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What’s Up (Literally) With Cloud Seeding?

April 19, 2024

Dubai, one of the richest and driest cities in the world, is flooding. Many have been quick to blame cloud seeding, a climate-engineering strategy the city uses to wrench every drop of moisture it can from its dry air.

Is cloud seeding really to blame? And regardless of if it is… what even is it?

 

What is Cloud Seeding?

Clouds form around nuclei, small particles that cool the area around them, causing water to condense and form the early wisps of clouds. These clouds then grow and take shape as more water condenses in the atmosphere, forming larger clouds. Eventually, clouds break, giving way to a deluge of rain.

It stands to reason then, that if there were more of these small particles in the atmosphere, more clouds would form and there would be more rain. Governments in dry areas, like state governments of the American Southwest and city, state, and national governments in the Middle East and South Asia have been quick to turn to cloud seeding as an opportunity to increase rainfall and bring more moisture down from the atmosphere. Specifically, governmental cloud seeding programs tend to use planes and cannons to seed clouds with dry ice and liquid iodine, creating more cloud nuclei and thus more and denser clouds (Borenstein and Peterson, 2024).

While the practice makes sense in theory, weather scientists disagree on whether or not it actually works (Al Hosari et al., 2021). In particular, the difficulty of proving anything weather related due to the complexity of cloud formation processes and shifting climatic patterns has made cloud seeding a difficult to test subject (Bruintjes, 1999).

Some studies, like this one by the United Arab Emirates’ National Center for Meteorology, argue in favor of cloud seeding, highlighting results that prove an average increase in annual rainfall by 23% over seeded areas (Al Hosari et al., 2021). The center reports that cloud seeding practices have increased storm volume by over 150%, area cover by just over 70% and lifetime by roughly 65% (Al Hosari et al., 2021). Surely such explosive results are significant?

And yet, many scientists still cast doubt. A study by the American Meteorological Association argues against cloud seeding, stating that the variation in “precipitation formation mechanisms” between locations and at a location given shifting climatic patterns weaken the scientific credibility of cloud seeding’s reported progress (Bruintjes, 1999). As AP News reports, “cloud seeding, although decades old… has been hard to prove [to do] very much” (Borenstein and Peterson, 2024).

 

Did Cloud Seeding Cause Dubai’s Floods?

The resounding consensus: no.

According to AP News, while many blame cloud seeding, it certainly is not the cause of Dubai’s recent floods. The record breaking storm was “forecast days in advance” and more or less expected by climate scientists (Borenstein and Peterson, 2024). Cloud seeding, while potentially effective, can not create water out of moisture-less air, and to state that cloud seeding could cause the deluge is “akin to [believing in] perpetual motion technology” (Borenstein and Peterson, 2024). At its best, cloud seeding can bring more water from the atmosphere down to the ground, but it cannot create moisture where there wasn’t any before. Regardless of whether cloud seeding works – the storm must’ve gotten moisture from somewhere.

Where did that moisture come from? Scientists blame climate change. As the climate warms, so does the atmosphere, and warmer air can hold more moisture (Borenstein and Peterson, 2024). The strangely large concentrations of moisture in an otherwise dry region are likely due to a warming global climate, one that allows for larger build-ups of moisture and stronger storms. These storms, termed “whopper storms” have been predicted (with statistical significance) to increase over the southeastern Arabian Peninsula, forewarning of more storms to come (Borenstein and Peterson, 2024).

At the end of the day, cloud seeding did not cause Dubai’s record-breaking storm. Climate change did.

 

Citations

Al Hosari, T., Al Mandous, A., Wehbe, Y., Shalaby, A., Al Shamsi, N., Al Naqbi, H., Al Yazeedi, O., Al Mazroui, A., & Farrah, S. (2021). The UAE Cloud Seeding Program: A statistical and physical evaluation. Atmosphere, 12(8), 1013. https://doi.org/10.3390/atmos12081013

Borenstein, S., & Peterson, B. (2024, April 18). Here’s why experts don’t think cloud seeding played a role in Dubai’s downpour. AP News. https://apnews.com/article/dubai-united-arab-emirates-oman-flooding-cloud-seeding-2f8c12854017e11ac7438579646b3758

Bruintjes, R. T. (1999).  A Review of Cloud Seeding Experiments to Enhance Precipitation and Some New Prospects. Bulletin of the American Meteorological Society, 80(5), 805–820. https://doi.org/10.1175/1520-0477(1999)080<0805:AROCSE>2.0.CO;2

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  Timothy Durham was convicted of rape when a test showed his DNA genotype matched the DNA recovered from the crime scene. Eye witnesses testified that Durham was in a different state at the time of the incident, but he wasn’t exonerated from his 3000 year sentence until follow up DNA analysis showed that the preliminary forensic analysis used in the trial was misinterpreted (Thomas et al.). DNA testing serves an invaluable part in our criminal justice system, but it comes with complications and bias that can lead to wrongful convictions and misinformed juries.  

