Can geoengineering blunt El Niño’s fury? 10%

By Carolyn Gramling0%

7/8/2026, 6:00:00 PM

BS Summary: This article contains 28 faulty reasoning types, including Pessimism Bias, Post Hoc (False Cause), and Optimism Bias, with Negativity Bias as the most egregious example at 17.4% saturation with 91 hits. Analysis detected 928 faulty-reasoning hits from 523 analyzed words, generating a BS Score of 26.6% and a BS Rank of 10% (19,158 of 21,121 articles). This article is better (less manipulative) than 90.70% of the article peer group.

Jessica Wan/Univ. of Chicago 
All of the injections made the simulated El Niños weaker than the actual events . 
But how much weaker depended on the timing, the team found. 
For the 2015–2016 event, for example, injecting particles from June through the following February led to the strongest cooling. 
But starting those injections in December  essentially at the 11th hour  led to the least cooling. 
That’s probably because by that time, the El Niño dynamics are well under way and any cooling is more localized, the team suggests. 
Using MCB to directly target large El Niños “is really interesting and very new,” says Daniele Visioni, a climate scientist at Cornell University not involved in the study. 
And “the fact that it looks like this could work is a really good indication that it is something worth thinking about.” 
Earth officially entered its most recent El Niño phase in June. 
Computer simulations of current conditions in the Pacific suggest that it has the potential to be a “super El Niño.” 
MCB isn’t anywhere close to being on the menu to mitigate this year’s El Niño, Wan says  there are big hurdles, including engineering constraints and sociological barriers, such as who should determine whether these interventions are worth any possible negative climate consequences. 
Many researchers remain leery about tinkering with the climate. 
“There are many, many unanswered questions and uncertainties as to the viability of MCB,” says James Haywood, a climate scientist at the University of Exeter in England not involved in the new study. 
Previous research by Haywood and his colleagues simulating the effects of MCB found that cooling the eastern Pacific might produce a “mega La Niña” many times stronger than previously seen, he says. 
La Niña is generally thought of as the gentler sibling  on the whole, it brings cooler temperatures and milder weather events. 
“But the impacts of both El Niño and La Niña are heterogeneous” around the planet, and not everyone suffers or benefits from either, Wan says. 
Visioni notes that “this is in no way the final answer…. 
But it’s important to have these kinds of studies that keep the door open. 
Considering that large El Niños produce a lot of damages, I think asking the question is worth it.” 
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Article reasoning-pattern comparisonThis article: 3.6%Carolyn Gramling: 0.9%Science News: 2.6%Confirmation Bias3.6%This article: 2.9%Carolyn Gramling: 0.7%Science News: 1.1%Anchoring Bias2.9%This article: 5.9%Carolyn Gramling: 3.0%Science News: 2.5%Availability Heuristic5.9%This article: 4.2%Carolyn Gramling: 1.1%Science News: 1.5%Representativeness Heuristic4.2%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.5%Hindsight Bias0.0%This article: 0.0%Carolyn Gramling: 0.0%Science News: 2.5%Overconfidence Bias0.0%This article: 1.1%Carolyn Gramling: 0.6%Science News: 4.2%Framing Effect1.1%This article: 3.4%Carolyn Gramling: 0.9%Science News: 0.3%Loss Aversion3.4%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.3%Status Quo Bias0.0%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.2%Sunk Cost Effect0.0%This article: 12.2%Carolyn Gramling: 5.2%Science News: 4.2%Optimism Bias12.2%This article: 14.5%Carolyn Gramling: 3.6%Science News: 1.2%Pessimism Bias14.5%This article: 17.4%Carolyn Gramling: 5.2%Science News: 4.1%Negativity Bias17.4%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.4%Self-Serving Bias0.0%This article: 8.2%Carolyn Gramling: 2.1%Science News: 0.5%Fundamental Attribution Error8.2%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.1%Actor-Observer Bias0.0%This article: 1.7%Carolyn Gramling: 0.4%Science News: 0.1%In-Group Bias1.7%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.1%Out-Group Homogeneity Bias0.0%This article: 5.4%Carolyn Gramling: 1.3%Science News: 1.4%Halo Effect5.4%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.0%Horn Effect0.0%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.0%Dunning-Kruger Effect0.0%This article: 1.7%Carolyn Gramling: 0.4%Science News: 0.8%Recency Bias1.7%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.3%Primacy Effect0.0%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.1%Blind-Spot Bias0.0%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.2%Ad Hominem0.0%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.2%Straw Man0.0%This article: 11.7%Carolyn Gramling: 2.9%Science News: 4.5%Appeal to Authority11.7%This article: 8.2%Carolyn Gramling: 2.9%Science News: 1.1%False Dilemma8.2%This article: 0.0%Carolyn Gramling: 0.0%Science News: 1.1%Slippery Slope0.0%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.1%Circular Reasoning0.0%This article: 3.4%Carolyn Gramling: 0.9%Science News: 4.0%Hasty Generalization3.4%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.0%Red Herring0.0%This article: 2.1%Carolyn Gramling: 0.5%Science News: 0.4%Bandwagon2.1%This article: 5.4%Carolyn Gramling: 3.1%Science News: 2.6%Appeal to Emotion5.4%This article: 7.6%Carolyn Gramling: 1.9%Science News: 0.5%Begging the Question7.6%This article: 12.6%Carolyn Gramling: 3.2%Science News: 2.5%Post Hoc (False Cause)12.6%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.0%Tu Quoque0.0%This article: 8.2%Carolyn Gramling: 2.1%Science News: 0.4%Burden of Proof8.2%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.2%Appeal to Nature0.0%This article: 4.2%Carolyn Gramling: 1.1%Science News: 0.2%Composition/Division4.2%This article: 0.0%Carolyn Gramling: 0.0%Science News: 1.2%Anecdotal0.0%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.1%No True Scotsman0.0%This article: 6.5%Carolyn Gramling: 1.6%Science News: 1.6%Ambiguity (Equivocation)6.5%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.0%Gambler’s Fallacy0.0%This article: 2.7%Carolyn Gramling: 0.7%Science News: 0.2%Middle Ground2.7%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.1%Personal Incredulity0.0%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.0%Special Pleading0.0%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.0%Genetic Fallacy0.0%This article: 4.2%Carolyn Gramling: 1.1%Science News: 0.8%Unattributed Quote4.2%This article: 5.5%Carolyn Gramling: 1.4%Science News: 0.7%Quote-first Misdirection5.5%This article: 4.4%Carolyn Gramling: 1.4%Science News: 2.6%Biased Writer Voice4.4%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.9%Indoctrination0.0%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.4%Politically Left Leaning Bias0.0%This article: 0.0%Carolyn Gramling: 0.0%Science News: 0.0%Politically Right Leaning Bias0.0%This article: 8.2%Carolyn Gramling: 2.1%Science News: 0.4%Attempt to Sell a Product or S…8.2%

