Key breakthrough links changes in length-of-day with climate prediction
Scientists made a key breakthrough in the quest to accurately predict fluctuations in the rotation of the Earth and so the length of the day
[Oct 5, 2022: Louise Vennells, University of Exeter]
Scientists used state-of-the-art mathematical modelling to show how fluctuations in the length of the day can be predicted more than a year in advance. (CREDIT: Creative Commons)
Scientists have made a key breakthrough in the quest to accurately predict fluctuations in the rotation of the Earth and so the length of the day - potentially opening up new predictions for the effects of climate change.
A team of scientists, led by Professor Adam Scaife from the University of Exeter, has used state-of-the-art mathematical modelling to show how fluctuations in the length of the day can be predicted more than a year in advance – significantly longer than currently possible.
The team suggest this long-range forecasting also originates from a new atmospheric source for long-range predictability of weather and climate changes.
Crucially, the research shows a definitive link between geodesy – or accurately measuring and understanding the shape, size, orientation and gravity on Earth – and climate prediction.
The study is published in leading journal Nature Geoscience.
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Professor Scaife, a climate expert from the University of Exeter’s Mathematics department said: “While the changes in day length are tiny, they are important for applications that require very accurate time measurements like GPS.”
Angular momentum has long been known to play a fundamental role in the structure and variability of the Earth’s atmosphere.
As the Earth spins around its axis, its overall mass and rotation result in what appears to be a steady rotation. However, surface wind changes and changes in high and low-pressure patterns can change this and if the atmosphere speeds up due to stronger winds, the Earth’s rotation consequently slows down, causing the length of day to increase.
a, Variations in the length of day (LOD) showing the prominent interannual variability of around 0.5 × 10−3 s in observations (black) and the first year of ensemble mean model predictions starting in November each year (red). b, Correlation of predicted seasonal length-of-day anomalies in the ensemble mean with length-of-day anomalies from single model ensemble members (black), with radio telescope observations of Earth’s rotation (blue) and with atmospheric reanalysis (red). The perfect model predictability (black) is smoother than the prediction skill against observations (red, blue) due to averaging of the correlations with each ensemble member in the model case. Note the non-monotonic variation with lead time and the peaks at leads of 3 and 15 months in winter. Statistical significance at the 95% level according to a one-sided t test for positive correlations is shown by the dotted line. (CREDIT: Nature Geoscience)
However, until now the long-range predictability of these fluctuations in the length of the day was unknown.
The new study shows that fluctuations in atmospheric angular momentum and the length of day are predictable out to more than a year ahead and that the atmospheric changes have an important influence on regional weather and climate.
a,b, Correlation between the ensemble mean predicted AAM (from forecasts started in November) and the following observed winter NAO (a) and Pacific jet-stream winds (b). Forecasts were started in November, and the NAO and jet-stream winds are predicted at a lead time of 13 months for all years between 1960 and 2017, inclusive. The NAO is the two-point difference in sea-level pressure between the Azores and Iceland, and the jet-stream wind is the zonal mean wind at 300 hPa and 60° N averaged over the Pacific (150° E to 150° W). The correlation with the following winter NAO and winds is plotted at each latitude and for each month as the forecasts progress. Positive correlations indicate that AAM anomalies precede the same-sign NAO and winds in the following winter as expected. Note the poleward migration with lead time (months), consistent with predictability arising from the poleward-migrating AAM anomalies. Hatching shows regions where the correlation between AAM and NAO is significant at the 90% level according to a one-sided t test. (CREDIT: Nature Geoscience)
Using a range of forecasts from a dynamical climate model, the scientists were able to predict signals in the atmosphere that spread slowly and coherently towards the poles.
These signals precede changes in extratropical climate via the North Atlantic Oscillation and the extratropical jet stream. These new findings point to a source of long-range predictability from within the atmosphere that will help us to understand and better predict weather and climate.
Professor Scaife added: “We usually look to the ocean for long range prediction signals but these new results show that long range forecasts can also be driven from within the atmosphere.”
Note: Materials provided above by the University of Exeter. Content may be edited for style and length.
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