A windier world

A new paper in Nature Climate Change reports a reversal in “terrestrial stilling” since 2010 – i.e. global wind speeds, thought to be in decline thanks to deforestation and real estate development, actually stopped slowing around 2010 and have been climbing since.

The paper’s authors, a group of researchers from China, France, Singapore, Spain, the UK and the US, argue that the result can be explained by “decadal ocean-atmosphere oscillations” and conclude with further analysis that the increase “has increased potential wind energy by 17 ± 2% for 2010 to 2017, boosting the US wind power capacity factor by ~2.5% and explains half the increase in the US wind capacity factor since 2010.”

Now that we have some data to support the theory that both terrestrial and oceanic processes affect wind speeds and to what extent, the authors propose building models to predict wind speeds in advance and engineer wind turbines accordingly to maximise power generation.

This seems like a silver lining but it isn’t.

Global heating does seem to be influencing wind speeds. To quote from the paper again: “The ocean-atmosphere oscillations, characterised as the decadal variations in [mainly three climate indices] can therefore explain the decadal variation in wind speed (that is, the long-term stilling and the recent reversal).” This in turn empowers wind turbines to produce more energy and correspondingly lowers demand from non-renewable sources.

DOI: 10.1038/s41558-019-0622-6

However, three of the major sources of greenhouse gas emissions are concrete, plastics and steel manufacturing – and all three materials are required in not insubstantial quantities to build a wind turbine. So far from being a happy outcome of global heating, the increase in average regional wind speed – which the authors say could last for up to a decade – could drive the construction of more or, significantly, different turbines which in turn causes more greenhouses gases to be released into the atmosphere.

Finally, while the authors estimate the “global mean annual wind speed” increased from 3.13 m/s in 2010 to 3.3 m/s in 2017, the increase in the amount of energy entering a wind turbine is distributed unevenly by location: “22 ± 2% for North America, 22 ± 4% for Europe and 11 ± 4% for Asia”. Assuming these calculations are reliable, the figures suggest industrialised nations have a stronger incentive to capitalise on the newfound stilling reversal (from the same paper: “We find that the capacity factor for wind generation in the US is highly and significantly correlated with the variation in the cube of regional-average wind speed”).

On the other hand Asia, which still has a weaker incentive, will continue to bear a disproportionate brunt of the climate crisis. To quote from an article published in The Wire Science today,

… as it happens, the idea that ‘green technology’ can help save the environment is dangerous because it glosses over the alternatives’ ills. In a bid to reduce the extraction of hydrocarbons for fuel as well as to manufacture components for more efficient electronic and mechanical systems, industrialists around the world have been extracting a wide array of minerals and metals, destroying entire ecosystems and displacing hundreds of thousands of people. It’s as if one injustice has replaced another.

Godwin Vasanth Bosco, The Wire Science, December 2, 2019

The climate and the A.I.

A few days ago, the New York Times and other major international publications sounded the alarm over a new study that claimed various coastal cities around the world would be underwater to different degrees by 2050. However, something seemed off; it couldn’t have been straightforward for the authors of the study to plot how much the sea-level rise would affect India’s coastal settlements. Specifically, the numbers required to calculate how many people in a city would be underwater aren’t readily available in India, if at all they do exist. Without this bit of information, it’s easy to disproportionately over- or underestimate certain outcomes for India on the basis of simulations and models. And earlier this evening, as if on cue, this thread appeared:

This post isn’t a declaration of smugness (although it is tempting) but to turn your attention to one of Palanichamy’s tweets in the thread:

One of the biggest differences between the developed and the developing worlds is clean, reliable, accessible data. There’s a reason USAfacts.org exists whereas in India, data discovery is as painstaking a part of the journalistic process as is reporting on it and getting the report published. Government records are fairly recent. They’re not always available at the same location on the web (data.gov.in has been remedying this to some extent). They’re often incomplete or not machine-readable. Every so often, the government doesn’t even publish the data – or changes how it’s obtained, rendering the latest dataset incompatible with previous versions.

This is why attempts to model Indian situations and similar situations in significantly different parts of the world (i.e. developed and developing, not India and, say, Mexico) in the same study are likely to deviate from reality: the authors might have extrapolated the data for the Indian situation using methods derived from non-native datasets. According to Palanichamy, the sea-level rise study took AI’s help for this – and herein lies the rub. With this study itself as an example, there are only going to be more – and potentially more sensational – efforts to determine the effects of continued global heating on coastal assets, whether cities or factories, paralleling greater investments to deal with the consequences.

In this scenario, AI, and algorithms in general, will only play a more prominent part in determining how, when and where our attention and money should be spent, and controlling the extent to which people think scientists’ predictions and reality are in agreement. Obviously the deeper problem here lies with the entities responsible for collecting and publishing the data – and aren’t doing so – but given how the climate crisis is forcing the world’s governments to rapidly globalise their action plans, the developing world needs to inculcate the courage and clarity to slow down, and scrutinise the AI and other tools scientists use to offer their recommendations.

It’s not a straightforward road from having the data to knowing what it implies for a city in India, a city in Australia and a city in Canada.