ve made
❖   A color-coded world map that highlights which
    areas are, right now, reporting significant
    temperature anomalies

❖   Interactive controls that allow readers to
    examine conditions in local areas they care
    about

Slide 12

Let’s have a look
We have a prototype that
we’re preparing to launch in
the weeks ahead.

Slide 13

The road to the finish line
❖   Calculate the number of people living in extreme
    conditions each day and identify today’s
    hotspots

❖   Forecast the temperature events into the
    near-term future, just like a traditional weather
    forecast

❖   Fuel reporting by our enterprise team into the
    impact of extreme temperatures

Slide 14

We hear from you
❖   Beyond temperature, what climate
    data would be most useful to you?
❖   What's a climate question your team
    gets asked that you can't currently
    answer with data?
❖   How could the news media do a
    better job covering climate with
    data?

Recording

Show the timestamped transcript
  1. This is what my wife calls my Bible salesman photo.
  2. Yeah.
  3. Accurate, yes?
  4. Yeah.
  5. No lies detected.
  6. So I've got a little presentation here I want to walk you through
  7. that I think I'm able to click my way to.
  8. No, wait.
  9. I'm clicking the wrong slides.
  10. My friend's in the back.
  11. Can we flip?
  12. Anyone else take the Acela up this morning?
  13. I was on the 5 o'clock from Penn Station.
  14. So if I get a little delirious, that's why.
  15. All right.
  16. We're waiting for the deck.
  17. We're going to take questions at the end.
  18. And you can ask me about my presentation, really,
  19. anything you want to know about Reuters News.
  20. I work in Times Square every single day, five days a week.
  21. And there you find the New York newsroom of Reuters News,
  22. which I think you probably have some familiarity with,
  23. one of the world's largest news organizations with people all around it.
  24. And I'm here to introduce you to a new project that we are,
  25. I think, publicly speaking about for the first time today at this event.
  26. And we'll launch in the coming weeks.
  27. This is kind of a sneak peek.
  28. And we call it the Reuters Climate Monitor.
  29. So what is that?
  30. I'm going to talk you through who I am,
  31. kind of what my team does,
  32. and then this new approach we're taking towards climate data as we go.
  33. Please put your questions in.
  34. All right.
  35. So I'm Ben Welsh.
  36. I'm from Iowa.
  37. Any other Hawkeyes here?
  38. All right.
  39. Any other Caitlin Clark fans here?
  40. Can I go for that?
  41. Yeah, okay.
  42. And I've been working at Reuters in New York for three years now,
  43. and I'm one of these nerds in the newsroom.
  44. I'm a reporter, an editor, and a computer programmer.
  45. And I lead a specialized team that works on automating data
  46. to cover breaking news for our service.
  47. What does that mean?
  48. Well, I'll give you an example.
  49. So our goal is really to build off these types of data applications
  50. that have become part of news,
  51. but you might not always think of as software.
  52. These are the live election results we look at on election night, right?
  53. I bet people in this room spent a lot of time
  54. refreshing their local coronavirus tracker, you know,
  55. when we were in the middle of the pandemic.
  56. And those data apps have become kind of almost by accident
  57. some of the most successful things that news organizations publish
  58. on their dot-com websites and in their apps.
  59. And what teams like mine at all the different news organizations
  60. really around the world have tried to do in the last few years
  61. is kind of retool and create miniature assembly lines
  62. inside of our information factories
  63. that kind of manufacture these types of products, right?
  64. And they're really just automated software systems,
  65. data pipelines that go to your local election office, right?
  66. Gather the results, bring them back, clean them up,
  67. and then put them up on a beautiful map
  68. before your eyes, right?
  69. You're familiar with this.
  70. And that approach works not just for elections,
  71. but really for all kinds of different stuff.
  72. And so my group is this new little assembly line inside Reuters
  73. trying to manufacture as many of those widgets as we can.
  74. And so like an example that is on our homepage
  75. like five or six times a week
  76. is we have all these automations
  77. that go get the latest economics and financial data
  78. from like a kind of business terminal type situation
  79. or a government website.
  80. And it doesn't just bring them down
  81. and allow us to analyze them.
  82. It goes all the way through out into live charts
  83. that go up onto our website, right?
  84. So we've got hundreds and hundreds
  85. of these breaking news automations
  86. that help us cover markets, economics, public opinion,
  87. other things.
  88. And that's been a success for us.
  89. And so we're trying to,
  90. and so we try to aim for the biggest stories
  91. so that when we invest in these automations,
  92. we can get continual use of them
  93. and really cover the top stuff.
  94. So the last few weeks, a lot of oil prices charts, right?
  95. I've been sitting on our homepage
  96. coming out of these automations.
  97. And we're looking to expand that approach
  98. beyond just stuff having to do with money
  99. and markets and that kind of thing
  100. into other major breaking news events,
  101. which of course extreme weather is a great example of, right?
  102. And so last year we rolled out a new hurricane tracking system
  103. that allows us to watch cyclones
  104. not just around Florida in the United States
  105. but really in Asia and elsewhere in the world
  106. so that we're able to cover those events
  107. kind of as they happen in this same style.
  108. And our next goal and what we've been working on
  109. is bringing that approach to climate
  110. and to the stuff this conference is about.
  111. So this guy right here, this is my boss.
  112. This is Simon Robinson.
  113. Don't tell him I'm doing this.
  114. And he's the executive editor of Reuters in London.
  115. And he came to me with a challenge