523 words analyzed.

Speakers

8speakers53%attributed speech245writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 6 words • 0.0% coverageJessica Wan • 4 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 23 words • 100.0% coverageDaniele Visioni • 28 words • 0.0% coverageDaniele Visioni • 22 words • 100.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageJessica Wan • 43 words • 100.0% coverageWriter's voice • 9 words • 0.0% coverageJames Haywood • 33 words • 0.0% coverageJames Haywood • 32 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageJessica Wan • 25 words • 0.0% coverageDaniele Visioni • 11 words • 0.0% coverageDaniele Visioni • 14 words • 0.0% coverageDaniele Visioni • 18 words • 0.0% coverageWriter's voice • 7 words • 100.0% coverageWriter's voice • 13 words • 0.0% coverageMićo Tatalović • 6 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageFechi Inyama • 6 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 4 words • 0.0% coverageCarolyn Gramling • 6 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageCarolyn Gramling • 6 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageJavier Barbuzano • 6 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageYujia Huang • 6 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageCarolyn Gramling • 6 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageCarolyn Gramling • 6 words • 0.0% coverage
Selected voice

Jessica Wan

60%flagged-word coverage
72 attributed words26% of attributed speech89% writer coverage
0%30.0%60.0%Attempt to Sell a Product +59.7 ptsWriter: 0.0%Jessica Wan: 59.7%59.7%Biased Writer Voice-9.4 ptsWriter: 9.4%Jessica Wan: 0.0%0.0%Quote-first Misdirection-2.9 ptsWriter: 2.9%Jessica Wan: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.