Background:

Forensic genetics aims to identify the origin of a biological sample. It can be traced back to Karl Landsteiner who discovered human ABO blood groups. With blood groups, suspects could be eliminated based on their blood type. For example, if a suspect had type A blood and there was type B blood left at the scene by the perpetrator, the investigators could rule out the suspect. Unfortunately, the reverse didn’t work. If the suspect and perpetrator both had the same blood type, it can’t be concluded that the suspect was guilty. Blood identifiers evolved into 8 distinct categories which improved the ability to eliminate suspects, but could only conclude that (if the system matched) it might be the perpetrator or this person might be related to the perpetrator (Jobling and Gill). The field evolved in the 1980s with the discovery of Minisatellites, lines of repetitive DNA that don’t encode proteins. Minisatellite patterns in DNA create what we know today as the DNA fingerprint, and it has a much higher accuracy rate than previous methods (Augustyn et al.). Around the same time, a breakthrough for victims of sexual assault was discovered. In the case of assault, bodily fluids of both the victim and the assaulter can get mixed. A process called differential lysis was created which could selectively increase the concentration of sperm in a fluid sample thereby allowing for accurate DNA testing (Jobling and Gill). Forensics evolved to use single-locus probe testing after several years because of its ease of interpretation. It looked at specific minisatellites that had high variability between subjects. This was the first DNA-based method to be used in a criminal investigation. Two murder homicides from several years apart were connected using DNA evidence. The evidence was then used to exonerate an innocent man, and the guilty party was found when he tried to evade mandatory DNA testing for all men in the small town. His guilt was then reinforced when the DNA profiles matched, and he gave a confession (Jobling and Gill). Finding DNA at a crime scene doesn’t guarantee that you’ll find a murderer. There are issues with non-pure samples and limited samples that make DNA work difficult, and accreditation of facilities and new techniques and take time (Jobling and Gill).  

Subjectivity of DNA Interpretation:

In a 2009 publication, the National Academy of Science discusses the possibility for subjective influence in lab based forensics. The interpretation bias happens in complex cases where, for example, there could be a mixture of DNA. A study published in 2011 looked specifically at this issue. A gang rape happened in Georgia, and the mixture in DNA from the case was used to prosecute one suspect after another suspect took a plea bargain to testify against him. With DNA mixtures, there are three possible conclusions: suspect excluded, suspect cannot be excluded, and inconclusive. If the suspect is excluded, it means that there’s little to no possibility that his DNA is part of the sample. If the suspect cannot be excluded, it means that there’s a possibility that his DNA is part of the sample. In the trial, the DNA examiners who knew the context of the case concluded that the suspect being prosecuted could not be excluded. The 2011 study gave the DNA evidence from this case and the DNA profiles of the suspects to 17 forensic examiners. These examiners had no context of the case, and they all worked independently. Of the 17, only 1 came to the same conclusion (suspect cannot be excluded) as the examiners who knew the context of the case. Twelve of the 17 concluded that the suspect who was prosecuted and convicted had no relation to the DNA found in the sample. Not only does this show that subjectivity occurs due to the context of the case, but the 17 examiners came up with different findings based on their past experiences meaning that not all DNA analysis is objective. Some organizations like the Scientific Working Group on DNA Analysis Methods say that quantitative statistical evidence should be presented with every categorical conclusion, but the US doesn’t require this (Dror and Hampikian).

False Positives:

There are two categories that cause wrongful identification in a DNA report: coincidental matches and false positives. Coincidental matches are DNA matches between two unrelated people. The probability of these matches is about 1 to 500,000 for white individuals and about 1 to 350,000 for black individuals. This small probability of coincidental matches makes people feel confident in DNA evidence. False positives, on the other hand, are matches between DNA and test DNA that result from issues in the forensics lab. There have been multiple cases of forensics labs accidentally switching victim and suspected rapist DNA which lead to convictions that were later overturned when the lab realized the error. There are also cases of misreading or misunderstanding a sample. In a case from 1993, a man was convicted of rape because someone interpreted a mixed sample of victim and perpetrator DNA as an individual sample. The conviction was again overturned. To prevent false convictions, retesting DNA is allowed, but it’s not an efficient solution. Many people are pushing for statistical information on the probability of false positives, so that the jurors can make accurate judgements based on DNA evidence (Thomas et al.).  

Conclusion:

DNA analysis is a useful tool for the criminal justice system, but subjectivity can be an issue in forensic testing. To combat wrongful convictions, there are organizations like The Innocence Project who use DNA testing to free the innocent, but reformed forensic testing standards should be put in place to prevent subjectivity and to ensure juries are correctly informed during trials. https://www.innocenceproject.org/    

Citation:

Augustyn , Adam, et al. “DNA Finerprinting.” Encyclopedia Britannica , 26 Apr. 2018, www.britannica.com/science/DNA-fingerprinting#ref153186. Dror, Itiel E., and Greg Hampikian. “Subjectivity and Bias in Forensic DNA Mixture Interpretation.” Science & Justice, vol. 51, no. 4, 2011, pp. 204–208., doi:10.1016/j.scijus.2011.08.004. Jobling, Mark A., and Peter Gill. “Encoded Evidence: DNA in Forensic Analysis.” Nature Reviews: Genetics, vol. 5, no. 10, Oct. 2004, pp. 739–751., doi:10.1038/nrg1455. Thompson, William C, et al. “How the Probability of a False Positive Affects the Value of DNA Evidence.” Journal of Forensic Sciences, vol. 48, no. 1, Jan. 2003, pp. 1–8., doi:10.1520/jfs2001171.