  116. which was could our newsroom use data
  117. and this approach I've been telling you about
  118. to cover the day-to-day kind of impact of climate change
  119. because he really saw a kind of gap in our coverage.
  120. If you think about you have a major heat wave
  121. in the Mediterranean last summer
  122. and for us at Reuters that's a big story
  123. that's going to dominate the top of the file,
  124. the homepage every day for maybe even a week, right?
  125. But in that coverage we only have
  126. the kind of traditional weather and temperature data
  127. and it just seemed to him that there's this kind of gap
  128. where the context of climate
  129. is kind of missing from those major storylines.
  130. And so his challenge was how could we try to address that
  131. or could we try to address that?
  132. And it was the first time I really thought about it
  133. and, you know, I definitely want feedback from you all
  134. on how we're doing as we go through this
  135. because you know more about it than me.
  136. But it seemed to me that one of the problems was
  137. is that unlike the stock market data
  138. or the oil prices data,
  139. which updates every second, right,
  140. almost around the clock, right,
  141. a lot of climate data is a little slower moving, right,
  142. where it might tell you,
  143. well, the Earth's temperature was this last month
  144. or last year, not today or tomorrow, right?
  145. And a lot of climate data,
  146. for very good scientific reasons,
  147. is measured in these kind of arcane statistics
  148. that aren't how the average person thinks about the weather.
  149. The average temperature is a great example, right,
  150. where the most climate goals, as I understand it,
  151. are set using the average temperature,
  152. but if I asked any of you,
  153. what's the average temperature in Boston today,
  154. you probably never even thought about it, right?
  155. People think in highs and lows.
  156. And so my feeling was that if we're going to address this,
  157. we had to take those things on.
  158. And so I went out to kind of,
  159. on a little reporting trip to see,
  160. could we get faster climate data
  161. that is in more relatable statistics
  162. and that isn't just entirely global either,
  163. but maybe can relate to people like where they live,
  164. like the weather report does.
  165. And so we surveyed what everybody else did,
  166. I interviewed a bunch of nerdy nerds,
  167. and we found the answer is yes,
  168. it actually might be possible to pull off such a thing.
  169. And if you guys want to get into all the details
  170. of the era five data set and all that stuff,
  171. I'm happy to go down the road,
  172. but we kind of came up with a rough methodology
  173. that we think will allow us just getting started
  174. in our first steps into this coverage
  175. to create a daily dashboard
  176. that automatically compares today's temperature,
  177. tomorrow's temperature,
  178. against the historic record,
  179. against what used to be normal
  180. before widespread global climate change
  181. for Boston, for Swisher, Iowa,
  182. where I grew up,
  183. for anywhere on earth, right?
  184. And then deliver a comprehensive view
  185. of today's, how weird today's temperature is, right?
  186. The thing we all ask ourselves
  187. when we're close to one of these events
  188. by measuring what people call an anomaly,
  189. just the difference between today
  190. and what used to be normal.
  191. And we've created and we're preparing to launch
  192. a color-coded world map
  193. that will update each day and highlight right now
  194. where the most significant temperature anomalies are on earth,
  195. the interactive controls will allow you
  196. to search your hometown, where your mom lives,
  197. wherever you'd like to go,
  198. whatever you're interested in,
  199. and find out the differences there
  200. in the same way that the weather report can.
  201. We're calling it the Reuters Climate Monitor,
  202. and we have a prototype
  203. that this is the screenshot of
  204. that we're preparing to launch.
  205. And we were going to try maybe
  206. to open up the webpage in the back,
  207. and this is just a quick look at the demo page,
  208. you know, where you're able to see
  209. today's kind of forecast,
  210. the red areas are higher,
  211. the blue are lower,
  212. and you'd be able to search anywhere you wanted
  213. and zoom in and get a report, right?
  214. We can jump back.
  215. And that's just the beginning, right?
  216. So after we get this up and out,
  217. we have immediate plans to follow on
  218. with stuff that we hope will make it more newsy.
  219. We're going to try to calculate the number of people
  220. living in extreme conditions every day,
  221. today, tomorrow, the day after,
  222. so that we're able to roll up
  223. those summaries of how many million people, etc.,
  224. into our coverage.
  225. And that, to me, is delivering that type of context
  226. that Simon thought was missing from those reports.
  227. So on the very day that we're writing about,
  228. an unusual temperature event somewhere,
  229. we're able to get that context automatically in, right?
  230. We're going to try to forecast it out
  231. further into the future,
  232. and we're going to use this analysis,
  233. we actually currently are using this analysis
  234. to kind of fling correspondence around the world
  235. to areas where we've seen significant climate change
  236. already bubbling up out of this data
  237. over the last 30, 40 years,
  238. and report on the impacts of that.
  239. And so it's going to serve as a reporting tool
  240. for enterprise as well.
  241. I want to hear from you.
  242. So this is just the very beginning.
  243. It might seem kind of basic to you guys.
  244. I don't know.
  245. I'm curious to learn.
  246. But in this room with experts,
  247. I would love to know answers to questions like these
  248. and how you think a news organization like Reuters
  249. could and should or ought to better use data
  250. to cover climate change.
  251. Thank you.
  252. Thank you.
  253. Thank you.
  254. Thank you.
  255. Thank you.
  256. Thank you.

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