418.57 ppm: tracking weekly climate news

Welcome to our regular segment on new climate news stories you might have missed. The title refers to the daily average global concentration of carbon dioxide within the Earth’s atmosphere in parts per million (ppm). Meaning that for every million air particles, currently about 418.57 of them are CO2.

Predicting the damage caused by incoming storms

High-resolution climate models predict that climate change will produce more frequent and intense storms in the future, but new research is helping us to prepare for and mitigate storm damage before the extreme weather even occurs.

Climate experts and engineers have created a model to forecast adverse weather consequences on electricity distribution networks. The model can be used up to 24 hours before extreme weather events.

“Our model has the potential to change the way we manage weather and climate risks to our infrastructure networks,” says lead author Dr Sean Wilkinson, of Newcastle University, UK.

“While electricity network operators already prepare extra resources when a storm approaches, predicting how many power lines may be blown down and where these are likely to be located will allow them to better target the necessary resources to more quickly repair any damage.”

The report is published in Climate Risk Management. Its authors suggest that the framework could also be applied to other infrastructure systems and types of weather events.

Greenland ice sheet melting due to falling water

The Greenland ice sheet, the world’s second largest, is the largest single contributor to global sea level rise, with ice loss tied to both melting and ice discharge. A new study has observed previously unknown extremely high rates of melting at the ice sheet’s base, which was found to be caused by huge quantities of meltwater descending from the sheet’s surface to its bed, a kilometre or more below.

Researchers found that the meltwater’s gravitational energy, formed at the surface, is converted to heat when it falls to the base through large cracks in the ice, resulting in the largest heat source found beneath ice sheet.

This has led to phenomenally high rates of melting at its base, which is currently not yet included in projections of global sea level rise, and is the first concrete evidence of an ice-sheet mass-loss mechanism.

The study was published in Proceedings of the National Academy of Sciences.

Water flowing into a moulin and down to the bed of store glacier greenland. Credit poul christoffersen 850
Water flowing into a moulin and down to the bed of Store Glacier, Greenland. Credit: Poul Christoffersen

The jet stream behind Storm Eunice is moving northwards

Just this week the winter northern hemisphere jet stream has brought storms Dudley, Eunice, and Franklin to the UK. Jet streams are fast bands of air that flow around the globe around 10 kilometres above the Earth’s surface and have significant influence on storm activity and temperature patterns.

Now, new research shows that during the 141-year period from 1871 to 2011, the average position of the jet stream over the North Atlantic and Eurasia moved northwards by up to 330 kilometres, and its average speed increased by 8% to approximately 212 km/h.

The study used data from the Twentieth Century Reanalysis (20CR) project – a dataset of weather covering 1836 to 2015 created by the US National Oceanic and Atmospheric Administration (NOAA). The research was published in Climate Dynamics.

Their results also showed that the jet’s variability and trends differed on a regional basis across the North Atlantic, North Pacific, Eurasia, and North America, which is important for making climate predictions and developing plans to combat climate change.

Warming climate will result in reduced corn production

According to a new study corn production will be reduced in the future, regardless of which widely accepted climate model comes closest to predicting the amount of warming that occurs due to climate change.

The potential effects of future climate change on irrigated and rain-fed corn yields in the US Great Plains was estimated using the AquaCrop computer model – a crop-growth simulation developed by the UN’s Food and Agriculture Organization.

“In our study, depending on the atmospheric greenhouse gas concentrations and associated level of warming, we saw declines in rain-fed corn yields ranging from 2.2% to 21.5%,” explains lead researcher Suat Irmack, professor in the Department of Agricultural and Biological Engineering at Pennsylvania State University, US.

“Under those same greenhouse gas concentrations, the range of declines was lower for irrigated yields – from 3.7% to 15.6%, due to irrigation technologies providing more stable crop growth conditions under water- and temperature-stress.”

This research, published in Agricultural Water Management, is valuable for understanding how climate change might impact food supply given that corn is used for both animal feed and human consumption.

An aerial view of a corn field being watered by center pivot irrigation.
The study site in Nebraska is representative of agricultural management practices in the region and represents the most densely irrigated area in the Central Plains, which is a sub region of the Great Plains. Credit: Suat Irmak/Penn State

Monitoring Arctic permafrost with satellites, supercomputers, and deep learning

Permafrost is a layer of ground that’s been permanently frozen for two or more years. It’s an essential factor in Earth’s climate as it contains large amounts of methane and carbon dioxide. However, soil thawing caused by global warming is expected to accelerate the release of greenhouse gasses into the atmosphere – creating a feedback loop that will exacerbate climate change.

One of the most distinctive features of permafrost are ice wedges (caused by freezing and melting of soil in the tundra), which produce recognisable polygons in satellite imagery. The shape and dimension of them can provide important information about the status and rate of change in permafrost region, but they are difficult to analyse conventionally.

Instead, a new study has used a neural network to do the analysis. Researchers used deep learning – a machine learning technique that teaches computers to do what humans can – to train their computer model with hand-annotated images of 50,000 individual polygons.

After feeding satellite imagery into the neural network and testing it on un-annotated images they are now achieving accuracy rates of 80–90%. The ice-wedge data will be available for rapid analysis on the new Permafrost Discovery Gateway The research was published in Photogrammetric Engineering & Remote Sensing.

“Permafrost isn’t characterised at these spatial scales in climate models,” says Dr Anna Liljedahl of the Woodwell Climate Research Centre in the US. “This study will help us derive a baseline and also see how changes are occurring over time.”

A screenshot of the progress of an automated ice-wedge polygon prediction underway.
Progress of automated ice-wedge polygon prediction from sub-meter resolution commercial satellite imagery. So far, researchers have mapped more than 1 billion individual-ice wedge polygons across the Arctic tundra. In addition to polygon outline, each detected ice-wedge polygon comes with a suite of analysis-ready attributes, such as ice-wedge polygon type, size, length, and width. Credit: Chandi Witharana/University of Connecticut

Please login to favourite this article.